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"implementation", "start_line": 1, "end_line": 50, "span_ids": ["docstring"], "tokens": 325}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "# Configuration file for the Sphinx documentation builder.\n#\n# This file only contains a selection of the most common options. For a full\n# list see the documentation:\n# https://www.sphinx-doc.org/en/master/usage/configuration.html\n\n# -- Path setup --------------------------------------------------------------\n\n# If extensions (or modules to document with autodoc) are in another directory,\n# add these directories to sys.path here. If the directory is relative to the\n# documentation root, use os.path.abspath to make it absolute, like shown here.\n#\nimport os\nimport subprocess\nimport sys\n\nsys.path.insert(0, os.path.abspath(\"..\"))\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), \"..\", \"..\")))\nprint(sys.path)\n\nimport monai  # noqa: E402\n\n# -- Project information -----------------------------------------------------\nproject = \"MONAI\"\ncopyright = \"MONAI Consortium\"\nauthor = \"MONAI Contributors\"\n\n# The full version, including alpha/beta/rc tags\nshort_version = monai.__version__.split(\"+\")[0]\nrelease = short_version\nversion = short_version\n\n# List of patterns, relative to source directory, that match files and\n# directories to ignore when looking for source files.\n# This pattern also affects html_static_path and html_extra_path.\nexclude_patterns = [\n    \"transforms\",\n    \"networks\",\n    \"metrics\",\n    \"engines\",\n    \"data\",\n    \"apps\",\n    \"config\",\n    \"handlers\",\n    \"losses\",\n    \"visualize\",\n    \"utils\",\n    \"inferers\",\n    \"optimizers\",\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/docs/source/conf.py_generate_apidocs_generate_apidocs.subprocess_check_call_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/docs/source/conf.py_generate_apidocs_generate_apidocs.subprocess_check_call_", "embedding": null, "metadata": {"file_path": "docs/source/conf.py", "file_name": "conf.py", "file_type": "text/x-python", "category": "implementation", "start_line": 52, "end_line": 67, "span_ids": ["generate_apidocs"], "tokens": 205}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def generate_apidocs(*args):\n    \"\"\"Generate API docs automatically by trawling the available modules\"\"\"\n    module_path = os.path.abspath(os.path.join(os.path.dirname(__file__), \"..\", \"..\", \"monai\"))\n    output_path = os.path.abspath(os.path.join(os.path.dirname(__file__), \"apidocs\"))\n    apidoc_command_path = \"sphinx-apidoc\"\n    if hasattr(sys, \"real_prefix\"):  # called from a virtualenv\n        apidoc_command_path = os.path.join(sys.prefix, \"bin\", \"sphinx-apidoc\")\n        apidoc_command_path = os.path.abspath(apidoc_command_path)\n    print(f\"output_path {output_path}\")\n    print(f\"module_path {module_path}\")\n    subprocess.check_call(\n        [apidoc_command_path, \"-e\"]\n        + [\"-o\", output_path]\n        + [module_path]\n        + [os.path.join(module_path, p) for p in exclude_patterns]\n    )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/__init__.py_os_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/__init__.py_os_", "embedding": null, "metadata": {"file_path": "monai/__init__.py", "file_name": "__init__.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 65, "span_ids": ["docstring"], "tokens": 353}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport sys\n\nfrom ._version import get_versions\n\nPY_REQUIRED_MAJOR = 3\nPY_REQUIRED_MINOR = 7\n\nversion_dict = get_versions()\n__version__: str = version_dict.get(\"version\", \"0+unknown\")\n__revision_id__: str = version_dict.get(\"full-revisionid\")\ndel get_versions, version_dict\n\n__copyright__ = \"(c) MONAI Consortium\"\n\n__basedir__ = os.path.dirname(__file__)\n\nif sys.version_info.major != PY_REQUIRED_MAJOR or sys.version_info.minor < PY_REQUIRED_MINOR:\n    import warnings\n\n    warnings.warn(\n        f\"MONAI requires Python {PY_REQUIRED_MAJOR}.{PY_REQUIRED_MINOR} or higher. \"\n        f\"But the current Python is: {sys.version}\",\n        category=RuntimeWarning,\n    )\n\nfrom .utils.module import load_submodules  # noqa: E402\n\n# handlers_* have some external decorators the users may not have installed\n# *.so files and folder \"_C\" may not exist when the cpp extensions are not compiled\nexcludes = \"(^(monai.handlers))|((\\\\.so)$)|(^(monai._C))\"\n\n# load directory modules only, skip loading individual files\nload_submodules(sys.modules[__name__], False, exclude_pattern=excludes)\n\n# load all modules, this will trigger all export decorations\nload_submodules(sys.modules[__name__], True, exclude_pattern=excludes)\n\n__all__ = [\n    \"apps\",\n    \"config\",\n    \"data\",\n    \"engines\",\n    \"handlers\",\n    \"inferers\",\n    \"losses\",\n    \"metrics\",\n    \"networks\",\n    \"optimizers\",\n    \"transforms\",\n    \"utils\",\n    \"visualize\",\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/_version.py__This_file_helps_to_comp_sys": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/_version.py__This_file_helps_to_comp_sys", "embedding": null, "metadata": {"file_path": "monai/_version.py", "file_name": "_version.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2, "end_line": 17, "span_ids": ["docstring"], "tokens": 129}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "# This file helps to compute a version number in source trees obtained from\n# git-archive tarball (such as those provided by githubs download-from-tag\n# feature). Distribution tarballs (built by setup.py sdist) and build\n# directories (produced by setup.py build) will contain a much shorter file\n# that just contains the computed version number.\n\n# This file is released into the public domain. Generated by\n# versioneer-0.19 (https://github.com/python-versioneer/python-versioneer)\n\n\"\"\"Git implementation of _version.py.\"\"\"\n\nimport errno\nimport os\nimport re\nimport subprocess\nimport sys", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/_version.py_get_keywords_get_keywords.return.keywords": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/_version.py_get_keywords_get_keywords.return.keywords", "embedding": null, "metadata": {"file_path": "monai/_version.py", "file_name": "_version.py", "file_type": "text/x-python", "category": "implementation", "start_line": 19, "end_line": 29, "span_ids": ["get_keywords"], "tokens": 129}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_keywords():\n    \"\"\"Get the keywords needed to look up the version information.\"\"\"\n    # these strings will be replaced by git during git-archive.\n    # setup.py/versioneer.py will grep for the variable names, so they must\n    # each be defined on a line of their own. _version.py will just call\n    # get_keywords().\n    git_refnames = \"$Format:%d$\"\n    git_full = \"$Format:%H$\"\n    git_date = \"$Format:%ci$\"\n    keywords = {\"refnames\": git_refnames, \"full\": git_full, \"date\": git_date}\n    return keywords", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/_version.py_VersioneerConfig_register_vcs_handler.return.decorate": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/_version.py_VersioneerConfig_register_vcs_handler.return.decorate", "embedding": null, "metadata": {"file_path": "monai/_version.py", "file_name": "_version.py", "file_type": "text/x-python", "category": "implementation", "start_line": 33, "end_line": 67, "span_ids": ["VersioneerConfig", "impl", "NotThisMethod", "register_vcs_handler", "get_config"], "tokens": 234}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class VersioneerConfig:\n    \"\"\"Container for Versioneer configuration parameters.\"\"\"\n\n\ndef get_config():\n    \"\"\"Create, populate and return the VersioneerConfig() object.\"\"\"\n    # these strings are filled in when 'setup.py versioneer' creates\n    # _version.py\n    cfg = VersioneerConfig()\n    cfg.VCS = \"git\"\n    cfg.style = \"pep440\"\n    cfg.tag_prefix = \"\"\n    cfg.parentdir_prefix = \"\"\n    cfg.versionfile_source = \"monai/_version.py\"\n    cfg.verbose = False\n    return cfg\n\n\nclass NotThisMethod(Exception):\n    \"\"\"Exception raised if a method is not valid for the current scenario.\"\"\"\n\n\nLONG_VERSION_PY = {}\nHANDLERS = {}\n\n\ndef register_vcs_handler(vcs, method):  # decorator\n    \"\"\"Create decorator to mark a method as the handler of a VCS.\"\"\"\n    def decorate(f):\n        \"\"\"Store f in HANDLERS[vcs][method].\"\"\"\n        if vcs not in HANDLERS:\n            HANDLERS[vcs] = {}\n        HANDLERS[vcs][method] = f\n        return f\n    return decorate", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/_version.py_run_command_run_command.return.stdout_p_returncode": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/_version.py_run_command_run_command.return.stdout_p_returncode", "embedding": null, "metadata": {"file_path": "monai/_version.py", "file_name": "_version.py", "file_type": "text/x-python", "category": "implementation", "start_line": 70, "end_line": 102, "span_ids": ["run_command"], "tokens": 274}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False,\n                env=None):\n    \"\"\"Call the given command(s).\"\"\"\n    assert isinstance(commands, list)\n    p = None\n    for c in commands:\n        try:\n            dispcmd = str([c] + args)\n            # remember shell=False, so use git.cmd on windows, not just git\n            p = subprocess.Popen([c] + args, cwd=cwd, env=env,\n                                 stdout=subprocess.PIPE,\n                                 stderr=(subprocess.PIPE if hide_stderr\n                                         else None))\n            break\n        except EnvironmentError:\n            e = sys.exc_info()[1]\n            if e.errno == errno.ENOENT:\n                continue\n            if verbose:\n                print(\"unable to run %s\" % dispcmd)\n                print(e)\n            return None, None\n    else:\n        if verbose:\n            print(\"unable to find command, tried %s\" % (commands,))\n        return None, None\n    stdout = p.communicate()[0].strip().decode()\n    if p.returncode != 0:\n        if verbose:\n            print(\"unable to run %s (error)\" % dispcmd)\n            print(\"stdout was %s\" % stdout)\n        return None, p.returncode\n    return stdout, p.returncode", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/_version.py_versions_from_parentdir_versions_from_parentdir.raise_NotThisMethod_root": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/_version.py_versions_from_parentdir_versions_from_parentdir.raise_NotThisMethod_root", "embedding": null, "metadata": {"file_path": "monai/_version.py", "file_name": "_version.py", "file_type": "text/x-python", "category": "implementation", "start_line": 106, "end_line": 128, "span_ids": ["versions_from_parentdir"], "tokens": 205}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def versions_from_parentdir(parentdir_prefix, root, verbose):\n    \"\"\"Try to determine the version from the parent directory name.\n\n    Source tarballs conventionally unpack into a directory that includes both\n    the project name and a version string. We will also support searching up\n    two directory levels for an appropriately named parent directory\n    \"\"\"\n    rootdirs = []\n\n    for i in range(3):\n        dirname = os.path.basename(root)\n        if dirname.startswith(parentdir_prefix):\n            return {\"version\": dirname[len(parentdir_prefix):],\n                    \"full-revisionid\": None,\n                    \"dirty\": False, \"error\": None, \"date\": None}\n        else:\n            rootdirs.append(root)\n            root = os.path.dirname(root)  # up a level\n\n    if verbose:\n        print(\"Tried directories %s but none started with prefix %s\" %\n              (str(rootdirs), parentdir_prefix))\n    raise NotThisMethod(\"rootdir doesn't start with parentdir_prefix\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/_version.py_git_get_keywords_git_get_keywords.return.keywords": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/_version.py_git_get_keywords_git_get_keywords.return.keywords", "embedding": null, "metadata": {"file_path": "monai/_version.py", "file_name": "_version.py", "file_type": "text/x-python", "category": "implementation", "start_line": 131, "end_line": 157, "span_ids": ["git_get_keywords"], "tokens": 255}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@register_vcs_handler(\"git\", \"get_keywords\")\ndef git_get_keywords(versionfile_abs):\n    \"\"\"Extract version information from the given file.\"\"\"\n    # the code embedded in _version.py can just fetch the value of these\n    # keywords. When used from setup.py, we don't want to import _version.py,\n    # so we do it with a regexp instead. This function is not used from\n    # _version.py.\n    keywords = {}\n    try:\n        f = open(versionfile_abs, \"r\")\n        for line in f.readlines():\n            if line.strip().startswith(\"git_refnames =\"):\n                mo = re.search(r'=\\s*\"(.*)\"', line)\n                if mo:\n                    keywords[\"refnames\"] = mo.group(1)\n            if line.strip().startswith(\"git_full =\"):\n                mo = re.search(r'=\\s*\"(.*)\"', line)\n                if mo:\n                    keywords[\"full\"] = mo.group(1)\n            if line.strip().startswith(\"git_date =\"):\n                mo = re.search(r'=\\s*\"(.*)\"', line)\n                if mo:\n                    keywords[\"date\"] = mo.group(1)\n        f.close()\n    except EnvironmentError:\n        pass\n    return keywords", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/_version.py_git_versions_from_keywords_git_versions_from_keywords.return._version_0_unknown_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/_version.py_git_versions_from_keywords_git_versions_from_keywords.return._version_0_unknown_", "embedding": null, "metadata": {"file_path": "monai/_version.py", "file_name": "_version.py", "file_type": "text/x-python", "category": "implementation", "start_line": 159, "end_line": 215, "span_ids": ["git_versions_from_keywords"], "tokens": 745}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@register_vcs_handler(\"git\", \"keywords\")\ndef git_versions_from_keywords(keywords, tag_prefix, verbose):\n    \"\"\"Get version information from git keywords.\"\"\"\n    if not keywords:\n        raise NotThisMethod(\"no keywords at all, weird\")\n    date = keywords.get(\"date\")\n    if date is not None:\n        # Use only the last line.  Previous lines may contain GPG signature\n        # information.\n        date = date.splitlines()[-1]\n\n        # git-2.2.0 added \"%cI\", which expands to an ISO-8601 -compliant\n        # datestamp. However we prefer \"%ci\" (which expands to an \"ISO-8601\n        # -like\" string, which we must then edit to make compliant), because\n        # it's been around since git-1.5.3, and it's too difficult to\n        # discover which version we're using, or to work around using an\n        # older one.\n        date = date.strip().replace(\" \", \"T\", 1).replace(\" \", \"\", 1)\n    refnames = keywords[\"refnames\"].strip()\n    if refnames.startswith(\"$Format\"):\n        if verbose:\n            print(\"keywords are unexpanded, not using\")\n        raise NotThisMethod(\"unexpanded keywords, not a git-archive tarball\")\n    refs = set([r.strip() for r in refnames.strip(\"()\").split(\",\")])\n    # starting in git-1.8.3, tags are listed as \"tag: foo-1.0\" instead of\n    # just \"foo-1.0\". If we see a \"tag: \" prefix, prefer those.\n    TAG = \"tag: \"\n    tags = set([r[len(TAG):] for r in refs if r.startswith(TAG)])\n    if not tags:\n        # Either we're using git < 1.8.3, or there really are no tags. We use\n        # a heuristic: assume all version tags have a digit. The old git %d\n        # expansion behaves like git log --decorate=short and strips out the\n        # refs/heads/ and refs/tags/ prefixes that would let us distinguish\n        # between branches and tags. By ignoring refnames without digits, we\n        # filter out many common branch names like \"release\" and\n        # \"stabilization\", as well as \"HEAD\" and \"master\".\n        tags = set([r for r in refs if re.search(r'\\d', r)])\n        if verbose:\n            print(\"discarding '%s', no digits\" % \",\".join(refs - tags))\n    if verbose:\n        print(\"likely tags: %s\" % \",\".join(sorted(tags)))\n    for ref in sorted(tags):\n        # sorting will prefer e.g. \"2.0\" over \"2.0rc1\"\n        if ref.startswith(tag_prefix):\n            r = ref[len(tag_prefix):]\n            if verbose:\n                print(\"picking %s\" % r)\n            return {\"version\": r,\n                    \"full-revisionid\": keywords[\"full\"].strip(),\n                    \"dirty\": False, \"error\": None,\n                    \"date\": date}\n    # no suitable tags, so version is \"0+unknown\", but full hex is still there\n    if verbose:\n        print(\"no suitable tags, using unknown + full revision id\")\n    return {\"version\": \"0+unknown\",\n            \"full-revisionid\": keywords[\"full\"].strip(),\n            \"dirty\": False, \"error\": \"no suitable tags\", \"date\": None}", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/_version.py_git_pieces_from_vcs_git_pieces_from_vcs.return.pieces": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/_version.py_git_pieces_from_vcs_git_pieces_from_vcs.return.pieces", "embedding": null, "metadata": {"file_path": "monai/_version.py", "file_name": "_version.py", "file_type": "text/x-python", "category": "implementation", "start_line": 218, "end_line": 310, "span_ids": ["git_pieces_from_vcs"], "tokens": 905}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@register_vcs_handler(\"git\", \"pieces_from_vcs\")\ndef git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command):\n    \"\"\"Get version from 'git describe' in the root of the source tree.\n\n    This only gets called if the git-archive 'subst' keywords were *not*\n    expanded, and _version.py hasn't already been rewritten with a short\n    version string, meaning we're inside a checked out source tree.\n    \"\"\"\n    GITS = [\"git\"]\n    if sys.platform == \"win32\":\n        GITS = [\"git.cmd\", \"git.exe\"]\n\n    out, rc = run_command(GITS, [\"rev-parse\", \"--git-dir\"], cwd=root,\n                          hide_stderr=True)\n    if rc != 0:\n        if verbose:\n            print(\"Directory %s not under git control\" % root)\n        raise NotThisMethod(\"'git rev-parse --git-dir' returned error\")\n\n    # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty]\n    # if there isn't one, this yields HEX[-dirty] (no NUM)\n    describe_out, rc = run_command(GITS, [\"describe\", \"--tags\", \"--dirty\",\n                                          \"--always\", \"--long\",\n                                          \"--match\", \"%s*\" % tag_prefix],\n                                   cwd=root)\n    # --long was added in git-1.5.5\n    if describe_out is None:\n        raise NotThisMethod(\"'git describe' failed\")\n    describe_out = describe_out.strip()\n    full_out, rc = run_command(GITS, [\"rev-parse\", \"HEAD\"], cwd=root)\n    if full_out is None:\n        raise NotThisMethod(\"'git rev-parse' failed\")\n    full_out = full_out.strip()\n\n    pieces = {}\n    pieces[\"long\"] = full_out\n    pieces[\"short\"] = full_out[:7]  # maybe improved later\n    pieces[\"error\"] = None\n\n    # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty]\n    # TAG might have hyphens.\n    git_describe = describe_out\n\n    # look for -dirty suffix\n    dirty = git_describe.endswith(\"-dirty\")\n    pieces[\"dirty\"] = dirty\n    if dirty:\n        git_describe = git_describe[:git_describe.rindex(\"-dirty\")]\n\n    # now we have TAG-NUM-gHEX or HEX\n\n    if \"-\" in git_describe:\n        # TAG-NUM-gHEX\n        mo = re.search(r'^(.+)-(\\d+)-g([0-9a-f]+)$', git_describe)\n        if not mo:\n            # unparseable. Maybe git-describe is misbehaving?\n            pieces[\"error\"] = (\"unable to parse git-describe output: '%s'\"\n                               % describe_out)\n            return pieces\n\n        # tag\n        full_tag = mo.group(1)\n        if not full_tag.startswith(tag_prefix):\n            if verbose:\n                fmt = \"tag '%s' doesn't start with prefix '%s'\"\n                print(fmt % (full_tag, tag_prefix))\n            pieces[\"error\"] = (\"tag '%s' doesn't start with prefix '%s'\"\n                               % (full_tag, tag_prefix))\n            return pieces\n        pieces[\"closest-tag\"] = full_tag[len(tag_prefix):]\n\n        # distance: number of commits since tag\n        pieces[\"distance\"] = int(mo.group(2))\n\n        # commit: short hex revision ID\n        pieces[\"short\"] = mo.group(3)\n\n    else:\n        # HEX: no tags\n        pieces[\"closest-tag\"] = None\n        count_out, rc = run_command(GITS, [\"rev-list\", \"HEAD\", \"--count\"],\n                                    cwd=root)\n        pieces[\"distance\"] = int(count_out)  # total number of commits\n\n    # commit date: see ISO-8601 comment in git_versions_from_keywords()\n    date = run_command(GITS, [\"show\", \"-s\", \"--format=%ci\", \"HEAD\"],\n                       cwd=root)[0].strip()\n    # Use only the last line.  Previous lines may contain GPG signature\n    # information.\n    date = date.splitlines()[-1]\n    pieces[\"date\"] = date.strip().replace(\" \", \"T\", 1).replace(\" \", \"\", 1)\n\n    return pieces", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/_version.py_plus_or_dot_render_pep440.return.rendered": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/_version.py_plus_or_dot_render_pep440.return.rendered", "embedding": null, "metadata": {"file_path": "monai/_version.py", "file_name": "_version.py", "file_type": "text/x-python", "category": "implementation", "start_line": 307, "end_line": 336, "span_ids": ["plus_or_dot", "render_pep440"], "tokens": 258}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def plus_or_dot(pieces):\n    \"\"\"Return a + if we don't already have one, else return a .\"\"\"\n    if \"+\" in pieces.get(\"closest-tag\", \"\"):\n        return \".\"\n    return \"+\"\n\n\ndef render_pep440(pieces):\n    \"\"\"Build up version string, with post-release \"local version identifier\".\n\n    Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you\n    get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty\n\n    Exceptions:\n    1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]\n    \"\"\"\n    if pieces[\"closest-tag\"]:\n        rendered = pieces[\"closest-tag\"]\n        if pieces[\"distance\"] or pieces[\"dirty\"]:\n            rendered += plus_or_dot(pieces)\n            rendered += \"%d.g%s\" % (pieces[\"distance\"], pieces[\"short\"])\n            if pieces[\"dirty\"]:\n                rendered += \".dirty\"\n    else:\n        # exception #1\n        rendered = \"0+untagged.%d.g%s\" % (pieces[\"distance\"],\n                                          pieces[\"short\"])\n        if pieces[\"dirty\"]:\n            rendered += \".dirty\"\n    return rendered", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/_version.py_render_pep440_old_render_pep440_old.return.rendered": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/_version.py_render_pep440_old_render_pep440_old.return.rendered", "embedding": null, "metadata": {"file_path": "monai/_version.py", "file_name": "_version.py", "file_type": "text/x-python", "category": "implementation", "start_line": 382, "end_line": 401, "span_ids": ["render_pep440_old"], "tokens": 143}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def render_pep440_old(pieces):\n    \"\"\"TAG[.postDISTANCE[.dev0]] .\n\n    The \".dev0\" means dirty.\n\n    Exceptions:\n    1: no tags. 0.postDISTANCE[.dev0]\n    \"\"\"\n    if pieces[\"closest-tag\"]:\n        rendered = pieces[\"closest-tag\"]\n        if pieces[\"distance\"] or pieces[\"dirty\"]:\n            rendered += \".post%d\" % pieces[\"distance\"]\n            if pieces[\"dirty\"]:\n                rendered += \".dev0\"\n    else:\n        # exception #1\n        rendered = \"0.post%d\" % pieces[\"distance\"]\n        if pieces[\"dirty\"]:\n            rendered += \".dev0\"\n    return rendered", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/_version.py_render_git_describe_render_git_describe.return.rendered": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/_version.py_render_git_describe_render_git_describe.return.rendered", "embedding": null, "metadata": {"file_path": "monai/_version.py", "file_name": "_version.py", "file_type": "text/x-python", "category": "implementation", "start_line": 404, "end_line": 421, "span_ids": ["render_git_describe"], "tokens": 129}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def render_git_describe(pieces):\n    \"\"\"TAG[-DISTANCE-gHEX][-dirty].\n\n    Like 'git describe --tags --dirty --always'.\n\n    Exceptions:\n    1: no tags. HEX[-dirty]  (note: no 'g' prefix)\n    \"\"\"\n    if pieces[\"closest-tag\"]:\n        rendered = pieces[\"closest-tag\"]\n        if pieces[\"distance\"]:\n            rendered += \"-%d-g%s\" % (pieces[\"distance\"], pieces[\"short\"])\n    else:\n        # exception #1\n        rendered = pieces[\"short\"]\n    if pieces[\"dirty\"]:\n        rendered += \"-dirty\"\n    return rendered", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/_version.py_render_git_describe_long_render_git_describe_long.return.rendered": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/_version.py_render_git_describe_long_render_git_describe_long.return.rendered", "embedding": null, "metadata": {"file_path": "monai/_version.py", "file_name": "_version.py", "file_type": "text/x-python", "category": "implementation", "start_line": 424, "end_line": 441, "span_ids": ["render_git_describe_long"], "tokens": 133}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def render_git_describe_long(pieces):\n    \"\"\"TAG-DISTANCE-gHEX[-dirty].\n\n    Like 'git describe --tags --dirty --always -long'.\n    The distance/hash is unconditional.\n\n    Exceptions:\n    1: no tags. HEX[-dirty]  (note: no 'g' prefix)\n    \"\"\"\n    if pieces[\"closest-tag\"]:\n        rendered = pieces[\"closest-tag\"]\n        rendered += \"-%d-g%s\" % (pieces[\"distance\"], pieces[\"short\"])\n    else:\n        # exception #1\n        rendered = pieces[\"short\"]\n    if pieces[\"dirty\"]:\n        rendered += \"-dirty\"\n    return rendered", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/_version.py_render_render.return._version_rendered_fu": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/_version.py_render_render.return._version_rendered_fu", "embedding": null, "metadata": {"file_path": "monai/_version.py", "file_name": "_version.py", "file_type": "text/x-python", "category": "implementation", "start_line": 444, "end_line": 473, "span_ids": ["render"], "tokens": 264}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def render(pieces, style):\n    \"\"\"Render the given version pieces into the requested style.\"\"\"\n    if pieces[\"error\"]:\n        return {\"version\": \"unknown\",\n                \"full-revisionid\": pieces.get(\"long\"),\n                \"dirty\": None,\n                \"error\": pieces[\"error\"],\n                \"date\": None}\n\n    if not style or style == \"default\":\n        style = \"pep440\"  # the default\n\n    if style == \"pep440\":\n        rendered = render_pep440(pieces)\n    elif style == \"pep440-pre\":\n        rendered = render_pep440_pre(pieces)\n    elif style == \"pep440-post\":\n        rendered = render_pep440_post(pieces)\n    elif style == \"pep440-old\":\n        rendered = render_pep440_old(pieces)\n    elif style == \"git-describe\":\n        rendered = render_git_describe(pieces)\n    elif style == \"git-describe-long\":\n        rendered = render_git_describe_long(pieces)\n    else:\n        raise ValueError(\"unknown style '%s'\" % style)\n\n    return {\"version\": rendered, \"full-revisionid\": pieces[\"long\"],\n            \"dirty\": pieces[\"dirty\"], \"error\": None,\n            \"date\": pieces.get(\"date\")}", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/_version.py_get_versions_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/_version.py_get_versions_", "embedding": null, "metadata": {"file_path": "monai/_version.py", "file_name": "_version.py", "file_type": "text/x-python", "category": "implementation", "start_line": 482, "end_line": 526, "span_ids": ["get_versions"], "tokens": 365}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_versions():\n    \"\"\"Get version information or return default if unable to do so.\"\"\"\n    # I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have\n    # __file__, we can work backwards from there to the root. Some\n    # py2exe/bbfreeze/non-CPython implementations don't do __file__, in which\n    # case we can only use expanded keywords.\n\n    cfg = get_config()\n    verbose = cfg.verbose\n\n    try:\n        return git_versions_from_keywords(get_keywords(), cfg.tag_prefix,\n                                          verbose)\n    except NotThisMethod:\n        pass\n\n    try:\n        root = os.path.realpath(__file__)\n        # versionfile_source is the relative path from the top of the source\n        # tree (where the .git directory might live) to this file. Invert\n        # this to find the root from __file__.\n        for i in cfg.versionfile_source.split('/'):\n            root = os.path.dirname(root)\n    except NameError:\n        return {\"version\": \"0+unknown\", \"full-revisionid\": None,\n                \"dirty\": None,\n                \"error\": \"unable to find root of source tree\",\n                \"date\": None}\n\n    try:\n        pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose)\n        return render(pieces, cfg.style)\n    except NotThisMethod:\n        pass\n\n    try:\n        if cfg.parentdir_prefix:\n            return versions_from_parentdir(cfg.parentdir_prefix, root, verbose)\n    except NotThisMethod:\n        pass\n\n    return {\"version\": \"0+unknown\", \"full-revisionid\": None,\n            \"dirty\": None,\n            \"error\": \"unable to compute version\", \"date\": None}", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/datasets.py_DecathlonDataset_DecathlonDataset._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/datasets.py_DecathlonDataset_DecathlonDataset._", "embedding": null, "metadata": {"file_path": "monai/apps/datasets.py", "file_name": "datasets.py", "file_type": "text/x-python", "category": "implementation", "start_line": 174, "end_line": 233, "span_ids": ["DecathlonDataset"], "tokens": 824}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DecathlonDataset(Randomizable, CacheDataset):\n    \"\"\"\n    The Dataset to automatically download the data of Medical Segmentation Decathlon challenge\n    (http://medicaldecathlon.com/) and generate items for training, validation or test.\n    It will also load these properties from the JSON config file of dataset. user can call `get_properties()`\n    to get specified properties or all the properties loaded.\n    It's based on :py:class:`monai.data.CacheDataset` to accelerate the training process.\n\n    Args:\n        root_dir: user's local directory for caching and loading the MSD datasets.\n        task: which task to download and execute: one of list (\"Task01_BrainTumour\", \"Task02_Heart\",\n            \"Task03_Liver\", \"Task04_Hippocampus\", \"Task05_Prostate\", \"Task06_Lung\", \"Task07_Pancreas\",\n            \"Task08_HepaticVessel\", \"Task09_Spleen\", \"Task10_Colon\").\n        section: expected data section, can be: `training`, `validation` or `test`.\n        transform: transforms to execute operations on input data.\n            for further usage, use `AddChanneld` or `AsChannelFirstd` to convert the shape to [C, H, W, D].\n        download: whether to download and extract the Decathlon from resource link, default is False.\n            if expected file already exists, skip downloading even set it to True.\n        val_frac: percentage of of validation fraction in the whole dataset, default is 0.2.\n            user can manually copy tar file or dataset folder to the root directory.\n        seed: random seed to randomly shuffle the datalist before splitting into training and validation, default is 0.\n            note to set same seed for `training` and `validation` sections.\n        cache_num: number of items to be cached. Default is `sys.maxsize`.\n            will take the minimum of (cache_num, data_length x cache_rate, data_length).\n        cache_rate: percentage of cached data in total, default is 1.0 (cache all).\n            will take the minimum of (cache_num, data_length x cache_rate, data_length).\n        num_workers: the number of worker threads to use.\n            if 0 a single thread will be used. Default is 0.\n        progress: whether to display a progress bar when downloading dataset and computing the transform cache content.\n        copy_cache: whether to `deepcopy` the cache content before applying the random transforms,\n            default to `True`. if the random transforms don't modify the cached content\n            (for example, randomly crop from the cached image and deepcopy the crop region)\n            or if every cache item is only used once in a `multi-processing` environment,\n            may set `copy=False` for better performance.\n\n    Raises:\n        ValueError: When ``root_dir`` is not a directory.\n        ValueError: When ``task`` is not one of [\"Task01_BrainTumour\", \"Task02_Heart\",\n            \"Task03_Liver\", \"Task04_Hippocampus\", \"Task05_Prostate\", \"Task06_Lung\", \"Task07_Pancreas\",\n            \"Task08_HepaticVessel\", \"Task09_Spleen\", \"Task10_Colon\"].\n        RuntimeError: When ``dataset_dir`` doesn't exist and downloading is not selected (``download=False``).\n\n    Example::\n\n        transform = Compose(\n            [\n                LoadImaged(keys=[\"image\", \"label\"]),\n                AddChanneld(keys=[\"image\", \"label\"]),\n                ScaleIntensityd(keys=\"image\"),\n                ToTensord(keys=[\"image\", \"label\"]),\n            ]\n        )\n\n        val_data = DecathlonDataset(\n            root_dir=\"./\", task=\"Task09_Spleen\", transform=transform, section=\"validation\", seed=12345, download=True\n        )\n\n        print(val_data[0][\"image\"], val_data[0][\"label\"])\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/datasets.py_DecathlonDataset.resource_DecathlonDataset.md5._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/datasets.py_DecathlonDataset.resource_DecathlonDataset.md5._", "embedding": null, "metadata": {"file_path": "monai/apps/datasets.py", "file_name": "datasets.py", "file_type": "text/x-python", "category": "implementation", "start_line": 193, "end_line": 216, "span_ids": ["DecathlonDataset"], "tokens": 628}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DecathlonDataset(Randomizable, CacheDataset):\n\n    resource = {\n        \"Task01_BrainTumour\": \"https://msd-for-monai.s3-us-west-2.amazonaws.com/Task01_BrainTumour.tar\",\n        \"Task02_Heart\": \"https://msd-for-monai.s3-us-west-2.amazonaws.com/Task02_Heart.tar\",\n        \"Task03_Liver\": \"https://msd-for-monai.s3-us-west-2.amazonaws.com/Task03_Liver.tar\",\n        \"Task04_Hippocampus\": \"https://msd-for-monai.s3-us-west-2.amazonaws.com/Task04_Hippocampus.tar\",\n        \"Task05_Prostate\": \"https://msd-for-monai.s3-us-west-2.amazonaws.com/Task05_Prostate.tar\",\n        \"Task06_Lung\": \"https://msd-for-monai.s3-us-west-2.amazonaws.com/Task06_Lung.tar\",\n        \"Task07_Pancreas\": \"https://msd-for-monai.s3-us-west-2.amazonaws.com/Task07_Pancreas.tar\",\n        \"Task08_HepaticVessel\": \"https://msd-for-monai.s3-us-west-2.amazonaws.com/Task08_HepaticVessel.tar\",\n        \"Task09_Spleen\": \"https://msd-for-monai.s3-us-west-2.amazonaws.com/Task09_Spleen.tar\",\n        \"Task10_Colon\": \"https://msd-for-monai.s3-us-west-2.amazonaws.com/Task10_Colon.tar\",\n    }\n    md5 = {\n        \"Task01_BrainTumour\": \"240a19d752f0d9e9101544901065d872\",\n        \"Task02_Heart\": \"06ee59366e1e5124267b774dbd654057\",\n        \"Task03_Liver\": \"a90ec6c4aa7f6a3d087205e23d4e6397\",\n        \"Task04_Hippocampus\": \"9d24dba78a72977dbd1d2e110310f31b\",\n        \"Task05_Prostate\": \"35138f08b1efaef89d7424d2bcc928db\",\n        \"Task06_Lung\": \"8afd997733c7fc0432f71255ba4e52dc\",\n        \"Task07_Pancreas\": \"4f7080cfca169fa8066d17ce6eb061e4\",\n        \"Task08_HepaticVessel\": \"641d79e80ec66453921d997fbf12a29c\",\n        \"Task09_Spleen\": \"410d4a301da4e5b2f6f86ec3ddba524e\",\n        \"Task10_Colon\": \"bad7a188931dc2f6acf72b08eb6202d0\",\n    }", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/__init__.py_CSVSaver_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/__init__.py_CSVSaver_", "embedding": null, "metadata": {"file_path": "monai/data/__init__.py", "file_name": "__init__.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 75, "span_ids": ["imports:19", "docstring"], "tokens": 457}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from .csv_saver import CSVSaver\nfrom .dataloader import DataLoader\nfrom .dataset import (\n    ArrayDataset,\n    CacheDataset,\n    CacheNTransDataset,\n    CSVDataset,\n    Dataset,\n    DatasetFunc,\n    LMDBDataset,\n    NPZDictItemDataset,\n    PersistentDataset,\n    SmartCacheDataset,\n    ZipDataset,\n)\nfrom .dataset_summary import DatasetSummary\nfrom .decathlon_datalist import (\n    check_missing_files,\n    create_cross_validation_datalist,\n    load_decathlon_datalist,\n    load_decathlon_properties,\n)\nfrom .grid_dataset import GridPatchDataset, PatchDataset, PatchIter\nfrom .image_dataset import ImageDataset\nfrom .image_reader import ImageReader, ITKReader, NibabelReader, NumpyReader, PILReader, WSIReader\nfrom .iterable_dataset import CSVIterableDataset, IterableDataset, ShuffleBuffer\nfrom .nifti_saver import NiftiSaver\nfrom .nifti_writer import write_nifti\nfrom .png_saver import PNGSaver\nfrom .png_writer import write_png\nfrom .samplers import DistributedSampler, DistributedWeightedRandomSampler\nfrom .synthetic import create_test_image_2d, create_test_image_3d\nfrom .test_time_augmentation import TestTimeAugmentation\nfrom .thread_buffer import ThreadBuffer, ThreadDataLoader\nfrom .torchscript_utils import load_net_with_metadata, save_net_with_metadata\nfrom .utils import (\n    compute_importance_map,\n    compute_shape_offset,\n    convert_tables_to_dicts,\n    correct_nifti_header_if_necessary,\n    create_file_basename,\n    decollate_batch,\n    dense_patch_slices,\n    get_random_patch,\n    get_valid_patch_size,\n    is_supported_format,\n    iter_patch,\n    iter_patch_slices,\n    json_hashing,\n    list_data_collate,\n    pad_list_data_collate,\n    partition_dataset,\n    partition_dataset_classes,\n    pickle_hashing,\n    rectify_header_sform_qform,\n    resample_datalist,\n    select_cross_validation_folds,\n    set_rnd,\n    sorted_dict,\n    to_affine_nd,\n    worker_init_fn,\n    zoom_affine,\n)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/csv_saver.py_CSVSaver.save_CSVSaver.save.self__cache_dict_save_key": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/csv_saver.py_CSVSaver.save_CSVSaver.save.self__cache_dict_save_key", "embedding": null, "metadata": {"file_path": "monai/data/csv_saver.py", "file_name": "csv_saver.py", "file_type": "text/x-python", "category": "implementation", "start_line": 74, "end_line": 88, "span_ids": ["CSVSaver.save"], "tokens": 185}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CSVSaver:\n\n    def save(self, data: Union[torch.Tensor, np.ndarray], meta_data: Optional[Dict] = None) -> None:\n        \"\"\"Save data into the cache dictionary. The metadata should have the following key:\n            - ``'filename_or_obj'`` -- save the data corresponding to file name or object.\n        If meta_data is None, use the default index from 0 to save data instead.\n\n        Args:\n            data: target data content that save into cache.\n            meta_data: the meta data information corresponding to the data.\n\n        \"\"\"\n        save_key = meta_data[Key.FILENAME_OR_OBJ] if meta_data else str(self._data_index)\n        self._data_index += 1\n        if isinstance(data, torch.Tensor):\n            data = data.detach().cpu().numpy()\n        self._cache_dict[save_key] = np.asarray(data, dtype=float)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/csv_saver.py_CSVSaver.save_batch_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/csv_saver.py_CSVSaver.save_batch_", "embedding": null, "metadata": {"file_path": "monai/data/csv_saver.py", "file_name": "csv_saver.py", "file_type": "text/x-python", "category": "implementation", "start_line": 96, "end_line": 118, "span_ids": ["CSVSaver.get_cache", "CSVSaver.save_batch", "CSVSaver.reset_cache"], "tokens": 196}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CSVSaver:\n\n    def save_batch(self, batch_data: Union[torch.Tensor, np.ndarray], meta_data: Optional[Dict] = None) -> None:\n        \"\"\"Save a batch of data into the cache dictionary.\n\n        Args:\n            batch_data: target batch data content that save into cache.\n            meta_data: every key-value in the meta_data is corresponding to 1 batch of data.\n\n        \"\"\"\n        for i, data in enumerate(batch_data):  # save a batch of files\n            self.save(data, {k: meta_data[k][i] for k in meta_data} if meta_data else None)\n\n        if self.flush:\n            self.finalize()\n\n    def get_cache(self) -> OrderedDict:\n        \"\"\"Get the cache dictionary, key is filename and value is the corresponding data\"\"\"\n\n        return self._cache_dict\n\n    def reset_cache(self) -> None:\n        \"\"\"Clear the cache dictionary content\"\"\"\n        self._cache_dict.clear()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/nifti_saver.py_NiftiSaver.save_batch_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/nifti_saver.py_NiftiSaver.save_batch_", "embedding": null, "metadata": {"file_path": "monai/data/nifti_saver.py", "file_name": "nifti_saver.py", "file_type": "text/x-python", "category": "implementation", "start_line": 180, "end_line": 201, "span_ids": ["NiftiSaver.save_batch"], "tokens": 297}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class NiftiSaver:\n\n    def save_batch(self, batch_data: Union[torch.Tensor, np.ndarray], meta_data: Optional[Dict] = None) -> None:\n        \"\"\"\n        Save a batch of data into NIfTI format files.\n\n        Spatially it supports up to three dimensions, that is, H, HW, HWD for\n        1D, 2D, 3D respectively (with resampling supports for 2D and 3D only).\n\n        When saving multiple time steps or multiple channels `batch_data`,\n        time and/or modality axes should be appended after the batch dimensions.\n        For example, the shape of a batch of 2D eight-class\n        segmentation probabilities to be saved could be `(batch, 8, 64, 64)`;\n        in this case each item in the batch will be saved as (64, 64, 1, 8)\n        NIfTI file (the third dimension is reserved as a spatial dimension).\n\n        Args:\n            batch_data: target batch data content that save into NIfTI format.\n            meta_data: every key-value in the meta_data is corresponding to a batch of data.\n\n        \"\"\"\n        for i, data in enumerate(batch_data):  # save a batch of files\n            self.save(data=data, meta_data={k: meta_data[k][i] for k in meta_data} if meta_data is not None else None)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/nifti_writer.py_from_typing_import_Option_write_nifti._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/nifti_writer.py_from_typing_import_Option_write_nifti._", "embedding": null, "metadata": {"file_path": "monai/data/nifti_writer.py", "file_name": "nifti_writer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 100, "span_ids": ["write_nifti", "docstring"], "tokens": 1218}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Optional, Sequence, Union\n\nimport numpy as np\nimport torch\n\nfrom monai.config import DtypeLike\nfrom monai.config.type_definitions import NdarrayOrTensor\nfrom monai.data.utils import compute_shape_offset, to_affine_nd\nfrom monai.networks.layers import AffineTransform\nfrom monai.utils import GridSampleMode, GridSamplePadMode, optional_import\nfrom monai.utils.type_conversion import convert_data_type\n\nnib, _ = optional_import(\"nibabel\")\n\n\ndef write_nifti(\n    data: NdarrayOrTensor,\n    file_name: str,\n    affine: Optional[NdarrayOrTensor] = None,\n    target_affine: Optional[np.ndarray] = None,\n    resample: bool = True,\n    output_spatial_shape: Union[Sequence[int], np.ndarray, None] = None,\n    mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,\n    padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER,\n    align_corners: bool = False,\n    dtype: DtypeLike = np.float64,\n    output_dtype: DtypeLike = np.float32,\n) -> None:\n    \"\"\"\n    Write numpy data into NIfTI files to disk.  This function converts data\n    into the coordinate system defined by `target_affine` when `target_affine`\n    is specified.\n\n    If the coordinate transform between `affine` and `target_affine` could be\n    achieved by simply transposing and flipping `data`, no resampling will\n    happen.  otherwise this function will resample `data` using the coordinate\n    transform computed from `affine` and `target_affine`.  Note that the shape\n    of the resampled `data` may subject to some rounding errors. For example,\n    resampling a 20x20 pixel image from pixel size (1.5, 1.5)-mm to (3.0,\n    3.0)-mm space will return a 10x10-pixel image.  However, resampling a\n    20x20-pixel image from pixel size (2.0, 2.0)-mm to (3.0, 3.0)-mma space\n    will output a 14x14-pixel image, where the image shape is rounded from\n    13.333x13.333 pixels. In this case `output_spatial_shape` could be specified so\n    that this function writes image data to a designated shape.\n\n    The saved `affine` matrix follows:\n    - If `affine` equals to `target_affine`, save the data with `target_affine`.\n    - If `resample=False`, transform `affine` to `new_affine` based on the orientation\n    of `target_affine` and save the data with `new_affine`.\n    - If `resample=True`, save the data with `target_affine`, if explicitly specify\n    the `output_spatial_shape`, the shape of saved data is not computed by `target_affine`.\n    - If `target_affine` is None, set `target_affine=affine` and save.\n    - If `affine` and `target_affine` are None, the data will be saved with an identity\n    matrix as the image affine.\n\n    This function assumes the NIfTI dimension notations.\n    Spatially it supports up to three dimensions, that is, H, HW, HWD for\n    1D, 2D, 3D respectively.\n    When saving multiple time steps or multiple channels `data`, time and/or\n    modality axes should be appended after the first three dimensions.  For\n    example, shape of 2D eight-class segmentation probabilities to be saved\n    could be `(64, 64, 1, 8)`. Also, data in shape (64, 64, 8), (64, 64, 8, 1)\n    will be considered as a single-channel 3D image.\n\n    Args:\n        data: input data to write to file.\n        file_name: expected file name that saved on disk.\n        affine: the current affine of `data`. Defaults to `np.eye(4)`\n        target_affine: before saving\n            the (`data`, `affine`) as a Nifti1Image,\n            transform the data into the coordinates defined by `target_affine`.\n        resample: whether to run resampling when the target affine\n            could not be achieved by swapping/flipping data axes.\n        output_spatial_shape: spatial shape of the output image.\n            This option is used when resample = True.\n        mode: {``\"bilinear\"``, ``\"nearest\"``}\n            This option is used when ``resample = True``.\n            Interpolation mode to calculate output values. Defaults to ``\"bilinear\"``.\n            See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n        padding_mode: {``\"zeros\"``, ``\"border\"``, ``\"reflection\"``}\n            This option is used when ``resample = True``.\n            Padding mode for outside grid values. Defaults to ``\"border\"``.\n            See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n        align_corners: Geometrically, we consider the pixels of the input as squares rather than points.\n            See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n        dtype: data type for resampling computation. Defaults to ``np.float64`` for best precision.\n            If None, use the data type of input data.\n        output_dtype: data type for saving data. Defaults to ``np.float32``.\n    \"\"\"\n    # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/nifti_writer.py_write_nifti.if_data_ndim_3_mult_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/nifti_writer.py_write_nifti.if_data_ndim_3_mult_", "embedding": null, "metadata": {"file_path": "monai/data/nifti_writer.py", "file_name": "nifti_writer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 143, "end_line": 170, "span_ids": ["write_nifti"], "tokens": 547}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def write_nifti(\n    data: NdarrayOrTensor,\n    file_name: str,\n    affine: Optional[NdarrayOrTensor] = None,\n    target_affine: Optional[np.ndarray] = None,\n    resample: bool = True,\n    output_spatial_shape: Union[Sequence[int], np.ndarray, None] = None,\n    mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,\n    padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER,\n    align_corners: bool = False,\n    dtype: DtypeLike = np.float64,\n    output_dtype: DtypeLike = np.float32,\n) -> None:\n    # ... other code\n    if data.ndim > 3:  # multi channel, resampling each channel\n        while len(output_spatial_shape_) < 3:\n            output_spatial_shape_ = output_spatial_shape_ + [1]\n        spatial_shape, channel_shape = data.shape[:3], data.shape[3:]\n        data_np: np.ndarray = data.reshape(list(spatial_shape) + [-1])  # type: ignore\n        data_np = np.moveaxis(data_np, -1, 0)  # channel first for pytorch\n        data_torch = affine_xform(\n            torch.as_tensor(np.ascontiguousarray(data_np, dtype=dtype)).unsqueeze(0),\n            torch.as_tensor(np.ascontiguousarray(transform, dtype=dtype)),\n            spatial_size=output_spatial_shape_[:3],\n        )\n        data_np = data_torch.squeeze(0).detach().cpu().numpy()\n        data_np = np.moveaxis(data_np, 0, -1)  # channel last for nifti\n        data_np = data_np.reshape(list(data_np.shape[:3]) + list(channel_shape))\n    else:  # single channel image, need to expand to have batch and channel\n        while len(output_spatial_shape_) < len(data.shape):\n            output_spatial_shape_ = output_spatial_shape_ + [1]\n        data_torch = affine_xform(\n            torch.as_tensor(np.ascontiguousarray(data, dtype=dtype)[None, None]),\n            torch.as_tensor(np.ascontiguousarray(transform, dtype=dtype)),\n            spatial_size=output_spatial_shape_[: len(data.shape)],\n        )\n        data_np = data_torch.squeeze(0).squeeze(0).detach().cpu().numpy()\n\n    results_img = nib.Nifti1Image(data_np.astype(output_dtype, copy=False), to_affine_nd(3, target_affine))\n    nib.save(results_img, file_name)\n    return", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/png_saver.py_PNGSaver.save_batch_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/png_saver.py_PNGSaver.save_batch_", "embedding": null, "metadata": {"file_path": "monai/data/png_saver.py", "file_name": "png_saver.py", "file_type": "text/x-python", "category": "implementation", "start_line": 125, "end_line": 135, "span_ids": ["PNGSaver.save_batch"], "tokens": 135}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class PNGSaver:\n\n    def save_batch(self, batch_data: Union[torch.Tensor, np.ndarray], meta_data: Optional[Dict] = None) -> None:\n        \"\"\"Save a batch of data into png format files.\n\n        Args:\n            batch_data: target batch data content that save into png format.\n            meta_data: every key-value in the meta_data is corresponding to a batch of data.\n\n        \"\"\"\n        for i, data in enumerate(batch_data):  # save a batch of files\n            self.save(data=data, meta_data={k: meta_data[k][i] for k in meta_data} if meta_data is not None else None)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/png_writer.py_from_typing_import_Option_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/png_writer.py_from_typing_import_Option_", "embedding": null, "metadata": {"file_path": "monai/data/png_writer.py", "file_name": "png_writer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 87, "span_ids": ["write_png", "docstring"], "tokens": 899}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Optional, Sequence, Union\n\nimport numpy as np\n\nfrom monai.transforms.spatial.array import Resize\nfrom monai.utils import InterpolateMode, ensure_tuple_rep, look_up_option, optional_import\n\nImage, _ = optional_import(\"PIL\", name=\"Image\")\n\n\ndef write_png(\n    data: np.ndarray,\n    file_name: str,\n    output_spatial_shape: Optional[Sequence[int]] = None,\n    mode: Union[InterpolateMode, str] = InterpolateMode.BICUBIC,\n    scale: Optional[int] = None,\n) -> None:\n    \"\"\"\n    Write numpy data into png files to disk.\n    Spatially it supports HW for 2D.(H,W) or (H,W,3) or (H,W,4).\n    If `scale` is None, expect the input data in `np.uint8` or `np.uint16` type.\n    It's based on the Image module in PIL library:\n    https://pillow.readthedocs.io/en/stable/reference/Image.html\n\n    Args:\n        data: input data to write to file.\n        file_name: expected file name that saved on disk.\n        output_spatial_shape: spatial shape of the output image.\n        mode: {``\"nearest\"``, ``\"linear\"``, ``\"bilinear\"``, ``\"bicubic\"``, ``\"trilinear\"``, ``\"area\"``}\n            The interpolation mode. Defaults to ``\"bicubic\"``.\n            See also: https://pytorch.org/docs/stable/nn.functional.html#interpolate\n        scale: {``255``, ``65535``} postprocess data by clipping to [0, 1] and scaling to\n            [0, 255] (uint8) or [0, 65535] (uint16). Default is None to disable scaling.\n\n    Raises:\n        ValueError: When ``scale`` is not one of [255, 65535].\n\n    \"\"\"\n    if not isinstance(data, np.ndarray):\n        raise ValueError(\"input data must be numpy array.\")\n    if len(data.shape) == 3 and data.shape[2] == 1:  # PIL Image can't save image with 1 channel\n        data = data.squeeze(2)\n    if output_spatial_shape is not None:\n        output_spatial_shape_ = ensure_tuple_rep(output_spatial_shape, 2)\n        mode = look_up_option(mode, InterpolateMode)\n        align_corners = None if mode in (InterpolateMode.NEAREST, InterpolateMode.AREA) else False\n        xform = Resize(spatial_size=output_spatial_shape_, mode=mode, align_corners=align_corners)\n        _min, _max = np.min(data), np.max(data)\n        if len(data.shape) == 3:\n            data = np.moveaxis(data, -1, 0)  # to channel first\n            data = xform(data)  # type: ignore\n            data = np.moveaxis(data, 0, -1)\n        else:  # (H, W)\n            data = np.expand_dims(data, 0)  # make a channel\n            data = xform(data)[0]  # type: ignore\n        if mode != InterpolateMode.NEAREST:\n            data = np.clip(data, _min, _max)  # type: ignore\n\n    if scale is not None:\n        data = np.clip(data, 0.0, 1.0)  # type: ignore # png writer only can scale data in range [0, 1]\n        if scale == np.iinfo(np.uint8).max:\n            data = (scale * data).astype(np.uint8, copy=False)\n        elif scale == np.iinfo(np.uint16).max:\n            data = (scale * data).astype(np.uint16, copy=False)\n        else:\n            raise ValueError(f\"Unsupported scale: {scale}, available options are [255, 65535]\")\n\n    # PNG data must be int number\n    if data.dtype not in (np.uint8, np.uint16):  # type: ignore\n        data = data.astype(np.uint8, copy=False)\n\n    data = np.moveaxis(data, 0, 1)\n    img = Image.fromarray(data)\n    img.save(file_name, \"PNG\")\n    return", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/synthetic.py_from_typing_import_Option_create_test_image_2d.return.noisyimage_labels": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/synthetic.py_from_typing_import_Option_create_test_image_2d.return.noisyimage_labels", "embedding": null, "metadata": {"file_path": "monai/data/synthetic.py", "file_name": "synthetic.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 91, "span_ids": ["create_test_image_2d", "docstring"], "tokens": 885}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Optional, Tuple\n\nimport numpy as np\n\nfrom monai.transforms.utils import rescale_array\n\n__all__ = [\"create_test_image_2d\", \"create_test_image_3d\"]\n\n\ndef create_test_image_2d(\n    width: int,\n    height: int,\n    num_objs: int = 12,\n    rad_max: int = 30,\n    rad_min: int = 5,\n    noise_max: float = 0.0,\n    num_seg_classes: int = 5,\n    channel_dim: Optional[int] = None,\n    random_state: Optional[np.random.RandomState] = None,\n) -> Tuple[np.ndarray, np.ndarray]:\n    \"\"\"\n    Return a noisy 2D image with `num_objs` circles and a 2D mask image. The maximum and minimum radii of the circles\n    are given as `rad_max` and `rad_min`. The mask will have `num_seg_classes` number of classes for segmentations labeled\n    sequentially from 1, plus a background class represented as 0. If `noise_max` is greater than 0 then noise will be\n    added to the image taken from the uniform distribution on range `[0,noise_max)`. If `channel_dim` is None, will create\n    an image without channel dimension, otherwise create an image with channel dimension as first dim or last dim.\n\n    Args:\n        width: width of the image. The value should be larger than `2 * rad_max`.\n        height: height of the image. The value should be larger than `2 * rad_max`.\n        num_objs: number of circles to generate. Defaults to `12`.\n        rad_max: maximum circle radius. Defaults to `30`.\n        rad_min: minimum circle radius. Defaults to `5`.\n        noise_max: if greater than 0 then noise will be added to the image taken from\n            the uniform distribution on range `[0,noise_max)`. Defaults to `0`.\n        num_seg_classes: number of classes for segmentations. Defaults to `5`.\n        channel_dim: if None, create an image without channel dimension, otherwise create\n            an image with channel dimension as first dim or last dim. Defaults to `None`.\n        random_state: the random generator to use. Defaults to `np.random`.\n    \"\"\"\n\n    if rad_max <= rad_min:\n        raise ValueError(\"`rad_min` should be less than `rad_max`.\")\n    if rad_min < 1:\n        raise ValueError(\"`rad_min` should be no less than 1.\")\n    min_size = min(width, height)\n    if min_size <= 2 * rad_max:\n        raise ValueError(\"the minimal size of the image should be larger than `2 * rad_max`.\")\n\n    image = np.zeros((width, height))\n    rs: np.random.RandomState = np.random.random.__self__ if random_state is None else random_state  # type: ignore\n\n    for _ in range(num_objs):\n        x = rs.randint(rad_max, width - rad_max)\n        y = rs.randint(rad_max, height - rad_max)\n        rad = rs.randint(rad_min, rad_max)\n        spy, spx = np.ogrid[-x : width - x, -y : height - y]\n        circle = (spx * spx + spy * spy) <= rad * rad\n\n        if num_seg_classes > 1:\n            image[circle] = np.ceil(rs.random() * num_seg_classes)\n        else:\n            image[circle] = rs.random() * 0.5 + 0.5\n\n    labels = np.ceil(image).astype(np.int32, copy=False)\n\n    norm = rs.uniform(0, num_seg_classes * noise_max, size=image.shape)\n    noisyimage: np.ndarray = rescale_array(np.maximum(image, norm))  # type: ignore\n\n    if channel_dim is not None:\n        if not (isinstance(channel_dim, int) and channel_dim in (-1, 0, 2)):\n            raise AssertionError(\"invalid channel dim.\")\n        if channel_dim == 0:\n            noisyimage = noisyimage[None]\n            labels = labels[None]\n        else:\n            noisyimage = noisyimage[..., None]\n            labels = labels[..., None]\n\n    return noisyimage, labels", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/synthetic.py_create_test_image_3d_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/synthetic.py_create_test_image_3d_", "embedding": null, "metadata": {"file_path": "monai/data/synthetic.py", "file_name": "synthetic.py", "file_type": "text/x-python", "category": "implementation", "start_line": 94, "end_line": 164, "span_ids": ["create_test_image_3d"], "tokens": 781}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def create_test_image_3d(\n    height: int,\n    width: int,\n    depth: int,\n    num_objs: int = 12,\n    rad_max: int = 30,\n    rad_min: int = 5,\n    noise_max: float = 0.0,\n    num_seg_classes: int = 5,\n    channel_dim: Optional[int] = None,\n    random_state: Optional[np.random.RandomState] = None,\n) -> Tuple[np.ndarray, np.ndarray]:\n    \"\"\"\n    Return a noisy 3D image and segmentation.\n\n    Args:\n        height: height of the image. The value should be larger than `2 * rad_max`.\n        width: width of the image. The value should be larger than `2 * rad_max`.\n        depth: depth of the image. The value should be larger than `2 * rad_max`.\n        num_objs: number of circles to generate. Defaults to `12`.\n        rad_max: maximum circle radius. Defaults to `30`.\n        rad_min: minimum circle radius. Defaults to `5`.\n        noise_max: if greater than 0 then noise will be added to the image taken from\n            the uniform distribution on range `[0,noise_max)`. Defaults to `0`.\n        num_seg_classes: number of classes for segmentations. Defaults to `5`.\n        channel_dim: if None, create an image without channel dimension, otherwise create\n            an image with channel dimension as first dim or last dim. Defaults to `None`.\n        random_state: the random generator to use. Defaults to `np.random`.\n\n    See also:\n        :py:meth:`~create_test_image_2d`\n    \"\"\"\n\n    if rad_max <= rad_min:\n        raise ValueError(\"`rad_min` should be less than `rad_max`.\")\n    if rad_min < 1:\n        raise ValueError(\"`rad_min` should be no less than 1.\")\n    min_size = min(width, height, depth)\n    if min_size <= 2 * rad_max:\n        raise ValueError(\"the minimal size of the image should be larger than `2 * rad_max`.\")\n\n    image = np.zeros((width, height, depth))\n    rs: np.random.RandomState = np.random.random.__self__ if random_state is None else random_state  # type: ignore\n\n    for _ in range(num_objs):\n        x = rs.randint(rad_max, width - rad_max)\n        y = rs.randint(rad_max, height - rad_max)\n        z = rs.randint(rad_max, depth - rad_max)\n        rad = rs.randint(rad_min, rad_max)\n        spy, spx, spz = np.ogrid[-x : width - x, -y : height - y, -z : depth - z]\n        circle = (spx * spx + spy * spy + spz * spz) <= rad * rad\n\n        if num_seg_classes > 1:\n            image[circle] = np.ceil(rs.random() * num_seg_classes)\n        else:\n            image[circle] = rs.random() * 0.5 + 0.5\n\n    labels = np.ceil(image).astype(np.int32, copy=False)\n\n    norm = rs.uniform(0, num_seg_classes * noise_max, size=image.shape)\n    noisyimage: np.ndarray = rescale_array(np.maximum(image, norm))  # type: ignore\n\n    if channel_dim is not None:\n        if not (isinstance(channel_dim, int) and channel_dim in (-1, 0, 3)):\n            raise AssertionError(\"invalid channel dim.\")\n        noisyimage, labels = (\n            (noisyimage[None], labels[None]) if channel_dim == 0 else (noisyimage[..., None], labels[..., None])\n        )\n\n    return noisyimage, labels", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_iter_patch_slices_iter_patch_slices.for_position_in_product_.yield_tuple_slice_s_s_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_iter_patch_slices_iter_patch_slices.for_position_in_product_.yield_tuple_slice_s_s_", "embedding": null, "metadata": {"file_path": "monai/data/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 53, "end_line": 80, "span_ids": ["iter_patch_slices"], "tokens": 313}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def iter_patch_slices(\n    dims: Sequence[int], patch_size: Union[Sequence[int], int], start_pos: Sequence[int] = ()\n) -> Generator[Tuple[slice, ...], None, None]:\n    \"\"\"\n    Yield successive tuples of slices defining patches of size `patch_size` from an array of dimensions `dims`. The\n    iteration starts from position `start_pos` in the array, or starting at the origin if this isn't provided. Each\n    patch is chosen in a contiguous grid using a first dimension as least significant ordering.\n\n    Args:\n        dims: dimensions of array to iterate over\n        patch_size: size of patches to generate slices for, 0 or None selects whole dimension\n        start_pos: starting position in the array, default is 0 for each dimension\n\n    Yields:\n        Tuples of slice objects defining each patch\n    \"\"\"\n\n    # ensure patchSize and startPos are the right length\n    ndim = len(dims)\n    patch_size_ = get_valid_patch_size(dims, patch_size)\n    start_pos = ensure_tuple_size(start_pos, ndim)\n\n    # collect the ranges to step over each dimension\n    ranges = tuple(starmap(range, zip(start_pos, dims, patch_size_)))\n\n    # choose patches by applying product to the ranges\n    for position in product(*ranges[::-1]):  # reverse ranges order to iterate in index order\n        yield tuple(slice(s, s + p) for s, p in zip(position[::-1], patch_size_))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_iter_patch_iter_patch.if_copy_back_.arr_arrpad_slices_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_iter_patch_iter_patch.if_copy_back_.arr_arrpad_slices_", "embedding": null, "metadata": {"file_path": "monai/data/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 177, "end_line": 236, "span_ids": ["iter_patch"], "tokens": 750}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def iter_patch(\n    arr: np.ndarray,\n    patch_size: Union[Sequence[int], int] = 0,\n    start_pos: Sequence[int] = (),\n    copy_back: bool = True,\n    mode: Union[NumpyPadMode, str] = NumpyPadMode.WRAP,\n    **pad_opts: Dict,\n):\n    \"\"\"\n    Yield successive patches from `arr` of size `patch_size`. The iteration can start from position `start_pos` in `arr`\n    but drawing from a padded array extended by the `patch_size` in each dimension (so these coordinates can be negative\n    to start in the padded region). If `copy_back` is True the values from each patch are written back to `arr`.\n\n    Args:\n        arr: array to iterate over\n        patch_size: size of patches to generate slices for, 0 or None selects whole dimension\n        start_pos: starting position in the array, default is 0 for each dimension\n        copy_back: if True data from the yielded patches is copied back to `arr` once the generator completes\n        mode: {``\"constant\"``, ``\"edge\"``, ``\"linear_ramp\"``, ``\"maximum\"``, ``\"mean\"``,\n            ``\"median\"``, ``\"minimum\"``, ``\"reflect\"``, ``\"symmetric\"``, ``\"wrap\"``, ``\"empty\"``}\n            One of the listed string values or a user supplied function. Defaults to ``\"wrap\"``.\n            See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html\n        pad_opts: padding options, see `numpy.pad`\n\n    Yields:\n        Patches of array data from `arr` which are views into a padded array which can be modified, if `copy_back` is\n        True these changes will be reflected in `arr` once the iteration completes.\n\n    Note:\n        coordinate format is:\n\n            [1st_dim_start, 1st_dim_end,\n             2nd_dim_start, 2nd_dim_end,\n             ...,\n             Nth_dim_start, Nth_dim_end]]\n\n    \"\"\"\n    # ensure patchSize and startPos are the right length\n    patch_size_ = get_valid_patch_size(arr.shape, patch_size)\n    start_pos = ensure_tuple_size(start_pos, arr.ndim)\n\n    # pad image by maximum values needed to ensure patches are taken from inside an image\n    arrpad = np.pad(arr, tuple((p, p) for p in patch_size_), look_up_option(mode, NumpyPadMode).value, **pad_opts)\n\n    # choose a start position in the padded image\n    start_pos_padded = tuple(s + p for s, p in zip(start_pos, patch_size_))\n\n    # choose a size to iterate over which is smaller than the actual padded image to prevent producing\n    # patches which are only in the padded regions\n    iter_size = tuple(s + p for s, p in zip(arr.shape, patch_size_))\n\n    for slices in iter_patch_slices(iter_size, patch_size_, start_pos_padded):\n        # compensate original image padding\n        coords_no_pad = tuple((coord.start - p, coord.stop - p) for coord, p in zip(slices, patch_size_))\n        yield arrpad[slices], np.asarray(coords_no_pad)  # data and coords (in numpy; works with torch loader)\n\n    # copy back data from the padded image if required\n    if copy_back:\n        slices = tuple(slice(p, p + s) for p, s in zip(patch_size_, arr.shape))\n        arr[...] = arrpad[slices]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_get_valid_patch_size_get_valid_patch_size.return.tuple_min_ms_ps_or_ms_f": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_get_valid_patch_size_get_valid_patch_size.return.tuple_min_ms_ps_or_ms_f", "embedding": null, "metadata": {"file_path": "monai/data/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 198, "end_line": 209, "span_ids": ["get_valid_patch_size"], "tokens": 211}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_valid_patch_size(image_size: Sequence[int], patch_size: Union[Sequence[int], int]) -> Tuple[int, ...]:\n    \"\"\"\n    Given an image of dimensions `image_size`, return a patch size tuple taking the dimension from `patch_size` if this is\n    not 0/None. Otherwise, or if `patch_size` is shorter than `image_size`, the dimension from `image_size` is taken. This ensures\n    the returned patch size is within the bounds of `image_size`. If `patch_size` is a single number this is interpreted as a\n    patch of the same dimensionality of `image_size` with that size in each dimension.\n    \"\"\"\n    ndim = len(image_size)\n    patch_size_ = ensure_tuple_size(patch_size, ndim)\n\n    # ensure patch size dimensions are not larger than image dimension, if a dimension is None or 0 use whole dimension\n    return tuple(min(ms, ps or ms) for ms, ps in zip(image_size, patch_size_))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_correct_nifti_header_if_necessary_correct_nifti_header_if_necessary.return.img_nii": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_correct_nifti_header_if_necessary_correct_nifti_header_if_necessary.return.img_nii", "embedding": null, "metadata": {"file_path": "monai/data/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 486, "end_line": 506, "span_ids": ["correct_nifti_header_if_necessary"], "tokens": 217}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def correct_nifti_header_if_necessary(img_nii):\n    \"\"\"\n    Check nifti object header's format, update the header if needed.\n    In the updated image pixdim matches the affine.\n\n    Args:\n        img_nii: nifti image object\n    \"\"\"\n    if img_nii.header.get(\"dim\") is None:\n        return img_nii  # not nifti?\n    dim = img_nii.header[\"dim\"][0]\n    if dim >= 5:\n        return img_nii  # do nothing for high-dimensional array\n    # check that affine matches zooms\n    pixdim = np.asarray(img_nii.header.get_zooms())[:dim]\n    norm_affine = np.sqrt(np.sum(np.square(img_nii.affine[:dim, :dim]), 0))\n    if np.allclose(pixdim, norm_affine):\n        return img_nii\n    if hasattr(img_nii, \"get_sform\"):\n        return rectify_header_sform_qform(img_nii)\n    return img_nii", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_rectify_header_sform_qform_rectify_header_sform_qform.return.img_nii": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_rectify_header_sform_qform_rectify_header_sform_qform.return.img_nii", "embedding": null, "metadata": {"file_path": "monai/data/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 259, "end_line": 294, "span_ids": ["rectify_header_sform_qform"], "tokens": 388}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def rectify_header_sform_qform(img_nii):\n    \"\"\"\n    Look at the sform and qform of the nifti object and correct it if any\n    incompatibilities with pixel dimensions\n\n    Adapted from https://github.com/NifTK/NiftyNet/blob/v0.6.0/niftynet/io/misc_io.py\n\n    Args:\n        img_nii: nifti image object\n    \"\"\"\n    d = img_nii.header[\"dim\"][0]\n    pixdim = np.asarray(img_nii.header.get_zooms())[:d]\n    sform, qform = img_nii.get_sform(), img_nii.get_qform()\n    norm_sform = np.sqrt(np.sum(np.square(sform[:d, :d]), 0))\n    norm_qform = np.sqrt(np.sum(np.square(qform[:d, :d]), 0))\n    sform_mismatch = not np.allclose(norm_sform, pixdim)\n    qform_mismatch = not np.allclose(norm_qform, pixdim)\n\n    if img_nii.header[\"sform_code\"] != 0:\n        if not sform_mismatch:\n            return img_nii\n        if not qform_mismatch:\n            img_nii.set_sform(img_nii.get_qform())\n            return img_nii\n    if img_nii.header[\"qform_code\"] != 0:\n        if not qform_mismatch:\n            return img_nii\n        if not sform_mismatch:\n            img_nii.set_qform(img_nii.get_sform())\n            return img_nii\n\n    norm = np.sqrt(np.sum(np.square(img_nii.affine[:d, :d]), 0))\n    warnings.warn(f\"Modifying image pixdim from {pixdim} to {norm}\")\n\n    img_nii.header.set_zooms(norm)\n    return img_nii", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_zoom_affine_zoom_affine.return.new_affine": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_zoom_affine_zoom_affine.return.new_affine", "embedding": null, "metadata": {"file_path": "monai/data/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 542, "end_line": 591, "span_ids": ["zoom_affine"], "tokens": 566}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def zoom_affine(affine: np.ndarray, scale: Union[np.ndarray, Sequence[float]], diagonal: bool = True):\n    \"\"\"\n    To make column norm of `affine` the same as `scale`.  If diagonal is False,\n    returns an affine that combines orthogonal rotation and the new scale.\n    This is done by first decomposing `affine`, then setting the zoom factors to\n    `scale`, and composing a new affine; the shearing factors are removed.  If\n    diagonal is True, returns a diagonal matrix, the scaling factors are set\n    to the diagonal elements.  This function always return an affine with zero\n    translations.\n\n    Args:\n        affine (nxn matrix): a square matrix.\n        scale: new scaling factor along each dimension. if the components of the `scale` are non-positive values,\n            will use the corresponding components of the original pixdim, which is computed from the `affine`.\n        diagonal: whether to return a diagonal scaling matrix.\n            Defaults to True.\n\n    Raises:\n        ValueError: When ``affine`` is not a square matrix.\n        ValueError: When ``scale`` contains a nonpositive scalar.\n\n    Returns:\n        the updated `n x n` affine.\n\n    \"\"\"\n\n    affine = np.array(affine, dtype=float, copy=True)\n    if len(affine) != len(affine[0]):\n        raise ValueError(f\"affine must be n x n, got {len(affine)} x {len(affine[0])}.\")\n    scale_np = np.array(scale, dtype=float, copy=True)\n\n    d = len(affine) - 1\n    # compute original pixdim\n    norm = np.sqrt(np.sum(np.square(affine), 0))[:-1]\n    if len(scale_np) < d:  # defaults based on affine\n        scale_np = np.append(scale_np, norm[len(scale_np) :])\n    scale_np = scale_np[:d]\n    scale_np = np.asarray(fall_back_tuple(scale_np, norm))\n\n    scale_np[scale_np == 0] = 1.0\n    if diagonal:\n        return np.diag(np.append(scale_np, [1.0]))\n    rzs = affine[:-1, :-1]  # rotation zoom scale\n    zs = np.linalg.cholesky(rzs.T @ rzs).T\n    rotation = rzs @ np.linalg.inv(zs)\n    s = np.sign(np.diag(zs)) * np.abs(scale_np)\n    # construct new affine with rotation and zoom\n    new_affine = np.eye(len(affine))\n    new_affine[:-1, :-1] = rotation @ np.diag(s)\n    return new_affine", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_to_affine_nd_to_affine_nd.return.new_affine": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_to_affine_nd_to_affine_nd.return.new_affine", "embedding": null, "metadata": {"file_path": "monai/data/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 580, "end_line": 620, "span_ids": ["to_affine_nd"], "tokens": 505}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def to_affine_nd(r: Union[np.ndarray, int], affine: np.ndarray) -> np.ndarray:\n    \"\"\"\n    Using elements from affine, to create a new affine matrix by\n    assigning the rotation/zoom/scaling matrix and the translation vector.\n\n    when ``r`` is an integer, output is an (r+1)x(r+1) matrix,\n    where the top left kxk elements are copied from ``affine``,\n    the last column of the output affine is copied from ``affine``'s last column.\n    `k` is determined by `min(r, len(affine) - 1)`.\n\n    when ``r`` is an affine matrix, the output has the same as ``r``,\n    the top left kxk elements are  copied from ``affine``,\n    the last column of the output affine is copied from ``affine``'s last column.\n    `k` is determined by `min(len(r) - 1, len(affine) - 1)`.\n\n    Args:\n        r (int or matrix): number of spatial dimensions or an output affine to be filled.\n        affine (matrix): 2D affine matrix\n\n    Raises:\n        ValueError: When ``affine`` dimensions is not 2.\n        ValueError: When ``r`` is nonpositive.\n\n    Returns:\n        an (r+1) x (r+1) matrix\n\n    \"\"\"\n    affine_np = np.array(affine, dtype=np.float64)\n    if affine_np.ndim != 2:\n        raise ValueError(f\"affine must have 2 dimensions, got {affine_np.ndim}.\")\n    new_affine = np.array(r, dtype=np.float64, copy=True)\n    if new_affine.ndim == 0:\n        sr: int = int(new_affine.astype(np.uint))\n        if not np.isfinite(sr) or sr < 0:\n            raise ValueError(f\"r must be positive, got {sr}.\")\n        new_affine = np.eye(sr + 1, dtype=np.float64)\n    d = max(min(len(new_affine) - 1, len(affine_np) - 1), 1)\n    new_affine[:d, :d] = affine_np[:d, :d]\n    if d > 1:\n        new_affine[:d, -1] = affine_np[:d, -1]\n    return new_affine", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/evaluator.py_EnsembleEvaluator._iteration_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/evaluator.py_EnsembleEvaluator._iteration_", "embedding": null, "metadata": {"file_path": "monai/engines/evaluator.py", "file_name": "evaluator.py", "file_type": "text/x-python", "category": "implementation", "start_line": 352, "end_line": 401, "span_ids": ["EnsembleEvaluator._iteration"], "tokens": 442}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class EnsembleEvaluator(Evaluator):\n\n    def _iteration(self, engine: Engine, batchdata: Dict[str, torch.Tensor]):\n        \"\"\"\n        callback function for the Supervised Evaluation processing logic of 1 iteration in Ignite Engine.\n        Return below items in a dictionary:\n            - IMAGE: image Tensor data for model input, already moved to device.\n            - LABEL: label Tensor data corresponding to the image, already moved to device.\n            - pred_keys[0]: prediction result of network 0.\n            - pred_keys[1]: prediction result of network 1.\n            - ... ...\n            - pred_keys[N]: prediction result of network N.\n\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n            batchdata: input data for this iteration, usually can be dictionary or tuple of Tensor data.\n\n        Raises:\n            ValueError: When ``batchdata`` is None.\n\n        \"\"\"\n        if batchdata is None:\n            raise ValueError(\"Must provide batch data for current iteration.\")\n        batch = self.prepare_batch(batchdata, engine.state.device, engine.non_blocking)  # type: ignore\n        if len(batch) == 2:\n            inputs, targets = batch\n            args: Tuple = ()\n            kwargs: Dict = {}\n        else:\n            inputs, targets, args, kwargs = batch\n\n        # put iteration outputs into engine.state\n        engine.state.output = {Keys.IMAGE: inputs, Keys.LABEL: targets}  # type: ignore\n\n        for idx, network in enumerate(self.networks):\n            with self.mode(network):\n                if self.amp:\n                    with torch.cuda.amp.autocast():\n                        if isinstance(engine.state.output, dict):\n                            engine.state.output.update(\n                                {self.pred_keys[idx]: self.inferer(inputs, network, *args, **kwargs)}\n                            )\n                else:\n                    if isinstance(engine.state.output, dict):\n                        engine.state.output.update(\n                            {self.pred_keys[idx]: self.inferer(inputs, network, *args, **kwargs)}\n                        )\n        engine.fire_event(IterationEvents.FORWARD_COMPLETED)\n        engine.fire_event(IterationEvents.MODEL_COMPLETED)\n\n        return engine.state.output", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/multi_gpu_supervised_trainer.py_create_multigpu_supervised_trainer_create_multigpu_supervised_trainer.return.create_supervised_trainer": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/multi_gpu_supervised_trainer.py_create_multigpu_supervised_trainer_create_multigpu_supervised_trainer.return.create_supervised_trainer", "embedding": null, "metadata": {"file_path": "monai/engines/multi_gpu_supervised_trainer.py", "file_name": "multi_gpu_supervised_trainer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 50, "end_line": 96, "span_ids": ["create_multigpu_supervised_trainer"], "tokens": 434}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def create_multigpu_supervised_trainer(\n    net: torch.nn.Module,\n    optimizer: Optimizer,\n    loss_fn: Callable,\n    devices: Optional[Sequence[torch.device]] = None,\n    non_blocking: bool = False,\n    prepare_batch: Callable = _prepare_batch,\n    output_transform: Callable = _default_transform,\n    distributed: bool = False,\n):\n    \"\"\"\n    Derived from `create_supervised_trainer` in Ignite.\n\n    Factory function for creating a trainer for supervised models.\n\n    Args:\n        net: the network to train.\n        optimizer: the optimizer to use.\n        loss_fn: the loss function to use.\n        devices: device(s) type specification (default: None).\n            Applies to both model and batches. None is all devices used, empty list is CPU only.\n        non_blocking: if True and this copy is between CPU and GPU, the copy may occur asynchronously\n            with respect to the host. For other cases, this argument has no effect.\n        prepare_batch: function that receives `batch`, `device`, `non_blocking` and outputs\n            tuple of tensors `(batch_x, batch_y)`.\n        output_transform: function that receives 'x', 'y', 'y_pred', 'loss' and returns value\n            to be assigned to engine's state.output after each iteration. Default is returning `loss.item()`.\n        distributed: whether convert model to `DistributedDataParallel`, if have multiple devices, use\n            the first device as output device.\n\n    Returns:\n        Engine: a trainer engine with supervised update function.\n\n    Note:\n        `engine.state.output` for this engine is defined by `output_transform` parameter and is the loss\n        of the processed batch by default.\n    \"\"\"\n\n    devices_ = get_devices_spec(devices)\n    if distributed:\n        net = DistributedDataParallel(net, device_ids=devices_)\n    elif len(devices_) > 1:\n        net = DataParallel(net)\n\n    return create_supervised_trainer(\n        net, optimizer, loss_fn, devices_[0], non_blocking, prepare_batch, output_transform\n    )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/multi_gpu_supervised_trainer.py_create_multigpu_supervised_evaluator_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/multi_gpu_supervised_trainer.py_create_multigpu_supervised_evaluator_", "embedding": null, "metadata": {"file_path": "monai/engines/multi_gpu_supervised_trainer.py", "file_name": "multi_gpu_supervised_trainer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 99, "end_line": 144, "span_ids": ["create_multigpu_supervised_evaluator"], "tokens": 447}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def create_multigpu_supervised_evaluator(\n    net: torch.nn.Module,\n    metrics: Optional[Dict[str, Metric]] = None,\n    devices: Optional[Sequence[torch.device]] = None,\n    non_blocking: bool = False,\n    prepare_batch: Callable = _prepare_batch,\n    output_transform: Callable = _default_eval_transform,\n    distributed: bool = False,\n):\n    \"\"\"\n    Derived from `create_supervised_evaluator` in Ignite.\n\n    Factory function for creating an evaluator for supervised models.\n\n    Args:\n        net: the model to train.\n        metrics: a map of metric names to Metrics.\n        devices: device(s) type specification (default: None).\n            Applies to both model and batches. None is all devices used, empty list is CPU only.\n        non_blocking: if True and this copy is between CPU and GPU, the copy may occur asynchronously\n            with respect to the host. For other cases, this argument has no effect.\n        prepare_batch: function that receives `batch`, `device`, `non_blocking` and outputs\n            tuple of tensors `(batch_x, batch_y)`.\n        output_transform: function that receives 'x', 'y', 'y_pred' and returns value\n            to be assigned to engine's state.output after each iteration. Default is returning `(y_pred, y,)`\n            which fits output expected by metrics. If you change it you should use `output_transform` in metrics.\n        distributed: whether convert model to `DistributedDataParallel`, if have multiple devices, use\n            the first device as output device.\n\n    Note:\n        `engine.state.output` for this engine is defined by `output_transform` parameter and is\n        a tuple of `(batch_pred, batch_y)` by default.\n\n    Returns:\n        Engine: an evaluator engine with supervised inference function.\n    \"\"\"\n\n    devices_ = get_devices_spec(devices)\n\n    if distributed:\n        net = DistributedDataParallel(net, device_ids=devices_)\n    elif len(devices_) > 1:\n        net = DataParallel(net)\n\n    return create_supervised_evaluator(net, metrics, devices_[0], non_blocking, prepare_batch, output_transform)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/trainer.py_Trainer_Trainer.get_train_stats.return._total_epochs_self_sta": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/trainer.py_Trainer_Trainer.get_train_stats.return._total_epochs_self_sta", "embedding": null, "metadata": {"file_path": "monai/engines/trainer.py", "file_name": "trainer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 33, "end_line": 49, "span_ids": ["Trainer.run", "Trainer.get_train_stats", "Trainer"], "tokens": 118}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Trainer(Workflow):\n    \"\"\"\n    Base class for all kinds of trainers, inherits from Workflow.\n\n    \"\"\"\n\n    def run(self) -> None:\n        \"\"\"\n        Execute training based on Ignite Engine.\n        If call this function multiple times, it will continuously run from the previous state.\n\n        \"\"\"\n        self.scaler = torch.cuda.amp.GradScaler() if self.amp else None\n        super().run()\n\n    def get_train_stats(self) -> Dict[str, float]:\n        return {\"total_epochs\": self.state.max_epochs, \"total_iterations\": self.state.epoch_length}", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/workflow.py_Workflow_Workflow._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/workflow.py_Workflow_Workflow._", "embedding": null, "metadata": {"file_path": "monai/engines/workflow.py", "file_name": "workflow.py", "file_type": "text/x-python", "category": "implementation", "start_line": 53, "end_line": 104, "span_ids": ["Workflow"], "tokens": 793}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Workflow(IgniteEngine):  # type: ignore[valid-type, misc] # due to optional_import\n    \"\"\"\n    Workflow defines the core work process inheriting from Ignite engine.\n    All trainer, validator and evaluator share this same workflow as base class,\n    because they all can be treated as same Ignite engine loops.\n    It initializes all the sharable data in Ignite engine.state.\n    And attach additional processing logics to Ignite engine based on Event-Handler mechanism.\n\n    Users should consider inheriting from `trainer` or `evaluator` to develop more trainers or evaluators.\n\n    Args:\n        device: an object representing the device on which to run.\n        max_epochs: the total epoch number for engine to run, validator and evaluator have only 1 epoch.\n        data_loader: Ignite engine use data_loader to run, must be Iterable or torch.DataLoader.\n        epoch_length: number of iterations for one epoch, default to `len(data_loader)`.\n        non_blocking: if True and this copy is between CPU and GPU, the copy may occur asynchronously\n            with respect to the host. For other cases, this argument has no effect.\n        prepare_batch: function to parse expected data (usually `image`, `label` and other network args)\n            from `engine.state.batch` for every iteration, for more details please refer to:\n            https://pytorch.org/ignite/generated/ignite.engine.create_supervised_trainer.html.\n        iteration_update: the callable function for every iteration, expect to accept `engine`\n            and `engine.state.batch` as inputs, return data will be stored in `engine.state.output`.\n            if not provided, use `self._iteration()` instead. for more details please refer to:\n            https://pytorch.org/ignite/generated/ignite.engine.engine.Engine.html.\n        postprocessing: execute additional transformation for the model output data.\n            Typically, several Tensor based transforms composed by `Compose`.\n        key_metric: compute metric when every iteration completed, and save average value to\n            engine.state.metrics when epoch completed. key_metric is the main metric to compare and save the\n            checkpoint into files.\n        additional_metrics: more Ignite metrics that also attach to Ignite Engine.\n        metric_cmp_fn: function to compare current key metric with previous best key metric value,\n            it must accept 2 args (current_metric, previous_best) and return a bool result: if `True`, will update\n            `best_metric` and `best_metric_epoch` with current metric and epoch, default to `greater than`.\n        handlers: every handler is a set of Ignite Event-Handlers, must have `attach` function, like:\n            CheckpointHandler, StatsHandler, SegmentationSaver, etc.\n        amp: whether to enable auto-mixed-precision training or inference, default is False.\n        event_names: additional custom ignite events that will register to the engine.\n            new events can be a list of str or `ignite.engine.events.EventEnum`.\n        event_to_attr: a dictionary to map an event to a state attribute, then add to `engine.state`.\n            for more details, check: https://pytorch.org/ignite/generated/ignite.engine.engine.Engine.html\n            #ignite.engine.engine.Engine.register_events.\n        decollate: whether to decollate the batch-first data to a list of data after model computation,\n            recommend `decollate=True` when `postprocessing` uses components from `monai.transforms`.\n            default to `True`.\n\n    Raises:\n        TypeError: When ``device`` is not a ``torch.Device``.\n        TypeError: When ``data_loader`` is not a ``torch.utils.data.DataLoader``.\n        TypeError: When ``key_metric`` is not a ``Optional[dict]``.\n        TypeError: When ``additional_metrics`` is not a ``Optional[dict]``.\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/__init__.py_CheckpointLoader_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/__init__.py_CheckpointLoader_", "embedding": null, "metadata": {"file_path": "monai/handlers/__init__.py", "file_name": "__init__.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 38, "span_ids": ["docstring"], "tokens": 304}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from .checkpoint_loader import CheckpointLoader\nfrom .checkpoint_saver import CheckpointSaver\nfrom .classification_saver import ClassificationSaver\nfrom .confusion_matrix import ConfusionMatrix\nfrom .decollate_batch import DecollateBatch\nfrom .earlystop_handler import EarlyStopHandler\nfrom .garbage_collector import GarbageCollector\nfrom .hausdorff_distance import HausdorffDistance\nfrom .ignite_metric import IgniteMetric\nfrom .lr_schedule_handler import LrScheduleHandler\nfrom .mean_dice import MeanDice\nfrom .metric_logger import MetricLogger, MetricLoggerKeys\nfrom .metrics_saver import MetricsSaver\nfrom .mlflow_handler import MLFlowHandler\nfrom .nvtx_handlers import MarkHandler, RangeHandler, RangePopHandler, RangePushHandler\nfrom .parameter_scheduler import ParamSchedulerHandler\nfrom .postprocessing import PostProcessing\nfrom .regression_metrics import MeanAbsoluteError, MeanSquaredError, PeakSignalToNoiseRatio, RootMeanSquaredError\nfrom .roc_auc import ROCAUC\nfrom .segmentation_saver import SegmentationSaver\nfrom .smartcache_handler import SmartCacheHandler\nfrom .stats_handler import StatsHandler\nfrom .surface_distance import SurfaceDistance\nfrom .tensorboard_handlers import TensorBoardHandler, TensorBoardImageHandler, TensorBoardStatsHandler\nfrom .utils import from_engine, stopping_fn_from_loss, stopping_fn_from_metric, write_metrics_reports\nfrom .validation_handler import ValidationHandler", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/checkpoint_saver.py_CheckpointSaver_CheckpointSaver._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/checkpoint_saver.py_CheckpointSaver_CheckpointSaver._", "embedding": null, "metadata": {"file_path": "monai/handlers/checkpoint_saver.py", "file_name": "checkpoint_saver.py", "file_type": "text/x-python", "category": "implementation", "start_line": 30, "end_line": 84, "span_ids": ["CheckpointSaver"], "tokens": 701}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CheckpointSaver:\n    \"\"\"\n    CheckpointSaver acts as an Ignite handler to save checkpoint data into files.\n    It supports to save according to metrics result, epoch number, iteration number\n    and last model or exception.\n\n    Args:\n        save_dir: the target directory to save the checkpoints.\n        save_dict: source objects that save to the checkpoint. examples::\n\n            {'network': net, 'optimizer': optimizer, 'lr_scheduler': lr_scheduler}\n\n        name: identifier of logging.logger to use, if None, defaulting to ``engine.logger``.\n        file_prefix: prefix for the filenames to which objects will be saved.\n        save_final: whether to save checkpoint or session at final iteration or exception.\n            If checkpoints are to be saved when an exception is raised, put this handler before\n            `StatsHandler` in the handler list, because the logic with Ignite can only trigger\n            the first attached handler for `EXCEPTION_RAISED` event.\n        final_filename: set a fixed filename to save the final model if `save_final=True`.\n            If None, default to `checkpoint_final_iteration=N.pt`.\n        save_key_metric: whether to save checkpoint or session when the value of key_metric is\n            higher than all the previous values during training.keep 4 decimal places of metric,\n            checkpoint name is: {file_prefix}_key_metric=0.XXXX.pth.\n        key_metric_name: the name of key_metric in ignite metrics dictionary.\n            If None, use `engine.state.key_metric` instead.\n        key_metric_n_saved: save top N checkpoints or sessions, sorted by the value of key\n            metric in descending order.\n        key_metric_filename: set a fixed filename to set the best metric model, if not None,\n            `key_metric_n_saved` should be 1 and only keep the best metric model.\n        key_metric_save_state: whether to save the tracking list of key metric in the checkpoint file.\n            if `True`, then will save an object in the checkpoint file with key `checkpointer` to be\n            consistent with the `include_self` arg of `Checkpoint` in ignite:\n            https://pytorch.org/ignite/v0.4.5/generated/ignite.handlers.checkpoint.Checkpoint.html.\n            typically, it's used to resume training and compare current metric with previous N values.\n        key_metric_greater_or_equal: if `True`, the latest equally scored model is stored. Otherwise,\n            save the the first equally scored model. default to `False`.\n        key_metric_negative_sign: whether adding a negative sign to the metric score to compare metrics,\n            because for error-like metrics, smaller is better(objects with larger score are retained).\n            default to `False`.\n        epoch_level: save checkpoint during training for every N epochs or every N iterations.\n            `True` is epoch level, `False` is iteration level.\n        save_interval: save checkpoint every N epochs, default is 0 to save no checkpoint.\n        n_saved: save latest N checkpoints of epoch level or iteration level, 'None' is to save all.\n\n    Note:\n        CheckpointHandler can be used during training, validation or evaluation.\n        example of saved files:\n\n            - checkpoint_iteration=400.pt\n            - checkpoint_iteration=800.pt\n            - checkpoint_epoch=1.pt\n            - checkpoint_final_iteration=1000.pt\n            - checkpoint_key_metric=0.9387.pt\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/checkpoint_saver.py_CheckpointSaver.attach_CheckpointSaver.attach.if_self__interval_checkpo.if_self_epoch_level_.else_.engine_add_event_handler_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/checkpoint_saver.py_CheckpointSaver.attach_CheckpointSaver.attach.if_self__interval_checkpo.if_self_epoch_level_.else_.engine_add_event_handler_", "embedding": null, "metadata": {"file_path": "monai/handlers/checkpoint_saver.py", "file_name": "checkpoint_saver.py", "file_type": "text/x-python", "category": "implementation", "start_line": 141, "end_line": 157, "span_ids": ["CheckpointSaver.attach"], "tokens": 183}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CheckpointSaver:\n\n    def attach(self, engine: Engine) -> None:\n        \"\"\"\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n        \"\"\"\n        if self._name is None:\n            self.logger = engine.logger\n        if self._final_checkpoint is not None:\n            engine.add_event_handler(Events.COMPLETED, self.completed)\n            engine.add_event_handler(Events.EXCEPTION_RAISED, self.exception_raised)\n        if self._key_metric_checkpoint is not None:\n            engine.add_event_handler(Events.EPOCH_COMPLETED, self.metrics_completed)\n        if self._interval_checkpoint is not None:\n            if self.epoch_level:\n                engine.add_event_handler(Events.EPOCH_COMPLETED(every=self.save_interval), self.interval_completed)\n            else:\n                engine.add_event_handler(Events.ITERATION_COMPLETED(every=self.save_interval), self.interval_completed)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/lr_schedule_handler.py_LrScheduleHandler_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/lr_schedule_handler.py_LrScheduleHandler_", "embedding": null, "metadata": {"file_path": "monai/handlers/lr_schedule_handler.py", "file_name": "lr_schedule_handler.py", "file_type": "text/x-python", "category": "implementation", "start_line": 26, "end_line": 85, "span_ids": ["LrScheduleHandler.__init__", "LrScheduleHandler", "LrScheduleHandler.__call__", "LrScheduleHandler.attach"], "tokens": 510}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LrScheduleHandler:\n    \"\"\"\n    Ignite handler to update the Learning Rate based on PyTorch LR scheduler.\n    \"\"\"\n\n    def __init__(\n        self,\n        lr_scheduler: Union[_LRScheduler, ReduceLROnPlateau],\n        print_lr: bool = True,\n        name: Optional[str] = None,\n        epoch_level: bool = True,\n        step_transform: Callable[[Engine], Any] = lambda engine: (),\n    ) -> None:\n        \"\"\"\n        Args:\n            lr_scheduler: typically, lr_scheduler should be PyTorch\n                lr_scheduler object. If customized version, must have `step` and `get_last_lr` methods.\n            print_lr: whether to print out the latest learning rate with logging.\n            name: identifier of logging.logger to use, if None, defaulting to ``engine.logger``.\n            epoch_level: execute lr_scheduler.step() after every epoch or every iteration.\n                `True` is epoch level, `False` is iteration level.\n            step_transform: a callable that is used to transform the information from `engine`\n                to expected input data of lr_scheduler.step() function if necessary.\n\n        Raises:\n            TypeError: When ``step_transform`` is not ``callable``.\n\n        \"\"\"\n        self.lr_scheduler = lr_scheduler\n        self.print_lr = print_lr\n        self.logger = logging.getLogger(name)\n        self.epoch_level = epoch_level\n        if not callable(step_transform):\n            raise TypeError(f\"step_transform must be callable but is {type(step_transform).__name__}.\")\n        self.step_transform = step_transform\n\n        self._name = name\n\n    def attach(self, engine: Engine) -> None:\n        \"\"\"\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n        \"\"\"\n        if self._name is None:\n            self.logger = engine.logger\n        if self.epoch_level:\n            engine.add_event_handler(Events.EPOCH_COMPLETED, self)\n        else:\n            engine.add_event_handler(Events.ITERATION_COMPLETED, self)\n\n    def __call__(self, engine: Engine) -> None:\n        \"\"\"\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n        \"\"\"\n        args = ensure_tuple(self.step_transform(engine))\n        self.lr_scheduler.step(*args)\n        if self.print_lr:\n            self.logger.info(f\"Current learning rate: {self.lr_scheduler._last_lr[0]}\")  # type: ignore[union-attr]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/segmentation_saver.py_SegmentationSaver.attach_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/segmentation_saver.py_SegmentationSaver.attach_", "embedding": null, "metadata": {"file_path": "monai/handlers/segmentation_saver.py", "file_name": "segmentation_saver.py", "file_type": "text/x-python", "category": "implementation", "start_line": 148, "end_line": 174, "span_ids": ["SegmentationSaver.__call__", "SegmentationSaver.attach"], "tokens": 278}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@deprecated(since=\"0.6.0\", removed=\"0.9.0\", msg_suffix=\"Please consider using `SaveImage[d]` transform instead.\")\nclass SegmentationSaver:\n\n    def attach(self, engine: Engine) -> None:\n        \"\"\"\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n        \"\"\"\n        if self._name is None:\n            self.logger = engine.logger\n        if not engine.has_event_handler(self, Events.ITERATION_COMPLETED):\n            engine.add_event_handler(Events.ITERATION_COMPLETED, self)\n\n    def __call__(self, engine: Engine) -> None:\n        \"\"\"\n        This method assumes self.batch_transform will extract metadata from the input batch.\n        Output file datatype is determined from ``engine.state.output.dtype``.\n\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n        \"\"\"\n        meta_data = self.batch_transform(engine.state.batch)\n        if isinstance(meta_data, dict):\n            # decollate the `dictionary of list` to `list of dictionaries`\n            meta_data = decollate_batch(meta_data)\n        engine_output = self.output_transform(engine.state.output)\n        for m, o in zip(meta_data, engine_output):\n            self._saver(o, m)\n        self.logger.info(\"model outputs saved into files.\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/stats_handler.py_StatsHandler_StatsHandler.__init__.if_logger_handler_is_not_.self_logger_addHandler_lo": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/stats_handler.py_StatsHandler_StatsHandler.__init__.if_logger_handler_is_not_.self_logger_addHandler_lo", "embedding": null, "metadata": {"file_path": "monai/handlers/stats_handler.py", "file_name": "stats_handler.py", "file_type": "text/x-python", "category": "implementation", "start_line": 31, "end_line": 99, "span_ids": ["StatsHandler.__init__", "StatsHandler"], "tokens": 767}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class StatsHandler:\n    \"\"\"\n    StatsHandler defines a set of Ignite Event-handlers for all the log printing logics.\n    It can be used for any Ignite Engine(trainer, validator and evaluator).\n    And it can support logging for epoch level and iteration level with pre-defined loggers.\n\n    Default behaviors:\n        - When EPOCH_COMPLETED, logs ``engine.state.metrics`` using ``self.logger``.\n        - When ITERATION_COMPLETED, logs\n          ``self.output_transform(engine.state.output)`` using ``self.logger``.\n\n    Usage example is available in the tutorial:\n    https://github.com/Project-MONAI/tutorials/blob/master/3d_segmentation/unet_segmentation_3d_ignite.ipynb.\n\n    \"\"\"\n\n    def __init__(\n        self,\n        epoch_print_logger: Optional[Callable[[Engine], Any]] = None,\n        iteration_print_logger: Optional[Callable[[Engine], Any]] = None,\n        output_transform: Callable = lambda x: x[0],\n        global_epoch_transform: Callable = lambda x: x,\n        state_attributes: Optional[Sequence[str]] = None,\n        name: Optional[str] = None,\n        tag_name: str = DEFAULT_TAG,\n        key_var_format: str = DEFAULT_KEY_VAL_FORMAT,\n        logger_handler: Optional[logging.Handler] = None,\n    ) -> None:\n        \"\"\"\n\n        Args:\n            epoch_print_logger: customized callable printer for epoch level logging.\n                Must accept parameter \"engine\", use default printer if None.\n            iteration_print_logger: customized callable printer for iteration level logging.\n                Must accept parameter \"engine\", use default printer if None.\n            output_transform: a callable that is used to transform the\n                ``ignite.engine.state.output`` into a scalar to print, or a dictionary of {key: scalar}.\n                In the latter case, the output string will be formatted as key: value.\n                By default this value logging happens when every iteration completed.\n                The default behavior is to print loss from output[0] as output is a decollated list\n                and we replicated loss value for every item of the decollated list.\n                `engine.state` and `output_transform` inherit from the ignite concept:\n                https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial:\n                https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb.\n            global_epoch_transform: a callable that is used to customize global epoch number.\n                For example, in evaluation, the evaluator engine might want to print synced epoch number\n                with the trainer engine.\n            state_attributes: expected attributes from `engine.state`, if provided, will extract them\n                when epoch completed.\n            name: identifier of logging.logger to use, defaulting to ``engine.logger``.\n            tag_name: when iteration output is a scalar, tag_name is used to print\n                tag_name: scalar_value to logger. Defaults to ``'Loss'``.\n            key_var_format: a formatting string to control the output string format of key: value.\n            logger_handler: add additional handler to handle the stats data: save to file, etc.\n                Add existing python logging handlers: https://docs.python.org/3/library/logging.handlers.html\n        \"\"\"\n\n        self.epoch_print_logger = epoch_print_logger\n        self.iteration_print_logger = iteration_print_logger\n        self.output_transform = output_transform\n        self.global_epoch_transform = global_epoch_transform\n        self.state_attributes = state_attributes\n        self.logger = logging.getLogger(name)\n        self._name = name\n\n        self.tag_name = tag_name\n        self.key_var_format = key_var_format\n        if logger_handler is not None:\n            self.logger.addHandler(logger_handler)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/stats_handler.py_StatsHandler.attach_StatsHandler.attach.None_3.engine_add_event_handler_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/stats_handler.py_StatsHandler.attach_StatsHandler.attach.None_3.engine_add_event_handler_", "embedding": null, "metadata": {"file_path": "monai/handlers/stats_handler.py", "file_name": "stats_handler.py", "file_type": "text/x-python", "category": "implementation", "start_line": 88, "end_line": 103, "span_ids": ["StatsHandler.attach"], "tokens": 173}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class StatsHandler:\n\n    def attach(self, engine: Engine) -> None:\n        \"\"\"\n        Register a set of Ignite Event-Handlers to a specified Ignite engine.\n\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n\n        \"\"\"\n        if self._name is None:\n            self.logger = engine.logger\n        if not engine.has_event_handler(self.iteration_completed, Events.ITERATION_COMPLETED):\n            engine.add_event_handler(Events.ITERATION_COMPLETED, self.iteration_completed)\n        if not engine.has_event_handler(self.epoch_completed, Events.EPOCH_COMPLETED):\n            engine.add_event_handler(Events.EPOCH_COMPLETED, self.epoch_completed)\n        if not engine.has_event_handler(self.exception_raised, Events.EXCEPTION_RAISED):\n            engine.add_event_handler(Events.EXCEPTION_RAISED, self.exception_raised)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/stats_handler.py_StatsHandler._default_iteration_print_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/stats_handler.py_StatsHandler._default_iteration_print_", "embedding": null, "metadata": {"file_path": "monai/handlers/stats_handler.py", "file_name": "stats_handler.py", "file_type": "text/x-python", "category": "implementation", "start_line": 196, "end_line": 249, "span_ids": ["StatsHandler._default_iteration_print"], "tokens": 494}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class StatsHandler:\n\n    def _default_iteration_print(self, engine: Engine) -> None:\n        \"\"\"\n        Execute iteration log operation based on Ignite `engine.state.output` data.\n        Print the values from `self.output_transform(engine.state.output)`.\n        Since `engine.state.output` is a decollated list and we replicated the loss value for every item\n        of the decollated list, the default behavior is to print the loss from `output[0]`.\n\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n\n        \"\"\"\n        loss = self.output_transform(engine.state.output)\n        if loss is None:\n            return  # no printing if the output is empty\n\n        out_str = \"\"\n        if isinstance(loss, dict):  # print dictionary items\n            for name in sorted(loss):\n                value = loss[name]\n                if not is_scalar(value):\n                    warnings.warn(\n                        \"ignoring non-scalar output in StatsHandler,\"\n                        \" make sure `output_transform(engine.state.output)` returns\"\n                        \" a scalar or dictionary of key and scalar pairs to avoid this warning.\"\n                        \" {}:{}\".format(name, type(value))\n                    )\n                    continue  # not printing multi dimensional output\n                out_str += self.key_var_format.format(name, value.item() if isinstance(value, torch.Tensor) else value)\n        elif is_scalar(loss):  # not printing multi dimensional output\n            out_str += self.key_var_format.format(\n                self.tag_name, loss.item() if isinstance(loss, torch.Tensor) else loss\n            )\n        else:\n            warnings.warn(\n                \"ignoring non-scalar output in StatsHandler,\"\n                \" make sure `output_transform(engine.state.output)` returns\"\n                \" a scalar or a dictionary of key and scalar pairs to avoid this warning.\"\n                \" {}\".format(type(loss))\n            )\n\n        if not out_str:\n            return  # no value to print\n\n        num_iterations = engine.state.epoch_length\n        current_iteration = engine.state.iteration\n        if num_iterations is not None:\n            current_iteration = (current_iteration - 1) % num_iterations + 1\n        current_epoch = engine.state.epoch\n        num_epochs = engine.state.max_epochs\n\n        base_str = f\"Epoch: {current_epoch}/{num_epochs}, Iter: {current_iteration}/{num_iterations} --\"\n\n        self.logger.info(\" \".join([base_str, out_str]))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/tensorboard_handlers.py_TensorBoardStatsHandler.attach_TensorBoardStatsHandler.attach.None_1.engine_add_event_handler_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/tensorboard_handlers.py_TensorBoardStatsHandler.attach_TensorBoardStatsHandler.attach.None_1.engine_add_event_handler_", "embedding": null, "metadata": {"file_path": "monai/handlers/tensorboard_handlers.py", "file_name": "tensorboard_handlers.py", "file_type": "text/x-python", "category": "implementation", "start_line": 118, "end_line": 131, "span_ids": ["TensorBoardStatsHandler.attach"], "tokens": 143}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TensorBoardStatsHandler(TensorBoardHandler):\n\n    def attach(self, engine: Engine) -> None:\n        \"\"\"\n        Register a set of Ignite Event-Handlers to a specified Ignite engine.\n\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n\n        \"\"\"\n        if not engine.has_event_handler(self.iteration_completed, Events.ITERATION_COMPLETED):\n            engine.add_event_handler(\n                Events.ITERATION_COMPLETED(every=self.iteration_interval), self.iteration_completed\n            )\n        if not engine.has_event_handler(self.epoch_completed, Events.EPOCH_COMPLETED):\n            engine.add_event_handler(Events.EPOCH_COMPLETED(every=self.epoch_interval), self.epoch_completed)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/tensorboard_handlers.py_TensorBoardStatsHandler.epoch_completed_TensorBoardStatsHandler.iteration_completed.if_self_iteration_event_w.else_.self__default_iteration_w": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/tensorboard_handlers.py_TensorBoardStatsHandler.epoch_completed_TensorBoardStatsHandler.iteration_completed.if_self_iteration_event_w.else_.self__default_iteration_w", "embedding": null, "metadata": {"file_path": "monai/handlers/tensorboard_handlers.py", "file_name": "tensorboard_handlers.py", "file_type": "text/x-python", "category": "implementation", "start_line": 147, "end_line": 173, "span_ids": ["TensorBoardStatsHandler.iteration_completed", "TensorBoardStatsHandler.epoch_completed"], "tokens": 218}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TensorBoardStatsHandler(TensorBoardHandler):\n\n    def epoch_completed(self, engine: Engine) -> None:\n        \"\"\"\n        Handler for train or validation/evaluation epoch completed Event.\n        Write epoch level events, default values are from Ignite `engine.state.metrics` dict.\n\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n\n        \"\"\"\n        if self.epoch_event_writer is not None:\n            self.epoch_event_writer(engine, self._writer)\n        else:\n            self._default_epoch_writer(engine, self._writer)\n\n    def iteration_completed(self, engine: Engine) -> None:\n        \"\"\"\n        Handler for train or validation/evaluation iteration completed Event.\n        Write iteration level events, default values are from Ignite `engine.state.output`.\n\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n\n        \"\"\"\n        if self.iteration_event_writer is not None:\n            self.iteration_event_writer(engine, self._writer)\n        else:\n            self._default_iteration_writer(engine, self._writer)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/tensorboard_handlers.py_TensorBoardStatsHandler._default_epoch_writer_TensorBoardStatsHandler._default_epoch_writer.writer_flush_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/tensorboard_handlers.py_TensorBoardStatsHandler._default_epoch_writer_TensorBoardStatsHandler._default_epoch_writer.writer_flush_", "embedding": null, "metadata": {"file_path": "monai/handlers/tensorboard_handlers.py", "file_name": "tensorboard_handlers.py", "file_type": "text/x-python", "category": "implementation", "start_line": 191, "end_line": 211, "span_ids": ["TensorBoardStatsHandler._default_epoch_writer"], "tokens": 205}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TensorBoardStatsHandler(TensorBoardHandler):\n\n    def _default_epoch_writer(self, engine: Engine, writer: SummaryWriter) -> None:\n        \"\"\"\n        Execute epoch level event write operation.\n        Default to write the values from Ignite `engine.state.metrics` dict and\n        write the values of specified attributes of `engine.state`.\n\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n            writer: TensorBoard or TensorBoardX writer, passed or created in TensorBoardHandler.\n\n        \"\"\"\n        current_epoch = self.global_epoch_transform(engine.state.epoch)\n        summary_dict = engine.state.metrics\n        for name, value in summary_dict.items():\n            if is_scalar(value):\n                self._write_scalar(engine, writer, name, value, current_epoch)\n\n        if self.state_attributes is not None:\n            for attr in self.state_attributes:\n                self._write_scalar(engine, writer, attr, getattr(engine.state, attr, None), current_epoch)\n        writer.flush()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/tensorboard_handlers.py_TensorBoardStatsHandler._default_iteration_writer_TensorBoardStatsHandler._default_iteration_writer.writer_flush_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/tensorboard_handlers.py_TensorBoardStatsHandler._default_iteration_writer_TensorBoardStatsHandler._default_iteration_writer.writer_flush_", "embedding": null, "metadata": {"file_path": "monai/handlers/tensorboard_handlers.py", "file_name": "tensorboard_handlers.py", "file_type": "text/x-python", "category": "implementation", "start_line": 213, "end_line": 261, "span_ids": ["TensorBoardStatsHandler._default_iteration_writer"], "tokens": 448}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TensorBoardStatsHandler(TensorBoardHandler):\n\n    def _default_iteration_writer(self, engine: Engine, writer: SummaryWriter) -> None:\n        \"\"\"\n        Execute iteration level event write operation based on Ignite `engine.state.output` data.\n        Extract the values from `self.output_transform(engine.state.output)`.\n        Since `engine.state.output` is a decollated list and we replicated the loss value for every item\n        of the decollated list, the default behavior is to track the loss from `output[0]`.\n\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n            writer: TensorBoard  or TensorBoardX writer, passed or created in TensorBoardHandler.\n\n        \"\"\"\n        loss = self.output_transform(engine.state.output)\n        if loss is None:\n            return  # do nothing if output is empty\n        if isinstance(loss, dict):\n            for name in sorted(loss):\n                value = loss[name]\n                if not is_scalar(value):\n                    warnings.warn(\n                        \"ignoring non-scalar output in TensorBoardStatsHandler,\"\n                        \" make sure `output_transform(engine.state.output)` returns\"\n                        \" a scalar or dictionary of key and scalar pairs to avoid this warning.\"\n                        \" {}:{}\".format(name, type(value))\n                    )\n                    continue  # not plot multi dimensional output\n                self._write_scalar(\n                    _engine=engine,\n                    writer=writer,\n                    tag=name,\n                    value=value.item() if isinstance(value, torch.Tensor) else value,\n                    step=engine.state.iteration,\n                )\n        elif is_scalar(loss):  # not printing multi dimensional output\n            self._write_scalar(\n                _engine=engine,\n                writer=writer,\n                tag=self.tag_name,\n                value=loss.item() if isinstance(loss, torch.Tensor) else loss,\n                step=engine.state.iteration,\n            )\n        else:\n            warnings.warn(\n                \"ignoring non-scalar output in TensorBoardStatsHandler,\"\n                \" make sure `output_transform(engine.state.output)` returns\"\n                \" a scalar or a dictionary of key and scalar pairs to avoid this warning.\"\n                \" {}\".format(type(loss))\n            )\n        writer.flush()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/validation_handler.py_from_typing_import_TYPE_C_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/validation_handler.py_from_typing_import_TYPE_C_", "embedding": null, "metadata": {"file_path": "monai/handlers/validation_handler.py", "file_name": "validation_handler.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 77, "span_ids": ["ValidationHandler.__init__", "ValidationHandler.set_validator", "ValidationHandler.__call__", "docstring", "ValidationHandler.attach", "ValidationHandler"], "tokens": 557}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import TYPE_CHECKING, Optional\n\nfrom monai.config import IgniteInfo\nfrom monai.engines.evaluator import Evaluator\nfrom monai.utils import min_version, optional_import\n\nEvents, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Events\")\nif TYPE_CHECKING:\n    from ignite.engine import Engine\nelse:\n    Engine, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Engine\")\n\n\nclass ValidationHandler:\n    \"\"\"\n    Attach validator to the trainer engine in Ignite.\n    It can support to execute validation every N epochs or every N iterations.\n\n    \"\"\"\n\n    def __init__(self, interval: int, validator: Optional[Evaluator] = None, epoch_level: bool = True) -> None:\n        \"\"\"\n        Args:\n            interval: do validation every N epochs or every N iterations during training.\n            validator: run the validator when trigger validation, suppose to be Evaluator.\n                if None, should call `set_validator()` before training.\n            epoch_level: execute validation every N epochs or N iterations.\n                `True` is epoch level, `False` is iteration level.\n\n        Raises:\n            TypeError: When ``validator`` is not a ``monai.engines.evaluator.Evaluator``.\n\n        \"\"\"\n        if validator is not None and not isinstance(validator, Evaluator):\n            raise TypeError(f\"validator must be a monai.engines.evaluator.Evaluator but is {type(validator).__name__}.\")\n        self.validator = validator\n        self.interval = interval\n        self.epoch_level = epoch_level\n\n    def set_validator(self, validator: Evaluator):\n        \"\"\"\n        Set validator if not setting in the __init__().\n        \"\"\"\n        if not isinstance(validator, Evaluator):\n            raise TypeError(f\"validator must be a monai.engines.evaluator.Evaluator but is {type(validator).__name__}.\")\n        self.validator = validator\n\n    def attach(self, engine: Engine) -> None:\n        \"\"\"\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n        \"\"\"\n        if self.epoch_level:\n            engine.add_event_handler(Events.EPOCH_COMPLETED(every=self.interval), self)\n        else:\n            engine.add_event_handler(Events.ITERATION_COMPLETED(every=self.interval), self)\n\n    def __call__(self, engine: Engine) -> None:\n        \"\"\"\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n        \"\"\"\n        if self.validator is None:\n            raise RuntimeError(\"please set validator in __init__() or call `set_validator()` before training.\")\n        self.validator.run(engine.state.epoch)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/inferers/utils.py__get_scan_interval_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/inferers/utils.py__get_scan_interval_", "embedding": null, "metadata": {"file_path": "monai/inferers/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 144, "end_line": 166, "span_ids": ["_get_scan_interval"], "tokens": 212}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _get_scan_interval(\n    image_size: Sequence[int], roi_size: Sequence[int], num_spatial_dims: int, overlap: float\n) -> Tuple[int, ...]:\n    \"\"\"\n    Compute scan interval according to the image size, roi size and overlap.\n    Scan interval will be `int((1 - overlap) * roi_size)`, if interval is 0,\n    use 1 instead to make sure sliding window works.\n\n    \"\"\"\n    if len(image_size) != num_spatial_dims:\n        raise ValueError(\"image coord different from spatial dims.\")\n    if len(roi_size) != num_spatial_dims:\n        raise ValueError(\"roi coord different from spatial dims.\")\n\n    scan_interval = []\n    for i in range(num_spatial_dims):\n        if roi_size[i] == image_size[i]:\n            scan_interval.append(int(roi_size[i]))\n        else:\n            interval = int(roi_size[i] * (1 - overlap))\n            scan_interval.append(interval if interval > 0 else 1)\n    return tuple(scan_interval)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/tversky.py_TverskyLoss.forward_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/tversky.py_TverskyLoss.forward_", "embedding": null, "metadata": {"file_path": "monai/losses/tversky.py", "file_name": "tversky.py", "file_type": "text/x-python", "category": "implementation", "start_line": 96, "end_line": 162, "span_ids": ["TverskyLoss.forward"], "tokens": 578}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TverskyLoss(_Loss):\n\n    def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:\n        \"\"\"\n        Args:\n            input: the shape should be BNH[WD].\n            target: the shape should be BNH[WD].\n\n        Raises:\n            ValueError: When ``self.reduction`` is not one of [\"mean\", \"sum\", \"none\"].\n\n        \"\"\"\n        if self.sigmoid:\n            input = torch.sigmoid(input)\n\n        n_pred_ch = input.shape[1]\n        if self.softmax:\n            if n_pred_ch == 1:\n                warnings.warn(\"single channel prediction, `softmax=True` ignored.\")\n            else:\n                input = torch.softmax(input, 1)\n\n        if self.other_act is not None:\n            input = self.other_act(input)\n\n        if self.to_onehot_y:\n            if n_pred_ch == 1:\n                warnings.warn(\"single channel prediction, `to_onehot_y=True` ignored.\")\n            else:\n                target = one_hot(target, num_classes=n_pred_ch)\n\n        if not self.include_background:\n            if n_pred_ch == 1:\n                warnings.warn(\"single channel prediction, `include_background=False` ignored.\")\n            else:\n                # if skipping background, removing first channel\n                target = target[:, 1:]\n                input = input[:, 1:]\n\n        if target.shape != input.shape:\n            raise AssertionError(f\"ground truth has differing shape ({target.shape}) from input ({input.shape})\")\n\n        p0 = input\n        p1 = 1 - p0\n        g0 = target\n        g1 = 1 - g0\n\n        # reducing only spatial dimensions (not batch nor channels)\n        reduce_axis: List[int] = torch.arange(2, len(input.shape)).tolist()\n        if self.batch:\n            # reducing spatial dimensions and batch\n            reduce_axis = [0] + reduce_axis\n\n        tp = torch.sum(p0 * g0, reduce_axis)\n        fp = self.alpha * torch.sum(p0 * g1, reduce_axis)\n        fn = self.beta * torch.sum(p1 * g0, reduce_axis)\n        numerator = tp + self.smooth_nr\n        denominator = tp + fp + fn + self.smooth_dr\n\n        score: torch.Tensor = 1.0 - numerator / denominator\n\n        if self.reduction == LossReduction.SUM.value:\n            return torch.sum(score)  # sum over the batch and channel dims\n        if self.reduction == LossReduction.NONE.value:\n            return score  # returns [N, num_classes] losses\n        if self.reduction == LossReduction.MEAN.value:\n            return torch.mean(score)\n        raise ValueError(f'Unsupported reduction: {self.reduction}, available options are [\"mean\", \"sum\", \"none\"].')", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/downsample.py_from_typing_import_Option_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/downsample.py_from_typing_import_Option_", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/downsample.py", "file_name": "downsample.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 62, "span_ids": ["MaxAvgPool", "MaxAvgPool.__init__", "MaxAvgPool.forward", "docstring"], "tokens": 412}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Optional, Sequence, Union\n\nimport torch\nimport torch.nn as nn\n\nfrom monai.networks.layers.factories import Pool\nfrom monai.utils import ensure_tuple_rep\n\n\nclass MaxAvgPool(nn.Module):\n    \"\"\"\n    Downsample with both maxpooling and avgpooling,\n    double the channel size by concatenating the downsampled feature maps.\n    \"\"\"\n\n    def __init__(\n        self,\n        spatial_dims: int,\n        kernel_size: Union[Sequence[int], int],\n        stride: Optional[Union[Sequence[int], int]] = None,\n        padding: Union[Sequence[int], int] = 0,\n        ceil_mode: bool = False,\n    ) -> None:\n        \"\"\"\n        Args:\n            spatial_dims: number of spatial dimensions of the input image.\n            kernel_size: the kernel size of both pooling operations.\n            stride: the stride of the window. Default value is `kernel_size`.\n            padding: implicit zero padding to be added to both pooling operations.\n            ceil_mode: when True, will use ceil instead of floor to compute the output shape.\n        \"\"\"\n        super().__init__()\n        _params = {\n            \"kernel_size\": ensure_tuple_rep(kernel_size, spatial_dims),\n            \"stride\": None if stride is None else ensure_tuple_rep(stride, spatial_dims),\n            \"padding\": ensure_tuple_rep(padding, spatial_dims),\n            \"ceil_mode\": ceil_mode,\n        }\n        self.max_pool = Pool[Pool.MAX, spatial_dims](**_params)\n        self.avg_pool = Pool[Pool.AVG, spatial_dims](**_params)\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        \"\"\"\n        Args:\n            x: Tensor in shape (batch, channel, spatial_1[, spatial_2, ...]).\n\n        Returns:\n            Tensor in shape (batch, 2*channel, spatial_1[, spatial_2, ...]).\n        \"\"\"\n        return torch.cat([self.max_pool(x), self.avg_pool(x)], dim=1)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/factories.py_from_typing_import_Any_C_LayerFactory.add_factory_callable.self.__doc__._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/factories.py_from_typing_import_Any_C_LayerFactory.add_factory_callable.self.__doc__._", "embedding": null, "metadata": {"file_path": "monai/networks/layers/factories.py", "file_name": "factories.py", "file_type": "text/x-python", "category": "implementation", "start_line": 63, "end_line": 100, "span_ids": ["LayerFactory", "LayerFactory.add_factory_callable", "docstring:11", "LayerFactory.__init__", "LayerFactory.names"], "tokens": 287}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Any, Callable, Dict, Tuple, Type, Union\n\nimport torch.nn as nn\n\nfrom monai.utils import look_up_option\n\n__all__ = [\"LayerFactory\", \"Dropout\", \"Norm\", \"Act\", \"Conv\", \"Pool\", \"Pad\", \"split_args\"]\n\n\nclass LayerFactory:\n    \"\"\"\n    Factory object for creating layers, this uses given factory functions to actually produce the types or constructing\n    callables. These functions are referred to by name and can be added at any time.\n    \"\"\"\n\n    def __init__(self) -> None:\n        self.factories: Dict[str, Callable] = {}\n\n    @property\n    def names(self) -> Tuple[str, ...]:\n        \"\"\"\n        Produces all factory names.\n        \"\"\"\n\n        return tuple(self.factories)\n\n    def add_factory_callable(self, name: str, func: Callable) -> None:\n        \"\"\"\n        Add the factory function to this object under the given name.\n        \"\"\"\n\n        self.factories[name.upper()] = func\n        self.__doc__ = (\n            \"The supported member\"\n            + (\"s are: \" if len(self.names) > 1 else \" is: \")\n            + \", \".join(f\"``{name}``\" for name in self.names)\n            + \".\\nPlease see :py:class:`monai.networks.layers.split_args` for additional args parsing.\"\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/factories.py_LayerFactory.__getitem___LayerFactory.__getattr__.return.super___getattribute___": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/factories.py_LayerFactory.__getitem___LayerFactory.__getattr__.return.super___getattribute___", "embedding": null, "metadata": {"file_path": "monai/networks/layers/factories.py", "file_name": "factories.py", "file_type": "text/x-python", "category": "implementation", "start_line": 126, "end_line": 153, "span_ids": ["LayerFactory.__getitem__", "LayerFactory.__getattr__"], "tokens": 228}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LayerFactory:\n\n    def __getitem__(self, args) -> Any:\n        \"\"\"\n        Get the given name or name/arguments pair. If `args` is a callable it is assumed to be the constructor\n        itself and is returned, otherwise it should be the factory name or a pair containing the name and arguments.\n        \"\"\"\n\n        # `args[0]` is actually a type or constructor\n        if callable(args):\n            return args\n\n        # `args` is a factory name or a name with arguments\n        if isinstance(args, str):\n            name_obj, args = args, ()\n        else:\n            name_obj, *args = args\n\n        return self.get_constructor(name_obj, *args)\n\n    def __getattr__(self, key):\n        \"\"\"\n        If `key` is a factory name, return it, otherwise behave as inherited. This allows referring to factory names\n        as if they were constants, eg. `Fact.FOO` for a factory Fact with factory function foo.\n        \"\"\"\n\n        if key in self.factories:\n            return key\n\n        return super().__getattribute__(key)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/classifier.py_from_typing_import_Option_Classifier.__init__.if_last_act_is_not_None_.self_final_add_module_la": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/classifier.py_from_typing_import_Option_Classifier.__init__.if_last_act_is_not_None_.self_final_add_module_la", "embedding": null, "metadata": {"file_path": "monai/networks/nets/classifier.py", "file_name": "classifier.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 63, "span_ids": ["Classifier.__init__", "Classifier", "docstring"], "tokens": 481}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Optional, Sequence, Union\n\nimport torch\nimport torch.nn as nn\n\nfrom monai.networks.layers.factories import Act, Norm, split_args\nfrom monai.networks.nets.regressor import Regressor\n\n__all__ = [\"Classifier\", \"Discriminator\", \"Critic\"]\n\n\nclass Classifier(Regressor):\n    \"\"\"\n    Defines a classification network from Regressor by specifying the output shape as a single dimensional tensor\n    with size equal to the number of classes to predict. The final activation function can also be specified, eg.\n    softmax or sigmoid.\n\n    Args:\n        in_shape: tuple of integers stating the dimension of the input tensor (minus batch dimension)\n        classes: integer stating the dimension of the final output tensor\n        channels: tuple of integers stating the output channels of each convolutional layer\n        strides: tuple of integers stating the stride (downscale factor) of each convolutional layer\n        kernel_size: integer or tuple of integers stating size of convolutional kernels\n        num_res_units: integer stating number of convolutions in residual units, 0 means no residual units\n        act: name or type defining activation layers\n        norm: name or type defining normalization layers\n        dropout: optional float value in range [0, 1] stating dropout probability for layers, None for no dropout\n        bias: boolean stating if convolution layers should have a bias component\n        last_act: name defining the last activation layer\n    \"\"\"\n\n    def __init__(\n        self,\n        in_shape: Sequence[int],\n        classes: int,\n        channels: Sequence[int],\n        strides: Sequence[int],\n        kernel_size: Union[Sequence[int], int] = 3,\n        num_res_units: int = 2,\n        act=Act.PRELU,\n        norm=Norm.INSTANCE,\n        dropout: Optional[float] = None,\n        bias: bool = True,\n        last_act: Optional[str] = None,\n    ) -> None:\n        super().__init__(in_shape, (classes,), channels, strides, kernel_size, num_res_units, act, norm, dropout, bias)\n\n        if last_act is not None:\n            last_act_name, last_act_args = split_args(last_act)\n            last_act_type = Act[last_act_name]\n\n            self.final.add_module(\"lastact\", last_act_type(**last_act_args))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/classifier.py_Discriminator_Discriminator.__init__.super___init___in_shape": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/classifier.py_Discriminator_Discriminator.__init__.super___init___in_shape", "embedding": null, "metadata": {"file_path": "monai/networks/nets/classifier.py", "file_name": "classifier.py", "file_type": "text/x-python", "category": "implementation", "start_line": 66, "end_line": 97, "span_ids": ["Discriminator.__init__", "Discriminator"], "tokens": 346}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Discriminator(Classifier):\n    \"\"\"\n    Defines a discriminator network from Classifier with a single output value and sigmoid activation by default. This\n    is meant for use with GANs or other applications requiring a generic discriminator network.\n\n    Args:\n        in_shape: tuple of integers stating the dimension of the input tensor (minus batch dimension)\n        channels: tuple of integers stating the output channels of each convolutional layer\n        strides: tuple of integers stating the stride (downscale factor) of each convolutional layer\n        kernel_size: integer or tuple of integers stating size of convolutional kernels\n        num_res_units: integer stating number of convolutions in residual units, 0 means no residual units\n        act: name or type defining activation layers\n        norm: name or type defining normalization layers\n        dropout: optional float value in range [0, 1] stating dropout probability for layers, None for no dropout\n        bias: boolean stating if convolution layers should have a bias component\n        last_act: name defining the last activation layer\n    \"\"\"\n\n    def __init__(\n        self,\n        in_shape: Sequence[int],\n        channels: Sequence[int],\n        strides: Sequence[int],\n        kernel_size: Union[Sequence[int], int] = 3,\n        num_res_units: int = 2,\n        act=Act.PRELU,\n        norm=Norm.INSTANCE,\n        dropout: Optional[float] = 0.25,\n        bias: bool = True,\n        last_act=Act.SIGMOID,\n    ) -> None:\n        super().__init__(in_shape, 1, channels, strides, kernel_size, num_res_units, act, norm, dropout, bias, last_act)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/classifier.py_Critic_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/classifier.py_Critic_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/classifier.py", "file_name": "classifier.py", "file_type": "text/x-python", "category": "implementation", "start_line": 100, "end_line": 140, "span_ids": ["Critic", "Critic.forward", "Critic.__init__", "Critic._get_final_layer"], "tokens": 418}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Critic(Classifier):\n    \"\"\"\n    Defines a critic network from Classifier with a single output value and no final activation. The final layer is\n    `nn.Flatten` instead of `nn.Linear`, the final result is computed as the mean over the first dimension. This is\n    meant to be used with Wasserstein GANs.\n\n    Args:\n        in_shape: tuple of integers stating the dimension of the input tensor (minus batch dimension)\n        channels: tuple of integers stating the output channels of each convolutional layer\n        strides: tuple of integers stating the stride (downscale factor) of each convolutional layer\n        kernel_size: integer or tuple of integers stating size of convolutional kernels\n        num_res_units: integer stating number of convolutions in residual units, 0 means no residual units\n        act: name or type defining activation layers\n        norm: name or type defining normalization layers\n        dropout: optional float value in range [0, 1] stating dropout probability for layers, None for no dropout\n        bias: boolean stating if convolution layers should have a bias component\n    \"\"\"\n\n    def __init__(\n        self,\n        in_shape: Sequence[int],\n        channels: Sequence[int],\n        strides: Sequence[int],\n        kernel_size: Union[Sequence[int], int] = 3,\n        num_res_units: int = 2,\n        act=Act.PRELU,\n        norm=Norm.INSTANCE,\n        dropout: Optional[float] = 0.25,\n        bias: bool = True,\n    ) -> None:\n        super().__init__(in_shape, 1, channels, strides, kernel_size, num_res_units, act, norm, dropout, bias, None)\n\n    def _get_final_layer(self, in_shape: Sequence[int]):\n        return nn.Flatten()\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        x = self.net(x)\n        x = self.final(x)\n        x = x.mean(1)\n        return x.view((x.shape[0], -1))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/densenet.py__DenseBlock__DenseBlock.__init__.for_i_in_range_layers_.self_add_module_denselay": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/densenet.py__DenseBlock__DenseBlock.__init__.for_i_in_range_layers_.self_add_module_denselay", "embedding": null, "metadata": {"file_path": "monai/networks/nets/densenet.py", "file_name": "densenet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 88, "end_line": 116, "span_ids": ["_DenseBlock.__init__", "_DenseBlock"], "tokens": 277}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class _DenseBlock(nn.Sequential):\n    def __init__(\n        self,\n        spatial_dims: int,\n        layers: int,\n        in_channels: int,\n        bn_size: int,\n        growth_rate: int,\n        dropout_prob: float,\n        act: Union[str, tuple] = (\"relu\", {\"inplace\": True}),\n        norm: Union[str, tuple] = \"batch\",\n    ) -> None:\n        \"\"\"\n        Args:\n            spatial_dims: number of spatial dimensions of the input image.\n            layers: number of layers in the block.\n            in_channels: number of the input channel.\n            bn_size: multiplicative factor for number of bottle neck layers.\n                (i.e. bn_size * k features in the bottleneck layer)\n            growth_rate: how many filters to add each layer (k in paper).\n            dropout_prob: dropout rate after each dense layer.\n            act: activation type and arguments. Defaults to relu.\n            norm: feature normalization type and arguments. Defaults to batch norm.\n        \"\"\"\n        super().__init__()\n        for i in range(layers):\n            layer = _DenseLayer(spatial_dims, in_channels, growth_rate, bn_size, dropout_prob, act=act, norm=norm)\n            in_channels += growth_rate\n            self.add_module(\"denselayer%d\" % (i + 1), layer)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/densenet.py__Transition__Transition.__init__.self_add_module_pool_p": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/densenet.py__Transition__Transition.__init__.self_add_module_pool_p", "embedding": null, "metadata": {"file_path": "monai/networks/nets/densenet.py", "file_name": "densenet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 119, "end_line": 144, "span_ids": ["_Transition", "_Transition.__init__"], "tokens": 248}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class _Transition(nn.Sequential):\n    def __init__(\n        self,\n        spatial_dims: int,\n        in_channels: int,\n        out_channels: int,\n        act: Union[str, tuple] = (\"relu\", {\"inplace\": True}),\n        norm: Union[str, tuple] = \"batch\",\n    ) -> None:\n        \"\"\"\n        Args:\n            spatial_dims: number of spatial dimensions of the input image.\n            in_channels: number of the input channel.\n            out_channels: number of the output classes.\n            act: activation type and arguments. Defaults to relu.\n            norm: feature normalization type and arguments. Defaults to batch norm.\n        \"\"\"\n        super().__init__()\n\n        conv_type: Callable = Conv[Conv.CONV, spatial_dims]\n        pool_type: Callable = Pool[Pool.AVG, spatial_dims]\n\n        self.add_module(\"norm\", get_norm_layer(name=norm, spatial_dims=spatial_dims, channels=in_channels))\n        self.add_module(\"relu\", get_act_layer(name=act))\n        self.add_module(\"conv\", conv_type(in_channels, out_channels, kernel_size=1, bias=False))\n        self.add_module(\"pool\", pool_type(kernel_size=2, stride=2))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/densenet.py_DenseNet_DenseNet._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/densenet.py_DenseNet_DenseNet._", "embedding": null, "metadata": {"file_path": "monai/networks/nets/densenet.py", "file_name": "densenet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 147, "end_line": 164, "span_ids": ["DenseNet"], "tokens": 213}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DenseNet(nn.Module):\n    \"\"\"\n    Densenet based on: `Densely Connected Convolutional Networks <https://arxiv.org/pdf/1608.06993.pdf>`_.\n    Adapted from PyTorch Hub 2D version: https://pytorch.org/vision/stable/models.html#id16.\n\n    Args:\n        spatial_dims: number of spatial dimensions of the input image.\n        in_channels: number of the input channel.\n        out_channels: number of the output classes.\n        init_features: number of filters in the first convolution layer.\n        growth_rate: how many filters to add each layer (k in paper).\n        block_config: how many layers in each pooling block.\n        bn_size: multiplicative factor for number of bottle neck layers.\n            (i.e. bn_size * k features in the bottleneck layer)\n        act: activation type and arguments. Defaults to relu.\n        norm: feature normalization type and arguments. Defaults to batch norm.\n        dropout_prob: dropout rate after each dense layer.\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/densenet.py_DenseNet.__init___DenseNet.forward.return.x": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/densenet.py_DenseNet.__init___DenseNet.forward.return.x", "embedding": null, "metadata": {"file_path": "monai/networks/nets/densenet.py", "file_name": "densenet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 166, "end_line": 249, "span_ids": ["DenseNet.__init__", "DenseNet.forward"], "tokens": 765}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DenseNet(nn.Module):\n\n    def __init__(\n        self,\n        spatial_dims: int,\n        in_channels: int,\n        out_channels: int,\n        init_features: int = 64,\n        growth_rate: int = 32,\n        block_config: Sequence[int] = (6, 12, 24, 16),\n        bn_size: int = 4,\n        act: Union[str, tuple] = (\"relu\", {\"inplace\": True}),\n        norm: Union[str, tuple] = \"batch\",\n        dropout_prob: float = 0.0,\n    ) -> None:\n\n        super().__init__()\n\n        conv_type: Type[Union[nn.Conv1d, nn.Conv2d, nn.Conv3d]] = Conv[Conv.CONV, spatial_dims]\n        pool_type: Type[Union[nn.MaxPool1d, nn.MaxPool2d, nn.MaxPool3d]] = Pool[Pool.MAX, spatial_dims]\n        avg_pool_type: Type[Union[nn.AdaptiveAvgPool1d, nn.AdaptiveAvgPool2d, nn.AdaptiveAvgPool3d]] = Pool[\n            Pool.ADAPTIVEAVG, spatial_dims\n        ]\n\n        self.features = nn.Sequential(\n            OrderedDict(\n                [\n                    (\"conv0\", conv_type(in_channels, init_features, kernel_size=7, stride=2, padding=3, bias=False)),\n                    (\"norm0\", get_norm_layer(name=norm, spatial_dims=spatial_dims, channels=init_features)),\n                    (\"relu0\", get_act_layer(name=act)),\n                    (\"pool0\", pool_type(kernel_size=3, stride=2, padding=1)),\n                ]\n            )\n        )\n\n        in_channels = init_features\n        for i, num_layers in enumerate(block_config):\n            block = _DenseBlock(\n                spatial_dims=spatial_dims,\n                layers=num_layers,\n                in_channels=in_channels,\n                bn_size=bn_size,\n                growth_rate=growth_rate,\n                dropout_prob=dropout_prob,\n                act=act,\n                norm=norm,\n            )\n            self.features.add_module(f\"denseblock{i + 1}\", block)\n            in_channels += num_layers * growth_rate\n            if i == len(block_config) - 1:\n                self.features.add_module(\n                    \"norm5\", get_norm_layer(name=norm, spatial_dims=spatial_dims, channels=in_channels)\n                )\n            else:\n                _out_channels = in_channels // 2\n                trans = _Transition(\n                    spatial_dims, in_channels=in_channels, out_channels=_out_channels, act=act, norm=norm\n                )\n                self.features.add_module(f\"transition{i + 1}\", trans)\n                in_channels = _out_channels\n\n        # pooling and classification\n        self.class_layers = nn.Sequential(\n            OrderedDict(\n                [\n                    (\"relu\", get_act_layer(name=act)),\n                    (\"pool\", avg_pool_type(1)),\n                    (\"flatten\", nn.Flatten(1)),\n                    (\"out\", nn.Linear(in_channels, out_channels)),\n                ]\n            )\n        )\n\n        for m in self.modules():\n            if isinstance(m, conv_type):\n                nn.init.kaiming_normal_(torch.as_tensor(m.weight))\n            elif isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d)):\n                nn.init.constant_(torch.as_tensor(m.weight), 1)\n                nn.init.constant_(torch.as_tensor(m.bias), 0)\n            elif isinstance(m, nn.Linear):\n                nn.init.constant_(torch.as_tensor(m.bias), 0)\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        x = self.features(x)\n        x = self.class_layers(x)\n        return x", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/generator.py_from_typing_import_Option_Generator._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/generator.py_from_typing_import_Option_Generator._", "embedding": null, "metadata": {"file_path": "monai/networks/nets/generator.py", "file_name": "generator.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 57, "span_ids": ["Generator", "docstring"], "tokens": 621}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Optional, Sequence, Union\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\n\nfrom monai.networks.blocks import Convolution, ResidualUnit\nfrom monai.networks.layers.factories import Act, Norm\nfrom monai.networks.layers.simplelayers import Reshape\nfrom monai.utils import ensure_tuple, ensure_tuple_rep\n\n\nclass Generator(nn.Module):\n    \"\"\"\n    Defines a simple generator network accepting a latent vector and through a sequence of convolution layers\n    constructs an output tensor of greater size and high dimensionality. The method `_get_layer` is used to\n    create each of these layers, override this method to define layers beyond the default\n    :py:class:`monai.networks.blocks.Convolution` or :py:class:`monai.networks.blocks.ResidualUnit` layers.\n\n    The layers are constructed using the values in the `channels` and `strides` arguments, the number being defined by\n    the length of these (which must match). Input is first passed through a :py:class:`torch.nn.Linear` layer to\n    convert the input vector to an image tensor with dimensions `start_shape`. This passes through the convolution\n    layers and is progressively upsampled if the `strides` values are greater than 1 using transpose convolutions. The\n    size of the final output is defined by the `start_shape` dimension and the amount of upsampling done through\n    strides. In the default definition the size of the output's spatial dimensions will be that of `start_shape`\n    multiplied by the product of `strides`, thus the example network below upsamples an starting size of (64, 8, 8)\n    to (1, 64, 64) since its `strides` are (2, 2, 2).\n\n    Args:\n        latent_shape: tuple of integers stating the dimension of the input latent vector (minus batch dimension)\n        start_shape: tuple of integers stating the dimension of the tensor to pass to convolution subnetwork\n        channels: tuple of integers stating the output channels of each convolutional layer\n        strides: tuple of integers stating the stride (upscale factor) of each convolutional layer\n        kernel_size: integer or tuple of integers stating size of convolutional kernels\n        num_res_units: integer stating number of convolutions in residual units, 0 means no residual units\n        act: name or type defining activation layers\n        norm: name or type defining normalization layers\n        dropout: optional float value in range [0, 1] stating dropout probability for layers, None for no dropout\n        bias: boolean stating if convolution layers should have a bias component\n\n    Examples::\n\n        # 3 layers, latent input vector of shape (42, 24), output volume of shape (1, 64, 64)\n        net = Generator((42, 24), (64, 8, 8), (32, 16, 1), (2, 2, 2))\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/generator.py_Generator.__init___Generator.__init__.for_i_c_s_in_enumerat.echannel.c": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/generator.py_Generator.__init___Generator.__init__.for_i_c_s_in_enumerat.echannel.c", "embedding": null, "metadata": {"file_path": "monai/networks/nets/generator.py", "file_name": "generator.py", "file_type": "text/x-python", "category": "implementation", "start_line": 59, "end_line": 99, "span_ids": ["Generator.__init__"], "tokens": 350}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Generator(nn.Module):\n\n    def __init__(\n        self,\n        latent_shape: Sequence[int],\n        start_shape: Sequence[int],\n        channels: Sequence[int],\n        strides: Sequence[int],\n        kernel_size: Union[Sequence[int], int] = 3,\n        num_res_units: int = 2,\n        act=Act.PRELU,\n        norm=Norm.INSTANCE,\n        dropout: Optional[float] = None,\n        bias: bool = True,\n    ) -> None:\n        super().__init__()\n\n        self.in_channels, *self.start_shape = ensure_tuple(start_shape)\n        self.dimensions = len(self.start_shape)\n\n        self.latent_shape = ensure_tuple(latent_shape)\n        self.channels = ensure_tuple(channels)\n        self.strides = ensure_tuple(strides)\n        self.kernel_size = ensure_tuple_rep(kernel_size, self.dimensions)\n        self.num_res_units = num_res_units\n        self.act = act\n        self.norm = norm\n        self.dropout = dropout\n        self.bias = bias\n\n        self.flatten = nn.Flatten()\n        self.linear = nn.Linear(int(np.prod(self.latent_shape)), int(np.prod(start_shape)))\n        self.reshape = Reshape(*start_shape)\n        self.conv = nn.Sequential()\n\n        echannel = self.in_channels\n\n        # transform tensor of shape `start_shape' into output shape through transposed convolutions and residual units\n        for i, (c, s) in enumerate(zip(channels, strides)):\n            is_last = i == len(channels) - 1\n            layer = self._get_layer(echannel, c, s, is_last)\n            self.conv.add_module(\"layer_%i\" % i, layer)\n            echannel = c", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/generator.py_Generator._get_layer_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/generator.py_Generator._get_layer_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/generator.py", "file_name": "generator.py", "file_type": "text/x-python", "category": "implementation", "start_line": 101, "end_line": 150, "span_ids": ["Generator.forward", "Generator._get_layer"], "tokens": 342}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Generator(nn.Module):\n\n    def _get_layer(\n        self, in_channels: int, out_channels: int, strides: int, is_last: bool\n    ) -> Union[Convolution, nn.Sequential]:\n        \"\"\"\n        Returns a layer accepting inputs with `in_channels` number of channels and producing outputs of `out_channels`\n        number of channels. The `strides` indicates upsampling factor, ie. transpose convolutional stride. If `is_last`\n        is True this is the final layer and is not expected to include activation and normalization layers.\n        \"\"\"\n\n        layer: Union[Convolution, nn.Sequential]\n\n        layer = Convolution(\n            in_channels=in_channels,\n            strides=strides,\n            is_transposed=True,\n            conv_only=is_last or self.num_res_units > 0,\n            spatial_dims=self.dimensions,\n            out_channels=out_channels,\n            kernel_size=self.kernel_size,\n            act=self.act,\n            norm=self.norm,\n            dropout=self.dropout,\n            bias=self.bias,\n        )\n\n        if self.num_res_units > 0:\n            ru = ResidualUnit(\n                in_channels=out_channels,\n                subunits=self.num_res_units,\n                last_conv_only=is_last,\n                spatial_dims=self.dimensions,\n                out_channels=out_channels,\n                kernel_size=self.kernel_size,\n                act=self.act,\n                norm=self.norm,\n                dropout=self.dropout,\n                bias=self.bias,\n            )\n\n            layer = nn.Sequential(layer, ru)\n\n        return layer\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        x = self.flatten(x)\n        x = self.linear(x)\n        x = self.reshape(x)\n        x = self.conv(x)\n        return x", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/highresnet.py_HighResNet_HighResNet._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/highresnet.py_HighResNet_HighResNet._", "embedding": null, "metadata": {"file_path": "monai/networks/nets/highresnet.py", "file_name": "highresnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 105, "end_line": 134, "span_ids": ["HighResNet"], "tokens": 357}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class HighResNet(nn.Module):\n    \"\"\"\n    Reimplementation of highres3dnet based on\n    Li et al., \"On the compactness, efficiency, and representation of 3D\n    convolutional networks: Brain parcellation as a pretext task\", IPMI '17\n\n    Adapted from:\n    https://github.com/NifTK/NiftyNet/blob/v0.6.0/niftynet/network/highres3dnet.py\n    https://github.com/fepegar/highresnet\n\n    Args:\n        spatial_dims: number of spatial dimensions of the input image.\n        in_channels: number of input channels.\n        out_channels: number of output channels.\n        norm_type: feature normalization type and arguments.\n            Defaults to ``(\"batch\", {\"affine\": True})``.\n        acti_type: activation type and arguments.\n            Defaults to ``(\"relu\", {\"inplace\": True})``.\n        dropout_prob: probability of the feature map to be zeroed\n            (only applies to the penultimate conv layer).\n        bias: whether to have a bias term in convolution blocks. Defaults to False.\n            According to `Performance Tuning Guide <https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html>`_,\n            if a conv layer is directly followed by a batch norm layer, bias should be False.\n        layer_params: specifying key parameters of each layer/block.\n        channel_matching: {``\"pad\"``, ``\"project\"``}\n            Specifies handling residual branch and conv branch channel mismatches. Defaults to ``\"pad\"``.\n\n            - ``\"pad\"``: with zero padding.\n            - ``\"project\"``: with a trainable conv with kernel size one.\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/highresnet.py_HighResNet.__init___": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/highresnet.py_HighResNet.__init___", "embedding": null, "metadata": {"file_path": "monai/networks/nets/highresnet.py", "file_name": "highresnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 136, "end_line": 225, "span_ids": ["HighResNet.forward", "HighResNet.__init__"], "tokens": 684}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class HighResNet(nn.Module):\n\n    def __init__(\n        self,\n        spatial_dims: int = 3,\n        in_channels: int = 1,\n        out_channels: int = 1,\n        norm_type: Union[str, tuple] = (\"batch\", {\"affine\": True}),\n        acti_type: Union[str, tuple] = (\"relu\", {\"inplace\": True}),\n        dropout_prob: Optional[Union[Tuple, str, float]] = 0.0,\n        bias: bool = False,\n        layer_params: Sequence[Dict] = DEFAULT_LAYER_PARAMS_3D,\n        channel_matching: Union[ChannelMatching, str] = ChannelMatching.PAD,\n    ) -> None:\n\n        super().__init__()\n        blocks = nn.ModuleList()\n\n        # initial conv layer\n        params = layer_params[0]\n        _in_chns, _out_chns = in_channels, params[\"n_features\"]\n        blocks.append(\n            Convolution(\n                spatial_dims=spatial_dims,\n                in_channels=_in_chns,\n                out_channels=_out_chns,\n                kernel_size=params[\"kernel_size\"],\n                adn_ordering=\"NA\",\n                act=acti_type,\n                norm=norm_type,\n                bias=bias,\n            )\n        )\n\n        # residual blocks\n        for (idx, params) in enumerate(layer_params[1:-2]):  # res blocks except the 1st and last two conv layers.\n            _in_chns, _out_chns = _out_chns, params[\"n_features\"]\n            _dilation = 2 ** idx\n            for _ in range(params[\"repeat\"]):\n                blocks.append(\n                    HighResBlock(\n                        spatial_dims=spatial_dims,\n                        in_channels=_in_chns,\n                        out_channels=_out_chns,\n                        kernels=params[\"kernels\"],\n                        dilation=_dilation,\n                        norm_type=norm_type,\n                        acti_type=acti_type,\n                        bias=bias,\n                        channel_matching=channel_matching,\n                    )\n                )\n                _in_chns = _out_chns\n\n        # final conv layers\n        params = layer_params[-2]\n        _in_chns, _out_chns = _out_chns, params[\"n_features\"]\n        blocks.append(\n            Convolution(\n                spatial_dims=spatial_dims,\n                in_channels=_in_chns,\n                out_channels=_out_chns,\n                kernel_size=params[\"kernel_size\"],\n                adn_ordering=\"NAD\",\n                act=acti_type,\n                norm=norm_type,\n                bias=bias,\n                dropout=dropout_prob,\n            )\n        )\n\n        params = layer_params[-1]\n        _in_chns = _out_chns\n        blocks.append(\n            Convolution(\n                spatial_dims=spatial_dims,\n                in_channels=_in_chns,\n                out_channels=out_channels,\n                kernel_size=params[\"kernel_size\"],\n                adn_ordering=\"NAD\",\n                act=acti_type,\n                norm=norm_type,\n                bias=bias,\n                dropout=dropout_prob,\n            )\n        )\n\n        self.blocks = nn.Sequential(*blocks)\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        return torch.as_tensor(self.blocks(x))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/regressor.py_Regressor._get_layer_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/regressor.py_Regressor._get_layer_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/regressor.py", "file_name": "regressor.py", "file_type": "text/x-python", "category": "implementation", "start_line": 102, "end_line": 152, "span_ids": ["Regressor.forward", "Regressor._get_final_layer", "Regressor._get_layer"], "tokens": 365}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Regressor(nn.Module):\n\n    def _get_layer(\n        self, in_channels: int, out_channels: int, strides: int, is_last: bool\n    ) -> Union[ResidualUnit, Convolution]:\n        \"\"\"\n        Returns a layer accepting inputs with `in_channels` number of channels and producing outputs of `out_channels`\n        number of channels. The `strides` indicates downsampling factor, ie. convolutional stride. If `is_last`\n        is True this is the final layer and is not expected to include activation and normalization layers.\n        \"\"\"\n\n        layer: Union[ResidualUnit, Convolution]\n\n        if self.num_res_units > 0:\n            layer = ResidualUnit(\n                subunits=self.num_res_units,\n                last_conv_only=is_last,\n                spatial_dims=self.dimensions,\n                in_channels=in_channels,\n                out_channels=out_channels,\n                strides=strides,\n                kernel_size=self.kernel_size,\n                act=self.act,\n                norm=self.norm,\n                dropout=self.dropout,\n                bias=self.bias,\n            )\n        else:\n            layer = Convolution(\n                conv_only=is_last,\n                spatial_dims=self.dimensions,\n                in_channels=in_channels,\n                out_channels=out_channels,\n                strides=strides,\n                kernel_size=self.kernel_size,\n                act=self.act,\n                norm=self.norm,\n                dropout=self.dropout,\n                bias=self.bias,\n            )\n\n        return layer\n\n    def _get_final_layer(self, in_shape: Sequence[int]):\n        linear = nn.Linear(int(np.product(in_shape)), int(np.product(self.out_shape)))\n        return nn.Sequential(nn.Flatten(), linear)\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        x = self.net(x)\n        x = self.final(x)\n        x = self.reshape(x)\n        return x", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/unet.py_UNet._get_down_layer_UNet._get_bottom_layer.return.self__get_down_layer_in_c": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/unet.py_UNet._get_down_layer_UNet._get_bottom_layer.return.self__get_down_layer_in_c", "embedding": null, "metadata": {"file_path": "monai/networks/nets/unet.py", "file_name": "unet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 193, "end_line": 236, "span_ids": ["UNet._get_down_layer", "UNet._get_bottom_layer"], "tokens": 298}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@export(\"monai.networks.nets\")\n@alias(\"Unet\")\nclass UNet(nn.Module):\n\n    def _get_down_layer(self, in_channels: int, out_channels: int, strides: int, is_top: bool) -> nn.Module:\n        \"\"\"\n        Args:\n            in_channels: number of input channels.\n            out_channels: number of output channels.\n            strides: convolution stride.\n            is_top: True if this is the top block.\n        \"\"\"\n        mod: nn.Module\n        if self.num_res_units > 0:\n\n            mod = ResidualUnit(\n                self.dimensions,\n                in_channels,\n                out_channels,\n                strides=strides,\n                kernel_size=self.kernel_size,\n                subunits=self.num_res_units,\n                act=self.act,\n                norm=self.norm,\n                dropout=self.dropout,\n                bias=self.bias,\n            )\n            return mod\n        mod = Convolution(\n            self.dimensions,\n            in_channels,\n            out_channels,\n            strides=strides,\n            kernel_size=self.kernel_size,\n            act=self.act,\n            norm=self.norm,\n            dropout=self.dropout,\n            bias=self.bias,\n        )\n        return mod\n\n    def _get_bottom_layer(self, in_channels: int, out_channels: int) -> nn.Module:\n        \"\"\"\n        Args:\n            in_channels: number of input channels.\n            out_channels: number of output channels.\n        \"\"\"\n        return self._get_down_layer(in_channels, out_channels, 1, False)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/unet.py_UNet._get_up_layer_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/unet.py_UNet._get_up_layer_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/unet.py", "file_name": "unet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 238, "end_line": 286, "span_ids": ["impl:3", "UNet.forward", "UNet._get_up_layer"], "tokens": 299}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@export(\"monai.networks.nets\")\n@alias(\"Unet\")\nclass UNet(nn.Module):\n\n    def _get_up_layer(self, in_channels: int, out_channels: int, strides: int, is_top: bool) -> nn.Module:\n        \"\"\"\n        Args:\n            in_channels: number of input channels.\n            out_channels: number of output channels.\n            strides: convolution stride.\n            is_top: True if this is the top block.\n        \"\"\"\n        conv: Union[Convolution, nn.Sequential]\n\n        conv = Convolution(\n            self.dimensions,\n            in_channels,\n            out_channels,\n            strides=strides,\n            kernel_size=self.up_kernel_size,\n            act=self.act,\n            norm=self.norm,\n            dropout=self.dropout,\n            bias=self.bias,\n            conv_only=is_top and self.num_res_units == 0,\n            is_transposed=True,\n        )\n\n        if self.num_res_units > 0:\n            ru = ResidualUnit(\n                self.dimensions,\n                out_channels,\n                out_channels,\n                strides=1,\n                kernel_size=self.kernel_size,\n                subunits=1,\n                act=self.act,\n                norm=self.norm,\n                dropout=self.dropout,\n                bias=self.bias,\n                last_conv_only=is_top,\n            )\n            conv = nn.Sequential(conv, ru)\n\n        return conv\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        x = self.model(x)\n        return x\n\n\nUnet = UNet", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/utils.py_normalize_transform_normalize_transform.return.norm": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/utils.py_normalize_transform_normalize_transform.return.norm", "embedding": null, "metadata": {"file_path": "monai/networks/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 82, "end_line": 114, "span_ids": ["normalize_transform"], "tokens": 372}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def normalize_transform(\n    shape: Sequence[int],\n    device: Optional[torch.device] = None,\n    dtype: Optional[torch.dtype] = None,\n    align_corners: bool = False,\n) -> torch.Tensor:\n    \"\"\"\n    Compute an affine matrix according to the input shape.\n    The transform normalizes the homogeneous image coordinates to the\n    range of `[-1, 1]`.\n\n    Args:\n        shape: input spatial shape\n        device: device on which the returned affine will be allocated.\n        dtype: data type of the returned affine\n        align_corners: if True, consider -1 and 1 to refer to the centers of the\n            corner pixels rather than the image corners.\n            See also: https://pytorch.org/docs/stable/nn.functional.html#torch.nn.functional.grid_sample\n    \"\"\"\n    norm = torch.tensor(shape, dtype=torch.float64, device=device)  # no in-place change\n    if align_corners:\n        norm[norm <= 1.0] = 2.0\n        norm = 2.0 / (norm - 1.0)\n        norm = torch.diag(torch.cat((norm, torch.ones((1,), dtype=torch.float64, device=device))))\n        norm[:-1, -1] = -1.0\n    else:\n        norm[norm <= 0.0] = 2.0\n        norm = 2.0 / norm\n        norm = torch.diag(torch.cat((norm, torch.ones((1,), dtype=torch.float64, device=device))))\n        norm[:-1, -1] = 1.0 / torch.tensor(shape, dtype=torch.float64, device=device) - 1.0\n    norm = norm.unsqueeze(0).to(dtype=dtype)\n    norm.requires_grad = False\n    return norm", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/adaptors.py_adaptor._inner_adaptor.return._inner": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/adaptors.py_adaptor._inner_adaptor.return._inner", "embedding": null, "metadata": {"file_path": "monai/transforms/adaptors.py", "file_name": "adaptors.py", "file_type": "text/x-python", "category": "implementation", "start_line": 149, "end_line": 214, "span_ids": ["adaptor"], "tokens": 569}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@_monai_export(\"monai.transforms\")\ndef adaptor(function, outputs, inputs=None):\n    # ... other code\n\n    def _inner(ditems):\n\n        sig = FunctionSignature(function)\n\n        if sig.found_kwargs:\n            must_be_types_or_none(\"inputs\", inputs, (dict,))\n            # we just forward all arguments unless we have been provided an input map\n            if inputs is None:\n                dinputs = dict(ditems)\n            else:\n                # dict\n                dinputs = map_names(ditems, inputs)\n\n        else:\n            # no **kwargs\n            # select only items from the method signature\n            dinputs = {k: v for k, v in ditems.items() if k in sig.non_var_parameters}\n            must_be_types_or_none(\"inputs\", inputs, (str, list, tuple, dict))\n            if inputs is None:\n                pass\n            elif isinstance(inputs, str):\n                if len(sig.non_var_parameters) != 1:\n                    raise ValueError(\"if 'inputs' is a string, function may only have a single non-variadic parameter\")\n                dinputs = {inputs: ditems[inputs]}\n            elif isinstance(inputs, (list, tuple)):\n                dinputs = {k: dinputs[k] for k in inputs}\n            else:\n                # dict\n                dinputs = map_only_names(ditems, inputs)\n\n        ret = function(**dinputs)\n\n        # now the mapping back to the output dictionary depends on outputs and what was returned from the function\n        op = outputs\n        if isinstance(ret, dict):\n            must_be_types_or_none(\"outputs\", op, (dict,))\n            if op is not None:\n                ret = {v: ret[k] for k, v in op.items()}\n        elif isinstance(ret, (list, tuple)):\n            if len(ret) == 1:\n                must_be_types(\"outputs\", op, (str, list, tuple))\n            else:\n                must_be_types(\"outputs\", op, (list, tuple))\n\n            if isinstance(op, str):\n                op = [op]\n\n            if len(ret) != len(outputs):\n                raise ValueError(\"'outputs' must have the same length as the number of elements that were returned\")\n\n            ret = dict(zip(op, ret))\n        else:\n            must_be_types(\"outputs\", op, (str, list, tuple))\n            if isinstance(op, (list, tuple)):\n                if len(op) != 1:\n                    raise ValueError(\"'outputs' must be of length one if it is a list or tuple\")\n                op = op[0]\n            ret = {op: ret}\n\n        ditems = dict(ditems)\n        for k, v in ret.items():\n            ditems[k] = v\n\n        return ditems\n\n    return _inner", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/adaptors.py_apply_alias_to_kwargs.return._inner": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/adaptors.py_apply_alias_to_kwargs.return._inner", "embedding": null, "metadata": {"file_path": "monai/transforms/adaptors.py", "file_name": "adaptors.py", "file_type": "text/x-python", "category": "implementation", "start_line": 213, "end_line": 239, "span_ids": ["apply_alias", "to_kwargs"], "tokens": 145}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@_monai_export(\"monai.transforms\")\ndef apply_alias(fn, name_map):\n    def _inner(data):\n\n        # map names\n        pre_call = dict(data)\n        for _from, _to in name_map.items():\n            pre_call[_to] = pre_call.pop(_from)\n\n        # execute\n        post_call = fn(pre_call)\n\n        # map names back\n        for _from, _to in name_map.items():\n            post_call[_from] = post_call.pop(_to)\n\n        return post_call\n\n    return _inner\n\n\n@_monai_export(\"monai.transforms\")\ndef to_kwargs(fn):\n    def _inner(data):\n        return fn(**data)\n\n    return _inner", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/adaptors.py_FunctionSignature_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/adaptors.py_FunctionSignature_", "embedding": null, "metadata": {"file_path": "monai/transforms/adaptors.py", "file_name": "adaptors.py", "file_type": "text/x-python", "category": "implementation", "start_line": 244, "end_line": 268, "span_ids": ["FunctionSignature.__repr__", "FunctionSignature", "FunctionSignature.__str__", "FunctionSignature.__init__"], "tokens": 194}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class FunctionSignature:\n    def __init__(self, function: Callable) -> None:\n        import inspect\n\n        sfn = inspect.signature(function)\n        self.found_args = False\n        self.found_kwargs = False\n        self.defaults = {}\n        self.non_var_parameters = set()\n        for p in sfn.parameters.values():\n            if p.kind is inspect.Parameter.VAR_POSITIONAL:\n                self.found_args = True\n            if p.kind is inspect.Parameter.VAR_KEYWORD:\n                self.found_kwargs = True\n            else:\n                self.non_var_parameters.add(p.name)\n                self.defaults[p.name] = p.default is not p.empty\n\n    def __repr__(self) -> str:\n        s = \"<class 'FunctionSignature': found_args={}, found_kwargs={}, defaults={}\"\n        return s.format(self.found_args, self.found_kwargs, self.defaults)\n\n    def __str__(self) -> str:\n        return self.__repr__()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/__init__.py__": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/__init__.py__", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/__init__.py", "file_name": "__init__.py", "file_type": "text/x-python", "category": "implementation", "start_line": 11, "end_line": 11, "span_ids": [], "tokens": 0}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_DivisiblePad.__call___DivisiblePad.__call__.return.spatial_pad_img_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_DivisiblePad.__call___DivisiblePad.__call__.return.spatial_pad_img_", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 329, "end_line": 347, "span_ids": ["DivisiblePad.__call__"], "tokens": 296}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DivisiblePad(Transform):\n\n    def __call__(\n        self, img: NdarrayOrTensor, mode: Optional[Union[NumpyPadMode, PytorchPadMode, str]] = None\n    ) -> NdarrayOrTensor:\n        \"\"\"\n        Args:\n            img: data to be transformed, assuming `img` is channel-first\n                and padding doesn't apply to the channel dim.\n            mode: available modes for numpy array:{``\"constant\"``, ``\"edge\"``, ``\"linear_ramp\"``, ``\"maximum\"``,\n                ``\"mean\"``, ``\"median\"``, ``\"minimum\"``, ``\"reflect\"``, ``\"symmetric\"``, ``\"wrap\"``, ``\"empty\"``}\n                available modes for PyTorch Tensor: {``\"constant\"``, ``\"reflect\"``, ``\"replicate\"``, ``\"circular\"``}.\n                One of the listed string values or a user supplied function. Defaults to `self.mode`.\n                See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html\n                https://pytorch.org/docs/stable/generated/torch.nn.functional.pad.html\n\n        \"\"\"\n        new_size = compute_divisible_spatial_size(spatial_shape=img.shape[1:], k=self.k)\n        spatial_pad = SpatialPad(spatial_size=new_size, method=self.method, mode=mode or self.mode, **self.kwargs)\n\n        return spatial_pad(img)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_RandSpatialCrop_RandSpatialCrop.__init__.self._slices.None": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_RandSpatialCrop_RandSpatialCrop.__init__.self._slices.None", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 483, "end_line": 521, "span_ids": ["RandSpatialCrop.__init__", "RandSpatialCrop"], "tokens": 513}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandSpatialCrop(Randomizable, Transform):\n    \"\"\"\n    Crop image with random size or specific size ROI. It can crop at a random position as center\n    or at the image center. And allows to set the minimum and maximum size to limit the randomly generated ROI.\n\n    Note: even `random_size=False`, if a dimension of the expected ROI size is bigger than the input image size,\n    will not crop that dimension. So the cropped result may be smaller than the expected ROI, and the cropped results\n    of several images may not have exactly the same shape.\n\n    Args:\n        roi_size: if `random_size` is True, it specifies the minimum crop region.\n            if `random_size` is False, it specifies the expected ROI size to crop. e.g. [224, 224, 128]\n            if a dimension of ROI size is bigger than image size, will not crop that dimension of the image.\n            If its components have non-positive values, the corresponding size of input image will be used.\n            for example: if the spatial size of input data is [40, 40, 40] and `roi_size=[32, 64, -1]`,\n            the spatial size of output data will be [32, 40, 40].\n        max_roi_size: if `random_size` is True and `roi_size` specifies the min crop region size, `max_roi_size`\n            can specify the max crop region size. if None, defaults to the input image size.\n            if its components have non-positive values, the corresponding size of input image will be used.\n        random_center: crop at random position as center or the image center.\n        random_size: crop with random size or specific size ROI.\n            if True, the actual size is sampled from `randint(roi_size, max_roi_size + 1)`.\n    \"\"\"\n\n    backend = CenterSpatialCrop.backend\n\n    def __init__(\n        self,\n        roi_size: Union[Sequence[int], int],\n        max_roi_size: Optional[Union[Sequence[int], int]] = None,\n        random_center: bool = True,\n        random_size: bool = True,\n    ) -> None:\n        self.roi_size = roi_size\n        self.max_roi_size = max_roi_size\n        self.random_center = random_center\n        self.random_size = random_size\n        self._size: Optional[Sequence[int]] = None\n        self._slices: Optional[Tuple[slice, ...]] = None", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_RandSpatialCropSamples_RandSpatialCropSamples.__call__.return._self_cropper_img_for___": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_RandSpatialCropSamples_RandSpatialCropSamples.__call__.return._self_cropper_img_for___", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 595, "end_line": 656, "span_ids": ["RandSpatialCropSamples", "RandSpatialCropSamples.__call__", "RandSpatialCropSamples.__init__", "RandSpatialCropSamples.set_random_state", "RandSpatialCropSamples.randomize"], "tokens": 706}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandSpatialCropSamples(Randomizable, Transform):\n    \"\"\"\n    Crop image with random size or specific size ROI to generate a list of N samples.\n    It can crop at a random position as center or at the image center. And allows to set\n    the minimum size to limit the randomly generated ROI.\n    It will return a list of cropped images.\n\n    Note: even `random_size=False`, if a dimension of the expected ROI size is bigger than the input image size,\n    will not crop that dimension. So the cropped result may be smaller than the expected ROI, and the cropped\n    results of several images may not have exactly the same shape.\n\n    Args:\n        roi_size: if `random_size` is True, it specifies the minimum crop region.\n            if `random_size` is False, it specifies the expected ROI size to crop. e.g. [224, 224, 128]\n            if a dimension of ROI size is bigger than image size, will not crop that dimension of the image.\n            If its components have non-positive values, the corresponding size of input image will be used.\n            for example: if the spatial size of input data is [40, 40, 40] and `roi_size=[32, 64, -1]`,\n            the spatial size of output data will be [32, 40, 40].\n        num_samples: number of samples (crop regions) to take in the returned list.\n        max_roi_size: if `random_size` is True and `roi_size` specifies the min crop region size, `max_roi_size`\n            can specify the max crop region size. if None, defaults to the input image size.\n            if its components have non-positive values, the corresponding size of input image will be used.\n        random_center: crop at random position as center or the image center.\n        random_size: crop with random size or specific size ROI.\n            The actual size is sampled from `randint(roi_size, img_size)`.\n\n    Raises:\n        ValueError: When ``num_samples`` is nonpositive.\n\n    \"\"\"\n\n    backend = RandSpatialCrop.backend\n\n    def __init__(\n        self,\n        roi_size: Union[Sequence[int], int],\n        num_samples: int,\n        max_roi_size: Optional[Union[Sequence[int], int]] = None,\n        random_center: bool = True,\n        random_size: bool = True,\n    ) -> None:\n        if num_samples < 1:\n            raise ValueError(f\"num_samples must be positive, got {num_samples}.\")\n        self.num_samples = num_samples\n        self.cropper = RandSpatialCrop(roi_size, max_roi_size, random_center, random_size)\n\n    def set_random_state(\n        self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None\n    ) -> \"RandSpatialCropSamples\":\n        super().set_random_state(seed, state)\n        self.cropper.set_random_state(seed, state)\n        return self\n\n    def randomize(self, data: Optional[Any] = None) -> None:\n        pass\n\n    def __call__(self, img: NdarrayOrTensor) -> List[NdarrayOrTensor]:\n        \"\"\"\n        Apply the transform to `img`, assuming `img` is channel-first and\n        cropping doesn't change the channel dim.\n        \"\"\"\n        return [self.cropper(img) for _ in range(self.num_samples)]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_SpatialPadd_SpatialPadd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_SpatialPadd_SpatialPadd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 107, "end_line": 156, "span_ids": ["SpatialPadd", "SpatialPadd.__init__", "SpatialPadd.__call__"], "tokens": 677}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SpatialPadd(MapTransform, InvertibleTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.SpatialPad`.\n    Performs padding to the data, symmetric for all sides or all on one side for each dimension.\n    \"\"\"\n\n    backend = SpatialPad.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        spatial_size: Union[Sequence[int], int],\n        method: Union[Method, str] = Method.SYMMETRIC,\n        mode: PadModeSequence = NumpyPadMode.CONSTANT,\n        allow_missing_keys: bool = False,\n        **kwargs,\n    ) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to be transformed.\n                See also: :py:class:`monai.transforms.compose.MapTransform`\n            spatial_size: the spatial size of output data after padding, if a dimension of the input\n                data size is bigger than the pad size, will not pad that dimension.\n                If its components have non-positive values, the corresponding size of input image will be used.\n                for example: if the spatial size of input data is [30, 30, 30] and `spatial_size=[32, 25, -1]`,\n                the spatial size of output data will be [32, 30, 30].\n            method: {``\"symmetric\"``, ``\"end\"``}\n                Pad image symmetrically on every side or only pad at the end sides. Defaults to ``\"symmetric\"``.\n            mode: available modes for numpy array:{``\"constant\"``, ``\"edge\"``, ``\"linear_ramp\"``, ``\"maximum\"``,\n                ``\"mean\"``, ``\"median\"``, ``\"minimum\"``, ``\"reflect\"``, ``\"symmetric\"``, ``\"wrap\"``, ``\"empty\"``}\n                available modes for PyTorch Tensor: {``\"constant\"``, ``\"reflect\"``, ``\"replicate\"``, ``\"circular\"``}.\n                One of the listed string values or a user supplied function. Defaults to ``\"constant\"``.\n                See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html\n                https://pytorch.org/docs/stable/generated/torch.nn.functional.pad.html\n                It also can be a sequence of string, each element corresponds to a key in ``keys``.\n            allow_missing_keys: don't raise exception if key is missing.\n            kwargs: other arguments for the `np.pad` or `torch.pad` function.\n                note that `np.pad` treats channel dimension as the first dimension.\n\n        \"\"\"\n        super().__init__(keys, allow_missing_keys)\n        self.mode = ensure_tuple_rep(mode, len(self.keys))\n        self.padder = SpatialPad(spatial_size, method, **kwargs)\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key, m in self.key_iterator(d, self.mode):\n            self.push_transform(d, key, extra_info={\"mode\": m.value if isinstance(m, Enum) else m})\n            d[key] = self.padder(d[key], mode=m)\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_BorderPadd_BorderPadd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_BorderPadd_BorderPadd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 179, "end_line": 231, "span_ids": ["BorderPadd.__init__", "BorderPadd.__call__", "BorderPadd"], "tokens": 720}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class BorderPadd(MapTransform, InvertibleTransform):\n    \"\"\"\n    Pad the input data by adding specified borders to every dimension.\n    Dictionary-based wrapper of :py:class:`monai.transforms.BorderPad`.\n    \"\"\"\n\n    backend = BorderPad.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        spatial_border: Union[Sequence[int], int],\n        mode: PadModeSequence = NumpyPadMode.CONSTANT,\n        allow_missing_keys: bool = False,\n        **kwargs,\n    ) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to be transformed.\n                See also: :py:class:`monai.transforms.compose.MapTransform`\n            spatial_border: specified size for every spatial border. it can be 3 shapes:\n\n                - single int number, pad all the borders with the same size.\n                - length equals the length of image shape, pad every spatial dimension separately.\n                  for example, image shape(CHW) is [1, 4, 4], spatial_border is [2, 1],\n                  pad every border of H dim with 2, pad every border of W dim with 1, result shape is [1, 8, 6].\n                - length equals 2 x (length of image shape), pad every border of every dimension separately.\n                  for example, image shape(CHW) is [1, 4, 4], spatial_border is [1, 2, 3, 4], pad top of H dim with 1,\n                  pad bottom of H dim with 2, pad left of W dim with 3, pad right of W dim with 4.\n                  the result shape is [1, 7, 11].\n\n            mode: available modes for numpy array:{``\"constant\"``, ``\"edge\"``, ``\"linear_ramp\"``, ``\"maximum\"``,\n                ``\"mean\"``, ``\"median\"``, ``\"minimum\"``, ``\"reflect\"``, ``\"symmetric\"``, ``\"wrap\"``, ``\"empty\"``}\n                available modes for PyTorch Tensor: {``\"constant\"``, ``\"reflect\"``, ``\"replicate\"``, ``\"circular\"``}.\n                One of the listed string values or a user supplied function. Defaults to ``\"constant\"``.\n                See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html\n                https://pytorch.org/docs/stable/generated/torch.nn.functional.pad.html\n                It also can be a sequence of string, each element corresponds to a key in ``keys``.\n            allow_missing_keys: don't raise exception if key is missing.\n            kwargs: other arguments for the `np.pad` or `torch.pad` function.\n                note that `np.pad` treats channel dimension as the first dimension.\n\n        \"\"\"\n        super().__init__(keys, allow_missing_keys)\n        self.mode = ensure_tuple_rep(mode, len(self.keys))\n        self.padder = BorderPad(spatial_border=spatial_border, **kwargs)\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key, m in self.key_iterator(d, self.mode):\n            self.push_transform(d, key, extra_info={\"mode\": m.value if isinstance(m, Enum) else m})\n            d[key] = self.padder(d[key], mode=m)\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_DivisiblePadd_DivisiblePadd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_DivisiblePadd_DivisiblePadd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 258, "end_line": 307, "span_ids": ["DivisiblePadd.__init__", "DivisiblePadd", "DivisiblePadd.__call__"], "tokens": 633}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DivisiblePadd(MapTransform, InvertibleTransform):\n    \"\"\"\n    Pad the input data, so that the spatial sizes are divisible by `k`.\n    Dictionary-based wrapper of :py:class:`monai.transforms.DivisiblePad`.\n    \"\"\"\n\n    backend = DivisiblePad.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        k: Union[Sequence[int], int],\n        mode: PadModeSequence = NumpyPadMode.CONSTANT,\n        method: Union[Method, str] = Method.SYMMETRIC,\n        allow_missing_keys: bool = False,\n        **kwargs,\n    ) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to be transformed.\n                See also: :py:class:`monai.transforms.compose.MapTransform`\n            k: the target k for each spatial dimension.\n                if `k` is negative or 0, the original size is preserved.\n                if `k` is an int, the same `k` be applied to all the input spatial dimensions.\n            mode: available modes for numpy array:{``\"constant\"``, ``\"edge\"``, ``\"linear_ramp\"``, ``\"maximum\"``,\n                ``\"mean\"``, ``\"median\"``, ``\"minimum\"``, ``\"reflect\"``, ``\"symmetric\"``, ``\"wrap\"``, ``\"empty\"``}\n                available modes for PyTorch Tensor: {``\"constant\"``, ``\"reflect\"``, ``\"replicate\"``, ``\"circular\"``}.\n                One of the listed string values or a user supplied function. Defaults to ``\"constant\"``.\n                See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html\n                https://pytorch.org/docs/stable/generated/torch.nn.functional.pad.html\n                It also can be a sequence of string, each element corresponds to a key in ``keys``.\n            method: {``\"symmetric\"``, ``\"end\"``}\n                Pad image symmetrically on every side or only pad at the end sides. Defaults to ``\"symmetric\"``.\n            allow_missing_keys: don't raise exception if key is missing.\n            kwargs: other arguments for the `np.pad` or `torch.pad` function.\n                note that `np.pad` treats channel dimension as the first dimension.\n\n        See also :py:class:`monai.transforms.SpatialPad`\n\n        \"\"\"\n        super().__init__(keys, allow_missing_keys)\n        self.mode = ensure_tuple_rep(mode, len(self.keys))\n        self.padder = DivisiblePad(k=k, method=method, **kwargs)\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key, m in self.key_iterator(d, self.mode):\n            self.push_transform(d, key, extra_info={\"mode\": m.value if isinstance(m, Enum) else m})\n            d[key] = self.padder(d[key], mode=m)\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_SpatialCropd_SpatialCropd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_SpatialCropd_SpatialCropd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 328, "end_line": 376, "span_ids": ["SpatialCropd", "SpatialCropd.__call__", "SpatialCropd.__init__"], "tokens": 515}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SpatialCropd(MapTransform, InvertibleTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.SpatialCrop`.\n    General purpose cropper to produce sub-volume region of interest (ROI).\n    If a dimension of the expected ROI size is bigger than the input image size, will not crop that dimension.\n    So the cropped result may be smaller than the expected ROI, and the cropped results of several images may\n    not have exactly the same shape.\n    It can support to crop ND spatial (channel-first) data.\n\n    The cropped region can be parameterised in various ways:\n        - a list of slices for each spatial dimension (allows for use of -ve indexing and `None`)\n        - a spatial center and size\n        - the start and end coordinates of the ROI\n    \"\"\"\n\n    backend = SpatialCrop.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        roi_center: Optional[Sequence[int]] = None,\n        roi_size: Optional[Sequence[int]] = None,\n        roi_start: Optional[Sequence[int]] = None,\n        roi_end: Optional[Sequence[int]] = None,\n        roi_slices: Optional[Sequence[slice]] = None,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to be transformed.\n                See also: :py:class:`monai.transforms.compose.MapTransform`\n            roi_center: voxel coordinates for center of the crop ROI.\n            roi_size: size of the crop ROI, if a dimension of ROI size is bigger than image size,\n                will not crop that dimension of the image.\n            roi_start: voxel coordinates for start of the crop ROI.\n            roi_end: voxel coordinates for end of the crop ROI, if a coordinate is out of image,\n                use the end coordinate of image.\n            roi_slices: list of slices for each of the spatial dimensions.\n            allow_missing_keys: don't raise exception if key is missing.\n        \"\"\"\n        super().__init__(keys, allow_missing_keys)\n        self.cropper = SpatialCrop(roi_center, roi_size, roi_start, roi_end, roi_slices)\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key in self.key_iterator(d):\n            self.push_transform(d, key)\n            d[key] = self.cropper(d[key])\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_CenterSpatialCropd_CenterSpatialCropd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_CenterSpatialCropd_CenterSpatialCropd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 400, "end_line": 432, "span_ids": ["CenterSpatialCropd.__call__", "CenterSpatialCropd", "CenterSpatialCropd.__init__"], "tokens": 392}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CenterSpatialCropd(MapTransform, InvertibleTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.CenterSpatialCrop`.\n    If a dimension of the expected ROI size is bigger than the input image size, will not crop that dimension.\n    So the cropped result may be smaller than the expected ROI, and the cropped results of several images may\n    not have exactly the same shape.\n\n    Args:\n        keys: keys of the corresponding items to be transformed.\n            See also: monai.transforms.MapTransform\n        roi_size: the size of the crop region e.g. [224,224,128]\n            if a dimension of ROI size is bigger than image size, will not crop that dimension of the image.\n            If its components have non-positive values, the corresponding size of input image will be used.\n            for example: if the spatial size of input data is [40, 40, 40] and `roi_size=[32, 64, -1]`,\n            the spatial size of output data will be [32, 40, 40].\n        allow_missing_keys: don't raise exception if key is missing.\n    \"\"\"\n\n    backend = CenterSpatialCrop.backend\n\n    def __init__(\n        self, keys: KeysCollection, roi_size: Union[Sequence[int], int], allow_missing_keys: bool = False\n    ) -> None:\n        super().__init__(keys, allow_missing_keys)\n        self.cropper = CenterSpatialCrop(roi_size)\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key in self.key_iterator(d):\n            orig_size = d[key].shape[1:]\n            d[key] = self.cropper(d[key])\n            self.push_transform(d, key, orig_size=orig_size)\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_RandSpatialCropd_RandSpatialCropd.__init__.self._size.None": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_RandSpatialCropd_RandSpatialCropd.__init__.self._size.None", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 513, "end_line": 560, "span_ids": ["RandSpatialCropd", "RandSpatialCropd.__init__"], "tokens": 627}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandSpatialCropd(Randomizable, MapTransform, InvertibleTransform):\n    \"\"\"\n    Dictionary-based version :py:class:`monai.transforms.RandSpatialCrop`.\n    Crop image with random size or specific size ROI. It can crop at a random position as\n    center or at the image center. And allows to set the minimum and maximum size to limit the randomly\n    generated ROI. Suppose all the expected fields specified by `keys` have same shape.\n\n    Note: even `random_size=False`, if a dimension of the expected ROI size is bigger than the input image size,\n    will not crop that dimension. So the cropped result may be smaller than the expected ROI, and the cropped\n    results of several images may not have exactly the same shape.\n\n    Args:\n        keys: keys of the corresponding items to be transformed.\n            See also: monai.transforms.MapTransform\n        roi_size: if `random_size` is True, it specifies the minimum crop region.\n            if `random_size` is False, it specifies the expected ROI size to crop. e.g. [224, 224, 128]\n            if a dimension of ROI size is bigger than image size, will not crop that dimension of the image.\n            If its components have non-positive values, the corresponding size of input image will be used.\n            for example: if the spatial size of input data is [40, 40, 40] and `roi_size=[32, 64, -1]`,\n            the spatial size of output data will be [32, 40, 40].\n        max_roi_size: if `random_size` is True and `roi_size` specifies the min crop region size, `max_roi_size`\n            can specify the max crop region size. if None, defaults to the input image size.\n            if its components have non-positive values, the corresponding size of input image will be used.\n        random_center: crop at random position as center or the image center.\n        random_size: crop with random size or specific size ROI.\n            if True, the actual size is sampled from:\n            `randint(roi_scale * image spatial size, max_roi_scale * image spatial size + 1)`.\n        allow_missing_keys: don't raise exception if key is missing.\n    \"\"\"\n\n    backend = CenterSpatialCrop.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        roi_size: Union[Sequence[int], int],\n        max_roi_size: Optional[Union[Sequence[int], int]] = None,\n        random_center: bool = True,\n        random_size: bool = True,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        MapTransform.__init__(self, keys, allow_missing_keys)\n        self.roi_size = roi_size\n        self.max_roi_size = max_roi_size\n        self.random_center = random_center\n        self.random_size = random_size\n        self._slices: Optional[Tuple[slice, ...]] = None\n        self._size: Optional[Sequence[int]] = None", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_RandCropByPosNegLabeld.__call___RandCropByPosNegLabeld.__call__.return.results": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_RandCropByPosNegLabeld.__call___RandCropByPosNegLabeld.__call__.return.results", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1139, "end_line": 1172, "span_ids": ["RandCropByPosNegLabeld.__call__"], "tokens": 452}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandCropByPosNegLabeld(Randomizable, MapTransform, InvertibleTransform):\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> List[Dict[Hashable, NdarrayOrTensor]]:\n        d = dict(data)\n        label = d[self.label_key]\n        image = d[self.image_key] if self.image_key else None\n        fg_indices = d.pop(self.fg_indices_key, None) if self.fg_indices_key is not None else None\n        bg_indices = d.pop(self.bg_indices_key, None) if self.bg_indices_key is not None else None\n\n        self.randomize(label, fg_indices, bg_indices, image)\n        if not isinstance(self.spatial_size, tuple):\n            raise ValueError(\"spatial_size must be a valid tuple.\")\n        if self.centers is None:\n            raise ValueError(\"no available ROI centers to crop.\")\n\n        # initialize returned list with shallow copy to preserve key ordering\n        results: List[Dict[Hashable, NdarrayOrTensor]] = [dict(d) for _ in range(self.num_samples)]\n\n        for i, center in enumerate(self.centers):\n            # fill in the extra keys with unmodified data\n            for key in set(d.keys()).difference(set(self.keys)):\n                results[i][key] = deepcopy(d[key])\n            for key in self.key_iterator(d):\n                img = d[key]\n                cropper = SpatialCrop(roi_center=tuple(center), roi_size=self.spatial_size)\n                orig_size = img.shape[1:]\n                results[i][key] = cropper(img)\n                self.push_transform(results[i], key, extra_info={\"center\": center}, orig_size=orig_size)\n            # add `patch_index` to the meta data\n            for key, meta_key, meta_key_postfix in self.key_iterator(d, self.meta_keys, self.meta_key_postfix):\n                meta_key = meta_key or f\"{key}_{meta_key_postfix}\"\n                if meta_key not in results[i]:\n                    results[i][meta_key] = {}  # type: ignore\n                results[i][meta_key][Key.PATCH_INDEX] = i  # type: ignore\n\n        return results", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_SpatialPadD_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_SpatialPadD_", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1503, "end_line": 1518, "span_ids": ["impl:7"], "tokens": 238}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "SpatialPadD = SpatialPadDict = SpatialPadd\nBorderPadD = BorderPadDict = BorderPadd\nDivisiblePadD = DivisiblePadDict = DivisiblePadd\nSpatialCropD = SpatialCropDict = SpatialCropd\nCenterSpatialCropD = CenterSpatialCropDict = CenterSpatialCropd\nCenterScaleCropD = CenterScaleCropDict = CenterScaleCropd\nRandSpatialCropD = RandSpatialCropDict = RandSpatialCropd\nRandScaleCropD = RandScaleCropDict = RandScaleCropd\nRandSpatialCropSamplesD = RandSpatialCropSamplesDict = RandSpatialCropSamplesd\nCropForegroundD = CropForegroundDict = CropForegroundd\nRandWeightedCropD = RandWeightedCropDict = RandWeightedCropd\nRandCropByPosNegLabelD = RandCropByPosNegLabelDict = RandCropByPosNegLabeld\nRandCropByLabelClassesD = RandCropByLabelClassesDict = RandCropByLabelClassesd\nResizeWithPadOrCropD = ResizeWithPadOrCropDict = ResizeWithPadOrCropd\nBoundingRectD = BoundingRectDict = BoundingRectd", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/__init__.py__": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/__init__.py__", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/__init__.py", "file_name": "__init__.py", "file_type": "text/x-python", "category": "implementation", "start_line": 11, "end_line": 11, "span_ids": [], "tokens": 0}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_ScaleIntensityd_ScaleIntensityd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_ScaleIntensityd_ScaleIntensityd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 483, "end_line": 523, "span_ids": ["ScaleIntensityd", "ScaleIntensityd.__call__", "ScaleIntensityd.__init__"], "tokens": 420}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ScaleIntensityd(MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.ScaleIntensity`.\n    Scale the intensity of input image to the given value range (minv, maxv).\n    If `minv` and `maxv` not provided, use `factor` to scale image by ``v = v * (1 + factor)``.\n    \"\"\"\n\n    backend = ScaleIntensity.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        minv: Optional[float] = 0.0,\n        maxv: Optional[float] = 1.0,\n        factor: Optional[float] = None,\n        channel_wise: bool = False,\n        dtype: DtypeLike = np.float32,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to be transformed.\n                See also: :py:class:`monai.transforms.compose.MapTransform`\n            minv: minimum value of output data.\n            maxv: maximum value of output data.\n            factor: factor scale by ``v = v * (1 + factor)``. In order to use\n                this parameter, please set both `minv` and `maxv` into None.\n            channel_wise: if True, scale on each channel separately. Please ensure\n                that the first dimension represents the channel of the image if True.\n            dtype: output data type, if None, same as input image. defaults to float32.\n            allow_missing_keys: don't raise exception if key is missing.\n\n        \"\"\"\n        super().__init__(keys, allow_missing_keys)\n        self.scaler = ScaleIntensity(minv, maxv, factor, channel_wise, dtype)\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key in self.key_iterator(d):\n            d[key] = self.scaler(d[key])\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_RandScaleIntensityd_RandScaleIntensityd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_RandScaleIntensityd_RandScaleIntensityd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 526, "end_line": 574, "span_ids": ["RandScaleIntensityd.__call__", "RandScaleIntensityd.__init__", "RandScaleIntensityd", "RandScaleIntensityd.set_random_state"], "tokens": 442}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandScaleIntensityd(RandomizableTransform, MapTransform):\n    \"\"\"\n    Dictionary-based version :py:class:`monai.transforms.RandScaleIntensity`.\n    \"\"\"\n\n    backend = RandScaleIntensity.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        factors: Union[Tuple[float, float], float],\n        prob: float = 0.1,\n        dtype: DtypeLike = np.float32,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to be transformed.\n                See also: :py:class:`monai.transforms.compose.MapTransform`\n            factors: factor range to randomly scale by ``v = v * (1 + factor)``.\n                if single number, factor value is picked from (-factors, factors).\n            prob: probability of rotating.\n                (Default 0.1, with 10% probability it returns a rotated array.)\n            dtype: output data type, if None, same as input image. defaults to float32.\n            allow_missing_keys: don't raise exception if key is missing.\n\n        \"\"\"\n        MapTransform.__init__(self, keys, allow_missing_keys)\n        RandomizableTransform.__init__(self, prob)\n        self.scaler = RandScaleIntensity(factors=factors, dtype=dtype, prob=1.0)\n\n    def set_random_state(\n        self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None\n    ) -> \"RandScaleIntensityd\":\n        super().set_random_state(seed, state)\n        self.scaler.set_random_state(seed, state)\n        return self\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        self.randomize(None)\n        if not self._do_transform:\n            return d\n\n        # all the keys share the same random scale factor\n        self.scaler.randomize(None)\n        for key in self.key_iterator(d):\n            d[key] = self.scaler(d[key], randomize=False)\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_NormalizeIntensityd_NormalizeIntensityd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_NormalizeIntensityd_NormalizeIntensityd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 650, "end_line": 687, "span_ids": ["NormalizeIntensityd.__call__", "NormalizeIntensityd.__init__", "NormalizeIntensityd"], "tokens": 363}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class NormalizeIntensityd(MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.NormalizeIntensity`.\n    This transform can normalize only non-zero values or entire image, and can also calculate\n    mean and std on each channel separately.\n\n    Args:\n        keys: keys of the corresponding items to be transformed.\n            See also: monai.transforms.MapTransform\n        subtrahend: the amount to subtract by (usually the mean)\n        divisor: the amount to divide by (usually the standard deviation)\n        nonzero: whether only normalize non-zero values.\n        channel_wise: if True, calculate on each channel separately, otherwise, calculate on\n            the entire image directly. default to False.\n        dtype: output data type, if None, same as input image. defaults to float32.\n        allow_missing_keys: don't raise exception if key is missing.\n    \"\"\"\n\n    backend = NormalizeIntensity.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        subtrahend: Optional[NdarrayOrTensor] = None,\n        divisor: Optional[NdarrayOrTensor] = None,\n        nonzero: bool = False,\n        channel_wise: bool = False,\n        dtype: DtypeLike = np.float32,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        super().__init__(keys, allow_missing_keys)\n        self.normalizer = NormalizeIntensity(subtrahend, divisor, nonzero, channel_wise, dtype)\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key in self.key_iterator(d):\n            d[key] = self.normalizer(d[key])\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_ThresholdIntensityd_ThresholdIntensityd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_ThresholdIntensityd_ThresholdIntensityd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 668, "end_line": 698, "span_ids": ["ThresholdIntensityd.__init__", "ThresholdIntensityd", "ThresholdIntensityd.__call__"], "tokens": 253}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ThresholdIntensityd(MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.ThresholdIntensity`.\n\n    Args:\n        keys: keys of the corresponding items to be transformed.\n            See also: monai.transforms.MapTransform\n        threshold: the threshold to filter intensity values.\n        above: filter values above the threshold or below the threshold, default is True.\n        cval: value to fill the remaining parts of the image, default is 0.\n        allow_missing_keys: don't raise exception if key is missing.\n    \"\"\"\n\n    backend = ThresholdIntensity.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        threshold: float,\n        above: bool = True,\n        cval: float = 0.0,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        super().__init__(keys, allow_missing_keys)\n        self.filter = ThresholdIntensity(threshold, above, cval)\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key in self.key_iterator(d):\n            d[key] = self.filter(d[key])\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_ScaleIntensityRanged_ScaleIntensityRanged.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_ScaleIntensityRanged_ScaleIntensityRanged.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 703, "end_line": 739, "span_ids": ["ScaleIntensityRanged.__call__", "ScaleIntensityRanged.__init__", "ScaleIntensityRanged"], "tokens": 312}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ScaleIntensityRanged(MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.ScaleIntensityRange`.\n\n    Args:\n        keys: keys of the corresponding items to be transformed.\n            See also: monai.transforms.MapTransform\n        a_min: intensity original range min.\n        a_max: intensity original range max.\n        b_min: intensity target range min.\n        b_max: intensity target range max.\n        clip: whether to perform clip after scaling.\n        dtype: output data type, if None, same as input image. defaults to float32.\n        allow_missing_keys: don't raise exception if key is missing.\n    \"\"\"\n\n    backend = ScaleIntensityRange.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        a_min: float,\n        a_max: float,\n        b_min: Optional[float] = None,\n        b_max: Optional[float] = None,\n        clip: bool = False,\n        dtype: DtypeLike = np.float32,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        super().__init__(keys, allow_missing_keys)\n        self.scaler = ScaleIntensityRange(a_min, a_max, b_min, b_max, clip, dtype)\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key in self.key_iterator(d):\n            d[key] = self.scaler(d[key])\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_AdjustContrastd_AdjustContrastd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_AdjustContrastd_AdjustContrastd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 738, "end_line": 762, "span_ids": ["AdjustContrastd", "AdjustContrastd.__call__", "AdjustContrastd.__init__"], "tokens": 234}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AdjustContrastd(MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.AdjustContrast`.\n    Changes image intensity by gamma. Each pixel/voxel intensity is updated as:\n\n        `x = ((x - min) / intensity_range) ^ gamma * intensity_range + min`\n\n    Args:\n        keys: keys of the corresponding items to be transformed.\n            See also: monai.transforms.MapTransform\n        gamma: gamma value to adjust the contrast as function.\n        allow_missing_keys: don't raise exception if key is missing.\n    \"\"\"\n\n    backend = AdjustContrast.backend\n\n    def __init__(self, keys: KeysCollection, gamma: float, allow_missing_keys: bool = False) -> None:\n        super().__init__(keys, allow_missing_keys)\n        self.adjuster = AdjustContrast(gamma)\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key in self.key_iterator(d):\n            d[key] = self.adjuster(d[key])\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_RandAdjustContrastd_RandAdjustContrastd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_RandAdjustContrastd_RandAdjustContrastd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 765, "end_line": 811, "span_ids": ["RandAdjustContrastd", "RandAdjustContrastd.set_random_state", "RandAdjustContrastd.__call__", "RandAdjustContrastd.__init__"], "tokens": 435}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandAdjustContrastd(RandomizableTransform, MapTransform):\n    \"\"\"\n    Dictionary-based version :py:class:`monai.transforms.RandAdjustContrast`.\n    Randomly changes image intensity by gamma. Each pixel/voxel intensity is updated as:\n\n        `x = ((x - min) / intensity_range) ^ gamma * intensity_range + min`\n\n    Args:\n        keys: keys of the corresponding items to be transformed.\n            See also: monai.transforms.MapTransform\n        prob: Probability of adjustment.\n        gamma: Range of gamma values.\n            If single number, value is picked from (0.5, gamma), default is (0.5, 4.5).\n        allow_missing_keys: don't raise exception if key is missing.\n    \"\"\"\n\n    backend = RandAdjustContrast.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        prob: float = 0.1,\n        gamma: Union[Tuple[float, float], float] = (0.5, 4.5),\n        allow_missing_keys: bool = False,\n    ) -> None:\n        MapTransform.__init__(self, keys, allow_missing_keys)\n        RandomizableTransform.__init__(self, prob)\n        self.adjuster = RandAdjustContrast(gamma=gamma, prob=1.0)\n\n    def set_random_state(\n        self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None\n    ) -> \"RandAdjustContrastd\":\n        super().set_random_state(seed, state)\n        self.adjuster.set_random_state(seed, state)\n        return self\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        self.randomize(None)\n        if not self._do_transform:\n            return d\n\n        # all the keys share the same random gamma value\n        self.adjuster.randomize(None)\n        for key in self.key_iterator(d):\n            d[key] = self.adjuster(d[key], randomize=False)\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_ScaleIntensityRangePercentilesd_ScaleIntensityRangePercentilesd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_ScaleIntensityRangePercentilesd_ScaleIntensityRangePercentilesd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 838, "end_line": 879, "span_ids": ["ScaleIntensityRangePercentilesd", "ScaleIntensityRangePercentilesd.__init__", "ScaleIntensityRangePercentilesd.__call__"], "tokens": 367}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ScaleIntensityRangePercentilesd(MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.ScaleIntensityRangePercentiles`.\n\n    Args:\n        keys: keys of the corresponding items to be transformed.\n            See also: monai.transforms.MapTransform\n        lower: lower percentile.\n        upper: upper percentile.\n        b_min: intensity target range min.\n        b_max: intensity target range max.\n        clip: whether to perform clip after scaling.\n        relative: whether to scale to the corresponding percentiles of [b_min, b_max]\n        channel_wise: if True, compute intensity percentile and normalize every channel separately.\n            default to False.\n        dtype: output data type, if None, same as input image. defaults to float32.\n        allow_missing_keys: don't raise exception if key is missing.\n    \"\"\"\n\n    backend = ScaleIntensityRangePercentiles.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        lower: float,\n        upper: float,\n        b_min: Optional[float],\n        b_max: Optional[float],\n        clip: bool = False,\n        relative: bool = False,\n        channel_wise: bool = False,\n        dtype: DtypeLike = np.float32,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        super().__init__(keys, allow_missing_keys)\n        self.scaler = ScaleIntensityRangePercentiles(lower, upper, b_min, b_max, clip, relative, channel_wise, dtype)\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key in self.key_iterator(d):\n            d[key] = self.scaler(d[key])\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_MaskIntensityd_MaskIntensityd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_MaskIntensityd_MaskIntensityd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 853, "end_line": 892, "span_ids": ["MaskIntensityd", "MaskIntensityd.__call__", "MaskIntensityd.__init__"], "tokens": 397}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class MaskIntensityd(MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.MaskIntensity`.\n\n    Args:\n        keys: keys of the corresponding items to be transformed.\n            See also: :py:class:`monai.transforms.compose.MapTransform`\n        mask_data: if mask data is single channel, apply to every channel\n            of input image. if multiple channels, the channel number must\n            match input data. the intensity values of input image corresponding\n            to the selected values in the mask data will keep the original value,\n            others will be set to `0`. if None, will extract the mask data from\n            input data based on `mask_key`.\n        mask_key: the key to extract mask data from input dictionary, only works\n            when `mask_data` is None.\n        select_fn: function to select valid values of the `mask_data`, default is\n            to select `values > 0`.\n        allow_missing_keys: don't raise exception if key is missing.\n\n    \"\"\"\n\n    backend = MaskIntensity.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        mask_data: Optional[NdarrayOrTensor] = None,\n        mask_key: Optional[str] = None,\n        select_fn: Callable = is_positive,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        super().__init__(keys, allow_missing_keys)\n        self.converter = MaskIntensity(mask_data=mask_data, select_fn=select_fn)\n        self.mask_key = mask_key if mask_data is None else None\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key in self.key_iterator(d):\n            d[key] = self.converter(d[key], d[self.mask_key]) if self.mask_key is not None else self.converter(d[key])\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_RandGaussianNoiseD_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_RandGaussianNoiseD_", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1654, "end_line": 1683, "span_ids": ["impl:3"], "tokens": 485}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "RandGaussianNoiseD = RandGaussianNoiseDict = RandGaussianNoised\nRandRicianNoiseD = RandRicianNoiseDict = RandRicianNoised\nShiftIntensityD = ShiftIntensityDict = ShiftIntensityd\nRandShiftIntensityD = RandShiftIntensityDict = RandShiftIntensityd\nStdShiftIntensityD = StdShiftIntensityDict = StdShiftIntensityd\nRandStdShiftIntensityD = RandStdShiftIntensityDict = RandStdShiftIntensityd\nRandBiasFieldD = RandBiasFieldDict = RandBiasFieldd\nScaleIntensityD = ScaleIntensityDict = ScaleIntensityd\nRandScaleIntensityD = RandScaleIntensityDict = RandScaleIntensityd\nNormalizeIntensityD = NormalizeIntensityDict = NormalizeIntensityd\nThresholdIntensityD = ThresholdIntensityDict = ThresholdIntensityd\nScaleIntensityRangeD = ScaleIntensityRangeDict = ScaleIntensityRanged\nAdjustContrastD = AdjustContrastDict = AdjustContrastd\nRandAdjustContrastD = RandAdjustContrastDict = RandAdjustContrastd\nScaleIntensityRangePercentilesD = ScaleIntensityRangePercentilesDict = ScaleIntensityRangePercentilesd\nMaskIntensityD = MaskIntensityDict = MaskIntensityd\nSavitzkyGolaySmoothD = SavitzkyGolaySmoothDict = SavitzkyGolaySmoothd\nGaussianSmoothD = GaussianSmoothDict = GaussianSmoothd\nRandGaussianSmoothD = RandGaussianSmoothDict = RandGaussianSmoothd\nGaussianSharpenD = GaussianSharpenDict = GaussianSharpend\nRandGaussianSharpenD = RandGaussianSharpenDict = RandGaussianSharpend\nRandHistogramShiftD = RandHistogramShiftDict = RandHistogramShiftd\nRandGibbsNoiseD = RandGibbsNoiseDict = RandGibbsNoised\nGibbsNoiseD = GibbsNoiseDict = GibbsNoised\nKSpaceSpikeNoiseD = KSpaceSpikeNoiseDict = KSpaceSpikeNoised\nRandKSpaceSpikeNoiseD = RandKSpaceSpikeNoiseDict = RandKSpaceSpikeNoised\nRandCoarseDropoutD = RandCoarseDropoutDict = RandCoarseDropoutd\nHistogramNormalizeD = HistogramNormalizeDict = HistogramNormalized\nRandCoarseShuffleD = RandCoarseShuffleDict = RandCoarseShuffled", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/io/__init__.py__": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/io/__init__.py__", "embedding": null, "metadata": {"file_path": "monai/transforms/io/__init__.py", "file_name": "__init__.py", "file_type": "text/x-python", "category": "implementation", "start_line": 11, "end_line": 11, "span_ids": [], "tokens": 0}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/__init__.py__": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/__init__.py__", "embedding": null, "metadata": {"file_path": "monai/transforms/post/__init__.py", "file_name": "__init__.py", "file_type": "text/x-python", "category": "implementation", "start_line": 11, "end_line": 11, "span_ids": [], "tokens": 0}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/array.py_Activations_Activations.__init__.self.other.other": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/array.py_Activations_Activations.__init__.self.other.other", "embedding": null, "metadata": {"file_path": "monai/transforms/post/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 45, "end_line": 69, "span_ids": ["Activations.__init__", "Activations"], "tokens": 217}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Activations(Transform):\n    \"\"\"\n    Add activation operations to the model output, typically `Sigmoid` or `Softmax`.\n\n    Args:\n        sigmoid: whether to execute sigmoid function on model output before transform.\n            Defaults to ``False``.\n        softmax: whether to execute softmax function on model output before transform.\n            Defaults to ``False``.\n        other: callable function to execute other activation layers, for example:\n            `other = lambda x: torch.tanh(x)`. Defaults to ``None``.\n\n    Raises:\n        TypeError: When ``other`` is not an ``Optional[Callable]``.\n\n    \"\"\"\n\n    backend = [TransformBackends.TORCH]\n\n    def __init__(self, sigmoid: bool = False, softmax: bool = False, other: Optional[Callable] = None) -> None:\n        self.sigmoid = sigmoid\n        self.softmax = softmax\n        if other is not None and not callable(other):\n            raise TypeError(f\"other must be None or callable but is {type(other).__name__}.\")\n        self.other = other", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/array.py_KeepLargestConnectedComponent_KeepLargestConnectedComponent._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/array.py_KeepLargestConnectedComponent_KeepLargestConnectedComponent._", "embedding": null, "metadata": {"file_path": "monai/transforms/post/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 220, "end_line": 264, "span_ids": ["KeepLargestConnectedComponent"], "tokens": 777}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class KeepLargestConnectedComponent(Transform):\n    \"\"\"\n    Keeps only the largest connected component in the image.\n    This transform can be used as a post-processing step to clean up over-segment areas in model output.\n\n    The input is assumed to be a channel-first PyTorch Tensor:\n      1) With shape (1, spatial_dim1[, spatial_dim2, ...]) and the values correspond to expected labels.\n      2) With shape (C, spatial_dim1[, spatial_dim2, ...]) and the values should be 0, 1 on each labels.\n\n    Note:\n        For single channel data, 0 will be treated as background and the over-segment pixels will be set to 0.\n        For one-hot data, the over-segment pixels will be set to 0 in its channel.\n\n    For example:\n    Use KeepLargestConnectedComponent with applied_labels=[1], connectivity=1::\n\n       [1, 0, 0]         [0, 0, 0]\n       [0, 1, 1]    =>   [0, 1 ,1]\n       [0, 1, 1]         [0, 1, 1]\n\n    Use KeepLargestConnectedComponent with applied_labels[1, 2], independent=False, connectivity=1::\n\n      [0, 0, 1, 0 ,0]           [0, 0, 1, 0 ,0]\n      [0, 2, 1, 1 ,1]           [0, 2, 1, 1 ,1]\n      [1, 2, 1, 0 ,0]    =>     [1, 2, 1, 0 ,0]\n      [1, 2, 0, 1 ,0]           [1, 2, 0, 0 ,0]\n      [2, 2, 0, 0 ,2]           [2, 2, 0, 0 ,0]\n\n    Use KeepLargestConnectedComponent with applied_labels[1, 2], independent=True, connectivity=1::\n\n      [0, 0, 1, 0 ,0]           [0, 0, 1, 0 ,0]\n      [0, 2, 1, 1 ,1]           [0, 2, 1, 1 ,1]\n      [1, 2, 1, 0 ,0]    =>     [0, 2, 1, 0 ,0]\n      [1, 2, 0, 1 ,0]           [0, 2, 0, 0 ,0]\n      [2, 2, 0, 0 ,2]           [2, 2, 0, 0 ,0]\n\n    Use KeepLargestConnectedComponent with applied_labels[1, 2], independent=False, connectivity=2::\n\n      [0, 0, 1, 0 ,0]           [0, 0, 1, 0 ,0]\n      [0, 2, 1, 1 ,1]           [0, 2, 1, 1 ,1]\n      [1, 2, 1, 0 ,0]    =>     [1, 2, 1, 0 ,0]\n      [1, 2, 0, 1 ,0]           [1, 2, 0, 1 ,0]\n      [2, 2, 0, 0 ,2]           [2, 2, 0, 0 ,2]\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/array.py_LabelToContour_LabelToContour.__init__.self.kernel_type.kernel_type": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/array.py_LabelToContour_LabelToContour.__init__.self.kernel_type.kernel_type", "embedding": null, "metadata": {"file_path": "monai/transforms/post/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 464, "end_line": 482, "span_ids": ["LabelToContour", "LabelToContour.__init__"], "tokens": 160}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LabelToContour(Transform):\n    \"\"\"\n    Return the contour of binary input images that only compose of 0 and 1, with Laplacian kernel\n    set as default for edge detection. Typical usage is to plot the edge of label or segmentation output.\n\n    Args:\n        kernel_type: the method applied to do edge detection, default is \"Laplace\".\n\n    Raises:\n        NotImplementedError: When ``kernel_type`` is not \"Laplace\".\n\n    \"\"\"\n\n    backend = [TransformBackends.TORCH]\n\n    def __init__(self, kernel_type: str = \"Laplace\") -> None:\n        if kernel_type != \"Laplace\":\n            raise NotImplementedError('Currently only kernel_type=\"Laplace\" is supported.')\n        self.kernel_type = kernel_type", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/array.py_MeanEnsemble_MeanEnsemble.__init__.self.weights.torch_as_tensor_weights_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/array.py_MeanEnsemble_MeanEnsemble.__init__.self.weights.torch_as_tensor_weights_", "embedding": null, "metadata": {"file_path": "monai/transforms/post/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 534, "end_line": 560, "span_ids": ["MeanEnsemble", "MeanEnsemble.__init__"], "tokens": 419}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class MeanEnsemble(Ensemble, Transform):\n    \"\"\"\n    Execute mean ensemble on the input data.\n    The input data can be a list or tuple of PyTorch Tensor with shape: [C[, H, W, D]],\n    Or a single PyTorch Tensor with shape: [E, C[, H, W, D]], the `E` dimension represents\n    the output data from different models.\n    Typically, the input data is model output of segmentation task or classification task.\n    And it also can support to add `weights` for the input data.\n\n    Args:\n        weights: can be a list or tuple of numbers for input data with shape: [E, C, H, W[, D]].\n            or a Numpy ndarray or a PyTorch Tensor data.\n            the `weights` will be added to input data from highest dimension, for example:\n            1. if the `weights` only has 1 dimension, it will be added to the `E` dimension of input data.\n            2. if the `weights` has 2 dimensions, it will be added to `E` and `C` dimensions.\n            it's a typical practice to add weights for different classes:\n            to ensemble 3 segmentation model outputs, every output has 4 channels(classes),\n            so the input data shape can be: [3, 4, H, W, D].\n            and add different `weights` for different classes, so the `weights` shape can be: [3, 4].\n            for example: `weights = [[1, 2, 3, 4], [4, 3, 2, 1], [1, 1, 1, 1]]`.\n\n    \"\"\"\n\n    backend = [TransformBackends.TORCH]\n\n    def __init__(self, weights: Optional[Union[Sequence[float], NdarrayOrTensor]] = None) -> None:\n        self.weights = torch.as_tensor(weights, dtype=torch.float) if weights is not None else None", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/dictionary.py_Activationsd_Activationsd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/dictionary.py_Activationsd_Activationsd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/post/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 82, "end_line": 122, "span_ids": ["Activationsd.__call__", "Activationsd", "Activationsd.__init__"], "tokens": 421}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Activationsd(MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.AddActivations`.\n    Add activation layers to the input data specified by `keys`.\n    \"\"\"\n\n    backend = Activations.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        sigmoid: Union[Sequence[bool], bool] = False,\n        softmax: Union[Sequence[bool], bool] = False,\n        other: Optional[Union[Sequence[Callable], Callable]] = None,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to model output and label.\n                See also: :py:class:`monai.transforms.compose.MapTransform`\n            sigmoid: whether to execute sigmoid function on model output before transform.\n                it also can be a sequence of bool, each element corresponds to a key in ``keys``.\n            softmax: whether to execute softmax function on model output before transform.\n                it also can be a sequence of bool, each element corresponds to a key in ``keys``.\n            other: callable function to execute other activation layers,\n                for example: `other = torch.tanh`. it also can be a sequence of Callable, each\n                element corresponds to a key in ``keys``.\n            allow_missing_keys: don't raise exception if key is missing.\n\n        \"\"\"\n        super().__init__(keys, allow_missing_keys)\n        self.sigmoid = ensure_tuple_rep(sigmoid, len(self.keys))\n        self.softmax = ensure_tuple_rep(softmax, len(self.keys))\n        self.other = ensure_tuple_rep(other, len(self.keys))\n        self.converter = Activations()\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key, sigmoid, softmax, other in self.key_iterator(d, self.sigmoid, self.softmax, self.other):\n            d[key] = self.converter(d[key], sigmoid, softmax, other)\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/dictionary.py_KeepLargestConnectedComponentd_KeepLargestConnectedComponentd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/dictionary.py_KeepLargestConnectedComponentd_KeepLargestConnectedComponentd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/post/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 189, "end_line": 229, "span_ids": ["KeepLargestConnectedComponentd.__init__", "KeepLargestConnectedComponentd", "KeepLargestConnectedComponentd.__call__"], "tokens": 400}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class KeepLargestConnectedComponentd(MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.KeepLargestConnectedComponent`.\n    \"\"\"\n\n    backend = KeepLargestConnectedComponent.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        applied_labels: Union[Sequence[int], int],\n        independent: bool = True,\n        connectivity: Optional[int] = None,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to be transformed.\n                See also: :py:class:`monai.transforms.compose.MapTransform`\n            applied_labels: Labels for applying the connected component on.\n                If only one channel. The pixel whose value is not in this list will remain unchanged.\n                If the data is in one-hot format, this is the channel indices to apply transform.\n            independent: whether to treat ``applied_labels`` as a union of foreground labels.\n                If ``True``, the connected component analysis will be performed on each foreground label independently\n                and return the intersection of the largest components.\n                If ``False``, the analysis will be performed on the union of foreground labels.\n                default is `True`.\n            connectivity: Maximum number of orthogonal hops to consider a pixel/voxel as a neighbor.\n                Accepted values are ranging from  1 to input.ndim. If ``None``, a full\n                connectivity of ``input.ndim`` is used.\n            allow_missing_keys: don't raise exception if key is missing.\n\n        \"\"\"\n        super().__init__(keys, allow_missing_keys)\n        self.converter = KeepLargestConnectedComponent(applied_labels, independent, connectivity)\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key in self.key_iterator(d):\n            d[key] = self.converter(d[key])\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/dictionary.py_LabelToContourd_LabelToContourd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/dictionary.py_LabelToContourd_LabelToContourd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/post/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 297, "end_line": 320, "span_ids": ["LabelToContourd.__init__", "LabelToContourd", "LabelToContourd.__call__"], "tokens": 221}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LabelToContourd(MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.LabelToContour`.\n    \"\"\"\n\n    backend = LabelToContour.backend\n\n    def __init__(self, keys: KeysCollection, kernel_type: str = \"Laplace\", allow_missing_keys: bool = False) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to be transformed.\n                See also: :py:class:`monai.transforms.compose.MapTransform`\n            kernel_type: the method applied to do edge detection, default is \"Laplace\".\n            allow_missing_keys: don't raise exception if key is missing.\n\n        \"\"\"\n        super().__init__(keys, allow_missing_keys)\n        self.converter = LabelToContour(kernel_type=kernel_type)\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key in self.key_iterator(d):\n            d[key] = self.converter(d[key])\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/__init__.py__": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/__init__.py__", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/__init__.py", "file_name": "__init__.py", "file_type": "text/x-python", "category": "implementation", "start_line": 11, "end_line": 11, "span_ids": [], "tokens": 0}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_Resize_Resize.__init__.self.align_corners.align_corners": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_Resize_Resize.__init__.self.align_corners.align_corners", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 366, "end_line": 401, "span_ids": ["Resize.__init__", "Resize"], "tokens": 485}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Resize(Transform):\n    \"\"\"\n    Resize the input image to given spatial size (with scaling, not cropping/padding).\n    Implemented using :py:class:`torch.nn.functional.interpolate`.\n\n    Args:\n        spatial_size: expected shape of spatial dimensions after resize operation.\n            if some components of the `spatial_size` are non-positive values, the transform will use the\n            corresponding components of img size. For example, `spatial_size=(32, -1)` will be adapted\n            to `(32, 64)` if the second spatial dimension size of img is `64`.\n        size_mode: should be \"all\" or \"longest\", if \"all\", will use `spatial_size` for all the spatial dims,\n            if \"longest\", rescale the image so that only the longest side is equal to specified `spatial_size`,\n            which must be an int number in this case, keeping the aspect ratio of the initial image, refer to:\n            https://albumentations.ai/docs/api_reference/augmentations/geometric/resize/\n            #albumentations.augmentations.geometric.resize.LongestMaxSize.\n        mode: {``\"nearest\"``, ``\"linear\"``, ``\"bilinear\"``, ``\"bicubic\"``, ``\"trilinear\"``, ``\"area\"``}\n            The interpolation mode. Defaults to ``\"area\"``.\n            See also: https://pytorch.org/docs/stable/nn.functional.html#interpolate\n        align_corners: This only has an effect when mode is\n            'linear', 'bilinear', 'bicubic' or 'trilinear'. Default: None.\n            See also: https://pytorch.org/docs/stable/nn.functional.html#interpolate\n    \"\"\"\n\n    backend = [TransformBackends.TORCH]\n\n    def __init__(\n        self,\n        spatial_size: Union[Sequence[int], int],\n        size_mode: str = \"all\",\n        mode: Union[InterpolateMode, str] = InterpolateMode.AREA,\n        align_corners: Optional[bool] = None,\n    ) -> None:\n        self.size_mode = look_up_option(size_mode, [\"all\", \"longest\"])\n        self.spatial_size = spatial_size\n        self.mode: InterpolateMode = look_up_option(mode, InterpolateMode)\n        self.align_corners = align_corners", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_RandFlip_RandFlip.__call__.return.self_flipper_img_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_RandFlip_RandFlip.__call__.return.self_flipper_img_", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 871, "end_line": 900, "span_ids": ["RandFlip", "RandFlip.__init__", "RandFlip.__call__"], "tokens": 243}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandFlip(RandomizableTransform):\n    \"\"\"\n    Randomly flips the image along axes. Preserves shape.\n    See numpy.flip for additional details.\n    https://docs.scipy.org/doc/numpy/reference/generated/numpy.flip.html\n\n    Args:\n        prob: Probability of flipping.\n        spatial_axis: Spatial axes along which to flip over. Default is None.\n    \"\"\"\n\n    backend = Flip.backend\n\n    def __init__(self, prob: float = 0.1, spatial_axis: Optional[Union[Sequence[int], int]] = None) -> None:\n        RandomizableTransform.__init__(self, prob)\n        self.flipper = Flip(spatial_axis=spatial_axis)\n\n    def __call__(self, img: NdarrayOrTensor, randomize: bool = True) -> NdarrayOrTensor:\n        \"\"\"\n        Args:\n            img: channel first array, must have shape: (num_channels, H[, W, ..., ]),\n            randomize: whether to execute `randomize()` function first, default to True.\n        \"\"\"\n        if randomize:\n            self.randomize(None)\n\n        if not self._do_transform:\n            return img\n\n        return self.flipper(img)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_RandDeformGrid_RandDeformGrid.randomize.self.rand_mag.self_R_uniform_self_magni": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_RandDeformGrid_RandDeformGrid.randomize.self.rand_mag.self_R_uniform_self_magni", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1270, "end_line": 1306, "span_ids": ["RandDeformGrid", "RandDeformGrid.__init__", "RandDeformGrid.randomize"], "tokens": 330}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandDeformGrid(Randomizable, Transform):\n    \"\"\"\n    Generate random deformation grid.\n    \"\"\"\n\n    backend = [TransformBackends.TORCH]\n\n    def __init__(\n        self,\n        spacing: Union[Sequence[float], float],\n        magnitude_range: Tuple[float, float],\n        as_tensor_output: bool = True,\n        device: Optional[torch.device] = None,\n    ) -> None:\n        \"\"\"\n        Args:\n            spacing: spacing of the grid in 2D or 3D.\n                e.g., spacing=(1, 1) indicates pixel-wise deformation in 2D,\n                spacing=(1, 1, 1) indicates voxel-wise deformation in 3D,\n                spacing=(2, 2) indicates deformation field defined on every other pixel in 2D.\n            magnitude_range: the random offsets will be generated from\n                `uniform[magnitude[0], magnitude[1])`.\n            as_tensor_output: whether to output tensor instead of numpy array.\n                defaults to True.\n            device: device to store the output grid data.\n        \"\"\"\n        self.spacing = spacing\n        self.magnitude = magnitude_range\n\n        self.rand_mag = 1.0\n        self.as_tensor_output = as_tensor_output\n        self.random_offset: np.ndarray\n        self.device = device\n\n    def randomize(self, grid_size: Sequence[int]) -> None:\n        self.random_offset = self.R.normal(size=([len(grid_size)] + list(grid_size))).astype(np.float32, copy=False)\n        self.rand_mag = self.R.uniform(self.magnitude[0], self.magnitude[1])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_RandDeformGrid.__call___RandDeformGrid.__call__.return.control_grid": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_RandDeformGrid.__call___RandDeformGrid.__call__.return.control_grid", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1308, "end_line": 1320, "span_ids": ["RandDeformGrid.__call__"], "tokens": 161}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandDeformGrid(Randomizable, Transform):\n\n    def __call__(self, spatial_size: Sequence[int]):\n        \"\"\"\n        Args:\n            spatial_size: spatial size of the grid.\n        \"\"\"\n        self.spacing = fall_back_tuple(self.spacing, (1.0,) * len(spatial_size))\n        control_grid = create_control_grid(spatial_size, self.spacing, device=self.device, backend=\"torch\")\n        self.randomize(control_grid.shape[1:])\n        _offset, *_ = convert_to_dst_type(self.rand_mag * self.random_offset, control_grid)\n        control_grid[: len(spatial_size)] += _offset\n        if not self.as_tensor_output:\n            control_grid, *_ = convert_data_type(control_grid, output_type=np.ndarray, dtype=np.float32)\n        return control_grid", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_Resample_Resample.__init__.self.device.device": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_Resample_Resample.__init__.self.device.device", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1323, "end_line": 1354, "span_ids": ["Resample.__init__", "Resample"], "tokens": 339}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Resample(Transform):\n\n    backend = [TransformBackends.TORCH]\n\n    @deprecated_arg(name=\"as_tensor_output\", since=\"0.6\")\n    def __init__(\n        self,\n        mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,\n        padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER,\n        as_tensor_output: bool = True,\n        device: Optional[torch.device] = None,\n    ) -> None:\n        \"\"\"\n        computes output image using values from `img`, locations from `grid` using pytorch.\n        supports spatially 2D or 3D (num_channels, H, W[, D]).\n\n        Args:\n            mode: {``\"bilinear\"``, ``\"nearest\"``}\n                Interpolation mode to calculate output values. Defaults to ``\"bilinear\"``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            padding_mode: {``\"zeros\"``, ``\"border\"``, ``\"reflection\"``}\n                Padding mode for outside grid values. Defaults to ``\"border\"``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            device: device on which the tensor will be allocated.\n\n        .. deprecated:: 0.6.0\n            ``as_tensor_output`` is deprecated.\n\n        \"\"\"\n        self.mode: GridSampleMode = look_up_option(mode, GridSampleMode)\n        self.padding_mode: GridSamplePadMode = look_up_option(padding_mode, GridSamplePadMode)\n        self.device = device", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_Rand2DElastic.set_random_state_Rand2DElastic.randomize.self_rand_affine_grid_ran": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_Rand2DElastic.set_random_state_Rand2DElastic.randomize.self_rand_affine_grid_ran", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1806, "end_line": 1819, "span_ids": ["Rand2DElastic.randomize", "Rand2DElastic.set_random_state"], "tokens": 139}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Rand2DElastic(RandomizableTransform):\n\n    def set_random_state(\n        self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None\n    ) -> \"Rand2DElastic\":\n        self.deform_grid.set_random_state(seed, state)\n        self.rand_affine_grid.set_random_state(seed, state)\n        super().set_random_state(seed, state)\n        return self\n\n    def randomize(self, spatial_size: Sequence[int]) -> None:\n        super().randomize(None)\n        if not self._do_transform:\n            return None\n        self.deform_grid.randomize(spatial_size)\n        self.rand_affine_grid.randomize()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_Rand3DElastic_Rand3DElastic.__init__.self.sigma.1_0": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_Rand3DElastic_Rand3DElastic.__init__.self.sigma.1_0", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1867, "end_line": 1963, "span_ids": ["Rand3DElastic.__init__", "Rand3DElastic"], "tokens": 1221}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Rand3DElastic(RandomizableTransform):\n    \"\"\"\n    Random elastic deformation and affine in 3D.\n    A tutorial is available: https://github.com/Project-MONAI/tutorials/blob/0.6.0/modules/transforms_demo_2d.ipynb.\n\n    \"\"\"\n\n    backend = Resample.backend\n\n    @deprecated_arg(name=\"as_tensor_output\", since=\"0.6\")\n    def __init__(\n        self,\n        sigma_range: Tuple[float, float],\n        magnitude_range: Tuple[float, float],\n        prob: float = 0.1,\n        rotate_range: RandRange = None,\n        shear_range: RandRange = None,\n        translate_range: RandRange = None,\n        scale_range: RandRange = None,\n        spatial_size: Optional[Union[Tuple[int, int, int], int]] = None,\n        mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,\n        padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.REFLECTION,\n        as_tensor_output: bool = False,\n        device: Optional[torch.device] = None,\n    ) -> None:\n        \"\"\"\n        Args:\n            sigma_range: a Gaussian kernel with standard deviation sampled from\n                ``uniform[sigma_range[0], sigma_range[1])`` will be used to smooth the random offset grid.\n            magnitude_range: the random offsets on the grid will be generated from\n                ``uniform[magnitude[0], magnitude[1])``.\n            prob: probability of returning a randomized elastic transform.\n                defaults to 0.1, with 10% chance returns a randomized elastic transform,\n                otherwise returns a ``spatial_size`` centered area extracted from the input image.\n            rotate_range: angle range in radians. If element `i` is a pair of (min, max) values, then\n                `uniform[-rotate_range[i][0], rotate_range[i][1])` will be used to generate the rotation parameter\n                for the `i`th spatial dimension. If not, `uniform[-rotate_range[i], rotate_range[i])` will be used.\n                This can be altered on a per-dimension basis. E.g., `((0,3), 1, ...)`: for dim0, rotation will be\n                in range `[0, 3]`, and for dim1 `[-1, 1]` will be used. Setting a single value will use `[-x, x]`\n                for dim0 and nothing for the remaining dimensions.\n            shear_range: shear range with format matching `rotate_range`, it defines the range to randomly select\n                shearing factors(a tuple of 6 floats for 3D) for affine matrix, take a 3D affine as example::\n\n                    [\n                        [1.0, params[0], params[1], 0.0],\n                        [params[2], 1.0, params[3], 0.0],\n                        [params[4], params[5], 1.0, 0.0],\n                        [0.0, 0.0, 0.0, 1.0],\n                    ]\n\n            translate_range: translate range with format matching `rotate_range`, it defines the range to randomly\n                select voxel to translate for every spatial dims.\n            scale_range: scaling range with format matching `rotate_range`. it defines the range to randomly select\n                the scale factor to translate for every spatial dims. A value of 1.0 is added to the result.\n                This allows 0 to correspond to no change (i.e., a scaling of 1.0).\n            spatial_size: specifying output image spatial size [h, w, d].\n                if `spatial_size` and `self.spatial_size` are not defined, or smaller than 1,\n                the transform will use the spatial size of `img`.\n                if some components of the `spatial_size` are non-positive values, the transform will use the\n                corresponding components of img size. For example, `spatial_size=(32, 32, -1)` will be adapted\n                to `(32, 32, 64)` if the third spatial dimension size of img is `64`.\n            mode: {``\"bilinear\"``, ``\"nearest\"``}\n                Interpolation mode to calculate output values. Defaults to ``\"bilinear\"``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            padding_mode: {``\"zeros\"``, ``\"border\"``, ``\"reflection\"``}\n                Padding mode for outside grid values. Defaults to ``\"reflection\"``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            device: device on which the tensor will be allocated.\n\n        See also:\n            - :py:class:`RandAffineGrid` for the random affine parameters configurations.\n            - :py:class:`Affine` for the affine transformation parameters configurations.\n\n        .. deprecated:: 0.6.0\n            ``as_tensor_output`` is deprecated.\n\n        \"\"\"\n        RandomizableTransform.__init__(self, prob)\n        self.rand_affine_grid = RandAffineGrid(\n            rotate_range=rotate_range,\n            shear_range=shear_range,\n            translate_range=translate_range,\n            scale_range=scale_range,\n            device=device,\n        )\n        self.resampler = Resample(device=device)\n\n        self.sigma_range = sigma_range\n        self.magnitude_range = magnitude_range\n        self.spatial_size = spatial_size\n        self.mode: GridSampleMode = look_up_option(mode, GridSampleMode)\n        self.padding_mode: GridSamplePadMode = look_up_option(padding_mode, GridSamplePadMode)\n        self.device = device\n\n        self.rand_offset: np.ndarray\n        self.magnitude = 1.0\n        self.sigma = 1.0", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_Rand3DElastic.set_random_state_Rand3DElastic.randomize.self_rand_affine_grid_ran": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_Rand3DElastic.set_random_state_Rand3DElastic.randomize.self_rand_affine_grid_ran", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1970, "end_line": 1984, "span_ids": ["Rand3DElastic.randomize", "Rand3DElastic.set_random_state"], "tokens": 188}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Rand3DElastic(RandomizableTransform):\n\n    def set_random_state(\n        self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None\n    ) -> \"Rand3DElastic\":\n        self.rand_affine_grid.set_random_state(seed, state)\n        super().set_random_state(seed, state)\n        return self\n\n    def randomize(self, grid_size: Sequence[int]) -> None:\n        super().randomize(None)\n        if not self._do_transform:\n            return None\n        self.rand_offset = self.R.uniform(-1.0, 1.0, [3] + list(grid_size)).astype(np.float32, copy=False)\n        self.magnitude = self.R.uniform(self.magnitude_range[0], self.magnitude_range[1])\n        self.sigma = self.R.uniform(self.sigma_range[0], self.sigma_range[1])\n        self.rand_affine_grid.randomize()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Spacingd.__call___Spacingd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Spacingd.__call___Spacingd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 229, "end_line": 266, "span_ids": ["Spacingd.__call__"], "tokens": 383}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Spacingd(MapTransform, InvertibleTransform):\n\n    def __call__(\n        self, data: Mapping[Union[Hashable, str], Dict[str, NdarrayOrTensor]]\n    ) -> Dict[Hashable, NdarrayOrTensor]:\n        d: Dict = dict(data)\n        for key, mode, padding_mode, align_corners, dtype, meta_key, meta_key_postfix in self.key_iterator(\n            d, self.mode, self.padding_mode, self.align_corners, self.dtype, self.meta_keys, self.meta_key_postfix\n        ):\n            meta_key = meta_key or f\"{key}_{meta_key_postfix}\"\n            # create metadata if necessary\n            if meta_key not in d:\n                d[meta_key] = {\"affine\": None}\n            meta_data = d[meta_key]\n            # resample array of each corresponding key\n            # using affine fetched from d[affine_key]\n            original_spatial_shape = d[key].shape[1:]\n            d[key], old_affine, new_affine = self.spacing_transform(\n                data_array=d[key],\n                affine=meta_data[\"affine\"],\n                mode=mode,\n                padding_mode=padding_mode,\n                align_corners=align_corners,\n                dtype=dtype,\n            )\n            self.push_transform(\n                d,\n                key,\n                extra_info={\n                    \"meta_key\": meta_key,\n                    \"old_affine\": old_affine,\n                    \"mode\": mode.value if isinstance(mode, Enum) else mode,\n                    \"padding_mode\": padding_mode.value if isinstance(padding_mode, Enum) else padding_mode,\n                    \"align_corners\": align_corners if align_corners is not None else TraceKeys.NONE,\n                },\n                orig_size=original_spatial_shape,\n            )\n            # set the 'affine' key\n            meta_data[\"affine\"] = new_affine\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Rotate90d_Rotate90d.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Rotate90d_Rotate90d.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 397, "end_line": 422, "span_ids": ["Rotate90d.__call__", "Rotate90d.__init__", "Rotate90d"], "tokens": 249}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Rotate90d(MapTransform, InvertibleTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.Rotate90`.\n    \"\"\"\n\n    backend = Rotate90.backend\n\n    def __init__(\n        self, keys: KeysCollection, k: int = 1, spatial_axes: Tuple[int, int] = (0, 1), allow_missing_keys: bool = False\n    ) -> None:\n        \"\"\"\n        Args:\n            k: number of times to rotate by 90 degrees.\n            spatial_axes: 2 int numbers, defines the plane to rotate with 2 spatial axes.\n                Default: (0, 1), this is the first two axis in spatial dimensions.\n            allow_missing_keys: don't raise exception if key is missing.\n        \"\"\"\n        super().__init__(keys, allow_missing_keys)\n        self.rotator = Rotate90(k, spatial_axes)\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key in self.key_iterator(d):\n            self.push_transform(d, key)\n            d[key] = self.rotator(d[key])\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_RandAffined_RandAffined.__init__.self.padding_mode.ensure_tuple_rep_padding_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_RandAffined_RandAffined.__init__.self.padding_mode.ensure_tuple_rep_padding_", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 708, "end_line": 800, "span_ids": ["RandAffined.__init__", "RandAffined"], "tokens": 1238}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandAffined(RandomizableTransform, MapTransform, InvertibleTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.RandAffine`.\n    \"\"\"\n\n    backend = RandAffine.backend\n\n    @deprecated_arg(name=\"as_tensor_output\", since=\"0.6\")\n    def __init__(\n        self,\n        keys: KeysCollection,\n        spatial_size: Optional[Union[Sequence[int], int]] = None,\n        prob: float = 0.1,\n        rotate_range: Optional[Union[Sequence[Union[Tuple[float, float], float]], float]] = None,\n        shear_range: Optional[Union[Sequence[Union[Tuple[float, float], float]], float]] = None,\n        translate_range: Optional[Union[Sequence[Union[Tuple[float, float], float]], float]] = None,\n        scale_range: Optional[Union[Sequence[Union[Tuple[float, float], float]], float]] = None,\n        mode: GridSampleModeSequence = GridSampleMode.BILINEAR,\n        padding_mode: GridSamplePadModeSequence = GridSamplePadMode.REFLECTION,\n        cache_grid: bool = False,\n        as_tensor_output: bool = True,\n        device: Optional[torch.device] = None,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to be transformed.\n            spatial_size: output image spatial size.\n                if `spatial_size` and `self.spatial_size` are not defined, or smaller than 1,\n                the transform will use the spatial size of `img`.\n                if some components of the `spatial_size` are non-positive values, the transform will use the\n                corresponding components of img size. For example, `spatial_size=(32, -1)` will be adapted\n                to `(32, 64)` if the second spatial dimension size of img is `64`.\n            prob: probability of returning a randomized affine grid.\n                defaults to 0.1, with 10% chance returns a randomized grid.\n            rotate_range: angle range in radians. If element `i` is a pair of (min, max) values, then\n                `uniform[-rotate_range[i][0], rotate_range[i][1])` will be used to generate the rotation parameter\n                for the `i`th spatial dimension. If not, `uniform[-rotate_range[i], rotate_range[i])` will be used.\n                This can be altered on a per-dimension basis. E.g., `((0,3), 1, ...)`: for dim0, rotation will be\n                in range `[0, 3]`, and for dim1 `[-1, 1]` will be used. Setting a single value will use `[-x, x]`\n                for dim0 and nothing for the remaining dimensions.\n            shear_range: shear range with format matching `rotate_range`, it defines the range to randomly select\n                shearing factors(a tuple of 2 floats for 2D, a tuple of 6 floats for 3D) for affine matrix,\n                take a 3D affine as example::\n\n                    [\n                        [1.0, params[0], params[1], 0.0],\n                        [params[2], 1.0, params[3], 0.0],\n                        [params[4], params[5], 1.0, 0.0],\n                        [0.0, 0.0, 0.0, 1.0],\n                    ]\n\n            translate_range: translate range with format matching `rotate_range`, it defines the range to randomly\n                select pixel/voxel to translate for every spatial dims.\n            scale_range: scaling range with format matching `rotate_range`. it defines the range to randomly select\n                the scale factor to translate for every spatial dims. A value of 1.0 is added to the result.\n                This allows 0 to correspond to no change (i.e., a scaling of 1.0).\n            mode: {``\"bilinear\"``, ``\"nearest\"``}\n                Interpolation mode to calculate output values. Defaults to ``\"bilinear\"``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n                It also can be a sequence of string, each element corresponds to a key in ``keys``.\n            padding_mode: {``\"zeros\"``, ``\"border\"``, ``\"reflection\"``}\n                Padding mode for outside grid values. Defaults to ``\"reflection\"``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n                It also can be a sequence of string, each element corresponds to a key in ``keys``.\n            cache_grid: whether to cache the identity sampling grid.\n                If the spatial size is not dynamically defined by input image, enabling this option could\n                accelerate the transform.\n            device: device on which the tensor will be allocated.\n            allow_missing_keys: don't raise exception if key is missing.\n\n        See also:\n            - :py:class:`monai.transforms.compose.MapTransform`\n            - :py:class:`RandAffineGrid` for the random affine parameters configurations.\n\n        .. deprecated:: 0.6.0\n            ``as_tensor_output`` is deprecated.\n\n        \"\"\"\n        MapTransform.__init__(self, keys, allow_missing_keys)\n        RandomizableTransform.__init__(self, prob)\n        self.rand_affine = RandAffine(\n            prob=1.0,  # because probability handled in this class\n            rotate_range=rotate_range,\n            shear_range=shear_range,\n            translate_range=translate_range,\n            scale_range=scale_range,\n            spatial_size=spatial_size,\n            cache_grid=cache_grid,\n            device=device,\n        )\n        self.mode = ensure_tuple_rep(mode, len(self.keys))\n        self.padding_mode = ensure_tuple_rep(padding_mode, len(self.keys))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_RandAffined.set_random_state_RandAffined.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_RandAffined.set_random_state_RandAffined.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 810, "end_line": 855, "span_ids": ["RandAffined.set_random_state", "RandAffined.__call__"], "tokens": 490}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandAffined(RandomizableTransform, MapTransform, InvertibleTransform):\n\n    def set_random_state(\n        self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None\n    ) -> \"RandAffined\":\n        self.rand_affine.set_random_state(seed, state)\n        super().set_random_state(seed, state)\n        return self\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        first_key: Union[Hashable, List] = self.first_key(d)\n        if first_key == []:\n            return d\n\n        self.randomize(None)\n        # all the keys share the same random Affine factor\n        self.rand_affine.randomize()\n\n        device = self.rand_affine.resampler.device\n        spatial_size = d[first_key].shape[1:]  # type: ignore\n        sp_size = fall_back_tuple(self.rand_affine.spatial_size, spatial_size)\n        # change image size or do random transform\n        do_resampling = self._do_transform or (sp_size != ensure_tuple(spatial_size))\n        affine: torch.Tensor = torch.eye(len(sp_size) + 1, dtype=torch.float64, device=device)\n        # converting affine to tensor because the resampler currently only support torch backend\n        grid = None\n        if do_resampling:  # need to prepare grid\n            grid = self.rand_affine.get_identity_grid(sp_size)\n            if self._do_transform:  # add some random factors\n                grid = self.rand_affine.rand_affine_grid(grid=grid)\n                affine = self.rand_affine.rand_affine_grid.get_transformation_matrix()  # type: ignore[assignment]\n\n        for key, mode, padding_mode in self.key_iterator(d, self.mode, self.padding_mode):\n            self.push_transform(\n                d,\n                key,\n                extra_info={\n                    \"affine\": affine,\n                    \"mode\": mode.value if isinstance(mode, Enum) else mode,\n                    \"padding_mode\": padding_mode.value if isinstance(padding_mode, Enum) else padding_mode,\n                },\n            )\n            # do the transform\n            if do_resampling:\n                d[key] = self.rand_affine.resampler(d[key], grid, mode=mode, padding_mode=padding_mode)\n\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Rand2DElasticd_Rand2DElasticd.__init__.self.padding_mode.ensure_tuple_rep_padding_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Rand2DElasticd_Rand2DElasticd.__init__.self.padding_mode.ensure_tuple_rep_padding_", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 871, "end_line": 964, "span_ids": ["Rand2DElasticd", "Rand2DElasticd.__init__"], "tokens": 1244}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Rand2DElasticd(RandomizableTransform, MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.Rand2DElastic`.\n    \"\"\"\n\n    backend = Rand2DElastic.backend\n\n    @deprecated_arg(name=\"as_tensor_output\", since=\"0.6\")\n    def __init__(\n        self,\n        keys: KeysCollection,\n        spacing: Union[Tuple[float, float], float],\n        magnitude_range: Tuple[float, float],\n        spatial_size: Optional[Union[Tuple[int, int], int]] = None,\n        prob: float = 0.1,\n        rotate_range: Optional[Union[Sequence[Union[Tuple[float, float], float]], float]] = None,\n        shear_range: Optional[Union[Sequence[Union[Tuple[float, float], float]], float]] = None,\n        translate_range: Optional[Union[Sequence[Union[Tuple[float, float], float]], float]] = None,\n        scale_range: Optional[Union[Sequence[Union[Tuple[float, float], float]], float]] = None,\n        mode: GridSampleModeSequence = GridSampleMode.BILINEAR,\n        padding_mode: GridSamplePadModeSequence = GridSamplePadMode.REFLECTION,\n        as_tensor_output: bool = False,\n        device: Optional[torch.device] = None,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to be transformed.\n            spacing: distance in between the control points.\n            magnitude_range: 2 int numbers, the random offsets will be generated from\n                ``uniform[magnitude[0], magnitude[1])``.\n            spatial_size: specifying output image spatial size [h, w].\n                if `spatial_size` and `self.spatial_size` are not defined, or smaller than 1,\n                the transform will use the spatial size of `img`.\n                if some components of the `spatial_size` are non-positive values, the transform will use the\n                corresponding components of img size. For example, `spatial_size=(32, -1)` will be adapted\n                to `(32, 64)` if the second spatial dimension size of img is `64`.\n            prob: probability of returning a randomized affine grid.\n                defaults to 0.1, with 10% chance returns a randomized grid,\n                otherwise returns a ``spatial_size`` centered area extracted from the input image.\n            rotate_range: angle range in radians. If element `i` is a pair of (min, max) values, then\n                `uniform[-rotate_range[i][0], rotate_range[i][1])` will be used to generate the rotation parameter\n                for the `i`th spatial dimension. If not, `uniform[-rotate_range[i], rotate_range[i])` will be used.\n                This can be altered on a per-dimension basis. E.g., `((0,3), 1, ...)`: for dim0, rotation will be\n                in range `[0, 3]`, and for dim1 `[-1, 1]` will be used. Setting a single value will use `[-x, x]`\n                for dim0 and nothing for the remaining dimensions.\n            shear_range: shear range with format matching `rotate_range`, it defines the range to randomly select\n                shearing factors(a tuple of 2 floats for 2D) for affine matrix, take a 2D affine as example::\n\n                    [\n                        [1.0, params[0], 0.0],\n                        [params[1], 1.0, 0.0],\n                        [0.0, 0.0, 1.0],\n                    ]\n\n            translate_range: translate range with format matching `rotate_range`, it defines the range to randomly\n                select pixel to translate for every spatial dims.\n            scale_range: scaling range with format matching `rotate_range`. it defines the range to randomly select\n                the scale factor to translate for every spatial dims. A value of 1.0 is added to the result.\n                This allows 0 to correspond to no change (i.e., a scaling of 1.0).\n            mode: {``\"bilinear\"``, ``\"nearest\"``}\n                Interpolation mode to calculate output values. Defaults to ``\"bilinear\"``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n                It also can be a sequence of string, each element corresponds to a key in ``keys``.\n            padding_mode: {``\"zeros\"``, ``\"border\"``, ``\"reflection\"``}\n                Padding mode for outside grid values. Defaults to ``\"reflection\"``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n                It also can be a sequence of string, each element corresponds to a key in ``keys``.\n            device: device on which the tensor will be allocated.\n            allow_missing_keys: don't raise exception if key is missing.\n\n        See also:\n            - :py:class:`RandAffineGrid` for the random affine parameters configurations.\n            - :py:class:`Affine` for the affine transformation parameters configurations.\n\n        .. deprecated:: 0.6.0\n            ``as_tensor_output`` is deprecated.\n\n        \"\"\"\n        MapTransform.__init__(self, keys, allow_missing_keys)\n        RandomizableTransform.__init__(self, prob)\n        self.rand_2d_elastic = Rand2DElastic(\n            spacing=spacing,\n            magnitude_range=magnitude_range,\n            prob=1.0,  # because probability controlled by this class\n            rotate_range=rotate_range,\n            shear_range=shear_range,\n            translate_range=translate_range,\n            scale_range=scale_range,\n            spatial_size=spatial_size,\n            device=device,\n        )\n        self.mode = ensure_tuple_rep(mode, len(self.keys))\n        self.padding_mode = ensure_tuple_rep(padding_mode, len(self.keys))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Rand2DElasticd.set_random_state_Rand2DElasticd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Rand2DElasticd.set_random_state_Rand2DElasticd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 978, "end_line": 1014, "span_ids": ["Rand2DElasticd.__call__", "Rand2DElasticd.set_random_state"], "tokens": 429}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Rand2DElasticd(RandomizableTransform, MapTransform):\n\n    def set_random_state(\n        self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None\n    ) -> \"Rand2DElasticd\":\n        self.rand_2d_elastic.set_random_state(seed, state)\n        super().set_random_state(seed, state)\n        return self\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        first_key: Union[Hashable, List] = self.first_key(d)\n        if first_key == []:\n            return d\n\n        self.randomize(None)\n\n        sp_size = fall_back_tuple(self.rand_2d_elastic.spatial_size, d[first_key].shape[1:])  # type: ignore\n        # all the keys share the same random elastic factor\n        self.rand_2d_elastic.randomize(sp_size)\n\n        if self._do_transform:\n            grid = self.rand_2d_elastic.deform_grid(spatial_size=sp_size)\n            grid = self.rand_2d_elastic.rand_affine_grid(grid=grid)\n            grid = torch.nn.functional.interpolate(  # type: ignore\n                recompute_scale_factor=True,\n                input=grid.unsqueeze(0),\n                scale_factor=ensure_tuple_rep(self.rand_2d_elastic.deform_grid.spacing, 2),\n                mode=InterpolateMode.BICUBIC.value,\n                align_corners=False,\n            )\n            grid = CenterSpatialCrop(roi_size=sp_size)(grid[0])\n        else:\n            _device = self.rand_2d_elastic.deform_grid.device\n            grid = create_grid(spatial_size=sp_size, device=_device, backend=\"torch\")\n\n        for key, mode, padding_mode in self.key_iterator(d, self.mode, self.padding_mode):\n            d[key] = self.rand_2d_elastic.resampler(d[key], grid, mode=mode, padding_mode=padding_mode)\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Rand3DElasticd_Rand3DElasticd.__init__.self.padding_mode.ensure_tuple_rep_padding_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Rand3DElasticd_Rand3DElasticd.__init__.self.padding_mode.ensure_tuple_rep_padding_", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1001, "end_line": 1096, "span_ids": ["Rand3DElasticd", "Rand3DElasticd.__init__"], "tokens": 1312}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Rand3DElasticd(RandomizableTransform, MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.Rand3DElastic`.\n    \"\"\"\n\n    backend = Rand3DElastic.backend\n\n    @deprecated_arg(name=\"as_tensor_output\", since=\"0.6\")\n    def __init__(\n        self,\n        keys: KeysCollection,\n        sigma_range: Tuple[float, float],\n        magnitude_range: Tuple[float, float],\n        spatial_size: Optional[Union[Tuple[int, int, int], int]] = None,\n        prob: float = 0.1,\n        rotate_range: Optional[Union[Sequence[Union[Tuple[float, float], float]], float]] = None,\n        shear_range: Optional[Union[Sequence[Union[Tuple[float, float], float]], float]] = None,\n        translate_range: Optional[Union[Sequence[Union[Tuple[float, float], float]], float]] = None,\n        scale_range: Optional[Union[Sequence[Union[Tuple[float, float], float]], float]] = None,\n        mode: GridSampleModeSequence = GridSampleMode.BILINEAR,\n        padding_mode: GridSamplePadModeSequence = GridSamplePadMode.REFLECTION,\n        as_tensor_output: bool = False,\n        device: Optional[torch.device] = None,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to be transformed.\n            sigma_range: a Gaussian kernel with standard deviation sampled from\n                ``uniform[sigma_range[0], sigma_range[1])`` will be used to smooth the random offset grid.\n            magnitude_range: the random offsets on the grid will be generated from\n                ``uniform[magnitude[0], magnitude[1])``.\n            spatial_size: specifying output image spatial size [h, w, d].\n                if `spatial_size` and `self.spatial_size` are not defined, or smaller than 1,\n                the transform will use the spatial size of `img`.\n                if some components of the `spatial_size` are non-positive values, the transform will use the\n                corresponding components of img size. For example, `spatial_size=(32, 32, -1)` will be adapted\n                to `(32, 32, 64)` if the third spatial dimension size of img is `64`.\n            prob: probability of returning a randomized affine grid.\n                defaults to 0.1, with 10% chance returns a randomized grid,\n                otherwise returns a ``spatial_size`` centered area extracted from the input image.\n            rotate_range: angle range in radians. If element `i` is a pair of (min, max) values, then\n                `uniform[-rotate_range[i][0], rotate_range[i][1])` will be used to generate the rotation parameter\n                for the `i`th spatial dimension. If not, `uniform[-rotate_range[i], rotate_range[i])` will be used.\n                This can be altered on a per-dimension basis. E.g., `((0,3), 1, ...)`: for dim0, rotation will be\n                in range `[0, 3]`, and for dim1 `[-1, 1]` will be used. Setting a single value will use `[-x, x]`\n                for dim0 and nothing for the remaining dimensions.\n            shear_range: shear range with format matching `rotate_range`, it defines the range to randomly select\n                shearing factors(a tuple of 6 floats for 3D) for affine matrix, take a 3D affine as example::\n\n                    [\n                        [1.0, params[0], params[1], 0.0],\n                        [params[2], 1.0, params[3], 0.0],\n                        [params[4], params[5], 1.0, 0.0],\n                        [0.0, 0.0, 0.0, 1.0],\n                    ]\n\n            translate_range: translate range with format matching `rotate_range`, it defines the range to randomly\n                select voxel to translate for every spatial dims.\n            scale_range: scaling range with format matching `rotate_range`. it defines the range to randomly select\n                the scale factor to translate for every spatial dims. A value of 1.0 is added to the result.\n                This allows 0 to correspond to no change (i.e., a scaling of 1.0).\n            mode: {``\"bilinear\"``, ``\"nearest\"``}\n                Interpolation mode to calculate output values. Defaults to ``\"bilinear\"``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n                It also can be a sequence of string, each element corresponds to a key in ``keys``.\n            padding_mode: {``\"zeros\"``, ``\"border\"``, ``\"reflection\"``}\n                Padding mode for outside grid values. Defaults to ``\"reflection\"``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n                It also can be a sequence of string, each element corresponds to a key in ``keys``.\n            device: device on which the tensor will be allocated.\n            allow_missing_keys: don't raise exception if key is missing.\n\n        See also:\n            - :py:class:`RandAffineGrid` for the random affine parameters configurations.\n            - :py:class:`Affine` for the affine transformation parameters configurations.\n\n        .. deprecated:: 0.6.0\n            ``as_tensor_output`` is deprecated.\n\n        \"\"\"\n        MapTransform.__init__(self, keys, allow_missing_keys)\n        RandomizableTransform.__init__(self, prob)\n        self.rand_3d_elastic = Rand3DElastic(\n            sigma_range=sigma_range,\n            magnitude_range=magnitude_range,\n            prob=1.0,  # because probability controlled by this class\n            rotate_range=rotate_range,\n            shear_range=shear_range,\n            translate_range=translate_range,\n            scale_range=scale_range,\n            spatial_size=spatial_size,\n            device=device,\n        )\n        self.mode = ensure_tuple_rep(mode, len(self.keys))\n        self.padding_mode = ensure_tuple_rep(padding_mode, len(self.keys))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Rand3DElasticd.set_random_state_Rand3DElasticd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Rand3DElasticd.set_random_state_Rand3DElasticd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1114, "end_line": 1144, "span_ids": ["Rand3DElasticd.set_random_state", "Rand3DElasticd.__call__"], "tokens": 402}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Rand3DElasticd(RandomizableTransform, MapTransform):\n\n    def set_random_state(\n        self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None\n    ) -> \"Rand3DElasticd\":\n        self.rand_3d_elastic.set_random_state(seed, state)\n        super().set_random_state(seed, state)\n        return self\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        first_key: Union[Hashable, List] = self.first_key(d)\n        if first_key == []:\n            return d\n\n        self.randomize(None)\n\n        sp_size = fall_back_tuple(self.rand_3d_elastic.spatial_size, d[first_key].shape[1:])  # type: ignore\n        # all the keys share the same random elastic factor\n        self.rand_3d_elastic.randomize(sp_size)\n\n        _device = self.rand_3d_elastic.device\n        grid = create_grid(spatial_size=sp_size, device=_device, backend=\"torch\")\n        if self._do_transform:\n            device = self.rand_3d_elastic.device\n            gaussian = GaussianFilter(spatial_dims=3, sigma=self.rand_3d_elastic.sigma, truncated=3.0).to(device)\n            offset = torch.as_tensor(self.rand_3d_elastic.rand_offset, device=device).unsqueeze(0)\n            grid[:3] += gaussian(offset)[0] * self.rand_3d_elastic.magnitude\n            grid = self.rand_3d_elastic.rand_affine_grid(grid=grid)\n\n        for key, mode, padding_mode in self.key_iterator(d, self.mode, self.padding_mode):\n            d[key] = self.rand_3d_elastic.resampler(d[key], grid, mode=mode, padding_mode=padding_mode)\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_RandFlipd_RandFlipd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_RandFlipd_RandFlipd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1170, "end_line": 1212, "span_ids": ["RandFlipd", "RandFlipd.__init__", "RandFlipd.__call__", "RandFlipd.set_random_state"], "tokens": 363}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandFlipd(RandomizableTransform, MapTransform, InvertibleTransform):\n    \"\"\"\n    Dictionary-based version :py:class:`monai.transforms.RandFlip`.\n\n    See `numpy.flip` for additional details.\n    https://docs.scipy.org/doc/numpy/reference/generated/numpy.flip.html\n\n    Args:\n        keys: Keys to pick data for transformation.\n        prob: Probability of flipping.\n        spatial_axis: Spatial axes along which to flip over. Default is None.\n        allow_missing_keys: don't raise exception if key is missing.\n    \"\"\"\n\n    backend = RandFlip.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        prob: float = 0.1,\n        spatial_axis: Optional[Union[Sequence[int], int]] = None,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        MapTransform.__init__(self, keys, allow_missing_keys)\n        RandomizableTransform.__init__(self, prob)\n        self.flipper = RandFlip(prob=1.0, spatial_axis=spatial_axis)\n\n    def set_random_state(\n        self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None\n    ) -> \"RandFlipd\":\n        super().set_random_state(seed, state)\n        self.flipper.set_random_state(seed, state)\n        return self\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        self.randomize(None)\n\n        for key in self.key_iterator(d):\n            if self._do_transform:\n                d[key] = self.flipper(d[key], randomize=False)\n            self.push_transform(d, key)\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_RandRotated.__call___RandRotated.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_RandRotated.__call___RandRotated.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1456, "end_line": 1488, "span_ids": ["RandRotated.__call__"], "tokens": 280}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandRotated(RandomizableTransform, MapTransform, InvertibleTransform):\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        self.randomize(None)\n\n        # all the keys share the same random rotate angle\n        self.rand_rotate.randomize()\n        for key, mode, padding_mode, align_corners, dtype in self.key_iterator(\n            d, self.mode, self.padding_mode, self.align_corners, self.dtype\n        ):\n            if self._do_transform:\n                d[key], rot_mat = self.rand_rotate(\n                    d[key],\n                    mode=mode,\n                    padding_mode=padding_mode,\n                    align_corners=align_corners,\n                    dtype=dtype,\n                    randomize=False,\n                    get_matrix=True,\n                )\n            else:\n                rot_mat = np.eye(d[key].ndim)\n            self.push_transform(\n                d,\n                key,\n                orig_size=d[key].shape[1:],\n                extra_info={\n                    \"rot_mat\": rot_mat,\n                    \"mode\": mode.value if isinstance(mode, Enum) else mode,\n                    \"padding_mode\": padding_mode.value if isinstance(padding_mode, Enum) else padding_mode,\n                    \"align_corners\": align_corners if align_corners is not None else TraceKeys.NONE,\n                },\n            )\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_SpacingD_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_SpacingD_", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1872, "end_line": 1890, "span_ids": ["impl:12"], "tokens": 227}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "SpacingD = SpacingDict = Spacingd\nOrientationD = OrientationDict = Orientationd\nRotate90D = Rotate90Dict = Rotate90d\nRandRotate90D = RandRotate90Dict = RandRotate90d\nResizeD = ResizeDict = Resized\nAffineD = AffineDict = Affined\nRandAffineD = RandAffineDict = RandAffined\nRand2DElasticD = Rand2DElasticDict = Rand2DElasticd\nRand3DElasticD = Rand3DElasticDict = Rand3DElasticd\nFlipD = FlipDict = Flipd\nRandFlipD = RandFlipDict = RandFlipd\nGridDistortionD = GridDistortionDict = GridDistortiond\nRandGridDistortionD = RandGridDistortionDict = RandGridDistortiond\nRandAxisFlipD = RandAxisFlipDict = RandAxisFlipd\nRotateD = RotateDict = Rotated\nRandRotateD = RandRotateDict = RandRotated\nZoomD = ZoomDict = Zoomd\nRandZoomD = RandZoomDict = RandZoomd", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/__init__.py__": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/__init__.py__", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/__init__.py", "file_name": "__init__.py", "file_type": "text/x-python", "category": "implementation", "start_line": 11, "end_line": 11, "span_ids": [], "tokens": 0}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_AddChannel_AddChannel.__call__.return.img_None_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_AddChannel_AddChannel.__call__.return.img_None_", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 161, "end_line": 181, "span_ids": ["AddChannel.__call__", "AddChannel"], "tokens": 180}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AddChannel(Transform):\n    \"\"\"\n    Adds a 1-length channel dimension to the input image.\n\n    Most of the image transformations in ``monai.transforms``\n    assumes the input image is in the channel-first format, which has the shape\n    (num_channels, spatial_dim_1[, spatial_dim_2, ...]).\n\n    This transform could be used, for example, to convert a (spatial_dim_1[, spatial_dim_2, ...])\n    spatial image into the channel-first format so that the\n    multidimensional image array can be correctly interpreted by the other\n    transforms.\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:\n        \"\"\"\n        Apply the transform to `img`.\n        \"\"\"\n        return img[None]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_DataStats.__call___DataStats.__call__.return.img": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_DataStats.__call___DataStats.__call__.return.img", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 562, "end_line": 596, "span_ids": ["DataStats.__call__"], "tokens": 379}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataStats(Transform):\n\n    def __call__(\n        self,\n        img: NdarrayOrTensor,\n        prefix: Optional[str] = None,\n        data_type: Optional[bool] = None,\n        data_shape: Optional[bool] = None,\n        value_range: Optional[bool] = None,\n        data_value: Optional[bool] = None,\n        additional_info: Optional[Callable] = None,\n    ) -> NdarrayOrTensor:\n        \"\"\"\n        Apply the transform to `img`, optionally take arguments similar to the class constructor.\n        \"\"\"\n        lines = [f\"{prefix or self.prefix} statistics:\"]\n\n        if self.data_type if data_type is None else data_type:\n            lines.append(f\"Type: {type(img)} {img.dtype if hasattr(img, 'dtype') else None}\")\n        if self.data_shape if data_shape is None else data_shape:\n            lines.append(f\"Shape: {img.shape}\")\n        if self.value_range if value_range is None else value_range:\n            if isinstance(img, np.ndarray):\n                lines.append(f\"Value range: ({np.min(img)}, {np.max(img)})\")\n            elif isinstance(img, torch.Tensor):\n                lines.append(f\"Value range: ({torch.min(img)}, {torch.max(img)})\")\n            else:\n                lines.append(f\"Value range: (not a PyTorch or Numpy array, type: {type(img)})\")\n        if self.data_value if data_value is None else data_value:\n            lines.append(f\"Value: {img}\")\n        additional_info = self.additional_info if additional_info is None else additional_info\n        if additional_info is not None:\n            lines.append(f\"Additional info: {additional_info(img)}\")\n        separator = \"\\n\"\n        output = f\"{separator.join(lines)}\"\n        logging.getLogger(self._logger_name).info(output)\n        return img", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_SimulateDelay_SimulateDelay.__call__.return.img": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_SimulateDelay_SimulateDelay.__call__.return.img", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 599, "end_line": 630, "span_ids": ["SimulateDelay.__call__", "SimulateDelay.__init__", "SimulateDelay"], "tokens": 276}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SimulateDelay(Transform):\n    \"\"\"\n    This is a pass through transform to be used for testing purposes. It allows\n    adding fake behaviors that are useful for testing purposes to simulate\n    how large datasets behave without needing to test on large data sets.\n\n    For example, simulating slow NFS data transfers, or slow network transfers\n    in testing by adding explicit timing delays. Testing of small test data\n    can lead to incomplete understanding of real world issues, and may lead\n    to sub-optimal design choices.\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __init__(self, delay_time: float = 0.0) -> None:\n        \"\"\"\n        Args:\n            delay_time: The minimum amount of time, in fractions of seconds,\n                to accomplish this delay task.\n        \"\"\"\n        super().__init__()\n        self.delay_time: float = delay_time\n\n    def __call__(self, img: NdarrayOrTensor, delay_time: Optional[float] = None) -> NdarrayOrTensor:\n        \"\"\"\n        Args:\n            img: data remain unchanged throughout this transform.\n            delay_time: The minimum amount of time, in fractions of seconds,\n                to accomplish this delay task.\n        \"\"\"\n        time.sleep(self.delay_time if delay_time is None else delay_time)\n        return img", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_LabelToMask_LabelToMask.__init__.self.merge_channels.merge_channels": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_LabelToMask_LabelToMask.__init__.self.merge_channels.merge_channels", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 706, "end_line": 731, "span_ids": ["LabelToMask.__init__", "LabelToMask"], "tokens": 313}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LabelToMask(Transform):\n    \"\"\"\n    Convert labels to mask for other tasks. A typical usage is to convert segmentation labels\n    to mask data to pre-process images and then feed the images into classification network.\n    It can support single channel labels or One-Hot labels with specified `select_labels`.\n    For example, users can select `label value = [2, 3]` to construct mask data, or select the\n    second and the third channels of labels to construct mask data.\n    The output mask data can be a multiple channels binary data or a single channel binary\n    data that merges all the channels.\n\n    Args:\n        select_labels: labels to generate mask from. for 1 channel label, the `select_labels`\n            is the expected label values, like: [1, 2, 3]. for One-Hot format label, the\n            `select_labels` is the expected channel indices.\n        merge_channels: whether to use `np.any()` to merge the result on channel dim. if yes,\n            will return a single channel mask with binary data.\n\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __init__(  # pytype: disable=annotation-type-mismatch\n        self, select_labels: Union[Sequence[int], int], merge_channels: bool = False\n    ) -> None:  # pytype: disable=annotation-type-mismatch\n        self.select_labels = ensure_tuple(select_labels)\n        self.merge_channels = merge_channels", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_Identityd_Identityd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_Identityd_Identityd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 177, "end_line": 199, "span_ids": ["Identityd.__init__", "Identityd", "Identityd.__call__"], "tokens": 176}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Identityd(MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.Identity`.\n    \"\"\"\n\n    backend = Identity.backend\n\n    def __init__(self, keys: KeysCollection, allow_missing_keys: bool = False) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to be transformed.\n                See also: :py:class:`monai.transforms.compose.MapTransform`\n            allow_missing_keys: don't raise exception if key is missing.\n\n        \"\"\"\n        super().__init__(keys, allow_missing_keys)\n        self.identity = Identity()\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key in self.key_iterator(d):\n            d[key] = self.identity(d[key])\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_AsChannelFirstd_AsChannelFirstd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_AsChannelFirstd_AsChannelFirstd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 202, "end_line": 224, "span_ids": ["AsChannelFirstd.__init__", "AsChannelFirstd.__call__", "AsChannelFirstd"], "tokens": 216}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AsChannelFirstd(MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.AsChannelFirst`.\n    \"\"\"\n\n    backend = AsChannelFirst.backend\n\n    def __init__(self, keys: KeysCollection, channel_dim: int = -1, allow_missing_keys: bool = False) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to be transformed.\n                See also: :py:class:`monai.transforms.compose.MapTransform`\n            channel_dim: which dimension of input image is the channel, default is the last dimension.\n            allow_missing_keys: don't raise exception if key is missing.\n        \"\"\"\n        super().__init__(keys, allow_missing_keys)\n        self.converter = AsChannelFirst(channel_dim=channel_dim)\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key in self.key_iterator(d):\n            d[key] = self.converter(d[key])\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_AsChannelLastd_AsChannelLastd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_AsChannelLastd_AsChannelLastd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 227, "end_line": 249, "span_ids": ["AsChannelLastd.__call__", "AsChannelLastd.__init__", "AsChannelLastd"], "tokens": 216}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AsChannelLastd(MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.AsChannelLast`.\n    \"\"\"\n\n    backend = AsChannelLast.backend\n\n    def __init__(self, keys: KeysCollection, channel_dim: int = 0, allow_missing_keys: bool = False) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to be transformed.\n                See also: :py:class:`monai.transforms.compose.MapTransform`\n            channel_dim: which dimension of input image is the channel, default is the first dimension.\n            allow_missing_keys: don't raise exception if key is missing.\n        \"\"\"\n        super().__init__(keys, allow_missing_keys)\n        self.converter = AsChannelLast(channel_dim=channel_dim)\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key in self.key_iterator(d):\n            d[key] = self.converter(d[key])\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_AddChanneld_AddChanneld.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_AddChanneld_AddChanneld.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 252, "end_line": 273, "span_ids": ["AddChanneld.__call__", "AddChanneld.__init__", "AddChanneld"], "tokens": 183}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AddChanneld(MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.AddChannel`.\n    \"\"\"\n\n    backend = AddChannel.backend\n\n    def __init__(self, keys: KeysCollection, allow_missing_keys: bool = False) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to be transformed.\n                See also: :py:class:`monai.transforms.compose.MapTransform`\n            allow_missing_keys: don't raise exception if key is missing.\n        \"\"\"\n        super().__init__(keys, allow_missing_keys)\n        self.adder = AddChannel()\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key in self.key_iterator(d):\n            d[key] = self.adder(d[key])\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_RepeatChanneld_RepeatChanneld.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_RepeatChanneld_RepeatChanneld.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 317, "end_line": 339, "span_ids": ["RepeatChanneld.__init__", "RepeatChanneld", "RepeatChanneld.__call__"], "tokens": 201}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RepeatChanneld(MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.RepeatChannel`.\n    \"\"\"\n\n    backend = RepeatChannel.backend\n\n    def __init__(self, keys: KeysCollection, repeats: int, allow_missing_keys: bool = False) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to be transformed.\n                See also: :py:class:`monai.transforms.compose.MapTransform`\n            repeats: the number of repetitions for each element.\n            allow_missing_keys: don't raise exception if key is missing.\n        \"\"\"\n        super().__init__(keys, allow_missing_keys)\n        self.repeater = RepeatChannel(repeats)\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key in self.key_iterator(d):\n            d[key] = self.repeater(d[key])\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_CastToTyped_CastToTyped.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_CastToTyped_CastToTyped.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 413, "end_line": 445, "span_ids": ["CastToTyped", "CastToTyped.__init__", "CastToTyped.__call__"], "tokens": 282}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CastToTyped(MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.CastToType`.\n    \"\"\"\n\n    backend = CastToType.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        dtype: Union[Sequence[Union[DtypeLike, torch.dtype]], DtypeLike, torch.dtype] = np.float32,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to be transformed.\n                See also: :py:class:`monai.transforms.compose.MapTransform`\n            dtype: convert image to this data type, default is `np.float32`.\n                it also can be a sequence of dtypes or torch.dtype,\n                each element corresponds to a key in ``keys``.\n            allow_missing_keys: don't raise exception if key is missing.\n\n        \"\"\"\n        MapTransform.__init__(self, keys, allow_missing_keys)\n        self.dtype = ensure_tuple_rep(dtype, len(self.keys))\n        self.converter = CastToType()\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key, dtype in self.key_iterator(d, self.dtype):\n            d[key] = self.converter(d[key], dtype=dtype)\n\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_ToNumpyd_ToNumpyd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_ToNumpyd_ToNumpyd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 554, "end_line": 584, "span_ids": ["ToNumpyd.__init__", "ToNumpyd", "ToNumpyd.__call__"], "tokens": 289}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ToNumpyd(MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.ToNumpy`.\n    \"\"\"\n\n    backend = ToNumpy.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        dtype: DtypeLike = None,\n        wrap_sequence: bool = True,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to be transformed.\n                See also: :py:class:`monai.transforms.compose.MapTransform`\n            dtype: target data type when converting to numpy array.\n            wrap_sequence: if `False`, then lists will recursively call this function, default to `True`.\n                E.g., if `False`, `[1, 2]` -> `[array(1), array(2)]`, if `True`, then `[1, 2]` -> `array([1, 2])`.\n            allow_missing_keys: don't raise exception if key is missing.\n        \"\"\"\n        super().__init__(keys, allow_missing_keys)\n        self.converter = ToNumpy(dtype=dtype, wrap_sequence=wrap_sequence)\n\n    def __call__(self, data: Mapping[Hashable, Any]) -> Dict[Hashable, Any]:\n        d = dict(data)\n        for key in self.key_iterator(d):\n            d[key] = self.converter(d[key])\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_SqueezeDimd_SqueezeDimd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_SqueezeDimd_SqueezeDimd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 706, "end_line": 728, "span_ids": ["SqueezeDimd.__init__", "SqueezeDimd", "SqueezeDimd.__call__"], "tokens": 211}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SqueezeDimd(MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.SqueezeDim`.\n    \"\"\"\n\n    backend = SqueezeDim.backend\n\n    def __init__(self, keys: KeysCollection, dim: int = 0, allow_missing_keys: bool = False) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to be transformed.\n                See also: :py:class:`monai.transforms.compose.MapTransform`\n            dim: dimension to be squeezed. Default: 0 (the first dimension)\n            allow_missing_keys: don't raise exception if key is missing.\n        \"\"\"\n        super().__init__(keys, allow_missing_keys)\n        self.converter = SqueezeDim(dim=dim)\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key in self.key_iterator(d):\n            d[key] = self.converter(d[key])\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_SimulateDelayd_SimulateDelayd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_SimulateDelayd_SimulateDelayd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 793, "end_line": 820, "span_ids": ["SimulateDelayd.__init__", "SimulateDelayd.__call__", "SimulateDelayd"], "tokens": 276}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SimulateDelayd(MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.SimulateDelay`.\n    \"\"\"\n\n    backend = SimulateDelay.backend\n\n    def __init__(\n        self, keys: KeysCollection, delay_time: Union[Sequence[float], float] = 0.0, allow_missing_keys: bool = False\n    ) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to be transformed.\n                See also: :py:class:`monai.transforms.compose.MapTransform`\n            delay_time: The minimum amount of time, in fractions of seconds, to accomplish this identity task.\n                It also can be a sequence of string, each element corresponds to a key in ``keys``.\n            allow_missing_keys: don't raise exception if key is missing.\n\n        \"\"\"\n        super().__init__(keys, allow_missing_keys)\n        self.delay_time = ensure_tuple_rep(delay_time, len(self.keys))\n        self.delayer = SimulateDelay()\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key, delay_time in self.key_iterator(d, self.delay_time):\n            d[key] = self.delayer(d[key], delay_time=delay_time)\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_ConcatItemsd_ConcatItemsd.__init__.self.dim.dim": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_ConcatItemsd_ConcatItemsd.__init__.self.dim.dim", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 882, "end_line": 901, "span_ids": ["ConcatItemsd", "ConcatItemsd.__init__"], "tokens": 198}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ConcatItemsd(MapTransform):\n    \"\"\"\n    Concatenate specified items from data dictionary together on the first dim to construct a big array.\n    Expect all the items are numpy array or PyTorch Tensor.\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __init__(self, keys: KeysCollection, name: str, dim: int = 0, allow_missing_keys: bool = False) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to be concatenated together.\n                See also: :py:class:`monai.transforms.compose.MapTransform`\n            name: the name corresponding to the key to store the concatenated data.\n            dim: on which dimension to concatenate the items, default is 0.\n            allow_missing_keys: don't raise exception if key is missing.\n        \"\"\"\n        super().__init__(keys, allow_missing_keys)\n        self.name = name\n        self.dim = dim", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_ConcatItemsd.__call___ConcatItemsd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_ConcatItemsd.__call___ConcatItemsd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 922, "end_line": 948, "span_ids": ["ConcatItemsd.__call__"], "tokens": 241}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ConcatItemsd(MapTransform):\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        \"\"\"\n        Raises:\n            TypeError: When items in ``data`` differ in type.\n            TypeError: When the item type is not in ``Union[numpy.ndarray, torch.Tensor]``.\n\n        \"\"\"\n        d = dict(data)\n        output = []\n        data_type = None\n        for key in self.key_iterator(d):\n            if data_type is None:\n                data_type = type(d[key])\n            elif not isinstance(d[key], data_type):\n                raise TypeError(\"All items in data must have the same type.\")\n            output.append(d[key])\n\n        if len(output) == 0:\n            return d\n\n        if data_type is np.ndarray:\n            d[self.name] = np.concatenate(output, axis=self.dim)\n        elif data_type is torch.Tensor:\n            d[self.name] = torch.cat(output, dim=self.dim)  # type: ignore\n        else:\n            raise TypeError(f\"Unsupported data type: {data_type}, available options are (numpy.ndarray, torch.Tensor).\")\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_LabelToMaskd_LabelToMaskd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_LabelToMaskd_LabelToMaskd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1056, "end_line": 1089, "span_ids": ["LabelToMaskd", "LabelToMaskd.__init__", "LabelToMaskd.__call__"], "tokens": 331}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LabelToMaskd(MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.LabelToMask`.\n\n    Args:\n        keys: keys of the corresponding items to be transformed.\n            See also: :py:class:`monai.transforms.compose.MapTransform`\n        select_labels: labels to generate mask from. for 1 channel label, the `select_labels`\n            is the expected label values, like: [1, 2, 3]. for One-Hot format label, the\n            `select_labels` is the expected channel indices.\n        merge_channels: whether to use `np.any()` to merge the result on channel dim.\n            if yes, will return a single channel mask with binary data.\n        allow_missing_keys: don't raise exception if key is missing.\n\n    \"\"\"\n\n    backend = LabelToMask.backend\n\n    def __init__(  # pytype: disable=annotation-type-mismatch\n        self,\n        keys: KeysCollection,\n        select_labels: Union[Sequence[int], int],\n        merge_channels: bool = False,\n        allow_missing_keys: bool = False,\n    ) -> None:  # pytype: disable=annotation-type-mismatch\n        super().__init__(keys, allow_missing_keys)\n        self.converter = LabelToMask(select_labels=select_labels, merge_channels=merge_channels)\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key in self.key_iterator(d):\n            d[key] = self.converter(d[key])\n\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_IdentityD_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_IdentityD_", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1629, "end_line": 1668, "span_ids": ["impl:3"], "tokens": 538}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "IdentityD = IdentityDict = Identityd\nAsChannelFirstD = AsChannelFirstDict = AsChannelFirstd\nAsChannelLastD = AsChannelLastDict = AsChannelLastd\nAddChannelD = AddChannelDict = AddChanneld\nEnsureChannelFirstD = EnsureChannelFirstDict = EnsureChannelFirstd\nRemoveRepeatedChannelD = RemoveRepeatedChannelDict = RemoveRepeatedChanneld\nRepeatChannelD = RepeatChannelDict = RepeatChanneld\nSplitChannelD = SplitChannelDict = SplitChanneld\nCastToTypeD = CastToTypeDict = CastToTyped\nToTensorD = ToTensorDict = ToTensord\nEnsureTypeD = EnsureTypeDict = EnsureTyped\nToNumpyD = ToNumpyDict = ToNumpyd\nToCupyD = ToCupyDict = ToCupyd\nToPILD = ToPILDict = ToPILd\nTransposeD = TransposeDict = Transposed\nDeleteItemsD = DeleteItemsDict = DeleteItemsd\nSelectItemsD = SelectItemsDict = SelectItemsd\nSqueezeDimD = SqueezeDimDict = SqueezeDimd\nDataStatsD = DataStatsDict = DataStatsd\nSimulateDelayD = SimulateDelayDict = SimulateDelayd\nCopyItemsD = CopyItemsDict = CopyItemsd\nConcatItemsD = ConcatItemsDict = ConcatItemsd\nLambdaD = LambdaDict = Lambdad\nLabelToMaskD = LabelToMaskDict = LabelToMaskd\nFgBgToIndicesD = FgBgToIndicesDict = FgBgToIndicesd\nClassesToIndicesD = ClassesToIndicesDict = ClassesToIndicesd\nConvertToMultiChannelBasedOnBratsClassesD = (\n    ConvertToMultiChannelBasedOnBratsClassesDict\n) = ConvertToMultiChannelBasedOnBratsClassesd\nAddExtremePointsChannelD = AddExtremePointsChannelDict = AddExtremePointsChanneld\nTorchVisionD = TorchVisionDict = TorchVisiond\nRandTorchVisionD = RandTorchVisionDict = RandTorchVisiond\nRandLambdaD = RandLambdaDict = RandLambdad\nMapLabelValueD = MapLabelValueDict = MapLabelValued\nIntensityStatsD = IntensityStatsDict = IntensityStatsd\nToDeviceD = ToDeviceDict = ToDeviced\nCuCIMD = CuCIMDict = CuCIMd\nRandCuCIMD = RandCuCIMDict = RandCuCIMd\nAddCoordinateChannelsD = AddCoordinateChannelsDict = AddCoordinateChannelsd", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_rescale_array_rescale_array._rescale_by_minv_and_max": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_rescale_array_rescale_array._rescale_by_minv_and_max", "embedding": null, "metadata": {"file_path": "monai/transforms/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 151, "end_line": 179, "span_ids": ["rescale_array"], "tokens": 310}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def rescale_array(\n    arr: NdarrayOrTensor,\n    minv: Optional[float] = 0.0,\n    maxv: Optional[float] = 1.0,\n    dtype: Optional[Union[DtypeLike, torch.dtype]] = np.float32,\n) -> NdarrayOrTensor:\n    \"\"\"\n    Rescale the values of numpy array `arr` to be from `minv` to `maxv`.\n    If either `minv` or `maxv` is None, it returns `(a - min_a) / (max_a - min_a)`.\n\n    Args:\n        arr: input array to rescale.\n        minv: minimum value of target rescaled array.\n        maxv: maxmum value of target rescaled array.\n        dtype: if not None, convert input array to dtype before computation.\n\n    \"\"\"\n    if dtype is not None:\n        arr, *_ = convert_data_type(arr, dtype=dtype)\n    mina = arr.min()\n    maxa = arr.max()\n\n    if mina == maxa:\n        return arr * minv if minv is not None else arr\n\n    norm = (arr - mina) / (maxa - mina)  # normalize the array first\n    if (minv is None) or (maxv is None):\n        return norm\n    return (norm * (maxv - minv)) + minv  # rescale by minv and maxv, which is the normalized array by default", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_copypaste_arrays_copypaste_arrays.return.tuple_srcslices_tuple_d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_copypaste_arrays_copypaste_arrays.return.tuple_srcslices_tuple_d", "embedding": null, "metadata": {"file_path": "monai/transforms/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 191, "end_line": 244, "span_ids": ["copypaste_arrays"], "tokens": 658}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def copypaste_arrays(\n    src_shape, dest_shape, srccenter: Sequence[int], destcenter: Sequence[int], dims: Sequence[Optional[int]]\n) -> Tuple[Tuple[slice, ...], Tuple[slice, ...]]:\n    \"\"\"\n    Calculate the slices to copy a sliced area of array in `src_shape` into array in `dest_shape`.\n\n    The area has dimensions `dims` (use 0 or None to copy everything in that dimension),\n    the source area is centered at `srccenter` index in `src` and copied into area centered at `destcenter` in `dest`.\n    The dimensions of the copied area will be clipped to fit within the\n    source and destination arrays so a smaller area may be copied than expected. Return value is the tuples of slice\n    objects indexing the copied area in `src`, and those indexing the copy area in `dest`.\n\n    Example\n\n    .. code-block:: python\n\n        src_shape = (6,6)\n        src = np.random.randint(0,10,src_shape)\n        dest = np.zeros_like(src)\n        srcslices, destslices = copypaste_arrays(src_shape, dest.shape, (3, 2),(2, 1),(3, 4))\n        dest[destslices] = src[srcslices]\n        print(src)\n        print(dest)\n\n        >>> [[9 5 6 6 9 6]\n             [4 3 5 6 1 2]\n             [0 7 3 2 4 1]\n             [3 0 0 1 5 1]\n             [9 4 7 1 8 2]\n             [6 6 5 8 6 7]]\n            [[0 0 0 0 0 0]\n             [7 3 2 4 0 0]\n             [0 0 1 5 0 0]\n             [4 7 1 8 0 0]\n             [0 0 0 0 0 0]\n             [0 0 0 0 0 0]]\n\n    \"\"\"\n    s_ndim = len(src_shape)\n    d_ndim = len(dest_shape)\n    srcslices = [slice(None)] * s_ndim\n    destslices = [slice(None)] * d_ndim\n\n    for i, ss, ds, sc, dc, dim in zip(range(s_ndim), src_shape, dest_shape, srccenter, destcenter, dims):\n        if dim:\n            # dimension before midpoint, clip to size fitting in both arrays\n            d1 = np.clip(dim // 2, 0, min(sc, dc))\n            # dimension after midpoint, clip to size fitting in both arrays\n            d2 = np.clip(dim // 2 + 1, 0, min(ss - sc, ds - dc))\n\n            srcslices[i] = slice(sc - d1, sc + d2)\n            destslices[i] = slice(dc - d1, dc + d2)\n\n    return tuple(srcslices), tuple(destslices)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_generate_spatial_bounding_box_generate_spatial_bounding_box.return.box_start_box_end": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_generate_spatial_bounding_box_generate_spatial_bounding_box.return.box_start_box_end", "embedding": null, "metadata": {"file_path": "monai/transforms/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 869, "end_line": 921, "span_ids": ["generate_spatial_bounding_box"], "tokens": 601}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def generate_spatial_bounding_box(\n    img: NdarrayOrTensor,\n    select_fn: Callable = is_positive,\n    channel_indices: Optional[IndexSelection] = None,\n    margin: Union[Sequence[int], int] = 0,\n) -> Tuple[List[int], List[int]]:\n    \"\"\"\n    generate the spatial bounding box of foreground in the image with start-end positions (inclusive).\n    Users can define arbitrary function to select expected foreground from the whole image or specified channels.\n    And it can also add margin to every dim of the bounding box.\n    The output format of the coordinates is:\n\n        [1st_spatial_dim_start, 2nd_spatial_dim_start, ..., Nth_spatial_dim_start],\n        [1st_spatial_dim_end, 2nd_spatial_dim_end, ..., Nth_spatial_dim_end]\n\n    The bounding boxes edges are aligned with the input image edges.\n    This function returns [-1, -1, ...], [-1, -1, ...] if there's no positive intensity.\n\n    Args:\n        img: source image to generate bounding box from.\n        select_fn: function to select expected foreground, default is to select values > 0.\n        channel_indices: if defined, select foreground only on the specified channels\n            of image. if None, select foreground on the whole image.\n        margin: add margin value to spatial dims of the bounding box, if only 1 value provided, use it for all dims.\n    \"\"\"\n    data = img[list(ensure_tuple(channel_indices))] if channel_indices is not None else img\n    data = select_fn(data).any(0)\n    ndim = len(data.shape)\n    margin = ensure_tuple_rep(margin, ndim)\n    for m in margin:\n        if m < 0:\n            raise ValueError(\"margin value should not be negative number.\")\n\n    box_start = [0] * ndim\n    box_end = [0] * ndim\n\n    for di, ax in enumerate(itertools.combinations(reversed(range(ndim)), ndim - 1)):\n        dt = data\n        if len(ax) != 0:\n            dt = any_np_pt(dt, ax)\n\n        if not dt.any():\n            # if no foreground, return all zero bounding box coords\n            return [0] * ndim, [0] * ndim\n\n        arg_max = where(dt == dt.max())[0]\n        min_d = max(arg_max[0] - margin[di], 0)\n        max_d = arg_max[-1] + margin[di] + 1\n\n        box_start[di] = min_d.detach().cpu().item() if isinstance(min_d, torch.Tensor) else min_d  # type: ignore\n        box_end[di] = max_d.detach().cpu().item() if isinstance(max_d, torch.Tensor) else max_d  # type: ignore\n\n    return box_start, box_end", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/aliases.py_resolve_name_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/aliases.py_resolve_name_", "embedding": null, "metadata": {"file_path": "monai/utils/aliases.py", "file_name": "aliases.py", "file_type": "text/x-python", "category": "implementation", "start_line": 46, "end_line": 103, "span_ids": ["resolve_name"], "tokens": 618}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def resolve_name(name):\n    \"\"\"\n    Search for the declaration (function or class) with the given name. This will first search the list of aliases to\n    see if it was declared with this aliased name, then search treating `name` as a fully qualified name, then search\n    the loaded modules for one having a declaration with the given name. If no declaration is found, raise ValueError.\n\n    Raises:\n        ValueError: When the module is not found.\n        ValueError: When the module does not have the specified member.\n        ValueError: When multiple modules with the declaration name are found.\n        ValueError: When no module with the specified member is found.\n\n    \"\"\"\n    # attempt to resolve an alias\n    with alias_lock:\n        obj = GlobalAliases.get(name)\n\n    if name in GlobalAliases and obj is None:\n        raise AssertionError\n\n    # attempt to resolve a qualified name\n    if obj is None and \".\" in name:\n        modname, declname = name.rsplit(\".\", 1)\n\n        try:\n            mod = importlib.import_module(modname)\n            obj = getattr(mod, declname, None)\n        except ModuleNotFoundError as not_found_err:\n            raise ValueError(f\"Module {modname!r} not found.\") from not_found_err\n\n        if obj is None:\n            raise ValueError(f\"Module {modname!r} does not have member {declname!r}.\")\n\n    # attempt to resolve a simple name\n    if obj is None:\n        # Get all modules having the declaration/import, need to check here that getattr returns something which doesn't\n        # equate to False since in places __getattr__ returns 0 incorrectly:\n        # https://github.com/tensorflow/tensorboard/blob/a22566561d2b4fea408755a951ac9eaf3a156f8e/tensorboard/compat/tensorflow_stub/pywrap_tensorflow.py#L35  # noqa: B950\n        mods = [m for m in list(sys.modules.values()) if getattr(m, name, None)]\n\n        if len(mods) > 0:  # found modules with this declaration or import\n            if len(mods) > 1:  # found multiple modules, need to determine if ambiguous or just multiple imports\n                foundmods = {inspect.getmodule(getattr(m, name)) for m in mods}  # resolve imports\n                foundmods = {m for m in foundmods if m is not None}\n\n                if len(foundmods) > 1:  # found multiple declarations with the same name\n                    modnames = [m.__name__ for m in foundmods]\n                    msg = f\"Multiple modules ({modnames!r}) with declaration name {name!r} found, resolution is ambiguous.\"\n                    raise ValueError(msg)\n                mods = list(foundmods)\n\n            obj = getattr(mods[0], name)\n\n        if obj is None:\n            raise ValueError(f\"No module with member {name!r} found.\")\n\n    return obj", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/misc.py_fall_back_tuple_fall_back_tuple.return.tuple_use_the_default": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/misc.py_fall_back_tuple_fall_back_tuple.return.tuple_use_the_default", "embedding": null, "metadata": {"file_path": "monai/utils/misc.py", "file_name": "misc.py", "file_type": "text/x-python", "category": "implementation", "start_line": 134, "end_line": 177, "span_ids": ["fall_back_tuple"], "tokens": 466}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def fall_back_tuple(\n    user_provided: Any, default: Union[Sequence, np.ndarray], func: Callable = lambda x: x and x > 0\n) -> Tuple[Any, ...]:\n    \"\"\"\n    Refine `user_provided` according to the `default`, and returns as a validated tuple.\n\n    The validation is done for each element in `user_provided` using `func`.\n    If `func(user_provided[idx])` returns False, the corresponding `default[idx]` will be used\n    as the fallback.\n\n    Typically used when `user_provided` is a tuple of window size provided by the user,\n    `default` is defined by data, this function returns an updated `user_provided` with its non-positive\n    components replaced by the corresponding components from `default`.\n\n    Args:\n        user_provided: item to be validated.\n        default: a sequence used to provided the fallbacks.\n        func: a Callable to validate every components of `user_provided`.\n\n    Examples::\n\n        >>> fall_back_tuple((1, 2), (32, 32))\n        (1, 2)\n        >>> fall_back_tuple(None, (32, 32))\n        (32, 32)\n        >>> fall_back_tuple((-1, 10), (32, 32))\n        (32, 10)\n        >>> fall_back_tuple((-1, None), (32, 32))\n        (32, 32)\n        >>> fall_back_tuple((1, None), (32, 32))\n        (1, 32)\n        >>> fall_back_tuple(0, (32, 32))\n        (32, 32)\n        >>> fall_back_tuple(range(3), (32, 64, 48))\n        (32, 1, 2)\n        >>> fall_back_tuple([0], (32, 32))\n        ValueError: Sequence must have length 2, got length 1.\n\n    \"\"\"\n    ndim = len(default)\n    user = ensure_tuple_rep(user_provided, ndim)\n    return tuple(  # use the default values if user provided is not valid\n        user_c if func(user_c) else default_c for default_c, user_c in zip(default, user)\n    )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/misc.py_is_scalar_tensor_get_seed.return._seed": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/misc.py_is_scalar_tensor_get_seed.return._seed", "embedding": null, "metadata": {"file_path": "monai/utils/misc.py", "file_name": "misc.py", "file_type": "text/x-python", "category": "implementation", "start_line": 185, "end_line": 215, "span_ids": ["is_scalar", "is_scalar_tensor", "progress_bar", "get_seed"], "tokens": 287}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def is_scalar_tensor(val: Any) -> bool:\n    return isinstance(val, torch.Tensor) and val.ndim == 0\n\n\ndef is_scalar(val: Any) -> bool:\n    if isinstance(val, torch.Tensor) and val.ndim == 0:\n        return True\n    return bool(np.isscalar(val))\n\n\ndef progress_bar(index: int, count: int, desc: Optional[str] = None, bar_len: int = 30, newline: bool = False) -> None:\n    \"\"\"print a progress bar to track some time consuming task.\n\n    Args:\n        index: current status in progress.\n        count: total steps of the progress.\n        desc: description of the progress bar, if not None, show before the progress bar.\n        bar_len: the total length of the bar on screen, default is 30 char.\n        newline: whether to print in a new line for every index.\n    \"\"\"\n    end = \"\\r\" if not newline else \"\\r\\n\"\n    filled_len = int(bar_len * index // count)\n    bar = f\"{desc} \" if desc is not None else \"\"\n    bar += \"[\" + \"=\" * filled_len + \" \" * (bar_len - filled_len) + \"]\"\n    print(f\"{index}/{count} {bar}\", end=end)\n    if index == count:\n        print(\"\")\n\n\ndef get_seed() -> Optional[int]:\n    return _seed", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/module.py_optional_import_optional_import.msg.descriptor_format_actual_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/module.py_optional_import_optional_import.msg.descriptor_format_actual_", "embedding": null, "metadata": {"file_path": "monai/utils/module.py", "file_name": "module.py", "file_type": "text/x-python", "category": "implementation", "start_line": 134, "end_line": 210, "span_ids": ["optional_import"], "tokens": 759}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def optional_import(\n    module: str,\n    version: str = \"\",\n    version_checker: Callable[..., bool] = min_version,\n    name: str = \"\",\n    descriptor: str = OPTIONAL_IMPORT_MSG_FMT,\n    version_args=None,\n    allow_namespace_pkg: bool = False,\n) -> Tuple[Any, bool]:\n    \"\"\"\n    Imports an optional module specified by `module` string.\n    Any importing related exceptions will be stored, and exceptions raise lazily\n    when attempting to use the failed-to-import module.\n\n    Args:\n        module: name of the module to be imported.\n        version: version string used by the version_checker.\n        version_checker: a callable to check the module version, Defaults to monai.utils.min_version.\n        name: a non-module attribute (such as method/class) to import from the imported module.\n        descriptor: a format string for the final error message when using a not imported module.\n        version_args: additional parameters to the version checker.\n        allow_namespace_pkg: whether importing a namespace package is allowed. Defaults to False.\n\n    Returns:\n        The imported module and a boolean flag indicating whether the import is successful.\n\n    Examples::\n\n        >>> torch, flag = optional_import('torch', '1.1')\n        >>> print(torch, flag)\n        <module 'torch' from 'python/lib/python3.6/site-packages/torch/__init__.py'> True\n\n        >>> the_module, flag = optional_import('unknown_module')\n        >>> print(flag)\n        False\n        >>> the_module.method  # trying to access a module which is not imported\n        OptionalImportError: import unknown_module (No module named 'unknown_module').\n\n        >>> torch, flag = optional_import('torch', '42', exact_version)\n        >>> torch.nn  # trying to access a module for which there isn't a proper version imported\n        OptionalImportError: import torch (requires version '42' by 'exact_version').\n\n        >>> conv, flag = optional_import('torch.nn.functional', '1.0', name='conv1d')\n        >>> print(conv)\n        <built-in method conv1d of type object at 0x11a49eac0>\n\n        >>> conv, flag = optional_import('torch.nn.functional', '42', name='conv1d')\n        >>> conv()  # trying to use a function from the not successfully imported module (due to unmatched version)\n        OptionalImportError: from torch.nn.functional import conv1d (requires version '42' by 'min_version').\n    \"\"\"\n\n    tb = None\n    exception_str = \"\"\n    if name:\n        actual_cmd = f\"from {module} import {name}\"\n    else:\n        actual_cmd = f\"import {module}\"\n    try:\n        pkg = __import__(module)  # top level module\n        the_module = import_module(module)\n        if not allow_namespace_pkg:\n            is_namespace = getattr(the_module, \"__file__\", None) is None and hasattr(the_module, \"__path__\")\n            if is_namespace:\n                raise AssertionError\n        if name:  # user specified to load class/function/... from the module\n            the_module = getattr(the_module, name)\n    except Exception as import_exception:  # any exceptions during import\n        tb = import_exception.__traceback__\n        exception_str = f\"{import_exception}\"\n    else:  # found the module\n        if version_args and version_checker(pkg, f\"{version}\", version_args):\n            return the_module, True\n        if not version_args and version_checker(pkg, f\"{version}\"):\n            return the_module, True\n\n    # preparing lazy error message\n    msg = descriptor.format(actual_cmd)\n    # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/img2tensorboard.py_make_animated_gif_summary_make_animated_gif_summary.return.summary_op": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/img2tensorboard.py_make_animated_gif_summary_make_animated_gif_summary.return.summary_op", "embedding": null, "metadata": {"file_path": "monai/visualize/img2tensorboard.py", "file_name": "img2tensorboard.py", "file_type": "text/x-python", "category": "implementation", "start_line": 71, "end_line": 104, "span_ids": ["make_animated_gif_summary"], "tokens": 362}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def make_animated_gif_summary(\n    tag: str,\n    image: Union[np.ndarray, torch.Tensor],\n    writer=None,\n    max_out: int = 3,\n    frame_dim: int = -3,\n    scale_factor: float = 1.0,\n) -> Summary:\n    \"\"\"Creates an animated gif out of an image tensor in 'CHWD' format and returns Summary.\n\n    Args:\n        tag: Data identifier\n        image: The image, expected to be in `CHWD` format\n        writer: the tensorboard writer to plot image\n        max_out: maximum number of image channels to animate through\n        frame_dim: the dimension used as frames for GIF image, expect input data shape as `CHWD`,\n            default to `-3` (the first spatial dim)\n        scale_factor: amount to multiply values by.\n            if the image data is between 0 and 1, using 255 for this value will scale it to displayable range\n    \"\"\"\n\n    suffix = \"/image\" if max_out == 1 else \"/image/{}\"\n    # GIF image has no channel dim, reduce the spatial dim index if positive\n    frame_dim = frame_dim - 1 if frame_dim > 0 else frame_dim\n\n    summary_op = []\n    for it_i in range(min(max_out, list(image.shape)[0])):\n        one_channel_img: Union[torch.Tensor, np.ndarray] = (\n            image[it_i, :, :, :].squeeze(dim=0) if isinstance(image, torch.Tensor) else image[it_i, :, :, :]\n        )\n        summary_op.append(\n            _image3_animated_gif(tag + suffix.format(it_i), one_channel_img, writer, frame_dim, scale_factor)\n        )\n    return summary_op", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/__init__.py_sys_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/__init__.py_sys_", "embedding": null, "metadata": {"file_path": "tests/__init__.py", "file_name": "__init__.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 38, "span_ids": ["_enter_pr_4800", "impl", "docstring"], "tokens": 200}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import sys\nimport unittest\nimport warnings\n\n\ndef _enter_pr_4800(self):\n    \"\"\"\n    code from https://github.com/python/cpython/pull/4800\n    \"\"\"\n    # The __warningregistry__'s need to be in a pristine state for tests\n    # to work properly.\n    for v in list(sys.modules.values()):\n        if getattr(v, \"__warningregistry__\", None):\n            v.__warningregistry__ = {}\n    self.warnings_manager = warnings.catch_warnings(record=True)\n    self.warnings = self.warnings_manager.__enter__()\n    warnings.simplefilter(\"always\", self.expected)\n    return self\n\n\n# workaround for https://bugs.python.org/issue29620\ntry:\n    # Suppression for issue #494:  tests/__init__.py:34: error: Cannot assign to a method\n    unittest.case._AssertWarnsContext.__enter__ = _enter_pr_4800  # type: ignore\nexcept AttributeError:\n    pass", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_activationsd.py_TestActivationsd_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_activationsd.py_TestActivationsd_", "embedding": null, "metadata": {"file_path": "tests/test_activationsd.py", "file_name": "test_activationsd.py", "file_type": "text/x-python", "category": "test", "start_line": 50, "end_line": 63, "span_ids": ["TestActivationsd.test_value_shape", "TestActivationsd", "impl:7"], "tokens": 136}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestActivationsd(unittest.TestCase):\n    @parameterized.expand(TEST_CASES)\n    def test_value_shape(self, input_param, test_input, output, expected_shape):\n        result = Activationsd(**input_param)(test_input)\n        assert_allclose(result[\"pred\"], output[\"pred\"], rtol=1e-3)\n        self.assertTupleEqual(result[\"pred\"].shape, expected_shape)\n        if \"label\" in result:\n            assert_allclose(result[\"label\"], output[\"label\"], rtol=1e-3)\n            self.assertTupleEqual(result[\"label\"].shape, expected_shape)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_adaptors.py_TestAdaptors.test_multi_in_single_out_TestAdaptors.test_multi_in_single_out.None_2.self_assertEqual_dres_lb": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_adaptors.py_TestAdaptors.test_multi_in_single_out_TestAdaptors.test_multi_in_single_out.None_2.self_assertEqual_dres_lb", "embedding": null, "metadata": {"file_path": "tests/test_adaptors.py", "file_name": "test_adaptors.py", "file_type": "text/x-python", "category": "test", "start_line": 56, "end_line": 85, "span_ids": ["TestAdaptors.test_multi_in_single_out"], "tokens": 320}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestAdaptors(unittest.TestCase):\n\n    def test_multi_in_single_out(self):\n        def foo(image, label):\n            return image * label\n\n        it = itertools.product([\"image\", [\"image\"]], [None, [\"image\", \"label\"], {\"image\": \"image\", \"label\": \"label\"}])\n\n        for i in it:\n            d = {\"image\": 2, \"label\": 3}\n            dres = adaptor(foo, i[0], i[1])(d)\n            self.assertEqual(dres[\"image\"], 6)\n            self.assertEqual(dres[\"label\"], 3)\n\n        it = itertools.product(\n            [\"newimage\", [\"newimage\"]], [None, [\"image\", \"label\"], {\"image\": \"image\", \"label\": \"label\"}]\n        )\n\n        for i in it:\n            d = {\"image\": 2, \"label\": 3}\n            dres = adaptor(foo, i[0], i[1])(d)\n            self.assertEqual(dres[\"image\"], 2)\n            self.assertEqual(dres[\"label\"], 3)\n            self.assertEqual(dres[\"newimage\"], 6)\n\n        it = itertools.product([\"img\", [\"img\"]], [{\"img\": \"image\", \"lbl\": \"label\"}])\n\n        for i in it:\n            d = {\"img\": 2, \"lbl\": 3}\n            dres = adaptor(foo, i[0], i[1])(d)\n            self.assertEqual(dres[\"img\"], 6)\n            self.assertEqual(dres[\"lbl\"], 3)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_adaptors.py_TestAdaptors.test_default_arg_single_out_TestAdaptors.test_dict_out.self_assertEqual_dres_b_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_adaptors.py_TestAdaptors.test_default_arg_single_out_TestAdaptors.test_dict_out.self_assertEqual_dres_b_", "embedding": null, "metadata": {"file_path": "tests/test_adaptors.py", "file_name": "test_adaptors.py", "file_type": "text/x-python", "category": "test", "start_line": 87, "end_line": 118, "span_ids": ["TestAdaptors.test_multi_out", "TestAdaptors.test_dict_out", "TestAdaptors.test_default_arg_single_out"], "tokens": 270}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestAdaptors(unittest.TestCase):\n\n    def test_default_arg_single_out(self):\n        def foo(a, b=2):\n            return a * b\n\n        d = {\"a\": 5}\n        dres = adaptor(foo, \"c\")(d)\n        self.assertEqual(dres[\"c\"], 10)\n\n        d = {\"b\": 5}\n        with self.assertRaises(TypeError):\n            dres = adaptor(foo, \"c\")(d)\n\n    def test_multi_out(self):\n        def foo(a, b):\n            return a * b, a / b\n\n        d = {\"a\": 3, \"b\": 4}\n        dres = adaptor(foo, [\"c\", \"d\"])(d)\n        self.assertEqual(dres[\"c\"], 12)\n        self.assertEqual(dres[\"d\"], 3 / 4)\n\n    def test_dict_out(self):\n        def foo(a):\n            return {\"a\": a * 2}\n\n        d = {\"a\": 2}\n        dres = adaptor(foo, {\"a\": \"a\"})(d)\n        self.assertEqual(dres[\"a\"], 4)\n\n        d = {\"b\": 2}\n        dres = adaptor(foo, {\"a\": \"b\"}, {\"b\": \"a\"})(d)\n        self.assertEqual(dres[\"b\"], 4)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_adaptors.py_TestApplyAlias_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_adaptors.py_TestApplyAlias_", "embedding": null, "metadata": {"file_path": "tests/test_adaptors.py", "file_name": "test_adaptors.py", "file_type": "text/x-python", "category": "test", "start_line": 121, "end_line": 149, "span_ids": ["TestToKwargs", "TestToKwargs.test_to_kwargs", "TestApplyAlias", "TestApplyAlias.test_apply_alias"], "tokens": 203}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestApplyAlias(unittest.TestCase):\n    def test_apply_alias(self):\n        def foo(d):\n            d[\"x\"] *= 2\n            return d\n\n        d = {\"a\": 1, \"b\": 3}\n        result = apply_alias(foo, {\"b\": \"x\"})(d)\n        self.assertDictEqual({\"a\": 1, \"b\": 6}, result)\n\n\nclass TestToKwargs(unittest.TestCase):\n    def test_to_kwargs(self):\n        def foo(**kwargs):\n            results = {k: v * 2 for k, v in kwargs.items()}\n            return results\n\n        def compose_like(fn, data):\n            data = fn(data)\n            return data\n\n        d = {\"a\": 1, \"b\": 2}\n\n        actual = compose_like(to_kwargs(foo), d)\n        self.assertDictEqual(actual, {\"a\": 2, \"b\": 4})\n\n        with self.assertRaises(TypeError):\n            actual = compose_like(foo, d)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_add_channeld.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_add_channeld.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_add_channeld.py", "file_name": "test_add_channeld.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 41, "span_ids": ["TestAddChanneld.test_shape", "TestAddChanneld", "impl:5", "docstring"], "tokens": 202}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import AddChanneld\nfrom tests.utils import TEST_NDARRAYS\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    TESTS.append(\n        [\n            {\"keys\": [\"img\", \"seg\"]},\n            {\"img\": p(np.array([[0, 1], [1, 2]])), \"seg\": p(np.array([[0, 1], [1, 2]]))},\n            (1, 2, 2),\n        ]\n    )\n\n\nclass TestAddChanneld(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_shape(self, input_param, input_data, expected_shape):\n        result = AddChanneld(**input_param)(input_data)\n        self.assertEqual(result[\"img\"].shape, expected_shape)\n        self.assertEqual(result[\"seg\"].shape, expected_shape)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_adjust_contrast.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_adjust_contrast.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_adjust_contrast.py", "file_name": "test_adjust_contrast.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 45, "span_ids": ["TestAdjustContrast.test_correct_results", "impl:7", "TestAdjustContrast", "docstring"], "tokens": 260}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import AdjustContrast\nfrom tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, assert_allclose\n\nTEST_CASE_1 = [1.0]\n\nTEST_CASE_2 = [0.5]\n\nTEST_CASE_3 = [4.5]\n\n\nclass TestAdjustContrast(NumpyImageTestCase2D):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3])\n    def test_correct_results(self, gamma):\n        adjuster = AdjustContrast(gamma=gamma)\n        for p in TEST_NDARRAYS:\n            result = adjuster(p(self.imt))\n            if gamma == 1.0:\n                expected = self.imt\n            else:\n                epsilon = 1e-7\n                img_min = self.imt.min()\n                img_range = self.imt.max() - img_min\n                expected = np.power(((self.imt - img_min) / float(img_range + epsilon)), gamma) * img_range + img_min\n            assert_allclose(expected, result, rtol=1e-05, type_test=False)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_adjust_contrastd.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_adjust_contrastd.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_adjust_contrastd.py", "file_name": "test_adjust_contrastd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 45, "span_ids": ["TestAdjustContrastd.test_correct_results", "TestAdjustContrastd", "impl:7", "docstring"], "tokens": 270}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import AdjustContrastd\nfrom tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, assert_allclose\n\nTEST_CASE_1 = [1.0]\n\nTEST_CASE_2 = [0.5]\n\nTEST_CASE_3 = [4.5]\n\n\nclass TestAdjustContrastd(NumpyImageTestCase2D):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3])\n    def test_correct_results(self, gamma):\n        adjuster = AdjustContrastd(\"img\", gamma=gamma)\n        for p in TEST_NDARRAYS:\n            result = adjuster({\"img\": p(self.imt)})\n            if gamma == 1.0:\n                expected = self.imt\n            else:\n                epsilon = 1e-7\n                img_min = self.imt.min()\n                img_range = self.imt.max() - img_min\n                expected = np.power(((self.imt - img_min) / float(img_range + epsilon)), gamma) * img_range + img_min\n            assert_allclose(expected, result[\"img\"], rtol=1e-05, type_test=False)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affine_transform.py_unittest_TEST_NORM_CASES._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affine_transform.py_unittest_TEST_NORM_CASES._", "embedding": null, "metadata": {"file_path": "tests/test_affine_transform.py", "file_name": "test_affine_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 33, "span_ids": ["docstring"], "tokens": 372}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks import normalize_transform, to_norm_affine\nfrom monai.networks.layers import AffineTransform\nfrom tests.utils import is_tf32_env\n\n_rtol = 1e-4 if not is_tf32_env() else 5e-3\n\nTEST_NORM_CASES = [\n    [(4, 5), True, [[[0.666667, 0, -1], [0, 0.5, -1], [0, 0, 1]]]],\n    [\n        (2, 4, 5),\n        True,\n        [[[2.0, 0.0, 0.0, -1.0], [0.0, 0.6666667, 0.0, -1.0], [0.0, 0.0, 0.5, -1.0], [0.0, 0.0, 0.0, 1.0]]],\n    ],\n    [(4, 5), False, [[[0.5, 0.0, -0.75], [0.0, 0.4, -0.8], [0.0, 0.0, 1.0]]]],\n    [(2, 4, 5), False, [[[1.0, 0.0, 0.0, -0.5], [0.0, 0.5, 0.0, -0.75], [0.0, 0.0, 0.4, -0.8], [0.0, 0.0, 0.0, 1.0]]]],\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affine_transform.py_TEST_TO_NORM_AFFINE_CASES_TEST_ILL_TO_NORM_AFFINE_CASES._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affine_transform.py_TEST_TO_NORM_AFFINE_CASES_TEST_ILL_TO_NORM_AFFINE_CASES._", "embedding": null, "metadata": {"file_path": "tests/test_affine_transform.py", "file_name": "test_affine_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 35, "end_line": 70, "span_ids": ["impl:7", "docstring"], "tokens": 720}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "TEST_TO_NORM_AFFINE_CASES = [\n    [\n        [[[1, 0, 0], [0, 1, 0], [0, 0, 1]]],\n        (4, 6),\n        (5, 3),\n        True,\n        [[[1.3333334, 0.0, 0.33333337], [0.0, 0.4, -0.6], [0.0, 0.0, 1.0]]],\n    ],\n    [\n        [[[1, 0, 0], [0, 1, 0], [0, 0, 1]]],\n        (4, 6),\n        (5, 3),\n        False,\n        [[[1.25, 0.0, 0.25], [0.0, 0.5, -0.5], [0.0, 0.0, 1.0]]],\n    ],\n    [\n        [[[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]]],\n        (2, 4, 6),\n        (3, 5, 3),\n        True,\n        [[[2.0, 0.0, 0.0, 1.0], [0.0, 1.3333334, 0.0, 0.33333337], [0.0, 0.0, 0.4, -0.6], [0.0, 0.0, 0.0, 1.0]]],\n    ],\n    [\n        [[[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]]],\n        (2, 4, 6),\n        (3, 5, 3),\n        False,\n        [[[1.5, 0.0, 0.0, 0.5], [0.0, 1.25, 0.0, 0.25], [0.0, 0.0, 0.5, -0.5], [0.0, 0.0, 0.0, 1.0]]],\n    ],\n]\n\nTEST_ILL_TO_NORM_AFFINE_CASES = [\n    [[[[1, 0, 0], [0, 1, 0], [0, 0, 1]]], (3, 4, 6), (3, 5, 3), False],\n    [[[[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]]], (4, 6), (3, 5, 3), True],\n    [[[[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0]]], (4, 6), (3, 5, 3), True],\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affine_transform.py_TestNormTransform_TestNormTransform.test_norm_xform.if_torch_cuda_is_availabl.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affine_transform.py_TestNormTransform_TestNormTransform.test_norm_xform.if_torch_cuda_is_availabl.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_affine_transform.py", "file_name": "test_affine_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 70, "end_line": 83, "span_ids": ["TestNormTransform.test_norm_xform", "TestNormTransform"], "tokens": 157}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestNormTransform(unittest.TestCase):\n    @parameterized.expand(TEST_NORM_CASES)\n    def test_norm_xform(self, input_shape, align_corners, expected):\n        norm = normalize_transform(\n            input_shape, device=torch.device(\"cpu:0\"), dtype=torch.float32, align_corners=align_corners\n        )\n        norm = norm.detach().cpu().numpy()\n        np.testing.assert_allclose(norm, expected, atol=1e-6)\n        if torch.cuda.is_available():\n            norm = normalize_transform(\n                input_shape, device=torch.device(\"cuda:0\"), dtype=torch.float32, align_corners=align_corners\n            )\n            norm = norm.detach().cpu().numpy()\n            np.testing.assert_allclose(norm, expected, atol=1e-4)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affine_transform.py_TestToNormAffine_TestToNormAffine.test_to_norm_affine.if_torch_cuda_is_availabl.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affine_transform.py_TestToNormAffine_TestToNormAffine.test_to_norm_affine.if_torch_cuda_is_availabl.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_affine_transform.py", "file_name": "test_affine_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 89, "end_line": 101, "span_ids": ["TestToNormAffine.test_to_norm_affine", "TestToNormAffine"], "tokens": 215}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestToNormAffine(unittest.TestCase):\n    @parameterized.expand(TEST_TO_NORM_AFFINE_CASES)\n    def test_to_norm_affine(self, affine, src_size, dst_size, align_corners, expected):\n        affine = torch.as_tensor(affine, device=torch.device(\"cpu:0\"), dtype=torch.float32)\n        new_affine = to_norm_affine(affine, src_size, dst_size, align_corners)\n        new_affine = new_affine.detach().cpu().numpy()\n        np.testing.assert_allclose(new_affine, expected, atol=1e-6)\n\n        if torch.cuda.is_available():\n            affine = torch.as_tensor(affine, device=torch.device(\"cuda:0\"), dtype=torch.float32)\n            new_affine = to_norm_affine(affine, src_size, dst_size, align_corners)\n            new_affine = new_affine.detach().cpu().numpy()\n            np.testing.assert_allclose(new_affine, expected, atol=1e-5, rtol=_rtol)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affine_transform.py_TestAffineTransform_TestAffineTransform.test_affine_shift.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affine_transform.py_TestAffineTransform_TestAffineTransform.test_affine_shift.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_affine_transform.py", "file_name": "test_affine_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 112, "end_line": 119, "span_ids": ["TestAffineTransform", "TestAffineTransform.test_affine_shift"], "tokens": 203}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestAffineTransform(unittest.TestCase):\n    def test_affine_shift(self):\n        affine = torch.as_tensor([[1.0, 0.0, 0.0], [0.0, 1.0, -1.0]])\n        image = torch.as_tensor([[[[4.0, 1.0, 3.0, 2.0], [7.0, 6.0, 8.0, 5.0], [3.0, 5.0, 3.0, 6.0]]]])\n        out = AffineTransform()(image, affine)\n        out = out.detach().cpu().numpy()\n        expected = [[[[0, 4, 1, 3], [0, 7, 6, 8], [0, 3, 5, 3]]]]\n        np.testing.assert_allclose(out, expected, atol=1e-5, rtol=_rtol)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affine_transform.py_TestAffineTransform.test_affine_shift_1_TestAffineTransform.test_affine_shift_1.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affine_transform.py_TestAffineTransform.test_affine_shift_1_TestAffineTransform.test_affine_shift_1.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_affine_transform.py", "file_name": "test_affine_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 121, "end_line": 127, "span_ids": ["TestAffineTransform.test_affine_shift_1"], "tokens": 205}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestAffineTransform(unittest.TestCase):\n\n    def test_affine_shift_1(self):\n        affine = torch.as_tensor([[1.0, 0.0, -1.0], [0.0, 1.0, -1.0]])\n        image = torch.as_tensor([[[[4.0, 1.0, 3.0, 2.0], [7.0, 6.0, 8.0, 5.0], [3.0, 5.0, 3.0, 6.0]]]])\n        out = AffineTransform()(image, affine)\n        out = out.detach().cpu().numpy()\n        expected = [[[[0, 0, 0, 0], [0, 4, 1, 3], [0, 7, 6, 8]]]]\n        np.testing.assert_allclose(out, expected, atol=1e-5, rtol=_rtol)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affine_transform.py_TestAffineTransform.test_affine_shift_2_TestAffineTransform.test_affine_shift_2.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affine_transform.py_TestAffineTransform.test_affine_shift_2_TestAffineTransform.test_affine_shift_2.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_affine_transform.py", "file_name": "test_affine_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 129, "end_line": 135, "span_ids": ["TestAffineTransform.test_affine_shift_2"], "tokens": 205}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestAffineTransform(unittest.TestCase):\n\n    def test_affine_shift_2(self):\n        affine = torch.as_tensor([[1.0, 0.0, -1.0], [0.0, 1.0, 0.0]])\n        image = torch.as_tensor([[[[4.0, 1.0, 3.0, 2.0], [7.0, 6.0, 8.0, 5.0], [3.0, 5.0, 3.0, 6.0]]]])\n        out = AffineTransform()(image, affine)\n        out = out.detach().cpu().numpy()\n        expected = [[[[0, 0, 0, 0], [4, 1, 3, 2], [7, 6, 8, 5]]]]\n        np.testing.assert_allclose(out, expected, atol=1e-5, rtol=_rtol)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affine_transform.py_TestAffineTransform.test_zoom_TestAffineTransform.test_zoom.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affine_transform.py_TestAffineTransform.test_zoom_TestAffineTransform.test_zoom.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_affine_transform.py", "file_name": "test_affine_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 137, "end_line": 142, "span_ids": ["TestAffineTransform.test_zoom"], "tokens": 146}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestAffineTransform(unittest.TestCase):\n\n    def test_zoom(self):\n        affine = torch.as_tensor([[1.0, 0.0, 0.0], [0.0, 2.0, 0.0]])\n        image = torch.arange(1.0, 13.0).view(1, 1, 3, 4).to(device=torch.device(\"cpu:0\"))\n        out = AffineTransform((3, 2))(image, affine)\n        expected = [[[[1, 3], [5, 7], [9, 11]]]]\n        np.testing.assert_allclose(out, expected, atol=1e-5, rtol=_rtol)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affine_transform.py_TestAffineTransform.test_zoom_1_TestAffineTransform.test_zoom_1.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affine_transform.py_TestAffineTransform.test_zoom_1_TestAffineTransform.test_zoom_1.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_affine_transform.py", "file_name": "test_affine_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 144, "end_line": 149, "span_ids": ["TestAffineTransform.test_zoom_1"], "tokens": 135}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestAffineTransform(unittest.TestCase):\n\n    def test_zoom_1(self):\n        affine = torch.as_tensor([[2.0, 0.0, 0.0], [0.0, 1.0, 0.0]])\n        image = torch.arange(1.0, 13.0).view(1, 1, 3, 4).to(device=torch.device(\"cpu:0\"))\n        out = AffineTransform()(image, affine, (1, 4))\n        expected = [[[[1, 2, 3, 4]]]]\n        np.testing.assert_allclose(out, expected, atol=_rtol)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affine_transform.py_TestAffineTransform.test_zoom_2_TestAffineTransform.test_zoom_2.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affine_transform.py_TestAffineTransform.test_zoom_2_TestAffineTransform.test_zoom_2.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_affine_transform.py", "file_name": "test_affine_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 151, "end_line": 156, "span_ids": ["TestAffineTransform.test_zoom_2"], "tokens": 141}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestAffineTransform(unittest.TestCase):\n\n    def test_zoom_2(self):\n        affine = torch.as_tensor([[2.0, 0.0, 0.0], [0.0, 2.0, 0.0]], dtype=torch.float32)\n        image = torch.arange(1.0, 13.0).view(1, 1, 3, 4).to(device=torch.device(\"cpu:0\"))\n        out = AffineTransform((1, 2))(image, affine)\n        expected = [[[[1, 3]]]]\n        np.testing.assert_allclose(out, expected, atol=1e-5, rtol=_rtol)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affine_transform.py_TestAffineTransform.test_affine_transform_minimum_TestAffineTransform.test_affine_transform_minimum.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affine_transform.py_TestAffineTransform.test_affine_transform_minimum_TestAffineTransform.test_affine_transform_minimum.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_affine_transform.py", "file_name": "test_affine_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 158, "end_line": 175, "span_ids": ["TestAffineTransform.test_affine_transform_minimum"], "tokens": 311}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestAffineTransform(unittest.TestCase):\n\n    def test_affine_transform_minimum(self):\n        t = np.pi / 3\n        affine = [[np.cos(t), -np.sin(t), 0], [np.sin(t), np.cos(t), 0], [0, 0, 1]]\n        affine = torch.as_tensor(affine, device=torch.device(\"cpu:0\"), dtype=torch.float32)\n        image = torch.arange(24.0).view(1, 1, 4, 6).to(device=torch.device(\"cpu:0\"))\n        out = AffineTransform()(image, affine)\n        out = out.detach().cpu().numpy()\n        expected = [\n            [\n                [\n                    [0.0, 0.06698727, 0.0, 0.0, 0.0, 0.0],\n                    [3.8660254, 0.86602557, 0.0, 0.0, 0.0, 0.0],\n                    [7.732051, 3.035899, 0.73205125, 0.0, 0.0, 0.0],\n                    [11.598076, 6.901923, 2.7631402, 0.0, 0.0, 0.0],\n                ]\n            ]\n        ]\n        np.testing.assert_allclose(out, expected, atol=1e-3, rtol=_rtol)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affine_transform.py_TestAffineTransform.test_affine_transform_2d_TestAffineTransform.test_affine_transform_2d.if_torch_cuda_is_availabl.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affine_transform.py_TestAffineTransform.test_affine_transform_2d_TestAffineTransform.test_affine_transform_2d.if_torch_cuda_is_availabl.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_affine_transform.py", "file_name": "test_affine_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 177, "end_line": 211, "span_ids": ["TestAffineTransform.test_affine_transform_2d"], "tokens": 564}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestAffineTransform(unittest.TestCase):\n\n    def test_affine_transform_2d(self):\n        t = np.pi / 3\n        affine = [[np.cos(t), -np.sin(t), 0], [np.sin(t), np.cos(t), 0], [0, 0, 1]]\n        affine = torch.as_tensor(affine, device=torch.device(\"cpu:0\"), dtype=torch.float32)\n        image = torch.arange(24.0).view(1, 1, 4, 6).to(device=torch.device(\"cpu:0\"))\n        xform = AffineTransform((3, 4), padding_mode=\"border\", align_corners=True, mode=\"bilinear\")\n        out = xform(image, affine)\n        out = out.detach().cpu().numpy()\n        expected = [\n            [\n                [\n                    [7.1525574e-07, 4.9999994e-01, 1.0000000e00, 1.4999999e00],\n                    [3.8660259e00, 1.3660253e00, 1.8660252e00, 2.3660252e00],\n                    [7.7320518e00, 3.0358994e00, 2.7320509e00, 3.2320507e00],\n                ]\n            ]\n        ]\n        np.testing.assert_allclose(out, expected, atol=1e-3, rtol=_rtol)\n\n        if torch.cuda.is_available():\n            affine = torch.as_tensor(affine, device=torch.device(\"cuda:0\"), dtype=torch.float32)\n            image = torch.arange(24.0).view(1, 1, 4, 6).to(device=torch.device(\"cuda:0\"))\n            xform = AffineTransform(padding_mode=\"border\", align_corners=True, mode=\"bilinear\")\n            out = xform(image, affine, (3, 4))\n            out = out.detach().cpu().numpy()\n            expected = [\n                [\n                    [\n                        [7.1525574e-07, 4.9999994e-01, 1.0000000e00, 1.4999999e00],\n                        [3.8660259e00, 1.3660253e00, 1.8660252e00, 2.3660252e00],\n                        [7.7320518e00, 3.0358994e00, 2.7320509e00, 3.2320507e00],\n                    ]\n                ]\n            ]\n            np.testing.assert_allclose(out, expected, atol=5e-3)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affine_transform.py_TestAffineTransform.test_affine_transform_3d_TestAffineTransform.test_affine_transform_3d.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affine_transform.py_TestAffineTransform.test_affine_transform_3d_TestAffineTransform.test_affine_transform_3d.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_affine_transform.py", "file_name": "test_affine_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 213, "end_line": 237, "span_ids": ["TestAffineTransform.test_affine_transform_3d"], "tokens": 525}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestAffineTransform(unittest.TestCase):\n\n    def test_affine_transform_3d(self):\n        t = np.pi / 3\n        affine = [[1, 0, 0, 0], [0.0, np.cos(t), -np.sin(t), 0], [0, np.sin(t), np.cos(t), 0], [0, 0, 0, 1]]\n        affine = torch.as_tensor(affine, device=torch.device(\"cpu:0\"), dtype=torch.float32)\n        image = torch.arange(48.0).view(2, 1, 4, 2, 3).to(device=torch.device(\"cpu:0\"))\n        xform = AffineTransform((3, 4, 2), padding_mode=\"border\", align_corners=False, mode=\"bilinear\")\n        out = xform(image, affine)\n        out = out.detach().cpu().numpy()\n        expected = [\n            [\n                [\n                    [[0.00000006, 0.5000001], [2.3660254, 1.3660254], [4.732051, 2.4019241], [5.0, 3.9019237]],\n                    [[6.0, 6.5], [8.366026, 7.3660254], [10.732051, 8.401924], [11.0, 9.901924]],\n                    [[12.0, 12.5], [14.366026, 13.366025], [16.732052, 14.401924], [17.0, 15.901923]],\n                ]\n            ],\n            [\n                [\n                    [[24.0, 24.5], [26.366024, 25.366024], [28.732052, 26.401924], [29.0, 27.901924]],\n                    [[30.0, 30.5], [32.366028, 31.366026], [34.732048, 32.401924], [35.0, 33.901924]],\n                    [[36.0, 36.5], [38.366024, 37.366024], [40.73205, 38.401924], [41.0, 39.901924]],\n                ]\n            ],\n        ]\n        np.testing.assert_allclose(out, expected, atol=1e-4, rtol=_rtol)\n        # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affine_transform.py_TestAffineTransform.test_affine_transform_3d.if_torch_cuda_is_availabl_TestAffineTransform.test_affine_transform_3d.if_torch_cuda_is_availabl.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affine_transform.py_TestAffineTransform.test_affine_transform_3d.if_torch_cuda_is_availabl_TestAffineTransform.test_affine_transform_3d.if_torch_cuda_is_availabl.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_affine_transform.py", "file_name": "test_affine_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 239, "end_line": 261, "span_ids": ["TestAffineTransform.test_affine_transform_3d"], "tokens": 460}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestAffineTransform(unittest.TestCase):\n\n    def test_affine_transform_3d(self):\n        # ... other code\n\n        if torch.cuda.is_available():\n            affine = torch.as_tensor(affine, device=torch.device(\"cuda:0\"), dtype=torch.float32)\n            image = torch.arange(48.0).view(2, 1, 4, 2, 3).to(device=torch.device(\"cuda:0\"))\n            xform = AffineTransform(padding_mode=\"border\", align_corners=False, mode=\"bilinear\")\n            out = xform(image, affine, (3, 4, 2))\n            out = out.detach().cpu().numpy()\n            expected = [\n                [\n                    [\n                        [[0.00000006, 0.5000001], [2.3660254, 1.3660254], [4.732051, 2.4019241], [5.0, 3.9019237]],\n                        [[6.0, 6.5], [8.366026, 7.3660254], [10.732051, 8.401924], [11.0, 9.901924]],\n                        [[12.0, 12.5], [14.366026, 13.366025], [16.732052, 14.401924], [17.0, 15.901923]],\n                    ]\n                ],\n                [\n                    [\n                        [[24.0, 24.5], [26.366024, 25.366024], [28.732052, 26.401924], [29.0, 27.901924]],\n                        [[30.0, 30.5], [32.366028, 31.366026], [34.732048, 32.401924], [35.0, 33.901924]],\n                        [[36.0, 36.5], [38.366024, 37.366024], [40.73205, 38.401924], [41.0, 39.901924]],\n                    ]\n                ],\n            ]\n            np.testing.assert_allclose(out, expected, atol=5e-3)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affine_transform.py_TestAffineTransform.test_ill_affine_transform_TestAffineTransform.test_ill_affine_transform.None_3.xform_image_affine_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affine_transform.py_TestAffineTransform.test_ill_affine_transform_TestAffineTransform.test_ill_affine_transform.None_3.xform_image_affine_", "embedding": null, "metadata": {"file_path": "tests/test_affine_transform.py", "file_name": "test_affine_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 260, "end_line": 293, "span_ids": ["TestAffineTransform.test_ill_affine_transform"], "tokens": 720}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestAffineTransform(unittest.TestCase):\n\n    def test_ill_affine_transform(self):\n        with self.assertRaises(ValueError):  # image too small\n            t = np.pi / 3\n            affine = [[1, 0, 0, 0], [0.0, np.cos(t), -np.sin(t), 0], [0, np.sin(t), np.cos(t), 0], [0, 0, 0, 1]]\n            affine = torch.as_tensor(affine, device=torch.device(\"cpu:0\"), dtype=torch.float32)\n            xform = AffineTransform((3, 4, 2), padding_mode=\"border\", align_corners=False, mode=\"bilinear\")\n            xform(torch.as_tensor([1.0, 2.0, 3.0]), affine)\n\n        with self.assertRaises(ValueError):  # output shape too small\n            t = np.pi / 3\n            affine = [[1, 0, 0, 0], [0.0, np.cos(t), -np.sin(t), 0], [0, np.sin(t), np.cos(t), 0], [0, 0, 0, 1]]\n            affine = torch.as_tensor(affine, device=torch.device(\"cpu:0\"), dtype=torch.float32)\n            image = torch.arange(48).view(2, 1, 4, 2, 3).to(device=torch.device(\"cpu:0\"))\n            xform = AffineTransform((3, 4), padding_mode=\"border\", align_corners=False, mode=\"bilinear\")\n            xform(image, affine)\n\n        with self.assertRaises(ValueError):  # incorrect affine\n            t = np.pi / 3\n            affine = [[1, 0, 0, 0], [0.0, np.cos(t), -np.sin(t), 0], [0, np.sin(t), np.cos(t), 0], [0, 0, 0, 1]]\n            affine = torch.as_tensor(affine, device=torch.device(\"cpu:0\"), dtype=torch.float32)\n            affine = affine.unsqueeze(0).unsqueeze(0)\n            image = torch.arange(48).view(2, 1, 4, 2, 3).to(device=torch.device(\"cpu:0\"))\n            xform = AffineTransform((2, 3, 4), padding_mode=\"border\", align_corners=False, mode=\"bilinear\")\n            xform(image, affine)\n\n        with self.assertRaises(ValueError):  # batch doesn't match\n            t = np.pi / 3\n            affine = [[1, 0, 0, 0], [0.0, np.cos(t), -np.sin(t), 0], [0, np.sin(t), np.cos(t), 0], [0, 0, 0, 1]]\n            affine = torch.as_tensor(affine, device=torch.device(\"cpu:0\"), dtype=torch.float32)\n            affine = affine.unsqueeze(0)\n            affine = affine.repeat(3, 1, 1)\n            image = torch.arange(48).view(2, 1, 4, 2, 3).to(device=torch.device(\"cpu:0\"))\n            xform = AffineTransform((2, 3, 4), padding_mode=\"border\", align_corners=False, mode=\"bilinear\")\n            xform(image, affine)\n        # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affine_transform.py_TestAffineTransform.test_forward_2d_TestAffineTransform.test_forward_2d.None_5": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affine_transform.py_TestAffineTransform.test_forward_2d_TestAffineTransform.test_forward_2d.None_5", "embedding": null, "metadata": {"file_path": "tests/test_affine_transform.py", "file_name": "test_affine_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 316, "end_line": 338, "span_ids": ["TestAffineTransform.test_forward_2d"], "tokens": 328}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestAffineTransform(unittest.TestCase):\n\n    def test_forward_2d(self):\n        x = torch.rand(2, 1, 4, 4)\n        theta = torch.Tensor([[[0, -1, 0], [1, 0, 0]]]).repeat(2, 1, 1)\n        grid = torch.nn.functional.affine_grid(theta, x.size(), align_corners=False)\n        expected = torch.nn.functional.grid_sample(x, grid, align_corners=False)\n        expected = expected.detach().cpu().numpy()\n\n        actual = AffineTransform(normalized=True, reverse_indexing=False)(x, theta)\n        actual = actual.detach().cpu().numpy()\n        np.testing.assert_allclose(actual, expected)\n        np.testing.assert_allclose(list(theta.shape), [2, 2, 3])\n\n        theta = torch.Tensor([[0, -1, 0], [1, 0, 0]])\n        actual = AffineTransform(normalized=True, reverse_indexing=False)(x, theta)\n        actual = actual.detach().cpu().numpy()\n        np.testing.assert_allclose(actual, expected)\n        np.testing.assert_allclose(list(theta.shape), [2, 3])\n\n        theta = torch.Tensor([[[0, -1, 0], [1, 0, 0]]])\n        actual = AffineTransform(normalized=True, reverse_indexing=False)(x, theta)\n        actual = actual.detach().cpu().numpy()\n        np.testing.assert_allclose(actual, expected)\n        np.testing.assert_allclose(list(theta.shape), [1, 2, 3])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affine_transform.py_TestAffineTransform.test_forward_3d_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affine_transform.py_TestAffineTransform.test_forward_3d_", "embedding": null, "metadata": {"file_path": "tests/test_affine_transform.py", "file_name": "test_affine_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 343, "end_line": 370, "span_ids": ["impl:9", "TestAffineTransform.test_forward_3d"], "tokens": 397}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestAffineTransform(unittest.TestCase):\n\n    def test_forward_3d(self):\n        x = torch.rand(2, 1, 4, 4, 4)\n        theta = torch.Tensor([[[0, 0, -1, 0], [1, 0, 0, 0], [0, 0, 1, 0]]]).repeat(2, 1, 1)\n        grid = torch.nn.functional.affine_grid(theta, x.size(), align_corners=False)\n        expected = torch.nn.functional.grid_sample(x, grid, align_corners=False)\n        expected = expected.detach().cpu().numpy()\n\n        actual = AffineTransform(normalized=True, reverse_indexing=False)(x, theta)\n        actual = actual.detach().cpu().numpy()\n        np.testing.assert_allclose(actual, expected)\n        np.testing.assert_allclose(list(theta.shape), [2, 3, 4])\n\n        theta = torch.Tensor([[0, 0, -1, 0], [1, 0, 0, 0], [0, 0, 1, 0]])\n        actual = AffineTransform(normalized=True, reverse_indexing=False)(x, theta)\n        actual = actual.detach().cpu().numpy()\n        np.testing.assert_allclose(actual, expected)\n        np.testing.assert_allclose(list(theta.shape), [3, 4])\n\n        theta = torch.Tensor([[[0, 0, -1, 0], [1, 0, 0, 0], [0, 0, 1, 0]]])\n        actual = AffineTransform(normalized=True, reverse_indexing=False)(x, theta)\n        actual = actual.detach().cpu().numpy()\n        np.testing.assert_allclose(actual, expected)\n        np.testing.assert_allclose(list(theta.shape), [1, 3, 4])\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_as_channel_firstd.py_TestAsChannelFirstd_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_as_channel_firstd.py_TestAsChannelFirstd_", "embedding": null, "metadata": {"file_path": "tests/test_as_channel_firstd.py", "file_name": "test_as_channel_firstd.py", "file_type": "text/x-python", "category": "test", "start_line": 27, "end_line": 43, "span_ids": ["TestAsChannelFirstd", "TestAsChannelFirstd.test_shape", "impl:7"], "tokens": 191}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestAsChannelFirstd(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_shape(self, in_type, input_param, expected_shape):\n        test_data = {\n            \"image\": in_type(np.random.randint(0, 2, size=[1, 2, 3, 4])),\n            \"label\": in_type(np.random.randint(0, 2, size=[1, 2, 3, 4])),\n            \"extra\": in_type(np.random.randint(0, 2, size=[1, 2, 3, 4])),\n        }\n        result = AsChannelFirstd(**input_param)(test_data)\n        self.assertTupleEqual(result[\"image\"].shape, expected_shape)\n        self.assertTupleEqual(result[\"label\"].shape, expected_shape)\n        self.assertTupleEqual(result[\"extra\"].shape, expected_shape)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_as_channel_last.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_as_channel_last.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_as_channel_last.py", "file_name": "test_as_channel_last.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 37, "span_ids": ["TestAsChannelLast", "TestAsChannelLast.test_shape", "impl:7", "docstring"], "tokens": 221}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import AsChannelLast\nfrom tests.utils import TEST_NDARRAYS\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    TESTS.append([p, {\"channel_dim\": 0}, (2, 3, 4, 1)])\n    TESTS.append([p, {\"channel_dim\": 1}, (1, 3, 4, 2)])\n    TESTS.append([p, {\"channel_dim\": 3}, (1, 2, 3, 4)])\n\n\nclass TestAsChannelLast(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_shape(self, in_type, input_param, expected_shape):\n        test_data = in_type(np.random.randint(0, 2, size=[1, 2, 3, 4]))\n        result = AsChannelLast(**input_param)(test_data)\n        self.assertTupleEqual(result.shape, expected_shape)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_as_channel_lastd.py_TestAsChannelLastd_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_as_channel_lastd.py_TestAsChannelLastd_", "embedding": null, "metadata": {"file_path": "tests/test_as_channel_lastd.py", "file_name": "test_as_channel_lastd.py", "file_type": "text/x-python", "category": "test", "start_line": 27, "end_line": 43, "span_ids": ["TestAsChannelLastd.test_shape", "TestAsChannelLastd", "impl:7"], "tokens": 191}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestAsChannelLastd(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_shape(self, in_type, input_param, expected_shape):\n        test_data = {\n            \"image\": in_type(np.random.randint(0, 2, size=[1, 2, 3, 4])),\n            \"label\": in_type(np.random.randint(0, 2, size=[1, 2, 3, 4])),\n            \"extra\": in_type(np.random.randint(0, 2, size=[1, 2, 3, 4])),\n        }\n        result = AsChannelLastd(**input_param)(test_data)\n        self.assertTupleEqual(result[\"image\"].shape, expected_shape)\n        self.assertTupleEqual(result[\"label\"].shape, expected_shape)\n        self.assertTupleEqual(result[\"extra\"].shape, expected_shape)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_as_discrete.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_as_discrete.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_as_discrete.py", "file_name": "test_as_discrete.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 75, "span_ids": ["TestAsDiscrete", "TestAsDiscrete.test_value_shape", "impl:10", "docstring"], "tokens": 618}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nfrom parameterized import parameterized\n\nfrom monai.transforms import AsDiscrete\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTEST_CASES = []\nfor p in TEST_NDARRAYS:\n    TEST_CASES.append(\n        [\n            {\"argmax\": True, \"to_onehot\": None, \"threshold\": 0.5},\n            p([[[0.0, 1.0]], [[2.0, 3.0]]]),\n            p([[[1.0, 1.0]]]),\n            (1, 1, 2),\n        ]\n    )\n\n    TEST_CASES.append(\n        [\n            {\"argmax\": True, \"to_onehot\": 2, \"threshold\": 0.5},\n            p([[[0.0, 1.0]], [[2.0, 3.0]]]),\n            p([[[0.0, 0.0]], [[1.0, 1.0]]]),\n            (2, 1, 2),\n        ]\n    )\n\n    TEST_CASES.append(\n        [\n            {\"argmax\": False, \"to_onehot\": None, \"threshold\": 0.6},\n            p([[[0.0, 1.0], [2.0, 3.0]]]),\n            p([[[0.0, 1.0], [1.0, 1.0]]]),\n            (1, 2, 2),\n        ]\n    )\n\n    # test threshold = 0.0\n    TEST_CASES.append(\n        [\n            {\"argmax\": False, \"to_onehot\": None, \"threshold\": 0.0},\n            p([[[0.0, -1.0], [-2.0, 3.0]]]),\n            p([[[1.0, 0.0], [0.0, 1.0]]]),\n            (1, 2, 2),\n        ]\n    )\n\n    TEST_CASES.append([{\"argmax\": False, \"to_onehot\": 3}, p(1), p([0.0, 1.0, 0.0]), (3,)])\n\n    TEST_CASES.append(\n        [{\"rounding\": \"torchrounding\"}, p([[[0.123, 1.345], [2.567, 3.789]]]), p([[[0.0, 1.0], [3.0, 4.0]]]), (1, 2, 2)]\n    )\n\n\nclass TestAsDiscrete(unittest.TestCase):\n    @parameterized.expand(TEST_CASES)\n    def test_value_shape(self, input_param, img, out, expected_shape):\n        result = AsDiscrete(**input_param)(img)\n        assert_allclose(result, out, rtol=1e-3)\n        self.assertTupleEqual(result.shape, expected_shape)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_as_discreted.py_TestAsDiscreted_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_as_discreted.py_TestAsDiscreted_", "embedding": null, "metadata": {"file_path": "tests/test_as_discreted.py", "file_name": "test_as_discreted.py", "file_type": "text/x-python", "category": "test", "start_line": 84, "end_line": 97, "span_ids": ["TestAsDiscreted.test_value_shape", "TestAsDiscreted", "impl:10"], "tokens": 138}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestAsDiscreted(unittest.TestCase):\n    @parameterized.expand(TEST_CASES)\n    def test_value_shape(self, input_param, test_input, output, expected_shape):\n        result = AsDiscreted(**input_param)(test_input)\n        assert_allclose(result[\"pred\"], output[\"pred\"], rtol=1e-3)\n        self.assertTupleEqual(result[\"pred\"].shape, expected_shape)\n        if \"label\" in result:\n            assert_allclose(result[\"label\"], output[\"label\"], rtol=1e-3)\n            self.assertTupleEqual(result[\"label\"].shape, expected_shape)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_border_padd.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_border_padd.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_border_padd.py", "file_name": "test_border_padd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 61, "span_ids": ["TestBorderPadd.test_pad_shape", "impl:11", "TestBorderPadd", "docstring"], "tokens": 548}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import BorderPadd\nfrom monai.utils import NumpyPadMode\n\nTEST_CASE_1 = [\n    {\"keys\": [\"img\", \"seg\"], \"spatial_border\": 2, \"mode\": [\"constant\", \"edge\"]},\n    {\"img\": np.zeros((3, 8, 8, 4)), \"seg\": np.zeros((3, 8, 8, 4))},\n    np.zeros((3, 12, 12, 8)),\n]\n\nTEST_CASE_2 = [\n    {\"keys\": \"img\", \"spatial_border\": [1, 2, 3], \"mode\": \"constant\"},\n    {\"img\": np.zeros((3, 8, 8, 4))},\n    np.zeros((3, 10, 12, 10)),\n]\n\nTEST_CASE_3 = [\n    {\"keys\": \"img\", \"spatial_border\": [1, 2, 3, 4, 5, 6], \"mode\": \"constant\"},\n    {\"img\": np.zeros((3, 8, 8, 4))},\n    np.zeros((3, 11, 15, 15)),\n]\n\nTEST_CASE_4 = [\n    {\"keys\": [\"img\", \"seg\"], \"spatial_border\": 2, \"mode\": [\"constant\", NumpyPadMode.EDGE]},\n    {\"img\": np.zeros((3, 8, 8, 4)), \"seg\": np.zeros((3, 8, 8, 4))},\n    np.zeros((3, 12, 12, 8)),\n]\n\nTEST_CASE_5 = [\n    {\"keys\": [\"img\", \"seg\"], \"spatial_border\": 2, \"mode\": [NumpyPadMode.CONSTANT, NumpyPadMode.EDGE]},\n    {\"img\": np.zeros((3, 8, 8, 4)), \"seg\": np.zeros((3, 8, 8, 4))},\n    np.zeros((3, 12, 12, 8)),\n]\n\n\nclass TestBorderPadd(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5])\n    def test_pad_shape(self, input_param, input_data, expected_val):\n        padder = BorderPadd(**input_param)\n        result = padder(input_data)\n        self.assertAlmostEqual(result[\"img\"].shape, expected_val.shape)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cachedataset_parallel.py_TestCacheDatasetParallel_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cachedataset_parallel.py_TestCacheDatasetParallel_", "embedding": null, "metadata": {"file_path": "tests/test_cachedataset_parallel.py", "file_name": "test_cachedataset_parallel.py", "file_type": "text/x-python", "category": "test", "start_line": 30, "end_line": 54, "span_ids": ["TestCacheDatasetParallel", "impl:7", "TestCacheDatasetParallel.test_shape"], "tokens": 276}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestCacheDatasetParallel(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3])\n    def test_shape(self, num_workers, dataset_size, transform):\n        test_image = nib.Nifti1Image(np.random.randint(0, 2, size=[8, 8, 8]), np.eye(4))\n        with tempfile.TemporaryDirectory() as tempdir:\n            nib.save(test_image, os.path.join(tempdir, \"test_image1.nii.gz\"))\n            nib.save(test_image, os.path.join(tempdir, \"test_label1.nii.gz\"))\n            nib.save(test_image, os.path.join(tempdir, \"test_extra1.nii.gz\"))\n            test_data = [\n                {\n                    \"image\": os.path.join(tempdir, \"test_image1.nii.gz\"),\n                    \"label\": os.path.join(tempdir, \"test_label1.nii.gz\"),\n                    \"extra\": os.path.join(tempdir, \"test_extra1.nii.gz\"),\n                }\n            ] * dataset_size\n            dataset = CacheDataset(data=test_data, transform=transform, cache_rate=1, num_workers=num_workers)\n\n        self.assertEqual(len(dataset._cache), dataset.cache_num)\n        for i in range(dataset.cache_num):\n            self.assertIsNotNone(dataset._cache[i])\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_center_spatial_crop.py_TestCenterSpatialCrop_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_center_spatial_crop.py_TestCenterSpatialCrop_", "embedding": null, "metadata": {"file_path": "tests/test_center_spatial_crop.py", "file_name": "test_center_spatial_crop.py", "file_type": "text/x-python", "category": "test", "start_line": 37, "end_line": 53, "span_ids": ["impl:9", "TestCenterSpatialCrop.test_shape", "TestCenterSpatialCrop", "TestCenterSpatialCrop.test_value"], "tokens": 163}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestCenterSpatialCrop(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_0, TEST_CASE_1, TEST_CASE_3])\n    def test_shape(self, input_param, input_data, expected_shape):\n        result = CenterSpatialCrop(**input_param)(input_data)\n        self.assertEqual(isinstance(result, torch.Tensor), isinstance(input_data, torch.Tensor))\n        np.testing.assert_allclose(result.shape, expected_shape)\n\n    @parameterized.expand([TEST_CASE_2])\n    def test_value(self, input_param, input_data, expected_value):\n        result = CenterSpatialCrop(**input_param)(input_data)\n        self.assertEqual(isinstance(result, torch.Tensor), isinstance(input_data, torch.Tensor))\n        np.testing.assert_allclose(result, expected_value)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_center_spatial_cropd.py_TestCenterSpatialCropd_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_center_spatial_cropd.py_TestCenterSpatialCropd_", "embedding": null, "metadata": {"file_path": "tests/test_center_spatial_cropd.py", "file_name": "test_center_spatial_cropd.py", "file_type": "text/x-python", "category": "test", "start_line": 45, "end_line": 59, "span_ids": ["TestCenterSpatialCropd", "TestCenterSpatialCropd.test_value", "impl:10", "TestCenterSpatialCropd.test_shape"], "tokens": 127}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestCenterSpatialCropd(unittest.TestCase):\n    @parameterized.expand(TEST_SHAPES)\n    def test_shape(self, input_param, input_data, expected_shape):\n        result = CenterSpatialCropd(**input_param)(input_data)\n        self.assertTupleEqual(result[\"img\"].shape, expected_shape)\n\n    @parameterized.expand(TEST_CASES)\n    def test_value(self, input_param, input_data, expected_value):\n        result = CenterSpatialCropd(**input_param)(input_data)\n        assert_allclose(result[\"img\"], expected_value, type_test=False)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compose.py_TestCompose.test_list_dict_compose_TestCompose.test_list_dict_compose.for_item_in_value_.self_assertDictEqual_item": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compose.py_TestCompose.test_list_dict_compose_TestCompose.test_list_dict_compose.for_item_in_value_.self_assertDictEqual_item", "embedding": null, "metadata": {"file_path": "tests/test_compose.py", "file_name": "test_compose.py", "file_type": "text/x-python", "category": "test", "start_line": 47, "end_line": 67, "span_ids": ["TestCompose.test_list_dict_compose"], "tokens": 193}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestCompose(unittest.TestCase):\n\n    def test_list_dict_compose(self):\n        def a(d):  # transform to handle dict data\n            d = dict(d)\n            d[\"a\"] += 1\n            return d\n\n        def b(d):  # transform to generate a batch list of data\n            d = dict(d)\n            d[\"b\"] += 1\n            d = [d] * 5\n            return d\n\n        def c(d):  # transform to handle dict data\n            d = dict(d)\n            d[\"c\"] += 1\n            return d\n\n        transforms = Compose([a, a, b, c, c])\n        value = transforms({\"a\": 0, \"b\": 0, \"c\": 0})\n        for item in value:\n            self.assertDictEqual(item, {\"a\": 2, \"b\": 1, \"c\": 2})", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_meandice.py_TestComputeMeanDice.test_value_class_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_meandice.py_TestComputeMeanDice.test_value_class_", "embedding": null, "metadata": {"file_path": "tests/test_compute_meandice.py", "file_name": "test_compute_meandice.py", "file_type": "text/x-python", "category": "test", "start_line": 191, "end_line": 214, "span_ids": ["impl:21", "TestComputeMeanDice.test_value_class", "TestComputeMeanDice.test_nans_class"], "tokens": 236}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestComputeMeanDice(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_10])\n    def test_value_class(self, input_data, expected_value):\n\n        # same test as for compute_meandice\n        vals = {}\n        vals[\"y_pred\"] = input_data.pop(\"y_pred\")\n        vals[\"y\"] = input_data.pop(\"y\")\n        dice_metric = DiceMetric(**input_data, reduction=\"none\")\n        dice_metric(**vals)\n        result = dice_metric.aggregate()\n        np.testing.assert_allclose(result.cpu().numpy(), expected_value, atol=1e-4)\n\n    @parameterized.expand([TEST_CASE_4, TEST_CASE_5, TEST_CASE_6, TEST_CASE_7, TEST_CASE_8])\n    def test_nans_class(self, params, input_data, expected_value):\n\n        dice_metric = DiceMetric(**params)\n        dice_metric(**input_data)\n        result, _ = dice_metric.aggregate()\n        np.testing.assert_allclose(result.cpu().numpy(), expected_value, atol=1e-4)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_concat_itemsd.py_unittest_TestConcatItemsd.test_tensor_values.None_2": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_concat_itemsd.py_unittest_TestConcatItemsd.test_tensor_values.None_2", "embedding": null, "metadata": {"file_path": "tests/test_concat_itemsd.py", "file_name": "test_concat_itemsd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 31, "span_ids": ["TestConcatItemsd.test_tensor_values", "TestConcatItemsd", "docstring"], "tokens": 221}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\n\nfrom monai.transforms import ConcatItemsd\n\n\nclass TestConcatItemsd(unittest.TestCase):\n    def test_tensor_values(self):\n        device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu:0\")\n        input_data = {\n            \"img1\": torch.tensor([[0, 1], [1, 2]], device=device),\n            \"img2\": torch.tensor([[0, 1], [1, 2]], device=device),\n        }\n        result = ConcatItemsd(keys=[\"img1\", \"img2\"], name=\"cat_img\")(input_data)\n        self.assertTrue(\"cat_img\" in result)\n        result[\"cat_img\"] += 1\n        torch.testing.assert_allclose(result[\"img1\"], torch.tensor([[0, 1], [1, 2]], device=device))\n        torch.testing.assert_allclose(result[\"cat_img\"], torch.tensor([[1, 2], [2, 3], [1, 2], [2, 3]], device=device))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_convolutions.py_TestResidualUnit2D_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_convolutions.py_TestResidualUnit2D_", "embedding": null, "metadata": {"file_path": "tests/test_convolutions.py", "file_name": "test_convolutions.py", "file_type": "text/x-python", "category": "test", "start_line": 126, "end_line": 155, "span_ids": ["TestResidualUnit2D.test_conv_only1", "impl", "TestResidualUnit2D", "TestResidualUnit2D.test_stride1", "TestResidualUnit2D.test_dropout1", "TestResidualUnit2D.test_dilation1"], "tokens": 322}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestResidualUnit2D(TorchImageTestCase2D):\n    def test_conv_only1(self):\n        conv = ResidualUnit(2, 1, self.output_channels)\n        out = conv(self.imt)\n        expected_shape = (1, self.output_channels, self.im_shape[0], self.im_shape[1])\n        self.assertEqual(out.shape, expected_shape)\n\n    def test_stride1(self):\n        for strides in [2, [2, 2], (2, 2)]:\n            conv = ResidualUnit(2, 1, self.output_channels, strides=strides)\n            out = conv(self.imt)\n            expected_shape = (1, self.output_channels, self.im_shape[0] // 2, self.im_shape[1] // 2)\n            self.assertEqual(out.shape, expected_shape)\n\n    def test_dilation1(self):\n        conv = ResidualUnit(2, 1, self.output_channels, dilation=3)\n        out = conv(self.imt)\n        expected_shape = (1, self.output_channels, self.im_shape[0], self.im_shape[1])\n        self.assertEqual(out.shape, expected_shape)\n\n    def test_dropout1(self):\n        conv = ResidualUnit(2, 1, self.output_channels, dropout=0.15)\n        out = conv(self.imt)\n        expected_shape = (1, self.output_channels, self.im_shape[0], self.im_shape[1])\n        self.assertEqual(out.shape, expected_shape)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_copy_itemsd.py_unittest_TEST_CASE_4._img_seg_2_img": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_copy_itemsd.py_unittest_TEST_CASE_4._img_seg_2_img", "embedding": null, "metadata": {"file_path": "tests/test_copy_itemsd.py", "file_name": "test_copy_itemsd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 28, "span_ids": ["docstring"], "tokens": 141}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks import eval_mode\nfrom monai.transforms import CopyItemsd\nfrom monai.utils import ensure_tuple\n\nTEST_CASE_1 = [\"img\", 1, \"img_1\"]\n\nTEST_CASE_2 = [[\"img\", \"seg\"], 1, [\"img_1\", \"seg_1\"]]\n\nTEST_CASE_3 = [\"img\", 2, [\"img_1\", \"img_2\"]]\n\nTEST_CASE_4 = [[\"img\", \"seg\"], 2, [\"img_1\", \"seg_1\", \"img_2\", \"seg_2\"]]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_copy_itemsd.py_TestCopyItemsd.test_tensor_values_TestCopyItemsd.test_tensor_values.None_2": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_copy_itemsd.py_TestCopyItemsd.test_tensor_values_TestCopyItemsd.test_tensor_values.None_2", "embedding": null, "metadata": {"file_path": "tests/test_copy_itemsd.py", "file_name": "test_copy_itemsd.py", "file_type": "text/x-python", "category": "test", "start_line": 39, "end_line": 49, "span_ids": ["TestCopyItemsd.test_tensor_values"], "tokens": 189}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestCopyItemsd(unittest.TestCase):\n\n    def test_tensor_values(self):\n        device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu:0\")\n        input_data = {\n            \"img\": torch.tensor([[0, 1], [1, 2]], device=device),\n            \"seg\": torch.tensor([[0, 1], [1, 2]], device=device),\n        }\n        result = CopyItemsd(keys=\"img\", times=1, names=\"img_1\")(input_data)\n        self.assertTrue(\"img_1\" in result)\n        result[\"img_1\"] += 1\n        torch.testing.assert_allclose(result[\"img\"], torch.tensor([[0, 1], [1, 2]], device=device))\n        torch.testing.assert_allclose(result[\"img_1\"], torch.tensor([[1, 2], [2, 3]], device=device))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_create_grid_and_affine.py_TestCreateAffine_TestCreateAffine.test_create_rotate.None_4": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_create_grid_and_affine.py_TestCreateAffine_TestCreateAffine.test_create_rotate.None_4", "embedding": null, "metadata": {"file_path": "tests/test_create_grid_and_affine.py", "file_name": "test_create_grid_and_affine.py", "file_type": "text/x-python", "category": "test", "start_line": 154, "end_line": 207, "span_ids": ["TestCreateAffine", "TestCreateAffine.test_create_rotate"], "tokens": 626}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestCreateAffine(unittest.TestCase):\n    def test_create_rotate(self):\n        with self.assertRaisesRegex(TypeError, \"\"):\n            create_rotate(2, None)\n\n        with self.assertRaisesRegex(ValueError, \"\"):\n            create_rotate(5, 1)\n\n        test_assert(\n            create_rotate,\n            (2, 1.1),\n            np.array([[0.45359612, -0.89120736, 0.0], [0.89120736, 0.45359612, 0.0], [0.0, 0.0, 1.0]]),\n        )\n        test_assert(\n            create_rotate,\n            (3, 1.1),\n            np.array(\n                [\n                    [1.0, 0.0, 0.0, 0.0],\n                    [0.0, 0.45359612, -0.89120736, 0.0],\n                    [0.0, 0.89120736, 0.45359612, 0.0],\n                    [0.0, 0.0, 0.0, 1.0],\n                ]\n            ),\n        )\n        test_assert(\n            create_rotate,\n            (3, (1.1, 1)),\n            np.array(\n                [\n                    [0.54030231, 0.0, 0.84147098, 0.0],\n                    [0.74992513, 0.45359612, -0.48152139, 0.0],\n                    [-0.38168798, 0.89120736, 0.24507903, 0.0],\n                    [0.0, 0.0, 0.0, 1.0],\n                ]\n            ),\n        )\n        test_assert(\n            create_rotate,\n            (3, (1, 1, 1.1)),\n            np.array(\n                [\n                    [0.24507903, -0.48152139, 0.84147098, 0.0],\n                    [0.80270075, -0.38596121, -0.45464871, 0.0],\n                    [0.54369824, 0.78687425, 0.29192658, 0.0],\n                    [0.0, 0.0, 0.0, 1.0],\n                ]\n            ),\n        )\n        test_assert(\n            create_rotate,\n            (3, (0, 0, np.pi / 2)),\n            np.array([[0.0, -1.0, 0.0, 0.0], [1.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 1.0]]),\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_create_grid_and_affine.py_TestCreateAffine.test_create_shear_TestCreateAffine.test_create_shear.test_assert_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_create_grid_and_affine.py_TestCreateAffine.test_create_shear_TestCreateAffine.test_create_shear.test_assert_", "embedding": null, "metadata": {"file_path": "tests/test_create_grid_and_affine.py", "file_name": "test_create_grid_and_affine.py", "file_type": "text/x-python", "category": "test", "start_line": 209, "end_line": 216, "span_ids": ["TestCreateAffine.test_create_shear"], "tokens": 251}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestCreateAffine(unittest.TestCase):\n\n    def test_create_shear(self):\n        test_assert(create_shear, (2, 1.0), np.array([[1.0, 1.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]))\n        test_assert(create_shear, (2, (2.0, 3.0)), np.array([[1.0, 2.0, 0.0], [3.0, 1.0, 0.0], [0.0, 0.0, 1.0]]))\n        test_assert(\n            create_shear,\n            (3, 1.0),\n            np.array([[1.0, 1.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 1.0]]),\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_create_grid_and_affine.py_TestCreateAffine.test_create_scale_TestCreateAffine.test_create_scale.None_4": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_create_grid_and_affine.py_TestCreateAffine.test_create_scale_TestCreateAffine.test_create_scale.None_4", "embedding": null, "metadata": {"file_path": "tests/test_create_grid_and_affine.py", "file_name": "test_create_grid_and_affine.py", "file_type": "text/x-python", "category": "test", "start_line": 218, "end_line": 235, "span_ids": ["TestCreateAffine.test_create_scale"], "tokens": 465}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestCreateAffine(unittest.TestCase):\n\n    def test_create_scale(self):\n        test_assert(create_scale, (2, 2), np.array([[2.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]))\n        test_assert(create_scale, (2, [2, 2, 2]), np.array([[2.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 1.0]]))\n        test_assert(\n            create_scale,\n            (3, [1.5, 2.4]),\n            np.array([[1.5, 0.0, 0.0, 0.0], [0.0, 2.4, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 1.0]]),\n        )\n        test_assert(\n            create_scale,\n            (3, 1.5),\n            np.array([[1.5, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 1.0]]),\n        )\n        test_assert(\n            create_scale,\n            (3, [1, 2, 3, 4, 5]),\n            np.array([[1.0, 0.0, 0.0, 0.0], [0.0, 2.0, 0.0, 0.0], [0.0, 0.0, 3.0, 0.0], [0.0, 0.0, 0.0, 1.0]]),\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_create_grid_and_affine.py_TestCreateAffine.test_create_translate_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_create_grid_and_affine.py_TestCreateAffine.test_create_translate_", "embedding": null, "metadata": {"file_path": "tests/test_create_grid_and_affine.py", "file_name": "test_create_grid_and_affine.py", "file_type": "text/x-python", "category": "test", "start_line": 237, "end_line": 259, "span_ids": ["TestCreateAffine.test_create_translate", "impl"], "tokens": 477}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestCreateAffine(unittest.TestCase):\n\n    def test_create_translate(self):\n        test_assert(create_translate, (2, 2), np.array([[1.0, 0.0, 2.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]))\n        test_assert(create_translate, (2, [2, 2, 2]), np.array([[1.0, 0.0, 2.0], [0.0, 1.0, 2.0], [0.0, 0.0, 1.0]]))\n        test_assert(\n            create_translate,\n            (3, [1.5, 2.4]),\n            np.array([[1.0, 0.0, 0.0, 1.5], [0.0, 1.0, 0.0, 2.4], [0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 1.0]]),\n        )\n        test_assert(\n            create_translate,\n            (3, 1.5),\n            np.array([[1.0, 0.0, 0.0, 1.5], [0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 1.0]]),\n        )\n        test_assert(\n            create_translate,\n            (3, [1, 2, 3, 4, 5]),\n            np.array([[1.0, 0.0, 0.0, 1.0], [0.0, 1.0, 0.0, 2.0], [0.0, 0.0, 1.0, 3.0], [0.0, 0.0, 0.0, 1.0]]),\n        )\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_data_stats.py_TestDataStats_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_data_stats.py_TestDataStats_", "embedding": null, "metadata": {"file_path": "tests/test_data_stats.py", "file_name": "test_data_stats.py", "file_type": "text/x-python", "category": "test", "start_line": 135, "end_line": 171, "span_ids": ["impl:17", "TestDataStats.test_file", "TestDataStats.test_value", "TestDataStats"], "tokens": 303}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestDataStats(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5, TEST_CASE_6, TEST_CASE_7])\n    def test_value(self, input_param, input_data, expected_print):\n        transform = DataStats(**input_param)\n        _ = transform(input_data)\n        # self.assertEqual(transform.output, expected_print)\n\n    @parameterized.expand([TEST_CASE_8])\n    def test_file(self, input_data, expected_print):\n        with tempfile.TemporaryDirectory() as tempdir:\n            filename = os.path.join(tempdir, \"test_data_stats.log\")\n            handler = logging.FileHandler(filename, mode=\"w\")\n            handler.setLevel(logging.INFO)\n            input_param = {\n                \"prefix\": \"test data\",\n                \"data_type\": True,\n                \"data_shape\": True,\n                \"value_range\": True,\n                \"data_value\": True,\n                \"additional_info\": np.mean,\n                \"logger_handler\": handler,\n            }\n            transform = DataStats(**input_param)\n            _ = transform(input_data)\n            _logger = logging.getLogger(transform._logger_name)\n            for h in _logger.handlers[:]:\n                h.close()\n                _logger.removeHandler(h)\n            with open(filename) as f:\n                content = f.read()\n            if sys.platform != \"win32\":\n                self.assertEqual(content, expected_print)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_densenet.py_TestDENSENET_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_densenet.py_TestDENSENET_", "embedding": null, "metadata": {"file_path": "tests/test_densenet.py", "file_name": "test_densenet.py", "file_type": "text/x-python", "category": "test", "start_line": 104, "end_line": 121, "span_ids": ["TestDENSENET.test_densenet_shape", "TestDENSENET.test_script", "TestDENSENET", "impl:27"], "tokens": 141}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestDENSENET(unittest.TestCase):\n    @parameterized.expand(TEST_CASES)\n    def test_densenet_shape(self, model, input_param, input_shape, expected_shape):\n        net = model(**input_param).to(device)\n        with eval_mode(net):\n            result = net.forward(torch.randn(input_shape).to(device))\n            self.assertEqual(result.shape, expected_shape)\n\n    @parameterized.expand(TEST_SCRIPT_CASES)\n    def test_script(self, model, input_param, input_shape, expected_shape):\n        net = model(**input_param)\n        test_data = torch.randn(input_shape)\n        test_script_save(net, test_data)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dice_loss.py_unittest_TEST_CASES": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dice_loss.py_unittest_TEST_CASES", "embedding": null, "metadata": {"file_path": "tests/test_dice_loss.py", "file_name": "test_dice_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 162, "span_ids": ["docstring"], "tokens": 51}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.losses import DiceLoss\nfrom tests.utils import SkipIfBeforePyTorchVersion, test_script_save\n\nTEST_CASES =\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dice_loss.py_TestDiceLoss_TestDiceLoss.test_ill_opts.None_2.DiceLoss_reduction_None_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dice_loss.py_TestDiceLoss_TestDiceLoss.test_ill_opts.None_2.DiceLoss_reduction_None_", "embedding": null, "metadata": {"file_path": "tests/test_dice_loss.py", "file_name": "test_dice_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 114, "end_line": 133, "span_ids": ["TestDiceLoss.test_shape", "TestDiceLoss.test_ill_shape", "TestDiceLoss", "TestDiceLoss.test_ill_opts"], "tokens": 227}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestDiceLoss(unittest.TestCase):\n    @parameterized.expand(TEST_CASES)\n    def test_shape(self, input_param, input_data, expected_val):\n        result = DiceLoss(**input_param).forward(**input_data)\n        np.testing.assert_allclose(result.detach().cpu().numpy(), expected_val, rtol=1e-5)\n\n    def test_ill_shape(self):\n        loss = DiceLoss()\n        with self.assertRaisesRegex(AssertionError, \"\"):\n            loss.forward(torch.ones((1, 2, 3)), torch.ones((4, 5, 6)))\n\n    def test_ill_opts(self):\n        with self.assertRaisesRegex(ValueError, \"\"):\n            DiceLoss(sigmoid=True, softmax=True)\n        chn_input = torch.ones((1, 1, 3))\n        chn_target = torch.ones((1, 1, 3))\n        with self.assertRaisesRegex(ValueError, \"\"):\n            DiceLoss(reduction=\"unknown\")(chn_input, chn_target)\n        with self.assertRaisesRegex(ValueError, \"\"):\n            DiceLoss(reduction=None)(chn_input, chn_target)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dice_loss.py_TestDiceLoss.test_input_warnings_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dice_loss.py_TestDiceLoss.test_input_warnings_", "embedding": null, "metadata": {"file_path": "tests/test_dice_loss.py", "file_name": "test_dice_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 186, "end_line": 208, "span_ids": ["TestDiceLoss.test_script", "impl:3", "TestDiceLoss.test_input_warnings"], "tokens": 207}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestDiceLoss(unittest.TestCase):\n\n    def test_input_warnings(self):\n        chn_input = torch.ones((1, 1, 3))\n        chn_target = torch.ones((1, 1, 3))\n        with self.assertWarns(Warning):\n            loss = DiceLoss(include_background=False)\n            loss.forward(chn_input, chn_target)\n        with self.assertWarns(Warning):\n            loss = DiceLoss(softmax=True)\n            loss.forward(chn_input, chn_target)\n        with self.assertWarns(Warning):\n            loss = DiceLoss(to_onehot_y=True)\n            loss.forward(chn_input, chn_target)\n\n    @SkipIfBeforePyTorchVersion((1, 7, 0))\n    def test_script(self):\n        loss = DiceLoss()\n        test_input = torch.ones(2, 1, 8, 8)\n        test_script_save(loss, test_input, test_input)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_divisible_padd.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_divisible_padd.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_divisible_padd.py", "file_name": "test_divisible_padd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 44, "span_ids": ["TestDivisiblePadd.test_pad_shape", "TestDivisiblePadd", "impl:7", "docstring"], "tokens": 287}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import DivisiblePadd\n\nTEST_CASE_1 = [\n    {\"keys\": [\"img\"], \"k\": [4, 3, 2], \"mode\": \"constant\"},\n    {\"img\": np.zeros((3, 8, 8, 4))},\n    np.zeros((3, 8, 9, 4)),\n]\n\nTEST_CASE_2 = [\n    {\"keys\": [\"img\"], \"k\": 7, \"mode\": \"constant\", \"method\": \"end\"},\n    {\"img\": np.zeros((3, 8, 7))},\n    np.zeros((3, 14, 7)),\n]\n\nTEST_CASE_3 = [{\"keys\": [\"img\"], \"k\": 0, \"mode\": {\"constant\"}}, {\"img\": np.zeros((3, 8))}, np.zeros((3, 8))]\n\n\nclass TestDivisiblePadd(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3])\n    def test_pad_shape(self, input_param, input_data, expected_val):\n        padder = DivisiblePadd(**input_param)\n        result = padder(input_data)\n        np.testing.assert_allclose(result[\"img\"], expected_val)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_downsample_block.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_downsample_block.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_downsample_block.py", "file_name": "test_downsample_block.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 48, "span_ids": ["TestMaxAvgPool", "impl:3", "TestMaxAvgPool.test_shape", "docstring"], "tokens": 412}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks import eval_mode\nfrom monai.networks.blocks import MaxAvgPool\n\nTEST_CASES = [\n    [{\"spatial_dims\": 2, \"kernel_size\": 2}, (7, 4, 64, 48), (7, 8, 32, 24)],  # 4-channel 2D, batch 7\n    [{\"spatial_dims\": 1, \"kernel_size\": 4}, (16, 4, 63), (16, 8, 15)],  # 4-channel 1D, batch 16\n    [{\"spatial_dims\": 1, \"kernel_size\": 4, \"padding\": 1}, (16, 4, 63), (16, 8, 16)],  # 4-channel 1D, batch 16\n    [  # 4-channel 3D, batch 16\n        {\"spatial_dims\": 3, \"kernel_size\": 3, \"ceil_mode\": True},\n        (16, 4, 32, 24, 48),\n        (16, 8, 11, 8, 16),\n    ],\n    [  # 1-channel 3D, batch 16\n        {\"spatial_dims\": 3, \"kernel_size\": 3, \"ceil_mode\": False},\n        (16, 1, 32, 24, 48),\n        (16, 2, 10, 8, 16),\n    ],\n]\n\n\nclass TestMaxAvgPool(unittest.TestCase):\n    @parameterized.expand(TEST_CASES)\n    def test_shape(self, input_param, input_shape, expected_shape):\n        net = MaxAvgPool(**input_param)\n        with eval_mode(net):\n            result = net(torch.randn(input_shape))\n            self.assertEqual(result.shape, expected_shape)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_flipd.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_flipd.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_flipd.py", "file_name": "test_flipd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 44, "span_ids": ["TestFlipd.test_invalid_cases", "TestFlipd.test_correct_results", "impl:5", "docstring", "TestFlipd"], "tokens": 279}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import Flipd\nfrom tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, assert_allclose\n\nINVALID_CASES = [(\"wrong_axis\", [\"s\", 1], TypeError), (\"not_numbers\", \"s\", TypeError)]\n\nVALID_CASES = [(\"no_axis\", None), (\"one_axis\", 1), (\"many_axis\", [0, 1])]\n\n\nclass TestFlipd(NumpyImageTestCase2D):\n    @parameterized.expand(INVALID_CASES)\n    def test_invalid_cases(self, _, spatial_axis, raises):\n        with self.assertRaises(raises):\n            flip = Flipd(keys=\"img\", spatial_axis=spatial_axis)\n            flip({\"img\": self.imt[0]})\n\n    @parameterized.expand(VALID_CASES)\n    def test_correct_results(self, _, spatial_axis):\n        for p in TEST_NDARRAYS:\n            flip = Flipd(keys=\"img\", spatial_axis=spatial_axis)\n            expected = [np.flip(channel, spatial_axis) for channel in self.imt[0]]\n            expected = np.stack(expected)\n            result = flip({\"img\": p(self.imt[0])})[\"img\"]\n            assert_allclose(result, p(expected))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_focal_loss.py_unittest_TestFocalLoss.test_consistency_with_cross_entropy_2d.self_assertAlmostEqual_ma": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_focal_loss.py_unittest_TestFocalLoss.test_consistency_with_cross_entropy_2d.self_assertAlmostEqual_ma", "embedding": null, "metadata": {"file_path": "tests/test_focal_loss.py", "file_name": "test_focal_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 45, "span_ids": ["TestFocalLoss", "TestFocalLoss.test_consistency_with_cross_entropy_2d", "docstring"], "tokens": 337}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom monai.losses import FocalLoss\nfrom monai.networks import one_hot\nfrom tests.utils import SkipIfBeforePyTorchVersion, test_script_save\n\n\nclass TestFocalLoss(unittest.TestCase):\n    def test_consistency_with_cross_entropy_2d(self):\n        \"\"\"For gamma=0 the focal loss reduces to the cross entropy loss\"\"\"\n        focal_loss = FocalLoss(to_onehot_y=False, gamma=0.0, reduction=\"mean\", weight=1.0)\n        ce = nn.BCEWithLogitsLoss(reduction=\"mean\")\n        max_error = 0\n        class_num = 10\n        batch_size = 128\n        for _ in range(100):\n            # Create a random tensor of shape (batch_size, class_num, 8, 4)\n            x = torch.rand(batch_size, class_num, 8, 4, requires_grad=True)\n            # Create a random batch of classes\n            l = torch.randint(low=0, high=2, size=(batch_size, class_num, 8, 4)).float()\n            if torch.cuda.is_available():\n                x = x.cuda()\n                l = l.cuda()\n            output0 = focal_loss(x, l)\n            output1 = ce(x, l)\n            a = float(output0.cpu().detach())\n            b = float(output1.cpu().detach())\n            if abs(a - b) > max_error:\n                max_error = abs(a - b)\n        self.assertAlmostEqual(max_error, 0.0, places=3)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_focal_loss.py_TestFocalLoss.test_consistency_with_cross_entropy_classification_TestFocalLoss.test_consistency_with_cross_entropy_classification.self_assertAlmostEqual_ma": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_focal_loss.py_TestFocalLoss.test_consistency_with_cross_entropy_classification_TestFocalLoss.test_consistency_with_cross_entropy_classification.self_assertAlmostEqual_ma", "embedding": null, "metadata": {"file_path": "tests/test_focal_loss.py", "file_name": "test_focal_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 70, "end_line": 92, "span_ids": ["TestFocalLoss.test_consistency_with_cross_entropy_classification"], "tokens": 271}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestFocalLoss(unittest.TestCase):\n\n    def test_consistency_with_cross_entropy_classification(self):\n        \"\"\"for gamma=0 the focal loss reduces to the cross entropy loss\"\"\"\n        focal_loss = FocalLoss(to_onehot_y=True, gamma=0.0, reduction=\"mean\")\n        ce = nn.BCEWithLogitsLoss(reduction=\"mean\")\n        max_error = 0\n        class_num = 10\n        batch_size = 128\n        for _ in range(100):\n            # Create a random scores tensor of shape (batch_size, class_num)\n            x = torch.rand(batch_size, class_num, requires_grad=True)\n            # Create a random batch of classes\n            l = torch.randint(low=0, high=class_num, size=(batch_size, 1))\n            l = l.long()\n            if torch.cuda.is_available():\n                x = x.cuda()\n                l = l.cuda()\n            output0 = focal_loss(x, l)\n            output1 = ce(x, one_hot(l, num_classes=class_num))\n            a = float(output0.cpu().detach())\n            b = float(output1.cpu().detach())\n            if abs(a - b) > max_error:\n                max_error = abs(a - b)\n        self.assertAlmostEqual(max_error, 0.0, places=3)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_focal_loss.py_TestFocalLoss.test_bin_seg_2d_TestFocalLoss.test_bin_seg_2d.self_assertAlmostEqual_fo": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_focal_loss.py_TestFocalLoss.test_bin_seg_2d_TestFocalLoss.test_bin_seg_2d.self_assertAlmostEqual_fo", "embedding": null, "metadata": {"file_path": "tests/test_focal_loss.py", "file_name": "test_focal_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 118, "end_line": 131, "span_ids": ["TestFocalLoss.test_bin_seg_2d"], "tokens": 245}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestFocalLoss(unittest.TestCase):\n\n    def test_bin_seg_2d(self):\n        # define 2d examples\n        target = torch.tensor([[0, 0, 0, 0], [0, 1, 1, 0], [0, 1, 1, 0], [0, 0, 0, 0]])\n        # add another dimension corresponding to the batch (batch size = 1 here)\n        target = target.unsqueeze(0)  # shape (1, H, W)\n        pred_very_good = 100 * F.one_hot(target, num_classes=2).permute(0, 3, 1, 2).float() - 50.0\n\n        # initialize the mean dice loss\n        loss = FocalLoss(to_onehot_y=True)\n\n        # focal loss for pred_very_good should be close to 0\n        target = target.unsqueeze(1)  # shape (1, 1, H, W)\n        focal_loss_good = float(loss(pred_very_good, target).cpu())\n        self.assertAlmostEqual(focal_loss_good, 0.0, places=3)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_focal_loss.py_TestFocalLoss.test_empty_class_2d_TestFocalLoss.test_empty_class_2d.self_assertAlmostEqual_fo": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_focal_loss.py_TestFocalLoss.test_empty_class_2d_TestFocalLoss.test_empty_class_2d.self_assertAlmostEqual_fo", "embedding": null, "metadata": {"file_path": "tests/test_focal_loss.py", "file_name": "test_focal_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 133, "end_line": 147, "span_ids": ["TestFocalLoss.test_empty_class_2d"], "tokens": 253}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestFocalLoss(unittest.TestCase):\n\n    def test_empty_class_2d(self):\n        num_classes = 2\n        # define 2d examples\n        target = torch.tensor([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]])\n        # add another dimension corresponding to the batch (batch size = 1 here)\n        target = target.unsqueeze(0)  # shape (1, H, W)\n        pred_very_good = 1000 * F.one_hot(target, num_classes=num_classes).permute(0, 3, 1, 2).float() - 500.0\n\n        # initialize the mean dice loss\n        loss = FocalLoss(to_onehot_y=True)\n\n        # focal loss for pred_very_good should be close to 0\n        target = target.unsqueeze(1)  # shape (1, 1, H, W)\n        focal_loss_good = float(loss(pred_very_good, target).cpu())\n        self.assertAlmostEqual(focal_loss_good, 0.0, places=3)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_focal_loss.py_TestFocalLoss.test_multi_class_seg_2d_TestFocalLoss.test_multi_class_seg_2d.None_1": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_focal_loss.py_TestFocalLoss.test_multi_class_seg_2d_TestFocalLoss.test_multi_class_seg_2d.None_1", "embedding": null, "metadata": {"file_path": "tests/test_focal_loss.py", "file_name": "test_focal_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 149, "end_line": 168, "span_ids": ["TestFocalLoss.test_multi_class_seg_2d"], "tokens": 348}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestFocalLoss(unittest.TestCase):\n\n    def test_multi_class_seg_2d(self):\n        num_classes = 6  # labels 0 to 5\n        # define 2d examples\n        target = torch.tensor([[0, 0, 0, 0], [0, 1, 2, 0], [0, 3, 4, 0], [0, 0, 0, 0]])\n        # add another dimension corresponding to the batch (batch size = 1 here)\n        target = target.unsqueeze(0)  # shape (1, H, W)\n        pred_very_good = 1000 * F.one_hot(target, num_classes=num_classes).permute(0, 3, 1, 2).float() - 500.0\n        # initialize the mean dice loss\n        loss = FocalLoss(to_onehot_y=True)\n        loss_onehot = FocalLoss(to_onehot_y=False)\n\n        # focal loss for pred_very_good should be close to 0\n        target_one_hot = F.one_hot(target, num_classes=num_classes).permute(0, 3, 1, 2)  # test one hot\n        target = target.unsqueeze(1)  # shape (1, 1, H, W)\n\n        focal_loss_good = float(loss(pred_very_good, target).cpu())\n        self.assertAlmostEqual(focal_loss_good, 0.0, places=3)\n\n        focal_loss_good = float(loss_onehot(pred_very_good, target_one_hot).cpu())\n        self.assertAlmostEqual(focal_loss_good, 0.0, places=3)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_focal_loss.py_TestFocalLoss.test_bin_seg_3d_TestFocalLoss.test_bin_seg_3d.None_1": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_focal_loss.py_TestFocalLoss.test_bin_seg_3d_TestFocalLoss.test_bin_seg_3d.None_1", "embedding": null, "metadata": {"file_path": "tests/test_focal_loss.py", "file_name": "test_focal_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 170, "end_line": 198, "span_ids": ["TestFocalLoss.test_bin_seg_3d"], "tokens": 479}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestFocalLoss(unittest.TestCase):\n\n    def test_bin_seg_3d(self):\n        num_classes = 2  # labels 0, 1\n        # define 3d examples\n        target = torch.tensor(\n            [\n                # raw 0\n                [[0, 0, 0, 0], [0, 1, 1, 0], [0, 1, 1, 0], [0, 0, 0, 0]],\n                # raw 1\n                [[0, 0, 0, 0], [0, 1, 1, 0], [0, 1, 1, 0], [0, 0, 0, 0]],\n                # raw 2\n                [[0, 0, 0, 0], [0, 1, 1, 0], [0, 1, 1, 0], [0, 0, 0, 0]],\n            ]\n        )\n        # add another dimension corresponding to the batch (batch size = 1 here)\n        target = target.unsqueeze(0)  # shape (1, H, W, D)\n        target_one_hot = F.one_hot(target, num_classes=num_classes).permute(0, 4, 1, 2, 3)  # test one hot\n        pred_very_good = 1000 * F.one_hot(target, num_classes=num_classes).permute(0, 4, 1, 2, 3).float() - 500.0\n\n        # initialize the mean dice loss\n        loss = FocalLoss(to_onehot_y=True)\n        loss_onehot = FocalLoss(to_onehot_y=False)\n\n        # focal loss for pred_very_good should be close to 0\n        target = target.unsqueeze(1)  # shape (1, 1, H, W)\n        focal_loss_good = float(loss(pred_very_good, target).cpu())\n        self.assertAlmostEqual(focal_loss_good, 0.0, places=3)\n\n        focal_loss_good = float(loss_onehot(pred_very_good, target_one_hot).cpu())\n        self.assertAlmostEqual(focal_loss_good, 0.0, places=3)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gaussian_filter.py_GaussianFilterTestCase.test_2d_GaussianFilterTestCase.test_2d.if_torch_cuda_is_availabl.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gaussian_filter.py_GaussianFilterTestCase.test_2d_GaussianFilterTestCase.test_2d.if_torch_cuda_is_availabl.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_gaussian_filter.py", "file_name": "test_gaussian_filter.py", "file_type": "text/x-python", "category": "test", "start_line": 122, "end_line": 141, "span_ids": ["GaussianFilterTestCase.test_2d"], "tokens": 222}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GaussianFilterTestCase(unittest.TestCase):\n\n    @skip_if_quick\n    def test_2d(self):\n        a = torch.ones(1, 1, 3, 3)\n        g = GaussianFilter(2, 3, 3).to(torch.device(\"cpu:0\"))\n        expected = np.array(\n            [\n                [\n                    [\n                        [0.13239081, 0.13932934, 0.13239081],\n                        [0.13932936, 0.14663152, 0.13932936],\n                        [0.13239081, 0.13932934, 0.13239081],\n                    ]\n                ]\n            ]\n        )\n\n        np.testing.assert_allclose(g(a).cpu().numpy(), expected, rtol=1e-5)\n        if torch.cuda.is_available():\n            g = GaussianFilter(2, 3, 3).to(torch.device(\"cuda:0\"))\n            np.testing.assert_allclose(g(a.cuda()).cpu().numpy(), expected, rtol=1e-2)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gaussian_filter.py_GaussianFilterTestCase.test_3d_GaussianFilterTestCase.test_3d.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gaussian_filter.py_GaussianFilterTestCase.test_3d_GaussianFilterTestCase.test_3d.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_gaussian_filter.py", "file_name": "test_gaussian_filter.py", "file_type": "text/x-python", "category": "test", "start_line": 131, "end_line": 163, "span_ids": ["GaussianFilterTestCase.test_3d"], "tokens": 464}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GaussianFilterTestCase(unittest.TestCase):\n\n    def test_3d(self):\n        a = torch.ones(1, 1, 4, 3, 4)\n        g = GaussianFilter(3, 3, 3).to(torch.device(\"cpu:0\"))\n\n        expected = np.array(\n            [\n                [\n                    [\n                        [\n                            [0.07189433, 0.07911152, 0.07911152, 0.07189433],\n                            [0.07566228, 0.08325771, 0.08325771, 0.07566228],\n                            [0.07189433, 0.07911152, 0.07911152, 0.07189433],\n                        ],\n                        [\n                            [0.07911152, 0.08705322, 0.08705322, 0.07911152],\n                            [0.08325771, 0.09161563, 0.09161563, 0.08325771],\n                            [0.07911152, 0.08705322, 0.08705322, 0.07911152],\n                        ],\n                        [\n                            [0.07911152, 0.08705322, 0.08705322, 0.07911152],\n                            [0.08325771, 0.09161563, 0.09161563, 0.08325771],\n                            [0.07911152, 0.08705322, 0.08705322, 0.07911152],\n                        ],\n                        [\n                            [0.07189433, 0.07911152, 0.07911152, 0.07189433],\n                            [0.07566228, 0.08325771, 0.08325771, 0.07566228],\n                            [0.07189433, 0.07911152, 0.07911152, 0.07189433],\n                        ],\n                    ]\n                ]\n            ]\n        )\n        np.testing.assert_allclose(g(a).cpu().numpy(), expected, rtol=1e-5)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gaussian_filter.py_GaussianFilterTestCase.test_3d_sigmas_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gaussian_filter.py_GaussianFilterTestCase.test_3d_sigmas_", "embedding": null, "metadata": {"file_path": "tests/test_gaussian_filter.py", "file_name": "test_gaussian_filter.py", "file_type": "text/x-python", "category": "test", "start_line": 177, "end_line": 206, "span_ids": ["GaussianFilterTestCase.test_wrong_args", "impl:9", "GaussianFilterTestCase.test_3d_sigmas"], "tokens": 421}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GaussianFilterTestCase(unittest.TestCase):\n\n    def test_3d_sigmas(self):\n        a = torch.ones(1, 1, 4, 3, 2)\n        g = GaussianFilter(3, [3, 2, 1], 3).to(torch.device(\"cpu:0\"))\n\n        expected = np.array(\n            [\n                [\n                    [\n                        [[0.13690521, 0.13690521], [0.15181276, 0.15181276], [0.13690521, 0.13690521]],\n                        [[0.1506486, 0.15064861], [0.16705267, 0.16705267], [0.1506486, 0.15064861]],\n                        [[0.1506486, 0.15064861], [0.16705267, 0.16705267], [0.1506486, 0.15064861]],\n                        [[0.13690521, 0.13690521], [0.15181276, 0.15181276], [0.13690521, 0.13690521]],\n                    ]\n                ]\n            ]\n        )\n        np.testing.assert_allclose(g(a).cpu().numpy(), expected, rtol=1e-5)\n        if torch.cuda.is_available():\n            g = GaussianFilter(3, [3, 2, 1], 3).to(torch.device(\"cuda:0\"))\n            np.testing.assert_allclose(g(a.cuda()).cpu().numpy(), expected, rtol=1e-2)\n\n    def test_wrong_args(self):\n        with self.assertRaisesRegex(ValueError, \"\"):\n            GaussianFilter(3, [3, 2], 3).to(torch.device(\"cpu:0\"))\n        GaussianFilter(3, [3, 2, 1], 3).to(torch.device(\"cpu:0\"))  # test init\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_generalized_dice_loss.py_unittest_TEST_CASES": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_generalized_dice_loss.py_unittest_TEST_CASES", "embedding": null, "metadata": {"file_path": "tests/test_generalized_dice_loss.py", "file_name": "test_generalized_dice_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 145, "span_ids": ["docstring"], "tokens": 53}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.losses import GeneralizedDiceLoss\nfrom tests.utils import SkipIfBeforePyTorchVersion, test_script_save\n\nTEST_CASES =\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_generalized_dice_loss.py_TestGeneralizedDiceLoss_TestGeneralizedDiceLoss.test_ill_shape.with_self_assertRaisesReg.loss_forward_torch_ones_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_generalized_dice_loss.py_TestGeneralizedDiceLoss_TestGeneralizedDiceLoss.test_ill_shape.with_self_assertRaisesReg.loss_forward_torch_ones_", "embedding": null, "metadata": {"file_path": "tests/test_generalized_dice_loss.py", "file_name": "test_generalized_dice_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 114, "end_line": 123, "span_ids": ["TestGeneralizedDiceLoss.test_shape", "TestGeneralizedDiceLoss", "TestGeneralizedDiceLoss.test_ill_shape"], "tokens": 127}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestGeneralizedDiceLoss(unittest.TestCase):\n    @parameterized.expand(TEST_CASES)\n    def test_shape(self, input_param, input_data, expected_val):\n        result = GeneralizedDiceLoss(**input_param).forward(**input_data)\n        np.testing.assert_allclose(result.detach().cpu().numpy(), expected_val, rtol=1e-5)\n\n    def test_ill_shape(self):\n        loss = GeneralizedDiceLoss()\n        with self.assertRaisesRegex(AssertionError, \"\"):\n            loss.forward(torch.ones((1, 2, 3)), torch.ones((4, 5, 6)))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_generalized_dice_loss.py_TestGeneralizedDiceLoss.test_ill_opts_TestGeneralizedDiceLoss.test_ill_opts.None_2.GeneralizedDiceLoss_reduc": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_generalized_dice_loss.py_TestGeneralizedDiceLoss.test_ill_opts_TestGeneralizedDiceLoss.test_ill_opts.None_2.GeneralizedDiceLoss_reduc", "embedding": null, "metadata": {"file_path": "tests/test_generalized_dice_loss.py", "file_name": "test_generalized_dice_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 125, "end_line": 133, "span_ids": ["TestGeneralizedDiceLoss.test_ill_opts"], "tokens": 121}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestGeneralizedDiceLoss(unittest.TestCase):\n\n    def test_ill_opts(self):\n        with self.assertRaisesRegex(ValueError, \"\"):\n            GeneralizedDiceLoss(sigmoid=True, softmax=True)\n        chn_input = torch.ones((1, 1, 3))\n        chn_target = torch.ones((1, 1, 3))\n        with self.assertRaisesRegex(ValueError, \"\"):\n            GeneralizedDiceLoss(reduction=\"unknown\")(chn_input, chn_target)\n        with self.assertRaisesRegex(ValueError, \"\"):\n            GeneralizedDiceLoss(reduction=None)(chn_input, chn_target)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_generalized_dice_loss.py_TestGeneralizedDiceLoss.test_input_warnings_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_generalized_dice_loss.py_TestGeneralizedDiceLoss.test_input_warnings_", "embedding": null, "metadata": {"file_path": "tests/test_generalized_dice_loss.py", "file_name": "test_generalized_dice_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 169, "end_line": 191, "span_ids": ["TestGeneralizedDiceLoss.test_script", "impl:3", "TestGeneralizedDiceLoss.test_input_warnings"], "tokens": 217}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestGeneralizedDiceLoss(unittest.TestCase):\n\n    def test_input_warnings(self):\n        chn_input = torch.ones((1, 1, 3))\n        chn_target = torch.ones((1, 1, 3))\n        with self.assertWarns(Warning):\n            loss = GeneralizedDiceLoss(include_background=False)\n            loss.forward(chn_input, chn_target)\n        with self.assertWarns(Warning):\n            loss = GeneralizedDiceLoss(softmax=True)\n            loss.forward(chn_input, chn_target)\n        with self.assertWarns(Warning):\n            loss = GeneralizedDiceLoss(to_onehot_y=True)\n            loss.forward(chn_input, chn_target)\n\n    @SkipIfBeforePyTorchVersion((1, 7, 0))\n    def test_script(self):\n        loss = GeneralizedDiceLoss()\n        test_input = torch.ones(2, 1, 8, 8)\n        test_script_save(loss, test_input, test_input)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_generate_spatial_bounding_box.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_generate_spatial_bounding_box.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_generate_spatial_bounding_box.py", "file_name": "test_generate_spatial_bounding_box.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 98, "span_ids": ["impl:9", "TestGenerateSpatialBoundingBox", "TestGenerateSpatialBoundingBox.test_value", "docstring"], "tokens": 832}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import generate_spatial_bounding_box\nfrom tests.utils import TEST_NDARRAYS\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    TESTS.append(\n        [\n            {\n                \"img\": p(\n                    np.array([[[0, 0, 0, 0, 0], [0, 1, 2, 1, 0], [0, 2, 3, 2, 0], [0, 1, 2, 1, 0], [0, 0, 0, 0, 0]]])\n                ),\n                \"select_fn\": lambda x: x > 0,\n                \"channel_indices\": None,\n                \"margin\": 0,\n            },\n            ([1, 1], [4, 4]),\n        ]\n    )\n    TESTS.append(\n        [\n            {\n                \"img\": p(\n                    np.array([[[0, 0, 0, 0, 0], [0, 1, 1, 1, 0], [0, 1, 3, 1, 0], [0, 1, 1, 1, 0], [0, 0, 0, 0, 0]]])\n                ),\n                \"select_fn\": lambda x: x > 1,\n                \"channel_indices\": None,\n                \"margin\": 0,\n            },\n            ([2, 2], [3, 3]),\n        ]\n    )\n    TESTS.append(\n        [\n            {\n                \"img\": p(\n                    np.array([[[0, 0, 0, 0, 0], [0, 1, 2, 1, 0], [0, 2, 3, 2, 0], [0, 1, 2, 1, 0], [0, 0, 0, 0, 0]]])\n                ),\n                \"select_fn\": lambda x: x > 0,\n                \"channel_indices\": 0,\n                \"margin\": 0,\n            },\n            ([1, 1], [4, 4]),\n        ]\n    )\n    TESTS.append(\n        [\n            {\n                \"img\": p(\n                    np.array([[[0, 0, 0, 0, 0], [0, 1, 2, 1, 0], [0, 2, 3, 2, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]])\n                ),\n                \"select_fn\": lambda x: x > 0,\n                \"channel_indices\": None,\n                \"margin\": 1,\n            },\n            ([0, 0], [4, 5]),\n        ]\n    )\n    TESTS.append(\n        [\n            {\n                \"img\": p(\n                    np.array([[[0, 0, 0, 0, 0], [0, 1, 2, 1, 0], [0, 2, 3, 2, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]])\n                ),\n                \"select_fn\": lambda x: x > 0,\n                \"channel_indices\": None,\n                \"margin\": [2, 1],\n            },\n            ([0, 0], [5, 5]),\n        ]\n    )\n\n\nclass TestGenerateSpatialBoundingBox(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_value(self, input_data, expected_box):\n        result = generate_spatial_bounding_box(**input_data)\n        self.assertTupleEqual(result, expected_box)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_rocauc.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_rocauc.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_handler_rocauc.py", "file_name": "test_handler_rocauc.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 46, "span_ids": ["TestHandlerROCAUC", "TestHandlerROCAUC.test_compute", "impl", "docstring"], "tokens": 274}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\n\nfrom monai.handlers import ROCAUC\nfrom monai.transforms import Activations, AsDiscrete\n\n\nclass TestHandlerROCAUC(unittest.TestCase):\n    def test_compute(self):\n        auc_metric = ROCAUC()\n        act = Activations(softmax=True)\n        to_onehot = AsDiscrete(to_onehot=2)\n\n        y_pred = [torch.Tensor([0.1, 0.9]), torch.Tensor([0.3, 1.4])]\n        y = [torch.Tensor([0]), torch.Tensor([1])]\n        y_pred = [act(p) for p in y_pred]\n        y = [to_onehot(y_) for y_ in y]\n        auc_metric.update([y_pred, y])\n\n        y_pred = [torch.Tensor([0.2, 0.1]), torch.Tensor([0.1, 0.5])]\n        y = [torch.Tensor([0]), torch.Tensor([1])]\n        y_pred = [act(p) for p in y_pred]\n        y = [to_onehot(y_) for y_ in y]\n\n        auc_metric.update([y_pred, y])\n\n        auc = auc_metric.compute()\n        np.testing.assert_allclose(0.75, auc)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_segmentation_saver.py_TestHandlerSegmentationSaver.test_save_resized_content_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_segmentation_saver.py_TestHandlerSegmentationSaver.test_save_resized_content_", "embedding": null, "metadata": {"file_path": "tests/test_handler_segmentation_saver.py", "file_name": "test_handler_segmentation_saver.py", "file_type": "text/x-python", "category": "test", "start_line": 56, "end_line": 87, "span_ids": ["impl:5", "TestHandlerSegmentationSaver.test_save_resized_content"], "tokens": 339}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestHandlerSegmentationSaver(unittest.TestCase):\n\n    @parameterized.expand([TEST_CASE_0, TEST_CASE_1])\n    def test_save_resized_content(self, output_ext):\n        with tempfile.TemporaryDirectory() as tempdir:\n\n            # set up engine\n            def _train_func(engine, batch):\n                engine.state.batch = decollate_batch(batch)\n                return [torch.randint(0, 255, (1, 2, 2)).float() for _ in range(8)]\n\n            engine = Engine(_train_func)\n\n            # set up testing handler\n            saver = SegmentationSaver(output_dir=tempdir, output_postfix=\"seg\", output_ext=output_ext, scale=255)\n            saver.attach(engine)\n\n            data = [\n                {\n                    \"filename_or_obj\": [\"testfile\" + str(i) + \".nii.gz\" for i in range(8)],\n                    \"spatial_shape\": torch.tensor([[28, 28] for _ in range(8)]),\n                    \"affine\": torch.tensor([np.diag(np.ones(4)) * 5 for _ in range(8)]),\n                    \"original_affine\": torch.tensor([np.diag(np.ones(4)) * 1.0 for _ in range(8)]),\n                }\n            ]\n            engine.run(data, max_epochs=1)\n            for i in range(8):\n                filepath = os.path.join(\"testfile\" + str(i), \"testfile\" + str(i) + \"_seg\" + output_ext)\n                self.assertTrue(os.path.exists(os.path.join(tempdir, filepath)))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_stats.py_TestHandlerStats.test_loss_print_TestHandlerStats.test_loss_print.for_idx_line_in_enumerat.if_grep_match_line_.if_idx_in_1_2_3_6_7_.self_assertTrue_has_key_w": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_stats.py_TestHandlerStats.test_loss_print_TestHandlerStats.test_loss_print.for_idx_line_in_enumerat.if_grep_match_line_.if_idx_in_1_2_3_6_7_.self_assertTrue_has_key_w", "embedding": null, "metadata": {"file_path": "tests/test_handler_stats.py", "file_name": "test_handler_stats.py", "file_type": "text/x-python", "category": "test", "start_line": 61, "end_line": 88, "span_ids": ["TestHandlerStats.test_loss_print"], "tokens": 237}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestHandlerStats(unittest.TestCase):\n\n    def test_loss_print(self):\n        log_stream = StringIO()\n        log_handler = logging.StreamHandler(log_stream)\n        log_handler.setLevel(logging.INFO)\n        key_to_handler = \"test_logging\"\n        key_to_print = \"myLoss\"\n\n        # set up engine\n        def _train_func(engine, batch):\n            return [torch.tensor(0.0)]\n\n        engine = Engine(_train_func)\n\n        # set up testing handler\n        stats_handler = StatsHandler(name=key_to_handler, tag_name=key_to_print, logger_handler=log_handler)\n        stats_handler.attach(engine)\n\n        engine.run(range(3), max_epochs=2)\n\n        # check logging output\n        output_str = log_stream.getvalue()\n        log_handler.close()\n        grep = re.compile(f\".*{key_to_handler}.*\")\n        has_key_word = re.compile(f\".*{key_to_print}.*\")\n        for idx, line in enumerate(output_str.split(\"\\n\")):\n            if grep.match(line):\n                if idx in [1, 2, 3, 6, 7, 8]:\n                    self.assertTrue(has_key_word.match(line))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_stats.py_TestHandlerStats.test_loss_dict_TestHandlerStats.test_loss_dict.for_idx_line_in_enumerat.if_grep_match_line_.if_idx_in_1_2_3_6_7_.self_assertTrue_has_key_w": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_stats.py_TestHandlerStats.test_loss_dict_TestHandlerStats.test_loss_dict.for_idx_line_in_enumerat.if_grep_match_line_.if_idx_in_1_2_3_6_7_.self_assertTrue_has_key_w", "embedding": null, "metadata": {"file_path": "tests/test_handler_stats.py", "file_name": "test_handler_stats.py", "file_type": "text/x-python", "category": "test", "start_line": 90, "end_line": 119, "span_ids": ["TestHandlerStats.test_loss_dict"], "tokens": 248}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestHandlerStats(unittest.TestCase):\n\n    def test_loss_dict(self):\n        log_stream = StringIO()\n        log_handler = logging.StreamHandler(log_stream)\n        log_handler.setLevel(logging.INFO)\n        key_to_handler = \"test_logging\"\n        key_to_print = \"myLoss1\"\n\n        # set up engine\n        def _train_func(engine, batch):\n            return [torch.tensor(0.0)]\n\n        engine = Engine(_train_func)\n\n        # set up testing handler\n        stats_handler = StatsHandler(\n            name=key_to_handler, output_transform=lambda x: {key_to_print: x}, logger_handler=log_handler\n        )\n        stats_handler.attach(engine)\n\n        engine.run(range(3), max_epochs=2)\n\n        # check logging output\n        output_str = log_stream.getvalue()\n        log_handler.close()\n        grep = re.compile(f\".*{key_to_handler}.*\")\n        has_key_word = re.compile(f\".*{key_to_print}.*\")\n        for idx, line in enumerate(output_str.split(\"\\n\")):\n            if grep.match(line):\n                if idx in [1, 2, 3, 6, 7, 8]:\n                    self.assertTrue(has_key_word.match(line))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_tb_image.py_glob_TEST_CASES._20_20_2_20_20_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_tb_image.py_glob_TEST_CASES._20_20_2_20_20_", "embedding": null, "metadata": {"file_path": "tests/test_handler_tb_image.py", "file_name": "test_handler_tb_image.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 24, "span_ids": ["docstring"], "tokens": 117}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import glob\nimport tempfile\nimport unittest\n\nimport numpy as np\nimport torch\nfrom ignite.engine import Engine, Events\nfrom parameterized import parameterized\n\nfrom monai.data import decollate_batch\nfrom monai.handlers import TensorBoardImageHandler\n\nTEST_CASES = [[[20, 20]], [[2, 20, 20]], [[3, 20, 20]], [[20, 20, 20]], [[2, 20, 20, 20]], [[2, 2, 20, 20, 20]]]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_tb_image.py_TestHandlerTBImage_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_tb_image.py_TestHandlerTBImage_", "embedding": null, "metadata": {"file_path": "tests/test_handler_tb_image.py", "file_name": "test_handler_tb_image.py", "file_type": "text/x-python", "category": "test", "start_line": 27, "end_line": 55, "span_ids": ["TestHandlerTBImage.test_tb_image_shape", "impl:3", "TestHandlerTBImage"], "tokens": 210}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestHandlerTBImage(unittest.TestCase):\n    @parameterized.expand(TEST_CASES)\n    def test_tb_image_shape(self, shape):\n        with tempfile.TemporaryDirectory() as tempdir:\n\n            # set up engine\n            def _train_func(engine, batch):\n                engine.state.batch = decollate_batch(list(batch))\n                return [torch.zeros((1, 10, 10))]\n\n            engine = Engine(_train_func)\n\n            # set up testing handler\n            stats_handler = TensorBoardImageHandler(log_dir=tempdir)\n            engine.add_event_handler(Events.ITERATION_COMPLETED, stats_handler)\n\n            data = zip(\n                torch.as_tensor(np.random.normal(size=(10, 4, *shape))),\n                torch.as_tensor(np.random.normal(size=(10, 4, *shape))),\n            )\n            engine.run(data, epoch_length=10, max_epochs=1)\n            stats_handler.close()\n\n            self.assertTrue(len(glob.glob(tempdir)) > 0)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_tb_stats.py_TestHandlerTBStats.test_metrics_writer_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_tb_stats.py_TestHandlerTBStats.test_metrics_writer_", "embedding": null, "metadata": {"file_path": "tests/test_handler_tb_stats.py", "file_name": "test_handler_tb_stats.py", "file_type": "text/x-python", "category": "test", "start_line": 46, "end_line": 79, "span_ids": ["TestHandlerTBStats.test_metrics_writer", "impl"], "tokens": 250}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestHandlerTBStats(unittest.TestCase):\n\n    def test_metrics_writer(self):\n        with tempfile.TemporaryDirectory() as tempdir:\n\n            # set up engine\n            def _train_func(engine, batch):\n                return [batch + 1.0]\n\n            engine = Engine(_train_func)\n\n            # set up dummy metric\n            @engine.on(Events.EPOCH_COMPLETED)\n            def _update_metric(engine):\n                current_metric = engine.state.metrics.get(\"acc\", 0.1)\n                engine.state.metrics[\"acc\"] = current_metric + 0.1\n                engine.state.test = current_metric\n\n            # set up testing handler\n            writer = SummaryWriter(log_dir=tempdir)\n            stats_handler = TensorBoardStatsHandler(\n                summary_writer=writer,\n                output_transform=lambda x: {\"loss\": x[0] * 2.0},\n                global_epoch_transform=lambda x: x * 3.0,\n                state_attributes=[\"test\"],\n            )\n            stats_handler.attach(engine)\n            engine.run(range(3), max_epochs=2)\n            writer.close()\n            # check logging output\n            self.assertTrue(len(glob.glob(tempdir)) > 0)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_validation.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_validation.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_handler_validation.py", "file_name": "test_handler_validation.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 50, "span_ids": ["TestEvaluator", "TestHandlerValidation.test_content", "TestEvaluator._iteration", "impl", "docstring", "TestHandlerValidation"], "tokens": 207}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom ignite.engine import Engine\n\nfrom monai.data import Dataset\nfrom monai.engines import Evaluator\nfrom monai.handlers import ValidationHandler\n\n\nclass TestEvaluator(Evaluator):\n    def _iteration(self, engine, batchdata):\n        pass\n\n\nclass TestHandlerValidation(unittest.TestCase):\n    def test_content(self):\n        data = [0] * 8\n\n        # set up engine\n        def _train_func(engine, batch):\n            pass\n\n        engine = Engine(_train_func)\n\n        # set up testing handler\n        val_data_loader = torch.utils.data.DataLoader(Dataset(data))\n        evaluator = TestEvaluator(torch.device(\"cpu:0\"), val_data_loader)\n        saver = ValidationHandler(interval=2, validator=evaluator)\n        saver.attach(engine)\n\n        engine.run(data, max_epochs=5)\n        self.assertEqual(evaluator.state.max_epochs, 4)\n        self.assertEqual(evaluator.state.epoch_length, 8)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_header_correct.py_unittest_TestCorrection.test_correct.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_header_correct.py_unittest_TestCorrection.test_correct.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_header_correct.py", "file_name": "test_header_correct.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 28, "span_ids": ["TestCorrection", "TestCorrection.test_correct", "docstring"], "tokens": 197}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport nibabel as nib\nimport numpy as np\n\nfrom monai.data import correct_nifti_header_if_necessary\n\n\nclass TestCorrection(unittest.TestCase):\n    def test_correct(self):\n        test_img = nib.Nifti1Image(np.zeros((1, 2, 3)), np.eye(4))\n        test_img.header.set_zooms((100, 100, 100))\n        test_img = correct_nifti_header_if_necessary(test_img)\n        np.testing.assert_allclose(\n            test_img.affine,\n            np.array([[100.0, 0.0, 0.0, 0.0], [0.0, 100.0, 0.0, 0.0], [0.0, 0.0, 100.0, 0.0], [0.0, 0.0, 0.0, 1.0]]),\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_header_correct.py_TestCorrection.test_affine_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_header_correct.py_TestCorrection.test_affine_", "embedding": null, "metadata": {"file_path": "tests/test_header_correct.py", "file_name": "test_header_correct.py", "file_type": "text/x-python", "category": "test", "start_line": 30, "end_line": 41, "span_ids": ["TestCorrection.test_affine", "impl"], "tokens": 172}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestCorrection(unittest.TestCase):\n\n    def test_affine(self):\n        test_img = nib.Nifti1Image(np.zeros((1, 2, 3)), np.eye(4) * 20.0)\n        test_img = correct_nifti_header_if_necessary(test_img)\n        np.testing.assert_allclose(\n            test_img.affine,\n            np.array([[20.0, 0.0, 0.0, 0.0], [0.0, 20.0, 0.0, 0.0], [0.0, 0.0, 20.0, 0.0], [0.0, 0.0, 0.0, 20.0]]),\n        )\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_identity.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_identity.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_identity.py", "file_name": "test_identity.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 28, "span_ids": ["TestIdentity", "TestIdentity.test_identity", "impl", "docstring"], "tokens": 93}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nfrom monai.transforms.utility.array import Identity\nfrom tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, assert_allclose\n\n\nclass TestIdentity(NumpyImageTestCase2D):\n    def test_identity(self):\n        for p in TEST_NDARRAYS:\n            img = p(self.imt)\n            identity = Identity()\n            assert_allclose(img, identity(img))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_identityd.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_identityd.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_identityd.py", "file_name": "test_identityd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 30, "span_ids": ["TestIdentityd.test_identityd", "TestIdentityd", "impl", "docstring"], "tokens": 115}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nfrom monai.transforms.utility.dictionary import Identityd\nfrom tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, assert_allclose\n\n\nclass TestIdentityd(NumpyImageTestCase2D):\n    def test_identityd(self):\n        for p in TEST_NDARRAYS:\n            img = p(self.imt)\n            data = {}\n            data[\"img\"] = img\n            identity = Identityd(keys=data.keys())\n            assert_allclose(img, identity(data)[\"img\"])\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_classification_2d.py_os_MedNISTDataset.__getitem__.return.self_transforms_self_imag": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_classification_2d.py_os_MedNISTDataset.__getitem__.return.self_transforms_self_imag", "embedding": null, "metadata": {"file_path": "tests/test_integration_classification_2d.py", "file_name": "test_integration_classification_2d.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 59, "span_ids": ["MedNISTDataset", "MedNISTDataset.__len__", "MedNISTDataset.__init__", "docstring", "MedNISTDataset.__getitem__"], "tokens": 330}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport unittest\nimport warnings\nfrom urllib.error import ContentTooShortError, HTTPError\n\nimport numpy as np\nimport torch\nfrom torch.utils.data import DataLoader\n\nimport monai\nfrom monai.apps import download_and_extract\nfrom monai.data import decollate_batch\nfrom monai.metrics import ROCAUCMetric\nfrom monai.networks import eval_mode\nfrom monai.networks.nets import DenseNet121\nfrom monai.transforms import (\n    Activations,\n    AddChannel,\n    AsDiscrete,\n    Compose,\n    LoadImage,\n    RandFlip,\n    RandRotate,\n    RandZoom,\n    ScaleIntensity,\n    ToTensor,\n    Transpose,\n)\nfrom monai.utils import set_determinism\nfrom tests.testing_data.integration_answers import test_integration_value\nfrom tests.utils import DistTestCase, TimedCall, skip_if_quick\n\nTEST_DATA_URL = \"https://drive.google.com/uc?id=1QsnnkvZyJPcbRoV_ArW8SnE1OTuoVbKE\"\nMD5_VALUE = \"0bc7306e7427e00ad1c5526a6677552d\"\nTASK = \"integration_classification_2d\"\n\n\nclass MedNISTDataset(torch.utils.data.Dataset):\n    def __init__(self, image_files, labels, transforms):\n        self.image_files = image_files\n        self.labels = labels\n        self.transforms = transforms\n\n    def __len__(self):\n        return len(self.image_files)\n\n    def __getitem__(self, index):\n        return self.transforms(self.image_files[index]), self.labels[index]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_classification_2d.py_run_inference_test_run_inference_test.return.tps": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_classification_2d.py_run_inference_test_run_inference_test.return.tps", "embedding": null, "metadata": {"file_path": "tests/test_integration_classification_2d.py", "file_name": "test_integration_classification_2d.py", "file_type": "text/x-python", "category": "test", "start_line": 148, "end_line": 168, "span_ids": ["run_inference_test"], "tokens": 272}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def run_inference_test(root_dir, test_x, test_y, device=\"cuda:0\", num_workers=10):\n    # define transforms for image and classification\n    val_transforms = Compose([LoadImage(image_only=True), AddChannel(), ScaleIntensity(), ToTensor()])\n    val_ds = MedNISTDataset(test_x, test_y, val_transforms)\n    val_loader = DataLoader(val_ds, batch_size=300, num_workers=num_workers)\n\n    model = DenseNet121(spatial_dims=2, in_channels=1, out_channels=len(np.unique(test_y))).to(device)\n\n    model_filename = os.path.join(root_dir, \"best_metric_model.pth\")\n    model.load_state_dict(torch.load(model_filename))\n    y_true = []\n    y_pred = []\n    with eval_mode(model):\n        for test_data in val_loader:\n            test_images, test_labels = test_data[0].to(device), test_data[1].to(device)\n            pred = model(test_images).argmax(dim=1)\n            for i in range(len(pred)):\n                y_true.append(test_labels[i].item())\n                y_pred.append(pred[i].item())\n    tps = [np.sum((np.asarray(y_true) == idx) & (np.asarray(y_pred) == idx)) for idx in np.unique(test_y)]\n    return tps", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_classification_2d.py_IntegrationClassification2D.tearDown_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_classification_2d.py_IntegrationClassification2D.tearDown_", "embedding": null, "metadata": {"file_path": "tests/test_integration_classification_2d.py", "file_name": "test_integration_classification_2d.py", "file_type": "text/x-python", "category": "test", "start_line": 220, "end_line": 270, "span_ids": ["IntegrationClassification2D.tearDown", "IntegrationClassification2D.test_training", "impl:7", "IntegrationClassification2D.test_timing", "IntegrationClassification2D.train_and_infer"], "tokens": 471}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_quick\nclass IntegrationClassification2D(DistTestCase):\n\n    def tearDown(self):\n        set_determinism(seed=None)\n        try:\n            os.remove(os.path.join(self.data_dir, \"best_metric_model.pth\"))\n        except FileNotFoundError:\n            warnings.warn(\"not found best_metric_model.pth, training skipped?\")\n            pass\n\n    def train_and_infer(self, idx=0):\n        results = []\n        if not os.path.exists(os.path.join(self.data_dir, \"MedNIST\")):\n            # skip test if no MedNIST dataset\n            return results\n\n        set_determinism(seed=0)\n        losses, best_metric, best_metric_epoch = run_training_test(\n            self.data_dir, self.train_x, self.train_y, self.val_x, self.val_y, device=self.device\n        )\n        infer_metric = run_inference_test(self.data_dir, self.test_x, self.test_y, device=self.device)\n\n        print(f\"integration_classification_2d {losses}\")\n        print(\"best metric\", best_metric)\n        print(\"infer metric\", infer_metric)\n        # check training properties\n        self.assertTrue(test_integration_value(TASK, key=\"losses\", data=losses, rtol=1e-2))\n        self.assertTrue(test_integration_value(TASK, key=\"best_metric\", data=best_metric, rtol=1e-4))\n        np.testing.assert_allclose(best_metric_epoch, 4)\n        model_file = os.path.join(self.data_dir, \"best_metric_model.pth\")\n        self.assertTrue(os.path.exists(model_file))\n        # check inference properties\n        self.assertTrue(test_integration_value(TASK, key=\"infer_prop\", data=np.asarray(infer_metric), rtol=1))\n        results.extend(losses)\n        results.append(best_metric)\n        results.extend(infer_metric)\n        return results\n\n    def test_training(self):\n        repeated = []\n        for i in range(2):\n            results = self.train_and_infer(i)\n            repeated.append(results)\n        np.testing.assert_allclose(repeated[0], repeated[1])\n\n    @TimedCall(seconds=1000, skip_timing=not torch.cuda.is_available(), daemon=False)\n    def test_timing(self):\n        self.train_and_infer()\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_determinism.py_unittest_run_test._TestBatch.__len__.return.train_steps": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_determinism.py_unittest_run_test._TestBatch.__len__.return.train_steps", "embedding": null, "metadata": {"file_path": "tests/test_integration_determinism.py", "file_name": "test_integration_determinism.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 41, "span_ids": ["run_test", "docstring"], "tokens": 253}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom torch.utils.data import DataLoader, Dataset\n\nfrom monai.data import create_test_image_2d\nfrom monai.losses import DiceLoss\nfrom monai.networks.nets import UNet\nfrom monai.transforms import AddChannel, Compose, RandRotate90, RandSpatialCrop, ScaleIntensity, ToTensor\nfrom monai.utils import set_determinism\nfrom tests.utils import DistTestCase, TimedCall\n\n\ndef run_test(batch_size=64, train_steps=200, device=\"cuda:0\"):\n    class _TestBatch(Dataset):\n        def __init__(self, transforms):\n            self.transforms = transforms\n\n        def __getitem__(self, _unused_id):\n            im, seg = create_test_image_2d(128, 128, noise_max=1, num_objs=4, num_seg_classes=1)\n            seed = np.random.randint(2147483647)\n            self.transforms.set_random_state(seed=seed)\n            im = self.transforms(im)\n            self.transforms.set_random_state(seed=seed)\n            seg = self.transforms(seg)\n            return im, seg\n\n        def __len__(self):\n            return train_steps\n    # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_determinism.py_run_test.net_run_test.return.epoch_loss_step": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_determinism.py_run_test.net_run_test.return.epoch_loss_step", "embedding": null, "metadata": {"file_path": "tests/test_integration_determinism.py", "file_name": "test_integration_determinism.py", "file_type": "text/x-python", "category": "test", "start_line": 43, "end_line": 68, "span_ids": ["run_test"], "tokens": 246}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def run_test(batch_size=64, train_steps=200, device=\"cuda:0\"):\n    # ... other code\n\n    net = UNet(\n        spatial_dims=2, in_channels=1, out_channels=1, channels=(4, 8, 16, 32), strides=(2, 2, 2), num_res_units=2\n    ).to(device)\n\n    loss = DiceLoss(sigmoid=True)\n    opt = torch.optim.Adam(net.parameters(), 1e-2)\n    train_transforms = Compose(\n        [AddChannel(), ScaleIntensity(), RandSpatialCrop((96, 96), random_size=False), RandRotate90(), ToTensor()]\n    )\n\n    src = DataLoader(_TestBatch(train_transforms), batch_size=batch_size, shuffle=True)\n\n    net.train()\n    epoch_loss = 0\n    step = 0\n    for img, seg in src:\n        step += 1\n        opt.zero_grad()\n        output = net(img.to(device))\n        step_loss = loss(output, seg.to(device))\n        step_loss.backward()\n        opt.step()\n        epoch_loss += step_loss.item()\n    epoch_loss /= step\n\n    return epoch_loss, step", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_determinism.py_TestDeterminism_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_determinism.py_TestDeterminism_", "embedding": null, "metadata": {"file_path": "tests/test_integration_determinism.py", "file_name": "test_integration_determinism.py", "file_type": "text/x-python", "category": "test", "start_line": 71, "end_line": 89, "span_ids": ["TestDeterminism.tearDown", "impl", "TestDeterminism.setUp", "TestDeterminism", "TestDeterminism.test_training"], "tokens": 145}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestDeterminism(DistTestCase):\n    def setUp(self):\n        self.device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu:0\")\n\n    def tearDown(self):\n        set_determinism(seed=None)\n\n    @TimedCall(seconds=150)\n    def test_training(self):\n        set_determinism(seed=0)\n        loss, step = run_test(device=self.device)\n        print(f\"Deterministic loss {loss} at training step {step}\")\n        np.testing.assert_allclose(step, 4)\n        np.testing.assert_allclose(loss, 0.536134, rtol=1e-4)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_segmentation_3d.py_IntegrationSegmentation3D_IntegrationSegmentation3D.tearDown.shutil_rmtree_self_data_d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_segmentation_3d.py_IntegrationSegmentation3D_IntegrationSegmentation3D.tearDown.shutil_rmtree_self_data_d", "embedding": null, "metadata": {"file_path": "tests/test_integration_segmentation_3d.py", "file_name": "test_integration_segmentation_3d.py", "file_type": "text/x-python", "category": "test", "start_line": 229, "end_line": 246, "span_ids": ["IntegrationSegmentation3D", "IntegrationSegmentation3D.tearDown", "IntegrationSegmentation3D.setUp"], "tokens": 195}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_quick\nclass IntegrationSegmentation3D(DistTestCase):\n    def setUp(self):\n        set_determinism(seed=0)\n\n        self.data_dir = tempfile.mkdtemp()\n        for i in range(40):\n            im, seg = create_test_image_3d(128, 128, 128, num_seg_classes=1, channel_dim=-1)\n            n = nib.Nifti1Image(im, np.eye(4))\n            nib.save(n, os.path.join(self.data_dir, f\"img{i:d}.nii.gz\"))\n            n = nib.Nifti1Image(seg, np.eye(4))\n            nib.save(n, os.path.join(self.data_dir, f\"seg{i:d}.nii.gz\"))\n\n        self.device = \"cuda:0\" if torch.cuda.is_available() else \"cpu:0\"\n\n    def tearDown(self):\n        set_determinism(seed=None)\n        shutil.rmtree(self.data_dir)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_sliding_window.py_run_test_run_test.return.saved_name": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_sliding_window.py_run_test_run_test.return.saved_name", "embedding": null, "metadata": {"file_path": "tests/test_integration_sliding_window.py", "file_name": "test_integration_sliding_window.py", "file_type": "text/x-python", "category": "test", "start_line": 32, "end_line": 58, "span_ids": ["run_test"], "tokens": 323}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def run_test(batch_size, img_name, seg_name, output_dir, device=\"cuda:0\"):\n    ds = ImageDataset([img_name], [seg_name], transform=AddChannel(), seg_transform=AddChannel(), image_only=False)\n    loader = DataLoader(ds, batch_size=1, pin_memory=torch.cuda.is_available())\n\n    net = UNet(\n        spatial_dims=3, in_channels=1, out_channels=1, channels=(4, 8, 16, 32), strides=(2, 2, 2), num_res_units=2\n    ).to(device)\n    roi_size = (16, 32, 48)\n    sw_batch_size = batch_size\n\n    def _sliding_window_processor(_engine, batch):\n        img = batch[0]  # first item from ImageDataset is the input image\n        with eval_mode(net):\n            seg_probs = sliding_window_inference(img.to(device), roi_size, sw_batch_size, net, device=device)\n            return predict_segmentation(seg_probs)\n\n    infer_engine = Engine(_sliding_window_processor)\n\n    SegmentationSaver(  # 3rd item for image batch meta data\n        output_dir=output_dir, output_ext=\".nii.gz\", output_postfix=\"seg\", batch_transform=lambda x: x[2]\n    ).attach(infer_engine)\n\n    infer_engine.run(loader)\n\n    basename = os.path.basename(img_name)[: -len(\".nii.gz\")]\n    saved_name = os.path.join(output_dir, basename, f\"{basename}_seg.nii.gz\")\n    return saved_name", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_stn.py_compare_2d_compare_2d.return.model_img_a_detach_cpu": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_stn.py_compare_2d_compare_2d.return.model_img_a_detach_cpu", "embedding": null, "metadata": {"file_path": "tests/test_integration_stn.py", "file_name": "test_integration_stn.py", "file_type": "text/x-python", "category": "test", "start_line": 76, "end_line": 96, "span_ids": ["compare_2d"], "tokens": 281}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def compare_2d(is_ref=True, device=None, reverse_indexing=False):\n    batch_size = 32\n    img_a = [create_test_image_2d(28, 28, 5, rad_max=6, noise_max=1)[0][None] for _ in range(batch_size)]\n    img_b = [create_test_image_2d(28, 28, 5, rad_max=6, noise_max=1)[0][None] for _ in range(batch_size)]\n    img_a = np.stack(img_a, axis=0)\n    img_b = np.stack(img_b, axis=0)\n    img_a = torch.as_tensor(img_a, device=device)\n    img_b = torch.as_tensor(img_b, device=device)\n    model = STNBenchmark(is_ref=is_ref, reverse_indexing=reverse_indexing).to(device)\n    optimizer = optim.SGD(model.parameters(), lr=0.001)\n    model.train()\n    init_loss = None\n    for _ in range(20):\n        optimizer.zero_grad()\n        output_a = model(img_a)\n        loss = torch.mean((output_a - img_b) ** 2)\n        if init_loss is None:\n            init_loss = loss.item()\n        loss.backward()\n        optimizer.step()\n    return model(img_a).detach().cpu().numpy(), loss.item(), init_loss", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_stn.py_TestSpatialTransformerCore_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_stn.py_TestSpatialTransformerCore_", "embedding": null, "metadata": {"file_path": "tests/test_integration_stn.py", "file_name": "test_integration_stn.py", "file_type": "text/x-python", "category": "test", "start_line": 99, "end_line": 133, "span_ids": ["TestSpatialTransformerCore", "TestSpatialTransformerCore.tearDown", "impl", "TestSpatialTransformerCore.test_training", "TestSpatialTransformerCore.setUp"], "tokens": 317}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestSpatialTransformerCore(DistTestCase):\n    def setUp(self):\n        self.device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu:0\")\n\n    def tearDown(self):\n        set_determinism(seed=None)\n\n    @TimedCall(seconds=100)\n    def test_training(self):\n        \"\"\"\n        check that the quality AffineTransform backpropagation\n        \"\"\"\n        atol = 1e-5\n        set_determinism(seed=0)\n        out_ref, loss_ref, init_loss_ref = compare_2d(True, self.device)\n        print(out_ref.shape, loss_ref, init_loss_ref)\n\n        set_determinism(seed=0)\n        out, loss, init_loss = compare_2d(False, self.device)\n        print(out.shape, loss, init_loss)\n        np.testing.assert_allclose(out_ref, out, atol=atol)\n        np.testing.assert_allclose(init_loss_ref, init_loss, atol=atol)\n        np.testing.assert_allclose(loss_ref, loss, atol=atol)\n\n        set_determinism(seed=0)\n        out, loss, init_loss = compare_2d(False, self.device, True)\n        print(out.shape, loss, init_loss)\n        np.testing.assert_allclose(out_ref, out, atol=atol)\n        np.testing.assert_allclose(init_loss_ref, init_loss, atol=atol)\n        np.testing.assert_allclose(loss_ref, loss, atol=atol)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_workflows.py_run_inference_test_run_inference_test.return.evaluator_state_best_metr": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_workflows.py_run_inference_test_run_inference_test.return.evaluator_state_best_metr", "embedding": null, "metadata": {"file_path": "tests/test_integration_workflows.py", "file_name": "test_integration_workflows.py", "file_type": "text/x-python", "category": "test", "start_line": 212, "end_line": 277, "span_ids": ["run_inference_test"], "tokens": 634}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def run_inference_test(root_dir, model_file, device=\"cuda:0\", amp=False, num_workers=4):\n    images = sorted(glob(os.path.join(root_dir, \"im*.nii.gz\")))\n    segs = sorted(glob(os.path.join(root_dir, \"seg*.nii.gz\")))\n    val_files = [{\"image\": img, \"label\": seg} for img, seg in zip(images, segs)]\n\n    # define transforms for image and segmentation\n    val_transforms = Compose(\n        [\n            LoadImaged(keys=[\"image\", \"label\"]),\n            AsChannelFirstd(keys=[\"image\", \"label\"], channel_dim=-1),\n            ScaleIntensityd(keys=[\"image\", \"label\"]),\n            ToTensord(keys=[\"image\", \"label\"]),\n        ]\n    )\n\n    # create a validation data loader\n    val_ds = monai.data.Dataset(data=val_files, transform=val_transforms)\n    val_loader = monai.data.DataLoader(val_ds, batch_size=1, num_workers=num_workers)\n\n    # create UNet, DiceLoss and Adam optimizer\n    net = monai.networks.nets.UNet(\n        spatial_dims=3,\n        in_channels=1,\n        out_channels=1,\n        channels=(16, 32, 64, 128, 256),\n        strides=(2, 2, 2, 2),\n        num_res_units=2,\n    ).to(device)\n\n    val_postprocessing = Compose(\n        [\n            ToTensord(keys=[\"pred\", \"label\"]),\n            Activationsd(keys=\"pred\", sigmoid=True),\n            AsDiscreted(keys=\"pred\", threshold=0.5),\n            KeepLargestConnectedComponentd(keys=\"pred\", applied_labels=[1]),\n            # test the case that `pred` in `engine.state.output`, while `image_meta_dict` in `engine.state.batch`\n            SaveImaged(keys=\"pred\", meta_keys=\"image_meta_dict\", output_dir=root_dir, output_postfix=\"seg_transform\"),\n        ]\n    )\n    val_handlers = [\n        StatsHandler(output_transform=lambda x: None),\n        CheckpointLoader(load_path=f\"{model_file}\", load_dict={\"net\": net}),\n        SegmentationSaver(\n            output_dir=root_dir,\n            output_postfix=\"seg_handler\",\n            batch_transform=from_engine(\"image_meta_dict\"),\n            output_transform=from_engine(\"pred\"),\n        ),\n    ]\n\n    evaluator = SupervisedEvaluator(\n        device=device,\n        val_data_loader=val_loader,\n        network=net,\n        inferer=SlidingWindowInferer(roi_size=(96, 96, 96), sw_batch_size=4, overlap=0.5),\n        postprocessing=val_postprocessing,\n        key_val_metric={\n            \"val_mean_dice\": MeanDice(include_background=True, output_transform=from_engine([\"pred\", \"label\"]))\n        },\n        additional_metrics={\"val_acc\": Accuracy(output_transform=from_engine([\"pred\", \"label\"]))},\n        val_handlers=val_handlers,\n        amp=True if amp else False,\n    )\n    evaluator.run()\n\n    return evaluator.state.best_metric", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_workflows.py_IntegrationWorkflows_IntegrationWorkflows.tearDown.shutil_rmtree_self_data_d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_workflows.py_IntegrationWorkflows_IntegrationWorkflows.tearDown.shutil_rmtree_self_data_d", "embedding": null, "metadata": {"file_path": "tests/test_integration_workflows.py", "file_name": "test_integration_workflows.py", "file_type": "text/x-python", "category": "test", "start_line": 270, "end_line": 289, "span_ids": ["IntegrationWorkflows.tearDown", "IntegrationWorkflows.setUp", "IntegrationWorkflows"], "tokens": 213}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_quick\nclass IntegrationWorkflows(DistTestCase):\n    def setUp(self):\n        set_determinism(seed=0)\n\n        self.data_dir = tempfile.mkdtemp()\n        for i in range(40):\n            im, seg = create_test_image_3d(128, 128, 128, num_seg_classes=1, channel_dim=-1)\n            n = nib.Nifti1Image(im, np.eye(4))\n            nib.save(n, os.path.join(self.data_dir, f\"img{i:d}.nii.gz\"))\n            n = nib.Nifti1Image(seg, np.eye(4))\n            nib.save(n, os.path.join(self.data_dir, f\"seg{i:d}.nii.gz\"))\n\n        self.device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu:0\")\n        monai.config.print_config()\n        logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n\n    def tearDown(self):\n        set_determinism(seed=None)\n        shutil.rmtree(self.data_dir)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_label_to_contour.py_unittest_expected_output_for_cube": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_label_to_contour.py_unittest_expected_output_for_cube", "embedding": null, "metadata": {"file_path": "tests/test_label_to_contour.py", "file_name": "test_label_to_contour.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 101, "span_ids": ["docstring"], "tokens": 44}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\n\nfrom monai.transforms import LabelToContour\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nexpected_output_for_cube =\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_label_to_contour.py_gen_fixed_img_gen_fixed_img.return.img_expected_output_for_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_label_to_contour.py_gen_fixed_img_gen_fixed_img.return.img_expected_output_for_", "embedding": null, "metadata": {"file_path": "tests/test_label_to_contour.py", "file_name": "test_label_to_contour.py", "file_type": "text/x-python", "category": "test", "start_line": 117, "end_line": 139, "span_ids": ["gen_fixed_img"], "tokens": 301}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def gen_fixed_img(array_type):\n    img = np.array(\n        [\n            [0, 0, 0, 1, 1, 1, 1],\n            [0, 0, 0, 1, 1, 1, 1],\n            [0, 0, 1, 1, 1, 1, 1],\n            [0, 1, 1, 1, 1, 1, 1],\n            [1, 1, 1, 1, 1, 1, 1],\n        ],\n        dtype=np.float32,\n    )\n    batch_size, channels = 10, 6\n    img = array_type(np.tile(img, (batch_size, channels, 1, 1)))\n    expected_output_for_img = array_type(\n        [\n            [0, 0, 0, 1, 1, 1, 1],\n            [0, 0, 0, 1, 0, 0, 1],\n            [0, 0, 1, 1, 0, 0, 1],\n            [0, 1, 1, 0, 0, 0, 1],\n            [1, 1, 1, 1, 1, 1, 1],\n        ]\n    )\n    return img, expected_output_for_img", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_label_to_contour.py_TestContour_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_label_to_contour.py_TestContour_", "embedding": null, "metadata": {"file_path": "tests/test_label_to_contour.py", "file_name": "test_label_to_contour.py", "file_type": "text/x-python", "category": "test", "start_line": 142, "end_line": 178, "span_ids": ["impl:3", "TestContour", "TestContour.test_contour"], "tokens": 329}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestContour(unittest.TestCase):\n    def test_contour(self):\n        input_param = {\"kernel_type\": \"Laplace\"}\n\n        for p in TEST_NDARRAYS:\n            # check 5-dim input data\n            test_cube, expected_output = gen_fixed_cube(p)\n            for cube in test_cube:\n                test_result_cube = LabelToContour(**input_param)(cube)\n                self.assertEqual(test_result_cube.shape, cube.shape)\n\n                channels = cube.shape[0]\n                for channel in range(channels):\n                    assert_allclose(test_result_cube[channel, ...], expected_output)\n\n            # check 4-dim input data\n            test_img, expected_output = gen_fixed_img(p)\n            for img in test_img:\n                channels = img.shape[0]\n                test_result_img = LabelToContour(**input_param)(img)\n                self.assertEqual(test_result_img.shape, img.shape)\n\n                for channel in range(channels):\n                    assert_allclose(test_result_img[channel, ...], expected_output)\n\n        # check invalid input data\n        error_input = torch.rand(1, 2)\n        self.assertRaises(ValueError, LabelToContour(**input_param), error_input)\n        error_input = torch.rand(1, 2, 3, 4, 5)\n        self.assertRaises(ValueError, LabelToContour(**input_param), error_input)\n        error_input = np.random.rand(1, 2, 3, 4, 5)\n        self.assertRaises(ValueError, LabelToContour(**input_param), error_input)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_label_to_contourd.py_unittest_expected_output_for_cube": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_label_to_contourd.py_unittest_expected_output_for_cube", "embedding": null, "metadata": {"file_path": "tests/test_label_to_contourd.py", "file_name": "test_label_to_contourd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 101, "span_ids": ["docstring"], "tokens": 44}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\n\nfrom monai.transforms import LabelToContourd\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nexpected_output_for_cube =\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_label_to_contourd.py_gen_fixed_img_gen_fixed_img.return.img_expected_output_for_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_label_to_contourd.py_gen_fixed_img_gen_fixed_img.return.img_expected_output_for_", "embedding": null, "metadata": {"file_path": "tests/test_label_to_contourd.py", "file_name": "test_label_to_contourd.py", "file_type": "text/x-python", "category": "test", "start_line": 117, "end_line": 140, "span_ids": ["gen_fixed_img"], "tokens": 306}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def gen_fixed_img(array_type):\n    img = np.array(\n        [\n            [0, 0, 0, 1, 1, 1, 1],\n            [0, 0, 0, 1, 1, 1, 1],\n            [0, 0, 1, 1, 1, 1, 1],\n            [0, 1, 1, 1, 1, 1, 1],\n            [1, 1, 1, 1, 1, 1, 1],\n        ],\n        dtype=np.float32,\n    )\n    batch_size, channels = 10, 6\n    img = np.tile(img, (batch_size, channels, 1, 1))\n    img = array_type(img)\n    expected_output_for_img = array_type(\n        [\n            [0, 0, 0, 1, 1, 1, 1],\n            [0, 0, 0, 1, 0, 0, 1],\n            [0, 0, 1, 1, 0, 0, 1],\n            [0, 1, 1, 0, 0, 0, 1],\n            [1, 1, 1, 1, 1, 1, 1],\n        ]\n    )\n    return img, expected_output_for_img", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_label_to_contourd.py_TestContourd_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_label_to_contourd.py_TestContourd_", "embedding": null, "metadata": {"file_path": "tests/test_label_to_contourd.py", "file_name": "test_label_to_contourd.py", "file_type": "text/x-python", "category": "test", "start_line": 143, "end_line": 181, "span_ids": ["impl:3", "TestContourd", "TestContourd.test_contour"], "tokens": 369}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestContourd(unittest.TestCase):\n    def test_contour(self):\n        input_param = {\"keys\": \"img\", \"kernel_type\": \"Laplace\"}\n\n        for p in TEST_NDARRAYS:\n            # check 5-dim input data\n            test_cube, expected_output = gen_fixed_cube(p)\n            for cube in test_cube:\n                test_result_cube = LabelToContourd(**input_param)({\"img\": cube})\n                self.assertEqual(test_result_cube[\"img\"].shape, cube.shape)\n\n                test_result_np = test_result_cube[\"img\"]\n                channels = cube.shape[0]\n                for channel in range(channels):\n                    assert_allclose(test_result_np[channel, ...], expected_output)\n\n            # check 4-dim input data\n            test_img, expected_output = gen_fixed_img(p)\n            for img in test_img:\n                channels = img.shape[0]\n                test_result_img = LabelToContourd(**input_param)({\"img\": img})\n                self.assertEqual(test_result_img[\"img\"].shape, img.shape)\n\n                test_result_np = test_result_img[\"img\"]\n                for channel in range(channels):\n                    assert_allclose(test_result_np[channel, ...], expected_output)\n\n        # check invalid input data\n        error_input = {\"img\": torch.rand(1, 2)}\n        self.assertRaises(ValueError, LabelToContourd(**input_param), error_input)\n        error_input = {\"img\": np.random.rand(1, 2)}\n        self.assertRaises(ValueError, LabelToContourd(**input_param), error_input)\n        error_input = {\"img\": torch.rand(1, 2, 3, 4, 5)}\n        self.assertRaises(ValueError, LabelToContourd(**input_param), error_input)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_label_to_mask.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_label_to_mask.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_label_to_mask.py", "file_name": "test_label_to_mask.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 72, "span_ids": ["TestLabelToMask.test_value", "impl:9", "TestLabelToMask", "docstring"], "tokens": 786}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import LabelToMask\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    TESTS.append(\n        [\n            {\"select_labels\": [2, 3], \"merge_channels\": False},\n            p(np.array([[[1, 1, 1], [2, 2, 2], [3, 3, 3], [4, 4, 4], [5, 5, 5], [6, 6, 6]]])),\n            np.array([[[0, 0, 0], [1, 1, 1], [1, 1, 1], [0, 0, 0], [0, 0, 0], [0, 0, 0]]]),\n        ]\n    )\n    TESTS.append(\n        [\n            {\"select_labels\": 2, \"merge_channels\": False},\n            p(np.array([[[1, 1, 1], [2, 2, 2], [3, 3, 3], [4, 4, 4], [5, 5, 5], [6, 6, 6]]])),\n            np.array([[[0, 0, 0], [1, 1, 1], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]]]),\n        ]\n    )\n    TESTS.append(\n        [\n            {\"select_labels\": [1, 2], \"merge_channels\": False},\n            p(np.array([[[0, 0, 1], [0, 1, 0]], [[1, 0, 0], [0, 1, 1]], [[1, 0, 1], [1, 1, 0]]])),\n            np.array([[[1, 0, 0], [0, 1, 1]], [[1, 0, 1], [1, 1, 0]]]),\n        ]\n    )\n    TESTS.append(\n        [\n            {\"select_labels\": 2, \"merge_channels\": False},\n            p(np.array([[[0, 0, 1], [0, 1, 0]], [[1, 0, 0], [0, 1, 1]], [[1, 0, 1], [1, 1, 0]]])),\n            np.array([[[1, 0, 1], [1, 1, 0]]]),\n        ]\n    )\n    TESTS.append(\n        [\n            {\"select_labels\": [1, 2], \"merge_channels\": True},\n            p(np.array([[[0, 0, 1], [0, 1, 0]], [[1, 0, 0], [0, 1, 1]], [[1, 0, 1], [1, 1, 0]]])),\n            np.array([[[1, 0, 1], [1, 1, 1]]]),\n        ]\n    )\n\n\nclass TestLabelToMask(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_value(self, argments, image, expected_data):\n        result = LabelToMask(**argments)(image)\n        self.assertEqual(type(result), type(image))\n        if isinstance(result, torch.Tensor):\n            self.assertEqual(result.device, image.device)\n        assert_allclose(result, expected_data, type_test=False)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_lambda.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_lambda.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_lambda.py", "file_name": "test_lambda.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 42, "span_ids": ["impl", "TestLambda.test_lambda_identity", "docstring", "TestLambda", "TestLambda.test_lambda_slicing"], "tokens": 177}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nfrom monai.transforms.utility.array import Lambda\nfrom tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, assert_allclose\n\n\nclass TestLambda(NumpyImageTestCase2D):\n    def test_lambda_identity(self):\n        for p in TEST_NDARRAYS:\n            img = p(self.imt)\n\n            def identity_func(x):\n                return x\n\n            lambd = Lambda(func=identity_func)\n            assert_allclose(identity_func(img), lambd(img))\n\n    def test_lambda_slicing(self):\n        for p in TEST_NDARRAYS:\n            img = p(self.imt)\n\n            def slice_func(x):\n                return x[:, :, :6, ::2]\n\n            lambd = Lambda(func=slice_func)\n            assert_allclose(slice_func(img), lambd(img))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_lambdad.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_lambdad.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_lambdad.py", "file_name": "test_lambdad.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 48, "span_ids": ["impl", "TestLambdad", "docstring", "TestLambdad.test_lambdad_identity", "TestLambdad.test_lambdad_slicing"], "tokens": 284}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nfrom monai.transforms.utility.dictionary import Lambdad\nfrom tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, assert_allclose\n\n\nclass TestLambdad(NumpyImageTestCase2D):\n    def test_lambdad_identity(self):\n        for p in TEST_NDARRAYS:\n            img = p(self.imt)\n            data = {\"img\": img, \"prop\": 1.0}\n\n            def noise_func(x):\n                return x + 1.0\n\n            expected = {\"img\": noise_func(data[\"img\"]), \"prop\": 1.0}\n            ret = Lambdad(keys=[\"img\", \"prop\"], func=noise_func, overwrite=[True, False])(data)\n            assert_allclose(expected[\"img\"], ret[\"img\"])\n            assert_allclose(expected[\"prop\"], ret[\"prop\"])\n\n    def test_lambdad_slicing(self):\n        for p in TEST_NDARRAYS:\n            img = p(self.imt)\n            data = {\"img\": img}\n\n            def slice_func(x):\n                return x[:, :, :6, ::2]\n\n            lambd = Lambdad(keys=data.keys(), func=slice_func)\n            expected = {}\n            expected[\"img\"] = slice_func(data[\"img\"])\n            assert_allclose(expected[\"img\"], lambd(data)[\"img\"])\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_list_data_collate.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_list_data_collate.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_list_data_collate.py", "file_name": "test_list_data_collate.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 47, "span_ids": ["impl:21", "TestListDataCollate.test_type_shape", "TestListDataCollate", "docstring"], "tokens": 426}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.data import list_data_collate\n\na = {\"image\": np.array([1, 2, 3]), \"label\": np.array([4, 5, 6])}\nb = {\"image\": np.array([7, 8, 9]), \"label\": np.array([10, 11, 12])}\nc = {\"image\": np.array([13, 14, 15]), \"label\": np.array([16, 7, 18])}\nd = {\"image\": np.array([19, 20, 21]), \"label\": np.array([22, 23, 24])}\nTEST_CASE_1 = [[[a, b], [c, d]], dict, torch.Size([4, 3])]  # dataset returns a list of dictionary data\n\ne = (np.array([1, 2, 3]), np.array([4, 5, 6]))\nf = (np.array([7, 8, 9]), np.array([10, 11, 12]))\ng = (np.array([13, 14, 15]), np.array([16, 7, 18]))\nh = (np.array([19, 20, 21]), np.array([22, 23, 24]))\nTEST_CASE_2 = [[[e, f], [g, h]], list, torch.Size([4, 3])]  # dataset returns a list of tuple data\n\n\nclass TestListDataCollate(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2])\n    def test_type_shape(self, input_data, expected_type, expected_shape):\n        result = list_data_collate(input_data)\n        self.assertIsInstance(result, expected_type)\n        if isinstance(result, dict):\n            data = result[\"image\"]\n        else:\n            data = result[0]\n        self.assertEqual(data.shape, expected_shape)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_spacing_orientation.py_os_TestLoadSpacingOrientation.test_load_spacingd.None_5": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_spacing_orientation.py_os_TestLoadSpacingOrientation.test_load_spacingd.None_5", "embedding": null, "metadata": {"file_path": "tests/test_load_spacing_orientation.py", "file_name": "test_load_spacing_orientation.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 46, "span_ids": ["TestLoadSpacingOrientation.test_load_spacingd", "TestLoadSpacingOrientation", "docstring"], "tokens": 366}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport time\nimport unittest\n\nimport nibabel\nimport numpy as np\nfrom nibabel.processing import resample_to_output\nfrom parameterized import parameterized\n\nfrom monai.transforms import AddChanneld, LoadImaged, Orientationd, Spacingd\n\nFILES = tuple(\n    os.path.join(os.path.dirname(__file__), \"testing_data\", filename)\n    for filename in (\"anatomical.nii\", \"reoriented_anat_moved.nii\")\n)\n\n\nclass TestLoadSpacingOrientation(unittest.TestCase):\n    @parameterized.expand(FILES)\n    def test_load_spacingd(self, filename):\n        data = {\"image\": filename}\n        data_dict = LoadImaged(keys=\"image\")(data)\n        data_dict = AddChanneld(keys=\"image\")(data_dict)\n        t = time.time()\n        res_dict = Spacingd(keys=\"image\", pixdim=(1, 0.2, 1), diagonal=True, padding_mode=\"zeros\")(data_dict)\n        t1 = time.time()\n        print(f\"time monai: {t1 - t}\")\n        anat = nibabel.Nifti1Image(data_dict[\"image\"][0], data_dict[\"image_meta_dict\"][\"original_affine\"])\n        ref = resample_to_output(anat, (1, 0.2, 1), order=1)\n        t2 = time.time()\n        print(f\"time scipy: {t2 - t1}\")\n        self.assertTrue(t2 >= t1)\n        np.testing.assert_allclose(res_dict[\"image_meta_dict\"][\"affine\"], ref.affine)\n        np.testing.assert_allclose(res_dict[\"image\"].shape[1:], ref.shape)\n        np.testing.assert_allclose(ref.get_fdata(), res_dict[\"image\"][0], atol=0.05)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_spacing_orientation.py_TestLoadSpacingOrientation.test_load_spacingd_rotate_TestLoadSpacingOrientation.test_load_spacingd_rotate.if_anatomical_not_in_fi.else_.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_spacing_orientation.py_TestLoadSpacingOrientation.test_load_spacingd_rotate_TestLoadSpacingOrientation.test_load_spacingd_rotate.if_anatomical_not_in_fi.else_.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_load_spacing_orientation.py", "file_name": "test_load_spacing_orientation.py", "file_type": "text/x-python", "category": "test", "start_line": 48, "end_line": 73, "span_ids": ["TestLoadSpacingOrientation.test_load_spacingd_rotate"], "tokens": 421}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestLoadSpacingOrientation(unittest.TestCase):\n\n    @parameterized.expand(FILES)\n    def test_load_spacingd_rotate(self, filename):\n        data = {\"image\": filename}\n        data_dict = LoadImaged(keys=\"image\")(data)\n        data_dict = AddChanneld(keys=\"image\")(data_dict)\n        affine = data_dict[\"image_meta_dict\"][\"affine\"]\n        data_dict[\"image_meta_dict\"][\"original_affine\"] = data_dict[\"image_meta_dict\"][\"affine\"] = (\n            np.array([[0, 0, 1, 0], [0, 1, 0, 0], [-1, 0, 0, 0], [0, 0, 0, 1]]) @ affine\n        )\n        t = time.time()\n        res_dict = Spacingd(keys=\"image\", pixdim=(1, 2, 3), diagonal=True, padding_mode=\"zeros\")(data_dict)\n        t1 = time.time()\n        print(f\"time monai: {t1 - t}\")\n        anat = nibabel.Nifti1Image(data_dict[\"image\"][0], data_dict[\"image_meta_dict\"][\"original_affine\"])\n        ref = resample_to_output(anat, (1, 2, 3), order=1)\n        t2 = time.time()\n        print(f\"time scipy: {t2 - t1}\")\n        self.assertTrue(t2 >= t1)\n        np.testing.assert_allclose(res_dict[\"image_meta_dict\"][\"affine\"], ref.affine)\n        if \"anatomical\" not in filename:\n            np.testing.assert_allclose(res_dict[\"image\"].shape[1:], ref.shape)\n            np.testing.assert_allclose(ref.get_fdata(), res_dict[\"image\"][0], atol=0.05)\n        else:\n            # different from the ref implementation (shape computed by round\n            # instead of ceil)\n            np.testing.assert_allclose(ref.get_fdata()[..., :-1], res_dict[\"image\"][0], atol=0.05)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_spacing_orientation.py_TestLoadSpacingOrientation.test_load_spacingd_non_diag_TestLoadSpacingOrientation.test_load_spacingd_non_diag.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_spacing_orientation.py_TestLoadSpacingOrientation.test_load_spacingd_non_diag_TestLoadSpacingOrientation.test_load_spacingd_non_diag.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_load_spacing_orientation.py", "file_name": "test_load_spacing_orientation.py", "file_type": "text/x-python", "category": "test", "start_line": 75, "end_line": 94, "span_ids": ["TestLoadSpacingOrientation.test_load_spacingd_non_diag"], "tokens": 300}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestLoadSpacingOrientation(unittest.TestCase):\n\n    def test_load_spacingd_non_diag(self):\n        data = {\"image\": FILES[1]}\n        data_dict = LoadImaged(keys=\"image\")(data)\n        data_dict = AddChanneld(keys=\"image\")(data_dict)\n        affine = data_dict[\"image_meta_dict\"][\"affine\"]\n        data_dict[\"image_meta_dict\"][\"original_affine\"] = data_dict[\"image_meta_dict\"][\"affine\"] = (\n            np.array([[0, 0, 1, 0], [0, 1, 0, 0], [-1, 0, 0, 0], [0, 0, 0, 1]]) @ affine\n        )\n        res_dict = Spacingd(keys=\"image\", pixdim=(1, 2, 3), diagonal=False, padding_mode=\"zeros\")(data_dict)\n        np.testing.assert_allclose(\n            res_dict[\"image_meta_dict\"][\"affine\"],\n            np.array(\n                [\n                    [0.0, 0.0, 3.0, -27.599409],\n                    [0.0, 2.0, 0.0, -47.977585],\n                    [-1.0, 0.0, 0.0, 35.297897],\n                    [0.0, 0.0, 0.0, 1.0],\n                ]\n            ),\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_spacing_orientation.py_TestLoadSpacingOrientation.test_load_spacingd_rotate_non_diag_TestLoadSpacingOrientation.test_load_spacingd_rotate_non_diag.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_spacing_orientation.py_TestLoadSpacingOrientation.test_load_spacingd_rotate_non_diag_TestLoadSpacingOrientation.test_load_spacingd_rotate_non_diag.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_load_spacing_orientation.py", "file_name": "test_load_spacing_orientation.py", "file_type": "text/x-python", "category": "test", "start_line": 96, "end_line": 104, "span_ids": ["TestLoadSpacingOrientation.test_load_spacingd_rotate_non_diag"], "tokens": 195}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestLoadSpacingOrientation(unittest.TestCase):\n\n    def test_load_spacingd_rotate_non_diag(self):\n        data = {\"image\": FILES[0]}\n        data_dict = LoadImaged(keys=\"image\")(data)\n        data_dict = AddChanneld(keys=\"image\")(data_dict)\n        res_dict = Spacingd(keys=\"image\", pixdim=(1, 2, 3), diagonal=False, padding_mode=\"border\")(data_dict)\n        np.testing.assert_allclose(\n            res_dict[\"image_meta_dict\"][\"affine\"],\n            np.array([[-1.0, 0.0, 0.0, 32.0], [0.0, 2.0, 0.0, -40.0], [0.0, 0.0, 3.0, -16.0], [0.0, 0.0, 0.0, 1.0]]),\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_spacing_orientation.py_TestLoadSpacingOrientation.test_load_spacingd_rotate_non_diag_ornt_TestLoadSpacingOrientation.test_load_spacingd_rotate_non_diag_ornt.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_spacing_orientation.py_TestLoadSpacingOrientation.test_load_spacingd_rotate_non_diag_ornt_TestLoadSpacingOrientation.test_load_spacingd_rotate_non_diag_ornt.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_load_spacing_orientation.py", "file_name": "test_load_spacing_orientation.py", "file_type": "text/x-python", "category": "test", "start_line": 106, "end_line": 115, "span_ids": ["TestLoadSpacingOrientation.test_load_spacingd_rotate_non_diag_ornt"], "tokens": 216}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestLoadSpacingOrientation(unittest.TestCase):\n\n    def test_load_spacingd_rotate_non_diag_ornt(self):\n        data = {\"image\": FILES[0]}\n        data_dict = LoadImaged(keys=\"image\")(data)\n        data_dict = AddChanneld(keys=\"image\")(data_dict)\n        res_dict = Spacingd(keys=\"image\", pixdim=(1, 2, 3), diagonal=False, padding_mode=\"border\")(data_dict)\n        res_dict = Orientationd(keys=\"image\", axcodes=\"LPI\")(res_dict)\n        np.testing.assert_allclose(\n            res_dict[\"image_meta_dict\"][\"affine\"],\n            np.array([[-1.0, 0.0, 0.0, 32.0], [0.0, -2.0, 0.0, 40.0], [0.0, 0.0, -3.0, 32.0], [0.0, 0.0, 0.0, 1.0]]),\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_spacing_orientation.py_TestLoadSpacingOrientation.test_load_spacingd_non_diag_ornt_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_spacing_orientation.py_TestLoadSpacingOrientation.test_load_spacingd_non_diag_ornt_", "embedding": null, "metadata": {"file_path": "tests/test_load_spacing_orientation.py", "file_name": "test_load_spacing_orientation.py", "file_type": "text/x-python", "category": "test", "start_line": 117, "end_line": 142, "span_ids": ["impl:3", "TestLoadSpacingOrientation.test_load_spacingd_non_diag_ornt"], "tokens": 336}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestLoadSpacingOrientation(unittest.TestCase):\n\n    def test_load_spacingd_non_diag_ornt(self):\n        data = {\"image\": FILES[1]}\n        data_dict = LoadImaged(keys=\"image\")(data)\n        data_dict = AddChanneld(keys=\"image\")(data_dict)\n        affine = data_dict[\"image_meta_dict\"][\"affine\"]\n        data_dict[\"image_meta_dict\"][\"original_affine\"] = data_dict[\"image_meta_dict\"][\"affine\"] = (\n            np.array([[0, 0, 1, 0], [0, 1, 0, 0], [-1, 0, 0, 0], [0, 0, 0, 1]]) @ affine\n        )\n        res_dict = Spacingd(keys=\"image\", pixdim=(1, 2, 3), diagonal=False, padding_mode=\"border\")(data_dict)\n        res_dict = Orientationd(keys=\"image\", axcodes=\"LPI\")(res_dict)\n        np.testing.assert_allclose(\n            res_dict[\"image_meta_dict\"][\"affine\"],\n            np.array(\n                [\n                    [-3.0, 0.0, 0.0, 56.4005909],\n                    [0.0, -2.0, 0.0, 52.02241516],\n                    [0.0, 0.0, -1.0, 35.29789734],\n                    [0.0, 0.0, 0.0, 1.0],\n                ]\n            ),\n        )\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_map_transform.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_map_transform.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_map_transform.py", "file_name": "test_map_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 42, "span_ids": ["TestRandomizable.test_wrong_keys", "MapTest", "MapTest.__call__", "TestRandomizable.test_keys", "impl:5", "docstring", "TestRandomizable"], "tokens": 188}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nfrom parameterized import parameterized\n\nfrom monai.transforms import MapTransform\n\nTEST_CASES = [[\"item\", (\"item\",)], [None, (None,)], [[\"item1\", \"item2\"], (\"item1\", \"item2\")]]\n\nTEST_ILL_CASES = [[ValueError, []], [ValueError, ()], [TypeError, [[]]]]\n\n\nclass MapTest(MapTransform):\n    def __call__(self, data):\n        pass\n\n\nclass TestRandomizable(unittest.TestCase):\n    @parameterized.expand(TEST_CASES)\n    def test_keys(self, keys, expected):\n        transform = MapTest(keys=keys)\n        self.assertEqual(transform.keys, expected)\n\n    @parameterized.expand(TEST_ILL_CASES)\n    def test_wrong_keys(self, exception, keys):\n        with self.assertRaisesRegex(exception, \"\"):\n            MapTest(keys=keys)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_masked_dice_loss.py_unittest_TEST_CASES": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_masked_dice_loss.py_unittest_TEST_CASES", "embedding": null, "metadata": {"file_path": "tests/test_masked_dice_loss.py", "file_name": "test_masked_dice_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 115, "span_ids": ["docstring"], "tokens": 37}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.losses import MaskedDiceLoss\n\nTEST_CASES =\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_masked_dice_loss.py_TestDiceLoss_TestDiceLoss.test_ill_shape.with_self_assertRaisesReg.loss_forward_torch_ones_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_masked_dice_loss.py_TestDiceLoss_TestDiceLoss.test_ill_shape.with_self_assertRaisesReg.loss_forward_torch_ones_", "embedding": null, "metadata": {"file_path": "tests/test_masked_dice_loss.py", "file_name": "test_masked_dice_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 118, "end_line": 127, "span_ids": ["TestDiceLoss.test_shape", "TestDiceLoss.test_ill_shape", "TestDiceLoss"], "tokens": 125}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestDiceLoss(unittest.TestCase):\n    @parameterized.expand(TEST_CASES)\n    def test_shape(self, input_param, input_data, expected_val):\n        result = MaskedDiceLoss(**input_param).forward(**input_data)\n        np.testing.assert_allclose(result.detach().cpu().numpy(), expected_val, rtol=1e-5)\n\n    def test_ill_shape(self):\n        loss = MaskedDiceLoss()\n        with self.assertRaisesRegex(AssertionError, \"\"):\n            loss.forward(torch.ones((1, 2, 3)), torch.ones((4, 5, 6)))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_masked_dice_loss.py_TestDiceLoss.test_ill_opts_TestDiceLoss.test_ill_opts.None_2.MaskedDiceLoss_reduction_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_masked_dice_loss.py_TestDiceLoss.test_ill_opts_TestDiceLoss.test_ill_opts.None_2.MaskedDiceLoss_reduction_", "embedding": null, "metadata": {"file_path": "tests/test_masked_dice_loss.py", "file_name": "test_masked_dice_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 129, "end_line": 137, "span_ids": ["TestDiceLoss.test_ill_opts"], "tokens": 119}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestDiceLoss(unittest.TestCase):\n\n    def test_ill_opts(self):\n        with self.assertRaisesRegex(ValueError, \"\"):\n            MaskedDiceLoss(sigmoid=True, softmax=True)\n        chn_input = torch.ones((1, 1, 3))\n        chn_target = torch.ones((1, 1, 3))\n        with self.assertRaisesRegex(ValueError, \"\"):\n            MaskedDiceLoss(reduction=\"unknown\")(chn_input, chn_target)\n        with self.assertRaisesRegex(ValueError, \"\"):\n            MaskedDiceLoss(reduction=None)(chn_input, chn_target)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_masked_dice_loss.py_TestDiceLoss.test_input_warnings_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_masked_dice_loss.py_TestDiceLoss.test_input_warnings_", "embedding": null, "metadata": {"file_path": "tests/test_masked_dice_loss.py", "file_name": "test_masked_dice_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 139, "end_line": 155, "span_ids": ["impl:3", "TestDiceLoss.test_input_warnings"], "tokens": 153}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestDiceLoss(unittest.TestCase):\n\n    def test_input_warnings(self):\n        chn_input = torch.ones((1, 1, 3))\n        chn_target = torch.ones((1, 1, 3))\n        with self.assertWarns(Warning):\n            loss = MaskedDiceLoss(include_background=False)\n            loss.forward(chn_input, chn_target)\n        with self.assertWarns(Warning):\n            loss = MaskedDiceLoss(softmax=True)\n            loss.forward(chn_input, chn_target)\n        with self.assertWarns(Warning):\n            loss = MaskedDiceLoss(to_onehot_y=True)\n            loss.forward(chn_input, chn_target)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_mean_ensemble.py_TestMeanEnsemble_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_mean_ensemble.py_TestMeanEnsemble_", "embedding": null, "metadata": {"file_path": "tests/test_mean_ensemble.py", "file_name": "test_mean_ensemble.py", "file_type": "text/x-python", "category": "test", "start_line": 58, "end_line": 76, "span_ids": ["TestMeanEnsemble.test_cuda_value", "TestMeanEnsemble", "TestMeanEnsemble.test_value", "impl:10"], "tokens": 221}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestMeanEnsemble(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_value(self, input_param, img, expected_value):\n        result = MeanEnsemble(**input_param)(img)\n        assert_allclose(result, expected_value)\n\n    def test_cuda_value(self):\n        img = torch.stack([torch.ones(2, 2, 2, 2), torch.ones(2, 2, 2, 2) + 2])\n        expected_value = torch.ones(2, 2, 2, 2) * torch.tensor([2.5, 1.5]).reshape(1, 2, 1, 1)\n        if torch.cuda.is_available():\n            img = img.to(torch.device(\"cuda:0\"))\n            expected_value = expected_value.to(torch.device(\"cuda:0\"))\n        result = MeanEnsemble(torch.tensor([[[1, 3]], [[3, 1]]]))(img)\n        torch.testing.assert_allclose(result, expected_value)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_mean_ensembled.py_TestMeanEnsembled_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_mean_ensembled.py_TestMeanEnsembled_", "embedding": null, "metadata": {"file_path": "tests/test_mean_ensembled.py", "file_name": "test_mean_ensembled.py", "file_type": "text/x-python", "category": "test", "start_line": 72, "end_line": 90, "span_ids": ["TestMeanEnsembled.test_value", "impl:10", "TestMeanEnsembled", "TestMeanEnsembled.test_cuda_value"], "tokens": 232}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestMeanEnsembled(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_value(self, input_param, data, expected_value):\n        result = MeanEnsembled(**input_param)(data)\n        torch.testing.assert_allclose(result[\"output\"], expected_value)\n\n    def test_cuda_value(self):\n        img = torch.stack([torch.ones(2, 2, 2, 2), torch.ones(2, 2, 2, 2) + 2])\n        expected_value = torch.ones(2, 2, 2, 2) * torch.tensor([2.5, 1.5]).reshape(1, 2, 1, 1)\n        if torch.cuda.is_available():\n            img = img.to(torch.device(\"cuda:0\"))\n            expected_value = expected_value.to(torch.device(\"cuda:0\"))\n        result = MeanEnsembled(keys=\"output\", weights=torch.tensor([[[1, 3]], [[3, 1]]]))({\"output\": img})\n        assert_allclose(result[\"output\"], expected_value)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nifti_header_revise.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nifti_header_revise.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_nifti_header_revise.py", "file_name": "test_nifti_header_revise.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 40, "span_ids": ["impl", "TestRectifyHeaderSformQform", "TestRectifyHeaderSformQform.test_revise_q", "TestRectifyHeaderSformQform.test_revise_both", "docstring"], "tokens": 299}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport nibabel as nib\nimport numpy as np\n\nfrom monai.data import rectify_header_sform_qform\n\n\nclass TestRectifyHeaderSformQform(unittest.TestCase):\n    def test_revise_q(self):\n        img = nib.Nifti1Image(np.zeros((10, 10, 10)), np.eye(4))\n        img.header.set_zooms((0.1, 0.2, 0.3))\n        output = rectify_header_sform_qform(img)\n        expected = np.diag([0.1, 0.2, 0.3, 1.0])\n        np.testing.assert_allclose(output.affine, expected)\n\n    def test_revise_both(self):\n        img = nib.Nifti1Image(np.zeros((10, 10, 10)), np.eye(4))\n        img.header.set_sform(np.diag([5, 3, 4, 1]))\n        img.header.set_qform(np.diag([2, 3, 4, 1]))\n        img.header.set_zooms((0.1, 0.2, 0.3))\n        output = rectify_header_sform_qform(img)\n        expected = np.diag([0.1, 0.2, 0.3, 1.0])\n        np.testing.assert_allclose(output.affine, expected)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nifti_rw.py_TestNiftiLoadRead.test_write_5d_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nifti_rw.py_TestNiftiLoadRead.test_write_5d_", "embedding": null, "metadata": {"file_path": "tests/test_nifti_rw.py", "file_name": "test_nifti_rw.py", "file_type": "text/x-python", "category": "test", "start_line": 209, "end_line": 234, "span_ids": ["TestNiftiLoadRead.test_write_5d", "impl:14"], "tokens": 415}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestNiftiLoadRead(unittest.TestCase):\n\n    def test_write_5d(self):\n        with tempfile.TemporaryDirectory() as out_dir:\n            image_name = os.path.join(out_dir, \"test.nii.gz\")\n            for p in TEST_NDARRAYS:\n                img = p(np.arange(12).reshape((1, 1, 3, 2, 2)))\n                write_nifti(img, image_name, affine=np.diag([1]), target_affine=np.diag([1.4]))\n                out = nib.load(image_name)\n                np.testing.assert_allclose(\n                    out.get_fdata(),\n                    np.array([[[[[0.0, 1.0], [2.0, 3.0]], [[4.0, 5.0], [6.0, 7.0]], [[8.0, 9.0], [10.0, 11.0]]]]]),\n                )\n                np.testing.assert_allclose(out.affine, np.diag([1.4, 1, 1, 1]))\n\n                image_name = os.path.join(out_dir, \"test1.nii.gz\")\n                img = p(np.arange(10).reshape((1, 1, 5, 1, 2)))\n                write_nifti(\n                    img, image_name, affine=np.diag([1, 1, 1, 3, 3]), target_affine=np.diag([1.4, 2.0, 2, 3, 5])\n                )\n                out = nib.load(image_name)\n                np.testing.assert_allclose(out.get_fdata(), np.array([[[[[0.0, 1.0]], [[4.0, 5.0]], [[8.0, 9.0]]]]]))\n                np.testing.assert_allclose(out.affine, np.diag([1.4, 2, 2, 1]))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_normalize_intensityd.py_TestNormalizeIntensityd.test_channel_wise_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_normalize_intensityd.py_TestNormalizeIntensityd.test_channel_wise_", "embedding": null, "metadata": {"file_path": "tests/test_normalize_intensityd.py", "file_name": "test_normalize_intensityd.py", "file_type": "text/x-python", "category": "test", "start_line": 75, "end_line": 90, "span_ids": ["TestNormalizeIntensityd.test_channel_wise", "impl:8"], "tokens": 227}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestNormalizeIntensityd(NumpyImageTestCase2D):\n\n    @parameterized.expand([[p] for p in TEST_NDARRAYS])\n    def test_channel_wise(self, im_type):\n        key = \"img\"\n        normalizer = NormalizeIntensityd(keys=key, nonzero=True, channel_wise=True)\n        input_data = {key: im_type(np.array([[0.0, 3.0, 0.0, 4.0], [0.0, 4.0, 0.0, 5.0]]))}\n        normalized = normalizer(input_data)[key]\n        self.assertEqual(type(input_data[key]), type(normalized))\n        if isinstance(normalized, torch.Tensor):\n            self.assertEqual(input_data[key].device, normalized.device)\n        expected = np.array([[0.0, -1.0, 0.0, 1.0], [0.0, -1.0, 0.0, 1.0]])\n        assert_allclose(normalized, im_type(expected))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_optional_import.py_unittest_TestOptionalImport.test_import_wrong_number.None_2.print_my_module_randint_1": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_optional_import.py_unittest_TestOptionalImport.test_import_wrong_number.None_2.print_my_module_randint_1", "embedding": null, "metadata": {"file_path": "tests/test_optional_import.py", "file_name": "test_optional_import.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 45, "span_ids": ["TestOptionalImport", "TestOptionalImport.test_import_valid", "TestOptionalImport.test_import_wrong_number", "docstring", "TestOptionalImport.test_default"], "tokens": 265}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nfrom monai.utils import OptionalImportError, exact_version, optional_import\n\n\nclass TestOptionalImport(unittest.TestCase):\n    def test_default(self):\n        my_module, flag = optional_import(\"not_a_module\")\n        self.assertFalse(flag)\n        with self.assertRaises(OptionalImportError):\n            my_module.test\n\n        my_module, flag = optional_import(\"torch.randint\")\n        with self.assertRaises(OptionalImportError):\n            self.assertFalse(flag)\n            print(my_module.test)\n\n    def test_import_valid(self):\n        my_module, flag = optional_import(\"torch\")\n        self.assertTrue(flag)\n        print(my_module.randint(1, 2, (1, 2)))\n\n    def test_import_wrong_number(self):\n        my_module, flag = optional_import(\"torch\", \"42\")\n        with self.assertRaisesRegex(OptionalImportError, \"version\"):\n            my_module.nn\n        self.assertFalse(flag)\n        with self.assertRaisesRegex(OptionalImportError, \"version\"):\n            my_module.randint(1, 2, (1, 2))\n        with self.assertRaisesRegex(ValueError, \"invalid literal\"):\n            my_module, flag = optional_import(\"torch\", \"test\")  # version should be number.number\n            my_module.nn\n            self.assertTrue(flag)\n            print(my_module.randint(1, 2, (1, 2)))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_optional_import.py_TestOptionalImport.test_import_good_number_TestOptionalImport.test_import_good_number.None_5": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_optional_import.py_TestOptionalImport.test_import_good_number_TestOptionalImport.test_import_good_number.None_5", "embedding": null, "metadata": {"file_path": "tests/test_optional_import.py", "file_name": "test_optional_import.py", "file_type": "text/x-python", "category": "test", "start_line": 47, "end_line": 61, "span_ids": ["TestOptionalImport.test_import_good_number"], "tokens": 148}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestOptionalImport(unittest.TestCase):\n\n    def test_import_good_number(self):\n        my_module, flag = optional_import(\"torch\", \"0\")\n        my_module.nn\n        self.assertTrue(flag)\n        print(my_module.randint(1, 2, (1, 2)))\n\n        my_module, flag = optional_import(\"torch\", \"0.0.0.1\")\n        my_module.nn\n        self.assertTrue(flag)\n        print(my_module.randint(1, 2, (1, 2)))\n\n        my_module, flag = optional_import(\"torch\", \"1.1.0\")\n        my_module.nn\n        self.assertTrue(flag)\n        print(my_module.randint(1, 2, (1, 2)))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_optional_import.py_TestOptionalImport.test_import_exact_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_optional_import.py_TestOptionalImport.test_import_exact_", "embedding": null, "metadata": {"file_path": "tests/test_optional_import.py", "file_name": "test_optional_import.py", "file_type": "text/x-python", "category": "test", "start_line": 63, "end_line": 89, "span_ids": ["TestOptionalImport.test_import_exact", "TestOptionalImport.test_additional", "impl", "TestOptionalImport.test_import_method"], "tokens": 210}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestOptionalImport(unittest.TestCase):\n\n    def test_import_exact(self):\n        my_module, flag = optional_import(\"torch\", \"0\", exact_version)\n        with self.assertRaisesRegex(OptionalImportError, \"exact_version\"):\n            my_module.nn\n        self.assertFalse(flag)\n        with self.assertRaisesRegex(OptionalImportError, \"exact_version\"):\n            my_module.randint(1, 2, (1, 2))\n\n    def test_import_method(self):\n        nn, flag = optional_import(\"torch\", \"1.1\", name=\"nn\")\n        self.assertTrue(flag)\n        print(nn.functional)\n\n    def test_additional(self):\n        test_args = {\"a\": \"test\", \"b\": \"test\"}\n\n        def versioning(module, ver, a):\n            self.assertEqual(a, test_args)\n            return True\n\n        nn, flag = optional_import(\"torch\", \"1.1\", version_checker=versioning, name=\"nn\", version_args=test_args)\n        self.assertTrue(flag)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_orientation.py_unittest_TEST_CASES._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_orientation.py_unittest_TEST_CASES._", "embedding": null, "metadata": {"file_path": "tests/test_orientation.py", "file_name": "test_orientation.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 102, "span_ids": ["docstring"], "tokens": 1208}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport nibabel as nib\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import Orientation, create_rotate, create_translate\n\nTEST_CASES = [\n    [\n        {\"axcodes\": \"RAS\"},\n        np.arange(12).reshape((2, 1, 2, 3)),\n        {\"affine\": np.eye(4)},\n        np.arange(12).reshape((2, 1, 2, 3)),\n        \"RAS\",\n    ],\n    [\n        {\"axcodes\": \"ALS\"},\n        np.arange(12).reshape((2, 1, 2, 3)),\n        {\"affine\": np.diag([-1, -1, 1, 1])},\n        np.array([[[[3, 4, 5]], [[0, 1, 2]]], [[[9, 10, 11]], [[6, 7, 8]]]]),\n        \"ALS\",\n    ],\n    [\n        {\"axcodes\": \"RAS\"},\n        np.arange(12).reshape((2, 1, 2, 3)),\n        {\"affine\": np.diag([-1, -1, 1, 1])},\n        np.array([[[[3, 4, 5], [0, 1, 2]]], [[[9, 10, 11], [6, 7, 8]]]]),\n        \"RAS\",\n    ],\n    [\n        {\"axcodes\": \"AL\"},\n        np.arange(6).reshape((2, 1, 3)),\n        {\"affine\": np.eye(3)},\n        np.array([[[0], [1], [2]], [[3], [4], [5]]]),\n        \"AL\",\n    ],\n    [{\"axcodes\": \"L\"}, np.arange(6).reshape((2, 3)), {\"affine\": np.eye(2)}, np.array([[2, 1, 0], [5, 4, 3]]), \"L\"],\n    [{\"axcodes\": \"L\"}, np.arange(6).reshape((2, 3)), {\"affine\": np.eye(2)}, np.array([[2, 1, 0], [5, 4, 3]]), \"L\"],\n    [{\"axcodes\": \"L\"}, np.arange(6).reshape((2, 3)), {\"affine\": np.diag([-1, 1])}, np.arange(6).reshape((2, 3)), \"L\"],\n    [\n        {\"axcodes\": \"LPS\"},\n        np.arange(12).reshape((2, 1, 2, 3)),\n        {\n            \"affine\": create_translate(3, (10, 20, 30))\n            @ create_rotate(3, (np.pi / 2, np.pi / 2, np.pi / 4))\n            @ np.diag([-1, 1, 1, 1])\n        },\n        np.array([[[[2, 5]], [[1, 4]], [[0, 3]]], [[[8, 11]], [[7, 10]], [[6, 9]]]]),\n        \"LPS\",\n    ],\n    [\n        {\"as_closest_canonical\": True},\n        np.arange(12).reshape((2, 1, 2, 3)),\n        {\n            \"affine\": create_translate(3, (10, 20, 30))\n            @ create_rotate(3, (np.pi / 2, np.pi / 2, np.pi / 4))\n            @ np.diag([-1, 1, 1, 1])\n        },\n        np.array([[[[0, 3]], [[1, 4]], [[2, 5]]], [[[6, 9]], [[7, 10]], [[8, 11]]]]),\n        \"RAS\",\n    ],\n    [\n        {\"as_closest_canonical\": True},\n        np.arange(6).reshape((1, 2, 3)),\n        {\"affine\": create_translate(2, (10, 20)) @ create_rotate(2, (np.pi / 3)) @ np.diag([-1, -0.2, 1])},\n        np.array([[[3, 0], [4, 1], [5, 2]]]),\n        \"RA\",\n    ],\n    [\n        {\"axcodes\": \"LP\"},\n        np.arange(6).reshape((1, 2, 3)),\n        {\"affine\": create_translate(2, (10, 20)) @ create_rotate(2, (np.pi / 3)) @ np.diag([-1, -0.2, 1])},\n        np.array([[[2, 5], [1, 4], [0, 3]]]),\n        \"LP\",\n    ],\n    [\n        {\"axcodes\": \"LPID\", \"labels\": tuple(zip(\"LPIC\", \"RASD\"))},\n        np.zeros((1, 2, 3, 4, 5)),\n        {\"affine\": np.diag([-1, -0.2, -1, 1, 1])},\n        np.zeros((1, 2, 3, 4, 5)),\n        \"LPID\",\n    ],\n    [\n        {\"as_closest_canonical\": True, \"labels\": tuple(zip(\"LPIC\", \"RASD\"))},\n        np.zeros((1, 2, 3, 4, 5)),\n        {\"affine\": np.diag([-1, -0.2, -1, 1, 1])},\n        np.zeros((1, 2, 3, 4, 5)),\n        \"RASD\",\n    ],\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_orientation.py_ILL_CASES_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_orientation.py_ILL_CASES_", "embedding": null, "metadata": {"file_path": "tests/test_orientation.py", "file_name": "test_orientation.py", "file_type": "text/x-python", "category": "test", "start_line": 104, "end_line": 134, "span_ids": ["impl:3", "TestOrientationCase.test_bad_params", "TestOrientationCase", "TestOrientationCase.test_ornt", "impl:5"], "tokens": 287}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "ILL_CASES = [\n    # no axcodes or as_cloest_canonical\n    [{}, np.arange(6).reshape((2, 3)), \"L\"],\n    # too short axcodes\n    [{\"axcodes\": \"RA\"}, np.arange(12).reshape((2, 1, 2, 3)), {\"affine\": np.eye(4)}],\n]\n\n\nclass TestOrientationCase(unittest.TestCase):\n    @parameterized.expand(TEST_CASES)\n    def test_ornt(self, init_param, img, data_param, expected_data, expected_code):\n        ornt = Orientation(**init_param)\n        res = ornt(img, **data_param)\n        if not isinstance(res, tuple):\n            np.testing.assert_allclose(res, expected_data)\n            return\n        np.testing.assert_allclose(res[0], expected_data)\n        original_affine = data_param[\"affine\"]\n        np.testing.assert_allclose(original_affine, res[1])\n        new_code = nib.orientations.aff2axcodes(res[2], labels=ornt.labels)\n        self.assertEqual(\"\".join(new_code), expected_code)\n\n    @parameterized.expand(ILL_CASES)\n    def test_bad_params(self, init_param, img, data_param):\n        with self.assertRaises(ValueError):\n            Orientation(**init_param)(img, **data_param)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_orientationd.py_unittest_TestOrientationdCase.test_orntd.self_assertEqual_code_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_orientationd.py_unittest_TestOrientationdCase.test_orntd.self_assertEqual_code_", "embedding": null, "metadata": {"file_path": "tests/test_orientationd.py", "file_name": "test_orientationd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 27, "span_ids": ["TestOrientationdCase.test_orntd", "TestOrientationdCase", "docstring"], "tokens": 159}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport nibabel as nib\nimport numpy as np\n\nfrom monai.transforms import Orientationd\n\n\nclass TestOrientationdCase(unittest.TestCase):\n    def test_orntd(self):\n        data = {\"seg\": np.ones((2, 1, 2, 3)), \"seg_meta_dict\": {\"affine\": np.eye(4)}}\n        ornt = Orientationd(keys=\"seg\", axcodes=\"RAS\")\n        res = ornt(data)\n        np.testing.assert_allclose(res[\"seg\"].shape, (2, 1, 2, 3))\n        code = nib.aff2axcodes(res[\"seg_meta_dict\"][\"affine\"], ornt.ornt_transform.labels)\n        self.assertEqual(code, (\"R\", \"A\", \"S\"))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_orientationd.py_TestOrientationdCase.test_orntd_3d_TestOrientationdCase.test_orntd_3d.None_3": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_orientationd.py_TestOrientationdCase.test_orntd_3d_TestOrientationdCase.test_orntd_3d.None_3", "embedding": null, "metadata": {"file_path": "tests/test_orientationd.py", "file_name": "test_orientationd.py", "file_type": "text/x-python", "category": "test", "start_line": 29, "end_line": 43, "span_ids": ["TestOrientationdCase.test_orntd_3d"], "tokens": 243}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestOrientationdCase(unittest.TestCase):\n\n    def test_orntd_3d(self):\n        data = {\n            \"seg\": np.ones((2, 1, 2, 3)),\n            \"img\": np.ones((2, 1, 2, 3)),\n            \"seg_meta_dict\": {\"affine\": np.eye(4)},\n            \"img_meta_dict\": {\"affine\": np.eye(4)},\n        }\n        ornt = Orientationd(keys=(\"img\", \"seg\"), axcodes=\"PLI\")\n        res = ornt(data)\n        np.testing.assert_allclose(res[\"img\"].shape, (2, 2, 1, 3))\n        np.testing.assert_allclose(res[\"seg\"].shape, (2, 2, 1, 3))\n        code = nib.aff2axcodes(res[\"seg_meta_dict\"][\"affine\"], ornt.ornt_transform.labels)\n        self.assertEqual(code, (\"P\", \"L\", \"I\"))\n        code = nib.aff2axcodes(res[\"img_meta_dict\"][\"affine\"], ornt.ornt_transform.labels)\n        self.assertEqual(code, (\"P\", \"L\", \"I\"))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_orientationd.py_TestOrientationdCase.test_orntd_2d_TestOrientationdCase.test_orntd_2d.None_2": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_orientationd.py_TestOrientationdCase.test_orntd_2d_TestOrientationdCase.test_orntd_2d.None_2", "embedding": null, "metadata": {"file_path": "tests/test_orientationd.py", "file_name": "test_orientationd.py", "file_type": "text/x-python", "category": "test", "start_line": 45, "end_line": 58, "span_ids": ["TestOrientationdCase.test_orntd_2d"], "tokens": 210}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestOrientationdCase(unittest.TestCase):\n\n    def test_orntd_2d(self):\n        data = {\n            \"seg\": np.ones((2, 1, 3)),\n            \"img\": np.ones((2, 1, 3)),\n            \"seg_meta_dict\": {\"affine\": np.eye(4)},\n            \"img_meta_dict\": {\"affine\": np.eye(4)},\n        }\n        ornt = Orientationd(keys=(\"img\", \"seg\"), axcodes=\"PLI\")\n        res = ornt(data)\n        np.testing.assert_allclose(res[\"img\"].shape, (2, 3, 1))\n        code = nib.aff2axcodes(res[\"seg_meta_dict\"][\"affine\"], ornt.ornt_transform.labels)\n        self.assertEqual(code, (\"P\", \"L\", \"S\"))\n        code = nib.aff2axcodes(res[\"img_meta_dict\"][\"affine\"], ornt.ornt_transform.labels)\n        self.assertEqual(code, (\"P\", \"L\", \"S\"))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_orientationd.py_TestOrientationdCase.test_orntd_1d_TestOrientationdCase.test_orntd_1d.None_2": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_orientationd.py_TestOrientationdCase.test_orntd_1d_TestOrientationdCase.test_orntd_1d.None_2", "embedding": null, "metadata": {"file_path": "tests/test_orientationd.py", "file_name": "test_orientationd.py", "file_type": "text/x-python", "category": "test", "start_line": 60, "end_line": 73, "span_ids": ["TestOrientationdCase.test_orntd_1d"], "tokens": 200}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestOrientationdCase(unittest.TestCase):\n\n    def test_orntd_1d(self):\n        data = {\n            \"seg\": np.ones((2, 3)),\n            \"img\": np.ones((2, 3)),\n            \"seg_meta_dict\": {\"affine\": np.eye(4)},\n            \"img_meta_dict\": {\"affine\": np.eye(4)},\n        }\n        ornt = Orientationd(keys=(\"img\", \"seg\"), axcodes=\"L\")\n        res = ornt(data)\n        np.testing.assert_allclose(res[\"img\"].shape, (2, 3))\n        code = nib.aff2axcodes(res[\"seg_meta_dict\"][\"affine\"], ornt.ornt_transform.labels)\n        self.assertEqual(code, (\"L\", \"A\", \"S\"))\n        code = nib.aff2axcodes(res[\"img_meta_dict\"][\"affine\"], ornt.ornt_transform.labels)\n        self.assertEqual(code, (\"L\", \"A\", \"S\"))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_plot_2d_or_3d_image.py_glob_TEST_CASE_5._1_3_10_10_10_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_plot_2d_or_3d_image.py_glob_TEST_CASE_5._1_3_10_10_10_", "embedding": null, "metadata": {"file_path": "tests/test_plot_2d_or_3d_image.py", "file_name": "test_plot_2d_or_3d_image.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 34, "span_ids": ["docstring"], "tokens": 170}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import glob\nimport tempfile\nimport unittest\n\nimport torch\nfrom parameterized import parameterized\nfrom torch.utils.tensorboard import SummaryWriter\n\nfrom monai.utils import optional_import\nfrom monai.visualize import plot_2d_or_3d_image\nfrom tests.utils import SkipIfNoModule\n\nSummaryWriterX, _ = optional_import(\"tensorboardX\", name=\"SummaryWriter\")\n\nTEST_CASE_1 = [(1, 1, 10, 10)]\n\nTEST_CASE_2 = [(1, 3, 10, 10)]\n\nTEST_CASE_3 = [(1, 4, 10, 10)]\n\nTEST_CASE_4 = [(1, 1, 10, 10, 10)]\n\nTEST_CASE_5 = [(1, 3, 10, 10, 10)]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_query_memory.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_query_memory.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_query_memory.py", "file_name": "test_query_memory.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 27, "span_ids": ["impl", "TestQueryMemory", "TestQueryMemory.test_output_str", "docstring"], "tokens": 75}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nfrom tests.utils import query_memory\n\n\nclass TestQueryMemory(unittest.TestCase):\n    def test_output_str(self):\n        self.assertTrue(isinstance(query_memory(2), str))\n        all_device = query_memory(-1)\n        self.assertTrue(isinstance(all_device, str))\n        self.assertEqual(query_memory(\"test\"), \"\")\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_adjust_contrast.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_adjust_contrast.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_rand_adjust_contrast.py", "file_name": "test_rand_adjust_contrast.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 43, "span_ids": ["TestRandAdjustContrast", "TestRandAdjustContrast.test_correct_results", "impl:5", "docstring"], "tokens": 248}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import RandAdjustContrast\nfrom tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, assert_allclose\n\nTEST_CASE_1 = [(0.5, 4.5)]\n\nTEST_CASE_2 = [1.5]\n\n\nclass TestRandAdjustContrast(NumpyImageTestCase2D):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2])\n    def test_correct_results(self, gamma):\n        adjuster = RandAdjustContrast(prob=1.0, gamma=gamma)\n        for p in TEST_NDARRAYS:\n            result = adjuster(p(self.imt))\n            epsilon = 1e-7\n            img_min = self.imt.min()\n            img_range = self.imt.max() - img_min\n            expected = (\n                np.power(((self.imt - img_min) / float(img_range + epsilon)), adjuster.gamma_value) * img_range\n                + img_min\n            )\n            assert_allclose(expected, result, rtol=1e-05, type_test=False)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_adjust_contrastd.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_adjust_contrastd.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_rand_adjust_contrastd.py", "file_name": "test_rand_adjust_contrastd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 43, "span_ids": ["impl:5", "TestRandAdjustContrastd.test_correct_results", "TestRandAdjustContrastd", "docstring"], "tokens": 261}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import RandAdjustContrastd\nfrom tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, assert_allclose\n\nTEST_CASE_1 = [(0.5, 4.5)]\n\nTEST_CASE_2 = [1.5]\n\n\nclass TestRandAdjustContrastd(NumpyImageTestCase2D):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2])\n    def test_correct_results(self, gamma):\n        adjuster = RandAdjustContrastd(\"img\", prob=1.0, gamma=gamma)\n        for p in TEST_NDARRAYS:\n            result = adjuster({\"img\": p(self.imt)})\n            epsilon = 1e-7\n            img_min = self.imt.min()\n            img_range = self.imt.max() - img_min\n            expected = (\n                np.power(((self.imt - img_min) / float(img_range + epsilon)), adjuster.adjuster.gamma_value) * img_range\n                + img_min\n            )\n            assert_allclose(expected, result[\"img\"], rtol=1e-05, type_test=False)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_affine_grid.py_TestRandAffineGrid_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_affine_grid.py_TestRandAffineGrid_", "embedding": null, "metadata": {"file_path": "tests/test_rand_affine_grid.py", "file_name": "test_rand_affine_grid.py", "file_type": "text/x-python", "category": "test", "start_line": 198, "end_line": 211, "span_ids": ["impl:11", "TestRandAffineGrid.test_rand_affine_grid", "TestRandAffineGrid"], "tokens": 120}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandAffineGrid(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_rand_affine_grid(self, input_param, input_data, expected_val):\n        g = RandAffineGrid(**input_param)\n        g.set_random_state(123)\n        result = g(**input_data)\n        if \"device\" in input_data:\n            self.assertEqual(result.device, input_data[device])\n        assert_allclose(result, expected_val, type_test=False, rtol=_rtol, atol=1e-4)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_crop_by_pos_neg_labeld.py_TestRandCropByPosNegLabeld_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_crop_by_pos_neg_labeld.py_TestRandCropByPosNegLabeld_", "embedding": null, "metadata": {"file_path": "tests/test_rand_crop_by_pos_neg_labeld.py", "file_name": "test_rand_crop_by_pos_neg_labeld.py", "file_type": "text/x-python", "category": "test", "start_line": 122, "end_line": 152, "span_ids": ["TestRandCropByPosNegLabeld.convert_data_type", "impl:3", "TestRandCropByPosNegLabeld", "TestRandCropByPosNegLabeld.test_type_shape"], "tokens": 276}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandCropByPosNegLabeld(unittest.TestCase):\n    @staticmethod\n    def convert_data_type(im_type, d, keys=(\"img\", \"image\", \"label\")):\n        out = deepcopy(d)\n        for k, v in out.items():\n            if k in keys and isinstance(v, np.ndarray):\n                out[k] = im_type(v)\n        return out\n\n    @parameterized.expand(TESTS)\n    def test_type_shape(self, input_param, input_data, expected_shape):\n        for p in TEST_NDARRAYS:\n            input_param_mod = self.convert_data_type(p, input_param)\n            input_data_mod = self.convert_data_type(p, input_data)\n            cropper = RandCropByPosNegLabeld(**input_param_mod)\n            cropper.set_random_state(0)\n            result = cropper(input_data_mod)\n\n            self.assertIsInstance(result, list)\n\n            _len = len(tuple(input_data.keys()))\n            self.assertTupleEqual(tuple(result[0].keys())[:_len], tuple(input_data.keys()))\n            for k in (\"image\", \"extra\", \"label\"):\n                self.assertTupleEqual(result[0][k].shape, expected_shape)\n                for i, item in enumerate(result):\n                    self.assertEqual(item[k + \"_meta_dict\"][\"patch_index\"], i)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_elasticd_2d.py_TestRand2DElasticd_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_elasticd_2d.py_TestRand2DElasticd_", "embedding": null, "metadata": {"file_path": "tests/test_rand_elasticd_2d.py", "file_name": "test_rand_elasticd_2d.py", "file_type": "text/x-python", "category": "test", "start_line": 160, "end_line": 174, "span_ids": ["TestRand2DElasticd.test_rand_2d_elasticd", "impl:13", "TestRand2DElasticd"], "tokens": 129}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRand2DElasticd(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_rand_2d_elasticd(self, input_param, input_data, expected_val):\n        g = Rand2DElasticd(**input_param)\n        g.set_random_state(123)\n        res = g(input_data)\n        for key in res:\n            result = res[key]\n            expected = expected_val[key] if isinstance(expected_val, dict) else expected_val\n            assert_allclose(result, expected, rtol=_rtol, atol=5e-3)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_elasticd_3d.py_TestRand3DElasticd_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_elasticd_3d.py_TestRand3DElasticd_", "embedding": null, "metadata": {"file_path": "tests/test_rand_elasticd_3d.py", "file_name": "test_rand_elasticd_3d.py", "file_type": "text/x-python", "category": "test", "start_line": 139, "end_line": 153, "span_ids": ["impl:11", "TestRand3DElasticd.test_rand_3d_elasticd", "TestRand3DElasticd"], "tokens": 131}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRand3DElasticd(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_rand_3d_elasticd(self, input_param, input_data, expected_val):\n        g = Rand3DElasticd(**input_param)\n        g.set_random_state(123)\n        res = g(input_data)\n        for key in res:\n            result = res[key]\n            expected = expected_val[key] if isinstance(expected_val, dict) else expected_val\n            assert_allclose(result, expected, rtol=1e-4, atol=1e-4)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_flip.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_flip.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_rand_flip.py", "file_name": "test_rand_flip.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 45, "span_ids": ["TestRandFlip.test_correct_results", "TestRandFlip", "TestRandFlip.test_invalid_inputs", "impl:5", "docstring"], "tokens": 277}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import RandFlip\nfrom tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, assert_allclose\n\nINVALID_CASES = [(\"wrong_axis\", [\"s\", 1], TypeError), (\"not_numbers\", \"s\", TypeError)]\n\nVALID_CASES = [(\"no_axis\", None), (\"one_axis\", 1), (\"many_axis\", [0, 1])]\n\n\nclass TestRandFlip(NumpyImageTestCase2D):\n    @parameterized.expand(INVALID_CASES)\n    def test_invalid_inputs(self, _, spatial_axis, raises):\n        with self.assertRaises(raises):\n            flip = RandFlip(prob=1.0, spatial_axis=spatial_axis)\n            flip(self.imt[0])\n\n    @parameterized.expand(VALID_CASES)\n    def test_correct_results(self, _, spatial_axis):\n        for p in TEST_NDARRAYS:\n            im = p(self.imt[0])\n            flip = RandFlip(prob=1.0, spatial_axis=spatial_axis)\n            expected = [np.flip(channel, spatial_axis) for channel in self.imt[0]]\n            expected = np.stack(expected)\n            result = flip(im)\n            assert_allclose(result, p(expected))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_flipd.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_flipd.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_rand_flipd.py", "file_name": "test_rand_flipd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 36, "span_ids": ["TestRandFlipd.test_correct_results", "impl:3", "TestRandFlipd", "docstring"], "tokens": 205}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import RandFlipd\nfrom tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, assert_allclose\n\nVALID_CASES = [(\"no_axis\", None), (\"one_axis\", 1), (\"many_axis\", [0, 1])]\n\n\nclass TestRandFlipd(NumpyImageTestCase2D):\n    @parameterized.expand(VALID_CASES)\n    def test_correct_results(self, _, spatial_axis):\n        for p in TEST_NDARRAYS:\n            flip = RandFlipd(keys=\"img\", prob=1.0, spatial_axis=spatial_axis)\n            result = flip({\"img\": p(self.imt[0])})[\"img\"]\n            expected = [np.flip(channel, spatial_axis) for channel in self.imt[0]]\n            expected = np.stack(expected)\n            assert_allclose(result, p(expected))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_rotate.py_TestRandRotate3D_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_rotate.py_TestRandRotate3D_", "embedding": null, "metadata": {"file_path": "tests/test_rand_rotate.py", "file_name": "test_rand_rotate.py", "file_type": "text/x-python", "category": "test", "start_line": 95, "end_line": 115, "span_ids": ["TestRandRotate3D", "TestRandRotate3D.test_correct_results", "impl:15"], "tokens": 169}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandRotate3D(NumpyImageTestCase3D):\n    @parameterized.expand(TEST_CASES_3D)\n    def test_correct_results(self, im_type, x, y, z, keep_size, mode, padding_mode, align_corners, expected):\n        rotate_fn = RandRotate(\n            range_x=x,\n            range_y=y,\n            range_z=z,\n            prob=1.0,\n            keep_size=keep_size,\n            mode=mode,\n            padding_mode=padding_mode,\n            align_corners=align_corners,\n        )\n        rotate_fn.set_random_state(243)\n        rotated = rotate_fn(im_type(self.imt[0]))\n        torch.testing.assert_allclose(rotated.shape, expected, rtol=1e-7, atol=0)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_rotate90d.py_TestRandRotate90d.test_prob_k_spatial_axes_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_rotate90d.py_TestRandRotate90d.test_prob_k_spatial_axes_", "embedding": null, "metadata": {"file_path": "tests/test_rand_rotate90d.py", "file_name": "test_rand_rotate90d.py", "file_type": "text/x-python", "category": "test", "start_line": 51, "end_line": 70, "span_ids": ["TestRandRotate90d.test_prob_k_spatial_axes", "TestRandRotate90d.test_no_key", "impl"], "tokens": 212}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandRotate90d(NumpyImageTestCase2D):\n\n    def test_prob_k_spatial_axes(self):\n        key = \"test\"\n        rotate = RandRotate90d(keys=key, prob=1.0, max_k=2, spatial_axes=(0, 1))\n        for p in TEST_NDARRAYS:\n            rotate.set_random_state(234)\n            rotated = rotate({key: p(self.imt[0])})\n            expected = [np.rot90(channel, 1, (0, 1)) for channel in self.imt[0]]\n            expected = np.stack(expected)\n            assert_allclose(rotated[key], p(expected))\n\n    def test_no_key(self):\n        key = \"unknown\"\n        rotate = RandRotate90d(keys=key, prob=1.0, max_k=2, spatial_axes=(0, 1))\n        with self.assertRaisesRegex(KeyError, \"\"):\n            rotate({\"test\": self.imt[0]})\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_rotated.py_TestRandRotated3D_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_rotated.py_TestRandRotated3D_", "embedding": null, "metadata": {"file_path": "tests/test_rand_rotated.py", "file_name": "test_rand_rotated.py", "file_type": "text/x-python", "category": "test", "start_line": 141, "end_line": 162, "span_ids": ["TestRandRotated3D", "TestRandRotated3D.test_correct_shapes", "impl:19"], "tokens": 181}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandRotated3D(NumpyImageTestCase3D):\n    @parameterized.expand(TEST_CASES_3D)\n    def test_correct_shapes(self, im_type, x, y, z, keep_size, mode, padding_mode, align_corners, expected):\n        rotate_fn = RandRotated(\n            \"img\",\n            range_x=x,\n            range_y=y,\n            range_z=z,\n            prob=1.0,\n            keep_size=keep_size,\n            mode=mode,\n            padding_mode=padding_mode,\n            align_corners=align_corners,\n        )\n        rotate_fn.set_random_state(243)\n        rotated = rotate_fn({\"img\": im_type(self.imt[0]), \"seg\": im_type(self.segn[0])})\n        np.testing.assert_allclose(rotated[\"img\"].shape, expected)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_scale_intensity.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_scale_intensity.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_rand_scale_intensity.py", "file_name": "test_rand_scale_intensity.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 35, "span_ids": ["TestRandScaleIntensity", "TestRandScaleIntensity.test_value", "impl", "docstring"], "tokens": 190}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\n\nfrom monai.transforms import RandScaleIntensity\nfrom tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, assert_allclose\n\n\nclass TestRandScaleIntensity(NumpyImageTestCase2D):\n    def test_value(self):\n        for p in TEST_NDARRAYS:\n            scaler = RandScaleIntensity(factors=0.5, prob=1.0)\n            scaler.set_random_state(seed=0)\n            result = scaler(p(self.imt))\n            np.random.seed(0)\n            # simulate the randomize() of transform\n            np.random.random()\n            expected = p((self.imt * (1 + np.random.uniform(low=-0.5, high=0.5))).astype(np.float32))\n            assert_allclose(result, p(expected), rtol=1e-7, atol=0)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_scale_intensityd.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_scale_intensityd.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_rand_scale_intensityd.py", "file_name": "test_rand_scale_intensityd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 36, "span_ids": ["TestRandScaleIntensityd", "TestRandScaleIntensityd.test_value", "impl", "docstring"], "tokens": 193}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\n\nfrom monai.transforms import RandScaleIntensityd\nfrom tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, assert_allclose\n\n\nclass TestRandScaleIntensityd(NumpyImageTestCase2D):\n    def test_value(self):\n        key = \"img\"\n        for p in TEST_NDARRAYS:\n            scaler = RandScaleIntensityd(keys=[key], factors=0.5, prob=1.0)\n            scaler.set_random_state(seed=0)\n            result = scaler({key: p(self.imt)})\n            np.random.seed(0)\n            # simulate the randomize function of transform\n            np.random.random()\n            expected = (self.imt * (1 + np.random.uniform(low=-0.5, high=0.5))).astype(np.float32)\n            assert_allclose(result[key], p(expected))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_shift_intensity.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_shift_intensity.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_rand_shift_intensity.py", "file_name": "test_rand_shift_intensity.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 34, "span_ids": ["impl", "TestRandShiftIntensity", "TestRandShiftIntensity.test_value", "docstring"], "tokens": 157}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\n\nfrom monai.transforms import RandShiftIntensity\nfrom tests.utils import NumpyImageTestCase2D\n\n\nclass TestRandShiftIntensity(NumpyImageTestCase2D):\n    def test_value(self):\n        shifter = RandShiftIntensity(offsets=1.0, prob=1.0)\n        shifter.set_random_state(seed=0)\n        result = shifter(self.imt, factor=1.0)\n        np.random.seed(0)\n        # simulate the randomize() of transform\n        np.random.random()\n        expected = self.imt + np.random.uniform(low=-1.0, high=1.0)\n        np.testing.assert_allclose(result, expected)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_spatial_crop.py_TestRandSpatialCrop_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_spatial_crop.py_TestRandSpatialCrop_", "embedding": null, "metadata": {"file_path": "tests/test_rand_spatial_crop.py", "file_name": "test_rand_spatial_crop.py", "file_type": "text/x-python", "category": "test", "start_line": 52, "end_line": 76, "span_ids": ["TestRandSpatialCrop", "TestRandSpatialCrop.test_value", "TestRandSpatialCrop.test_random_shape", "TestRandSpatialCrop.test_shape", "impl:13"], "tokens": 273}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandSpatialCrop(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_0, TEST_CASE_1, TEST_CASE_2])\n    def test_shape(self, input_param, input_data, expected_shape):\n        result = RandSpatialCrop(**input_param)(input_data)\n        self.assertTupleEqual(result.shape, expected_shape)\n\n    @parameterized.expand([TEST_CASE_3])\n    def test_value(self, input_param, input_data):\n        for p in TEST_NDARRAYS:\n            cropper = RandSpatialCrop(**input_param)\n            result = cropper(p(input_data))\n            roi = [(2 - i // 2, 2 + i - i // 2) for i in cropper._size]\n            assert_allclose(result, input_data[:, roi[0][0] : roi[0][1], roi[1][0] : roi[1][1]], type_test=False)\n\n    @parameterized.expand([TEST_CASE_4, TEST_CASE_5])\n    def test_random_shape(self, input_param, input_data, expected_shape):\n        cropper = RandSpatialCrop(**input_param)\n        cropper.set_random_state(seed=123)\n        result = cropper(input_data)\n        self.assertTupleEqual(result.shape, expected_shape)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_spatial_cropd.py_TestRandSpatialCropd_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_spatial_cropd.py_TestRandSpatialCropd_", "embedding": null, "metadata": {"file_path": "tests/test_rand_spatial_cropd.py", "file_name": "test_rand_spatial_cropd.py", "file_type": "text/x-python", "category": "test", "start_line": 56, "end_line": 81, "span_ids": ["TestRandSpatialCropd.test_shape", "TestRandSpatialCropd.test_value", "TestRandSpatialCropd", "TestRandSpatialCropd.test_random_shape", "impl:13"], "tokens": 298}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandSpatialCropd(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_0, TEST_CASE_1, TEST_CASE_2])\n    def test_shape(self, input_param, input_data, expected_shape):\n        result = RandSpatialCropd(**input_param)(input_data)\n        self.assertTupleEqual(result[\"img\"].shape, expected_shape)\n\n    @parameterized.expand([TEST_CASE_3])\n    def test_value(self, input_param, input_data):\n        cropper = RandSpatialCropd(**input_param)\n        result = cropper(input_data)\n        roi = [(2 - i // 2, 2 + i - i // 2) for i in cropper._size]\n        np.testing.assert_allclose(result[\"img\"], input_data[\"img\"][:, roi[0][0] : roi[0][1], roi[1][0] : roi[1][1]])\n\n    @parameterized.expand([TEST_CASE_4, TEST_CASE_5])\n    def test_random_shape(self, input_param, input_data, expected_shape):\n        for p in TEST_NDARRAYS:\n            cropper = RandSpatialCropd(**input_param)\n            cropper.set_random_state(seed=123)\n            input_data[\"img\"] = p(input_data[\"img\"])\n            result = cropper(input_data)\n            self.assertTupleEqual(result[\"img\"].shape, expected_shape)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_randomizable.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_randomizable.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_randomizable.py", "file_name": "test_randomizable.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 49, "span_ids": ["TestRandomizable.test_state", "impl", "TestRandomizable.test_default", "RandTest.randomize", "docstring", "RandTest", "TestRandomizable", "TestRandomizable.test_seed"], "tokens": 214}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\n\nfrom monai.transforms.transform import Randomizable\n\n\nclass RandTest(Randomizable):\n    def randomize(self, data=None):\n        pass\n\n\nclass TestRandomizable(unittest.TestCase):\n    def test_default(self):\n        inst = RandTest()\n        r1 = inst.R.rand()\n        self.assertTrue(isinstance(inst.R, np.random.RandomState))\n        inst.set_random_state()\n        r2 = inst.R.rand()\n        self.assertNotAlmostEqual(r1, r2)\n\n    def test_seed(self):\n        inst = RandTest()\n        inst.set_random_state(seed=123)\n        self.assertAlmostEqual(inst.R.rand(), 0.69646918)\n        inst.set_random_state(123)\n        self.assertAlmostEqual(inst.R.rand(), 0.69646918)\n\n    def test_state(self):\n        inst = RandTest()\n        inst_r = np.random.RandomState(123)\n        inst.set_random_state(state=inst_r)\n        self.assertAlmostEqual(inst.R.rand(), 0.69646918)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_repeat_channel.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_repeat_channel.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_repeat_channel.py", "file_name": "test_repeat_channel.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 33, "span_ids": ["TestRepeatChannel", "TestRepeatChannel.test_shape", "impl:5", "docstring"], "tokens": 141}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nfrom parameterized import parameterized\n\nfrom monai.transforms import RepeatChannel\nfrom tests.utils import TEST_NDARRAYS\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    TESTS.append([{\"repeats\": 3}, p([[[0, 1], [1, 2]]]), (3, 2, 2)])\n\n\nclass TestRepeatChannel(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_shape(self, input_param, input_data, expected_shape):\n        result = RepeatChannel(**input_param)(input_data)\n        self.assertEqual(result.shape, expected_shape)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_repeat_channeld.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_repeat_channeld.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_repeat_channeld.py", "file_name": "test_repeat_channeld.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 40, "span_ids": ["TestRepeatChanneld.test_shape", "impl:5", "TestRepeatChanneld", "docstring"], "tokens": 195}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import RepeatChanneld\nfrom tests.utils import TEST_NDARRAYS\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    TESTS.append(\n        [\n            {\"keys\": [\"img\"], \"repeats\": 3},\n            {\"img\": p(np.array([[[0, 1], [1, 2]]])), \"seg\": p(np.array([[[0, 1], [1, 2]]]))},\n            (3, 2, 2),\n        ]\n    )\n\n\nclass TestRepeatChanneld(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_shape(self, input_param, input_data, expected_shape):\n        result = RepeatChanneld(**input_param)(input_data)\n        self.assertEqual(result[\"img\"].shape, expected_shape)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rotate.py_TestRotate3D.test_correct_shape_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rotate.py_TestRotate3D.test_correct_shape_", "embedding": null, "metadata": {"file_path": "tests/test_rotate.py", "file_name": "test_rotate.py", "file_type": "text/x-python", "category": "test", "start_line": 101, "end_line": 120, "span_ids": ["impl:23", "TestRotate3D.test_correct_shape", "TestRotate3D.test_ill_case"], "tokens": 197}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRotate3D(NumpyImageTestCase3D):\n\n    @parameterized.expand(TEST_CASES_SHAPE_3D)\n    def test_correct_shape(self, im_type, angle, mode, padding_mode, align_corners):\n        rotate_fn = Rotate(angle, True, align_corners=align_corners)\n        rotated = rotate_fn(im_type(self.imt[0]), mode=mode, padding_mode=padding_mode)\n        np.testing.assert_allclose(self.imt[0].shape, rotated.shape)\n\n    def test_ill_case(self):\n        for p in TEST_NDARRAYS:\n            rotate_fn = Rotate(10, True)\n            with self.assertRaises(ValueError):  # wrong shape\n                rotate_fn(p(self.imt))\n\n            rotate_fn = Rotate(10, keep_size=False)\n            with self.assertRaises(ValueError):  # wrong mode\n                rotate_fn(p(self.imt[0]), mode=\"trilinear\")\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rotate90d.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rotate90d.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_rotate90d.py", "file_name": "test_rotate90d.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 66, "span_ids": ["impl", "TestRotate90d.test_prob_k_spatial_axes", "TestRotate90d.test_rotate90_default", "TestRotate90d", "docstring", "TestRotate90d.test_spatial_axes", "TestRotate90d.test_k", "TestRotate90d.test_no_key"], "tokens": 501}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\n\nfrom monai.transforms import Rotate90d\nfrom tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, assert_allclose\n\n\nclass TestRotate90d(NumpyImageTestCase2D):\n    def test_rotate90_default(self):\n        key = \"test\"\n        rotate = Rotate90d(keys=key)\n        for p in TEST_NDARRAYS:\n            rotated = rotate({key: p(self.imt[0])})\n            expected = [np.rot90(channel, 1, (0, 1)) for channel in self.imt[0]]\n            expected = np.stack(expected)\n            assert_allclose(rotated[key], p(expected))\n\n    def test_k(self):\n        key = None\n        rotate = Rotate90d(keys=key, k=2)\n        for p in TEST_NDARRAYS:\n            rotated = rotate({key: p(self.imt[0])})\n            expected = [np.rot90(channel, 2, (0, 1)) for channel in self.imt[0]]\n            expected = np.stack(expected)\n            assert_allclose(rotated[key], p(expected))\n\n    def test_spatial_axes(self):\n        key = \"test\"\n        rotate = Rotate90d(keys=key, spatial_axes=(0, 1))\n        for p in TEST_NDARRAYS:\n            rotated = rotate({key: p(self.imt[0])})\n            expected = [np.rot90(channel, 1, (0, 1)) for channel in self.imt[0]]\n            expected = np.stack(expected)\n            assert_allclose(rotated[key], p(expected))\n\n    def test_prob_k_spatial_axes(self):\n        key = \"test\"\n        rotate = Rotate90d(keys=key, k=2, spatial_axes=(0, 1))\n        for p in TEST_NDARRAYS:\n            rotated = rotate({key: p(self.imt[0])})\n            expected = [np.rot90(channel, 2, (0, 1)) for channel in self.imt[0]]\n            expected = np.stack(expected)\n            assert_allclose(rotated[key], p(expected))\n\n    def test_no_key(self):\n        key = \"unknown\"\n        rotate = Rotate90d(keys=key)\n        with self.assertRaisesRegex(KeyError, \"\"):\n            rotate({\"test\": self.imt[0]})\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rotated.py_TestRotated2D_TestRotated2D.test_correct_results.self_assertLessEqual_np_c": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rotated.py_TestRotated2D_TestRotated2D.test_correct_results.self_assertLessEqual_np_c", "embedding": null, "metadata": {"file_path": "tests/test_rotated.py", "file_name": "test_rotated.py", "file_type": "text/x-python", "category": "test", "start_line": 40, "end_line": 66, "span_ids": ["TestRotated2D.test_correct_results", "TestRotated2D"], "tokens": 388}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRotated2D(NumpyImageTestCase2D):\n    @parameterized.expand(TEST_CASES_2D)\n    def test_correct_results(self, im_type, angle, keep_size, mode, padding_mode, align_corners):\n        rotate_fn = Rotated((\"img\", \"seg\"), angle, keep_size, (mode, \"nearest\"), padding_mode, align_corners)\n        rotated = rotate_fn({\"img\": im_type(self.imt[0]), \"seg\": im_type(self.segn[0])})\n        if keep_size:\n            np.testing.assert_allclose(self.imt[0].shape, rotated[\"img\"].shape)\n        _order = 0 if mode == \"nearest\" else 1\n        if padding_mode == \"border\":\n            _mode = \"nearest\"\n        elif padding_mode == \"reflection\":\n            _mode = \"reflect\"\n        else:\n            _mode = \"constant\"\n        expected = scipy.ndimage.rotate(\n            self.imt[0, 0], -np.rad2deg(angle), (0, 1), not keep_size, order=_order, mode=_mode, prefilter=False\n        )\n        for k, v in rotated.items():\n            rotated[k] = v.cpu() if isinstance(v, torch.Tensor) else v\n        good = np.sum(np.isclose(expected, rotated[\"img\"][0], atol=1e-3))\n        self.assertLessEqual(np.abs(good - expected.size), 5, \"diff at most 5 pixels\")\n\n        expected = scipy.ndimage.rotate(\n            self.segn[0, 0], -np.rad2deg(angle), (0, 1), not keep_size, order=0, mode=_mode, prefilter=False\n        )\n        expected = np.stack(expected).astype(int)\n        self.assertLessEqual(np.count_nonzero(expected != rotated[\"seg\"][0]), 30)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rotated.py_TestRotated3D_TestRotated3D.test_correct_results.self_assertLessEqual_np_c": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rotated.py_TestRotated3D_TestRotated3D.test_correct_results.self_assertLessEqual_np_c", "embedding": null, "metadata": {"file_path": "tests/test_rotated.py", "file_name": "test_rotated.py", "file_type": "text/x-python", "category": "test", "start_line": 69, "end_line": 95, "span_ids": ["TestRotated3D", "TestRotated3D.test_correct_results"], "tokens": 397}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRotated3D(NumpyImageTestCase3D):\n    @parameterized.expand(TEST_CASES_3D)\n    def test_correct_results(self, im_type, angle, keep_size, mode, padding_mode, align_corners):\n        rotate_fn = Rotated((\"img\", \"seg\"), [0, angle, 0], keep_size, (mode, \"nearest\"), padding_mode, align_corners)\n        rotated = rotate_fn({\"img\": im_type(self.imt[0]), \"seg\": im_type(self.segn[0])})\n        if keep_size:\n            np.testing.assert_allclose(self.imt[0].shape, rotated[\"img\"].shape)\n        _order = 0 if mode == \"nearest\" else 1\n        if padding_mode == \"border\":\n            _mode = \"nearest\"\n        elif padding_mode == \"reflection\":\n            _mode = \"reflect\"\n        else:\n            _mode = \"constant\"\n        expected = scipy.ndimage.rotate(\n            self.imt[0, 0], np.rad2deg(angle), (0, 2), not keep_size, order=_order, mode=_mode, prefilter=False\n        )\n        for k, v in rotated.items():\n            rotated[k] = v.cpu() if isinstance(v, torch.Tensor) else v\n        good = np.sum(np.isclose(expected.astype(np.float32), rotated[\"img\"][0], atol=1e-3))\n        self.assertLessEqual(np.abs(good - expected.size), 5, \"diff at most 5 voxels.\")\n\n        expected = scipy.ndimage.rotate(\n            self.segn[0, 0], np.rad2deg(angle), (0, 2), not keep_size, order=0, mode=_mode, prefilter=False\n        )\n        expected = np.stack(expected).astype(int)\n        self.assertLessEqual(np.count_nonzero(expected != rotated[\"seg\"][0]), 160)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rotated.py_TestRotated3DXY_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rotated.py_TestRotated3DXY_", "embedding": null, "metadata": {"file_path": "tests/test_rotated.py", "file_name": "test_rotated.py", "file_type": "text/x-python", "category": "test", "start_line": 98, "end_line": 129, "span_ids": ["TestRotated3DXY.test_correct_results", "impl:17", "TestRotated3DXY"], "tokens": 408}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRotated3DXY(NumpyImageTestCase3D):\n    @parameterized.expand(TEST_CASES_3D)\n    def test_correct_results(self, im_type, angle, keep_size, mode, padding_mode, align_corners):\n        rotate_fn = Rotated((\"img\", \"seg\"), [0, 0, angle], keep_size, (mode, \"nearest\"), padding_mode, align_corners)\n        rotated = rotate_fn({\"img\": im_type(self.imt[0]), \"seg\": im_type(self.segn[0])})\n        if keep_size:\n            np.testing.assert_allclose(self.imt[0].shape, rotated[\"img\"].shape)\n        _order = 0 if mode == \"nearest\" else 1\n        if padding_mode == \"border\":\n            _mode = \"nearest\"\n        elif padding_mode == \"reflection\":\n            _mode = \"reflect\"\n        else:\n            _mode = \"constant\"\n        expected = scipy.ndimage.rotate(\n            self.imt[0, 0], -np.rad2deg(angle), (0, 1), not keep_size, order=_order, mode=_mode, prefilter=False\n        )\n        for k, v in rotated.items():\n            rotated[k] = v.cpu() if isinstance(v, torch.Tensor) else v\n        good = np.sum(np.isclose(expected, rotated[\"img\"][0], atol=1e-3))\n        self.assertLessEqual(np.abs(good - expected.size), 5, \"diff at most 5 voxels\")\n\n        expected = scipy.ndimage.rotate(\n            self.segn[0, 0], -np.rad2deg(angle), (0, 1), not keep_size, order=0, mode=_mode, prefilter=False\n        )\n        expected = np.stack(expected).astype(int)\n        self.assertLessEqual(np.count_nonzero(expected != rotated[\"seg\"][0]), 160)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_scale_intensity_ranged.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_scale_intensity_ranged.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_scale_intensity_ranged.py", "file_name": "test_scale_intensity_ranged.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 39, "span_ids": ["impl", "IntensityScaleIntensityRanged.test_image_scale_intensity_ranged_none", "IntensityScaleIntensityRanged", "IntensityScaleIntensityRanged.test_image_scale_intensity_ranged", "docstring"], "tokens": 258}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nfrom monai.transforms import ScaleIntensityRanged\nfrom tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, assert_allclose\n\n\nclass IntensityScaleIntensityRanged(NumpyImageTestCase2D):\n    def test_image_scale_intensity_ranged(self):\n        key = \"img\"\n        scaler = ScaleIntensityRanged(keys=key, a_min=20, a_max=108, b_min=50, b_max=80)\n        for p in TEST_NDARRAYS:\n            scaled = scaler({key: p(self.imt)})\n            expected = (self.imt - 20) / 88\n            expected = expected * 30 + 50\n            assert_allclose(scaled[key], p(expected))\n\n    def test_image_scale_intensity_ranged_none(self):\n        key = \"img\"\n        scaler = ScaleIntensityRanged(keys=key, a_min=20, a_max=108, b_min=None, b_max=None)\n        for p in TEST_NDARRAYS:\n            scaled = scaler({key: p(self.imt)})\n            expected = (self.imt - 20) / 88\n            assert_allclose(scaled[key], p(expected))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_se_block.py_unittest_for_type_1_in_.for_type_2_in_.TEST_CASES_3D_append_test": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_se_block.py_unittest_for_type_1_in_.for_type_2_in_.TEST_CASES_3D_append_test", "embedding": null, "metadata": {"file_path": "tests/test_se_block.py", "file_name": "test_se_block.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 60, "span_ids": ["docstring"], "tokens": 519}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks import eval_mode\nfrom monai.networks.blocks import SEBlock\nfrom monai.networks.layers.factories import Act, Norm\nfrom tests.utils import test_script_save\n\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\nTEST_CASES = [\n    [\n        {\"spatial_dims\": 2, \"in_channels\": 4, \"n_chns_1\": 20, \"n_chns_2\": 30, \"n_chns_3\": 4, \"r\": 2},\n        (7, 4, 64, 48),  # 4-channel 2D, batch 7\n        (7, 4, 64, 48),\n    ],\n    [\n        {\"spatial_dims\": 1, \"in_channels\": 3, \"n_chns_1\": 20, \"n_chns_2\": 30, \"n_chns_3\": 40, \"r\": 5},\n        (16, 3, 63),  # 3-channel 1D, batch 16\n        (16, 40, 63),\n    ],\n]\n\nTEST_CASES_3D = []\nfor type_1 in (\n    {\"kernel_size\": 3, \"act\": Act.PRELU, \"norm\": Norm.INSTANCE},\n    {\"kernel_size\": 1, \"act\": None, \"norm\": Norm.INSTANCE},\n):\n    for type_2 in (\n        {\"kernel_size\": 3, \"act\": Act.PRELU, \"norm\": Norm.INSTANCE},\n        {\"kernel_size\": 1, \"act\": None, \"norm\": Norm.INSTANCE},\n    ):\n        test_case = [\n            {\n                \"spatial_dims\": 3,\n                \"in_channels\": 10,\n                \"r\": 3,\n                \"n_chns_1\": 3,\n                \"n_chns_2\": 5,\n                \"n_chns_3\": 11,\n                \"conv_param_1\": type_1,\n                \"conv_param_3\": type_2,\n            },\n            (16, 10, 32, 24, 48),  # 10-channel 3D, batch 16\n            (16, 11, 32, 24, 48),\n        ]\n        TEST_CASES_3D.append(test_case)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_se_block.py_TestSEBlockLayer_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_se_block.py_TestSEBlockLayer_", "embedding": null, "metadata": {"file_path": "tests/test_se_block.py", "file_name": "test_se_block.py", "file_type": "text/x-python", "category": "test", "start_line": 63, "end_line": 84, "span_ids": ["TestSEBlockLayer.test_script", "TestSEBlockLayer", "impl:12", "TestSEBlockLayer.test_shape", "TestSEBlockLayer.test_ill_arg"], "tokens": 197}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestSEBlockLayer(unittest.TestCase):\n    @parameterized.expand(TEST_CASES + TEST_CASES_3D)\n    def test_shape(self, input_param, input_shape, expected_shape):\n        net = SEBlock(**input_param).to(device)\n        with eval_mode(net):\n            result = net(torch.randn(input_shape).to(device))\n            self.assertEqual(result.shape, expected_shape)\n\n    def test_script(self):\n        input_param, input_shape, _ = TEST_CASES[0]\n        net = SEBlock(**input_param)\n        test_data = torch.randn(input_shape)\n        test_script_save(net, test_data)\n\n    def test_ill_arg(self):\n        with self.assertRaises(ValueError):\n            SEBlock(spatial_dims=1, in_channels=4, n_chns_1=2, n_chns_2=3, n_chns_3=4, r=100)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_seg_loss_integration.py_unittest_TEST_CASES._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_seg_loss_integration.py_unittest_TEST_CASES._", "embedding": null, "metadata": {"file_path": "tests/test_seg_loss_integration.py", "file_name": "test_seg_loss_integration.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 44, "span_ids": ["docstring"], "tokens": 707}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom parameterized import parameterized\n\nfrom monai.losses import DiceLoss, FocalLoss, GeneralizedDiceLoss, TverskyLoss\nfrom monai.networks import one_hot\n\nTEST_CASES = [\n    [DiceLoss, {\"to_onehot_y\": True, \"squared_pred\": True, \"smooth_nr\": 1e-4, \"smooth_dr\": 1e-4}, {}],\n    [DiceLoss, {\"to_onehot_y\": True, \"squared_pred\": True, \"smooth_nr\": 0, \"smooth_dr\": 1e-3}, {}],\n    [DiceLoss, {\"to_onehot_y\": False, \"squared_pred\": True, \"smooth_nr\": 0, \"smooth_dr\": 1e-3}, {}],\n    [DiceLoss, {\"to_onehot_y\": True, \"squared_pred\": True, \"batch\": True}, {}],\n    [DiceLoss, {\"to_onehot_y\": True, \"sigmoid\": True}, {}],\n    [DiceLoss, {\"to_onehot_y\": True, \"softmax\": True}, {}],\n    [FocalLoss, {\"to_onehot_y\": True, \"gamma\": 1.5, \"weight\": torch.tensor([1, 2])}, {}],\n    [FocalLoss, {\"to_onehot_y\": False, \"gamma\": 1.5, \"weight\": [1, 2]}, {}],\n    [FocalLoss, {\"to_onehot_y\": False, \"gamma\": 1.5, \"weight\": 1.0}, {}],\n    [FocalLoss, {\"to_onehot_y\": True, \"gamma\": 1.5}, {}],\n    [GeneralizedDiceLoss, {\"to_onehot_y\": True, \"softmax\": True}, {}],\n    [GeneralizedDiceLoss, {\"to_onehot_y\": True, \"sigmoid\": True}, {}],\n    [GeneralizedDiceLoss, {\"to_onehot_y\": True, \"sigmoid\": True, \"w_type\": \"simple\"}, {}],\n    [GeneralizedDiceLoss, {\"to_onehot_y\": True, \"sigmoid\": True, \"w_type\": \"uniform\"}, {}],\n    [GeneralizedDiceLoss, {\"to_onehot_y\": True, \"sigmoid\": True, \"w_type\": \"uniform\", \"batch\": True}, {}],\n    [GeneralizedDiceLoss, {\"to_onehot_y\": False, \"sigmoid\": True, \"w_type\": \"uniform\", \"batch\": True}, {}],\n    [TverskyLoss, {\"to_onehot_y\": True, \"softmax\": True, \"alpha\": 0.8, \"beta\": 0.2}, {}],\n    [TverskyLoss, {\"to_onehot_y\": True, \"softmax\": True, \"alpha\": 0.8, \"beta\": 0.2, \"batch\": True}, {}],\n    [TverskyLoss, {\"to_onehot_y\": True, \"softmax\": True, \"alpha\": 1.0, \"beta\": 0.0}, {}],\n    [TverskyLoss, {\"to_onehot_y\": False, \"softmax\": True, \"alpha\": 1.0, \"beta\": 0.0}, {}],\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_seg_loss_integration.py_TestSegLossIntegration_TestSegLossIntegration.test_convergence._define_a_one_layer_mode": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_seg_loss_integration.py_TestSegLossIntegration_TestSegLossIntegration.test_convergence._define_a_one_layer_mode", "embedding": null, "metadata": {"file_path": "tests/test_seg_loss_integration.py", "file_name": "test_seg_loss_integration.py", "file_type": "text/x-python", "category": "test", "start_line": 47, "end_line": 91, "span_ids": ["TestSegLossIntegration.setUp", "TestSegLossIntegration.test_convergence", "TestSegLossIntegration", "TestSegLossIntegration.tearDown"], "tokens": 473}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestSegLossIntegration(unittest.TestCase):\n    def setUp(self):\n        torch.backends.cudnn.deterministic = True\n        torch.backends.cudnn.benchmark = False\n        torch.manual_seed(0)\n        self.device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu:0\")\n\n    def tearDown(self):\n        torch.backends.cudnn.deterministic = False\n        torch.backends.cudnn.benchmark = True\n\n    @parameterized.expand(TEST_CASES)\n    def test_convergence(self, loss_type, loss_args, forward_args):\n        \"\"\"\n        The goal of this test is to assess if the gradient of the loss function\n        is correct by testing if we can train a one layer neural network\n        to segment one image.\n        We verify that the loss is decreasing in almost all SGD steps.\n        \"\"\"\n        learning_rate = 0.001\n        max_iter = 40\n\n        # define a simple 3d example\n        target_seg = torch.tensor(\n            [\n                [\n                    # raw 0\n                    [[0, 0, 0, 0], [0, 1, 1, 0], [0, 1, 1, 0], [0, 0, 0, 0]],\n                    # raw 1\n                    [[0, 0, 0, 0], [0, 1, 1, 0], [0, 1, 1, 0], [0, 0, 0, 0]],\n                    # raw 2\n                    [[0, 0, 0, 0], [0, 1, 1, 0], [0, 1, 1, 0], [0, 0, 0, 0]],\n                ]\n            ],\n            device=self.device,\n        )\n        target_seg = torch.unsqueeze(target_seg, dim=0)\n        image = 12 * target_seg + 27\n        image = image.float().to(self.device)\n        num_classes = 2\n        num_voxels = 3 * 4 * 4\n\n        target_onehot = one_hot(target_seg, num_classes=num_classes)\n\n        # define a one layer model\n        # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_seg_loss_integration.py_TestSegLossIntegration.test_convergence.OnelayerNet_TestSegLossIntegration.test_convergence.OnelayerNet.forward.return.x": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_seg_loss_integration.py_TestSegLossIntegration.test_convergence.OnelayerNet_TestSegLossIntegration.test_convergence.OnelayerNet.forward.return.x", "embedding": null, "metadata": {"file_path": "tests/test_seg_loss_integration.py", "file_name": "test_seg_loss_integration.py", "file_type": "text/x-python", "category": "test", "start_line": 92, "end_line": 105, "span_ids": ["TestSegLossIntegration.test_convergence"], "tokens": 178}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestSegLossIntegration(unittest.TestCase):\n\n    @parameterized.expand(TEST_CASES)\n    def test_convergence(self, loss_type, loss_args, forward_args):\n        # ... other code\n        class OnelayerNet(nn.Module):\n            def __init__(self):\n                super().__init__()\n                self.layer_1 = nn.Linear(num_voxels, 200)\n                self.acti = nn.ReLU()\n                self.layer_2 = nn.Linear(200, num_voxels * num_classes)\n\n            def forward(self, x):\n                x = x.view(-1, num_voxels)\n                x = self.layer_1(x)\n                x = self.acti(x)\n                x = self.layer_2(x)\n                x = x.view(-1, num_classes, 3, 4, 4)\n                return x\n        # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_seg_loss_integration.py_TestSegLossIntegration.test_convergence._initialise_the_network_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_seg_loss_integration.py_TestSegLossIntegration.test_convergence._initialise_the_network_", "embedding": null, "metadata": {"file_path": "tests/test_seg_loss_integration.py", "file_name": "test_seg_loss_integration.py", "file_type": "text/x-python", "category": "test", "start_line": 107, "end_line": 156, "span_ids": ["impl:3", "TestSegLossIntegration.test_convergence"], "tokens": 384}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestSegLossIntegration(unittest.TestCase):\n\n    @parameterized.expand(TEST_CASES)\n    def test_convergence(self, loss_type, loss_args, forward_args):\n\n        # initialise the network\n        net = OnelayerNet().to(self.device)\n\n        # initialize the loss\n        loss = loss_type(**loss_args)\n\n        # initialize a SGD optimizer\n        optimizer = optim.Adam(net.parameters(), lr=learning_rate)\n\n        loss_history = []\n        init_output = None\n\n        # train the network\n        for iter_i in range(max_iter):\n            # set the gradient to zero\n            optimizer.zero_grad()\n\n            # forward pass\n            output = net(image)\n            if init_output is None:\n                init_output = torch.argmax(output, 1).detach().cpu().numpy()\n\n            if loss_args[\"to_onehot_y\"] is False:\n                loss_val = loss(output, target_onehot, **forward_args)\n            else:\n                loss_val = loss(output, target_seg, **forward_args)\n\n            if iter_i % 10 == 0:\n                pred = torch.argmax(output, 1).detach().cpu().numpy()\n                gt = target_seg.detach().cpu().numpy()[:, 0]\n                print(f\"{loss_type.__name__} iter: {iter_i}, acc: {np.sum(pred == gt) / np.prod(pred.shape)}\")\n\n            # backward pass\n            loss_val.backward()\n            optimizer.step()\n\n            # stats\n            loss_history.append(loss_val.item())\n\n        pred = torch.argmax(output, 1).detach().cpu().numpy()\n        target = target_seg.detach().cpu().numpy()[:, 0]\n        # initial predictions are bad\n        self.assertTrue(not np.allclose(init_output, target))\n        # final predictions are good\n        np.testing.assert_allclose(pred, target)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_shift_intensity.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_shift_intensity.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_shift_intensity.py", "file_name": "test_shift_intensity.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 30, "span_ids": ["TestShiftIntensity.test_value", "TestShiftIntensity", "impl", "docstring"], "tokens": 99}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\n\nfrom monai.transforms import ShiftIntensity\nfrom tests.utils import NumpyImageTestCase2D\n\n\nclass TestShiftIntensity(NumpyImageTestCase2D):\n    def test_value(self):\n        shifter = ShiftIntensity(offset=1.0)\n        result = shifter(self.imt)\n        expected = self.imt + 1.0\n        np.testing.assert_allclose(result, expected)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_shift_intensityd.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_shift_intensityd.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_shift_intensityd.py", "file_name": "test_shift_intensityd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 42, "span_ids": ["impl", "TestShiftIntensityd.test_factor", "TestShiftIntensityd", "docstring", "TestShiftIntensityd.test_value"], "tokens": 255}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\n\nfrom monai.transforms import IntensityStatsd, ShiftIntensityd\nfrom tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, assert_allclose\n\n\nclass TestShiftIntensityd(NumpyImageTestCase2D):\n    def test_value(self):\n        key = \"img\"\n        for p in TEST_NDARRAYS:\n            shifter = ShiftIntensityd(keys=[key], offset=1.0)\n            result = shifter({key: p(self.imt)})\n            expected = self.imt + 1.0\n            assert_allclose(result[key], p(expected))\n\n    def test_factor(self):\n        key = \"img\"\n        stats = IntensityStatsd(keys=key, ops=\"max\", key_prefix=\"orig\")\n        shifter = ShiftIntensityd(keys=[key], offset=1.0, factor_key=[\"orig_max\"])\n        data = {key: self.imt, key + \"_meta_dict\": {\"affine\": None}}\n\n        result = shifter(stats(data))\n        expected = self.imt + 1.0 * np.nanmax(self.imt)\n        np.testing.assert_allclose(result[key], expected)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_simple_aspp.py_unittest_TEST_ILL_CASES._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_simple_aspp.py_unittest_TEST_ILL_CASES._", "embedding": null, "metadata": {"file_path": "tests/test_simple_aspp.py", "file_name": "test_simple_aspp.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 66, "span_ids": ["impl:3", "docstring"], "tokens": 590}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks import eval_mode\nfrom monai.networks.blocks import SimpleASPP\n\nTEST_CASES = [\n    [  # 32-channel 2D, batch 7\n        {\"spatial_dims\": 2, \"in_channels\": 32, \"conv_out_channels\": 3, \"norm_type\": (\"batch\", {\"affine\": False})},\n        (7, 32, 18, 20),\n        (7, 12, 18, 20),\n    ],\n    [  # 4-channel 1D, batch 16\n        {\"spatial_dims\": 1, \"in_channels\": 4, \"conv_out_channels\": 8, \"acti_type\": (\"PRELU\", {\"num_parameters\": 32})},\n        (16, 4, 17),\n        (16, 32, 17),\n    ],\n    [  # 3-channel 3D, batch 16\n        {\"spatial_dims\": 3, \"in_channels\": 3, \"conv_out_channels\": 2},\n        (16, 3, 17, 18, 19),\n        (16, 8, 17, 18, 19),\n    ],\n    [  # 3-channel 3D, batch 16\n        {\n            \"spatial_dims\": 3,\n            \"in_channels\": 3,\n            \"conv_out_channels\": 2,\n            \"kernel_sizes\": (1, 3, 3),\n            \"dilations\": (1, 2, 4),\n        },\n        (16, 3, 17, 18, 19),\n        (16, 6, 17, 18, 19),\n    ],\n]\n\nTEST_ILL_CASES = [\n    [  # 3-channel 3D, batch 16, wrong k and d sizes.\n        {\"spatial_dims\": 3, \"in_channels\": 3, \"conv_out_channels\": 2, \"kernel_sizes\": (1, 3, 3), \"dilations\": (1, 2)},\n        (16, 3, 17, 18, 19),\n        ValueError,\n    ],\n    [  # 3-channel 3D, batch 16, wrong k and d sizes.\n        {\n            \"spatial_dims\": 3,\n            \"in_channels\": 3,\n            \"conv_out_channels\": 2,\n            \"kernel_sizes\": (1, 3, 4),\n            \"dilations\": (1, 2, 3),\n        },\n        (16, 3, 17, 18, 19),\n        NotImplementedError,  # unknown padding k=4, d=3\n    ],\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_simple_aspp.py_TestChannelSELayer_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_simple_aspp.py_TestChannelSELayer_", "embedding": null, "metadata": {"file_path": "tests/test_simple_aspp.py", "file_name": "test_simple_aspp.py", "file_type": "text/x-python", "category": "test", "start_line": 69, "end_line": 85, "span_ids": ["TestChannelSELayer.test_shape", "impl:5", "TestChannelSELayer", "TestChannelSELayer.test_ill_args"], "tokens": 121}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestChannelSELayer(unittest.TestCase):\n    @parameterized.expand(TEST_CASES)\n    def test_shape(self, input_param, input_shape, expected_shape):\n        net = SimpleASPP(**input_param)\n        with eval_mode(net):\n            result = net(torch.randn(input_shape))\n            self.assertEqual(result.shape, expected_shape)\n\n    @parameterized.expand(TEST_ILL_CASES)\n    def test_ill_args(self, input_param, input_shape, error_type):\n        with self.assertRaises(error_type):\n            SimpleASPP(**input_param)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_spacing.py_TestSpacingCase_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_spacing.py_TestSpacingCase_", "embedding": null, "metadata": {"file_path": "tests/test_spacing.py", "file_name": "test_spacing.py", "file_type": "text/x-python", "category": "test", "start_line": 197, "end_line": 218, "span_ids": ["TestSpacingCase.test_spacing", "TestSpacingCase", "impl:19"], "tokens": 237}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestSpacingCase(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_spacing(self, in_type, init_param, img, data_param, expected_output):\n        _img = in_type(img)\n        output_data, _, new_affine = Spacing(**init_param)(_img, **data_param)\n        if isinstance(_img, torch.Tensor):\n            self.assertEqual(_img.device, output_data.device)\n            output_data = output_data.cpu()\n\n        np.testing.assert_allclose(output_data, expected_output, atol=1e-3, rtol=1e-3)\n        sr = len(output_data.shape) - 1\n        if isinstance(init_param[\"pixdim\"], float):\n            init_param[\"pixdim\"] = [init_param[\"pixdim\"]] * sr\n        init_pixdim = ensure_tuple(init_param[\"pixdim\"])\n        init_pixdim = init_param[\"pixdim\"][:sr]\n        norm = np.sqrt(np.sum(np.square(new_affine), axis=0))[:sr]\n        np.testing.assert_allclose(fall_back_tuple(init_pixdim, norm), norm)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_spatial_cropd.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_spatial_cropd.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_spatial_cropd.py", "file_name": "test_spatial_cropd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 68, "span_ids": ["TestSpatialCropd.test_shape", "impl:9", "TestSpatialCropd", "docstring"], "tokens": 556}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import SpatialCropd\nfrom tests.utils import TEST_NDARRAYS\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    TESTS.append(\n        [\n            {\"keys\": [\"img\"], \"roi_center\": [1, 1, 1], \"roi_size\": [2, 2, 2]},\n            {\"img\": p(np.random.randint(0, 2, size=[3, 3, 3, 3]))},\n            (3, 2, 2, 2),\n        ]\n    )\n    TESTS.append(\n        [\n            {\"keys\": [\"img\"], \"roi_start\": [0, 0, 0], \"roi_end\": [2, 2, 2]},\n            {\"img\": p(np.random.randint(0, 2, size=[3, 3, 3, 3]))},\n            (3, 2, 2, 2),\n        ]\n    )\n    TESTS.append(\n        [\n            {\"keys\": [\"img\"], \"roi_start\": [0, 0], \"roi_end\": [2, 2]},\n            {\"img\": p(np.random.randint(0, 2, size=[3, 3, 3, 3]))},\n            (3, 2, 2, 3),\n        ]\n    )\n    TESTS.append(\n        [\n            {\"keys\": [\"img\"], \"roi_start\": [0, 0, 0, 0, 0], \"roi_end\": [2, 2, 2, 2, 2]},\n            {\"img\": p(np.random.randint(0, 2, size=[3, 3, 3, 3]))},\n            (3, 2, 2, 2),\n        ]\n    )\n    TESTS.append(\n        [\n            {\"keys\": [\"img\"], \"roi_slices\": [slice(s, e) for s, e in zip([-1, -2, 0], [None, None, 2])]},\n            {\"img\": p(np.random.randint(0, 2, size=[3, 3, 3, 3]))},\n            (3, 1, 2, 2),\n        ]\n    )\n\n\nclass TestSpatialCropd(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_shape(self, input_param, input_data, expected_shape):\n        result = SpatialCropd(**input_param)(input_data)\n        self.assertTupleEqual(result[\"img\"].shape, expected_shape)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_spatial_padd.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_spatial_padd.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_spatial_padd.py", "file_name": "test_spatial_padd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 54, "span_ids": ["impl:9", "TestSpatialPadd.test_pad_shape", "TestSpatialPadd", "docstring"], "tokens": 418}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import SpatialPadd\n\nTEST_CASE_1 = [\n    {\"keys\": [\"img\"], \"spatial_size\": [15, 8, 8], \"method\": \"symmetric\", \"mode\": \"constant\"},\n    {\"img\": np.zeros((3, 8, 8, 4))},\n    np.zeros((3, 15, 8, 8)),\n]\n\nTEST_CASE_2 = [\n    {\"keys\": [\"img\"], \"spatial_size\": [15, 8, 8], \"method\": \"end\", \"mode\": \"constant\"},\n    {\"img\": np.zeros((3, 8, 8, 4))},\n    np.zeros((3, 15, 8, 8)),\n]\n\nTEST_CASE_3 = [\n    {\"keys\": [\"img\"], \"spatial_size\": [15, 8, 8], \"method\": \"end\", \"mode\": {\"constant\"}},\n    {\"img\": np.zeros((3, 8, 8, 4))},\n    np.zeros((3, 15, 8, 8)),\n]\n\nTEST_CASE_4 = [\n    {\"keys\": [\"img\"], \"spatial_size\": [15, 8, -1], \"method\": \"end\", \"mode\": {\"constant\"}},\n    {\"img\": np.zeros((3, 8, 4, 4))},\n    np.zeros((3, 15, 8, 4)),\n]\n\n\nclass TestSpatialPadd(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4])\n    def test_pad_shape(self, input_param, input_data, expected_val):\n        padder = SpatialPadd(**input_param)\n        result = padder(input_data)\n        np.testing.assert_allclose(result[\"img\"].shape, expected_val.shape)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_split_channel.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_split_channel.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_split_channel.py", "file_name": "test_split_channel.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 38, "span_ids": ["TestSplitChannel", "TestSplitChannel.test_shape", "impl:8", "docstring"], "tokens": 276}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import SplitChannel\nfrom tests.utils import TEST_NDARRAYS\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    TESTS.append([{\"channel_dim\": 1}, p(np.random.randint(2, size=(4, 3, 3, 4))), (4, 1, 3, 4)])\n    TESTS.append([{\"channel_dim\": 0}, p(np.random.randint(2, size=(3, 3, 4))), (1, 3, 4)])\n    TESTS.append([{\"channel_dim\": 2}, p(np.random.randint(2, size=(3, 2, 4))), (3, 2, 1)])\n    TESTS.append([{\"channel_dim\": -1}, p(np.random.randint(2, size=(3, 2, 4))), (3, 2, 1)])\n\n\nclass TestSplitChannel(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_shape(self, input_param, test_data, expected_shape):\n        result = SplitChannel(**input_param)(test_data)\n        for data in result:\n            self.assertTupleEqual(data.shape, expected_shape)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_split_channeld.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_split_channeld.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_split_channeld.py", "file_name": "test_split_channeld.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 68, "span_ids": ["impl:9", "TestSplitChanneld.test_shape", "TestSplitChanneld", "docstring"], "tokens": 495}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import SplitChanneld\nfrom tests.utils import TEST_NDARRAYS\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    TESTS.append(\n        [\n            {\"keys\": \"pred\", \"output_postfixes\": [\"cls1\", \"cls2\", \"cls3\"], \"channel_dim\": 1},\n            {\"pred\": p(np.random.randint(2, size=(4, 3, 3, 4)))},\n            (4, 1, 3, 4),\n        ]\n    )\n\n    TESTS.append(\n        [\n            {\"keys\": \"pred\", \"output_postfixes\": [\"cls1\", \"cls2\", \"cls3\"], \"channel_dim\": 0},\n            {\"pred\": p(np.random.randint(2, size=(3, 3, 4)))},\n            (1, 3, 4),\n        ]\n    )\n\n    TESTS.append(\n        [\n            {\"keys\": \"pred\", \"output_postfixes\": [\"cls1\", \"cls2\", \"cls3\", \"cls4\"], \"channel_dim\": 2},\n            {\"pred\": p(np.random.randint(2, size=(3, 2, 4)))},\n            (3, 2, 1),\n        ]\n    )\n\n    TESTS.append(\n        [\n            {\"keys\": \"pred\", \"output_postfixes\": [\"cls1\", \"cls2\", \"cls3\", \"cls4\"], \"channel_dim\": -1},\n            {\"pred\": p(np.random.randint(2, size=(3, 2, 4)))},\n            (3, 2, 1),\n        ]\n    )\n\n    TESTS.append([{\"keys\": \"pred\", \"channel_dim\": 1}, {\"pred\": p(np.random.randint(2, size=(3, 2, 4)))}, (3, 1, 4)])\n\n\nclass TestSplitChanneld(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_shape(self, input_param, test_data, expected_shape):\n        result = SplitChanneld(**input_param)(test_data)\n        for k, v in result.items():\n            if \"_\" in k:\n                self.assertTupleEqual(v.shape, expected_shape)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_squeezedimd.py_TestSqueezeDim_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_squeezedimd.py_TestSqueezeDim_", "embedding": null, "metadata": {"file_path": "tests/test_squeezedimd.py", "file_name": "test_squeezedimd.py", "file_type": "text/x-python", "category": "test", "start_line": 79, "end_line": 94, "span_ids": ["TestSqueezeDim.test_invalid_inputs", "TestSqueezeDim.test_shape", "TestSqueezeDim", "impl:10"], "tokens": 127}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestSqueezeDim(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_shape(self, input_param, test_data, expected_shape):\n        result = SqueezeDimd(**input_param)(test_data)\n        self.assertTupleEqual(result[\"img\"].shape, expected_shape)\n        self.assertTupleEqual(result[\"seg\"].shape, expected_shape)\n\n    @parameterized.expand(TESTS_FAIL)\n    def test_invalid_inputs(self, exception, input_param, test_data):\n        with self.assertRaises(exception):\n            SqueezeDimd(**input_param)(test_data)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_threshold_intensity.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_threshold_intensity.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_threshold_intensity.py", "file_name": "test_threshold_intensity.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 37, "span_ids": ["TestThresholdIntensity.test_value", "TestThresholdIntensity", "impl:7", "docstring"], "tokens": 292}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import ThresholdIntensity\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    TESTS.append([p, {\"threshold\": 5, \"above\": True, \"cval\": 0}, (0, 0, 0, 0, 0, 0, 6, 7, 8, 9)])\n    TESTS.append([p, {\"threshold\": 5, \"above\": False, \"cval\": 0}, (0, 1, 2, 3, 4, 0, 0, 0, 0, 0)])\n    TESTS.append([p, {\"threshold\": 5, \"above\": True, \"cval\": 5}, (5, 5, 5, 5, 5, 5, 6, 7, 8, 9)])\n\n\nclass TestThresholdIntensity(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_value(self, in_type, input_param, expected_value):\n        test_data = in_type(np.arange(10))\n        result = ThresholdIntensity(**input_param)(test_data)\n        assert_allclose(result, in_type(expected_value))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_threshold_intensityd.py_TestThresholdIntensityd_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_threshold_intensityd.py_TestThresholdIntensityd_", "embedding": null, "metadata": {"file_path": "tests/test_threshold_intensityd.py", "file_name": "test_threshold_intensityd.py", "file_type": "text/x-python", "category": "test", "start_line": 45, "end_line": 57, "span_ids": ["TestThresholdIntensityd", "TestThresholdIntensityd.test_value", "impl:7"], "tokens": 129}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestThresholdIntensityd(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_value(self, in_type, input_param, expected_value):\n        test_data = {\"image\": in_type(np.arange(10)), \"label\": in_type(np.arange(10)), \"extra\": in_type(np.arange(10))}\n        result = ThresholdIntensityd(**input_param)(test_data)\n        assert_allclose(result[\"image\"], in_type(expected_value))\n        assert_allclose(result[\"label\"], in_type(expected_value))\n        assert_allclose(result[\"extra\"], in_type(expected_value))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_to_onehot.py_unittest_TEST_CASE_4._no_channel_0D_batch": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_to_onehot.py_unittest_TEST_CASE_4._no_channel_0D_batch", "embedding": null, "metadata": {"file_path": "tests/test_to_onehot.py", "file_name": "test_to_onehot.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 41, "span_ids": ["docstring"], "tokens": 437}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks import one_hot\n\nTEST_CASE_1 = [  # single channel 2D, batch 3, shape (2, 1, 2, 2)\n    {\"labels\": torch.tensor([[[[0, 1], [1, 2]]], [[[2, 1], [1, 0]]]]), \"num_classes\": 3},\n    (2, 3, 2, 2),\n]\n\nTEST_CASE_2 = [  # single channel 1D, batch 2, shape (2, 1, 4)\n    {\"labels\": torch.tensor([[[1, 2, 2, 0]], [[2, 1, 0, 1]]]), \"num_classes\": 3},\n    (2, 3, 4),\n    np.array([[[0, 0, 0, 1], [1, 0, 0, 0], [0, 1, 1, 0]], [[0, 0, 1, 0], [0, 1, 0, 1], [1, 0, 0, 0]]]),\n]\n\nTEST_CASE_3 = [  # single channel 0D, batch 2, shape (2, 1)\n    {\"labels\": torch.tensor([[1.0], [2.0]]), \"num_classes\": 3},\n    (2, 3),\n    np.array([[0, 1, 0], [0, 0, 1]]),\n]\n\nTEST_CASE_4 = [  # no channel 0D, batch 3, shape (3)\n    {\"labels\": torch.tensor([1, 2, 0]), \"num_classes\": 3, \"dtype\": torch.long},\n    (3, 3),\n    np.array([[0, 1, 0], [0, 0, 1], [1, 0, 0]]),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_to_onehot.py_TestToOneHot_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_to_onehot.py_TestToOneHot_", "embedding": null, "metadata": {"file_path": "tests/test_to_onehot.py", "file_name": "test_to_onehot.py", "file_type": "text/x-python", "category": "test", "start_line": 44, "end_line": 61, "span_ids": ["impl:9", "TestToOneHot.test_shape", "TestToOneHot"], "tokens": 140}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestToOneHot(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4])\n    def test_shape(self, input_data, expected_shape, expected_result=None):\n        result = one_hot(**input_data)\n        self.assertEqual(result.shape, expected_shape)\n        if expected_result is not None:\n            self.assertTrue(np.allclose(expected_result, result.numpy()))\n\n        if \"dtype\" in input_data:\n            self.assertEqual(result.dtype, input_data[\"dtype\"])\n        else:\n            # by default, expecting float type\n            self.assertEqual(result.dtype, torch.float)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_tversky_loss.py_TestTverskyLoss_TestTverskyLoss.test_ill_shape.None_2.TverskyLoss_reduction_Non": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_tversky_loss.py_TestTverskyLoss_TestTverskyLoss.test_ill_shape.None_2.TverskyLoss_reduction_Non", "embedding": null, "metadata": {"file_path": "tests/test_tversky_loss.py", "file_name": "test_tversky_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 105, "end_line": 120, "span_ids": ["TestTverskyLoss.test_ill_shape", "TestTverskyLoss", "TestTverskyLoss.test_shape"], "tokens": 210}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestTverskyLoss(unittest.TestCase):\n    @parameterized.expand(TEST_CASES)\n    def test_shape(self, input_param, input_data, expected_val):\n        result = TverskyLoss(**input_param).forward(**input_data)\n        np.testing.assert_allclose(result.detach().cpu().numpy(), expected_val, rtol=1e-4)\n\n    def test_ill_shape(self):\n        loss = TverskyLoss()\n        with self.assertRaisesRegex(AssertionError, \"\"):\n            loss.forward(torch.ones((2, 2, 3)), torch.ones((4, 5, 6)))\n        chn_input = torch.ones((1, 1, 3))\n        chn_target = torch.ones((1, 1, 3))\n        with self.assertRaisesRegex(ValueError, \"\"):\n            TverskyLoss(reduction=\"unknown\")(chn_input, chn_target)\n        with self.assertRaisesRegex(ValueError, \"\"):\n            TverskyLoss(reduction=None)(chn_input, chn_target)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_tversky_loss.py_TestTverskyLoss.test_input_warnings_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_tversky_loss.py_TestTverskyLoss.test_input_warnings_", "embedding": null, "metadata": {"file_path": "tests/test_tversky_loss.py", "file_name": "test_tversky_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 174, "end_line": 196, "span_ids": ["impl:3", "TestTverskyLoss.test_input_warnings", "TestTverskyLoss.test_script"], "tokens": 217}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestTverskyLoss(unittest.TestCase):\n\n    def test_input_warnings(self):\n        chn_input = torch.ones((1, 1, 3))\n        chn_target = torch.ones((1, 1, 3))\n        with self.assertWarns(Warning):\n            loss = TverskyLoss(include_background=False)\n            loss.forward(chn_input, chn_target)\n        with self.assertWarns(Warning):\n            loss = TverskyLoss(softmax=True)\n            loss.forward(chn_input, chn_target)\n        with self.assertWarns(Warning):\n            loss = TverskyLoss(to_onehot_y=True)\n            loss.forward(chn_input, chn_target)\n\n    @SkipIfBeforePyTorchVersion((1, 7, 0))\n    def test_script(self):\n        loss = TverskyLoss()\n        test_input = torch.ones(2, 1, 8, 8)\n        test_script_save(loss, test_input, test_input)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_zoom.py_unittest_INVALID_CASES._None_None_bilinear": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_zoom.py_unittest_INVALID_CASES._None_None_bilinear", "embedding": null, "metadata": {"file_path": "tests/test_zoom.py", "file_name": "test_zoom.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 24, "span_ids": ["docstring"], "tokens": 125}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\nfrom scipy.ndimage import zoom as zoom_scipy\n\nfrom monai.transforms import Zoom\nfrom tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, assert_allclose\n\nVALID_CASES = [(1.5, \"nearest\"), (1.5, \"nearest\"), (0.8, \"bilinear\"), (0.8, \"area\")]\n\nINVALID_CASES = [((None, None), \"bilinear\", TypeError), ((0.9, 0.9), \"s\", ValueError)]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_zoom_affine.py_unittest_VALID_CASES._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_zoom_affine.py_unittest_VALID_CASES._", "embedding": null, "metadata": {"file_path": "tests/test_zoom_affine.py", "file_name": "test_zoom_affine.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 47, "span_ids": ["docstring"], "tokens": 643}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport nibabel as nib\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.data.utils import zoom_affine\n\nVALID_CASES = [\n    (\n        np.array([[2, 1, 4], [-1, -3, 5], [0, 0, 1]]),\n        (10, 20, 30),\n        np.array([[8.94427191, -8.94427191, 0], [-4.47213595, -17.88854382, 0], [0.0, 0.0, 1.0]]),\n    ),\n    (\n        np.array([[1, 0, 0, 4], [0, 2, 0, 5], [0, 0, 3, 6], [0, 0, 0, 1]]),\n        (10, 20, 30),\n        np.array([[10, 0, 0, 0], [0, 20, 0, 0], [0, 0, 30, 0], [0, 0, 0, 1]]),\n    ),\n    (\n        np.array([[1, 0, 0, 4], [0, 2, 0, 5], [0, 0, 3, 6], [0, 0, 0, 1]]),\n        (10, 20),\n        np.array([[10, 0, 0, 0], [0, 20, 0, 0], [0, 0, 3, 0], [0, 0, 0, 1]]),\n    ),\n    (\n        np.array([[1, 0, 0, 4], [0, 2, 0, 5], [0, 0, 3, 6], [0, 0, 0, 1]]),\n        (10,),\n        np.array([[10, 0, 0, 0], [0, 2, 0, 0], [0, 0, 3, 0], [0, 0, 0, 1]]),\n    ),\n    (\n        [[1, 0, 10], [0, 1, 20], [0, 0, 1]]\n        @ ([[0, -1, 0], [1, 0, 0], [0, 0, 1]] @ np.array([[2, 0.3, 0], [0, 3, 0], [0, 0, 1]])),\n        (4, 5, 6),\n        ([[0, -1, 0], [1, 0, 0], [0, 0, 1]] @ np.array([[4, 0, 0], [0, 5, 0], [0, 0, 1]])),\n    ),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_zoom_affine.py_DIAGONAL_CASES_DIAGONAL_CASES._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_zoom_affine.py_DIAGONAL_CASES_DIAGONAL_CASES._", "embedding": null, "metadata": {"file_path": "tests/test_zoom_affine.py", "file_name": "test_zoom_affine.py", "file_type": "text/x-python", "category": "test", "start_line": 49, "end_line": 61, "span_ids": ["impl:3"], "tokens": 291}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "DIAGONAL_CASES = [\n    (\n        np.array([[-1, 0, 0, 4], [0, 2, 0, 5], [0, 0, 3, 6], [0, 0, 0, 1]]),\n        (10, 20, 30),\n        np.array([[10, 0, 0, 0], [0, 20, 0, 0], [0, 0, 30, 0], [0, 0, 0, 1]]),\n    ),\n    (np.array([[2, 1, 4], [-1, -3, 5], [0, 0, 1]]), (10, 20, 30), np.array([[10, 0, 0], [0, 20, 0], [0.0, 0.0, 1.0]])),\n    (  # test default scale from affine\n        np.array([[2, 1, 4], [-1, -3, 5], [0, 0, 1]]),\n        (10,),\n        np.array([[10, 0, 0], [0, 3.162278, 0], [0.0, 0.0, 1.0]]),\n    ),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_zoom_affine.py_TestZoomAffine_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_zoom_affine.py_TestZoomAffine_", "embedding": null, "metadata": {"file_path": "tests/test_zoom_affine.py", "file_name": "test_zoom_affine.py", "file_type": "text/x-python", "category": "test", "start_line": 64, "end_line": 81, "span_ids": ["TestZoomAffine.test_diagonal", "TestZoomAffine.test_correct", "impl:5", "TestZoomAffine"], "tokens": 211}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestZoomAffine(unittest.TestCase):\n    @parameterized.expand(VALID_CASES)\n    def test_correct(self, affine, scale, expected):\n        output = zoom_affine(affine, scale, diagonal=False)\n        ornt_affine = nib.orientations.ornt2axcodes(nib.orientations.io_orientation(output))\n        ornt_output = nib.orientations.ornt2axcodes(nib.orientations.io_orientation(affine))\n        np.testing.assert_array_equal(ornt_affine, ornt_output)\n        np.testing.assert_allclose(output, expected, rtol=1e-6, atol=1e-6)\n\n    @parameterized.expand(DIAGONAL_CASES)\n    def test_diagonal(self, affine, scale, expected):\n        output = zoom_affine(affine, scale, diagonal=True)\n        np.testing.assert_allclose(output, expected, rtol=1e-6, atol=1e-6)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_zoomd.py_unittest_INVALID_CASES._no_zoom_None_bilin": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_zoomd.py_unittest_INVALID_CASES._no_zoom_None_bilin", "embedding": null, "metadata": {"file_path": "tests/test_zoomd.py", "file_name": "test_zoomd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 23, "span_ids": ["docstring"], "tokens": 121}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\nfrom scipy.ndimage import zoom as zoom_scipy\n\nfrom monai.transforms import Zoomd\nfrom tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, assert_allclose\n\nVALID_CASES = [(1.5, \"nearest\", False), (0.3, \"bilinear\", False), (0.8, \"bilinear\", False)]\n\nINVALID_CASES = [(\"no_zoom\", None, \"bilinear\", TypeError), (\"invalid_order\", 0.9, \"s\", ValueError)]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_zoomd.py_TestZoomd.test_keep_size_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_zoomd.py_TestZoomd.test_keep_size_", "embedding": null, "metadata": {"file_path": "tests/test_zoomd.py", "file_name": "test_zoomd.py", "file_type": "text/x-python", "category": "test", "start_line": 43, "end_line": 65, "span_ids": ["TestZoomd.test_keep_size", "impl:5", "TestZoomd.test_invalid_inputs"], "tokens": 243}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestZoomd(NumpyImageTestCase2D):\n\n    def test_keep_size(self):\n        key = \"img\"\n        zoom_fn = Zoomd(key, zoom=0.6, keep_size=True, padding_mode=\"constant\", constant_values=2)\n        for p in TEST_NDARRAYS:\n            zoomed = zoom_fn({key: p(self.imt[0])})\n            np.testing.assert_array_equal(zoomed[key].shape, self.imt.shape[1:])\n\n            zoom_fn = Zoomd(key, zoom=1.3, keep_size=True)\n            zoomed = zoom_fn({key: self.imt[0]})\n            self.assertTrue(np.array_equal(zoomed[key].shape, self.imt.shape[1:]))\n\n    @parameterized.expand(INVALID_CASES)\n    def test_invalid_inputs(self, _, zoom, mode, raises):\n        key = \"img\"\n        for p in TEST_NDARRAYS:\n            with self.assertRaises(raises):\n                zoom_fn = Zoomd(key, zoom=zoom, mode=mode)\n                zoom_fn({key: p(self.imt[0])})\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/utils.py_NumpyImageTestCase2D_TorchImageTestCase2D.setUp.self.segn.torch_tensor_self_segn_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/utils.py_NumpyImageTestCase2D_TorchImageTestCase2D.setUp.self.segn.torch_tensor_self_segn_", "embedding": null, "metadata": {"file_path": "tests/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 527, "end_line": 548, "span_ids": ["NumpyImageTestCase2D", "TorchImageTestCase2D.setUp", "TorchImageTestCase2D", "NumpyImageTestCase2D.setUp"], "tokens": 207}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class NumpyImageTestCase2D(unittest.TestCase):\n    im_shape = (128, 64)\n    input_channels = 1\n    output_channels = 4\n    num_classes = 3\n\n    def setUp(self):\n        im, msk = create_test_image_2d(\n            self.im_shape[0], self.im_shape[1], num_objs=4, rad_max=20, noise_max=0.0, num_seg_classes=self.num_classes\n        )\n\n        self.imt = im[None, None]\n        self.seg1 = (msk[None, None] > 0).astype(np.float32)\n        self.segn = msk[None, None]\n\n\nclass TorchImageTestCase2D(NumpyImageTestCase2D):\n    def setUp(self):\n        NumpyImageTestCase2D.setUp(self)\n        self.imt = torch.tensor(self.imt)\n        self.seg1 = torch.tensor(self.seg1)\n        self.segn = torch.tensor(self.segn)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/utils.py_query_memory_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/utils.py_query_memory_", "embedding": null, "metadata": {"file_path": "tests/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 632, "end_line": 658, "span_ids": ["query_memory", "impl:10"], "tokens": 290}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def query_memory(n=2):\n    \"\"\"\n    Find best n idle devices and return a string of device ids using the `nvidia-smi` command.\n    \"\"\"\n    bash_string = \"nvidia-smi --query-gpu=power.draw,temperature.gpu,memory.used --format=csv,noheader,nounits\"\n\n    try:\n        p1 = Popen(bash_string.split(), stdout=PIPE)\n        output, error = p1.communicate()\n        free_memory = [x.split(\",\") for x in output.decode(\"utf-8\").split(\"\\n\")[:-1]]\n        free_memory = np.asarray(free_memory, dtype=float).T\n        free_memory[1] += free_memory[0]  # combine 0/1 column measures\n        ids = np.lexsort(free_memory)[:n]\n    except (TypeError, IndexError, OSError):\n        ids = range(n) if isinstance(n, int) else []\n    return \",\".join(f\"{int(x)}\" for x in ids)\n\n\nTEST_NDARRAYS: Tuple[Callable] = (np.array, torch.as_tensor)  # type: ignore\nif torch.cuda.is_available():\n    gpu_tensor: Callable = partial(torch.as_tensor, device=\"cuda\")\n    TEST_NDARRAYS = TEST_NDARRAYS + (gpu_tensor,)  # type: ignore\n\n\nif __name__ == \"__main__\":\n    print(query_memory())", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_get_config_from_root_get_config_from_root.return.cfg": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_get_config_from_root_get_config_from_root.return.cfg", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 328, "end_line": 355, "span_ids": ["get_config_from_root"], "tokens": 293}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_config_from_root(root):\n    \"\"\"Read the project setup.cfg file to determine Versioneer config.\"\"\"\n    # This might raise EnvironmentError (if setup.cfg is missing), or\n    # configparser.NoSectionError (if it lacks a [versioneer] section), or\n    # configparser.NoOptionError (if it lacks \"VCS=\"). See the docstring at\n    # the top of versioneer.py for instructions on writing your setup.cfg .\n    setup_cfg = os.path.join(root, \"setup.cfg\")\n    parser = configparser.ConfigParser()\n    with open(setup_cfg, \"r\") as f:\n        parser.read_file(f)\n    VCS = parser.get(\"versioneer\", \"VCS\")  # mandatory\n\n    def get(parser, name):\n        if parser.has_option(\"versioneer\", name):\n            return parser.get(\"versioneer\", name)\n        return None\n\n    cfg = VersioneerConfig()\n    cfg.VCS = VCS\n    cfg.style = get(parser, \"style\") or \"\"\n    cfg.versionfile_source = get(parser, \"versionfile_source\")\n    cfg.versionfile_build = get(parser, \"versionfile_build\")\n    cfg.tag_prefix = get(parser, \"tag_prefix\")\n    if cfg.tag_prefix in (\"''\", '\"\"'):\n        cfg.tag_prefix = \"\"\n    cfg.parentdir_prefix = get(parser, \"parentdir_prefix\")\n    cfg.verbose = get(parser, \"verbose\")\n    return cfg", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_NotThisMethod_register_vcs_handler.return.decorate": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_NotThisMethod_register_vcs_handler.return.decorate", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 358, "end_line": 377, "span_ids": ["register_vcs_handler", "NotThisMethod", "impl"], "tokens": 128}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class NotThisMethod(Exception):\n    \"\"\"Exception raised if a method is not valid for the current scenario.\"\"\"\n\n\n# these dictionaries contain VCS-specific tools\nLONG_VERSION_PY = {}\nHANDLERS = {}\n\n\ndef register_vcs_handler(vcs, method):  # decorator\n    \"\"\"Create decorator to mark a method as the handler of a VCS.\"\"\"\n\n    def decorate(f):\n        \"\"\"Store f in HANDLERS[vcs][method].\"\"\"\n        if vcs not in HANDLERS:\n            HANDLERS[vcs] = {}\n        HANDLERS[vcs][method] = f\n        return f\n\n    return decorate", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_run_command_run_command.return.stdout_p_returncode": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_run_command_run_command.return.stdout_p_returncode", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 380, "end_line": 410, "span_ids": ["run_command"], "tokens": 273}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, env=None):\n    \"\"\"Call the given command(s).\"\"\"\n    assert isinstance(commands, list)\n    p = None\n    for c in commands:\n        try:\n            dispcmd = str([c] + args)\n            # remember shell=False, so use git.cmd on windows, not just git\n            p = subprocess.Popen(\n                [c] + args, cwd=cwd, env=env, stdout=subprocess.PIPE, stderr=(subprocess.PIPE if hide_stderr else None)\n            )\n            break\n        except EnvironmentError:\n            e = sys.exc_info()[1]\n            if e.errno == errno.ENOENT:\n                continue\n            if verbose:\n                print(\"unable to run %s\" % dispcmd)\n                print(e)\n            return None, None\n    else:\n        if verbose:\n            print(\"unable to find command, tried %s\" % (commands,))\n        return None, None\n    stdout = p.communicate()[0].strip().decode()\n    if p.returncode != 0:\n        if verbose:\n            print(\"unable to run %s (error)\" % dispcmd)\n            print(\"stdout was %s\" % stdout)\n        return None, p.returncode\n    return stdout, p.returncode", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_git_get_keywords_git_get_keywords.return.keywords": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_git_get_keywords_git_get_keywords.return.keywords", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 948, "end_line": 974, "span_ids": ["git_get_keywords"], "tokens": 255}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@register_vcs_handler(\"git\", \"get_keywords\")\ndef git_get_keywords(versionfile_abs):\n    \"\"\"Extract version information from the given file.\"\"\"\n    # the code embedded in _version.py can just fetch the value of these\n    # keywords. When used from setup.py, we don't want to import _version.py,\n    # so we do it with a regexp instead. This function is not used from\n    # _version.py.\n    keywords = {}\n    try:\n        f = open(versionfile_abs, \"r\")\n        for line in f.readlines():\n            if line.strip().startswith(\"git_refnames =\"):\n                mo = re.search(r'=\\s*\"(.*)\"', line)\n                if mo:\n                    keywords[\"refnames\"] = mo.group(1)\n            if line.strip().startswith(\"git_full =\"):\n                mo = re.search(r'=\\s*\"(.*)\"', line)\n                if mo:\n                    keywords[\"full\"] = mo.group(1)\n            if line.strip().startswith(\"git_date =\"):\n                mo = re.search(r'=\\s*\"(.*)\"', line)\n                if mo:\n                    keywords[\"date\"] = mo.group(1)\n        f.close()\n    except EnvironmentError:\n        pass\n    return keywords", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_git_versions_from_keywords_git_versions_from_keywords.return._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_git_versions_from_keywords_git_versions_from_keywords.return._", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 972, "end_line": 1035, "span_ids": ["git_versions_from_keywords"], "tokens": 756}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@register_vcs_handler(\"git\", \"keywords\")\ndef git_versions_from_keywords(keywords, tag_prefix, verbose):\n    \"\"\"Get version information from git keywords.\"\"\"\n    if not keywords:\n        raise NotThisMethod(\"no keywords at all, weird\")\n    date = keywords.get(\"date\")\n    if date is not None:\n        # Use only the last line.  Previous lines may contain GPG signature\n        # information.\n        date = date.splitlines()[-1]\n\n        # git-2.2.0 added \"%cI\", which expands to an ISO-8601 -compliant\n        # datestamp. However we prefer \"%ci\" (which expands to an \"ISO-8601\n        # -like\" string, which we must then edit to make compliant), because\n        # it's been around since git-1.5.3, and it's too difficult to\n        # discover which version we're using, or to work around using an\n        # older one.\n        date = date.strip().replace(\" \", \"T\", 1).replace(\" \", \"\", 1)\n    refnames = keywords[\"refnames\"].strip()\n    if refnames.startswith(\"$Format\"):\n        if verbose:\n            print(\"keywords are unexpanded, not using\")\n        raise NotThisMethod(\"unexpanded keywords, not a git-archive tarball\")\n    refs = set([r.strip() for r in refnames.strip(\"()\").split(\",\")])\n    # starting in git-1.8.3, tags are listed as \"tag: foo-1.0\" instead of\n    # just \"foo-1.0\". If we see a \"tag: \" prefix, prefer those.\n    TAG = \"tag: \"\n    tags = set([r[len(TAG) :] for r in refs if r.startswith(TAG)])\n    if not tags:\n        # Either we're using git < 1.8.3, or there really are no tags. We use\n        # a heuristic: assume all version tags have a digit. The old git %d\n        # expansion behaves like git log --decorate=short and strips out the\n        # refs/heads/ and refs/tags/ prefixes that would let us distinguish\n        # between branches and tags. By ignoring refnames without digits, we\n        # filter out many common branch names like \"release\" and\n        # \"stabilization\", as well as \"HEAD\" and \"master\".\n        tags = set([r for r in refs if re.search(r\"\\d\", r)])\n        if verbose:\n            print(\"discarding '%s', no digits\" % \",\".join(refs - tags))\n    if verbose:\n        print(\"likely tags: %s\" % \",\".join(sorted(tags)))\n    for ref in sorted(tags):\n        # sorting will prefer e.g. \"2.0\" over \"2.0rc1\"\n        if ref.startswith(tag_prefix):\n            r = ref[len(tag_prefix) :]\n            if verbose:\n                print(\"picking %s\" % r)\n            return {\n                \"version\": r,\n                \"full-revisionid\": keywords[\"full\"].strip(),\n                \"dirty\": False,\n                \"error\": None,\n                \"date\": date,\n            }\n    # no suitable tags, so version is \"0+unknown\", but full hex is still there\n    if verbose:\n        print(\"no suitable tags, using unknown + full revision id\")\n    return {\n        \"version\": \"0+unknown\",\n        \"full-revisionid\": keywords[\"full\"].strip(),\n        \"dirty\": False,\n        \"error\": \"no suitable tags\",\n        \"date\": None,\n    }", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_git_pieces_from_vcs_git_pieces_from_vcs.return.pieces": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_git_pieces_from_vcs_git_pieces_from_vcs.return.pieces", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1038, "end_line": 1124, "span_ids": ["git_pieces_from_vcs"], "tokens": 902}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@register_vcs_handler(\"git\", \"pieces_from_vcs\")\ndef git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command):\n    \"\"\"Get version from 'git describe' in the root of the source tree.\n\n    This only gets called if the git-archive 'subst' keywords were *not*\n    expanded, and _version.py hasn't already been rewritten with a short\n    version string, meaning we're inside a checked out source tree.\n    \"\"\"\n    GITS = [\"git\"]\n    if sys.platform == \"win32\":\n        GITS = [\"git.cmd\", \"git.exe\"]\n\n    out, rc = run_command(GITS, [\"rev-parse\", \"--git-dir\"], cwd=root, hide_stderr=True)\n    if rc != 0:\n        if verbose:\n            print(\"Directory %s not under git control\" % root)\n        raise NotThisMethod(\"'git rev-parse --git-dir' returned error\")\n\n    # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty]\n    # if there isn't one, this yields HEX[-dirty] (no NUM)\n    describe_out, rc = run_command(\n        GITS, [\"describe\", \"--tags\", \"--dirty\", \"--always\", \"--long\", \"--match\", \"%s*\" % tag_prefix], cwd=root\n    )\n    # --long was added in git-1.5.5\n    if describe_out is None:\n        raise NotThisMethod(\"'git describe' failed\")\n    describe_out = describe_out.strip()\n    full_out, rc = run_command(GITS, [\"rev-parse\", \"HEAD\"], cwd=root)\n    if full_out is None:\n        raise NotThisMethod(\"'git rev-parse' failed\")\n    full_out = full_out.strip()\n\n    pieces = {}\n    pieces[\"long\"] = full_out\n    pieces[\"short\"] = full_out[:7]  # maybe improved later\n    pieces[\"error\"] = None\n\n    # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty]\n    # TAG might have hyphens.\n    git_describe = describe_out\n\n    # look for -dirty suffix\n    dirty = git_describe.endswith(\"-dirty\")\n    pieces[\"dirty\"] = dirty\n    if dirty:\n        git_describe = git_describe[: git_describe.rindex(\"-dirty\")]\n\n    # now we have TAG-NUM-gHEX or HEX\n\n    if \"-\" in git_describe:\n        # TAG-NUM-gHEX\n        mo = re.search(r\"^(.+)-(\\d+)-g([0-9a-f]+)$\", git_describe)\n        if not mo:\n            # unparseable. Maybe git-describe is misbehaving?\n            pieces[\"error\"] = \"unable to parse git-describe output: '%s'\" % describe_out\n            return pieces\n\n        # tag\n        full_tag = mo.group(1)\n        if not full_tag.startswith(tag_prefix):\n            if verbose:\n                fmt = \"tag '%s' doesn't start with prefix '%s'\"\n                print(fmt % (full_tag, tag_prefix))\n            pieces[\"error\"] = \"tag '%s' doesn't start with prefix '%s'\" % (full_tag, tag_prefix)\n            return pieces\n        pieces[\"closest-tag\"] = full_tag[len(tag_prefix) :]\n\n        # distance: number of commits since tag\n        pieces[\"distance\"] = int(mo.group(2))\n\n        # commit: short hex revision ID\n        pieces[\"short\"] = mo.group(3)\n\n    else:\n        # HEX: no tags\n        pieces[\"closest-tag\"] = None\n        count_out, rc = run_command(GITS, [\"rev-list\", \"HEAD\", \"--count\"], cwd=root)\n        pieces[\"distance\"] = int(count_out)  # total number of commits\n\n    # commit date: see ISO-8601 comment in git_versions_from_keywords()\n    date = run_command(GITS, [\"show\", \"-s\", \"--format=%ci\", \"HEAD\"], cwd=root)[0].strip()\n    # Use only the last line.  Previous lines may contain GPG signature\n    # information.\n    date = date.splitlines()[-1]\n    pieces[\"date\"] = date.strip().replace(\" \", \"T\", 1).replace(\" \", \"\", 1)\n\n    return pieces", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_do_vcs_install_do_vcs_install.run_command_GITS_add_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_do_vcs_install_do_vcs_install.run_command_GITS_add_", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1125, "end_line": 1160, "span_ids": ["do_vcs_install"], "tokens": 300}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def do_vcs_install(manifest_in, versionfile_source, ipy):\n    \"\"\"Git-specific installation logic for Versioneer.\n\n    For Git, this means creating/changing .gitattributes to mark _version.py\n    for export-subst keyword substitution.\n    \"\"\"\n    GITS = [\"git\"]\n    if sys.platform == \"win32\":\n        GITS = [\"git.cmd\", \"git.exe\"]\n    files = [manifest_in, versionfile_source]\n    if ipy:\n        files.append(ipy)\n    try:\n        me = __file__\n        if me.endswith(\".pyc\") or me.endswith(\".pyo\"):\n            me = os.path.splitext(me)[0] + \".py\"\n        versioneer_file = os.path.relpath(me)\n    except NameError:\n        versioneer_file = \"versioneer.py\"\n    files.append(versioneer_file)\n    present = False\n    try:\n        f = open(\".gitattributes\", \"r\")\n        for line in f.readlines():\n            if line.strip().startswith(versionfile_source):\n                if \"export-subst\" in line.strip().split()[1:]:\n                    present = True\n        f.close()\n    except EnvironmentError:\n        pass\n    if not present:\n        f = open(\".gitattributes\", \"a+\")\n        f.write(\"%s export-subst\\n\" % versionfile_source)\n        f.close()\n        files.append(\".gitattributes\")\n    run_command(GITS, [\"add\", \"--\"] + files)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_versions_from_parentdir_versions_from_parentdir.raise_NotThisMethod_root": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_versions_from_parentdir_versions_from_parentdir.raise_NotThisMethod_root", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1163, "end_line": 1188, "span_ids": ["versions_from_parentdir"], "tokens": 211}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def versions_from_parentdir(parentdir_prefix, root, verbose):\n    \"\"\"Try to determine the version from the parent directory name.\n\n    Source tarballs conventionally unpack into a directory that includes both\n    the project name and a version string. We will also support searching up\n    two directory levels for an appropriately named parent directory\n    \"\"\"\n    rootdirs = []\n\n    for i in range(3):\n        dirname = os.path.basename(root)\n        if dirname.startswith(parentdir_prefix):\n            return {\n                \"version\": dirname[len(parentdir_prefix) :],\n                \"full-revisionid\": None,\n                \"dirty\": False,\n                \"error\": None,\n                \"date\": None,\n            }\n        else:\n            rootdirs.append(root)\n            root = os.path.dirname(root)  # up a level\n\n    if verbose:\n        print(\"Tried directories %s but none started with prefix %s\" % (str(rootdirs), parentdir_prefix))\n    raise NotThisMethod(\"rootdir doesn't start with parentdir_prefix\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_SHORT_VERSION_PY_versions_from_file.return.json_loads_mo_group_1_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_SHORT_VERSION_PY_versions_from_file.return.json_loads_mo_group_1_", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1193, "end_line": 1223, "span_ids": ["versions_from_file", "impl:6"], "tokens": 236}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "SHORT_VERSION_PY = \"\"\"\n# This file was generated by 'versioneer.py' (0.19) from\n# revision-control system data, or from the parent directory name of an\n# unpacked source archive. Distribution tarballs contain a pre-generated copy\n# of this file.\n\nimport json\n\nversion_json = '''\n%s\n'''  # END VERSION_JSON\n\n\ndef get_versions():\n    return json.loads(version_json)\n\"\"\"\n\n\ndef versions_from_file(filename):\n    \"\"\"Try to determine the version from _version.py if present.\"\"\"\n    try:\n        with open(filename) as f:\n            contents = f.read()\n    except EnvironmentError:\n        raise NotThisMethod(\"unable to read _version.py\")\n    mo = re.search(r\"version_json = '''\\n(.*)'''  # END VERSION_JSON\", contents, re.M | re.S)\n    if not mo:\n        mo = re.search(r\"version_json = '''\\r\\n(.*)'''  # END VERSION_JSON\", contents, re.M | re.S)\n    if not mo:\n        raise NotThisMethod(\"no version_json in _version.py\")\n    return json.loads(mo.group(1))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_write_to_version_file_plus_or_dot.return._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_write_to_version_file_plus_or_dot.return._", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1224, "end_line": 1238, "span_ids": ["plus_or_dot", "write_to_version_file"], "tokens": 134}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def write_to_version_file(filename, versions):\n    \"\"\"Write the given version number to the given _version.py file.\"\"\"\n    os.unlink(filename)\n    contents = json.dumps(versions, sort_keys=True, indent=1, separators=(\",\", \": \"))\n    with open(filename, \"w\") as f:\n        f.write(SHORT_VERSION_PY % contents)\n\n    print(\"set %s to '%s'\" % (filename, versions[\"version\"]))\n\n\ndef plus_or_dot(pieces):\n    \"\"\"Return a + if we don't already have one, else return a .\"\"\"\n    if \"+\" in pieces.get(\"closest-tag\", \"\"):\n        return \".\"\n    return \"+\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_render_pep440_post_render_pep440_post.return.rendered": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_render_pep440_post_render_pep440_post.return.rendered", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1281, "end_line": 1305, "span_ids": ["render_pep440_post"], "tokens": 217}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def render_pep440_post(pieces):\n    \"\"\"TAG[.postDISTANCE[.dev0]+gHEX] .\n\n    The \".dev0\" means dirty. Note that .dev0 sorts backwards\n    (a dirty tree will appear \"older\" than the corresponding clean one),\n    but you shouldn't be releasing software with -dirty anyways.\n\n    Exceptions:\n    1: no tags. 0.postDISTANCE[.dev0]\n    \"\"\"\n    if pieces[\"closest-tag\"]:\n        rendered = pieces[\"closest-tag\"]\n        if pieces[\"distance\"] or pieces[\"dirty\"]:\n            rendered += \".post%d\" % pieces[\"distance\"]\n            if pieces[\"dirty\"]:\n                rendered += \".dev0\"\n            rendered += plus_or_dot(pieces)\n            rendered += \"g%s\" % pieces[\"short\"]\n    else:\n        # exception #1\n        rendered = \"0.post%d\" % pieces[\"distance\"]\n        if pieces[\"dirty\"]:\n            rendered += \".dev0\"\n        rendered += \"+g%s\" % pieces[\"short\"]\n    return rendered", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_render_pep440_old_render_pep440_old.return.rendered": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_render_pep440_old_render_pep440_old.return.rendered", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1310, "end_line": 1329, "span_ids": ["render_pep440_old"], "tokens": 143}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def render_pep440_old(pieces):\n    \"\"\"TAG[.postDISTANCE[.dev0]] .\n\n    The \".dev0\" means dirty.\n\n    Exceptions:\n    1: no tags. 0.postDISTANCE[.dev0]\n    \"\"\"\n    if pieces[\"closest-tag\"]:\n        rendered = pieces[\"closest-tag\"]\n        if pieces[\"distance\"] or pieces[\"dirty\"]:\n            rendered += \".post%d\" % pieces[\"distance\"]\n            if pieces[\"dirty\"]:\n                rendered += \".dev0\"\n    else:\n        # exception #1\n        rendered = \"0.post%d\" % pieces[\"distance\"]\n        if pieces[\"dirty\"]:\n            rendered += \".dev0\"\n    return rendered", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_render_git_describe_render_git_describe.return.rendered": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_render_git_describe_render_git_describe.return.rendered", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1330, "end_line": 1347, "span_ids": ["render_git_describe"], "tokens": 129}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def render_git_describe(pieces):\n    \"\"\"TAG[-DISTANCE-gHEX][-dirty].\n\n    Like 'git describe --tags --dirty --always'.\n\n    Exceptions:\n    1: no tags. HEX[-dirty]  (note: no 'g' prefix)\n    \"\"\"\n    if pieces[\"closest-tag\"]:\n        rendered = pieces[\"closest-tag\"]\n        if pieces[\"distance\"]:\n            rendered += \"-%d-g%s\" % (pieces[\"distance\"], pieces[\"short\"])\n    else:\n        # exception #1\n        rendered = pieces[\"short\"]\n    if pieces[\"dirty\"]:\n        rendered += \"-dirty\"\n    return rendered", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_render_git_describe_long_render_git_describe_long.return.rendered": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_render_git_describe_long_render_git_describe_long.return.rendered", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1350, "end_line": 1367, "span_ids": ["render_git_describe_long"], "tokens": 133}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def render_git_describe_long(pieces):\n    \"\"\"TAG-DISTANCE-gHEX[-dirty].\n\n    Like 'git describe --tags --dirty --always -long'.\n    The distance/hash is unconditional.\n\n    Exceptions:\n    1: no tags. HEX[-dirty]  (note: no 'g' prefix)\n    \"\"\"\n    if pieces[\"closest-tag\"]:\n        rendered = pieces[\"closest-tag\"]\n        rendered += \"-%d-g%s\" % (pieces[\"distance\"], pieces[\"short\"])\n    else:\n        # exception #1\n        rendered = pieces[\"short\"]\n    if pieces[\"dirty\"]:\n        rendered += \"-dirty\"\n    return rendered", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_render_VersioneerBadRootError._The_project_root_direc": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_render_VersioneerBadRootError._The_project_root_direc", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1370, "end_line": 1409, "span_ids": ["VersioneerBadRootError", "render"], "tokens": 295}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def render(pieces, style):\n    \"\"\"Render the given version pieces into the requested style.\"\"\"\n    if pieces[\"error\"]:\n        return {\n            \"version\": \"unknown\",\n            \"full-revisionid\": pieces.get(\"long\"),\n            \"dirty\": None,\n            \"error\": pieces[\"error\"],\n            \"date\": None,\n        }\n\n    if not style or style == \"default\":\n        style = \"pep440\"  # the default\n\n    if style == \"pep440\":\n        rendered = render_pep440(pieces)\n    elif style == \"pep440-pre\":\n        rendered = render_pep440_pre(pieces)\n    elif style == \"pep440-post\":\n        rendered = render_pep440_post(pieces)\n    elif style == \"pep440-old\":\n        rendered = render_pep440_old(pieces)\n    elif style == \"git-describe\":\n        rendered = render_git_describe(pieces)\n    elif style == \"git-describe-long\":\n        rendered = render_git_describe_long(pieces)\n    else:\n        raise ValueError(\"unknown style '%s'\" % style)\n\n    return {\n        \"version\": rendered,\n        \"full-revisionid\": pieces[\"long\"],\n        \"dirty\": pieces[\"dirty\"],\n        \"error\": None,\n        \"date\": pieces.get(\"date\"),\n    }\n\n\nclass VersioneerBadRootError(Exception):\n    \"\"\"The project root directory is unknown or missing key files.\"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_get_versions_get_versions.return._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_get_versions_get_versions.return._", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1412, "end_line": 1488, "span_ids": ["get_versions"], "tokens": 619}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_versions(verbose=False):\n    \"\"\"Get the project version from whatever source is available.\n\n    Returns dict with two keys: 'version' and 'full'.\n    \"\"\"\n    if \"versioneer\" in sys.modules:\n        # see the discussion in cmdclass.py:get_cmdclass()\n        del sys.modules[\"versioneer\"]\n\n    root = get_root()\n    cfg = get_config_from_root(root)\n\n    assert cfg.VCS is not None, \"please set [versioneer]VCS= in setup.cfg\"\n    handlers = HANDLERS.get(cfg.VCS)\n    assert handlers, \"unrecognized VCS '%s'\" % cfg.VCS\n    verbose = verbose or cfg.verbose\n    assert cfg.versionfile_source is not None, \"please set versioneer.versionfile_source\"\n    assert cfg.tag_prefix is not None, \"please set versioneer.tag_prefix\"\n\n    versionfile_abs = os.path.join(root, cfg.versionfile_source)\n\n    # extract version from first of: _version.py, VCS command (e.g. 'git\n    # describe'), parentdir. This is meant to work for developers using a\n    # source checkout, for users of a tarball created by 'setup.py sdist',\n    # and for users of a tarball/zipball created by 'git archive' or github's\n    # download-from-tag feature or the equivalent in other VCSes.\n\n    get_keywords_f = handlers.get(\"get_keywords\")\n    from_keywords_f = handlers.get(\"keywords\")\n    if get_keywords_f and from_keywords_f:\n        try:\n            keywords = get_keywords_f(versionfile_abs)\n            ver = from_keywords_f(keywords, cfg.tag_prefix, verbose)\n            if verbose:\n                print(\"got version from expanded keyword %s\" % ver)\n            return ver\n        except NotThisMethod:\n            pass\n\n    try:\n        ver = versions_from_file(versionfile_abs)\n        if verbose:\n            print(\"got version from file %s %s\" % (versionfile_abs, ver))\n        return ver\n    except NotThisMethod:\n        pass\n\n    from_vcs_f = handlers.get(\"pieces_from_vcs\")\n    if from_vcs_f:\n        try:\n            pieces = from_vcs_f(cfg.tag_prefix, root, verbose)\n            ver = render(pieces, cfg.style)\n            if verbose:\n                print(\"got version from VCS %s\" % ver)\n            return ver\n        except NotThisMethod:\n            pass\n\n    try:\n        if cfg.parentdir_prefix:\n            ver = versions_from_parentdir(cfg.parentdir_prefix, root, verbose)\n            if verbose:\n                print(\"got version from parentdir %s\" % ver)\n            return ver\n    except NotThisMethod:\n        pass\n\n    if verbose:\n        print(\"unable to compute version\")\n\n    return {\n        \"version\": \"0+unknown\",\n        \"full-revisionid\": None,\n        \"dirty\": None,\n        \"error\": \"unable to compute version\",\n        \"date\": None,\n    }", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_get_version_get_cmdclass.from_distutils_core_impor": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_get_version_get_cmdclass.from_distutils_core_impor", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1493, "end_line": 1522, "span_ids": ["get_cmdclass", "get_version"], "tokens": 344}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_version():\n    \"\"\"Get the short version string for this project.\"\"\"\n    return get_versions()[\"version\"]\n\n\ndef get_cmdclass(cmdclass=None):\n    \"\"\"Get the custom setuptools/distutils subclasses used by Versioneer.\n\n    If the package uses a different cmdclass (e.g. one from numpy), it\n    should be provide as an argument.\n    \"\"\"\n    if \"versioneer\" in sys.modules:\n        del sys.modules[\"versioneer\"]\n        # this fixes the \"python setup.py develop\" case (also 'install' and\n        # 'easy_install .'), in which subdependencies of the main project are\n        # built (using setup.py bdist_egg) in the same python process. Assume\n        # a main project A and a dependency B, which use different versions\n        # of Versioneer. A's setup.py imports A's Versioneer, leaving it in\n        # sys.modules by the time B's setup.py is executed, causing B to run\n        # with the wrong versioneer. Setuptools wraps the sub-dep builds in a\n        # sandbox that restores sys.modules to it's pre-build state, so the\n        # parent is protected against the child's \"import versioneer\". By\n        # removing ourselves from sys.modules here, before the child build\n        # happens, we protect the child from the parent's versioneer too.\n        # Also see https://github.com/python-versioneer/python-versioneer/issues/52\n\n    cmds = {} if cmdclass is None else cmdclass.copy()\n\n    # we add \"version\" to both distutils and setuptools\n    from distutils.core import Command\n    # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_get_cmdclass.cmd_version_get_cmdclass.cmd_version.run.if_vers_error_.print_error_s_vers": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_get_cmdclass.cmd_version_get_cmdclass.cmd_version.run.if_vers_error_.print_error_s_vers", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1524, "end_line": 1542, "span_ids": ["get_cmdclass"], "tokens": 155}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_cmdclass(cmdclass=None):\n    # ... other code\n\n    class cmd_version(Command):\n        description = \"report generated version string\"\n        user_options = []\n        boolean_options = []\n\n        def initialize_options(self):\n            pass\n\n        def finalize_options(self):\n            pass\n\n        def run(self):\n            vers = get_versions(verbose=True)\n            print(\"Version: %s\" % vers[\"version\"])\n            print(\" full-revisionid: %s\" % vers.get(\"full-revisionid\"))\n            print(\" dirty: %s\" % vers.get(\"dirty\"))\n            print(\" date: %s\" % vers.get(\"date\"))\n            if vers[\"error\"]:\n                print(\" error: %s\" % vers[\"error\"])\n    # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_get_cmdclass.cmd_sdist_get_cmdclass.return.cmds": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_get_cmdclass.cmd_sdist_get_cmdclass.return.cmds", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1683, "end_line": 1705, "span_ids": ["get_cmdclass"], "tokens": 221}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_cmdclass(cmdclass=None):\n    # ... other code\n\n    class cmd_sdist(_sdist):\n        def run(self):\n            versions = get_versions()\n            self._versioneer_generated_versions = versions\n            # unless we update this, the command will keep using the old\n            # version\n            self.distribution.metadata.version = versions[\"version\"]\n            return _sdist.run(self)\n\n        def make_release_tree(self, base_dir, files):\n            root = get_root()\n            cfg = get_config_from_root(root)\n            _sdist.make_release_tree(self, base_dir, files)\n            # now locate _version.py in the new base_dir directory\n            # (remembering that it may be a hardlink) and replace it with an\n            # updated value\n            target_versionfile = os.path.join(base_dir, cfg.versionfile_source)\n            print(\"UPDATING %s\" % target_versionfile)\n            write_to_version_file(target_versionfile, self._versioneer_generated_versions)\n\n    cmds[\"sdist\"] = cmd_sdist\n\n    return cmds", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_CONFIG_ERROR_INIT_PY_SNIPPET._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_CONFIG_ERROR_INIT_PY_SNIPPET._", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1708, "end_line": 1749, "span_ids": ["impl:8"], "tokens": 243}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "CONFIG_ERROR = \"\"\"\nsetup.cfg is missing the necessary Versioneer configuration. You need\na section like:\n\n [versioneer]\n VCS = git\n style = pep440\n versionfile_source = src/myproject/_version.py\n versionfile_build = myproject/_version.py\n tag_prefix =\n parentdir_prefix = myproject-\n\nYou will also need to edit your setup.py to use the results:\n\n import versioneer\n setup(version=versioneer.get_version(),\n       cmdclass=versioneer.get_cmdclass(), ...)\n\nPlease read the docstring in ./versioneer.py for configuration instructions,\nedit setup.cfg, and re-run the installer or 'python versioneer.py setup'.\n\"\"\"\n\nSAMPLE_CONFIG = \"\"\"\n# See the docstring in versioneer.py for instructions. Note that you must\n# re-run 'versioneer.py setup' after changing this section, and commit the\n# resulting files.\n\n[versioneer]\n#VCS = git\n#style = pep440\n#versionfile_source =\n#versionfile_build =\n#tag_prefix =\n#parentdir_prefix =\n\n\"\"\"\n\nINIT_PY_SNIPPET = \"\"\"\nfrom ._version import get_versions\n__version__ = get_versions()['version']\ndel get_versions\n\"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_do_setup_do_setup.return.0": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_do_setup_do_setup.return.0", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1752, "end_line": 1831, "span_ids": ["do_setup"], "tokens": 763}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def do_setup():\n    \"\"\"Do main VCS-independent setup function for installing Versioneer.\"\"\"\n    root = get_root()\n    try:\n        cfg = get_config_from_root(root)\n    except (EnvironmentError, configparser.NoSectionError, configparser.NoOptionError) as e:\n        if isinstance(e, (EnvironmentError, configparser.NoSectionError)):\n            print(\"Adding sample versioneer config to setup.cfg\", file=sys.stderr)\n            with open(os.path.join(root, \"setup.cfg\"), \"a\") as f:\n                f.write(SAMPLE_CONFIG)\n        print(CONFIG_ERROR, file=sys.stderr)\n        return 1\n\n    print(\" creating %s\" % cfg.versionfile_source)\n    with open(cfg.versionfile_source, \"w\") as f:\n        LONG = LONG_VERSION_PY[cfg.VCS]\n        f.write(\n            LONG\n            % {\n                \"DOLLAR\": \"$\",\n                \"STYLE\": cfg.style,\n                \"TAG_PREFIX\": cfg.tag_prefix,\n                \"PARENTDIR_PREFIX\": cfg.parentdir_prefix,\n                \"VERSIONFILE_SOURCE\": cfg.versionfile_source,\n            }\n        )\n\n    ipy = os.path.join(os.path.dirname(cfg.versionfile_source), \"__init__.py\")\n    if os.path.exists(ipy):\n        try:\n            with open(ipy, \"r\") as f:\n                old = f.read()\n        except EnvironmentError:\n            old = \"\"\n        if INIT_PY_SNIPPET not in old:\n            print(\" appending to %s\" % ipy)\n            with open(ipy, \"a\") as f:\n                f.write(INIT_PY_SNIPPET)\n        else:\n            print(\" %s unmodified\" % ipy)\n    else:\n        print(\" %s doesn't exist, ok\" % ipy)\n        ipy = None\n\n    # Make sure both the top-level \"versioneer.py\" and versionfile_source\n    # (PKG/_version.py, used by runtime code) are in MANIFEST.in, so\n    # they'll be copied into source distributions. Pip won't be able to\n    # install the package without this.\n    manifest_in = os.path.join(root, \"MANIFEST.in\")\n    simple_includes = set()\n    try:\n        with open(manifest_in, \"r\") as f:\n            for line in f:\n                if line.startswith(\"include \"):\n                    for include in line.split()[1:]:\n                        simple_includes.add(include)\n    except EnvironmentError:\n        pass\n    # That doesn't cover everything MANIFEST.in can do\n    # (http://docs.python.org/2/distutils/sourcedist.html#commands), so\n    # it might give some false negatives. Appending redundant 'include'\n    # lines is safe, though.\n    if \"versioneer.py\" not in simple_includes:\n        print(\" appending 'versioneer.py' to MANIFEST.in\")\n        with open(manifest_in, \"a\") as f:\n            f.write(\"include versioneer.py\\n\")\n    else:\n        print(\" 'versioneer.py' already in MANIFEST.in\")\n    if cfg.versionfile_source not in simple_includes:\n        print(\" appending versionfile_source ('%s') to MANIFEST.in\" % cfg.versionfile_source)\n        with open(manifest_in, \"a\") as f:\n            f.write(\"include %s\\n\" % cfg.versionfile_source)\n    else:\n        print(\" versionfile_source already in MANIFEST.in\")\n\n    # Make VCS-specific changes. For git, this means creating/changing\n    # .gitattributes to mark _version.py for export-subst keyword\n    # substitution.\n    do_vcs_install(manifest_in, cfg.versionfile_source, ipy)\n    return 0", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_scan_setup_py_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_scan_setup_py_", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1834, "end_line": 1878, "span_ids": ["scan_setup_py", "impl:14"], "tokens": 351}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def scan_setup_py():\n    \"\"\"Validate the contents of setup.py against Versioneer's expectations.\"\"\"\n    found = set()\n    setters = False\n    errors = 0\n    with open(\"setup.py\", \"r\") as f:\n        for line in f.readlines():\n            if \"import versioneer\" in line:\n                found.add(\"import\")\n            if \"versioneer.get_cmdclass()\" in line:\n                found.add(\"cmdclass\")\n            if \"versioneer.get_version()\" in line:\n                found.add(\"get_version\")\n            if \"versioneer.VCS\" in line:\n                setters = True\n            if \"versioneer.versionfile_source\" in line:\n                setters = True\n    if len(found) != 3:\n        print(\"\")\n        print(\"Your setup.py appears to be missing some important items\")\n        print(\"(but I might be wrong). Please make sure it has something\")\n        print(\"roughly like the following:\")\n        print(\"\")\n        print(\" import versioneer\")\n        print(\" setup( version=versioneer.get_version(),\")\n        print(\"        cmdclass=versioneer.get_cmdclass(),  ...)\")\n        print(\"\")\n        errors += 1\n    if setters:\n        print(\"You should remove lines like 'versioneer.VCS = ' and\")\n        print(\"'versioneer.versionfile_source = ' . This configuration\")\n        print(\"now lives in setup.cfg, and should be removed from setup.py\")\n        print(\"\")\n        errors += 1\n    return errors\n\n\nif __name__ == \"__main__\":\n    cmd = sys.argv[1]\n    if cmd == \"setup\":\n        errors = do_setup()\n        errors += scan_setup_py()\n        if errors:\n            sys.exit(1)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/multi_gpu_supervised_trainer.py_from_typing_import_TYPE_C__default_eval_transform.return.y_pred_y": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/multi_gpu_supervised_trainer.py_from_typing_import_TYPE_C__default_eval_transform.return.y_pred_y", "embedding": null, "metadata": {"file_path": "monai/engines/multi_gpu_supervised_trainer.py", "file_name": "multi_gpu_supervised_trainer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 47, "span_ids": ["_default_eval_transform", "_default_transform", "docstring"], "tokens": 342}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import TYPE_CHECKING, Callable, Dict, Optional, Sequence, Tuple\n\nimport torch\nimport torch.nn\nfrom torch.nn.parallel import DataParallel, DistributedDataParallel\nfrom torch.optim.optimizer import Optimizer\n\nfrom monai.config import IgniteInfo\nfrom monai.engines.utils import get_devices_spec\nfrom monai.utils import min_version, optional_import\n\ncreate_supervised_trainer, _ = optional_import(\n    \"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"create_supervised_trainer\"\n)\ncreate_supervised_evaluator, _ = optional_import(\n    \"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"create_supervised_evaluator\"\n)\n_prepare_batch, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"_prepare_batch\")\nif TYPE_CHECKING:\n    from ignite.engine import Engine\n    from ignite.metrics import Metric\nelse:\n    Engine, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Engine\")\n    Metric, _ = optional_import(\"ignite.metrics\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Metric\")\n\n__all__ = [\"create_multigpu_supervised_trainer\", \"create_multigpu_supervised_evaluator\"]\n\n\ndef _default_transform(_x: torch.Tensor, _y: torch.Tensor, _y_pred: torch.Tensor, loss: torch.Tensor) -> float:\n    return loss.item()\n\n\ndef _default_eval_transform(\n    x: torch.Tensor, y: torch.Tensor, y_pred: torch.Tensor\n) -> Tuple[torch.Tensor, torch.Tensor]:\n    return y_pred, y", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/trainer.py_GanTrainer_GanTrainer._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/trainer.py_GanTrainer_GanTrainer._", "embedding": null, "metadata": {"file_path": "monai/engines/trainer.py", "file_name": "trainer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 219, "end_line": 275, "span_ids": ["GanTrainer"], "tokens": 891}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GanTrainer(Trainer):\n    \"\"\"\n    Generative adversarial network training based on Goodfellow et al. 2014 https://arxiv.org/abs/1406.266,\n    inherits from ``Trainer`` and ``Workflow``.\n\n    Training Loop: for each batch of data size `m`\n        1. Generate `m` fakes from random latent codes.\n        2. Update discriminator with these fakes and current batch reals, repeated d_train_steps times.\n        3. If g_update_latents, generate `m` fakes from new random latent codes.\n        4. Update generator with these fakes using discriminator feedback.\n\n    Args:\n        device: an object representing the device on which to run.\n        max_epochs: the total epoch number for engine to run.\n        train_data_loader: Core ignite engines uses `DataLoader` for training loop batchdata.\n        g_network: generator (G) network architecture.\n        g_optimizer: G optimizer function.\n        g_loss_function: G loss function for optimizer.\n        d_network: discriminator (D) network architecture.\n        d_optimizer: D optimizer function.\n        d_loss_function: D loss function for optimizer.\n        epoch_length: number of iterations for one epoch, default to `len(train_data_loader)`.\n        g_inferer: inference method to execute G model forward. Defaults to ``SimpleInferer()``.\n        d_inferer: inference method to execute D model forward. Defaults to ``SimpleInferer()``.\n        d_train_steps: number of times to update D with real data minibatch. Defaults to ``1``.\n        latent_shape: size of G input latent code. Defaults to ``64``.\n        non_blocking: if True and this copy is between CPU and GPU, the copy may occur asynchronously\n            with respect to the host. For other cases, this argument has no effect.\n        d_prepare_batch: callback function to prepare batchdata for D inferer.\n            Defaults to return ``GanKeys.REALS`` in batchdata dict. for more details please refer to:\n            https://pytorch.org/ignite/generated/ignite.engine.create_supervised_trainer.html.\n        g_prepare_batch: callback function to create batch of latent input for G inferer.\n            Defaults to return random latents. for more details please refer to:\n            https://pytorch.org/ignite/generated/ignite.engine.create_supervised_trainer.html.\n        g_update_latents: Calculate G loss with new latent codes. Defaults to ``True``.\n        iteration_update: the callable function for every iteration, expect to accept `engine`\n            and `engine.state.batch` as inputs, return data will be stored in `engine.state.output`.\n            if not provided, use `self._iteration()` instead. for more details please refer to:\n            https://pytorch.org/ignite/generated/ignite.engine.engine.Engine.html.\n        postprocessing: execute additional transformation for the model output data.\n            Typically, several Tensor based transforms composed by `Compose`.\n        key_train_metric: compute metric when every iteration completed, and save average value to\n            engine.state.metrics when epoch completed. key_train_metric is the main metric to compare and save the\n            checkpoint into files.\n        additional_metrics: more Ignite metrics that also attach to Ignite Engine.\n        metric_cmp_fn: function to compare current key metric with previous best key metric value,\n            it must accept 2 args (current_metric, previous_best) and return a bool result: if `True`, will update\n            `best_metric` and `best_metric_epoch` with current metric and epoch, default to `greater than`.\n        train_handlers: every handler is a set of Ignite Event-Handlers, must have `attach` function, like:\n            CheckpointHandler, StatsHandler, SegmentationSaver, etc.\n        decollate: whether to decollate the batch-first data to a list of data after model computation,\n            recommend `decollate=True` when `postprocessing` uses components from `monai.transforms`.\n            default to `True`.\n        optim_set_to_none: when calling `optimizer.zero_grad()`, instead of setting to zero, set the grads to None.\n            more details: https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html.\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/trainer.py_GanTrainer._iteration_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/trainer.py_GanTrainer._iteration_", "embedding": null, "metadata": {"file_path": "monai/engines/trainer.py", "file_name": "trainer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 331, "end_line": 395, "span_ids": ["GanTrainer._iteration"], "tokens": 563}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GanTrainer(Trainer):\n\n    def _iteration(\n        self, engine: Engine, batchdata: Union[Dict, Sequence]\n    ) -> Dict[str, Union[torch.Tensor, int, float, bool]]:\n        \"\"\"\n        Callback function for Adversarial Training processing logic of 1 iteration in Ignite Engine.\n\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n            batchdata: input data for this iteration, usually can be dictionary or tuple of Tensor data.\n\n        Raises:\n            ValueError: must provide batch data for current iteration.\n\n        \"\"\"\n        if batchdata is None:\n            raise ValueError(\"must provide batch data for current iteration.\")\n\n        d_input = self.prepare_batch(batchdata, engine.state.device, engine.non_blocking)  # type: ignore\n        batch_size = self.data_loader.batch_size  # type: ignore\n        g_input = self.g_prepare_batch(\n            num_latents=batch_size,\n            latent_size=self.latent_shape,\n            device=engine.state.device,  # type: ignore\n            non_blocking=engine.non_blocking,  # type: ignore\n        )\n        g_output = self.g_inferer(g_input, self.g_network)\n\n        # Train Discriminator\n        d_total_loss = torch.zeros(1)\n        for _ in range(self.d_train_steps):\n            # `set_to_none` only work from PyTorch 1.7.0\n            if not pytorch_after(1, 7):\n                self.d_optimizer.zero_grad()\n            else:\n                self.d_optimizer.zero_grad(set_to_none=self.optim_set_to_none)\n            dloss = self.d_loss_function(g_output, d_input)\n            dloss.backward()\n            self.d_optimizer.step()\n            d_total_loss += dloss.item()\n\n        # Train Generator\n        if self.g_update_latents:\n            g_input = self.g_prepare_batch(\n                num_latents=batch_size,\n                latent_size=self.latent_shape,\n                device=engine.state.device,  # type: ignore\n                non_blocking=engine.non_blocking,  # type: ignore\n            )\n        g_output = self.g_inferer(g_input, self.g_network)\n        if not pytorch_after(1, 7):\n            self.g_optimizer.zero_grad()\n        else:\n            self.g_optimizer.zero_grad(set_to_none=self.optim_set_to_none)\n        g_loss = self.g_loss_function(g_output)\n        g_loss.backward()\n        self.g_optimizer.step()\n\n        return {\n            GanKeys.REALS: d_input,\n            GanKeys.FAKES: g_output,\n            GanKeys.LATENTS: g_input,\n            GanKeys.GLOSS: g_loss.item(),\n            GanKeys.DLOSS: d_total_loss.item(),\n        }", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/checkpoint_loader.py_logging_if_TYPE_CHECKING_.else_.Engine___optional_impo": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/checkpoint_loader.py_logging_if_TYPE_CHECKING_.else_.Engine___optional_impo", "embedding": null, "metadata": {"file_path": "monai/handlers/checkpoint_loader.py", "file_name": "checkpoint_loader.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 27, "span_ids": ["docstring"], "tokens": 140}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import logging\nimport warnings\nfrom typing import TYPE_CHECKING, Dict, List, Optional\n\nimport torch\n\nfrom monai.config import IgniteInfo\nfrom monai.networks.utils import copy_model_state\nfrom monai.utils import min_version, optional_import\n\nEvents, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Events\")\nCheckpoint, _ = optional_import(\"ignite.handlers\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Checkpoint\")\nif TYPE_CHECKING:\n    from ignite.engine import Engine\nelse:\n    Engine, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Engine\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/checkpoint_loader.py_CheckpointLoader_CheckpointLoader.attach.engine_add_event_handler_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/checkpoint_loader.py_CheckpointLoader_CheckpointLoader.attach.engine_add_event_handler_", "embedding": null, "metadata": {"file_path": "monai/handlers/checkpoint_loader.py", "file_name": "checkpoint_loader.py", "file_type": "text/x-python", "category": "implementation", "start_line": 30, "end_line": 100, "span_ids": ["CheckpointLoader.attach", "CheckpointLoader", "CheckpointLoader.__init__"], "tokens": 760}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CheckpointLoader:\n    \"\"\"\n    CheckpointLoader acts as an Ignite handler to load checkpoint data from file.\n    It can load variables for network, optimizer, lr_scheduler, etc.\n    If saving checkpoint after `torch.nn.DataParallel`, need to save `model.module` instead\n    as PyTorch recommended and then use this loader to load the model.\n\n    Args:\n        load_path: the file path of checkpoint, it should be a PyTorch `pth` file.\n        load_dict: target objects that load checkpoint to. examples::\n\n            {'network': net, 'optimizer': optimizer, 'lr_scheduler': lr_scheduler}\n\n        name: identifier of logging.logger to use, if None, defaulting to ``engine.logger``.\n        map_location: when loading the module for distributed training/evaluation,\n            need to provide an appropriate map_location argument to prevent a process\n            to step into others\u2019 devices. If map_location is missing, torch.load will\n            first load the module to CPU and then copy each parameter to where it was\n            saved, which would result in all processes on the same machine using the\n            same set of devices.\n        strict: whether to strictly enforce that the keys and data shape in the `state_dict` of every item\n            of `load_dict` match the `state_dict` of the corresponding items of checkpoint, default to `True`.\n        strict_shape: whether to enforce the data shape of the matched layers in the checkpoint,\n            `if `False`, it will skip the layers that have different data shape with checkpoint content,\n            and ignore the `strict` arg. this can be useful advanced feature for transfer learning.\n            users should totally understand which layers will have different shape. default to `True`.\n\n    Note: if `strict_shape=False`, will only load checkpoint for `torch.nn.Module` and skip other\n        items in the `load_dict`. For example, if the shape of some layers in current model can't\n        match the checkpoint, the `parameter_group` of current optimizer may also can't match the\n        checkpoint, so skip loading checkpoint for optimizer.\n\n        For more details about loading checkpoint, please refer to:\n        https://pytorch.org/ignite/v0.4.5/generated/ignite.handlers.checkpoint.Checkpoint.html\n        #ignite.handlers.checkpoint.Checkpoint.load_objects.\n        https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.load_state_dict.\n\n    \"\"\"\n\n    def __init__(\n        self,\n        load_path: str,\n        load_dict: Dict,\n        name: Optional[str] = None,\n        map_location: Optional[Dict] = None,\n        strict: bool = True,\n        strict_shape: bool = True,\n    ) -> None:\n        if load_path is None:\n            raise AssertionError(\"must provide clear path to load checkpoint.\")\n        self.load_path = load_path\n        if load_dict is None or len(load_dict) <= 0:\n            raise AssertionError(\"must provide target objects to load.\")\n        self.logger = logging.getLogger(name)\n        self.load_dict = load_dict\n        self._name = name\n        self.map_location = map_location\n        if strict and not strict_shape:\n            warnings.warn(\"as `strict_shape` is already False, change `strict` to False.\")\n            strict = False\n        self.strict = strict\n        self.strict_shape = strict_shape\n\n    def attach(self, engine: Engine) -> None:\n        \"\"\"\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n        \"\"\"\n        if self._name is None:\n            self.logger = engine.logger\n        engine.add_event_handler(Events.STARTED, self)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/checkpoint_loader.py_CheckpointLoader.__call___": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/checkpoint_loader.py_CheckpointLoader.__call___", "embedding": null, "metadata": {"file_path": "monai/handlers/checkpoint_loader.py", "file_name": "checkpoint_loader.py", "file_type": "text/x-python", "category": "implementation", "start_line": 102, "end_line": 139, "span_ids": ["CheckpointLoader.__call__"], "tokens": 397}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CheckpointLoader:\n\n    def __call__(self, engine: Engine) -> None:\n        \"\"\"\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n        \"\"\"\n        checkpoint = torch.load(self.load_path, map_location=self.map_location)\n\n        k, _ = list(self.load_dict.items())[0]\n        # single object and checkpoint is directly a state_dict\n        if len(self.load_dict) == 1 and k not in checkpoint:\n            checkpoint = {k: checkpoint}\n\n        if not self.strict_shape:\n            pop_items: List[str] = []\n            for k, obj in self.load_dict.items():\n                if isinstance(obj, torch.nn.Module):\n                    # skip items that don't match key name or data shape\n                    checkpoint[k] = copy_model_state(obj, checkpoint, inplace=False)[0]\n                else:\n                    warnings.warn(\"`strict_shape` is False, load checkpoint for model, skip others in `load_dict`.\")\n                    pop_items.append(k)\n            for i in pop_items:\n                self.load_dict.pop(i)\n\n        # save current max epochs setting in the engine, don't overwrite it if larger than max_epochs in checkpoint\n        prior_max_epochs = engine.state.max_epochs\n        Checkpoint.load_objects(to_load=self.load_dict, checkpoint=checkpoint, strict=self.strict)\n        if prior_max_epochs is not None and engine.state.epoch > prior_max_epochs:\n            raise ValueError(\n                f\"Epoch count ({engine.state.epoch}) in checkpoint is larger than \"\n                f\"the `engine.state.max_epochs` ({prior_max_epochs}) of engine. To further train from checkpoint, \"\n                \"construct trainer with `max_epochs` larger than checkpoint's epoch count. \"\n                \"To use checkpoint for inference, no need to load state_dict for the engine.\"\n            )\n        engine.state.max_epochs = prior_max_epochs\n\n        self.logger.info(f\"Restored all variables from {self.load_path}\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/checkpoint_saver.py_CheckpointSaver.exception_raised_CheckpointSaver.exception_raised.raise_e": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/checkpoint_saver.py_CheckpointSaver.exception_raised_CheckpointSaver.exception_raised.raise_e", "embedding": null, "metadata": {"file_path": "monai/handlers/checkpoint_saver.py", "file_name": "checkpoint_saver.py", "file_type": "text/x-python", "category": "implementation", "start_line": 256, "end_line": 275, "span_ids": ["CheckpointSaver.exception_raised"], "tokens": 222}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CheckpointSaver:\n\n    def exception_raised(self, engine: Engine, e: Exception) -> None:\n        \"\"\"Callback for train or validation/evaluation exception raised Event.\n        Save current data as final checkpoint if configure save_final is True. This callback may be skipped\n        because the logic with Ignite can only trigger the first attached handler for `EXCEPTION_RAISED` event.\n\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n            e: the exception caught in Ignite during engine.run().\n        \"\"\"\n        if not callable(self._final_checkpoint):\n            raise AssertionError(\"Error: _final_checkpoint function not specified.\")\n        # delete previous saved final checkpoint if existing\n        self._delete_previous_final_ckpt()\n        self._final_checkpoint(engine)\n        if self.logger is None:\n            raise AssertionError\n        if not hasattr(self.logger, \"info\"):\n            raise AssertionError(\"Error, provided logger has not info attribute.\")\n        self.logger.info(f\"Exception raised, saved the last checkpoint: {self._final_checkpoint.last_checkpoint}\")\n        raise e", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/checkpoint_saver.py_CheckpointSaver.metrics_completed_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/checkpoint_saver.py_CheckpointSaver.metrics_completed_", "embedding": null, "metadata": {"file_path": "monai/handlers/checkpoint_saver.py", "file_name": "checkpoint_saver.py", "file_type": "text/x-python", "category": "implementation", "start_line": 277, "end_line": 305, "span_ids": ["CheckpointSaver.metrics_completed", "CheckpointSaver.interval_completed"], "tokens": 251}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CheckpointSaver:\n\n    def metrics_completed(self, engine: Engine) -> None:\n        \"\"\"Callback to compare metrics and save models in train or validation when epoch completed.\n\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n        \"\"\"\n        if not callable(self._key_metric_checkpoint):\n            raise AssertionError(\"Error: _key_metric_checkpoint function not specified.\")\n        self._key_metric_checkpoint(engine)\n\n    def interval_completed(self, engine: Engine) -> None:\n        \"\"\"Callback for train epoch/iteration completed Event.\n        Save checkpoint if configure save_interval = N\n\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n        \"\"\"\n        if not callable(self._interval_checkpoint):\n            raise AssertionError(\"Error: _interval_checkpoint function not specified.\")\n        self._interval_checkpoint(engine)\n        if self.logger is None:\n            raise AssertionError\n        if not hasattr(self.logger, \"info\"):\n            raise AssertionError(\"Error, provided logger has not info attribute.\")\n        if self.epoch_level:\n            self.logger.info(f\"Saved checkpoint at epoch: {engine.state.epoch}\")\n        else:\n            self.logger.info(f\"Saved checkpoint at iteration: {engine.state.iteration}\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/lr_schedule_handler.py_logging_if_TYPE_CHECKING_.else_.Engine___optional_impo": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/lr_schedule_handler.py_logging_if_TYPE_CHECKING_.else_.Engine___optional_impo", "embedding": null, "metadata": {"file_path": "monai/handlers/lr_schedule_handler.py", "file_name": "lr_schedule_handler.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 24, "span_ids": ["docstring"], "tokens": 122}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import logging\nfrom typing import TYPE_CHECKING, Any, Callable, Optional, Union\n\nfrom torch.optim.lr_scheduler import ReduceLROnPlateau, _LRScheduler\n\nfrom monai.config import IgniteInfo\nfrom monai.utils import ensure_tuple, min_version, optional_import\n\nEvents, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Events\")\nif TYPE_CHECKING:\n    from ignite.engine import Engine\nelse:\n    Engine, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Engine\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/segmentation_saver.py_logging_if_TYPE_CHECKING_.else_.Engine___optional_impo": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/segmentation_saver.py_logging_if_TYPE_CHECKING_.else_.Engine___optional_impo", "embedding": null, "metadata": {"file_path": "monai/handlers/segmentation_saver.py", "file_name": "segmentation_saver.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 26, "span_ids": ["docstring"], "tokens": 140}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import logging\nfrom typing import TYPE_CHECKING, Callable, Optional, Union\n\nimport numpy as np\n\nfrom monai.config import DtypeLike, IgniteInfo\nfrom monai.data import decollate_batch\nfrom monai.transforms import SaveImage\nfrom monai.utils import GridSampleMode, GridSamplePadMode, InterpolateMode, deprecated, min_version, optional_import\n\nEvents, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Events\")\nif TYPE_CHECKING:\n    from ignite.engine import Engine\nelse:\n    Engine, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Engine\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/stats_handler.py_logging_DEFAULT_TAG._Loss_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/stats_handler.py_logging_DEFAULT_TAG._Loss_", "embedding": null, "metadata": {"file_path": "monai/handlers/stats_handler.py", "file_name": "stats_handler.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 28, "span_ids": ["docstring"], "tokens": 128}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import logging\nimport warnings\nfrom typing import TYPE_CHECKING, Any, Callable, Optional, Sequence\n\nimport torch\n\nfrom monai.config import IgniteInfo\nfrom monai.utils import is_scalar, min_version, optional_import\n\nEvents, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Events\")\nif TYPE_CHECKING:\n    from ignite.engine import Engine\nelse:\n    Engine, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Engine\")\n\nDEFAULT_KEY_VAL_FORMAT = \"{}: {:.4f} \"\nDEFAULT_TAG = \"Loss\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/stats_handler.py_StatsHandler.epoch_completed_StatsHandler.iteration_completed.if_self_iteration_print_l.else_.self__default_iteration_p": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/stats_handler.py_StatsHandler.epoch_completed_StatsHandler.iteration_completed.if_self_iteration_print_l.else_.self__default_iteration_p", "embedding": null, "metadata": {"file_path": "monai/handlers/stats_handler.py", "file_name": "stats_handler.py", "file_type": "text/x-python", "category": "implementation", "start_line": 118, "end_line": 144, "span_ids": ["StatsHandler.epoch_completed", "StatsHandler.iteration_completed"], "tokens": 196}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class StatsHandler:\n\n    def epoch_completed(self, engine: Engine) -> None:\n        \"\"\"\n        Handler for train or validation/evaluation epoch completed Event.\n        Print epoch level log, default values are from Ignite `engine.state.metrics` dict.\n\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n\n        \"\"\"\n        if self.epoch_print_logger is not None:\n            self.epoch_print_logger(engine)\n        else:\n            self._default_epoch_print(engine)\n\n    def iteration_completed(self, engine: Engine) -> None:\n        \"\"\"\n        Handler for train or validation/evaluation iteration completed Event.\n        Print iteration level log, default values are from Ignite `engine.state.output`.\n\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n\n        \"\"\"\n        if self.iteration_print_logger is not None:\n            self.iteration_print_logger(engine)\n        else:\n            self._default_iteration_print(engine)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/stats_handler.py_StatsHandler.exception_raised_StatsHandler.exception_raised.raise_e": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/stats_handler.py_StatsHandler.exception_raised_StatsHandler.exception_raised.raise_e", "embedding": null, "metadata": {"file_path": "monai/handlers/stats_handler.py", "file_name": "stats_handler.py", "file_type": "text/x-python", "category": "implementation", "start_line": 146, "end_line": 158, "span_ids": ["StatsHandler.exception_raised"], "tokens": 118}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class StatsHandler:\n\n    def exception_raised(self, _engine: Engine, e: Exception) -> None:\n        \"\"\"\n        Handler for train or validation/evaluation exception raised Event.\n        Print the exception information and traceback. This callback may be skipped because the logic\n        with Ignite can only trigger the first attached handler for `EXCEPTION_RAISED` event.\n\n        Args:\n            _engine: Ignite Engine, unused argument.\n            e: the exception caught in Ignite during engine.run().\n\n        \"\"\"\n        self.logger.exception(f\"Exception: {e}\")\n        raise e", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/tensorboard_handlers.py_warnings_DEFAULT_TAG._Loss_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/tensorboard_handlers.py_warnings_DEFAULT_TAG._Loss_", "embedding": null, "metadata": {"file_path": "monai/handlers/tensorboard_handlers.py", "file_name": "tensorboard_handlers.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 31, "span_ids": ["docstring"], "tokens": 163}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import warnings\nfrom typing import TYPE_CHECKING, Any, Callable, Optional, Sequence\n\nimport numpy as np\nimport torch\n\nfrom monai.config import IgniteInfo\nfrom monai.utils import is_scalar, min_version, optional_import\nfrom monai.visualize import plot_2d_or_3d_image\n\nEvents, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Events\")\n\nif TYPE_CHECKING:\n    from ignite.engine import Engine\n    from torch.utils.tensorboard import SummaryWriter\nelse:\n    Engine, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Engine\")\n    SummaryWriter, _ = optional_import(\"torch.utils.tensorboard\", name=\"SummaryWriter\")\n\nDEFAULT_TAG = \"Loss\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/dice.py_warnings_DiceLoss._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/dice.py_warnings_DiceLoss._", "embedding": null, "metadata": {"file_path": "monai/losses/dice.py", "file_name": "dice.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 42, "span_ids": ["DiceLoss", "docstring"], "tokens": 312}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import warnings\nfrom typing import Callable, List, Optional, Sequence, Union\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.nn.modules.loss import _Loss\n\nfrom monai.losses.focal_loss import FocalLoss\nfrom monai.losses.spatial_mask import MaskedLoss\nfrom monai.networks import one_hot\nfrom monai.utils import LossReduction, Weight, look_up_option\n\n\nclass DiceLoss(_Loss):\n    \"\"\"\n    Compute average Dice loss between two tensors. It can support both multi-classes and multi-labels tasks.\n    The data `input` (BNHW[D] where N is number of classes) is compared with ground truth `target` (BNHW[D]).\n\n    Note that axis N of `input` is expected to be logits or probabilities for each class, if passing logits as input,\n    must set `sigmoid=True` or `softmax=True`, or specifying `other_act`. And the same axis of `target`\n    can be 1 or N (one-hot format).\n\n    The `smooth_nr` and `smooth_dr` parameters are values added to the intersection and union components of\n    the inter-over-union calculation to smooth results respectively, these values should be small.\n\n    The original paper: Milletari, F. et. al. (2016) V-Net: Fully Convolutional Neural Networks forVolumetric\n    Medical Image Segmentation, 3DV, 2016.\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/dice.py_GeneralizedDiceLoss.forward_GeneralizedDiceLoss.forward.return.f": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/dice.py_GeneralizedDiceLoss.forward_GeneralizedDiceLoss.forward.return.f", "embedding": null, "metadata": {"file_path": "monai/losses/dice.py", "file_name": "dice.py", "file_type": "text/x-python", "category": "implementation", "start_line": 301, "end_line": 374, "span_ids": ["GeneralizedDiceLoss.forward"], "tokens": 687}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GeneralizedDiceLoss(_Loss):\n\n    def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:\n        \"\"\"\n        Args:\n            input: the shape should be BNH[WD].\n            target: the shape should be BNH[WD].\n\n        Raises:\n            ValueError: When ``self.reduction`` is not one of [\"mean\", \"sum\", \"none\"].\n\n        \"\"\"\n        if self.sigmoid:\n            input = torch.sigmoid(input)\n        n_pred_ch = input.shape[1]\n        if self.softmax:\n            if n_pred_ch == 1:\n                warnings.warn(\"single channel prediction, `softmax=True` ignored.\")\n            else:\n                input = torch.softmax(input, 1)\n\n        if self.other_act is not None:\n            input = self.other_act(input)\n\n        if self.to_onehot_y:\n            if n_pred_ch == 1:\n                warnings.warn(\"single channel prediction, `to_onehot_y=True` ignored.\")\n            else:\n                target = one_hot(target, num_classes=n_pred_ch)\n\n        if not self.include_background:\n            if n_pred_ch == 1:\n                warnings.warn(\"single channel prediction, `include_background=False` ignored.\")\n            else:\n                # if skipping background, removing first channel\n                target = target[:, 1:]\n                input = input[:, 1:]\n\n        if target.shape != input.shape:\n            raise AssertionError(f\"ground truth has differing shape ({target.shape}) from input ({input.shape})\")\n\n        # reducing only spatial dimensions (not batch nor channels)\n        reduce_axis: List[int] = torch.arange(2, len(input.shape)).tolist()\n        if self.batch:\n            reduce_axis = [0] + reduce_axis\n        intersection = torch.sum(target * input, reduce_axis)\n\n        ground_o = torch.sum(target, reduce_axis)\n        pred_o = torch.sum(input, reduce_axis)\n\n        denominator = ground_o + pred_o\n\n        w = self.w_func(ground_o.float())\n        for b in w:\n            infs = torch.isinf(b)\n            b[infs] = 0.0\n            b[infs] = torch.max(b)\n\n        final_reduce_dim = 0 if self.batch else 1\n        numer = 2.0 * (intersection * w).sum(final_reduce_dim, keepdim=True) + self.smooth_nr\n        denom = (denominator * w).sum(final_reduce_dim, keepdim=True) + self.smooth_dr\n        f: torch.Tensor = 1.0 - (numer / denom)\n\n        if self.reduction == LossReduction.MEAN.value:\n            f = torch.mean(f)  # the batch and channel average\n        elif self.reduction == LossReduction.SUM.value:\n            f = torch.sum(f)  # sum over the batch and channel dims\n        elif self.reduction == LossReduction.NONE.value:\n            # If we are not computing voxelwise loss components at least\n            # make sure a none reduction maintains a broadcastable shape\n            broadcast_shape = list(f.shape[0:2]) + [1] * (len(input.shape) - 2)\n            f = f.view(broadcast_shape)\n        else:\n            raise ValueError(f'Unsupported reduction: {self.reduction}, available options are [\"mean\", \"sum\", \"none\"].')\n\n        return f", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/dice.py_GeneralizedWassersteinDiceLoss.wasserstein_distance_map_GeneralizedWassersteinDiceLoss.wasserstein_distance_map.return.wasserstein_map": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/dice.py_GeneralizedWassersteinDiceLoss.wasserstein_distance_map_GeneralizedWassersteinDiceLoss.wasserstein_distance_map.return.wasserstein_map", "embedding": null, "metadata": {"file_path": "monai/losses/dice.py", "file_name": "dice.py", "file_type": "text/x-python", "category": "implementation", "start_line": 502, "end_line": 538, "span_ids": ["GeneralizedWassersteinDiceLoss.wasserstein_distance_map"], "tokens": 414}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GeneralizedWassersteinDiceLoss(_Loss):\n\n    def wasserstein_distance_map(self, flat_proba: torch.Tensor, flat_target: torch.Tensor) -> torch.Tensor:\n        \"\"\"\n        Compute the voxel-wise Wasserstein distance between the\n        flattened prediction and the flattened labels (ground_truth) with respect\n        to the distance matrix on the label space M.\n        This corresponds to eq. 6 in:\n\n            Fidon L. et al. (2017) Generalised Wasserstein Dice Score for Imbalanced Multi-class\n            Segmentation using Holistic Convolutional Networks. BrainLes 2017.\n\n        Args:\n            flat_proba: the probabilities of input(predicted) tensor.\n            flat_target: the target tensor.\n        \"\"\"\n        # Turn the distance matrix to a map of identical matrix\n        m = torch.clone(torch.as_tensor(self.m)).to(flat_proba.device)\n        m_extended = torch.unsqueeze(m, dim=0)\n        m_extended = torch.unsqueeze(m_extended, dim=3)\n        m_extended = m_extended.expand((flat_proba.size(0), m_extended.size(1), m_extended.size(2), flat_proba.size(2)))\n\n        # Expand the feature dimensions of the target\n        flat_target_extended = torch.unsqueeze(flat_target, dim=1)\n        flat_target_extended = flat_target_extended.expand(\n            (flat_target.size(0), m_extended.size(1), flat_target.size(1))\n        )\n        flat_target_extended = torch.unsqueeze(flat_target_extended, dim=1)\n\n        # Extract the vector of class distances for the ground-truth label at each voxel\n        m_extended = torch.gather(m_extended, dim=1, index=flat_target_extended)\n        m_extended = torch.squeeze(m_extended, dim=1)\n\n        # Compute the wasserstein distance map\n        wasserstein_map = m_extended * flat_proba\n\n        # Sum over the classes\n        wasserstein_map = torch.sum(wasserstein_map, dim=1)\n        return wasserstein_map", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/convolutions.py_from_typing_import_Option_Convolution._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/convolutions.py_from_typing_import_Option_Convolution._", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/convolutions.py", "file_name": "convolutions.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 98, "span_ids": ["Convolution", "docstring"], "tokens": 763}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Optional, Sequence, Tuple, Union\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\n\nfrom monai.networks.blocks import ADN\nfrom monai.networks.layers.convutils import same_padding, stride_minus_kernel_padding\nfrom monai.networks.layers.factories import Conv\nfrom monai.utils.deprecate_utils import deprecated_arg\n\n\nclass Convolution(nn.Sequential):\n    \"\"\"\n    Constructs a convolution with normalization, optional dropout, and optional activation layers::\n\n        -- (Conv|ConvTrans) -- (Norm -- Dropout -- Acti) --\n\n    if ``conv_only`` set to ``True``::\n\n        -- (Conv|ConvTrans) --\n\n    For example:\n\n    .. code-block:: python\n\n        from monai.networks.blocks import Convolution\n\n        conv = Convolution(\n            dimensions=3,\n            in_channels=1,\n            out_channels=1,\n            adn_ordering=\"ADN\",\n            act=(\"prelu\", {\"init\": 0.2}),\n            dropout=0.1,\n            norm=(\"layer\", {\"normalized_shape\": (10, 10, 10)}),\n        )\n        print(conv)\n\n    output::\n\n        Convolution(\n          (conv): Conv3d(1, 1, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n          (adn): ADN(\n            (A): PReLU(num_parameters=1)\n            (D): Dropout(p=0.1, inplace=False)\n            (N): LayerNorm((10, 10, 10), eps=1e-05, elementwise_affine=True)\n          )\n        )\n\n    Args:\n        spatial_dims: number of spatial dimensions.\n        in_channels: number of input channels.\n        out_channels: number of output channels.\n        strides: convolution stride. Defaults to 1.\n        kernel_size: convolution kernel size. Defaults to 3.\n        adn_ordering: a string representing the ordering of activation, normalization, and dropout.\n            Defaults to \"NDA\".\n        act: activation type and arguments. Defaults to PReLU.\n        norm: feature normalization type and arguments. Defaults to instance norm.\n        dropout: dropout ratio. Defaults to no dropout.\n        dropout_dim: determine the spatial dimensions of dropout. Defaults to 1.\n\n            - When dropout_dim = 1, randomly zeroes some of the elements for each channel.\n            - When dropout_dim = 2, Randomly zeroes out entire channels (a channel is a 2D feature map).\n            - When dropout_dim = 3, Randomly zeroes out entire channels (a channel is a 3D feature map).\n\n            The value of dropout_dim should be no no larger than the value of `spatial_dims`.\n        dilation: dilation rate. Defaults to 1.\n        groups: controls the connections between inputs and outputs. Defaults to 1.\n        bias: whether to have a bias term. Defaults to True.\n        conv_only: whether to use the convolutional layer only. Defaults to False.\n        is_transposed: if True uses ConvTrans instead of Conv. Defaults to False.\n        padding: controls the amount of implicit zero-paddings on both sides for padding number of points\n            for each dimension. Defaults to None.\n        output_padding: controls the additional size added to one side of the output shape.\n            Defaults to None.\n\n    .. deprecated:: 0.6.0\n        ``dimensions`` is deprecated, use ``spatial_dims`` instead.\n\n    See also:\n\n        :py:class:`monai.networks.layers.Conv`\n        :py:class:`monai.networks.blocks.ADN`\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/convolutions.py_ResidualUnit_ResidualUnit._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/convolutions.py_ResidualUnit_ResidualUnit._", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/convolutions.py", "file_name": "convolutions.py", "file_type": "text/x-python", "category": "implementation", "start_line": 177, "end_line": 252, "span_ids": ["ResidualUnit"], "tokens": 711}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ResidualUnit(nn.Module):\n    \"\"\"\n    Residual module with multiple convolutions and a residual connection.\n\n    For example:\n\n    .. code-block:: python\n\n        from monai.networks.blocks import ResidualUnit\n\n        convs = ResidualUnit(\n            spatial_dims=3,\n            in_channels=1,\n            out_channels=1,\n            adn_ordering=\"AN\",\n            act=(\"prelu\", {\"init\": 0.2}),\n            norm=(\"layer\", {\"normalized_shape\": (10, 10, 10)}),\n        )\n        print(convs)\n\n    output::\n\n        ResidualUnit(\n          (conv): Sequential(\n            (unit0): Convolution(\n              (conv): Conv3d(1, 1, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n              (adn): ADN(\n                (A): PReLU(num_parameters=1)\n                (N): LayerNorm((10, 10, 10), eps=1e-05, elementwise_affine=True)\n              )\n            )\n            (unit1): Convolution(\n              (conv): Conv3d(1, 1, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n              (adn): ADN(\n                (A): PReLU(num_parameters=1)\n                (N): LayerNorm((10, 10, 10), eps=1e-05, elementwise_affine=True)\n              )\n            )\n          )\n          (residual): Identity()\n        )\n\n    Args:\n        spatial_dims: number of spatial dimensions.\n        in_channels: number of input channels.\n        out_channels: number of output channels.\n        strides: convolution stride. Defaults to 1.\n        kernel_size: convolution kernel size. Defaults to 3.\n        subunits: number of convolutions. Defaults to 2.\n        adn_ordering: a string representing the ordering of activation, normalization, and dropout.\n            Defaults to \"NDA\".\n        act: activation type and arguments. Defaults to PReLU.\n        norm: feature normalization type and arguments. Defaults to instance norm.\n        dropout: dropout ratio. Defaults to no dropout.\n        dropout_dim: determine the dimensions of dropout. Defaults to 1.\n\n            - When dropout_dim = 1, randomly zeroes some of the elements for each channel.\n            - When dropout_dim = 2, Randomly zero out entire channels (a channel is a 2D feature map).\n            - When dropout_dim = 3, Randomly zero out entire channels (a channel is a 3D feature map).\n\n            The value of dropout_dim should be no no larger than the value of `dimensions`.\n        dilation: dilation rate. Defaults to 1.\n        bias: whether to have a bias term. Defaults to True.\n        last_conv_only: for the last subunit, whether to use the convolutional layer only.\n            Defaults to False.\n        padding: controls the amount of implicit zero-paddings on both sides for padding number of points\n            for each dimension. Defaults to None.\n\n    .. deprecated:: 0.6.0\n        ``dimensions`` is deprecated, use ``spatial_dims`` instead.\n\n    See also:\n\n        :py:class:`monai.networks.blocks.Convolution`\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/convolutions.py_ResidualUnit.__init___": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/convolutions.py_ResidualUnit.__init___", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/convolutions.py", "file_name": "convolutions.py", "file_type": "text/x-python", "category": "implementation", "start_line": 254, "end_line": 327, "span_ids": ["ResidualUnit.forward", "ResidualUnit.__init__"], "tokens": 679}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ResidualUnit(nn.Module):\n\n    @deprecated_arg(name=\"dimensions\", since=\"0.6\", msg_suffix=\"Please use `spatial_dims` instead.\")\n    def __init__(\n        self,\n        spatial_dims: int,\n        in_channels: int,\n        out_channels: int,\n        strides: Union[Sequence[int], int] = 1,\n        kernel_size: Union[Sequence[int], int] = 3,\n        subunits: int = 2,\n        adn_ordering: str = \"NDA\",\n        act: Optional[Union[Tuple, str]] = \"PRELU\",\n        norm: Optional[Union[Tuple, str]] = \"INSTANCE\",\n        dropout: Optional[Union[Tuple, str, float]] = None,\n        dropout_dim: Optional[int] = 1,\n        dilation: Union[Sequence[int], int] = 1,\n        bias: bool = True,\n        last_conv_only: bool = False,\n        padding: Optional[Union[Sequence[int], int]] = None,\n        dimensions: Optional[int] = None,\n    ) -> None:\n        super().__init__()\n        self.dimensions = spatial_dims if dimensions is None else dimensions\n        self.in_channels = in_channels\n        self.out_channels = out_channels\n        self.conv = nn.Sequential()\n        self.residual = nn.Identity()\n        if not padding:\n            padding = same_padding(kernel_size, dilation)\n        schannels = in_channels\n        sstrides = strides\n        subunits = max(1, subunits)\n\n        for su in range(subunits):\n            conv_only = last_conv_only and su == (subunits - 1)\n            unit = Convolution(\n                self.dimensions,\n                schannels,\n                out_channels,\n                strides=sstrides,\n                kernel_size=kernel_size,\n                adn_ordering=adn_ordering,\n                act=act,\n                norm=norm,\n                dropout=dropout,\n                dropout_dim=dropout_dim,\n                dilation=dilation,\n                bias=bias,\n                conv_only=conv_only,\n                padding=padding,\n            )\n\n            self.conv.add_module(f\"unit{su:d}\", unit)\n\n            # after first loop set channels and strides to what they should be for subsequent units\n            schannels = out_channels\n            sstrides = 1\n\n        # apply convolution to input to change number of output channels and size to match that coming from self.conv\n        if np.prod(strides) != 1 or in_channels != out_channels:\n            rkernel_size = kernel_size\n            rpadding = padding\n\n            if np.prod(strides) == 1:  # if only adapting number of channels a 1x1 kernel is used with no padding\n                rkernel_size = 1\n                rpadding = 0\n\n            conv_type = Conv[Conv.CONV, self.dimensions]\n            self.residual = conv_type(in_channels, out_channels, rkernel_size, strides, rpadding, bias=bias)\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        res: torch.Tensor = self.residual(x)  # create the additive residual from x\n        cx: torch.Tensor = self.conv(x)  # apply x to sequence of operations\n        return cx + res  # add the residual to the output", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/fcn.py_from_typing_import_Type_GCN.forward.return.x": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/fcn.py_from_typing_import_Type_GCN.forward.return.x", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/fcn.py", "file_name": "fcn.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 57, "span_ids": ["GCN", "GCN.forward", "GCN.__init__", "docstring"], "tokens": 456}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Type\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom monai.networks.blocks.convolutions import Convolution\nfrom monai.networks.blocks.upsample import UpSample\nfrom monai.networks.layers.factories import Act, Conv, Norm\nfrom monai.utils import optional_import\n\nmodels, _ = optional_import(\"torchvision\", name=\"models\")\n\n\nclass GCN(nn.Module):\n    \"\"\"\n    The Global Convolutional Network module using large 1D\n    Kx1 and 1xK kernels to represent 2D kernels.\n    \"\"\"\n\n    def __init__(self, inplanes: int, planes: int, ks: int = 7):\n        \"\"\"\n        Args:\n            inplanes: number of input channels.\n            planes: number of output channels.\n            ks: kernel size for one dimension. Defaults to 7.\n        \"\"\"\n        super().__init__()\n\n        conv2d_type: Type[nn.Conv2d] = Conv[Conv.CONV, 2]\n        self.conv_l1 = conv2d_type(in_channels=inplanes, out_channels=planes, kernel_size=(ks, 1), padding=(ks // 2, 0))\n        self.conv_l2 = conv2d_type(in_channels=planes, out_channels=planes, kernel_size=(1, ks), padding=(0, ks // 2))\n        self.conv_r1 = conv2d_type(in_channels=inplanes, out_channels=planes, kernel_size=(1, ks), padding=(0, ks // 2))\n        self.conv_r2 = conv2d_type(in_channels=planes, out_channels=planes, kernel_size=(ks, 1), padding=(ks // 2, 0))\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        \"\"\"\n        Args:\n            x: in shape (batch, inplanes, spatial_1, spatial_2).\n        \"\"\"\n        x_l = self.conv_l1(x)\n        x_l = self.conv_l2(x_l)\n        x_r = self.conv_r1(x)\n        x_r = self.conv_r2(x_r)\n        x = x_l + x_r\n        return x", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/fcn.py_MCFCN_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/fcn.py_MCFCN_", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/fcn.py", "file_name": "fcn.py", "file_type": "text/x-python", "category": "implementation", "start_line": 196, "end_line": 243, "span_ids": ["MCFCN.forward", "MCFCN.__init__", "MCFCN"], "tokens": 360}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class MCFCN(FCN):\n    \"\"\"\n    The multi-channel version of the 2D FCN module.\n    Adds a projection layer to take arbitrary number of inputs.\n\n    Args:\n        in_channels: number of input channels. Defaults to 3.\n        out_channels: number of output channels. Defaults to 1.\n        upsample_mode: [``\"transpose\"``, ``\"bilinear\"``]\n            The mode of upsampling manipulations.\n            Using the second mode cannot guarantee the model's reproducibility. Defaults to ``bilinear``.\n\n            - ``transpose``, uses transposed convolution layers.\n            - ``bilinear``, uses bilinear interpolate.\n        pretrained: If True, returns a model pre-trained on ImageNet\n        progress: If True, displays a progress bar of the download to stderr.\n    \"\"\"\n\n    def __init__(\n        self,\n        in_channels: int = 3,\n        out_channels: int = 1,\n        upsample_mode: str = \"bilinear\",\n        pretrained: bool = True,\n        progress: bool = True,\n    ):\n        super().__init__(\n            out_channels=out_channels, upsample_mode=upsample_mode, pretrained=pretrained, progress=progress\n        )\n\n        self.init_proj = Convolution(\n            spatial_dims=2,\n            in_channels=in_channels,\n            out_channels=3,\n            kernel_size=1,\n            act=(\"relu\", {\"inplace\": True}),\n            norm=Norm.BATCH,\n            bias=False,\n        )\n\n    def forward(self, x: torch.Tensor):\n        \"\"\"\n        Args:\n            x: in shape (batch, in_channels, spatial_1, spatial_2).\n        \"\"\"\n        x = self.init_proj(x)\n        return super().forward(x)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/segresnet_block.py_ResBlock_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/segresnet_block.py_ResBlock_", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/segresnet_block.py", "file_name": "segresnet_block.py", "file_type": "text/x-python", "category": "implementation", "start_line": 45, "end_line": 95, "span_ids": ["ResBlock.__init__", "ResBlock.forward", "ResBlock"], "tokens": 398}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ResBlock(nn.Module):\n    \"\"\"\n    ResBlock employs skip connection and two convolution blocks and is used\n    in SegResNet based on `3D MRI brain tumor segmentation using autoencoder regularization\n    <https://arxiv.org/pdf/1810.11654.pdf>`_.\n    \"\"\"\n\n    def __init__(\n        self,\n        spatial_dims: int,\n        in_channels: int,\n        norm: Union[Tuple, str],\n        kernel_size: int = 3,\n        act: Union[Tuple, str] = (\"RELU\", {\"inplace\": True}),\n    ) -> None:\n        \"\"\"\n        Args:\n            spatial_dims: number of spatial dimensions, could be 1, 2 or 3.\n            in_channels: number of input channels.\n            norm: feature normalization type and arguments.\n            kernel_size: convolution kernel size, the value should be an odd number. Defaults to 3.\n            act: activation type and arguments. Defaults to ``RELU``.\n        \"\"\"\n\n        super().__init__()\n\n        if kernel_size % 2 != 1:\n            raise AssertionError(\"kernel_size should be an odd number.\")\n\n        self.norm1 = get_norm_layer(name=norm, spatial_dims=spatial_dims, channels=in_channels)\n        self.norm2 = get_norm_layer(name=norm, spatial_dims=spatial_dims, channels=in_channels)\n        self.act = get_act_layer(act)\n        self.conv1 = get_conv_layer(spatial_dims, in_channels=in_channels, out_channels=in_channels)\n        self.conv2 = get_conv_layer(spatial_dims, in_channels=in_channels, out_channels=in_channels)\n\n    def forward(self, x):\n\n        identity = x\n\n        x = self.norm1(x)\n        x = self.act(x)\n        x = self.conv1(x)\n\n        x = self.norm2(x)\n        x = self.act(x)\n        x = self.conv2(x)\n\n        x += identity\n\n        return x", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/squeeze_and_excitation.py_SEResNeXtBottleneck_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/squeeze_and_excitation.py_SEResNeXtBottleneck_", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/squeeze_and_excitation.py", "file_name": "squeeze_and_excitation.py", "file_type": "text/x-python", "category": "implementation", "start_line": 332, "end_line": 381, "span_ids": ["SEResNeXtBottleneck.__init__", "SEResNeXtBottleneck"], "tokens": 371}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SEResNeXtBottleneck(SEBlock):\n    \"\"\"\n    ResNeXt bottleneck type C with a Squeeze-and-Excitation module.\n    \"\"\"\n\n    expansion = 4\n\n    def __init__(\n        self,\n        spatial_dims: int,\n        inplanes: int,\n        planes: int,\n        groups: int,\n        reduction: int,\n        stride: int = 1,\n        downsample: Optional[Convolution] = None,\n        base_width: int = 4,\n    ) -> None:\n\n        conv_param_1 = {\n            \"strides\": 1,\n            \"kernel_size\": 1,\n            \"act\": (\"relu\", {\"inplace\": True}),\n            \"norm\": Norm.BATCH,\n            \"bias\": False,\n        }\n        conv_param_2 = {\n            \"strides\": stride,\n            \"kernel_size\": 3,\n            \"act\": (\"relu\", {\"inplace\": True}),\n            \"norm\": Norm.BATCH,\n            \"groups\": groups,\n            \"bias\": False,\n        }\n        conv_param_3 = {\"strides\": 1, \"kernel_size\": 1, \"act\": None, \"norm\": Norm.BATCH, \"bias\": False}\n        width = math.floor(planes * (base_width / 64)) * groups\n\n        super().__init__(\n            spatial_dims=spatial_dims,\n            in_channels=inplanes,\n            n_chns_1=width,\n            n_chns_2=width,\n            n_chns_3=planes * 4,\n            conv_param_1=conv_param_1,\n            conv_param_2=conv_param_2,\n            conv_param_3=conv_param_3,\n            project=downsample,\n            r=reduction,\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/convutils.py_calculate_out_shape_calculate_out_shape.return.out_shape_if_len_out_shap": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/convutils.py_calculate_out_shape_calculate_out_shape.return.out_shape_if_len_out_shap", "embedding": null, "metadata": {"file_path": "monai/networks/layers/convutils.py", "file_name": "convutils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 59, "end_line": 78, "span_ids": ["calculate_out_shape"], "tokens": 243}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def calculate_out_shape(\n    in_shape: Union[Sequence[int], int, np.ndarray],\n    kernel_size: Union[Sequence[int], int],\n    stride: Union[Sequence[int], int],\n    padding: Union[Sequence[int], int],\n) -> Union[Tuple[int, ...], int]:\n    \"\"\"\n    Calculate the output tensor shape when applying a convolution to a tensor of shape `inShape` with kernel size\n    `kernel_size`, stride value `stride`, and input padding value `padding`. All arguments can be scalars or multiple\n    values, return value is a scalar if all inputs are scalars.\n    \"\"\"\n    in_shape_np = np.atleast_1d(in_shape)\n    kernel_size_np = np.atleast_1d(kernel_size)\n    stride_np = np.atleast_1d(stride)\n    padding_np = np.atleast_1d(padding)\n\n    out_shape_np = ((in_shape_np - kernel_size_np + padding_np + padding_np) // stride_np) + 1\n    out_shape = tuple(int(s) for s in out_shape_np)\n\n    return out_shape if len(out_shape) > 1 else out_shape[0]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/simplelayers.py_LLTMFunction_LLTMFunction.backward.return.d_input_d_weights_d_bia": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/simplelayers.py_LLTMFunction_LLTMFunction.backward.return.d_input_d_weights_d_bia", "embedding": null, "metadata": {"file_path": "monai/networks/layers/simplelayers.py", "file_name": "simplelayers.py", "file_type": "text/x-python", "category": "implementation", "start_line": 161, "end_line": 176, "span_ids": ["LLTMFunction.forward", "LLTMFunction", "LLTMFunction.backward"], "tokens": 163}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LLTMFunction(Function):\n    @staticmethod\n    def forward(ctx, input, weights, bias, old_h, old_cell):\n        outputs = _C.lltm_forward(input, weights, bias, old_h, old_cell)\n        new_h, new_cell = outputs[:2]\n        variables = outputs[1:] + [weights]\n        ctx.save_for_backward(*variables)\n\n        return new_h, new_cell\n\n    @staticmethod\n    def backward(ctx, grad_h, grad_cell):\n        outputs = _C.lltm_backward(grad_h.contiguous(), grad_cell.contiguous(), *ctx.saved_tensors)\n        d_old_h, d_input, d_weights, d_bias, d_old_cell = outputs[:5]\n\n        return d_input, d_weights, d_bias, d_old_h, d_old_cell", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/simplelayers.py_LLTM_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/simplelayers.py_LLTM_", "embedding": null, "metadata": {"file_path": "monai/networks/layers/simplelayers.py", "file_name": "simplelayers.py", "file_type": "text/x-python", "category": "implementation", "start_line": 442, "end_line": 472, "span_ids": ["LLTM.forward", "LLTM", "LLTM.__init__", "LLTM.reset_parameters"], "tokens": 286}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LLTM(nn.Module):\n    \"\"\"\n    This recurrent unit is similar to an LSTM, but differs in that it lacks a forget\n    gate and uses an Exponential Linear Unit (ELU) as its internal activation function.\n    Because this unit never forgets, call it LLTM, or Long-Long-Term-Memory unit.\n    It has both C++ and CUDA implementation, automatically switch according to the\n    target device where put this module to.\n\n    Args:\n        input_features: size of input feature data\n        state_size: size of the state of recurrent unit\n\n    Referring to: https://pytorch.org/tutorials/advanced/cpp_extension.html\n    \"\"\"\n\n    def __init__(self, input_features: int, state_size: int):\n        super().__init__()\n        self.input_features = input_features\n        self.state_size = state_size\n        self.weights = nn.Parameter(torch.empty(3 * state_size, input_features + state_size))\n        self.bias = nn.Parameter(torch.empty(1, 3 * state_size))\n        self.reset_parameters()\n\n    def reset_parameters(self):\n        stdv = 1.0 / math.sqrt(self.state_size)\n        for weight in self.parameters():\n            weight.data.uniform_(-stdv, +stdv)\n\n    def forward(self, input, state):\n        return LLTMFunction.apply(input, self.weights, self.bias, *state)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/ahnet.py_math_Bottleneck3x3x1.__init__.self.pool.pool_type_kernel_size_1_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/ahnet.py_math_Bottleneck3x3x1.__init__.self.pool.pool_type_kernel_size_1_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/ahnet.py", "file_name": "ahnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 61, "span_ids": ["Bottleneck3x3x1.__init__", "Bottleneck3x3x1", "docstring"], "tokens": 432}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import math\nfrom typing import Optional, Sequence, Type, Union\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom monai.networks.blocks.fcn import FCN\nfrom monai.networks.layers.factories import Act, Conv, Norm, Pool\n\n__all__ = [\"AHnet\", \"Ahnet\", \"AHNet\"]\n\n\nclass Bottleneck3x3x1(nn.Module):\n\n    expansion = 4\n\n    def __init__(\n        self,\n        spatial_dims: int,\n        inplanes: int,\n        planes: int,\n        stride: Union[Sequence[int], int] = 1,\n        downsample: Optional[nn.Sequential] = None,\n    ) -> None:\n\n        super().__init__()\n\n        conv_type = Conv[Conv.CONV, spatial_dims]\n        norm_type: Type[Union[nn.BatchNorm2d, nn.BatchNorm3d]] = Norm[Norm.BATCH, spatial_dims]\n        pool_type: Type[Union[nn.MaxPool2d, nn.MaxPool3d]] = Pool[Pool.MAX, spatial_dims]\n        relu_type: Type[nn.ReLU] = Act[Act.RELU]\n\n        self.conv1 = conv_type(inplanes, planes, kernel_size=1, bias=False)\n        self.bn1 = norm_type(planes)\n        self.conv2 = conv_type(\n            planes,\n            planes,\n            kernel_size=(3, 3, 1)[-spatial_dims:],\n            stride=stride,\n            padding=(1, 1, 0)[-spatial_dims:],\n            bias=False,\n        )\n        self.bn2 = norm_type(planes)\n        self.conv3 = conv_type(planes, planes * 4, kernel_size=1, bias=False)\n        self.bn3 = norm_type(planes * 4)\n        self.relu = relu_type(inplace=True)\n        self.downsample = downsample\n        self.stride = stride\n        self.pool = pool_type(kernel_size=(1, 1, 2)[-spatial_dims:], stride=(1, 1, 2)[-spatial_dims:])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/ahnet.py_Bottleneck3x3x1.forward_Bottleneck3x3x1.forward.return.out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/ahnet.py_Bottleneck3x3x1.forward_Bottleneck3x3x1.forward.return.out", "embedding": null, "metadata": {"file_path": "monai/networks/nets/ahnet.py", "file_name": "ahnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 60, "end_line": 82, "span_ids": ["Bottleneck3x3x1.forward"], "tokens": 133}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Bottleneck3x3x1(nn.Module):\n\n    def forward(self, x):\n        residual = x\n\n        out = self.conv1(x)\n        out = self.bn1(out)\n        out = self.relu(out)\n\n        out = self.conv2(out)\n        out = self.bn2(out)\n        out = self.relu(out)\n\n        out = self.conv3(out)\n        out = self.bn3(out)\n\n        if self.downsample is not None:\n            residual = self.downsample(x)\n            if out.size() != residual.size():\n                out = self.pool(out)\n\n        out += residual\n        out = self.relu(out)\n\n        return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/ahnet.py_Projection_Projection.__init__.self_add_module_conv_c": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/ahnet.py_Projection_Projection.__init__.self_add_module_conv_c", "embedding": null, "metadata": {"file_path": "monai/networks/nets/ahnet.py", "file_name": "ahnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 88, "end_line": 98, "span_ids": ["Projection.__init__", "Projection"], "tokens": 145}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Projection(nn.Sequential):\n    def __init__(self, spatial_dims: int, num_input_features: int, num_output_features: int):\n        super().__init__()\n\n        conv_type = Conv[Conv.CONV, spatial_dims]\n        norm_type: Type[Union[nn.BatchNorm2d, nn.BatchNorm3d]] = Norm[Norm.BATCH, spatial_dims]\n        relu_type: Type[nn.ReLU] = Act[Act.RELU]\n\n        self.add_module(\"norm\", norm_type(num_input_features))\n        self.add_module(\"relu\", relu_type(inplace=True))\n        self.add_module(\"conv\", conv_type(num_input_features, num_output_features, kernel_size=1, stride=1, bias=False))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/ahnet.py_DenseBlock_DenseBlock.__init__.for_i_in_range_num_layers.self_add_module_denselay": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/ahnet.py_DenseBlock_DenseBlock.__init__.for_i_in_range_num_layers.self_add_module_denselay", "embedding": null, "metadata": {"file_path": "monai/networks/nets/ahnet.py", "file_name": "ahnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 101, "end_line": 116, "span_ids": ["DenseBlock.__init__", "DenseBlock"], "tokens": 118}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DenseBlock(nn.Sequential):\n    def __init__(\n        self,\n        spatial_dims: int,\n        num_layers: int,\n        num_input_features: int,\n        bn_size: int,\n        growth_rate: int,\n        dropout_prob: float,\n    ):\n        super().__init__()\n        for i in range(num_layers):\n            layer = Pseudo3DLayer(\n                spatial_dims, num_input_features + i * growth_rate, growth_rate, bn_size, dropout_prob\n            )\n            self.add_module(\"denselayer%d\" % (i + 1), layer)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/ahnet.py_UpTransition_UpTransition.__init__.if_upsample_mode_tran.else_.self_add_module_up_nn_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/ahnet.py_UpTransition_UpTransition.__init__.if_upsample_mode_tran.else_.self_add_module_up_nn_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/ahnet.py", "file_name": "ahnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 119, "end_line": 141, "span_ids": ["UpTransition.__init__", "UpTransition"], "tokens": 278}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class UpTransition(nn.Sequential):\n    def __init__(\n        self, spatial_dims: int, num_input_features: int, num_output_features: int, upsample_mode: str = \"transpose\"\n    ):\n        super().__init__()\n\n        conv_type = Conv[Conv.CONV, spatial_dims]\n        norm_type: Type[Union[nn.BatchNorm2d, nn.BatchNorm3d]] = Norm[Norm.BATCH, spatial_dims]\n        relu_type: Type[nn.ReLU] = Act[Act.RELU]\n\n        self.add_module(\"norm\", norm_type(num_input_features))\n        self.add_module(\"relu\", relu_type(inplace=True))\n        self.add_module(\"conv\", conv_type(num_input_features, num_output_features, kernel_size=1, stride=1, bias=False))\n        if upsample_mode == \"transpose\":\n            conv_trans_type = Conv[Conv.CONVTRANS, spatial_dims]\n            self.add_module(\n                \"up\", conv_trans_type(num_output_features, num_output_features, kernel_size=2, stride=2, bias=False)\n            )\n        else:\n            align_corners: Optional[bool] = None\n            if upsample_mode in [\"trilinear\", \"bilinear\"]:\n                align_corners = True\n            self.add_module(\"up\", nn.Upsample(scale_factor=2, mode=upsample_mode, align_corners=align_corners))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/ahnet.py_Final_Final.__init__.if_upsample_mode_tran.else_.self_add_module_up_nn_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/ahnet.py_Final_Final.__init__.if_upsample_mode_tran.else_.self_add_module_up_nn_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/ahnet.py", "file_name": "ahnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 144, "end_line": 176, "span_ids": ["Final.__init__", "Final"], "tokens": 317}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Final(nn.Sequential):\n    def __init__(\n        self, spatial_dims: int, num_input_features: int, num_output_features: int, upsample_mode: str = \"transpose\"\n    ):\n        super().__init__()\n\n        conv_type = Conv[Conv.CONV, spatial_dims]\n        norm_type: Type[Union[nn.BatchNorm2d, nn.BatchNorm3d]] = Norm[Norm.BATCH, spatial_dims]\n        relu_type: Type[nn.ReLU] = Act[Act.RELU]\n\n        self.add_module(\"norm\", norm_type(num_input_features))\n        self.add_module(\"relu\", relu_type(inplace=True))\n        self.add_module(\n            \"conv\",\n            conv_type(\n                num_input_features,\n                num_output_features,\n                kernel_size=(3, 3, 1)[-spatial_dims:],\n                stride=1,\n                padding=(1, 1, 0)[-spatial_dims:],\n                bias=False,\n            ),\n        )\n        if upsample_mode == \"transpose\":\n            conv_trans_type = Conv[Conv.CONVTRANS, spatial_dims]\n            self.add_module(\n                \"up\", conv_trans_type(num_output_features, num_output_features, kernel_size=2, stride=2, bias=False)\n            )\n        else:\n            align_corners: Optional[bool] = None\n            if upsample_mode in [\"trilinear\", \"bilinear\"]:\n                align_corners = True\n            self.add_module(\"up\", nn.Upsample(scale_factor=2, mode=upsample_mode, align_corners=align_corners))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/ahnet.py_Pseudo3DLayer_Pseudo3DLayer.__init__.self.dropout_prob.dropout_prob": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/ahnet.py_Pseudo3DLayer_Pseudo3DLayer.__init__.self.dropout_prob.dropout_prob", "embedding": null, "metadata": {"file_path": "monai/networks/nets/ahnet.py", "file_name": "ahnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 179, "end_line": 217, "span_ids": ["Pseudo3DLayer.__init__", "Pseudo3DLayer"], "tokens": 412}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Pseudo3DLayer(nn.Module):\n    def __init__(self, spatial_dims: int, num_input_features: int, growth_rate: int, bn_size: int, dropout_prob: float):\n        super().__init__()\n        # 1x1x1\n\n        conv_type = Conv[Conv.CONV, spatial_dims]\n        norm_type: Type[Union[nn.BatchNorm2d, nn.BatchNorm3d]] = Norm[Norm.BATCH, spatial_dims]\n        relu_type: Type[nn.ReLU] = Act[Act.RELU]\n\n        self.bn1 = norm_type(num_input_features)\n        self.relu1 = relu_type(inplace=True)\n        self.conv1 = conv_type(num_input_features, bn_size * growth_rate, kernel_size=1, stride=1, bias=False)\n        # 3x3x1\n        self.bn2 = norm_type(bn_size * growth_rate)\n        self.relu2 = relu_type(inplace=True)\n        self.conv2 = conv_type(\n            bn_size * growth_rate,\n            growth_rate,\n            kernel_size=(3, 3, 1)[-spatial_dims:],\n            stride=1,\n            padding=(1, 1, 0)[-spatial_dims:],\n            bias=False,\n        )\n        # 1x1x3\n        self.bn3 = norm_type(growth_rate)\n        self.relu3 = relu_type(inplace=True)\n        self.conv3 = conv_type(\n            growth_rate,\n            growth_rate,\n            kernel_size=(1, 1, 3)[-spatial_dims:],\n            stride=1,\n            padding=(0, 0, 1)[-spatial_dims:],\n            bias=False,\n        )\n        # 1x1x1\n        self.bn4 = norm_type(growth_rate)\n        self.relu4 = relu_type(inplace=True)\n        self.conv4 = conv_type(growth_rate, growth_rate, kernel_size=1, stride=1, bias=False)\n        self.dropout_prob = dropout_prob", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/ahnet.py_Pseudo3DLayer.forward_Pseudo3DLayer.forward.return.torch_cat_inx_new_featu": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/ahnet.py_Pseudo3DLayer.forward_Pseudo3DLayer.forward.return.torch_cat_inx_new_featu", "embedding": null, "metadata": {"file_path": "monai/networks/nets/ahnet.py", "file_name": "ahnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 217, "end_line": 240, "span_ids": ["Pseudo3DLayer.forward"], "tokens": 220}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Pseudo3DLayer(nn.Module):\n\n    def forward(self, x):\n        inx = x\n        x = self.bn1(x)\n        x = self.relu1(x)\n        x = self.conv1(x)\n\n        x = self.bn2(x)\n        x = self.relu2(x)\n        x3x3x1 = self.conv2(x)\n\n        x = self.bn3(x3x3x1)\n        x = self.relu3(x)\n        x1x1x3 = self.conv3(x)\n\n        x = x3x3x1 + x1x1x3\n        x = self.bn4(x)\n        x = self.relu4(x)\n        new_features = self.conv4(x)\n\n        self.dropout_prob = 0.0  # Dropout will make trouble!\n        # since we use the train mode for inference\n        if self.dropout_prob > 0.0:\n            new_features = F.dropout(new_features, p=self.dropout_prob, training=self.training)\n        return torch.cat([inx, new_features], 1)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/ahnet.py_PSP.forward_PSP.forward.return.x": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/ahnet.py_PSP.forward_PSP.forward.return.x", "embedding": null, "metadata": {"file_path": "monai/networks/nets/ahnet.py", "file_name": "ahnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 285, "end_line": 307, "span_ids": ["PSP.forward"], "tokens": 193}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class PSP(nn.Module):\n\n    def forward(self, x):\n        outputs = []\n        if self.upsample_mode == \"transpose\":\n            for (project_module, pool_module, up_module) in zip(\n                self.project_modules, self.pool_modules, self.up_modules\n            ):\n                output = up_module(project_module(pool_module(x)))\n                outputs.append(output)\n        else:\n            for (project_module, pool_module) in zip(self.project_modules, self.pool_modules):\n                interpolate_size = x.shape[2:]\n                align_corners: Optional[bool] = None\n                if self.upsample_mode in [\"trilinear\", \"bilinear\"]:\n                    align_corners = True\n                output = F.interpolate(\n                    project_module(pool_module(x)),\n                    size=interpolate_size,\n                    mode=self.upsample_mode,\n                    align_corners=align_corners,\n                )\n                outputs.append(output)\n        x = torch.cat(outputs, dim=1)\n        return x", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/ahnet.py_AHNet_AHNet._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/ahnet.py_AHNet_AHNet._", "embedding": null, "metadata": {"file_path": "monai/networks/nets/ahnet.py", "file_name": "ahnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 298, "end_line": 331, "span_ids": ["AHNet"], "tokens": 566}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AHNet(nn.Module):\n    \"\"\"\n    AHNet based on `Anisotropic Hybrid Network <https://arxiv.org/pdf/1711.08580.pdf>`_.\n    Adapted from `lsqshr's official code <https://github.com/lsqshr/AH-Net/blob/master/net3d.py>`_.\n    Except from the original network that supports 3D inputs, this implementation also supports 2D inputs.\n    According to the `tests for deconvolutions <https://github.com/Project-MONAI/MONAI/issues/1023>`_, using\n    ``\"transpose\"`` rather than linear interpolations is faster. Therefore, this implementation sets ``\"transpose\"``\n    as the default upsampling method.\n\n    To meet the requirements of the structure, the input size for each spatial dimension\n    (except the last one) should be: divisible by 2 ** (psp_block_num + 3) and no less than 32 in ``transpose`` mode,\n    and should be divisible by 32 and no less than 2 ** (psp_block_num + 3) in other upsample modes.\n    In addition, the input size for the last spatial dimension should be divisible by 32, and at least one spatial size\n    should be no less than 64.\n\n    Args:\n        layers: number of residual blocks for 4 layers of the network (layer1...layer4). Defaults to ``(3, 4, 6, 3)``.\n        spatial_dims: spatial dimension of the input data. Defaults to 3.\n        in_channels: number of input channels for the network. Default to 1.\n        out_channels: number of output channels for the network. Defaults to 1.\n        psp_block_num: the number of pyramid volumetric pooling modules used at the end of the network before the final\n            output layer for extracting multiscale features. The number should be an integer that belongs to [0,4]. Defaults\n            to 4.\n        upsample_mode: [``\"transpose\"``, ``\"bilinear\"``, ``\"trilinear\"``, ``nearest``]\n            The mode of upsampling manipulations.\n            Using the last two modes cannot guarantee the model's reproducibility. Defaults to ``transpose``.\n\n            - ``\"transpose\"``, uses transposed convolution layers.\n            - ``\"bilinear\"``, uses bilinear interpolate.\n            - ``\"trilinear\"``, uses trilinear interpolate.\n            - ``\"nearest\"``, uses nearest interpolate.\n        pretrained: whether to load pretrained weights from ResNet50 to initialize convolution layers, default to False.\n        progress: If True, displays a progress bar of the download of pretrained weights to stderr.\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/ahnet.py_AHNet._make_layer_AHNet._make_layer.return.nn_Sequential_layers_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/ahnet.py_AHNet._make_layer_AHNet._make_layer.return.nn_Sequential_layers_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/ahnet.py", "file_name": "ahnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 440, "end_line": 464, "span_ids": ["AHNet._make_layer"], "tokens": 244}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AHNet(nn.Module):\n\n    def _make_layer(self, block: Type[Bottleneck3x3x1], planes: int, blocks: int, stride: int = 1) -> nn.Sequential:\n        downsample = None\n        if stride != 1 or self.inplanes != planes * block.expansion:\n            downsample = nn.Sequential(\n                self.conv_type(\n                    self.inplanes,\n                    planes * block.expansion,\n                    kernel_size=1,\n                    stride=(stride, stride, 1)[: self.spatial_dims],\n                    bias=False,\n                ),\n                self.pool_type(\n                    kernel_size=(1, 1, stride)[: self.spatial_dims], stride=(1, 1, stride)[: self.spatial_dims]\n                ),\n                self.norm_type(planes * block.expansion),\n            )\n\n        layers = []\n        layers.append(\n            block(self.spatial_dims, self.inplanes, planes, (stride, stride, 1)[: self.spatial_dims], downsample)\n        )\n        self.inplanes = planes * block.expansion\n        for _ in range(1, blocks):\n            layers.append(block(self.spatial_dims, self.inplanes, planes))\n        return nn.Sequential(*layers)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/ahnet.py_AHNet.forward_AHNet.forward.return.self_final_x_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/ahnet.py_AHNet.forward_AHNet.forward.return.self_final_x_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/ahnet.py", "file_name": "ahnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 485, "end_line": 518, "span_ids": ["AHNet.forward"], "tokens": 275}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AHNet(nn.Module):\n\n    def forward(self, x):\n        x = self.conv1(x)\n        x = self.pool1(x)\n        x = self.bn0(x)\n        x = self.relu(x)\n        conv_x = x\n        x = self.maxpool(x)\n        pool_x = x\n\n        fm1 = self.layer1(x)\n        fm2 = self.layer2(fm1)\n        fm3 = self.layer3(fm2)\n        fm4 = self.layer4(fm3)\n\n        sum0 = self.up0(fm4) + fm3\n        d0 = self.dense0(sum0)\n\n        sum1 = self.up1(d0) + fm2\n        d1 = self.dense1(sum1)\n\n        sum2 = self.up2(d1) + fm1\n        d2 = self.dense2(sum2)\n\n        sum3 = self.trans1(d2) + pool_x\n        d3 = self.dense3(sum3)\n\n        sum4 = self.up3(d3) + conv_x\n        d4 = self.dense4(sum4)\n        if self.psp_block_num > 0:\n            psp = self.psp(d4)\n            x = torch.cat((psp, d4), dim=1)\n        else:\n            x = d4\n        return self.final(x)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/highresnet.py_from_typing_import_Dict__DEFAULT_LAYER_PARAMS_3D._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/highresnet.py_from_typing_import_Dict__DEFAULT_LAYER_PARAMS_3D._", "embedding": null, "metadata": {"file_path": "monai/networks/nets/highresnet.py", "file_name": "highresnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 33, "span_ids": ["docstring"], "tokens": 253}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Dict, Optional, Sequence, Tuple, Union\n\nimport torch\nimport torch.nn as nn\n\nfrom monai.networks.blocks import ADN, Convolution\nfrom monai.networks.layers.simplelayers import ChannelPad\nfrom monai.utils import ChannelMatching\n\n__all__ = [\"HighResBlock\", \"HighResNet\"]\n\nDEFAULT_LAYER_PARAMS_3D = (\n    # initial conv layer\n    {\"name\": \"conv_0\", \"n_features\": 16, \"kernel_size\": 3},\n    # residual blocks\n    {\"name\": \"res_1\", \"n_features\": 16, \"kernels\": (3, 3), \"repeat\": 3},\n    {\"name\": \"res_2\", \"n_features\": 32, \"kernels\": (3, 3), \"repeat\": 3},\n    {\"name\": \"res_3\", \"n_features\": 64, \"kernels\": (3, 3), \"repeat\": 3},\n    # final conv layers\n    {\"name\": \"conv_1\", \"n_features\": 80, \"kernel_size\": 1},\n    {\"name\": \"conv_2\", \"kernel_size\": 1},\n)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/vnet.py_from_typing_import_Dict__get_acti_layer.return.act_type_act_args_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/vnet.py_from_typing_import_Dict__get_acti_layer.return.act_type_act_args_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/vnet.py", "file_name": "vnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 28, "span_ids": ["get_acti_layer", "docstring"], "tokens": 138}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Dict, Optional, Tuple, Type, Union\n\nimport torch\nimport torch.nn as nn\n\nfrom monai.networks.blocks.convolutions import Convolution\nfrom monai.networks.layers.factories import Act, Conv, Dropout, Norm, split_args\n\n__all__ = [\"VNet\"]\n\n\ndef get_acti_layer(act: Union[Tuple[str, Dict], str], nchan: int = 0):\n    if act == \"prelu\":\n        act = (\"prelu\", {\"num_parameters\": nchan})\n    act_name, act_args = split_args(act)\n    act_type = Act[act_name]\n    return act_type(**act_args)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/vnet.py_LUConv__make_nconv.return.nn_Sequential_layers_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/vnet.py_LUConv__make_nconv.return.nn_Sequential_layers_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/vnet.py", "file_name": "vnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 31, "end_line": 56, "span_ids": ["LUConv.__init__", "LUConv.forward", "LUConv", "_make_nconv"], "tokens": 210}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LUConv(nn.Module):\n    def __init__(self, spatial_dims: int, nchan: int, act: Union[Tuple[str, Dict], str], bias: bool = False):\n        super().__init__()\n\n        self.act_function = get_acti_layer(act, nchan)\n        self.conv_block = Convolution(\n            spatial_dims=spatial_dims,\n            in_channels=nchan,\n            out_channels=nchan,\n            kernel_size=5,\n            act=None,\n            norm=Norm.BATCH,\n            bias=bias,\n        )\n\n    def forward(self, x):\n        out = self.conv_block(x)\n        out = self.act_function(out)\n        return out\n\n\ndef _make_nconv(spatial_dims: int, nchan: int, depth: int, act: Union[Tuple[str, Dict], str], bias: bool = False):\n    layers = []\n    for _ in range(depth):\n        layers.append(LUConv(spatial_dims, nchan, act, bias))\n    return nn.Sequential(*layers)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/vnet.py_InputTransition_InputTransition.forward.return.out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/vnet.py_InputTransition_InputTransition.forward.return.out", "embedding": null, "metadata": {"file_path": "monai/networks/nets/vnet.py", "file_name": "vnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 59, "end_line": 91, "span_ids": ["InputTransition.forward", "InputTransition.__init__", "InputTransition"], "tokens": 242}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class InputTransition(nn.Module):\n    def __init__(\n        self,\n        spatial_dims: int,\n        in_channels: int,\n        out_channels: int,\n        act: Union[Tuple[str, Dict], str],\n        bias: bool = False,\n    ):\n        super().__init__()\n\n        if 16 % in_channels != 0:\n            raise ValueError(f\"16 should be divisible by in_channels, got in_channels={in_channels}.\")\n\n        self.spatial_dims = spatial_dims\n        self.in_channels = in_channels\n        self.act_function = get_acti_layer(act, 16)\n        self.conv_block = Convolution(\n            spatial_dims=spatial_dims,\n            in_channels=in_channels,\n            out_channels=16,\n            kernel_size=5,\n            act=None,\n            norm=Norm.BATCH,\n            bias=bias,\n        )\n\n    def forward(self, x):\n        out = self.conv_block(x)\n        repeat_num = 16 // self.in_channels\n        x16 = x.repeat([1, repeat_num, 1, 1, 1][: self.spatial_dims + 2])\n        out = self.act_function(torch.add(out, x16))\n        return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/vnet.py_DownTransition_DownTransition.forward.return.out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/vnet.py_DownTransition_DownTransition.forward.return.out", "embedding": null, "metadata": {"file_path": "monai/networks/nets/vnet.py", "file_name": "vnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 94, "end_line": 127, "span_ids": ["DownTransition.__init__", "DownTransition", "DownTransition.forward"], "tokens": 354}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DownTransition(nn.Module):\n    def __init__(\n        self,\n        spatial_dims: int,\n        in_channels: int,\n        nconvs: int,\n        act: Union[Tuple[str, Dict], str],\n        dropout_prob: Optional[float] = None,\n        dropout_dim: int = 3,\n        bias: bool = False,\n    ):\n        super().__init__()\n\n        conv_type: Type[Union[nn.Conv2d, nn.Conv3d]] = Conv[Conv.CONV, spatial_dims]\n        norm_type: Type[Union[nn.BatchNorm2d, nn.BatchNorm3d]] = Norm[Norm.BATCH, spatial_dims]\n        dropout_type: Type[Union[nn.Dropout, nn.Dropout2d, nn.Dropout3d]] = Dropout[Dropout.DROPOUT, dropout_dim]\n\n        out_channels = 2 * in_channels\n        self.down_conv = conv_type(in_channels, out_channels, kernel_size=2, stride=2, bias=bias)\n        self.bn1 = norm_type(out_channels)\n        self.act_function1 = get_acti_layer(act, out_channels)\n        self.act_function2 = get_acti_layer(act, out_channels)\n        self.ops = _make_nconv(spatial_dims, out_channels, nconvs, act, bias)\n        self.dropout = dropout_type(dropout_prob) if dropout_prob is not None else None\n\n    def forward(self, x):\n        down = self.act_function1(self.bn1(self.down_conv(x)))\n        if self.dropout is not None:\n            out = self.dropout(down)\n        else:\n            out = down\n        out = self.ops(out)\n        out = self.act_function2(torch.add(out, down))\n        return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/vnet.py_UpTransition_UpTransition.forward.return.out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/vnet.py_UpTransition_UpTransition.forward.return.out", "embedding": null, "metadata": {"file_path": "monai/networks/nets/vnet.py", "file_name": "vnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 130, "end_line": 165, "span_ids": ["UpTransition.__init__", "UpTransition", "UpTransition.forward"], "tokens": 395}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class UpTransition(nn.Module):\n    def __init__(\n        self,\n        spatial_dims: int,\n        in_channels: int,\n        out_channels: int,\n        nconvs: int,\n        act: Union[Tuple[str, Dict], str],\n        dropout_prob: Optional[float] = None,\n        dropout_dim: int = 3,\n    ):\n        super().__init__()\n\n        conv_trans_type: Type[Union[nn.ConvTranspose2d, nn.ConvTranspose3d]] = Conv[Conv.CONVTRANS, spatial_dims]\n        norm_type: Type[Union[nn.BatchNorm2d, nn.BatchNorm3d]] = Norm[Norm.BATCH, spatial_dims]\n        dropout_type: Type[Union[nn.Dropout, nn.Dropout2d, nn.Dropout3d]] = Dropout[Dropout.DROPOUT, dropout_dim]\n\n        self.up_conv = conv_trans_type(in_channels, out_channels // 2, kernel_size=2, stride=2)\n        self.bn1 = norm_type(out_channels // 2)\n        self.dropout = dropout_type(dropout_prob) if dropout_prob is not None else None\n        self.dropout2 = dropout_type(0.5)\n        self.act_function1 = get_acti_layer(act, out_channels // 2)\n        self.act_function2 = get_acti_layer(act, out_channels)\n        self.ops = _make_nconv(spatial_dims, out_channels, nconvs, act)\n\n    def forward(self, x, skipx):\n        if self.dropout is not None:\n            out = self.dropout(x)\n        else:\n            out = x\n        skipxdo = self.dropout2(skipx)\n        out = self.act_function1(self.bn1(self.up_conv(out)))\n        xcat = torch.cat((out, skipxdo), 1)\n        out = self.ops(xcat)\n        out = self.act_function2(torch.add(out, xcat))\n        return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/vnet.py_OutputTransition_OutputTransition.forward.return.out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/vnet.py_OutputTransition_OutputTransition.forward.return.out", "embedding": null, "metadata": {"file_path": "monai/networks/nets/vnet.py", "file_name": "vnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 168, "end_line": 198, "span_ids": ["OutputTransition.__init__", "OutputTransition.forward", "OutputTransition"], "tokens": 218}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class OutputTransition(nn.Module):\n    def __init__(\n        self,\n        spatial_dims: int,\n        in_channels: int,\n        out_channels: int,\n        act: Union[Tuple[str, Dict], str],\n        bias: bool = False,\n    ):\n        super().__init__()\n\n        conv_type: Type[Union[nn.Conv2d, nn.Conv3d]] = Conv[Conv.CONV, spatial_dims]\n\n        self.act_function1 = get_acti_layer(act, out_channels)\n        self.conv_block = Convolution(\n            spatial_dims=spatial_dims,\n            in_channels=in_channels,\n            out_channels=out_channels,\n            kernel_size=5,\n            act=None,\n            norm=Norm.BATCH,\n            bias=bias,\n        )\n        self.conv2 = conv_type(out_channels, out_channels, kernel_size=1)\n\n    def forward(self, x):\n        # convolve 32 down to 2 channels\n        out = self.conv_block(x)\n        out = self.act_function1(out)\n        out = self.conv2(out)\n        return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/vnet.py_VNet_VNet.__init__.self.out_tr.OutputTransition_spatial_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/vnet.py_VNet_VNet.__init__.self.out_tr.OutputTransition_spatial_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/vnet.py", "file_name": "vnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 201, "end_line": 252, "span_ids": ["VNet", "VNet.__init__"], "tokens": 755}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class VNet(nn.Module):\n    \"\"\"\n    V-Net based on `Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation\n    <https://arxiv.org/pdf/1606.04797.pdf>`_.\n    Adapted from `the official Caffe implementation\n    <https://github.com/faustomilletari/VNet>`_. and `another pytorch implementation\n    <https://github.com/mattmacy/vnet.pytorch/blob/master/vnet.py>`_.\n    The model supports 2D or 3D inputs.\n\n    Args:\n        spatial_dims: spatial dimension of the input data. Defaults to 3.\n        in_channels: number of input channels for the network. Defaults to 1.\n            The value should meet the condition that ``16 % in_channels == 0``.\n        out_channels: number of output channels for the network. Defaults to 1.\n        act: activation type in the network. Defaults to ``(\"elu\", {\"inplace\": True})``.\n        dropout_prob: dropout ratio. Defaults to 0.5. Defaults to 3.\n        dropout_dim: determine the dimensions of dropout. Defaults to 3.\n\n            - ``dropout_dim = 1``, randomly zeroes some of the elements for each channel.\n            - ``dropout_dim = 2``, Randomly zeroes out entire channels (a channel is a 2D feature map).\n            - ``dropout_dim = 3``, Randomly zeroes out entire channels (a channel is a 3D feature map).\n        bias: whether to have a bias term in convolution blocks. Defaults to False.\n            According to `Performance Tuning Guide <https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html>`_,\n            if a conv layer is directly followed by a batch norm layer, bias should be False.\n\n    \"\"\"\n\n    def __init__(\n        self,\n        spatial_dims: int = 3,\n        in_channels: int = 1,\n        out_channels: int = 1,\n        act: Union[Tuple[str, Dict], str] = (\"elu\", {\"inplace\": True}),\n        dropout_prob: float = 0.5,\n        dropout_dim: int = 3,\n        bias: bool = False,\n    ):\n        super().__init__()\n\n        if spatial_dims not in (2, 3):\n            raise AssertionError(\"spatial_dims can only be 2 or 3.\")\n\n        self.in_tr = InputTransition(spatial_dims, in_channels, 16, act, bias=bias)\n        self.down_tr32 = DownTransition(spatial_dims, 16, 1, act, bias=bias)\n        self.down_tr64 = DownTransition(spatial_dims, 32, 2, act, bias=bias)\n        self.down_tr128 = DownTransition(spatial_dims, 64, 3, act, dropout_prob=dropout_prob, bias=bias)\n        self.down_tr256 = DownTransition(spatial_dims, 128, 2, act, dropout_prob=dropout_prob, bias=bias)\n        self.up_tr256 = UpTransition(spatial_dims, 256, 256, 2, act, dropout_prob=dropout_prob)\n        self.up_tr128 = UpTransition(spatial_dims, 256, 128, 2, act, dropout_prob=dropout_prob)\n        self.up_tr64 = UpTransition(spatial_dims, 128, 64, 1, act)\n        self.up_tr32 = UpTransition(spatial_dims, 64, 32, 1, act)\n        self.out_tr = OutputTransition(spatial_dims, 32, out_channels, act, bias=bias)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/vnet.py_VNet.forward_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/vnet.py_VNet.forward_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/vnet.py", "file_name": "vnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 217, "end_line": 229, "span_ids": ["VNet.forward"], "tokens": 126}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class VNet(nn.Module):\n\n    def forward(self, x):\n        out16 = self.in_tr(x)\n        out32 = self.down_tr32(out16)\n        out64 = self.down_tr64(out32)\n        out128 = self.down_tr128(out64)\n        out256 = self.down_tr256(out128)\n        x = self.up_tr256(out256, out128)\n        x = self.up_tr128(x, out64)\n        x = self.up_tr64(x, out32)\n        x = self.up_tr32(x, out16)\n        x = self.out_tr(x)\n        return x", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/utils.py_normal_init_normal_init.if_getattr_m_weight_N.elif_cname_find_BatchNor.nn_init_constant__m_bias_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/utils.py_normal_init_normal_init.if_getattr_m_weight_N.elif_cname_find_BatchNor.nn_init_constant__m_bias_", "embedding": null, "metadata": {"file_path": "monai/networks/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 152, "end_line": 170, "span_ids": ["normal_init"], "tokens": 266}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def normal_init(\n    m, std: float = 0.02, normal_func: Callable[[torch.Tensor, float, float], Any] = torch.nn.init.normal_\n) -> None:\n    \"\"\"\n    Initialize the weight and bias tensors of `m' and its submodules to values from a normal distribution with a\n    stddev of `std'. Weight tensors of convolution and linear modules are initialized with a mean of 0, batch\n    norm modules with a mean of 1. The callable `normal_func', used to assign values, should have the same arguments\n    as its default normal_(). This can be used with `nn.Module.apply` to visit submodules of a network.\n    \"\"\"\n    cname = m.__class__.__name__\n\n    if getattr(m, \"weight\", None) is not None and (cname.find(\"Conv\") != -1 or cname.find(\"Linear\") != -1):\n        normal_func(m.weight.data, 0.0, std)\n        if getattr(m, \"bias\", None) is not None:\n            nn.init.constant_(m.bias.data, 0.0)\n\n    elif cname.find(\"BatchNorm\") != -1:\n        normal_func(m.weight.data, 1.0, std)\n        nn.init.constant_(m.bias.data, 0)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/utils.py_icnr_init_icnr_init.conv_weight_data_copy__ke": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/utils.py_icnr_init_icnr_init.conv_weight_data_copy__ke", "embedding": null, "metadata": {"file_path": "monai/networks/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 173, "end_line": 190, "span_ids": ["icnr_init"], "tokens": 191}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def icnr_init(conv, upsample_factor, init=nn.init.kaiming_normal_):\n    \"\"\"\n    ICNR initialization for 2D/3D kernels adapted from Aitken et al.,2017 , \"Checkerboard artifact free\n    sub-pixel convolution\".\n    \"\"\"\n    out_channels, in_channels, *dims = conv.weight.shape\n    scale_factor = upsample_factor ** len(dims)\n\n    oc2 = int(out_channels / scale_factor)\n\n    kernel = torch.zeros([oc2, in_channels] + dims)\n    kernel = init(kernel)\n    kernel = kernel.transpose(0, 1)\n    kernel = kernel.reshape(oc2, in_channels, -1)\n    kernel = kernel.repeat(1, 1, scale_factor)\n    kernel = kernel.reshape([in_channels, out_channels] + dims)\n    kernel = kernel.transpose(0, 1)\n    conv.weight.data.copy_(kernel)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/adaptors.py_from_typing_import_Callab_adaptor.map_only_names.return._v_ditems_k_for_k_v_in": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/adaptors.py_from_typing_import_Callab_adaptor.map_only_names.return._v_ditems_k_for_k_v_in", "embedding": null, "metadata": {"file_path": "monai/transforms/adaptors.py", "file_name": "adaptors.py", "file_type": "text/x-python", "category": "implementation", "start_line": 125, "end_line": 147, "span_ids": ["adaptor", "imports"], "tokens": 218}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Callable\n\nfrom monai.utils import export as _monai_export\n\n__all__ = [\"adaptor\", \"apply_alias\", \"to_kwargs\", \"FunctionSignature\"]\n\n\n@_monai_export(\"monai.transforms\")\ndef adaptor(function, outputs, inputs=None):\n    def must_be_types_or_none(variable_name, variable, types):\n        if variable is not None:\n            if not isinstance(variable, types):\n                raise TypeError(f\"'{variable_name}' must be None or one of {types} but is {type(variable)}\")\n\n    def must_be_types(variable_name, variable, types):\n        if not isinstance(variable, types):\n            raise TypeError(f\"'{variable_name}' must be one of {types} but is {type(variable)}\")\n\n    def map_names(ditems, input_map):\n        return {input_map(k, k): v for k, v in ditems.items()}\n\n    def map_only_names(ditems, input_map):\n        return {v: ditems[k] for k, v in input_map.items()}\n    # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_RandSpatialCropd.randomize_RandSpatialCropd.randomize.if_self_random_center_.self._slices._slice_None_get_rand": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_RandSpatialCropd.randomize_RandSpatialCropd.randomize.if_self_random_center_.self._slices._slice_None_get_rand", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 562, "end_line": 571, "span_ids": ["RandSpatialCropd.randomize"], "tokens": 200}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandSpatialCropd(Randomizable, MapTransform, InvertibleTransform):\n\n    def randomize(self, img_size: Sequence[int]) -> None:\n        self._size = fall_back_tuple(self.roi_size, img_size)\n        if self.random_size:\n            max_size = img_size if self.max_roi_size is None else fall_back_tuple(self.max_roi_size, img_size)\n            if any(i > j for i, j in zip(self._size, max_size)):\n                raise ValueError(f\"min ROI size: {self._size} is bigger than max ROI size: {max_size}.\")\n            self._size = [self.R.randint(low=self._size[i], high=max_size[i] + 1) for i in range(len(img_size))]\n        if self.random_center:\n            valid_size = get_valid_patch_size(img_size, self._size)\n            self._slices = (slice(None),) + get_random_patch(img_size, valid_size, self.R)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_RandSpatialCropd.__call___RandSpatialCropd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_RandSpatialCropd.__call___RandSpatialCropd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 580, "end_line": 597, "span_ids": ["RandSpatialCropd.__call__"], "tokens": 215}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandSpatialCropd(Randomizable, MapTransform, InvertibleTransform):\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        first_key: Union[Hashable, List] = self.first_key(d)\n        if first_key == []:\n            return d\n\n        self.randomize(d[first_key].shape[1:])  # type: ignore\n        if self._size is None:\n            raise RuntimeError(\"self._size not specified.\")\n        for key in self.key_iterator(d):\n            if self.random_center:\n                self.push_transform(d, key, {\"slices\": [(i.start, i.stop) for i in self._slices[1:]]})  # type: ignore\n                d[key] = d[key][self._slices]\n            else:\n                self.push_transform(d, key)\n                cropper = CenterSpatialCrop(self._size)\n                d[key] = cropper(d[key])\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandGaussianSmooth_RandGaussianSmooth.__call__.return.GaussianSmooth_sigma_sigm": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandGaussianSmooth_RandGaussianSmooth.__call__.return.GaussianSmooth_sigma_sigm", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1105, "end_line": 1155, "span_ids": ["RandGaussianSmooth.__call__", "RandGaussianSmooth.randomize", "RandGaussianSmooth", "RandGaussianSmooth.__init__"], "tokens": 488}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandGaussianSmooth(RandomizableTransform):\n    \"\"\"\n    Apply Gaussian smooth to the input data based on randomly selected `sigma` parameters.\n\n    Args:\n        sigma_x: randomly select sigma value for the first spatial dimension.\n        sigma_y: randomly select sigma value for the second spatial dimension if have.\n        sigma_z: randomly select sigma value for the third spatial dimension if have.\n        prob: probability of Gaussian smooth.\n        approx: discrete Gaussian kernel type, available options are \"erf\", \"sampled\", and \"scalespace\".\n            see also :py:meth:`monai.networks.layers.GaussianFilter`.\n\n    \"\"\"\n\n    backend = GaussianSmooth.backend\n\n    def __init__(\n        self,\n        sigma_x: Tuple[float, float] = (0.25, 1.5),\n        sigma_y: Tuple[float, float] = (0.25, 1.5),\n        sigma_z: Tuple[float, float] = (0.25, 1.5),\n        prob: float = 0.1,\n        approx: str = \"erf\",\n    ) -> None:\n        RandomizableTransform.__init__(self, prob)\n        self.sigma_x = sigma_x\n        self.sigma_y = sigma_y\n        self.sigma_z = sigma_z\n        self.approx = approx\n\n        self.x = self.sigma_x[0]\n        self.y = self.sigma_y[0]\n        self.z = self.sigma_z[0]\n\n    def randomize(self, data: Optional[Any] = None) -> None:\n        super().randomize(None)\n        if not self._do_transform:\n            return None\n        self.x = self.R.uniform(low=self.sigma_x[0], high=self.sigma_x[1])\n        self.y = self.R.uniform(low=self.sigma_y[0], high=self.sigma_y[1])\n        self.z = self.R.uniform(low=self.sigma_z[0], high=self.sigma_z[1])\n\n    def __call__(self, img: NdarrayOrTensor, randomize: bool = True) -> NdarrayOrTensor:\n        if randomize:\n            self.randomize()\n\n        if not self._do_transform:\n            return img\n\n        sigma = ensure_tuple_size(tup=(self.x, self.y, self.z), dim=img.ndim - 1)\n        return GaussianSmooth(sigma=sigma, approx=self.approx)(img)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandGaussianSharpen.randomize_RandGaussianSharpen.randomize.self.a.self_R_uniform_low_self_a": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandGaussianSharpen.randomize_RandGaussianSharpen.randomize.self.a.self_R_uniform_low_self_a", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1267, "end_line": 1280, "span_ids": ["RandGaussianSharpen.randomize"], "tokens": 302}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandGaussianSharpen(RandomizableTransform):\n\n    def randomize(self, data: Optional[Any] = None) -> None:\n        super().randomize(None)\n        if not self._do_transform:\n            return None\n        self.x1 = self.R.uniform(low=self.sigma1_x[0], high=self.sigma1_x[1])\n        self.y1 = self.R.uniform(low=self.sigma1_y[0], high=self.sigma1_y[1])\n        self.z1 = self.R.uniform(low=self.sigma1_z[0], high=self.sigma1_z[1])\n        sigma2_x = (self.sigma2_x, self.x1) if not isinstance(self.sigma2_x, Iterable) else self.sigma2_x\n        sigma2_y = (self.sigma2_y, self.y1) if not isinstance(self.sigma2_y, Iterable) else self.sigma2_y\n        sigma2_z = (self.sigma2_z, self.z1) if not isinstance(self.sigma2_z, Iterable) else self.sigma2_z\n        self.x2 = self.R.uniform(low=sigma2_x[0], high=sigma2_x[1])\n        self.y2 = self.R.uniform(low=sigma2_y[0], high=sigma2_y[1])\n        self.z2 = self.R.uniform(low=sigma2_z[0], high=sigma2_z[1])\n        self.a = self.R.uniform(low=self.alpha[0], high=self.alpha[1])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_GaussianSmoothd_GaussianSmoothd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_GaussianSmoothd_GaussianSmoothd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 895, "end_line": 927, "span_ids": ["GaussianSmoothd", "GaussianSmoothd.__call__", "GaussianSmoothd.__init__"], "tokens": 297}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GaussianSmoothd(MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.GaussianSmooth`.\n\n    Args:\n        keys: keys of the corresponding items to be transformed.\n            See also: :py:class:`monai.transforms.compose.MapTransform`\n        sigma: if a list of values, must match the count of spatial dimensions of input data,\n            and apply every value in the list to 1 spatial dimension. if only 1 value provided,\n            use it for all spatial dimensions.\n        approx: discrete Gaussian kernel type, available options are \"erf\", \"sampled\", and \"scalespace\".\n            see also :py:meth:`monai.networks.layers.GaussianFilter`.\n        allow_missing_keys: don't raise exception if key is missing.\n\n    \"\"\"\n\n    backend = GaussianSmooth.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        sigma: Union[Sequence[float], float],\n        approx: str = \"erf\",\n        allow_missing_keys: bool = False,\n    ) -> None:\n        super().__init__(keys, allow_missing_keys)\n        self.converter = GaussianSmooth(sigma, approx=approx)\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key in self.key_iterator(d):\n            d[key] = self.converter(d[key])\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_RandGaussianSmoothd_RandGaussianSmoothd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_RandGaussianSmoothd_RandGaussianSmoothd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 930, "end_line": 982, "span_ids": ["RandGaussianSmoothd.__init__", "RandGaussianSmoothd", "RandGaussianSmoothd.set_random_state", "RandGaussianSmoothd.__call__"], "tokens": 518}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandGaussianSmoothd(RandomizableTransform, MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.GaussianSmooth`.\n\n    Args:\n        keys: keys of the corresponding items to be transformed.\n            See also: :py:class:`monai.transforms.compose.MapTransform`\n        sigma_x: randomly select sigma value for the first spatial dimension.\n        sigma_y: randomly select sigma value for the second spatial dimension if have.\n        sigma_z: randomly select sigma value for the third spatial dimension if have.\n        approx: discrete Gaussian kernel type, available options are \"erf\", \"sampled\", and \"scalespace\".\n            see also :py:meth:`monai.networks.layers.GaussianFilter`.\n        prob: probability of Gaussian smooth.\n        allow_missing_keys: don't raise exception if key is missing.\n\n    \"\"\"\n\n    backend = RandGaussianSmooth.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        sigma_x: Tuple[float, float] = (0.25, 1.5),\n        sigma_y: Tuple[float, float] = (0.25, 1.5),\n        sigma_z: Tuple[float, float] = (0.25, 1.5),\n        approx: str = \"erf\",\n        prob: float = 0.1,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        MapTransform.__init__(self, keys, allow_missing_keys)\n        RandomizableTransform.__init__(self, prob)\n        self.rand_smooth = RandGaussianSmooth(\n            sigma_x=sigma_x, sigma_y=sigma_y, sigma_z=sigma_z, approx=approx, prob=1.0\n        )\n\n    def set_random_state(\n        self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None\n    ) -> \"RandGaussianSmoothd\":\n        super().set_random_state(seed, state)\n        self.rand_smooth.set_random_state(seed, state)\n        return self\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        self.randomize(None)\n        if not self._do_transform:\n            return d\n\n        # all the keys share the same random sigma\n        self.rand_smooth.randomize(None)\n        for key in self.key_iterator(d):\n            d[key] = self.rand_smooth(d[key], randomize=False)\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_GaussianSharpend_GaussianSharpend.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_GaussianSharpend_GaussianSharpend.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 985, "end_line": 1023, "span_ids": ["GaussianSharpend.__init__", "GaussianSharpend.__call__", "GaussianSharpend"], "tokens": 420}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GaussianSharpend(MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.GaussianSharpen`.\n\n    Args:\n        keys: keys of the corresponding items to be transformed.\n            See also: :py:class:`monai.transforms.compose.MapTransform`\n        sigma1: sigma parameter for the first gaussian kernel. if a list of values, must match the count\n            of spatial dimensions of input data, and apply every value in the list to 1 spatial dimension.\n            if only 1 value provided, use it for all spatial dimensions.\n        sigma2: sigma parameter for the second gaussian kernel. if a list of values, must match the count\n            of spatial dimensions of input data, and apply every value in the list to 1 spatial dimension.\n            if only 1 value provided, use it for all spatial dimensions.\n        alpha: weight parameter to compute the final result.\n        approx: discrete Gaussian kernel type, available options are \"erf\", \"sampled\", and \"scalespace\".\n            see also :py:meth:`monai.networks.layers.GaussianFilter`.\n        allow_missing_keys: don't raise exception if key is missing.\n\n    \"\"\"\n\n    backend = GaussianSharpen.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        sigma1: Union[Sequence[float], float] = 3.0,\n        sigma2: Union[Sequence[float], float] = 1.0,\n        alpha: float = 30.0,\n        approx: str = \"erf\",\n        allow_missing_keys: bool = False,\n    ) -> None:\n        super().__init__(keys, allow_missing_keys)\n        self.converter = GaussianSharpen(sigma1, sigma2, alpha, approx=approx)\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key in self.key_iterator(d):\n            d[key] = self.converter(d[key])\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/array.py_VoteEnsemble_VoteEnsemble.__init__.self.num_classes.num_classes": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/array.py_VoteEnsemble_VoteEnsemble.__init__.self.num_classes.num_classes", "embedding": null, "metadata": {"file_path": "monai/transforms/post/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 577, "end_line": 600, "span_ids": ["VoteEnsemble.__init__", "VoteEnsemble"], "tokens": 245}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class VoteEnsemble(Ensemble, Transform):\n    \"\"\"\n    Execute vote ensemble on the input data.\n    The input data can be a list or tuple of PyTorch Tensor with shape: [C[, H, W, D]],\n    Or a single PyTorch Tensor with shape: [E[, C, H, W, D]], the `E` dimension represents\n    the output data from different models.\n    Typically, the input data is model output of segmentation task or classification task.\n\n    Note:\n        This vote transform expects the input data is discrete values. It can be multiple channels\n        data in One-Hot format or single channel data. It will vote to select the most common data\n        between items.\n        The output data has the same shape as every item of the input data.\n\n    Args:\n        num_classes: if the input is single channel data instead of One-Hot, we can't get class number\n            from channel, need to explicitly specify the number of classes to vote.\n\n    \"\"\"\n\n    backend = [TransformBackends.TORCH]\n\n    def __init__(self, num_classes: Optional[int] = None) -> None:\n        self.num_classes = num_classes", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/dictionary.py_MeanEnsembled_MeanEnsembled.__init__.super___init___keys_en": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/dictionary.py_MeanEnsembled_MeanEnsembled.__init__.super___init___keys_en", "embedding": null, "metadata": {"file_path": "monai/transforms/post/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 372, "end_line": 404, "span_ids": ["MeanEnsembled", "MeanEnsembled.__init__"], "tokens": 431}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class MeanEnsembled(Ensembled):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.MeanEnsemble`.\n    \"\"\"\n\n    backend = MeanEnsemble.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        output_key: Optional[str] = None,\n        weights: Optional[Union[Sequence[float], NdarrayOrTensor]] = None,\n    ) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to be stack and execute ensemble.\n                if only 1 key provided, suppose it's a PyTorch Tensor with data stacked on dimension `E`.\n            output_key: the key to store ensemble result in the dictionary.\n                if only 1 key provided in `keys`, `output_key` can be None and use `keys` as default.\n            weights: can be a list or tuple of numbers for input data with shape: [E, C, H, W[, D]].\n                or a Numpy ndarray or a PyTorch Tensor data.\n                the `weights` will be added to input data from highest dimension, for example:\n                1. if the `weights` only has 1 dimension, it will be added to the `E` dimension of input data.\n                2. if the `weights` has 2 dimensions, it will be added to `E` and `C` dimensions.\n                it's a typical practice to add weights for different classes:\n                to ensemble 3 segmentation model outputs, every output has 4 channels(classes),\n                so the input data shape can be: [3, 4, H, W, D].\n                and add different `weights` for different classes, so the `weights` shape can be: [3, 4].\n                for example: `weights = [[1, 2, 3, 4], [4, 3, 2, 1], [1, 1, 1, 1]]`.\n\n        \"\"\"\n        ensemble = MeanEnsemble(weights=weights)\n        super().__init__(keys, ensemble, output_key)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_RandRotate_RandRotate.__init__.self.z.0_0": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_RandRotate_RandRotate.__init__.self.z.0_0", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 755, "end_line": 816, "span_ids": ["RandRotate", "RandRotate.__init__"], "tokens": 764}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandRotate(RandomizableTransform):\n    \"\"\"\n    Randomly rotate the input arrays.\n\n    Args:\n        range_x: Range of rotation angle in radians in the plane defined by the first and second axes.\n            If single number, angle is uniformly sampled from (-range_x, range_x).\n        range_y: Range of rotation angle in radians in the plane defined by the first and third axes.\n            If single number, angle is uniformly sampled from (-range_y, range_y).\n        range_z: Range of rotation angle in radians in the plane defined by the second and third axes.\n            If single number, angle is uniformly sampled from (-range_z, range_z).\n        prob: Probability of rotation.\n        keep_size: If it is False, the output shape is adapted so that the\n            input array is contained completely in the output.\n            If it is True, the output shape is the same as the input. Default is True.\n        mode: {``\"bilinear\"``, ``\"nearest\"``}\n            Interpolation mode to calculate output values. Defaults to ``\"bilinear\"``.\n            See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n        padding_mode: {``\"zeros\"``, ``\"border\"``, ``\"reflection\"``}\n            Padding mode for outside grid values. Defaults to ``\"border\"``.\n            See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n        align_corners: Defaults to False.\n            See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n        dtype: data type for resampling computation. Defaults to ``np.float64`` for best precision.\n            If None, use the data type of input data. To be compatible with other modules,\n            the output data type is always ``np.float32``.\n    \"\"\"\n\n    backend = Rotate.backend\n\n    def __init__(\n        self,\n        range_x: Union[Tuple[float, float], float] = 0.0,\n        range_y: Union[Tuple[float, float], float] = 0.0,\n        range_z: Union[Tuple[float, float], float] = 0.0,\n        prob: float = 0.1,\n        keep_size: bool = True,\n        mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,\n        padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER,\n        align_corners: bool = False,\n        dtype: Union[DtypeLike, torch.dtype] = np.float64,\n    ) -> None:\n        RandomizableTransform.__init__(self, prob)\n        self.range_x = ensure_tuple(range_x)\n        if len(self.range_x) == 1:\n            self.range_x = tuple(sorted([-self.range_x[0], self.range_x[0]]))\n        self.range_y = ensure_tuple(range_y)\n        if len(self.range_y) == 1:\n            self.range_y = tuple(sorted([-self.range_y[0], self.range_y[0]]))\n        self.range_z = ensure_tuple(range_z)\n        if len(self.range_z) == 1:\n            self.range_z = tuple(sorted([-self.range_z[0], self.range_z[0]]))\n\n        self.keep_size = keep_size\n        self.mode: GridSampleMode = look_up_option(mode, GridSampleMode)\n        self.padding_mode: GridSamplePadMode = look_up_option(padding_mode, GridSamplePadMode)\n        self.align_corners = align_corners\n        self.dtype = dtype\n\n        self.x = 0.0\n        self.y = 0.0\n        self.z = 0.0", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Orientationd.__call___Orientationd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Orientationd.__call___Orientationd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 362, "end_line": 375, "span_ids": ["Orientationd.__call__"], "tokens": 209}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Orientationd(MapTransform, InvertibleTransform):\n\n    def __call__(\n        self, data: Mapping[Union[Hashable, str], Dict[str, NdarrayOrTensor]]\n    ) -> Dict[Hashable, NdarrayOrTensor]:\n        d: Dict = dict(data)\n        for key, meta_key, meta_key_postfix in self.key_iterator(d, self.meta_keys, self.meta_key_postfix):\n            meta_key = meta_key or f\"{key}_{meta_key_postfix}\"\n            # create metadata if necessary\n            if meta_key not in d:\n                d[meta_key] = {\"affine\": None}\n            meta_data = d[meta_key]\n            d[key], old_affine, new_affine = self.ornt_transform(d[key], affine=meta_data[\"affine\"])\n            self.push_transform(d, key, extra_info={\"meta_key\": meta_key, \"old_affine\": old_affine})\n            d[meta_key][\"affine\"] = new_affine\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_Lambda_Lambda.__init__.self.func.func": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_Lambda_Lambda.__init__.self.func.func", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 633, "end_line": 660, "span_ids": ["Lambda", "Lambda.__init__"], "tokens": 194}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Lambda(Transform):\n    \"\"\"\n    Apply a user-defined lambda as a transform.\n\n    For example:\n\n    .. code-block:: python\n        :emphasize-lines: 2\n\n        image = np.ones((10, 2, 2))\n        lambd = Lambda(func=lambda x: x[:4, :, :])\n        print(lambd(image).shape)\n        (4, 2, 2)\n\n    Args:\n        func: Lambda/function to be applied.\n\n    Raises:\n        TypeError: When ``func`` is not an ``Optional[Callable]``.\n\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __init__(self, func: Optional[Callable] = None) -> None:\n        if func is not None and not callable(func):\n            raise TypeError(f\"func must be None or callable but is {type(func).__name__}.\")\n        self.func = func", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/aliases.py_importlib_alias.return._outer": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/aliases.py_importlib_alias.return._outer", "embedding": null, "metadata": {"file_path": "monai/utils/aliases.py", "file_name": "aliases.py", "file_type": "text/x-python", "category": "implementation", "start_line": 16, "end_line": 43, "span_ids": ["alias", "docstring"], "tokens": 162}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import importlib\nimport inspect\nimport sys\nimport threading\n\nalias_lock = threading.RLock()\nGlobalAliases = {}\n\n__all__ = [\"alias\", \"resolve_name\"]\n\n\ndef alias(*names):\n    \"\"\"\n    Stores the decorated function or class in the global aliases table under the given names and as the `__aliases__`\n    member of the decorated object. This new member will contain all alias names declared for that object.\n    \"\"\"\n\n    def _outer(obj):\n        for n in names:\n            with alias_lock:\n                GlobalAliases[n] = obj\n\n        # set the member list __aliases__ to contain the alias names defined by the decorator for `obj`\n        obj.__aliases__ = getattr(obj, \"__aliases__\", ()) + tuple(names)\n\n        return obj\n\n    return _outer", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/misc.py_ensure_tuple_rep_ensure_tuple_rep.raise_ValueError_f_Sequen": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/misc.py_ensure_tuple_rep_ensure_tuple_rep.raise_ValueError_f_Sequen", "embedding": null, "metadata": {"file_path": "monai/utils/misc.py", "file_name": "misc.py", "file_type": "text/x-python", "category": "implementation", "start_line": 104, "end_line": 136, "span_ids": ["ensure_tuple_rep"], "tokens": 298}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def ensure_tuple_rep(tup: Any, dim: int) -> Tuple[Any, ...]:\n    \"\"\"\n    Returns a copy of `tup` with `dim` values by either shortened or duplicated input.\n\n    Raises:\n        ValueError: When ``tup`` is a sequence and ``tup`` length is not ``dim``.\n\n    Examples::\n\n        >>> ensure_tuple_rep(1, 3)\n        (1, 1, 1)\n        >>> ensure_tuple_rep(None, 3)\n        (None, None, None)\n        >>> ensure_tuple_rep('test', 3)\n        ('test', 'test', 'test')\n        >>> ensure_tuple_rep([1, 2, 3], 3)\n        (1, 2, 3)\n        >>> ensure_tuple_rep(range(3), 3)\n        (0, 1, 2)\n        >>> ensure_tuple_rep([1, 2], 3)\n        ValueError: Sequence must have length 3, got length 2.\n\n    \"\"\"\n    if isinstance(tup, torch.Tensor):\n        tup = tup.detach().cpu().numpy()\n    if isinstance(tup, np.ndarray):\n        tup = tup.tolist()\n    if not issequenceiterable(tup):\n        return (tup,) * dim\n    if len(tup) == dim:\n        return tuple(tup)\n\n    raise ValueError(f\"Sequence must have length {dim}, got {len(tup)}.\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_adaptors.py_itertools_TestAdaptors.test_single_in_single_out.None_4": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_adaptors.py_itertools_TestAdaptors.test_single_in_single_out.None_4", "embedding": null, "metadata": {"file_path": "tests/test_adaptors.py", "file_name": "test_adaptors.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 53, "span_ids": ["TestAdaptors.test_function_signature", "TestAdaptors.test_single_in_single_out", "TestAdaptors", "docstring"], "tokens": 338}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import itertools\nimport unittest\n\nfrom monai.transforms.adaptors import FunctionSignature, adaptor, apply_alias, to_kwargs\n\n\nclass TestAdaptors(unittest.TestCase):\n    def test_function_signature(self):\n        def foo(image, label=None, *a, **kw):\n            pass\n\n        f = FunctionSignature(foo)\n\n    def test_single_in_single_out(self):\n        def foo(image):\n            return image * 2\n\n        it = itertools.product([\"image\", [\"image\"]], [None, \"image\", [\"image\"], {\"image\": \"image\"}])\n        for i in it:\n            d = {\"image\": 2}\n            dres = adaptor(foo, i[0], i[1])(d)\n            self.assertEqual(dres[\"image\"], 4)\n\n        d = {\"image\": 2}\n        dres = adaptor(foo, \"image\")(d)\n        self.assertEqual(dres[\"image\"], 4)\n\n        d = {\"image\": 2}\n        dres = adaptor(foo, \"image\", \"image\")(d)\n        self.assertEqual(dres[\"image\"], 4)\n\n        d = {\"image\": 2}\n        dres = adaptor(foo, \"image\", {\"image\": \"image\"})(d)\n        self.assertEqual(dres[\"image\"], 4)\n\n        d = {\"img\": 2}\n        dres = adaptor(foo, \"img\", {\"img\": \"image\"})(d)\n        self.assertEqual(dres[\"img\"], 4)\n\n        d = {\"img\": 2}\n        dres = adaptor(foo, [\"img\"], {\"img\": \"image\"})(d)\n        self.assertEqual(dres[\"img\"], 4)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affine_transform.py_TestToNormAffine.test_to_norm_affine_ill_TestToNormAffine.test_to_norm_affine_ill.with_self_assertRaises_Va.to_norm_affine_affine_sr": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affine_transform.py_TestToNormAffine.test_to_norm_affine_ill_TestToNormAffine.test_to_norm_affine_ill.with_self_assertRaises_Va.to_norm_affine_affine_sr", "embedding": null, "metadata": {"file_path": "tests/test_affine_transform.py", "file_name": "test_affine_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 100, "end_line": 106, "span_ids": ["TestToNormAffine.test_to_norm_affine_ill"], "tokens": 119}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestToNormAffine(unittest.TestCase):\n\n    @parameterized.expand(TEST_ILL_TO_NORM_AFFINE_CASES)\n    def test_to_norm_affine_ill(self, affine, src_size, dst_size, align_corners):\n        with self.assertRaises(TypeError):\n            to_norm_affine(affine, src_size, dst_size, align_corners)\n        with self.assertRaises(ValueError):\n            affine = torch.as_tensor(affine, device=torch.device(\"cpu:0\"), dtype=torch.float32)\n            to_norm_affine(affine, src_size, dst_size, align_corners)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_arraydataset.py_TestArrayDataset_TestArrayDataset.test_shape.with_tempfile_TemporaryDi.None_13": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_arraydataset.py_TestArrayDataset_TestArrayDataset.test_shape.with_tempfile_TemporaryDi.None_13", "embedding": null, "metadata": {"file_path": "tests/test_arraydataset.py", "file_name": "test_arraydataset.py", "file_type": "text/x-python", "category": "test", "start_line": 57, "end_line": 90, "span_ids": ["TestArrayDataset", "TestArrayDataset.test_shape"], "tokens": 480}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestArrayDataset(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3])\n    def test_shape(self, img_transform, label_transform, indices, expected_shape):\n        test_image = nib.Nifti1Image(np.random.randint(0, 2, size=(128, 128, 128)), np.eye(4))\n        with tempfile.TemporaryDirectory() as tempdir:\n            test_image1 = os.path.join(tempdir, \"test_image1.nii.gz\")\n            test_seg1 = os.path.join(tempdir, \"test_seg1.nii.gz\")\n            test_image2 = os.path.join(tempdir, \"test_image2.nii.gz\")\n            test_seg2 = os.path.join(tempdir, \"test_seg2.nii.gz\")\n            nib.save(test_image, test_image1)\n            nib.save(test_image, test_seg1)\n            nib.save(test_image, test_image2)\n            nib.save(test_image, test_seg2)\n            test_images = [test_image1, test_image2]\n            test_segs = [test_seg1, test_seg2]\n            test_labels = [1, 1]\n            dataset = ArrayDataset(test_images, img_transform, test_segs, label_transform, test_labels, None)\n            self.assertEqual(len(dataset), 2)\n            dataset.set_random_state(1234)\n            data1 = dataset[0]\n            data2 = dataset[1]\n\n            self.assertTupleEqual(data1[indices[0]].shape, expected_shape)\n            self.assertTupleEqual(data1[indices[1]].shape, expected_shape)\n            np.testing.assert_allclose(data1[indices[0]], data1[indices[1]])\n            self.assertTupleEqual(data2[indices[0]].shape, expected_shape)\n            self.assertTupleEqual(data2[indices[1]].shape, expected_shape)\n            np.testing.assert_allclose(data2[indices[0]], data2[indices[0]])\n\n            dataset = ArrayDataset(test_images, img_transform, test_segs, label_transform, test_labels, None)\n            dataset.set_random_state(1234)\n            _ = dataset[0]\n            data2_new = dataset[1]\n            np.testing.assert_allclose(data2[indices[0]], data2_new[indices[0]], atol=1e-3)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_arraydataset.py_TestArrayDataset.test_default_none_TestArrayDataset.test_default_none.with_tempfile_TemporaryDi.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_arraydataset.py_TestArrayDataset.test_default_none_TestArrayDataset.test_default_none.with_tempfile_TemporaryDi.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_arraydataset.py", "file_name": "test_arraydataset.py", "file_type": "text/x-python", "category": "test", "start_line": 92, "end_line": 113, "span_ids": ["TestArrayDataset.test_default_none"], "tokens": 270}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestArrayDataset(unittest.TestCase):\n\n    @parameterized.expand([TEST_CASE_4])\n    def test_default_none(self, img_transform, expected_shape):\n        test_image = nib.Nifti1Image(np.random.randint(0, 2, size=(128, 128, 128)), np.eye(4))\n        with tempfile.TemporaryDirectory() as tempdir:\n            test_image1 = os.path.join(tempdir, \"test_image1.nii.gz\")\n            test_image2 = os.path.join(tempdir, \"test_image2.nii.gz\")\n            nib.save(test_image, test_image1)\n            nib.save(test_image, test_image2)\n            test_images = [test_image1, test_image2]\n            dataset = ArrayDataset(test_images, img_transform)\n            self.assertEqual(len(dataset), 2)\n            dataset.set_random_state(1234)\n            data1 = dataset[0]\n            data2 = dataset[1]\n            self.assertTupleEqual(data1.shape, expected_shape)\n            self.assertTupleEqual(data2.shape, expected_shape)\n\n            dataset = ArrayDataset(test_images, img_transform)\n            dataset.set_random_state(1234)\n            _ = dataset[0]\n            data2_new = dataset[1]\n            np.testing.assert_allclose(data2, data2_new, atol=1e-3)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_arraydataset.py_TestArrayDataset.test_dataloading_img_TestArrayDataset.test_dataloading_img.with_tempfile_TemporaryDi.None_6": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_arraydataset.py_TestArrayDataset.test_dataloading_img_TestArrayDataset.test_dataloading_img.with_tempfile_TemporaryDi.None_6", "embedding": null, "metadata": {"file_path": "tests/test_arraydataset.py", "file_name": "test_arraydataset.py", "file_type": "text/x-python", "category": "test", "start_line": 116, "end_line": 135, "span_ids": ["TestArrayDataset.test_dataloading_img"], "tokens": 282}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestArrayDataset(unittest.TestCase):\n\n    @parameterized.expand([TEST_CASE_4])\n    def test_dataloading_img(self, img_transform, expected_shape):\n        test_image = nib.Nifti1Image(np.random.randint(0, 2, size=(128, 128, 128)), np.eye(4))\n        with tempfile.TemporaryDirectory() as tempdir:\n            test_image1 = os.path.join(tempdir, \"test_image1.nii.gz\")\n            test_image2 = os.path.join(tempdir, \"test_image2.nii.gz\")\n            nib.save(test_image, test_image1)\n            nib.save(test_image, test_image2)\n            test_images = [test_image1, test_image2]\n            dataset = ArrayDataset(test_images, img_transform)\n            self.assertEqual(len(dataset), 2)\n            dataset.set_random_state(1234)\n            n_workers = 0 if sys.platform == \"win32\" else 2\n            loader = DataLoader(dataset, batch_size=10, num_workers=n_workers)\n            imgs = next(iter(loader))  # test batching\n            np.testing.assert_allclose(imgs.shape, [2] + list(expected_shape))\n\n            dataset.set_random_state(1234)\n            new_imgs = next(iter(loader))  # test batching\n            np.testing.assert_allclose(imgs, new_imgs, atol=1e-3)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_arraydataset.py_TestArrayDataset.test_dataloading_img_label_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_arraydataset.py_TestArrayDataset.test_dataloading_img_label_", "embedding": null, "metadata": {"file_path": "tests/test_arraydataset.py", "file_name": "test_arraydataset.py", "file_type": "text/x-python", "category": "test", "start_line": 137, "end_line": 166, "span_ids": ["impl:9", "TestArrayDataset.test_dataloading_img_label"], "tokens": 375}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestArrayDataset(unittest.TestCase):\n\n    @parameterized.expand([TEST_CASE_4])\n    def test_dataloading_img_label(self, img_transform, expected_shape):\n        test_image = nib.Nifti1Image(np.random.randint(0, 2, size=(128, 128, 128)), np.eye(4))\n        with tempfile.TemporaryDirectory() as tempdir:\n            test_image1 = os.path.join(tempdir, \"test_image1.nii.gz\")\n            test_image2 = os.path.join(tempdir, \"test_image2.nii.gz\")\n            test_label1 = os.path.join(tempdir, \"test_label1.nii.gz\")\n            test_label2 = os.path.join(tempdir, \"test_label2.nii.gz\")\n            nib.save(test_image, test_image1)\n            nib.save(test_image, test_image2)\n            nib.save(test_image, test_label1)\n            nib.save(test_image, test_label2)\n            test_images = [test_image1, test_image2]\n            test_labels = [test_label1, test_label2]\n            dataset = ArrayDataset(test_images, img_transform, test_labels, img_transform)\n            self.assertEqual(len(dataset), 2)\n            dataset.set_random_state(1234)\n            n_workers = 0 if sys.platform == \"win32\" else 2\n            loader = DataLoader(dataset, batch_size=10, num_workers=n_workers)\n            data = next(iter(loader))  # test batching\n            np.testing.assert_allclose(data[0].shape, [2] + list(expected_shape))\n\n            dataset.set_random_state(1234)\n            new_data = next(iter(loader))  # test batching\n            np.testing.assert_allclose(data[0], new_data[0], atol=1e-3)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_roc_auc.py_unittest_TEST_CASE_7._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_roc_auc.py_unittest_TEST_CASE_7._", "embedding": null, "metadata": {"file_path": "tests/test_compute_roc_auc.py", "file_name": "test_compute_roc_auc.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 69, "span_ids": ["impl:11", "docstring"], "tokens": 629}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.data import decollate_batch\nfrom monai.metrics import ROCAUCMetric, compute_roc_auc\nfrom monai.transforms import Activations, AsDiscrete, Compose, ToTensor\n\nTEST_CASE_1 = [\n    torch.tensor([[0.1, 0.9], [0.3, 1.4], [0.2, 0.1], [0.1, 0.5]]),\n    torch.tensor([[0], [1], [0], [1]]),\n    True,\n    2,\n    \"macro\",\n    0.75,\n]\n\nTEST_CASE_2 = [\n    torch.tensor([[0.5], [0.5], [0.2], [8.3]]),\n    torch.tensor([[0], [1], [0], [1]]),\n    False,\n    None,\n    \"macro\",\n    0.875,\n]\n\nTEST_CASE_3 = [torch.tensor([[0.5], [0.5], [0.2], [8.3]]), torch.tensor([0, 1, 0, 1]), False, None, \"macro\", 0.875]\n\nTEST_CASE_4 = [torch.tensor([0.5, 0.5, 0.2, 8.3]), torch.tensor([0, 1, 0, 1]), False, None, \"macro\", 0.875]\n\nTEST_CASE_5 = [\n    torch.tensor([[0.1, 0.9], [0.3, 1.4], [0.2, 0.1], [0.1, 0.5]]),\n    torch.tensor([[0], [1], [0], [1]]),\n    True,\n    2,\n    \"none\",\n    [0.75, 0.75],\n]\n\nTEST_CASE_6 = [\n    torch.tensor([[0.1, 0.9], [0.3, 1.4], [0.2, 0.1], [0.1, 0.5], [0.1, 0.5]]),\n    torch.tensor([[1, 0], [0, 1], [0, 0], [1, 1], [0, 1]]),\n    True,\n    None,\n    \"weighted\",\n    0.56667,\n]\n\nTEST_CASE_7 = [\n    torch.tensor([[0.1, 0.9], [0.3, 1.4], [0.2, 0.1], [0.1, 0.5], [0.1, 0.5]]),\n    torch.tensor([[1, 0], [0, 1], [0, 0], [1, 1], [0, 1]]),\n    True,\n    None,\n    \"micro\",\n    0.62,\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_csv_saver.py_csv_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_csv_saver.py_csv_", "embedding": null, "metadata": {"file_path": "tests/test_csv_saver.py", "file_name": "test_csv_saver.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 44, "span_ids": ["TestCSVSaver.test_saved_content", "TestCSVSaver", "impl", "docstring"], "tokens": 222}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import csv\nimport os\nimport tempfile\nimport unittest\n\nimport numpy as np\nimport torch\n\nfrom monai.data import CSVSaver\n\n\nclass TestCSVSaver(unittest.TestCase):\n    def test_saved_content(self):\n        with tempfile.TemporaryDirectory() as tempdir:\n            saver = CSVSaver(output_dir=tempdir, filename=\"predictions.csv\")\n            meta_data = {\"filename_or_obj\": [\"testfile\" + str(i) for i in range(8)]}\n            saver.save_batch(torch.zeros(8), meta_data)\n            saver.finalize()\n            filepath = os.path.join(tempdir, \"predictions.csv\")\n            self.assertTrue(os.path.exists(filepath))\n            with open(filepath) as f:\n                reader = csv.reader(f)\n                i = 0\n                for row in reader:\n                    self.assertEqual(row[0], \"testfile\" + str(i))\n                    self.assertEqual(np.array(row[1:]).astype(np.float32), 0.0)\n                    i += 1\n                self.assertEqual(i, 8)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dataset.py_os_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dataset.py_os_", "embedding": null, "metadata": {"file_path": "tests/test_dataset.py", "file_name": "test_dataset.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 93, "span_ids": ["TestDataset.test_shape", "impl:3", "TestDataset", "docstring"], "tokens": 864}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport tempfile\nimport unittest\n\nimport nibabel as nib\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.data import Dataset\nfrom monai.transforms import Compose, LoadImaged, SimulateDelayd\n\nTEST_CASE_1 = [(128, 128, 128)]\n\n\nclass TestDataset(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1])\n    def test_shape(self, expected_shape):\n        test_image = nib.Nifti1Image(np.random.randint(0, 2, size=[128, 128, 128]), np.eye(4))\n        with tempfile.TemporaryDirectory() as tempdir:\n            nib.save(test_image, os.path.join(tempdir, \"test_image1.nii.gz\"))\n            nib.save(test_image, os.path.join(tempdir, \"test_label1.nii.gz\"))\n            nib.save(test_image, os.path.join(tempdir, \"test_extra1.nii.gz\"))\n            nib.save(test_image, os.path.join(tempdir, \"test_image2.nii.gz\"))\n            nib.save(test_image, os.path.join(tempdir, \"test_label2.nii.gz\"))\n            nib.save(test_image, os.path.join(tempdir, \"test_extra2.nii.gz\"))\n            test_data = [\n                {\n                    \"image\": os.path.join(tempdir, \"test_image1.nii.gz\"),\n                    \"label\": os.path.join(tempdir, \"test_label1.nii.gz\"),\n                    \"extra\": os.path.join(tempdir, \"test_extra1.nii.gz\"),\n                },\n                {\n                    \"image\": os.path.join(tempdir, \"test_image2.nii.gz\"),\n                    \"label\": os.path.join(tempdir, \"test_label2.nii.gz\"),\n                    \"extra\": os.path.join(tempdir, \"test_extra2.nii.gz\"),\n                },\n            ]\n            test_transform = Compose(\n                [\n                    LoadImaged(keys=[\"image\", \"label\", \"extra\"]),\n                    SimulateDelayd(keys=[\"image\", \"label\", \"extra\"], delay_time=[1e-7, 1e-6, 1e-5]),\n                ]\n            )\n            dataset = Dataset(data=test_data, transform=test_transform)\n            data1 = dataset[0]\n            data2 = dataset[1]\n\n            self.assertTupleEqual(data1[\"image\"].shape, expected_shape)\n            self.assertTupleEqual(data1[\"label\"].shape, expected_shape)\n            self.assertTupleEqual(data1[\"extra\"].shape, expected_shape)\n            self.assertTupleEqual(data2[\"image\"].shape, expected_shape)\n            self.assertTupleEqual(data2[\"label\"].shape, expected_shape)\n            self.assertTupleEqual(data2[\"extra\"].shape, expected_shape)\n\n            dataset = Dataset(data=test_data, transform=LoadImaged(keys=[\"image\", \"label\", \"extra\"]))\n            data1_simple = dataset[0]\n            data2_simple = dataset[1]\n            data3_simple = dataset[-1]\n            data4_simple = dataset[[0, 1]]\n\n            self.assertTupleEqual(data1_simple[\"image\"].shape, expected_shape)\n            self.assertTupleEqual(data1_simple[\"label\"].shape, expected_shape)\n            self.assertTupleEqual(data1_simple[\"extra\"].shape, expected_shape)\n            self.assertTupleEqual(data2_simple[\"image\"].shape, expected_shape)\n            self.assertTupleEqual(data2_simple[\"label\"].shape, expected_shape)\n            self.assertTupleEqual(data2_simple[\"extra\"].shape, expected_shape)\n            self.assertTupleEqual(data3_simple[\"image\"].shape, expected_shape)\n            self.assertTupleEqual(data3_simple[\"label\"].shape, expected_shape)\n            self.assertTupleEqual(data3_simple[\"extra\"].shape, expected_shape)\n            self.assertTupleEqual(data4_simple[0][\"image\"].shape, expected_shape)\n            self.assertTupleEqual(data4_simple[1][\"label\"].shape, expected_shape)\n            self.assertTupleEqual(data4_simple[-1][\"extra\"].shape, expected_shape)\n\n            data4_list = dataset[0:1]\n            self.assertEqual(len(data4_list), 1)\n            for d in data4_list:\n                self.assertTupleEqual(d[\"image\"].shape, expected_shape)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_decathlondataset.py_os_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_decathlondataset.py_os_", "embedding": null, "metadata": {"file_path": "tests/test_decathlondataset.py", "file_name": "test_decathlondataset.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 97, "span_ids": ["TestDecathlonDataset", "TestDecathlonDataset.test_values", "impl", "docstring"], "tokens": 776}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport shutil\nimport unittest\nfrom pathlib import Path\nfrom urllib.error import ContentTooShortError, HTTPError\n\nfrom monai.apps import DecathlonDataset\nfrom monai.transforms import AddChanneld, Compose, LoadImaged, ScaleIntensityd, ToTensord\nfrom tests.utils import skip_if_quick\n\n\nclass TestDecathlonDataset(unittest.TestCase):\n    @skip_if_quick\n    def test_values(self):\n        testing_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"testing_data\")\n        transform = Compose(\n            [\n                LoadImaged(keys=[\"image\", \"label\"]),\n                AddChanneld(keys=[\"image\", \"label\"]),\n                ScaleIntensityd(keys=\"image\"),\n                ToTensord(keys=[\"image\", \"label\"]),\n            ]\n        )\n\n        def _test_dataset(dataset):\n            self.assertEqual(len(dataset), 52)\n            self.assertTrue(\"image\" in dataset[0])\n            self.assertTrue(\"label\" in dataset[0])\n            self.assertTrue(\"image_meta_dict\" in dataset[0])\n            self.assertTupleEqual(dataset[0][\"image\"].shape, (1, 36, 47, 44))\n\n        try:  # will start downloading if testing_dir doesn't have the Decathlon files\n            data = DecathlonDataset(\n                root_dir=testing_dir,\n                task=\"Task04_Hippocampus\",\n                transform=transform,\n                section=\"validation\",\n                download=True,\n                copy_cache=False,\n            )\n        except (ContentTooShortError, HTTPError, RuntimeError) as e:\n            print(str(e))\n            if isinstance(e, RuntimeError):\n                # FIXME: skip MD5 check as current downloading method may fail\n                self.assertTrue(str(e).startswith(\"md5 check\"))\n            return  # skipping this test due the network connection errors\n\n        _test_dataset(data)\n        data = DecathlonDataset(\n            root_dir=testing_dir, task=\"Task04_Hippocampus\", transform=transform, section=\"validation\", download=False\n        )\n        _test_dataset(data)\n        self.assertTrue(data[0][\"image_meta_dict\"][\"filename_or_obj\"].endswith(\"hippocampus_163.nii.gz\"))\n        self.assertTrue(data[0][\"label_meta_dict\"][\"filename_or_obj\"].endswith(\"hippocampus_163.nii.gz\"))\n        # test validation without transforms\n        data = DecathlonDataset(root_dir=testing_dir, task=\"Task04_Hippocampus\", section=\"validation\", download=False)\n        self.assertTupleEqual(data[0][\"image\"].shape, (36, 47, 44))\n        self.assertEqual(len(data), 52)\n        data = DecathlonDataset(root_dir=testing_dir, task=\"Task04_Hippocampus\", section=\"training\", download=False)\n        self.assertTupleEqual(data[0][\"image\"].shape, (34, 56, 31))\n        self.assertEqual(len(data), 208)\n\n        # test dataset properties\n        data = DecathlonDataset(\n            root_dir=Path(testing_dir), task=\"Task04_Hippocampus\", section=\"validation\", download=False\n        )\n        properties = data.get_properties(keys=\"labels\")\n        self.assertDictEqual(properties[\"labels\"], {\"0\": \"background\", \"1\": \"Anterior\", \"2\": \"Posterior\"})\n\n        shutil.rmtree(os.path.join(testing_dir, \"Task04_Hippocampus\"))\n        try:\n            DecathlonDataset(\n                root_dir=testing_dir,\n                task=\"Task04_Hippocampus\",\n                transform=transform,\n                section=\"validation\",\n                download=False,\n            )\n        except RuntimeError as e:\n            print(str(e))\n            self.assertTrue(str(e).startswith(\"Cannot find dataset directory\"))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ensemble_evaluator.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ensemble_evaluator.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_ensemble_evaluator.py", "file_name": "test_ensemble_evaluator.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 86, "span_ids": ["TestEnsembleEvaluator", "TestEnsembleEvaluator.test_content", "impl", "docstring"], "tokens": 533}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom ignite.engine import EventEnum, Events\n\nfrom monai.engines import EnsembleEvaluator\n\n\nclass TestEnsembleEvaluator(unittest.TestCase):\n    def test_content(self):\n        device = torch.device(\"cpu:0\")\n\n        class TestDataset(torch.utils.data.Dataset):\n            def __len__(self):\n                return 8\n\n            def __getitem__(self, index):\n                return {\"image\": torch.tensor([index]), \"label\": torch.zeros(1)}\n\n        val_loader = torch.utils.data.DataLoader(TestDataset())\n\n        class TestNet(torch.nn.Module):\n            def __init__(self, func):\n                super().__init__()\n                self.func = func\n\n            def forward(self, x):\n                return self.func(x)\n\n        net0 = TestNet(lambda x: x + 1)\n        net1 = TestNet(lambda x: x + 2)\n        net2 = TestNet(lambda x: x + 3)\n        net3 = TestNet(lambda x: x + 4)\n        net4 = TestNet(lambda x: x + 5)\n\n        class CustomEvents(EventEnum):\n            FOO_EVENT = \"foo_event\"\n            BAR_EVENT = \"bar_event\"\n\n        val_engine = EnsembleEvaluator(\n            device=device,\n            val_data_loader=val_loader,\n            networks=[net0, net1, net2, net3, net4],\n            pred_keys=[\"pred0\", \"pred1\", \"pred2\", \"pred3\", \"pred4\"],\n            event_names=[\"bwd_event\", \"opt_event\", CustomEvents],\n            event_to_attr={CustomEvents.FOO_EVENT: \"foo\", \"opt_event\": \"opt\"},\n        )\n\n        @val_engine.on(Events.ITERATION_COMPLETED)\n        def run_transform(engine):\n            for i in range(5):\n                expected_value = engine.state.iteration + i\n                torch.testing.assert_allclose(engine.state.output[0][f\"pred{i}\"].item(), expected_value)\n\n        @val_engine.on(Events.EPOCH_COMPLETED)\n        def trigger_custom_event():\n            val_engine.fire_event(CustomEvents.FOO_EVENT)\n            val_engine.fire_event(CustomEvents.BAR_EVENT)\n            val_engine.fire_event(\"bwd_event\")\n            val_engine.fire_event(\"opt_event\")\n\n        @val_engine.on(CustomEvents.FOO_EVENT)\n        def do_foo_op():\n            self.assertEqual(val_engine.state.foo, 0)\n\n        @val_engine.on(\"opt_event\")\n        def do_bar_op():\n            self.assertEqual(val_engine.state.opt, 0)\n\n        val_engine.run()\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gaussian_sharpen.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gaussian_sharpen.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_gaussian_sharpen.py", "file_name": "test_gaussian_sharpen.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 91, "span_ids": ["TestGaussianSharpen.test_value", "TestGaussianSharpen", "impl:7", "docstring"], "tokens": 796}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nfrom parameterized import parameterized\n\nfrom monai.transforms import GaussianSharpen\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTESTS = []\n\nfor p in TEST_NDARRAYS:\n    TESTS.append(\n        [\n            {},\n            p([[[1, 1, 1], [2, 2, 2], [3, 3, 3]], [[4, 4, 4], [5, 5, 5], [6, 6, 6]]]),\n            p(\n                [\n                    [\n                        [4.1081963, 3.4950666, 4.1081963],\n                        [3.7239995, 2.8491793, 3.7239995],\n                        [4.569839, 3.9529324, 4.569839],\n                    ],\n                    [[10.616725, 9.081067, 10.616725], [9.309998, 7.12295, 9.309998], [11.078365, 9.538931, 11.078365]],\n                ]\n            ),\n        ]\n    )\n\n    TESTS.append(\n        [\n            {\"sigma1\": 1.0, \"sigma2\": 0.75, \"alpha\": 20},\n            p([[[1, 1, 1], [2, 2, 2], [3, 3, 3]], [[4, 4, 4], [5, 5, 5], [6, 6, 6]]]),\n            p(\n                [\n                    [\n                        [4.513644, 4.869134, 4.513644],\n                        [8.467242, 9.4004135, 8.467242],\n                        [10.416813, 12.0653515, 10.416813],\n                    ],\n                    [\n                        [15.711488, 17.569994, 15.711488],\n                        [21.16811, 23.501041, 21.16811],\n                        [21.614658, 24.766209, 21.614658],\n                    ],\n                ]\n            ),\n        ]\n    )\n\n    TESTS.append(\n        [\n            {\"sigma1\": (0.5, 1.0), \"sigma2\": (0.5, 0.75), \"alpha\": 20},\n            p([[[1, 1, 1], [2, 2, 2], [3, 3, 3]], [[4, 4, 4], [5, 5, 5], [6, 6, 6]]]),\n            p(\n                [\n                    [\n                        [3.3324685, 3.335536, 3.3324673],\n                        [7.7666636, 8.16056, 7.7666636],\n                        [12.662973, 14.317837, 12.6629715],\n                    ],\n                    [\n                        [15.329051, 16.57557, 15.329051],\n                        [19.41665, 20.40139, 19.416655],\n                        [24.659554, 27.557873, 24.659554],\n                    ],\n                ]\n            ),\n        ]\n    )\n\n\nclass TestGaussianSharpen(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_value(self, argments, image, expected_data):\n        result = GaussianSharpen(**argments)(image)\n        assert_allclose(result, expected_data, atol=0, rtol=1e-4, type_test=False)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gaussian_sharpend.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gaussian_sharpend.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_gaussian_sharpend.py", "file_name": "test_gaussian_sharpend.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 91, "span_ids": ["TestGaussianSharpend.test_value", "TestGaussianSharpend", "impl:7", "docstring"], "tokens": 837}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import GaussianSharpend\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    TESTS.append(\n        [\n            {\"keys\": \"img\"},\n            {\"img\": p(np.array([[[1, 1, 1], [2, 2, 2], [3, 3, 3]], [[4, 4, 4], [5, 5, 5], [6, 6, 6]]]))},\n            np.array(\n                [\n                    [\n                        [4.1081963, 3.4950666, 4.1081963],\n                        [3.7239995, 2.8491793, 3.7239995],\n                        [4.569839, 3.9529324, 4.569839],\n                    ],\n                    [[10.616725, 9.081067, 10.616725], [9.309998, 7.12295, 9.309998], [11.078365, 9.538931, 11.078365]],\n                ]\n            ),\n        ]\n    )\n\n    TESTS.append(\n        [\n            {\"keys\": \"img\", \"sigma1\": 1.0, \"sigma2\": 0.75, \"alpha\": 20},\n            {\"img\": p(np.array([[[1, 1, 1], [2, 2, 2], [3, 3, 3]], [[4, 4, 4], [5, 5, 5], [6, 6, 6]]]))},\n            np.array(\n                [\n                    [\n                        [4.513644, 4.869134, 4.513644],\n                        [8.467242, 9.4004135, 8.467242],\n                        [10.416813, 12.0653515, 10.416813],\n                    ],\n                    [\n                        [15.711488, 17.569994, 15.711488],\n                        [21.16811, 23.501041, 21.16811],\n                        [21.614658, 24.766209, 21.614658],\n                    ],\n                ]\n            ),\n        ]\n    )\n\n    TESTS.append(\n        [\n            {\"keys\": \"img\", \"sigma1\": (0.5, 1.0), \"sigma2\": (0.5, 0.75), \"alpha\": 20},\n            {\"img\": p(np.array([[[1, 1, 1], [2, 2, 2], [3, 3, 3]], [[4, 4, 4], [5, 5, 5], [6, 6, 6]]]))},\n            np.array(\n                [\n                    [\n                        [3.3324685, 3.335536, 3.3324673],\n                        [7.7666636, 8.16056, 7.7666636],\n                        [12.662973, 14.317837, 12.6629715],\n                    ],\n                    [\n                        [15.329051, 16.57557, 15.329051],\n                        [19.41665, 20.40139, 19.416655],\n                        [24.659554, 27.557873, 24.659554],\n                    ],\n                ]\n            ),\n        ]\n    )\n\n\nclass TestGaussianSharpend(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_value(self, argments, image, expected_data):\n        result = GaussianSharpend(**argments)(image)\n        assert_allclose(result[\"img\"], expected_data, rtol=1e-4, type_test=False)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gaussian_smooth.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gaussian_smooth.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_gaussian_smooth.py", "file_name": "test_gaussian_smooth.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 95, "span_ids": ["TestGaussianSmooth", "impl:7", "TestGaussianSmooth.test_value", "docstring"], "tokens": 801}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nfrom parameterized import parameterized\n\nfrom monai.transforms import GaussianSmooth\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTESTS = []\n\nfor p in TEST_NDARRAYS:\n    TESTS.append(\n        [\n            {\"sigma\": 1.5},\n            p([[[1, 1, 1], [2, 2, 2], [3, 3, 3]], [[4, 4, 4], [5, 5, 5], [6, 6, 6]]]),\n            p(\n                [\n                    [\n                        [0.59167546, 0.69312394, 0.59167546],\n                        [0.7956997, 0.93213004, 0.7956997],\n                        [0.7668002, 0.8982755, 0.7668002],\n                    ],\n                    [\n                        [1.6105323, 1.8866735, 1.6105323],\n                        [1.9892492, 2.3303251, 1.9892492],\n                        [1.7856569, 2.091825, 1.7856569],\n                    ],\n                ]\n            ),\n        ]\n    )\n\n    TESTS.append(\n        [\n            {\"sigma\": 0.5},\n            p([[[1, 1, 1], [2, 2, 2], [3, 3, 3]], [[4, 4, 4], [5, 5, 5], [6, 6, 6]]]),\n            p(\n                [\n                    [\n                        [0.8424794, 0.99864554, 0.8424794],\n                        [1.678146, 1.9892154, 1.678146],\n                        [1.9889624, 2.3576462, 1.9889624],\n                    ],\n                    [\n                        [2.966061, 3.5158648, 2.966061],\n                        [4.1953645, 4.973038, 4.1953645],\n                        [4.112544, 4.8748655, 4.1125436],\n                    ],\n                ]\n            ),\n        ]\n    )\n\n    TESTS.append(\n        [\n            {\"sigma\": [1.5, 0.5]},\n            p([[[1, 1, 1], [2, 2, 2], [3, 3, 3]], [[4, 4, 4], [5, 5, 5], [6, 6, 6]]]),\n            p(\n                [\n                    [\n                        [0.8542037, 1.0125432, 0.8542037],\n                        [1.1487541, 1.3616928, 1.1487541],\n                        [1.1070318, 1.3122368, 1.1070318],\n                    ],\n                    [\n                        [2.3251305, 2.756128, 2.3251305],\n                        [2.8718853, 3.4042323, 2.8718853],\n                        [2.5779586, 3.0558217, 2.5779586],\n                    ],\n                ]\n            ),\n        ]\n    )\n\n\nclass TestGaussianSmooth(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_value(self, argments, image, expected_data):\n        result = GaussianSmooth(**argments)(image)\n        assert_allclose(result, expected_data, atol=0, rtol=1e-4, type_test=False)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gaussian_smoothd.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gaussian_smoothd.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_gaussian_smoothd.py", "file_name": "test_gaussian_smoothd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 95, "span_ids": ["TestGaussianSmoothd", "impl:7", "TestGaussianSmoothd.test_value", "docstring"], "tokens": 846}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import GaussianSmoothd\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    TESTS.append(\n        [\n            {\"keys\": \"img\", \"sigma\": 1.5},\n            {\"img\": p(np.array([[[1, 1, 1], [2, 2, 2], [3, 3, 3]], [[4, 4, 4], [5, 5, 5], [6, 6, 6]]]))},\n            np.array(\n                [\n                    [\n                        [0.59167546, 0.69312394, 0.59167546],\n                        [0.7956997, 0.93213004, 0.7956997],\n                        [0.7668002, 0.8982755, 0.7668002],\n                    ],\n                    [\n                        [1.6105323, 1.8866735, 1.6105323],\n                        [1.9892492, 2.3303251, 1.9892492],\n                        [1.7856569, 2.091825, 1.7856569],\n                    ],\n                ]\n            ),\n        ]\n    )\n\n    TESTS.append(\n        [\n            {\"keys\": \"img\", \"sigma\": 0.5},\n            {\"img\": p(np.array([[[1, 1, 1], [2, 2, 2], [3, 3, 3]], [[4, 4, 4], [5, 5, 5], [6, 6, 6]]]))},\n            np.array(\n                [\n                    [\n                        [0.8424794, 0.99864554, 0.8424794],\n                        [1.678146, 1.9892154, 1.678146],\n                        [1.9889624, 2.3576462, 1.9889624],\n                    ],\n                    [\n                        [2.966061, 3.5158648, 2.966061],\n                        [4.1953645, 4.973038, 4.1953645],\n                        [4.112544, 4.8748655, 4.1125436],\n                    ],\n                ]\n            ),\n        ]\n    )\n\n    TESTS.append(\n        [\n            {\"keys\": \"img\", \"sigma\": [1.5, 0.5]},\n            {\"img\": p(np.array([[[1, 1, 1], [2, 2, 2], [3, 3, 3]], [[4, 4, 4], [5, 5, 5], [6, 6, 6]]]))},\n            np.array(\n                [\n                    [\n                        [0.8542037, 1.0125432, 0.8542037],\n                        [1.1487541, 1.3616928, 1.1487541],\n                        [1.1070318, 1.3122368, 1.1070318],\n                    ],\n                    [\n                        [2.3251305, 2.756128, 2.3251305],\n                        [2.8718853, 3.4042323, 2.8718853],\n                        [2.5779586, 3.0558217, 2.5779586],\n                    ],\n                ]\n            ),\n        ]\n    )\n\n\nclass TestGaussianSmoothd(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_value(self, argments, image, expected_data):\n        result = GaussianSmoothd(**argments)(image)\n        assert_allclose(result[\"img\"], expected_data, rtol=1e-4, type_test=False)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_checkpoint_loader.py_TestHandlerCheckpointLoader.test_two_save_one_load_TestHandlerCheckpointLoader.test_two_save_one_load.with_tempfile_TemporaryDi.torch_testing_assert_allc": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_checkpoint_loader.py_TestHandlerCheckpointLoader.test_two_save_one_load_TestHandlerCheckpointLoader.test_two_save_one_load.with_tempfile_TemporaryDi.torch_testing_assert_allc", "embedding": null, "metadata": {"file_path": "tests/test_handler_checkpoint_loader.py", "file_name": "test_handler_checkpoint_loader.py", "file_type": "text/x-python", "category": "test", "start_line": 60, "end_line": 80, "span_ids": ["TestHandlerCheckpointLoader.test_two_save_one_load"], "tokens": 283}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestHandlerCheckpointLoader(unittest.TestCase):\n\n    def test_two_save_one_load(self):\n        logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n        net1 = torch.nn.PReLU()\n        optimizer = optim.SGD(net1.parameters(), lr=0.02)\n        data1 = net1.state_dict()\n        data1[\"weight\"] = torch.tensor([0.1])\n        net1.load_state_dict(data1)\n        net2 = torch.nn.PReLU()\n        data2 = net2.state_dict()\n        data2[\"weight\"] = torch.tensor([0.2])\n        net2.load_state_dict(data2)\n        with tempfile.TemporaryDirectory() as tempdir:\n            engine = Engine(lambda e, b: None)\n            save_dict = {\"net\": net1, \"opt\": optimizer}\n            CheckpointSaver(save_dir=tempdir, save_dict=save_dict, save_final=True).attach(engine)\n            engine.run([0] * 8, max_epochs=5)\n            path = tempdir + \"/checkpoint_final_iteration=40.pt\"\n            engine = Engine(lambda e, b: None)\n            CheckpointLoader(load_path=path, load_dict={\"net\": net2}, strict=True).attach(engine)\n            engine.run([0] * 8, max_epochs=1)\n            torch.testing.assert_allclose(net2.state_dict()[\"weight\"], torch.tensor([0.1]))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_checkpoint_saver.py_TestHandlerCheckpointSaver_TestHandlerCheckpointSaver.test_file.with_tempfile_TemporaryDi.for_filename_in_filenames.self_assertTrue_os_path_e": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_checkpoint_saver.py_TestHandlerCheckpointSaver_TestHandlerCheckpointSaver.test_file.with_tempfile_TemporaryDi.for_filename_in_filenames.self_assertTrue_os_path_e", "embedding": null, "metadata": {"file_path": "tests/test_handler_checkpoint_saver.py", "file_name": "test_handler_checkpoint_saver.py", "file_type": "text/x-python", "category": "test", "start_line": 113, "end_line": 171, "span_ids": ["TestHandlerCheckpointSaver", "TestHandlerCheckpointSaver.test_file"], "tokens": 390}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestHandlerCheckpointSaver(unittest.TestCase):\n    @parameterized.expand(\n        [TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5, TEST_CASE_6, TEST_CASE_7, TEST_CASE_8]\n    )\n    def test_file(\n        self,\n        save_final,\n        final_filename,\n        save_key_metric,\n        key_metric_name,\n        key_metric_n_saved,\n        key_metric_filename,\n        key_metric_save_state,\n        key_metric_greater_or_equal,\n        key_metric_negative_sign,\n        epoch_level,\n        save_interval,\n        n_saved,\n        filenames,\n        multi_devices=False,\n    ):\n        logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n        data = [0] * 8\n\n        # set up engine\n        def _train_func(engine, batch):\n            engine.state.metrics[\"val_loss\"] = engine.state.iteration\n\n        engine = Engine(_train_func)\n\n        # set up testing handler\n        net = torch.nn.PReLU()\n        if multi_devices:\n            net = torch.nn.DataParallel(net)\n        optimizer = optim.SGD(net.parameters(), lr=0.02)\n        with tempfile.TemporaryDirectory() as tempdir:\n            handler = CheckpointSaver(\n                tempdir,\n                {\"net\": net, \"opt\": optimizer},\n                \"CheckpointSaver\",\n                \"test\",\n                save_final,\n                final_filename,\n                save_key_metric,\n                key_metric_name,\n                key_metric_n_saved,\n                key_metric_filename,\n                key_metric_save_state,\n                key_metric_greater_or_equal,\n                key_metric_negative_sign,\n                epoch_level,\n                save_interval,\n                n_saved,\n            )\n            handler.attach(engine)\n            engine.run(data, max_epochs=2)\n            engine.run(data, max_epochs=5)\n            for filename in filenames:\n                self.assertTrue(os.path.exists(os.path.join(tempdir, filename)))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_classification_saver.py_csv_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_classification_saver.py_csv_", "embedding": null, "metadata": {"file_path": "tests/test_handler_classification_saver.py", "file_name": "test_handler_classification_saver.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 63, "span_ids": ["TestHandlerClassificationSaver", "TestHandlerClassificationSaver.test_saved_content", "impl", "docstring"], "tokens": 353}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import csv\nimport os\nimport tempfile\nimport unittest\n\nimport numpy as np\nimport torch\nfrom ignite.engine import Engine\n\nfrom monai.data import decollate_batch\nfrom monai.data.csv_saver import CSVSaver\nfrom monai.handlers import ClassificationSaver\n\n\nclass TestHandlerClassificationSaver(unittest.TestCase):\n    def test_saved_content(self):\n        with tempfile.TemporaryDirectory() as tempdir:\n\n            # set up engine\n            def _train_func(engine, batch):\n                engine.state.batch = decollate_batch(batch)\n                return [torch.zeros(1) for _ in range(8)]\n\n            engine = Engine(_train_func)\n\n            # set up testing handler\n            saver = CSVSaver(output_dir=tempdir, filename=\"predictions2.csv\")\n            ClassificationSaver(output_dir=tempdir, filename=\"predictions1.csv\").attach(engine)\n            ClassificationSaver(saver=saver).attach(engine)\n\n            data = [{\"filename_or_obj\": [\"testfile\" + str(i) for i in range(8)]}]\n            engine.run(data, max_epochs=1)\n\n            def _test_file(filename):\n                filepath = os.path.join(tempdir, filename)\n                self.assertTrue(os.path.exists(filepath))\n                with open(filepath) as f:\n                    reader = csv.reader(f)\n                    i = 0\n                    for row in reader:\n                        self.assertEqual(row[0], \"testfile\" + str(i))\n                        self.assertEqual(np.array(row[1:]).astype(np.float32), 0.0)\n                        i += 1\n                    self.assertEqual(i, 8)\n\n            _test_file(\"predictions1.csv\")\n            _test_file(\"predictions2.csv\")\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_lr_scheduler.py_logging_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_lr_scheduler.py_logging_", "embedding": null, "metadata": {"file_path": "tests/test_handler_lr_scheduler.py", "file_name": "test_handler_lr_scheduler.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 66, "span_ids": ["TestHandlerLrSchedule", "TestHandlerLrSchedule.test_content", "impl", "docstring"], "tokens": 368}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import logging\nimport sys\nimport unittest\n\nimport numpy as np\nimport torch\nfrom ignite.engine import Engine, Events\n\nfrom monai.handlers import LrScheduleHandler\n\n\nclass TestHandlerLrSchedule(unittest.TestCase):\n    def test_content(self):\n        logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n        data = [0] * 8\n\n        # set up engine\n        def _train_func(engine, batch):\n            pass\n\n        val_engine = Engine(_train_func)\n        train_engine = Engine(_train_func)\n\n        @train_engine.on(Events.EPOCH_COMPLETED)\n        def run_validation(engine):\n            val_engine.run(data)\n            val_engine.state.metrics[\"val_loss\"] = 1\n\n        # set up testing handler\n        net = torch.nn.PReLU()\n\n        def _reduce_lr_on_plateau():\n            optimizer = torch.optim.SGD(net.parameters(), 0.1)\n            lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=1)\n            handler = LrScheduleHandler(lr_scheduler, step_transform=lambda x: val_engine.state.metrics[\"val_loss\"])\n            handler.attach(train_engine)\n            return lr_scheduler\n\n        def _reduce_on_step():\n            optimizer = torch.optim.SGD(net.parameters(), 0.1)\n            lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=2, gamma=0.1)\n            handler = LrScheduleHandler(lr_scheduler)\n            handler.attach(train_engine)\n            return lr_scheduler\n\n        schedulers = _reduce_lr_on_plateau(), _reduce_on_step()\n\n        train_engine.run(data, max_epochs=5)\n        for scheduler in schedulers:\n            np.testing.assert_allclose(scheduler._last_lr[0], 0.001)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_segmentation_saver.py_os_TestHandlerSegmentationSaver.test_saved_content.with_tempfile_TemporaryDi.for_i_in_range_8_.self_assertTrue_os_path_e": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_segmentation_saver.py_os_TestHandlerSegmentationSaver.test_saved_content.with_tempfile_TemporaryDi.for_i_in_range_8_.self_assertTrue_os_path_e", "embedding": null, "metadata": {"file_path": "tests/test_handler_segmentation_saver.py", "file_name": "test_handler_segmentation_saver.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 54, "span_ids": ["TestHandlerSegmentationSaver.test_saved_content", "TestHandlerSegmentationSaver", "docstring"], "tokens": 337}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport tempfile\nimport unittest\n\nimport numpy as np\nimport torch\nfrom ignite.engine import Engine\nfrom parameterized import parameterized\n\nfrom monai.data import decollate_batch\nfrom monai.handlers import SegmentationSaver\n\nTEST_CASE_0 = [\".nii.gz\"]\n\nTEST_CASE_1 = [\".png\"]\n\n\nclass TestHandlerSegmentationSaver(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_0, TEST_CASE_1])\n    def test_saved_content(self, output_ext):\n        with tempfile.TemporaryDirectory() as tempdir:\n\n            # set up engine\n            def _train_func(engine, batch):\n                engine.state.batch = decollate_batch(batch)\n                return [torch.randint(0, 255, (1, 2, 2)).float() for _ in range(8)]\n\n            engine = Engine(_train_func)\n\n            # set up testing handler\n            saver = SegmentationSaver(output_dir=tempdir, output_postfix=\"seg\", output_ext=output_ext, scale=255)\n            saver.attach(engine)\n\n            data = [\n                {\n                    \"filename_or_obj\": [\"testfile\" + str(i) + \".nii.gz\" for i in range(8)],\n                    \"patch_index\": torch.tensor(list(range(8))),\n                }\n            ]\n            engine.run(data, max_epochs=1)\n            for i in range(8):\n                filepath = os.path.join(\"testfile\" + str(i), \"testfile\" + str(i) + \"_seg\" + f\"_{i}\" + output_ext)\n                self.assertTrue(os.path.exists(os.path.join(tempdir, filepath)))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_stats.py_logging_TestHandlerStats.test_metrics_print.for_idx_line_in_enumerat.if_grep_match_line_.if_idx_in_5_10_.self_assertTrue_has_key_w": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_stats.py_logging_TestHandlerStats.test_metrics_print.for_idx_line_in_enumerat.if_grep_match_line_.if_idx_in_5_10_.self_assertTrue_has_key_w", "embedding": null, "metadata": {"file_path": "tests/test_handler_stats.py", "file_name": "test_handler_stats.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 59, "span_ids": ["TestHandlerStats.test_metrics_print", "TestHandlerStats", "docstring"], "tokens": 316}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import logging\nimport os\nimport re\nimport tempfile\nimport unittest\nfrom io import StringIO\n\nimport torch\nfrom ignite.engine import Engine, Events\n\nfrom monai.handlers import StatsHandler\n\n\nclass TestHandlerStats(unittest.TestCase):\n    def test_metrics_print(self):\n        log_stream = StringIO()\n        log_handler = logging.StreamHandler(log_stream)\n        log_handler.setLevel(logging.INFO)\n        key_to_handler = \"test_logging\"\n        key_to_print = \"testing_metric\"\n\n        # set up engine\n        def _train_func(engine, batch):\n            return [torch.tensor(0.0)]\n\n        engine = Engine(_train_func)\n\n        # set up dummy metric\n        @engine.on(Events.EPOCH_COMPLETED)\n        def _update_metric(engine):\n            current_metric = engine.state.metrics.get(key_to_print, 0.1)\n            engine.state.metrics[key_to_print] = current_metric + 0.1\n\n        # set up testing handler\n        stats_handler = StatsHandler(name=key_to_handler, logger_handler=log_handler)\n        stats_handler.attach(engine)\n\n        engine.run(range(3), max_epochs=2)\n\n        # check logging output\n        output_str = log_stream.getvalue()\n        log_handler.close()\n        grep = re.compile(f\".*{key_to_handler}.*\")\n        has_key_word = re.compile(f\".*{key_to_print}.*\")\n        for idx, line in enumerate(output_str.split(\"\\n\")):\n            if grep.match(line):\n                if idx in [5, 10]:\n                    self.assertTrue(has_key_word.match(line))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_tb_stats.py_glob_TestHandlerTBStats.test_metrics_print.with_tempfile_TemporaryDi.self_assertTrue_len_glob_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_tb_stats.py_glob_TestHandlerTBStats.test_metrics_print.with_tempfile_TemporaryDi.self_assertTrue_len_glob_", "embedding": null, "metadata": {"file_path": "tests/test_handler_tb_stats.py", "file_name": "test_handler_tb_stats.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 44, "span_ids": ["TestHandlerTBStats.test_metrics_print", "TestHandlerTBStats", "docstring"], "tokens": 214}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import glob\nimport tempfile\nimport unittest\n\nfrom ignite.engine import Engine, Events\nfrom torch.utils.tensorboard import SummaryWriter\n\nfrom monai.handlers import TensorBoardStatsHandler\n\n\nclass TestHandlerTBStats(unittest.TestCase):\n    def test_metrics_print(self):\n        with tempfile.TemporaryDirectory() as tempdir:\n\n            # set up engine\n            def _train_func(engine, batch):\n                return [batch + 1.0]\n\n            engine = Engine(_train_func)\n\n            # set up dummy metric\n            @engine.on(Events.EPOCH_COMPLETED)\n            def _update_metric(engine):\n                current_metric = engine.state.metrics.get(\"acc\", 0.1)\n                engine.state.metrics[\"acc\"] = current_metric + 0.1\n\n            # set up testing handler\n            stats_handler = TensorBoardStatsHandler(log_dir=tempdir)\n            stats_handler.attach(engine)\n            engine.run(range(3), max_epochs=2)\n            stats_handler.close()\n            # check logging output\n            self.assertTrue(len(glob.glob(tempdir)) > 0)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_img2tensorboard.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_img2tensorboard.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_img2tensorboard.py", "file_name": "test_img2tensorboard.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 44, "span_ids": ["TestImg2Tensorboard.test_write_gray", "TestImg2Tensorboard", "impl", "docstring"], "tokens": 261}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport tensorboard\nimport torch\n\nfrom monai.visualize import make_animated_gif_summary\n\n\nclass TestImg2Tensorboard(unittest.TestCase):\n    def test_write_gray(self):\n        nparr = np.ones(shape=(1, 32, 32, 32), dtype=np.float32)\n        summary_object_np = make_animated_gif_summary(\n            tag=\"test_summary_nparr.png\", image=nparr, max_out=1, scale_factor=253.0\n        )\n        for s in summary_object_np:\n            assert isinstance(\n                s, tensorboard.compat.proto.summary_pb2.Summary\n            ), \"make_animated_gif_summary must return a tensorboard.summary object from numpy array\"\n\n        tensorarr = torch.tensor(nparr)\n        summary_object_tensor = make_animated_gif_summary(\n            tag=\"test_summary_tensorarr.png\", image=tensorarr, max_out=1, frame_dim=-1, scale_factor=253.0\n        )\n        for s in summary_object_tensor:\n            assert isinstance(\n                s, tensorboard.compat.proto.summary_pb2.Summary\n            ), \"make_animated_gif_summary must return a tensorboard.summary object from tensor input\"\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_workflows_gan.py_run_training_test_run_training_test.return.trainer_state": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_workflows_gan.py_run_training_test_run_training_test.return.trainer_state", "embedding": null, "metadata": {"file_path": "tests/test_integration_workflows_gan.py", "file_name": "test_integration_workflows_gan.py", "file_type": "text/x-python", "category": "test", "start_line": 36, "end_line": 125, "span_ids": ["run_training_test"], "tokens": 819}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def run_training_test(root_dir, device=\"cuda:0\"):\n    real_images = sorted(glob(os.path.join(root_dir, \"img*.nii.gz\")))\n    train_files = [{\"reals\": img} for img in zip(real_images)]\n\n    # prepare real data\n    train_transforms = Compose(\n        [\n            LoadImaged(keys=[\"reals\"]),\n            AsChannelFirstd(keys=[\"reals\"]),\n            ScaleIntensityd(keys=[\"reals\"]),\n            RandFlipd(keys=[\"reals\"], prob=0.5),\n            ToTensord(keys=[\"reals\"]),\n        ]\n    )\n    train_ds = monai.data.CacheDataset(data=train_files, transform=train_transforms, cache_rate=0.5)\n    train_loader = monai.data.DataLoader(train_ds, batch_size=2, shuffle=True, num_workers=4)\n\n    learning_rate = 2e-4\n    betas = (0.5, 0.999)\n    real_label = 1\n    fake_label = 0\n\n    # create discriminator\n    disc_net = Discriminator(\n        in_shape=(1, 64, 64), channels=(8, 16, 32, 64, 1), strides=(2, 2, 2, 2, 1), num_res_units=1, kernel_size=5\n    ).to(device)\n    disc_net.apply(normal_init)\n    disc_opt = torch.optim.Adam(disc_net.parameters(), learning_rate, betas=betas)\n    disc_loss_criterion = torch.nn.BCELoss()\n\n    def discriminator_loss(gen_images, real_images):\n        real = real_images.new_full((real_images.shape[0], 1), real_label)\n        gen = gen_images.new_full((gen_images.shape[0], 1), fake_label)\n        realloss = disc_loss_criterion(disc_net(real_images), real)\n        genloss = disc_loss_criterion(disc_net(gen_images.detach()), gen)\n        return torch.div(torch.add(realloss, genloss), 2)\n\n    # create generator\n    latent_size = 64\n    gen_net = Generator(\n        latent_shape=latent_size, start_shape=(latent_size, 8, 8), channels=[32, 16, 8, 1], strides=[2, 2, 2, 1]\n    )\n    gen_net.apply(normal_init)\n    gen_net.conv.add_module(\"activation\", torch.nn.Sigmoid())\n    gen_net = gen_net.to(device)\n    gen_opt = torch.optim.Adam(gen_net.parameters(), learning_rate, betas=betas)\n    gen_loss_criterion = torch.nn.BCELoss()\n\n    def generator_loss(gen_images):\n        output = disc_net(gen_images)\n        cats = output.new_full(output.shape, real_label)\n        return gen_loss_criterion(output, cats)\n\n    key_train_metric = None\n\n    train_handlers = [\n        StatsHandler(\n            name=\"training_loss\", output_transform=lambda x: {Keys.GLOSS: x[Keys.GLOSS], Keys.DLOSS: x[Keys.DLOSS]}\n        ),\n        TensorBoardStatsHandler(\n            log_dir=root_dir,\n            tag_name=\"training_loss\",\n            output_transform=lambda x: {Keys.GLOSS: x[Keys.GLOSS], Keys.DLOSS: x[Keys.DLOSS]},\n        ),\n        CheckpointSaver(\n            save_dir=root_dir, save_dict={\"g_net\": gen_net, \"d_net\": disc_net}, save_interval=2, epoch_level=True\n        ),\n    ]\n\n    disc_train_steps = 2\n    num_epochs = 5\n\n    trainer = GanTrainer(\n        device,\n        num_epochs,\n        train_loader,\n        gen_net,\n        gen_opt,\n        generator_loss,\n        disc_net,\n        disc_opt,\n        discriminator_loss,\n        d_train_steps=disc_train_steps,\n        latent_shape=latent_size,\n        key_train_metric=key_train_metric,\n        train_handlers=train_handlers,\n    )\n    trainer.run()\n\n    return trainer.state", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_workflows_gan.py_IntegrationWorkflowsGAN_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_workflows_gan.py_IntegrationWorkflowsGAN_", "embedding": null, "metadata": {"file_path": "tests/test_integration_workflows_gan.py", "file_name": "test_integration_workflows_gan.py", "file_type": "text/x-python", "category": "test", "start_line": 129, "end_line": 161, "span_ids": ["impl", "IntegrationWorkflowsGAN", "IntegrationWorkflowsGAN.tearDown", "IntegrationWorkflowsGAN.test_training", "IntegrationWorkflowsGAN.setUp"], "tokens": 266}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_quick\nclass IntegrationWorkflowsGAN(DistTestCase):\n    def setUp(self):\n        set_determinism(seed=0)\n\n        self.data_dir = tempfile.mkdtemp()\n        for i in range(40):\n            im, _ = create_test_image_2d(64, 64, num_objs=3, rad_max=14, num_seg_classes=1, channel_dim=-1)\n            n = nib.Nifti1Image(im, np.eye(4))\n            nib.save(n, os.path.join(self.data_dir, f\"img{i:d}.nii.gz\"))\n\n        self.device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu:0\")\n        monai.config.print_config()\n        logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n\n    def tearDown(self):\n        set_determinism(seed=None)\n        shutil.rmtree(self.data_dir)\n\n    @TimedCall(seconds=200, daemon=False)\n    def test_training(self):\n        torch.manual_seed(0)\n\n        finish_state = run_training_test(self.data_dir, device=self.device)\n\n        # assert GAN training finished\n        self.assertEqual(finish_state.iteration, 100)\n        self.assertEqual(finish_state.epoch, 5)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_list_to_dict.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_list_to_dict.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_list_to_dict.py", "file_name": "test_list_to_dict.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 41, "span_ids": ["impl:11", "TestListToDict.test_value_shape", "TestListToDict", "docstring"], "tokens": 386}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nfrom parameterized import parameterized\n\nfrom monai.utils import list_to_dict\n\nTEST_CASE_1 = [[\"a=1\", \"b=2\", \"c=3\", \"d=4\"], {\"a\": 1, \"b\": 2, \"c\": 3, \"d\": 4}]\n\nTEST_CASE_2 = [[\"a=a\", \"b=b\", \"c=c\", \"d=d\"], {\"a\": \"a\", \"b\": \"b\", \"c\": \"c\", \"d\": \"d\"}]\n\nTEST_CASE_3 = [[\"a=0.1\", \"b=0.2\", \"c=0.3\", \"d=0.4\"], {\"a\": 0.1, \"b\": 0.2, \"c\": 0.3, \"d\": 0.4}]\n\nTEST_CASE_4 = [[\"a=True\", \"b=TRUE\", \"c=false\", \"d=FALSE\"], {\"a\": True, \"b\": True, \"c\": False, \"d\": False}]\n\nTEST_CASE_5 = [\n    [\"a='1'\", \"b=2 \", \" c = 3\", \"d='test'\", \"'e'=0\", \"f\", \"g=None\"],\n    {\"a\": 1, \"b\": 2, \"c\": 3, \"d\": \"test\", \"e\": 0, \"f\": None, \"g\": None},\n]\n\n\nclass TestListToDict(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5])\n    def test_value_shape(self, input, output):\n        result = list_to_dict(input)\n        self.assertDictEqual(result, output)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_lltm.py_unittest_TEST_CASE_1._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_lltm.py_unittest_TEST_CASE_1._", "embedding": null, "metadata": {"file_path": "tests/test_lltm.py", "file_name": "test_lltm.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 26, "span_ids": ["docstring"], "tokens": 185}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks.layers import LLTM\nfrom tests.utils import SkipIfNoModule, is_tf32_env\n\n_rtol = 0.001 if is_tf32_env() else 0.0001\n\nTEST_CASE_1 = [\n    {\"input_features\": 32, \"state_size\": 2},\n    torch.tensor([[-0.1622, 0.1663], [0.5465, 0.0459], [-0.1436, 0.6171], [0.3632, -0.0111]]),\n    torch.tensor([[-1.3773, 0.3348], [0.8353, 1.3064], [-0.2179, 4.1739], [1.3045, -0.1444]]),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_lltm.py_TestLLTM_TestLLTM.test_value.None_3": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_lltm.py_TestLLTM_TestLLTM.test_value.None_3", "embedding": null, "metadata": {"file_path": "tests/test_lltm.py", "file_name": "test_lltm.py", "file_type": "text/x-python", "category": "test", "start_line": 27, "end_line": 39, "span_ids": ["TestLLTM", "TestLLTM.test_value"], "tokens": 172}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestLLTM(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1])\n    @SkipIfNoModule(\"monai._C\")\n    def test_value(self, input_param, expected_h, expected_c):\n        torch.manual_seed(0)\n        x = torch.randn(4, 32)\n        h = torch.randn(4, 2)\n        c = torch.randn(4, 2)\n        new_h, new_c = LLTM(**input_param)(x, (h, c))\n        (new_h.sum() + new_c.sum()).backward()\n\n        torch.testing.assert_allclose(new_h, expected_h, rtol=0.0001, atol=1e-04)\n        torch.testing.assert_allclose(new_c, expected_c, rtol=0.0001, atol=1e-04)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_lltm.py_TestLLTM.test_value_cuda_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_lltm.py_TestLLTM.test_value_cuda_", "embedding": null, "metadata": {"file_path": "tests/test_lltm.py", "file_name": "test_lltm.py", "file_type": "text/x-python", "category": "test", "start_line": 43, "end_line": 61, "span_ids": ["TestLLTM.test_value_cuda", "impl:5"], "tokens": 227}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestLLTM(unittest.TestCase):\n\n    @parameterized.expand([TEST_CASE_1])\n    @SkipIfNoModule(\"monai._C\")\n    def test_value_cuda(self, input_param, expected_h, expected_c):\n        device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu:0\")\n        torch.manual_seed(0)\n        x = torch.randn(4, 32).to(device)\n        h = torch.randn(4, 2).to(device)\n        c = torch.randn(4, 2).to(device)\n        lltm = LLTM(**input_param).to(device)\n        new_h, new_c = lltm(x, (h, c))\n        (new_h.sum() + new_c.sum()).backward()\n\n        torch.testing.assert_allclose(new_h, expected_h.to(device), rtol=_rtol, atol=0.001)\n        torch.testing.assert_allclose(new_c, expected_c.to(device), rtol=_rtol, atol=0.001)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_mednistdataset.py_os_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_mednistdataset.py_os_", "embedding": null, "metadata": {"file_path": "tests/test_mednistdataset.py", "file_name": "test_mednistdataset.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 79, "span_ids": ["TestMedNISTDataset", "impl:3", "TestMedNISTDataset.test_values", "docstring"], "tokens": 624}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport shutil\nimport unittest\nfrom pathlib import Path\nfrom urllib.error import ContentTooShortError, HTTPError\n\nfrom monai.apps import MedNISTDataset\nfrom monai.transforms import AddChanneld, Compose, LoadImaged, ScaleIntensityd, ToTensord\nfrom tests.utils import skip_if_quick\n\nMEDNIST_FULL_DATASET_LENGTH = 58954\n\n\nclass TestMedNISTDataset(unittest.TestCase):\n    @skip_if_quick\n    def test_values(self):\n        testing_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"testing_data\")\n        transform = Compose(\n            [\n                LoadImaged(keys=\"image\"),\n                AddChanneld(keys=\"image\"),\n                ScaleIntensityd(keys=\"image\"),\n                ToTensord(keys=[\"image\", \"label\"]),\n            ]\n        )\n\n        def _test_dataset(dataset):\n            self.assertEqual(len(dataset), int(MEDNIST_FULL_DATASET_LENGTH * dataset.test_frac))\n            self.assertTrue(\"image\" in dataset[0])\n            self.assertTrue(\"label\" in dataset[0])\n            self.assertTrue(\"image_meta_dict\" in dataset[0])\n            self.assertTupleEqual(dataset[0][\"image\"].shape, (1, 64, 64))\n\n        try:  # will start downloading if testing_dir doesn't have the MedNIST files\n            data = MedNISTDataset(\n                root_dir=testing_dir, transform=transform, section=\"test\", download=True, copy_cache=False\n            )\n        except (ContentTooShortError, HTTPError, RuntimeError) as e:\n            print(str(e))\n            if isinstance(e, RuntimeError):\n                # FIXME: skip MD5 check as current downloading method may fail\n                self.assertTrue(str(e).startswith(\"md5 check\"))\n            return  # skipping this test due the network connection errors\n\n        _test_dataset(data)\n\n        # testing from\n        data = MedNISTDataset(root_dir=Path(testing_dir), transform=transform, section=\"test\", download=False)\n        self.assertEqual(data.get_num_classes(), 6)\n        _test_dataset(data)\n        data = MedNISTDataset(root_dir=testing_dir, section=\"test\", download=False)\n        self.assertTupleEqual(data[0][\"image\"].shape, (64, 64))\n        # test same dataset length with different random seed\n        data = MedNISTDataset(root_dir=testing_dir, transform=transform, section=\"test\", download=False, seed=42)\n        _test_dataset(data)\n        self.assertEqual(data[0][\"class_name\"], \"AbdomenCT\")\n        self.assertEqual(data[0][\"label\"].cpu().item(), 0)\n        shutil.rmtree(os.path.join(testing_dir, \"MedNIST\"))\n        try:\n            MedNISTDataset(root_dir=testing_dir, transform=transform, section=\"test\", download=False)\n        except RuntimeError as e:\n            print(str(e))\n            self.assertTrue(str(e).startswith(\"Cannot find dataset directory\"))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nifti_saver.py_os_TestNiftiSaver.test_saved_content.with_tempfile_TemporaryDi.for_i_in_range_8_.self_assertTrue_os_path_e": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nifti_saver.py_os_TestNiftiSaver.test_saved_content.with_tempfile_TemporaryDi.for_i_in_range_8_.self_assertTrue_os_path_e", "embedding": null, "metadata": {"file_path": "tests/test_nifti_saver.py", "file_name": "test_nifti_saver.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 34, "span_ids": ["TestNiftiSaver.test_saved_content", "TestNiftiSaver", "docstring"], "tokens": 198}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport tempfile\nimport unittest\nfrom pathlib import Path\n\nimport numpy as np\nimport torch\n\nfrom monai.data import NiftiSaver\nfrom monai.transforms import LoadImage\n\n\nclass TestNiftiSaver(unittest.TestCase):\n    def test_saved_content(self):\n        with tempfile.TemporaryDirectory() as tempdir:\n\n            saver = NiftiSaver(output_dir=Path(tempdir), output_postfix=\"seg\", output_ext=\".nii.gz\")\n\n            meta_data = {\"filename_or_obj\": [\"testfile\" + str(i) + \".nii\" for i in range(8)]}\n            saver.save_batch(torch.zeros(8, 1, 2, 2), meta_data)\n            for i in range(8):\n                filepath = os.path.join(\"testfile\" + str(i), \"testfile\" + str(i) + \"_seg.nii.gz\")\n                self.assertTrue(os.path.exists(os.path.join(tempdir, filepath)))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nifti_saver.py_TestNiftiSaver.test_saved_resize_content_TestNiftiSaver.test_saved_resize_content.with_tempfile_TemporaryDi.for_i_in_range_8_.self_assertTrue_os_path_e": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nifti_saver.py_TestNiftiSaver.test_saved_resize_content_TestNiftiSaver.test_saved_resize_content.with_tempfile_TemporaryDi.for_i_in_range_8_.self_assertTrue_os_path_e", "embedding": null, "metadata": {"file_path": "tests/test_nifti_saver.py", "file_name": "test_nifti_saver.py", "file_type": "text/x-python", "category": "test", "start_line": 36, "end_line": 49, "span_ids": ["TestNiftiSaver.test_saved_resize_content"], "tokens": 215}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestNiftiSaver(unittest.TestCase):\n\n    def test_saved_resize_content(self):\n        with tempfile.TemporaryDirectory() as tempdir:\n\n            saver = NiftiSaver(output_dir=tempdir, output_postfix=\"seg\", output_ext=\".nii.gz\", dtype=np.float32)\n\n            meta_data = {\n                \"filename_or_obj\": [\"testfile\" + str(i) + \".nii\" for i in range(8)],\n                \"affine\": [np.diag(np.ones(4)) * 5] * 8,\n                \"original_affine\": [np.diag(np.ones(4)) * 1.0] * 8,\n            }\n            saver.save_batch(torch.randint(0, 255, (8, 8, 2, 2)), meta_data)\n            for i in range(8):\n                filepath = os.path.join(\"testfile\" + str(i), \"testfile\" + str(i) + \"_seg.nii.gz\")\n                self.assertTrue(os.path.exists(os.path.join(tempdir, filepath)))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_png_saver.py_os_TestPNGSaver.test_saved_content.with_tempfile_TemporaryDi.for_i_in_range_8_.self_assertTrue_os_path_e": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_png_saver.py_os_TestPNGSaver.test_saved_content.with_tempfile_TemporaryDi.for_i_in_range_8_.self_assertTrue_os_path_e", "embedding": null, "metadata": {"file_path": "tests/test_png_saver.py", "file_name": "test_png_saver.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 32, "span_ids": ["TestPNGSaver.test_saved_content", "TestPNGSaver", "docstring"], "tokens": 183}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport tempfile\nimport unittest\nfrom pathlib import Path\n\nimport torch\n\nfrom monai.data import PNGSaver\n\n\nclass TestPNGSaver(unittest.TestCase):\n    def test_saved_content(self):\n        with tempfile.TemporaryDirectory() as tempdir:\n\n            saver = PNGSaver(output_dir=tempdir, output_postfix=\"seg\", output_ext=\".png\", scale=255)\n\n            meta_data = {\"filename_or_obj\": [\"testfile\" + str(i) + \".jpg\" for i in range(8)]}\n            saver.save_batch(torch.randint(1, 200, (8, 1, 2, 2)), meta_data)\n            for i in range(8):\n                filepath = os.path.join(\"testfile\" + str(i), \"testfile\" + str(i) + \"_seg.png\")\n                self.assertTrue(os.path.exists(os.path.join(tempdir, filepath)))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_png_saver.py_TestPNGSaver.test_saved_content_three_channel_TestPNGSaver.test_saved_content_three_channel.with_tempfile_TemporaryDi.for_i_in_range_8_.self_assertTrue_os_path_e": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_png_saver.py_TestPNGSaver.test_saved_content_three_channel_TestPNGSaver.test_saved_content_three_channel.with_tempfile_TemporaryDi.for_i_in_range_8_.self_assertTrue_os_path_e", "embedding": null, "metadata": {"file_path": "tests/test_png_saver.py", "file_name": "test_png_saver.py", "file_type": "text/x-python", "category": "test", "start_line": 34, "end_line": 43, "span_ids": ["TestPNGSaver.test_saved_content_three_channel"], "tokens": 161}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestPNGSaver(unittest.TestCase):\n\n    def test_saved_content_three_channel(self):\n        with tempfile.TemporaryDirectory() as tempdir:\n\n            saver = PNGSaver(output_dir=Path(tempdir), output_postfix=\"seg\", output_ext=\".png\", scale=255)\n\n            meta_data = {\"filename_or_obj\": [\"testfile\" + str(i) + \".jpg\" for i in range(8)]}\n            saver.save_batch(torch.randint(1, 200, (8, 3, 2, 2)), meta_data)\n            for i in range(8):\n                filepath = os.path.join(\"testfile\" + str(i), \"testfile\" + str(i) + \"_seg.png\")\n                self.assertTrue(os.path.exists(os.path.join(tempdir, filepath)))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_gaussian_smooth.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_gaussian_smooth.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_rand_gaussian_smooth.py", "file_name": "test_rand_gaussian_smooth.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 97, "span_ids": ["TestRandGaussianSmooth.test_value", "TestRandGaussianSmooth", "impl:7", "docstring"], "tokens": 907}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import RandGaussianSmooth\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    TESTS.append(\n        [\n            {\"sigma_x\": (0.5, 1.5), \"prob\": 1.0},\n            p(np.array([[[1, 1, 1], [2, 2, 2], [3, 3, 3]], [[4, 4, 4], [5, 5, 5], [6, 6, 6]]])),\n            np.array(\n                [\n                    [\n                        [0.71806467, 0.9074683, 0.71806467],\n                        [1.0718315, 1.3545481, 1.0718315],\n                        [1.0337002, 1.306359, 1.0337002],\n                    ],\n                    [\n                        [2.0318885, 2.5678391, 2.0318885],\n                        [2.6795788, 3.3863702, 2.6795788],\n                        [2.3475242, 2.9667296, 2.3475242],\n                    ],\n                ]\n            ),\n        ]\n    )\n\n    TESTS.append(\n        [\n            {\"sigma_x\": (0.5, 1.5), \"sigma_y\": (0.5, 1.0), \"prob\": 1.0},\n            p(np.array([[[1, 1, 1], [2, 2, 2], [3, 3, 3]], [[4, 4, 4], [5, 5, 5], [6, 6, 6]]])),\n            np.array(\n                [\n                    [\n                        [0.7686928, 0.9848021, 0.7686928],\n                        [1.1474025, 1.4699818, 1.1474024],\n                        [1.1065826, 1.4176859, 1.1065826],\n                    ],\n                    [\n                        [2.1751494, 2.7866683, 2.1751497],\n                        [2.8685062, 3.6749542, 2.8685062],\n                        [2.5130394, 3.219552, 2.5130394],\n                    ],\n                ]\n            ),\n        ]\n    )\n\n    TESTS.append(\n        [\n            {\"sigma_x\": (0.5, 1.5), \"sigma_y\": (0.5, 1.0), \"approx\": \"scalespace\", \"prob\": 1.0},\n            p(np.array([[[1, 1, 1], [2, 2, 2], [3, 3, 3]], [[4, 4, 4], [5, 5, 5], [6, 6, 6]]])),\n            np.array(\n                [\n                    [\n                        [0.8128456, 0.96736777, 0.8128456],\n                        [1.2742369, 1.5164697, 1.2742369],\n                        [1.2800367, 1.5233722, 1.2800368],\n                    ],\n                    [\n                        [2.3825073, 2.8354228, 2.3825073],\n                        [3.1855922, 3.7911744, 3.1855922],\n                        [2.8496985, 3.391427, 2.8496985],\n                    ],\n                ]\n            ),\n        ]\n    )\n\n\nclass TestRandGaussianSmooth(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_value(self, argments, image, expected_data):\n        converter = RandGaussianSmooth(**argments)\n        converter.set_random_state(seed=0)\n        result = converter(image)\n        assert_allclose(result, expected_data, rtol=1e-4, type_test=False)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_segresnet_block.py_unittest_for_spatial_dims_in_range.for_in_channels_in_range_.for_kernel_size_in_1_3_.TEST_CASE_RESBLOCK_append": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_segresnet_block.py_unittest_for_spatial_dims_in_range.for_in_channels_in_range_.for_kernel_size_in_1_3_.TEST_CASE_RESBLOCK_append", "embedding": null, "metadata": {"file_path": "tests/test_segresnet_block.py", "file_name": "test_segresnet_block.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 35, "span_ids": ["docstring"], "tokens": 170}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks import eval_mode\nfrom monai.networks.blocks.segresnet_block import ResBlock\n\nTEST_CASE_RESBLOCK = []\nfor spatial_dims in range(2, 4):\n    for in_channels in range(1, 4):\n        for kernel_size in [1, 3]:\n            for norm in [\"group\", \"batch\", \"instance\"]:\n                test_case = [\n                    {\n                        \"spatial_dims\": spatial_dims,\n                        \"in_channels\": in_channels,\n                        \"kernel_size\": kernel_size,\n                        \"norm\": norm,\n                    },\n                    (2, in_channels, *([16] * spatial_dims)),\n                    (2, in_channels, *([16] * spatial_dims)),\n                ]\n            TEST_CASE_RESBLOCK.append(test_case)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_segresnet_block.py_TestResBlock_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_segresnet_block.py_TestResBlock_", "embedding": null, "metadata": {"file_path": "tests/test_segresnet_block.py", "file_name": "test_segresnet_block.py", "file_type": "text/x-python", "category": "test", "start_line": 38, "end_line": 55, "span_ids": ["impl:10", "TestResBlock.test_ill_arg", "TestResBlock", "TestResBlock.test_shape"], "tokens": 142}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestResBlock(unittest.TestCase):\n    @parameterized.expand(TEST_CASE_RESBLOCK)\n    def test_shape(self, input_param, input_shape, expected_shape):\n        net = ResBlock(**input_param)\n        with eval_mode(net):\n            result = net(torch.randn(input_shape))\n            self.assertEqual(result.shape, expected_shape)\n\n    def test_ill_arg(self):\n        with self.assertRaises(AssertionError):\n            ResBlock(spatial_dims=3, in_channels=8, norm=\"group\", kernel_size=2)\n        with self.assertRaises(ValueError):\n            ResBlock(spatial_dims=3, in_channels=8, norm=\"norm\")\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_simulatedelay.py_time_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_simulatedelay.py_time_", "embedding": null, "metadata": {"file_path": "tests/test_simulatedelay.py", "file_name": "test_simulatedelay.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 35, "span_ids": ["TestSimulateDelay.test_value", "TestSimulateDelay", "impl", "docstring"], "tokens": 161}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import time\nimport unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms.utility.array import SimulateDelay\nfrom tests.utils import NumpyImageTestCase2D\n\n\nclass TestSimulateDelay(NumpyImageTestCase2D):\n    @parameterized.expand([(0.45,), (1,)])\n    def test_value(self, delay_test_time: float):\n        resize = SimulateDelay(delay_time=delay_test_time)\n        start: float = time.time()\n        result = resize(self.imt[0])\n        stop: float = time.time()\n        measured_approximate: float = stop - start\n        np.testing.assert_allclose(delay_test_time, measured_approximate, rtol=0.5)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_simulatedelayd.py_time_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_simulatedelayd.py_time_", "embedding": null, "metadata": {"file_path": "tests/test_simulatedelayd.py", "file_name": "test_simulatedelayd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 35, "span_ids": ["TestSimulateDelay.test_value", "TestSimulateDelay", "impl", "docstring"], "tokens": 172}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import time\nimport unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms.utility.dictionary import SimulateDelayd\nfrom tests.utils import NumpyImageTestCase2D\n\n\nclass TestSimulateDelay(NumpyImageTestCase2D):\n    @parameterized.expand([(0.45,), (1,)])\n    def test_value(self, delay_test_time: float):\n        resize = SimulateDelayd(keys=\"imgd\", delay_time=delay_test_time)\n        start: float = time.time()\n        _ = resize({\"imgd\": self.imt[0]})\n        stop: float = time.time()\n        measured_approximate: float = stop - start\n        np.testing.assert_allclose(delay_test_time, measured_approximate, rtol=0.5)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_subpixel_upsample.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_subpixel_upsample.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_subpixel_upsample.py", "file_name": "test_subpixel_upsample.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 79, "span_ids": ["impl:27", "impl:17", "TestSUBPIXEL.test_subpixel_shape", "TestSUBPIXEL", "docstring"], "tokens": 646}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nimport torch.nn as nn\nfrom parameterized import parameterized\n\nfrom monai.networks import eval_mode\nfrom monai.networks.blocks import SubpixelUpsample\nfrom monai.networks.layers.factories import Conv\n\nTEST_CASE_SUBPIXEL = []\nfor inch in range(1, 5):\n    for dim in range(1, 4):\n        for factor in range(1, 3):\n            test_case = [\n                {\"spatial_dims\": dim, \"in_channels\": inch, \"scale_factor\": factor},\n                (2, inch, *([8] * dim)),\n                (2, inch, *([8 * factor] * dim)),\n            ]\n            TEST_CASE_SUBPIXEL.append(test_case)\n\nTEST_CASE_SUBPIXEL_2D_EXTRA = [\n    {\"spatial_dims\": 2, \"in_channels\": 2, \"scale_factor\": 3},\n    (2, 2, 8, 4),  # different size for H and W\n    (2, 2, 24, 12),\n]\n\nTEST_CASE_SUBPIXEL_3D_EXTRA = [\n    {\"spatial_dims\": 3, \"in_channels\": 1, \"scale_factor\": 2},\n    (2, 1, 16, 8, 4),  # different size for H, W and D\n    (2, 1, 32, 16, 8),\n]\n\nconv_block = nn.Sequential(\n    Conv[Conv.CONV, 3](1, 4, kernel_size=1), Conv[Conv.CONV, 3](4, 8, kernel_size=3, stride=1, padding=1)\n)\n\nTEST_CASE_SUBPIXEL_CONV_BLOCK_EXTRA = [\n    {\"spatial_dims\": 3, \"in_channels\": 1, \"scale_factor\": 2, \"conv_block\": conv_block},\n    (2, 1, 16, 8, 4),  # different size for H, W and D\n    (2, 1, 32, 16, 8),\n]\n\nTEST_CASE_SUBPIXEL.append(TEST_CASE_SUBPIXEL_2D_EXTRA)\nTEST_CASE_SUBPIXEL.append(TEST_CASE_SUBPIXEL_3D_EXTRA)\nTEST_CASE_SUBPIXEL.append(TEST_CASE_SUBPIXEL_CONV_BLOCK_EXTRA)\n\n\n# add every test back with the pad/pool sequential component omitted\nfor tests in list(TEST_CASE_SUBPIXEL):\n    args: dict = tests[0]  # type: ignore\n    args = dict(args)\n    args[\"apply_pad_pool\"] = False\n    TEST_CASE_SUBPIXEL.append([args, tests[1], tests[2]])\n\n\nclass TestSUBPIXEL(unittest.TestCase):\n    @parameterized.expand(TEST_CASE_SUBPIXEL)\n    def test_subpixel_shape(self, input_param, input_shape, expected_shape):\n        net = SubpixelUpsample(**input_param)\n        with eval_mode(net):\n            result = net.forward(torch.randn(input_shape))\n            self.assertEqual(result.shape, expected_shape)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_tversky_loss.py_unittest_TEST_CASES": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_tversky_loss.py_unittest_TEST_CASES", "embedding": null, "metadata": {"file_path": "tests/test_tversky_loss.py", "file_name": "test_tversky_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 154, "span_ids": ["docstring"], "tokens": 53}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.losses import TverskyLoss\nfrom tests.utils import SkipIfBeforePyTorchVersion, test_script_save\n\nTEST_CASES =\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_vnet.py_unittest_TEST_CASE_VNET_3D_3._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_vnet.py_unittest_TEST_CASE_VNET_3D_3._", "embedding": null, "metadata": {"file_path": "tests/test_vnet.py", "file_name": "test_vnet.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 52, "span_ids": ["impl:13", "docstring"], "tokens": 508}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks import eval_mode\nfrom monai.networks.nets import VNet\nfrom tests.utils import test_script_save\n\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\nTEST_CASE_VNET_2D_1 = [\n    {\"spatial_dims\": 2, \"in_channels\": 4, \"out_channels\": 1, \"act\": \"elu\", \"dropout_dim\": 1},\n    (1, 4, 32, 32),\n    (1, 1, 32, 32),\n]\nTEST_CASE_VNET_2D_2 = [\n    {\"spatial_dims\": 2, \"in_channels\": 2, \"out_channels\": 2, \"act\": \"prelu\", \"dropout_dim\": 2},\n    (1, 2, 32, 32),\n    (1, 2, 32, 32),\n]\nTEST_CASE_VNET_2D_3 = [\n    {\"spatial_dims\": 2, \"in_channels\": 1, \"out_channels\": 3, \"dropout_dim\": 3},\n    (1, 1, 32, 32),\n    (1, 3, 32, 32),\n]\nTEST_CASE_VNET_3D_1 = [\n    {\"spatial_dims\": 3, \"in_channels\": 4, \"out_channels\": 1, \"act\": \"elu\", \"dropout_dim\": 1},\n    (1, 4, 32, 32, 32),\n    (1, 1, 32, 32, 32),\n]\nTEST_CASE_VNET_3D_2 = [\n    {\"spatial_dims\": 3, \"in_channels\": 2, \"out_channels\": 2, \"act\": \"prelu\", \"dropout_dim\": 2},\n    (1, 2, 32, 32, 32),\n    (1, 2, 32, 32, 32),\n]\nTEST_CASE_VNET_3D_3 = [\n    {\"spatial_dims\": 3, \"in_channels\": 1, \"out_channels\": 3, \"dropout_dim\": 3},\n    (1, 1, 32, 32, 32),\n    (1, 3, 32, 32, 32),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_vnet.py_TestVNet_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_vnet.py_TestVNet_", "embedding": null, "metadata": {"file_path": "tests/test_vnet.py", "file_name": "test_vnet.py", "file_type": "text/x-python", "category": "test", "start_line": 55, "end_line": 80, "span_ids": ["impl:15", "TestVNet", "TestVNet.test_vnet_shape", "TestVNet.test_script"], "tokens": 216}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestVNet(unittest.TestCase):\n    @parameterized.expand(\n        [\n            TEST_CASE_VNET_2D_1,\n            TEST_CASE_VNET_2D_2,\n            TEST_CASE_VNET_2D_3,\n            TEST_CASE_VNET_3D_1,\n            TEST_CASE_VNET_3D_2,\n            TEST_CASE_VNET_3D_3,\n        ]\n    )\n    def test_vnet_shape(self, input_param, input_shape, expected_shape):\n        net = VNet(**input_param).to(device)\n        with eval_mode(net):\n            result = net.forward(torch.randn(input_shape).to(device))\n            self.assertEqual(result.shape, expected_shape)\n\n    def test_script(self):\n        net = VNet(spatial_dims=3, in_channels=1, out_channels=3, dropout_dim=3)\n        test_data = torch.randn(1, 1, 32, 32, 32)\n        test_script_save(net, test_data)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_vote_ensembled.py_TestVoteEnsembled_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_vote_ensembled.py_TestVoteEnsembled_", "embedding": null, "metadata": {"file_path": "tests/test_vote_ensembled.py", "file_name": "test_vote_ensembled.py", "file_type": "text/x-python", "category": "test", "start_line": 86, "end_line": 106, "span_ids": ["TestVoteEnsembled.test_cuda_value", "TestVoteEnsembled", "impl:9", "TestVoteEnsembled.test_value"], "tokens": 200}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestVoteEnsembled(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_value(self, input_param, img, expected_value):\n        result = VoteEnsembled(**input_param)(img)\n        assert_allclose(result[\"output\"], expected_value)\n\n    def test_cuda_value(self):\n        img = torch.stack(\n            [torch.tensor([[[[1]], [[0]]]]), torch.tensor([[[[1]], [[0]]]]), torch.tensor([[[[0]], [[1]]]])]\n        )\n        expected_value = torch.tensor([[[[1.0]], [[0.0]]]])\n        if torch.cuda.is_available():\n            img = img.to(torch.device(\"cuda:0\"))\n            expected_value = expected_value.to(torch.device(\"cuda:0\"))\n        result = VoteEnsembled(keys=\"output\", num_classes=None)({\"output\": img})\n        assert_allclose(result[\"output\"], expected_value)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/utils.py_check_hash_check_hash.return.True": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/utils.py_check_hash_check_hash.return.True", "embedding": null, "metadata": {"file_path": "monai/apps/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 116, "end_line": 145, "span_ids": ["check_hash"], "tokens": 301}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def check_hash(filepath: PathLike, val: Optional[str] = None, hash_type: str = \"md5\") -> bool:\n    \"\"\"\n    Verify hash signature of specified file.\n\n    Args:\n        filepath: path of source file to verify hash value.\n        val: expected hash value of the file.\n        hash_type: type of hash algorithm to use, default is `\"md5\"`.\n            The supported hash types are `\"md5\"`, `\"sha1\"`, `\"sha256\"`, `\"sha512\"`.\n            See also: :py:data:`monai.apps.utils.SUPPORTED_HASH_TYPES`.\n\n    \"\"\"\n    if val is None:\n        logger.info(f\"Expected {hash_type} is None, skip {hash_type} check for file {filepath}.\")\n        return True\n    actual_hash_func = look_up_option(hash_type.lower(), SUPPORTED_HASH_TYPES)\n    actual_hash = actual_hash_func()\n    try:\n        with open(filepath, \"rb\") as f:\n            for chunk in iter(lambda: f.read(1024 * 1024), b\"\"):\n                actual_hash.update(chunk)\n    except Exception as e:\n        logger.error(f\"Exception in check_hash: {e}\")\n        return False\n    if val != actual_hash.hexdigest():\n        logger.error(f\"check_hash failed {actual_hash.hexdigest()}.\")\n        return False\n\n    logger.info(f\"Verified '{_basename(filepath)}', {hash_type}: {val}.\")\n    return True", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/config/deviceconfig.py_os_get_config_values.return.output": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/config/deviceconfig.py_os_get_config_values.return.output", "embedding": null, "metadata": {"file_path": "monai/config/deviceconfig.py", "file_name": "deviceconfig.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 55, "span_ids": ["get_config_values", "docstring"], "tokens": 255}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport platform\nimport re\nimport sys\nfrom collections import OrderedDict\n\nimport numpy as np\nimport torch\n\nimport monai\nfrom monai.utils.module import OptionalImportError, get_package_version, optional_import\n\ntry:\n    _, HAS_EXT = optional_import(\"monai._C\")\n    USE_COMPILED = HAS_EXT and os.getenv(\"BUILD_MONAI\", \"0\") == \"1\"\nexcept (OptionalImportError, ImportError, AttributeError):\n    HAS_EXT = USE_COMPILED = False\n\npsutil, has_psutil = optional_import(\"psutil\")\npsutil_version = psutil.__version__ if has_psutil else \"NOT INSTALLED or UNKNOWN VERSION.\"\n\n__all__ = [\n    \"print_config\",\n    \"get_system_info\",\n    \"print_system_info\",\n    \"get_gpu_info\",\n    \"print_gpu_info\",\n    \"print_debug_info\",\n    \"USE_COMPILED\",\n    \"IgniteInfo\",\n]\n\n\ndef get_config_values():\n    \"\"\"\n    Read the package versions into a dictionary.\n    \"\"\"\n    output = OrderedDict()\n\n    output[\"MONAI\"] = monai.__version__\n    output[\"Numpy\"] = np.version.full_version\n    output[\"Pytorch\"] = torch.__version__\n\n    return output", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/config/deviceconfig.py_get_optional_config_values_get_optional_config_values.return.output": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/config/deviceconfig.py_get_optional_config_values_get_optional_config_values.return.output", "embedding": null, "metadata": {"file_path": "monai/config/deviceconfig.py", "file_name": "deviceconfig.py", "file_type": "text/x-python", "category": "implementation", "start_line": 58, "end_line": 79, "span_ids": ["get_optional_config_values"], "tokens": 227}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_optional_config_values():\n    \"\"\"\n    Read the optional package versions into a dictionary.\n    \"\"\"\n    output = OrderedDict()\n\n    output[\"Pytorch Ignite\"] = get_package_version(\"ignite\")\n    output[\"Nibabel\"] = get_package_version(\"nibabel\")\n    output[\"scikit-image\"] = get_package_version(\"skimage\")\n    output[\"Pillow\"] = get_package_version(\"PIL\")\n    output[\"Tensorboard\"] = get_package_version(\"tensorboard\")\n    output[\"gdown\"] = get_package_version(\"gdown\")\n    output[\"TorchVision\"] = get_package_version(\"torchvision\")\n    output[\"tqdm\"] = get_package_version(\"tqdm\")\n    output[\"lmdb\"] = get_package_version(\"lmdb\")\n    output[\"psutil\"] = psutil_version\n    output[\"pandas\"] = get_package_version(\"pandas\")\n    output[\"einops\"] = get_package_version(\"einops\")\n    output[\"transformers\"] = get_package_version(\"transformers\")\n    output[\"mlflow\"] = get_package_version(\"mlflow\")\n\n    return output", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_SmartCacheDataset_SmartCacheDataset._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_SmartCacheDataset_SmartCacheDataset._", "embedding": null, "metadata": {"file_path": "monai/data/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 672, "end_line": 737, "span_ids": ["SmartCacheDataset"], "tokens": 1017}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SmartCacheDataset(Randomizable, CacheDataset):\n    \"\"\"\n    Re-implementation of the SmartCache mechanism in NVIDIA Clara-train SDK.\n    At any time, the cache pool only keeps a subset of the whole dataset. In each epoch, only the items\n    in the cache are used for training. This ensures that data needed for training is readily available,\n    keeping GPU resources busy. Note that cached items may still have to go through a non-deterministic\n    transform sequence before being fed to GPU. At the same time, another thread is preparing replacement\n    items by applying the transform sequence to items not in cache. Once one epoch is completed, Smart\n    Cache replaces the same number of items with replacement items.\n    Smart Cache uses a simple `running window` algorithm to determine the cache content and replacement items.\n    Let N be the configured number of objects in cache; and R be the number of replacement objects (R = ceil(N * r),\n    where r is the configured replace rate).\n    For more details, please refer to:\n    https://docs.nvidia.com/clara/tlt-mi/clara-train-sdk-v3.0/nvmidl/additional_features/smart_cache.html#smart-cache\n    If passing slicing indices, will return a PyTorch Subset, for example: `data: Subset = dataset[1:4]`,\n    for more details, please check: https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset\n\n    For example, if we have 5 images: `[image1, image2, image3, image4, image5]`, and `cache_num=4`, `replace_rate=0.25`.\n    so the actual training images cached and replaced for every epoch are as below::\n\n        epoch 1: [image1, image2, image3, image4]\n        epoch 2: [image2, image3, image4, image5]\n        epoch 3: [image3, image4, image5, image1]\n        epoch 3: [image4, image5, image1, image2]\n        epoch N: [image[N % 5] ...]\n\n    The usage of `SmartCacheDataset` contains 4 steps:\n\n        1. Initialize `SmartCacheDataset` object and cache for the first epoch.\n        2. Call `start()` to run replacement thread in background.\n        3. Call `update_cache()` before every epoch to replace training items.\n        4. Call `shutdown()` when training ends.\n\n    During training call `set_data()` to update input data and recompute cache content, note to call\n    `shutdown()` to stop first, then update data and call `start()` to restart.\n\n    Note:\n        This replacement will not work for below cases:\n        1. Set the `multiprocessing_context` of DataLoader to `spawn`.\n        2. Run on windows(the default multiprocessing method is `spawn`) with `num_workers` greater than 0.\n        3. Set the `persistent_workers` of DataLoader to `True` with `num_workers` greater than 0.\n\n        If using MONAI workflows, please add `SmartCacheHandler` to the handler list of trainer,\n        otherwise, please make sure to call `start()`, `update_cache()`, `shutdown()` during training.\n\n    Args:\n        data: input data to load and transform to generate dataset for model.\n        transform: transforms to execute operations on input data.\n        replace_rate: percentage of the cached items to be replaced in every epoch.\n        cache_num: number of items to be cached. Default is `sys.maxsize`.\n            will take the minimum of (cache_num, data_length x cache_rate, data_length).\n        cache_rate: percentage of cached data in total, default is 1.0 (cache all).\n            will take the minimum of (cache_num, data_length x cache_rate, data_length).\n        num_init_workers: the number of worker threads to initialize the cache for first epoch.\n            If num_init_workers is None then the number returned by os.cpu_count() is used.\n        num_replace_workers: the number of worker threads to prepare the replacement cache for every epoch.\n            If num_replace_workers is None then the number returned by os.cpu_count() is used.\n        progress: whether to display a progress bar when caching for the first epoch.\n        shuffle: whether to shuffle the whole data list before preparing the cache content for first epoch.\n            it will not modify the original input data sequence in-place.\n        seed: random seed if shuffle is `True`, default to `0`.\n        copy_cache: whether to `deepcopy` the cache content before applying the random transforms,\n            default to `True`. if the random transforms don't modify the cache content\n            or every cache item is only used once in a `multi-processing` environment,\n            may set `copy=False` for better performance.\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_SmartCacheDataset.__init___SmartCacheDataset._restart.self__replace_mgr_start_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_SmartCacheDataset.__init___SmartCacheDataset._restart.self__replace_mgr_start_", "embedding": null, "metadata": {"file_path": "monai/data/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 739, "end_line": 843, "span_ids": ["SmartCacheDataset.set_data", "SmartCacheDataset._compute_data_idx", "SmartCacheDataset.__init__", "SmartCacheDataset.randomize", "SmartCacheDataset.is_started", "SmartCacheDataset._restart", "SmartCacheDataset.start"], "tokens": 794}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SmartCacheDataset(Randomizable, CacheDataset):\n\n    def __init__(\n        self,\n        data: Sequence,\n        transform: Union[Sequence[Callable], Callable],\n        replace_rate: float,\n        cache_num: int = sys.maxsize,\n        cache_rate: float = 1.0,\n        num_init_workers: Optional[int] = None,\n        num_replace_workers: Optional[int] = None,\n        progress: bool = True,\n        shuffle: bool = True,\n        seed: int = 0,\n        copy_cache: bool = True,\n    ) -> None:\n        if shuffle:\n            self.set_random_state(seed=seed)\n            data = copy(data)\n            self.randomize(data)\n        self.shuffle = shuffle\n\n        super().__init__(data, transform, cache_num, cache_rate, num_init_workers, progress, copy_cache)\n        if self._cache is None:\n            self._cache = self._fill_cache()\n        if self.cache_num >= len(data):\n            warnings.warn(\n                \"cache_num is greater or equal than dataset length, fall back to regular monai.data.CacheDataset.\"\n            )\n        if replace_rate <= 0:\n            raise ValueError(\"replace_rate must be greater than 0, otherwise, please use monai.data.CacheDataset.\")\n\n        self.num_replace_workers: Optional[int] = num_replace_workers\n        if self.num_replace_workers is not None:\n            self.num_replace_workers = max(int(self.num_replace_workers), 1)\n\n        self._total_num: int = len(data)\n        self._replace_num: int = min(math.ceil(self.cache_num * replace_rate), len(data) - self.cache_num)\n        self._replacements: List[Any] = [None for _ in range(self._replace_num)]\n        self._replace_data_idx: List[int] = list(range(self._replace_num))\n\n        self._start_pos: int = 0\n        self._update_lock: threading.Lock = threading.Lock()\n        self._round: int = 1\n        self._replace_done: bool = False\n        self._replace_mgr: Optional[threading.Thread] = None\n\n        self._compute_data_idx()\n\n    def set_data(self, data: Sequence):\n        \"\"\"\n        Set the input data and run deterministic transforms to generate cache content.\n\n        Note: should call `shutdown()` before calling this func.\n\n        \"\"\"\n        if self.is_started():\n            warnings.warn(\"SmartCacheDataset is not shutdown yet, shutdown it directly.\")\n            self.shutdown()\n\n        if self.shuffle:\n            data = copy(data)\n            self.randomize(data)\n        super().set_data(data)\n\n    def randomize(self, data: Sequence) -> None:\n        try:\n            self.R.shuffle(data)\n        except TypeError as e:\n            warnings.warn(f\"input data can't be shuffled in SmartCacheDataset with numpy.random.shuffle(): {e}.\")\n\n    def _compute_data_idx(self):\n        \"\"\"\n        Update the replacement data position in the total data.\n\n        \"\"\"\n        for i in range(self._replace_num):\n            pos: int = self._start_pos + self.cache_num + i\n            if pos >= self._total_num:\n                pos -= self._total_num\n            self._replace_data_idx[i] = pos\n\n    def is_started(self):\n        \"\"\"\n        Check whether the replacement thread is already started.\n\n        \"\"\"\n        if self._replace_mgr is None:\n            return False\n        return self._replace_mgr.is_alive()\n\n    def start(self):\n        \"\"\"\n        Start the background thread to replace training items for every epoch.\n\n        \"\"\"\n        if self._replace_mgr is None or not self.is_started():\n            self._restart()\n\n    def _restart(self):\n        \"\"\"\n        Restart background thread if killed for some reason.\n\n        \"\"\"\n        self._round = 1\n        self._replace_mgr = threading.Thread(target=self.manage_replacement, daemon=True)\n        self._replace_mgr.start()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_SmartCacheDataset.update_cache_SmartCacheDataset.__len__.return.self_cache_num": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_SmartCacheDataset.update_cache_SmartCacheDataset.__len__.return.self_cache_num", "embedding": null, "metadata": {"file_path": "monai/data/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 868, "end_line": 958, "span_ids": ["SmartCacheDataset._replace_cache_thread", "SmartCacheDataset._compute_replacements", "SmartCacheDataset._try_shutdown", "SmartCacheDataset._try_manage_replacement", "SmartCacheDataset.shutdown", "SmartCacheDataset.__len__", "SmartCacheDataset.manage_replacement", "SmartCacheDataset.update_cache"], "tokens": 543}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SmartCacheDataset(Randomizable, CacheDataset):\n\n    def update_cache(self):\n        \"\"\"\n        Update cache items for current epoch, need to call this function before every epoch.\n        If the cache has been shutdown before, need to restart the `_replace_mgr` thread.\n\n        \"\"\"\n        if not self._replace_mgr.is_alive():\n            self._restart()\n\n        # make sure update is done\n        while not self._try_update_cache():\n            time.sleep(0.01)\n\n    def _try_shutdown(self):\n        \"\"\"\n        Wait for thread lock to shut down the background thread.\n\n        \"\"\"\n        with self._update_lock:\n            if self._replace_done:\n                self._round = 0\n                self._start_pos = 0\n                self._compute_data_idx()\n                self._replace_done = False\n                return True\n            return False\n\n    def shutdown(self):\n        \"\"\"\n        Shut down the background thread for replacement.\n\n        \"\"\"\n        if not self.is_started():\n            return\n\n        # wait until replace mgr is done the current round\n        while not self._try_shutdown():\n            time.sleep(0.01)\n        self._replace_mgr.join()\n\n    def _replace_cache_thread(self, index: int):\n        \"\"\"\n        Execute deterministic transforms on the new data for replacement.\n\n        \"\"\"\n        pos: int = self._replace_data_idx[index]\n        self._replacements[index] = self._load_cache_item(pos)\n\n    def _compute_replacements(self):\n        \"\"\"\n        Compute expected items for the replacement of next epoch, execute deterministic transforms.\n        It can support multi-threads to accelerate the computation progress.\n\n        \"\"\"\n        with ThreadPool(self.num_replace_workers) as p:\n            p.map(self._replace_cache_thread, list(range(self._replace_num)))\n\n        self._replace_done = True\n\n    def _try_manage_replacement(self, check_round):\n        \"\"\"\n        Wait thread lock and replace training items in the background thread.\n\n        \"\"\"\n        with self._update_lock:\n            if self._round <= 0:\n                # shutdown replacement\n                self._replace_done = True\n                return True, -1\n\n            if self._round != check_round:\n                self._compute_replacements()\n            return False, self._round\n\n    def manage_replacement(self):\n        \"\"\"\n        Background thread for replacement.\n\n        \"\"\"\n        check_round: int = -1\n        done = False\n        while not done:\n            done, check_round = self._try_manage_replacement(check_round)\n            time.sleep(0.01)\n\n    def __len__(self):\n        \"\"\"\n        The dataset length is given by cache_num instead of len(data).\n\n        \"\"\"\n        return self.cache_num", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/decathlon_datalist.py__append_paths__append_paths.return.items": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/decathlon_datalist.py__append_paths__append_paths.return.items", "embedding": null, "metadata": {"file_path": "monai/data/decathlon_datalist.py", "file_name": "decathlon_datalist.py", "file_type": "text/x-python", "category": "implementation", "start_line": 63, "end_line": 83, "span_ids": ["_append_paths"], "tokens": 213}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _append_paths(base_dir: PathLike, is_segmentation: bool, items: List[Dict]) -> List[Dict]:\n    \"\"\"\n    Args:\n        base_dir: the base directory of the dataset.\n        is_segmentation: whether the datalist is for segmentation task.\n        items: list of data items, each of which is a dict keyed by element names.\n\n    Raises:\n        TypeError: When ``items`` contains a non ``dict``.\n\n    \"\"\"\n    for item in items:\n        if not isinstance(item, dict):\n            raise TypeError(f\"Every item in items must be a dict but got {type(item).__name__}.\")\n        for k, v in item.items():\n            if k == \"image\" or is_segmentation and k == \"label\":\n                item[k] = _compute_path(base_dir, v, check_path=False)\n            else:\n                # for other items, auto detect whether it's a valid path\n                item[k] = _compute_path(base_dir, v, check_path=True)\n    return items", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/decathlon_datalist.py_load_decathlon_datalist_load_decathlon_datalist.return._append_paths_base_dir_i": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/decathlon_datalist.py_load_decathlon_datalist_load_decathlon_datalist.return._append_paths_base_dir_i", "embedding": null, "metadata": {"file_path": "monai/data/decathlon_datalist.py", "file_name": "decathlon_datalist.py", "file_type": "text/x-python", "category": "implementation", "start_line": 86, "end_line": 131, "span_ids": ["load_decathlon_datalist"], "tokens": 443}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def load_decathlon_datalist(\n    data_list_file_path: PathLike,\n    is_segmentation: bool = True,\n    data_list_key: str = \"training\",\n    base_dir: Optional[PathLike] = None,\n) -> List[Dict]:\n    \"\"\"Load image/label paths of decathlon challenge from JSON file\n\n    Json file is similar to what you get from http://medicaldecathlon.com/\n    Those dataset.json files\n\n    Args:\n        data_list_file_path: the path to the json file of datalist.\n        is_segmentation: whether the datalist is for segmentation task, default is True.\n        data_list_key: the key to get a list of dictionary to be used, default is \"training\".\n        base_dir: the base directory of the dataset, if None, use the datalist directory.\n\n    Raises:\n        ValueError: When ``data_list_file_path`` does not point to a file.\n        ValueError: When ``data_list_key`` is not specified in the data list file.\n\n    Returns a list of data items, each of which is a dict keyed by element names, for example:\n\n    .. code-block::\n\n        [\n            {'image': '/workspace/data/chest_19.nii.gz',  'label': 0},\n            {'image': '/workspace/data/chest_31.nii.gz',  'label': 1}\n        ]\n\n    \"\"\"\n    data_list_file_path = Path(data_list_file_path)\n    if not data_list_file_path.is_file():\n        raise ValueError(f\"Data list file {data_list_file_path} does not exist.\")\n    with open(data_list_file_path) as json_file:\n        json_data = json.load(json_file)\n    if data_list_key not in json_data:\n        raise ValueError(f'Data list {data_list_key} not specified in \"{data_list_file_path}\".')\n    expected_data = json_data[data_list_key]\n    if data_list_key == \"test\":\n        expected_data = [{\"image\": i} for i in expected_data]\n\n    if base_dir is None:\n        base_dir = data_list_file_path.parent\n\n    return _append_paths(base_dir, is_segmentation, expected_data)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/grid_dataset.py_PatchDataset_PatchDataset.__len__.return.len_self_data_self_sam": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/grid_dataset.py_PatchDataset_PatchDataset.__len__.return.len_self_data_self_sam", "embedding": null, "metadata": {"file_path": "monai/data/grid_dataset.py", "file_name": "grid_dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 168, "end_line": 228, "span_ids": ["PatchDataset.__init__", "PatchDataset.__len__", "PatchDataset"], "tokens": 522}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class PatchDataset(Dataset):\n    \"\"\"\n    returns a patch from an image dataset.\n    The patches are generated by a user-specified callable `patch_func`,\n    and are optionally post-processed by `transform`.\n    For example, to generate random patch samples from an image dataset:\n\n    .. code-block:: python\n\n        import numpy as np\n\n        from monai.data import PatchDataset, DataLoader\n        from monai.transforms import RandSpatialCropSamples, RandShiftIntensity\n\n        # image dataset\n        images = [np.arange(16, dtype=float).reshape(1, 4, 4),\n                  np.arange(16, dtype=float).reshape(1, 4, 4)]\n        # image patch sampler\n        n_samples = 5\n        sampler = RandSpatialCropSamples(roi_size=(3, 3), num_samples=n_samples,\n                                         random_center=True, random_size=False)\n        # patch-level intensity shifts\n        patch_intensity = RandShiftIntensity(offsets=1.0, prob=1.0)\n        # construct the patch dataset\n        ds = PatchDataset(dataset=images,\n                          patch_func=sampler,\n                          samples_per_image=n_samples,\n                          transform=patch_intensity)\n\n        # use the patch dataset, length: len(images) x samplers_per_image\n        print(len(ds))\n\n        >>> 10\n\n        for item in DataLoader(ds, batch_size=2, shuffle=True, num_workers=2):\n            print(item.shape)\n\n        >>> torch.Size([2, 1, 3, 3])\n\n    \"\"\"\n\n    def __init__(\n        self, dataset: Sequence, patch_func: Callable, samples_per_image: int = 1, transform: Optional[Callable] = None\n    ) -> None:\n        \"\"\"\n        Args:\n            dataset: an image dataset to extract patches from.\n            patch_func: converts an input image (item from dataset) into a sequence of image patches.\n                patch_func(dataset[idx]) must return a sequence of patches (length `samples_per_image`).\n            samples_per_image: `patch_func` should return a sequence of `samples_per_image` elements.\n            transform: transform applied to each patch.\n        \"\"\"\n        super().__init__(data=dataset, transform=transform)\n\n        self.patch_func = patch_func\n        if samples_per_image <= 0:\n            raise ValueError(\"sampler_per_image must be a positive integer.\")\n        self.samples_per_image = int(samples_per_image)\n\n    def __len__(self) -> int:\n        return len(self.data) * self.samples_per_image", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_ITKReader.read_ITKReader.read.return.img__if_len_filenames_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_ITKReader.read_ITKReader.read.return.img__if_len_filenames_", "embedding": null, "metadata": {"file_path": "monai/data/image_reader.py", "file_name": "image_reader.py", "file_type": "text/x-python", "category": "implementation", "start_line": 180, "end_line": 218, "span_ids": ["ITKReader.read"], "tokens": 457}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@require_pkg(pkg_name=\"itk\")\nclass ITKReader(ImageReader):\n\n    def read(self, data: Union[Sequence[PathLike], PathLike], **kwargs):\n        \"\"\"\n        Read image data from specified file or files, it can read a list of `no-channel` images\n        and stack them together as multi-channels data in `get_data()`.\n        If passing directory path instead of file path, will treat it as DICOM images series and read.\n        Note that the returned object is ITK image object or list of ITK image objects.\n\n        Args:\n            data: file name or a list of file names to read,\n            kwargs: additional args for `itk.imread` API, will override `self.kwargs` for existing keys.\n                More details about available args:\n                https://github.com/InsightSoftwareConsortium/ITK/blob/master/Wrapping/Generators/Python/itkExtras.py\n\n        \"\"\"\n        img_ = []\n\n        filenames: Sequence[PathLike] = ensure_tuple(data)\n        kwargs_ = self.kwargs.copy()\n        kwargs_.update(kwargs)\n        for name in filenames:\n            name = f\"{name}\"\n            if Path(name).is_dir():\n                # read DICOM series\n                # https://itk.org/ITKExamples/src/IO/GDCM/ReadDICOMSeriesAndWrite3DImage\n                names_generator = itk.GDCMSeriesFileNames.New()\n                names_generator.SetUseSeriesDetails(True)\n                names_generator.AddSeriesRestriction(\"0008|0021\")  # Series Date\n                names_generator.SetDirectory(name)\n                series_uid = names_generator.GetSeriesUIDs()\n\n                if len(series_uid) < 1:\n                    raise FileNotFoundError(f\"no DICOMs in: {name}.\")\n                if len(series_uid) > 1:\n                    warnings.warn(f\"the directory: {name} contains more than one DICOM series.\")\n                series_identifier = series_uid[0] if not self.series_name else self.series_name\n                name = names_generator.GetFileNames(series_identifier)\n\n            img_.append(itk.imread(name, **kwargs_))\n        return img_ if len(filenames) > 1 else img_[0]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_ITKReader._get_meta_dict_ITKReader._get_meta_dict.return.meta_dict": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_ITKReader._get_meta_dict_ITKReader._get_meta_dict.return.meta_dict", "embedding": null, "metadata": {"file_path": "monai/data/image_reader.py", "file_name": "image_reader.py", "file_type": "text/x-python", "category": "implementation", "start_line": 249, "end_line": 261, "span_ids": ["ITKReader._get_meta_dict"], "tokens": 123}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@require_pkg(pkg_name=\"itk\")\nclass ITKReader(ImageReader):\n\n    def _get_meta_dict(self, img) -> Dict:\n        \"\"\"\n        Get all the meta data of the image and convert to dict type.\n\n        Args:\n            img: an ITK image object loaded from an image file.\n\n        \"\"\"\n        img_meta_dict = img.GetMetaDataDictionary()\n        meta_dict = {key: img_meta_dict[key] for key in img_meta_dict.GetKeys() if not key.startswith(\"ITK_\")}\n\n        meta_dict[\"spacing\"] = np.asarray(img.GetSpacing())\n        return meta_dict", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_NibabelReader.read_NibabelReader.read.return.img__if_len_filenames_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_NibabelReader.read_NibabelReader.read.return.img__if_len_filenames_", "embedding": null, "metadata": {"file_path": "monai/data/image_reader.py", "file_name": "image_reader.py", "file_type": "text/x-python", "category": "implementation", "start_line": 364, "end_line": 386, "span_ids": ["NibabelReader.read"], "tokens": 259}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@require_pkg(pkg_name=\"nibabel\")\nclass NibabelReader(ImageReader):\n\n    def read(self, data: Union[Sequence[PathLike], PathLike], **kwargs):\n        \"\"\"\n        Read image data from specified file or files, it can read a list of `no-channel` images\n        and stack them together as multi-channels data in `get_data()`.\n        Note that the returned object is Nibabel image object or list of Nibabel image objects.\n\n        Args:\n            data: file name or a list of file names to read.\n            kwargs: additional args for `nibabel.load` API, will override `self.kwargs` for existing keys.\n                More details about available args:\n                https://github.com/nipy/nibabel/blob/master/nibabel/loadsave.py\n\n        \"\"\"\n        img_: List[Nifti1Image] = []\n\n        filenames: Sequence[PathLike] = ensure_tuple(data)\n        kwargs_ = self.kwargs.copy()\n        kwargs_.update(kwargs)\n        for name in filenames:\n            img = nib.load(name, **kwargs_)\n            img = correct_nifti_header_if_necessary(img)\n            img_.append(img)\n        return img_ if len(filenames) > 1 else img_[0]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_NumpyReader_NumpyReader.verify_suffix.return.is_supported_format_filen": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_NumpyReader_NumpyReader.verify_suffix.return.is_supported_format_filen", "embedding": null, "metadata": {"file_path": "monai/data/image_reader.py", "file_name": "image_reader.py", "file_type": "text/x-python", "category": "implementation", "start_line": 484, "end_line": 516, "span_ids": ["NumpyReader.__init__", "NumpyReader.verify_suffix", "NumpyReader"], "tokens": 346}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class NumpyReader(ImageReader):\n    \"\"\"\n    Load NPY or NPZ format data based on Numpy library, they can be arrays or pickled objects.\n    A typical usage is to load the `mask` data for classification task.\n    It can load part of the npz file with specified `npz_keys`.\n\n    Args:\n        npz_keys: if loading npz file, only load the specified keys, if None, load all the items.\n            stack the loaded items together to construct a new first dimension.\n        channel_dim: if not None, explicitly specify the channel dim, otherwise, treat the array as no channel.\n        kwargs: additional args for `numpy.load` API except `allow_pickle`. more details about available args:\n            https://numpy.org/doc/stable/reference/generated/numpy.load.html\n\n    \"\"\"\n\n    def __init__(self, npz_keys: Optional[KeysCollection] = None, channel_dim: Optional[int] = None, **kwargs):\n        super().__init__()\n        if npz_keys is not None:\n            npz_keys = ensure_tuple(npz_keys)\n        self.npz_keys = npz_keys\n        self.channel_dim = channel_dim\n        self.kwargs = kwargs\n\n    def verify_suffix(self, filename: Union[Sequence[PathLike], PathLike]) -> bool:\n        \"\"\"\n        Verify whether the specified file or files format is supported by Numpy reader.\n\n        Args:\n            filename: file name or a list of file names to read.\n                if a list of files, verify all the suffixes.\n        \"\"\"\n        suffixes: Sequence[str] = [\"npz\", \"npy\"]\n        return is_supported_format(filename, suffixes)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_NumpyReader.read_NumpyReader.read.return.img__if_len_img__1_els": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_NumpyReader.read_NumpyReader.read.return.img__if_len_img__1_els", "embedding": null, "metadata": {"file_path": "monai/data/image_reader.py", "file_name": "image_reader.py", "file_type": "text/x-python", "category": "implementation", "start_line": 518, "end_line": 546, "span_ids": ["NumpyReader.read"], "tokens": 304}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class NumpyReader(ImageReader):\n\n    def read(self, data: Union[Sequence[PathLike], PathLike], **kwargs):\n        \"\"\"\n        Read image data from specified file or files, it can read a list of `no-channel` data files\n        and stack them together as multi-channels data in `get_data()`.\n        Note that the returned object is Numpy array or list of Numpy arrays.\n\n        Args:\n            data: file name or a list of file names to read.\n            kwargs: additional args for `numpy.load` API except `allow_pickle`, will override `self.kwargs` for existing keys.\n                More details about available args:\n                https://numpy.org/doc/stable/reference/generated/numpy.load.html\n\n        \"\"\"\n        img_: List[Nifti1Image] = []\n\n        filenames: Sequence[PathLike] = ensure_tuple(data)\n        kwargs_ = self.kwargs.copy()\n        kwargs_.update(kwargs)\n        for name in filenames:\n            img = np.load(name, allow_pickle=True, **kwargs_)\n            if Path(name).name.endswith(\".npz\"):\n                # load expected items from NPZ file\n                npz_keys = [f\"arr_{i}\" for i in range(len(img))] if self.npz_keys is None else self.npz_keys\n                for k in npz_keys:\n                    img_.append(img[k])\n            else:\n                img_.append(img)\n\n        return img_ if len(img_) > 1 else img_[0]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_PILReader.read_PILReader.read.return.img__if_len_filenames_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_PILReader.read_PILReader.read.return.img__if_len_filenames_", "embedding": null, "metadata": {"file_path": "monai/data/image_reader.py", "file_name": "image_reader.py", "file_type": "text/x-python", "category": "implementation", "start_line": 608, "end_line": 632, "span_ids": ["PILReader.read"], "tokens": 257}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@require_pkg(pkg_name=\"PIL\")\nclass PILReader(ImageReader):\n\n    def read(self, data: Union[Sequence[PathLike], PathLike, np.ndarray], **kwargs):\n        \"\"\"\n        Read image data from specified file or files, it can read a list of `no-channel` images\n        and stack them together as multi-channels data in `get_data()`.\n        Note that the returned object is PIL image or list of PIL image.\n\n        Args:\n            data: file name or a list of file names to read.\n            kwargs: additional args for `Image.open` API in `read()`, will override `self.kwargs` for existing keys.\n                Mode details about available args:\n                https://pillow.readthedocs.io/en/stable/reference/Image.html#PIL.Image.open\n\n        \"\"\"\n        img_: List[PILImage.Image] = []\n\n        filenames: Sequence[PathLike] = ensure_tuple(data)\n        kwargs_ = self.kwargs.copy()\n        kwargs_.update(kwargs)\n        for name in filenames:\n            img = PILImage.open(name, **kwargs_)\n            if callable(self.converter):\n                img = self.converter(img)\n            img_.append(img)\n\n        return img_ if len(filenames) > 1 else img_[0]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_get_random_patch_get_random_patch.return.tuple_slice_mc_mc_ps_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_get_random_patch_get_random_patch.return.tuple_slice_mc_mc_ps_", "embedding": null, "metadata": {"file_path": "monai/data/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 38, "end_line": 60, "span_ids": ["get_random_patch"], "tokens": 268}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_random_patch(\n    dims: Sequence[int], patch_size: Sequence[int], rand_state: Optional[np.random.RandomState] = None\n) -> Tuple[slice, ...]:\n    \"\"\"\n    Returns a tuple of slices to define a random patch in an array of shape `dims` with size `patch_size` or the as\n    close to it as possible within the given dimension. It is expected that `patch_size` is a valid patch for a source\n    of shape `dims` as returned by `get_valid_patch_size`.\n\n    Args:\n        dims: shape of source array\n        patch_size: shape of patch size to generate\n        rand_state: a random state object to generate random numbers from\n\n    Returns:\n        (tuple of slice): a tuple of slice objects defining the patch\n    \"\"\"\n\n    # choose the minimal corner of the patch\n    rand_int = np.random.randint if rand_state is None else rand_state.randint\n    min_corner = tuple(rand_int(0, ms - ps + 1) if ms > ps else 0 for ms, ps in zip(dims, patch_size))\n\n    # create the slices for each dimension which define the patch in the source array\n    return tuple(slice(mc, mc + ps) for mc, ps in zip(min_corner, patch_size))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_compute_importance_map_compute_importance_map.return.importance_map": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_compute_importance_map_compute_importance_map.return.importance_map", "embedding": null, "metadata": {"file_path": "monai/data/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 742, "end_line": 794, "span_ids": ["compute_importance_map"], "tokens": 547}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def compute_importance_map(\n    patch_size: Tuple[int, ...],\n    mode: Union[BlendMode, str] = BlendMode.CONSTANT,\n    sigma_scale: Union[Sequence[float], float] = 0.125,\n    device: Union[torch.device, int, str] = \"cpu\",\n) -> torch.Tensor:\n    \"\"\"Get importance map for different weight modes.\n\n    Args:\n        patch_size: Size of the required importance map. This should be either H, W [,D].\n        mode: {``\"constant\"``, ``\"gaussian\"``}\n            How to blend output of overlapping windows. Defaults to ``\"constant\"``.\n\n            - ``\"constant``\": gives equal weight to all predictions.\n            - ``\"gaussian``\": gives less weight to predictions on edges of windows.\n\n        sigma_scale: Sigma_scale to calculate sigma for each dimension\n            (sigma = sigma_scale * dim_size). Used for gaussian mode only.\n        device: Device to put importance map on.\n\n    Raises:\n        ValueError: When ``mode`` is not one of [\"constant\", \"gaussian\"].\n\n    Returns:\n        Tensor of size patch_size.\n\n    \"\"\"\n    mode = look_up_option(mode, BlendMode)\n    device = torch.device(device)  # type: ignore[arg-type]\n    if mode == BlendMode.CONSTANT:\n        importance_map = torch.ones(patch_size, device=device).float()\n    elif mode == BlendMode.GAUSSIAN:\n        center_coords = [i // 2 for i in patch_size]\n        sigma_scale = ensure_tuple_rep(sigma_scale, len(patch_size))\n        sigmas = [i * sigma_s for i, sigma_s in zip(patch_size, sigma_scale)]\n\n        importance_map = torch.zeros(patch_size, device=device)\n        importance_map[tuple(center_coords)] = 1\n        pt_gaussian = GaussianFilter(len(patch_size), sigmas).to(device=device, dtype=torch.float)\n        importance_map = pt_gaussian(importance_map.unsqueeze(0).unsqueeze(0))\n        importance_map = importance_map.squeeze(0).squeeze(0)\n        importance_map = importance_map / torch.max(importance_map)\n        importance_map = importance_map.float()\n\n        # importance_map cannot be 0, otherwise we may end up with nans!\n        min_non_zero = importance_map[importance_map != 0].min().item()\n        importance_map = torch.clamp(importance_map, min=min_non_zero)\n    else:\n        raise ValueError(\n            f\"Unsupported mode: {mode}, available options are [{BlendMode.CONSTANT}, {BlendMode.CONSTANT}].\"\n        )\n\n    return importance_map", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/smartcache_handler.py_from_typing_import_TYPE_C_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/smartcache_handler.py_from_typing_import_TYPE_C_", "embedding": null, "metadata": {"file_path": "monai/handlers/smartcache_handler.py", "file_name": "smartcache_handler.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 80, "span_ids": ["SmartCacheHandler.__init__", "SmartCacheHandler.completed", "SmartCacheHandler.started", "SmartCacheHandler.epoch_completed", "SmartCacheHandler.attach", "docstring", "SmartCacheHandler"], "tokens": 517}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import TYPE_CHECKING\n\nfrom monai.config import IgniteInfo\nfrom monai.data import SmartCacheDataset\nfrom monai.utils import min_version, optional_import\n\nEvents, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Events\")\nif TYPE_CHECKING:\n    from ignite.engine import Engine\nelse:\n    Engine, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Engine\")\n\n\nclass SmartCacheHandler:\n    \"\"\"\n    Attach SmartCache logic to the engine in Ignite.\n    Mainly include the `start`, `update_cache`, and `shutdown` functions of SmartCacheDataset.\n\n    \"\"\"\n\n    def __init__(self, smartcacher: SmartCacheDataset) -> None:\n        \"\"\"\n        Args:\n            smartcacher: predefined SmartCacheDataset, will attach it to the engine.\n\n        Raises:\n            TypeError: When ``smartcacher`` is not a ``monai.data.SmartCacheDataset``.\n\n        \"\"\"\n        if not isinstance(smartcacher, SmartCacheDataset):\n            raise TypeError(\"smartcacher must be a monai.data.SmartCacheDataset.\")\n        self.smartcacher = smartcacher\n\n    def attach(self, engine: Engine) -> None:\n        \"\"\"\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n        \"\"\"\n        engine.add_event_handler(Events.STARTED, self.started)\n        engine.add_event_handler(Events.EPOCH_COMPLETED, self.epoch_completed)\n        engine.add_event_handler(Events.COMPLETED, self.completed)\n\n    def started(self, engine: Engine) -> None:\n        \"\"\"Callback for train or validation/evaluation started Event.\n        Start the replacement thread of SmartCacheDataset.\n\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n        \"\"\"\n        self.smartcacher.start()\n\n    def epoch_completed(self, engine: Engine) -> None:\n        \"\"\"Callback for train or validation/evaluation epoch completed Event.\n        Update cache content with replacement data.\n\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n        \"\"\"\n        self.smartcacher.update_cache()\n\n    def completed(self, engine: Engine) -> None:\n        \"\"\"Callback for train or validation/evaluation completed Event.\n        Stop the replacement thread of SmartCacheDataset.\n\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n        \"\"\"\n        self.smartcacher.shutdown()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/hausdorff_distance.py_compute_percent_hausdorff_distance_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/hausdorff_distance.py_compute_percent_hausdorff_distance_", "embedding": null, "metadata": {"file_path": "monai/metrics/hausdorff_distance.py", "file_name": "hausdorff_distance.py", "file_type": "text/x-python", "category": "implementation", "start_line": 171, "end_line": 190, "span_ids": ["compute_percent_hausdorff_distance"], "tokens": 161}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def compute_percent_hausdorff_distance(\n    edges_pred: np.ndarray, edges_gt: np.ndarray, distance_metric: str = \"euclidean\", percentile: Optional[float] = None\n):\n    \"\"\"\n    This function is used to compute the directed Hausdorff distance.\n    \"\"\"\n\n    surface_distance = get_surface_distance(edges_pred, edges_gt, distance_metric=distance_metric)\n\n    # for both pred and gt do not have foreground\n    if surface_distance.shape == (0,):\n        return np.nan\n\n    if not percentile:\n        return surface_distance.max()\n\n    if 0 <= percentile <= 100:\n        return np.percentile(surface_distance, percentile)\n    raise ValueError(f\"percentile should be a value between 0 and 100, get {percentile}.\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/utils.py_get_surface_distance_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/utils.py_get_surface_distance_", "embedding": null, "metadata": {"file_path": "monai/metrics/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 167, "end_line": 200, "span_ids": ["get_surface_distance"], "tokens": 337}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_surface_distance(seg_pred: np.ndarray, seg_gt: np.ndarray, distance_metric: str = \"euclidean\") -> np.ndarray:\n    \"\"\"\n    This function is used to compute the surface distances from `seg_pred` to `seg_gt`.\n\n    Args:\n        seg_pred: the edge of the predictions.\n        seg_gt: the edge of the ground truth.\n        distance_metric: : [``\"euclidean\"``, ``\"chessboard\"``, ``\"taxicab\"``]\n            the metric used to compute surface distance. Defaults to ``\"euclidean\"``.\n\n            - ``\"euclidean\"``, uses Exact Euclidean distance transform.\n            - ``\"chessboard\"``, uses `chessboard` metric in chamfer type of transform.\n            - ``\"taxicab\"``, uses `taxicab` metric in chamfer type of transform.\n\n    Note:\n        If seg_pred or seg_gt is all 0, may result in nan/inf distance.\n\n    \"\"\"\n\n    if not np.any(seg_gt):\n        dis = np.inf * np.ones_like(seg_gt)\n    else:\n        if not np.any(seg_pred):\n            dis = np.inf * np.ones_like(seg_gt)\n            return np.asarray(dis[seg_gt])\n        if distance_metric == \"euclidean\":\n            dis = distance_transform_edt(~seg_gt)\n        elif distance_metric in {\"chessboard\", \"taxicab\"}:\n            dis = distance_transform_cdt(~seg_gt, metric=distance_metric)\n        else:\n            raise ValueError(f\"distance_metric {distance_metric} is not implemented.\")\n\n    return np.asarray(dis[seg_pred])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/dynunet_block.py_UnetBasicBlock_UnetBasicBlock.forward.return.out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/dynunet_block.py_UnetBasicBlock_UnetBasicBlock.forward.return.out", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/dynunet_block.py", "file_name": "dynunet_block.py", "file_type": "text/x-python", "category": "implementation", "start_line": 92, "end_line": 145, "span_ids": ["UnetBasicBlock", "UnetBasicBlock.forward", "UnetBasicBlock.__init__"], "tokens": 483}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class UnetBasicBlock(nn.Module):\n    \"\"\"\n    A CNN module module that can be used for DynUNet, based on:\n    `Automated Design of Deep Learning Methods for Biomedical Image Segmentation <https://arxiv.org/abs/1904.08128>`_.\n    `nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation <https://arxiv.org/abs/1809.10486>`_.\n\n    Args:\n        spatial_dims: number of spatial dimensions.\n        in_channels: number of input channels.\n        out_channels: number of output channels.\n        kernel_size: convolution kernel size.\n        stride: convolution stride.\n        norm_name: feature normalization type and arguments.\n        act_name: activation layer type and arguments.\n        dropout: dropout probability.\n\n    \"\"\"\n\n    def __init__(\n        self,\n        spatial_dims: int,\n        in_channels: int,\n        out_channels: int,\n        kernel_size: Union[Sequence[int], int],\n        stride: Union[Sequence[int], int],\n        norm_name: Union[Tuple, str],\n        act_name: Union[Tuple, str] = (\"leakyrelu\", {\"inplace\": True, \"negative_slope\": 0.01}),\n        dropout: Optional[Union[Tuple, str, float]] = None,\n    ):\n        super().__init__()\n        self.conv1 = get_conv_layer(\n            spatial_dims,\n            in_channels,\n            out_channels,\n            kernel_size=kernel_size,\n            stride=stride,\n            dropout=dropout,\n            conv_only=True,\n        )\n        self.conv2 = get_conv_layer(\n            spatial_dims, out_channels, out_channels, kernel_size=kernel_size, stride=1, dropout=dropout, conv_only=True\n        )\n        self.lrelu = get_act_layer(name=act_name)\n        self.norm1 = get_norm_layer(name=norm_name, spatial_dims=spatial_dims, channels=out_channels)\n        self.norm2 = get_norm_layer(name=norm_name, spatial_dims=spatial_dims, channels=out_channels)\n\n    def forward(self, inp):\n        out = self.conv1(inp)\n        out = self.norm1(out)\n        out = self.lrelu(out)\n        out = self.conv2(out)\n        out = self.norm2(out)\n        out = self.lrelu(out)\n        return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/dynunet_block.py_UnetUpBlock_UnetUpBlock.forward.return.out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/dynunet_block.py_UnetUpBlock_UnetUpBlock.forward.return.out", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/dynunet_block.py", "file_name": "dynunet_block.py", "file_type": "text/x-python", "category": "implementation", "start_line": 148, "end_line": 210, "span_ids": ["UnetUpBlock.__init__", "UnetUpBlock.forward", "UnetUpBlock"], "tokens": 511}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class UnetUpBlock(nn.Module):\n    \"\"\"\n    An upsampling module that can be used for DynUNet, based on:\n    `Automated Design of Deep Learning Methods for Biomedical Image Segmentation <https://arxiv.org/abs/1904.08128>`_.\n    `nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation <https://arxiv.org/abs/1809.10486>`_.\n\n    Args:\n        spatial_dims: number of spatial dimensions.\n        in_channels: number of input channels.\n        out_channels: number of output channels.\n        kernel_size: convolution kernel size.\n        stride: convolution stride.\n        upsample_kernel_size: convolution kernel size for transposed convolution layers.\n        norm_name: feature normalization type and arguments.\n        act_name: activation layer type and arguments.\n        dropout: dropout probability.\n        trans_bias: transposed convolution bias.\n\n    \"\"\"\n\n    def __init__(\n        self,\n        spatial_dims: int,\n        in_channels: int,\n        out_channels: int,\n        kernel_size: Union[Sequence[int], int],\n        stride: Union[Sequence[int], int],\n        upsample_kernel_size: Union[Sequence[int], int],\n        norm_name: Union[Tuple, str],\n        act_name: Union[Tuple, str] = (\"leakyrelu\", {\"inplace\": True, \"negative_slope\": 0.01}),\n        dropout: Optional[Union[Tuple, str, float]] = None,\n        trans_bias: bool = False,\n    ):\n        super().__init__()\n        upsample_stride = upsample_kernel_size\n        self.transp_conv = get_conv_layer(\n            spatial_dims,\n            in_channels,\n            out_channels,\n            kernel_size=upsample_kernel_size,\n            stride=upsample_stride,\n            dropout=dropout,\n            bias=trans_bias,\n            conv_only=True,\n            is_transposed=True,\n        )\n        self.conv_block = UnetBasicBlock(\n            spatial_dims,\n            out_channels + out_channels,\n            out_channels,\n            kernel_size=kernel_size,\n            stride=1,\n            dropout=dropout,\n            norm_name=norm_name,\n            act_name=act_name,\n        )\n\n    def forward(self, inp, skip):\n        # number of channels for skip should equals to out_channels\n        out = self.transp_conv(inp)\n        out = torch.cat((out, skip), dim=1)\n        out = self.conv_block(out)\n        return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/dynunet_block.py_get_padding_get_padding.return.padding_if_len_padding_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/dynunet_block.py_get_padding_get_padding.return.padding_if_len_padding_", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/dynunet_block.py", "file_name": "dynunet_block.py", "file_type": "text/x-python", "category": "implementation", "start_line": 250, "end_line": 261, "span_ids": ["get_padding"], "tokens": 140}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_padding(\n    kernel_size: Union[Sequence[int], int], stride: Union[Sequence[int], int]\n) -> Union[Tuple[int, ...], int]:\n\n    kernel_size_np = np.atleast_1d(kernel_size)\n    stride_np = np.atleast_1d(stride)\n    padding_np = (kernel_size_np - stride_np + 1) / 2\n    if np.min(padding_np) < 0:\n        raise AssertionError(\"padding value should not be negative, please change the kernel size and/or stride.\")\n    padding = tuple(int(p) for p in padding_np)\n\n    return padding if len(padding) > 1 else padding[0]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/dynunet_block.py_get_output_padding_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/dynunet_block.py_get_output_padding_", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/dynunet_block.py", "file_name": "dynunet_block.py", "file_type": "text/x-python", "category": "implementation", "start_line": 264, "end_line": 277, "span_ids": ["get_output_padding"], "tokens": 168}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_output_padding(\n    kernel_size: Union[Sequence[int], int], stride: Union[Sequence[int], int], padding: Union[Sequence[int], int]\n) -> Union[Tuple[int, ...], int]:\n    kernel_size_np = np.atleast_1d(kernel_size)\n    stride_np = np.atleast_1d(stride)\n    padding_np = np.atleast_1d(padding)\n\n    out_padding_np = 2 * padding_np + stride_np - kernel_size_np\n    if np.min(out_padding_np) < 0:\n        raise AssertionError(\"out_padding value should not be negative, please change the kernel size and/or stride.\")\n    out_padding = tuple(int(p) for p in out_padding_np)\n\n    return out_padding if len(out_padding) > 1 else out_padding[0]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/upsample.py_from_typing_import_Option_UpSample._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/upsample.py_from_typing_import_Option_UpSample._", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/upsample.py", "file_name": "upsample.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 35, "span_ids": ["UpSample", "docstring"], "tokens": 212}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Optional, Sequence, Tuple, Union\n\nimport torch\nimport torch.nn as nn\n\nfrom monai.networks.layers.factories import Conv, Pad, Pool\nfrom monai.networks.utils import icnr_init, pixelshuffle\nfrom monai.utils import InterpolateMode, UpsampleMode, deprecated_arg, ensure_tuple_rep, look_up_option\n\n__all__ = [\"Upsample\", \"UpSample\", \"SubpixelUpsample\", \"Subpixelupsample\", \"SubpixelUpSample\"]\n\n\nclass UpSample(nn.Sequential):\n    \"\"\"\n    Upsamples data by `scale_factor`.\n    Supported modes are:\n\n        - \"deconv\": uses a transposed convolution.\n        - \"nontrainable\": uses :py:class:`torch.nn.Upsample`.\n        - \"pixelshuffle\": uses :py:class:`monai.networks.blocks.SubpixelUpsample`.\n\n    This module can optionally take a pre-convolution\n    (often used to map the number of features from `in_channels` to `out_channels`).\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/upsample.py_UpSample.__init___UpSample.__init__.if_up_mode_UpsampleMod.else_.raise_NotImplementedError": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/upsample.py_UpSample.__init___UpSample.__init__.if_up_mode_UpsampleMod.else_.raise_NotImplementedError", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/upsample.py", "file_name": "upsample.py", "file_type": "text/x-python", "category": "implementation", "start_line": 37, "end_line": 148, "span_ids": ["UpSample.__init__"], "tokens": 1260}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class UpSample(nn.Sequential):\n\n    @deprecated_arg(\n        name=\"dimensions\", new_name=\"spatial_dims\", since=\"0.6\", msg_suffix=\"Please use `spatial_dims` instead.\"\n    )\n    def __init__(\n        self,\n        spatial_dims: int,\n        in_channels: Optional[int] = None,\n        out_channels: Optional[int] = None,\n        scale_factor: Union[Sequence[float], float] = 2,\n        size: Optional[Union[Tuple[int], int]] = None,\n        mode: Union[UpsampleMode, str] = UpsampleMode.DECONV,\n        pre_conv: Optional[Union[nn.Module, str]] = \"default\",\n        interp_mode: Union[InterpolateMode, str] = InterpolateMode.LINEAR,\n        align_corners: Optional[bool] = True,\n        bias: bool = True,\n        apply_pad_pool: bool = True,\n        dimensions: Optional[int] = None,\n    ) -> None:\n        \"\"\"\n        Args:\n            spatial_dims: number of spatial dimensions of the input image.\n            in_channels: number of channels of the input image.\n            out_channels: number of channels of the output image. Defaults to `in_channels`.\n            scale_factor: multiplier for spatial size. Has to match input size if it is a tuple. Defaults to 2.\n            size: spatial size of the output image.\n                Only used when ``mode`` is ``UpsampleMode.NONTRAINABLE``.\n                In torch.nn.functional.interpolate, only one of `size` or `scale_factor` should be defined,\n                thus if size is defined, `scale_factor` will not be used.\n                Defaults to None.\n            mode: {``\"deconv\"``, ``\"nontrainable\"``, ``\"pixelshuffle\"``}. Defaults to ``\"deconv\"``.\n            pre_conv: a conv block applied before upsampling. Defaults to \"default\".\n                When ``conv_block`` is ``\"default\"``, one reserved conv layer will be utilized when\n                Only used in the \"nontrainable\" or \"pixelshuffle\" mode.\n            interp_mode: {``\"nearest\"``, ``\"linear\"``, ``\"bilinear\"``, ``\"bicubic\"``, ``\"trilinear\"``}\n                Only used in the \"nontrainable\" mode.\n                If ends with ``\"linear\"`` will use ``spatial dims`` to determine the correct interpolation.\n                This corresponds to linear, bilinear, trilinear for 1D, 2D, and 3D respectively.\n                The interpolation mode. Defaults to ``\"linear\"``.\n                See also: https://pytorch.org/docs/stable/nn.html#upsample\n            align_corners: set the align_corners parameter of `torch.nn.Upsample`. Defaults to True.\n                Only used in the \"nontrainable\" mode.\n            bias: whether to have a bias term in the default preconv and deconv layers. Defaults to True.\n            apply_pad_pool: if True the upsampled tensor is padded then average pooling is applied with a kernel the\n                size of `scale_factor` with a stride of 1. See also: :py:class:`monai.networks.blocks.SubpixelUpsample`.\n                Only used in the \"pixelshuffle\" mode.\n\n        .. deprecated:: 0.6.0\n            ``dimensions`` is deprecated, use ``spatial_dims`` instead.\n        \"\"\"\n        super().__init__()\n        if dimensions is not None:\n            spatial_dims = dimensions\n        scale_factor_ = ensure_tuple_rep(scale_factor, spatial_dims)\n        up_mode = look_up_option(mode, UpsampleMode)\n        if up_mode == UpsampleMode.DECONV:\n            if not in_channels:\n                raise ValueError(f\"in_channels needs to be specified in the '{mode}' mode.\")\n            self.add_module(\n                \"deconv\",\n                Conv[Conv.CONVTRANS, spatial_dims](\n                    in_channels=in_channels,\n                    out_channels=out_channels or in_channels,\n                    kernel_size=scale_factor_,\n                    stride=scale_factor_,\n                    bias=bias,\n                ),\n            )\n        elif up_mode == UpsampleMode.NONTRAINABLE:\n            if pre_conv == \"default\" and (out_channels != in_channels):  # defaults to no conv if out_chns==in_chns\n                if not in_channels:\n                    raise ValueError(f\"in_channels needs to be specified in the '{mode}' mode.\")\n                self.add_module(\n                    \"preconv\",\n                    Conv[Conv.CONV, spatial_dims](\n                        in_channels=in_channels, out_channels=out_channels or in_channels, kernel_size=1, bias=bias\n                    ),\n                )\n            elif pre_conv is not None and pre_conv != \"default\":\n                self.add_module(\"preconv\", pre_conv)  # type: ignore\n            elif pre_conv is None and (out_channels != in_channels):\n                raise ValueError(\n                    \"in the nontrainable mode, if not setting pre_conv, out_channels should equal to in_channels.\"\n                )\n\n            interp_mode = InterpolateMode(interp_mode)\n            linear_mode = [InterpolateMode.LINEAR, InterpolateMode.BILINEAR, InterpolateMode.TRILINEAR]\n            if interp_mode in linear_mode:  # choose mode based on dimensions\n                interp_mode = linear_mode[spatial_dims - 1]\n            self.add_module(\n                \"upsample_non_trainable\",\n                nn.Upsample(\n                    size=size,\n                    scale_factor=None if size else scale_factor_,\n                    mode=interp_mode.value,\n                    align_corners=align_corners,\n                ),\n            )\n        elif up_mode == UpsampleMode.PIXELSHUFFLE:\n            self.add_module(\n                \"pixelshuffle\",\n                SubpixelUpsample(\n                    spatial_dims=spatial_dims,\n                    in_channels=in_channels,\n                    out_channels=out_channels,\n                    scale_factor=scale_factor_[0],  # isotropic\n                    conv_block=pre_conv,\n                    apply_pad_pool=apply_pad_pool,\n                    bias=bias,\n                ),\n            )\n        else:\n            raise NotImplementedError(f\"Unsupported upsampling mode {mode}.\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/upsample.py_SubpixelUpsample_SubpixelUpsample._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/upsample.py_SubpixelUpsample_SubpixelUpsample._", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/upsample.py", "file_name": "upsample.py", "file_type": "text/x-python", "category": "implementation", "start_line": 151, "end_line": 174, "span_ids": ["SubpixelUpsample"], "tokens": 264}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SubpixelUpsample(nn.Module):\n    \"\"\"\n    Upsample via using a subpixel CNN. This module supports 1D, 2D and 3D input images.\n    The module is consisted with two parts. First of all, a convolutional layer is employed\n    to increase the number of channels into: ``in_channels * (scale_factor ** dimensions)``.\n    Secondly, a pixel shuffle manipulation is utilized to aggregates the feature maps from\n    low resolution space and build the super resolution space.\n    The first part of the module is not fixed, a sequential layers can be used to replace the\n    default single layer.\n\n    See: Shi et al., 2016, \"Real-Time Single Image and Video Super-Resolution\n    Using a nEfficient Sub-Pixel Convolutional Neural Network.\"\n\n    See: Aitken et al., 2017, \"Checkerboard artifact free sub-pixel convolution\".\n\n    The idea comes from:\n    https://arxiv.org/abs/1609.05158\n\n    The pixel shuffle mechanism refers to:\n    https://pytorch.org/docs/stable/generated/torch.nn.PixelShuffle.html#torch.nn.PixelShuffle.\n    and:\n    https://github.com/pytorch/pytorch/pull/6340.\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/upsample.py_SubpixelUpsample.__init___SubpixelUpsample.__init__.if_apply_pad_pool_.self.pad_pool.nn_Sequential_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/upsample.py_SubpixelUpsample.__init___SubpixelUpsample.__init__.if_apply_pad_pool_.self.pad_pool.nn_Sequential_", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/upsample.py", "file_name": "upsample.py", "file_type": "text/x-python", "category": "implementation", "start_line": 176, "end_line": 242, "span_ids": ["SubpixelUpsample.__init__"], "tokens": 655}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SubpixelUpsample(nn.Module):\n\n    @deprecated_arg(\n        name=\"dimensions\", new_name=\"spatial_dims\", since=\"0.6\", msg_suffix=\"Please use `spatial_dims` instead.\"\n    )\n    def __init__(\n        self,\n        spatial_dims: int,\n        in_channels: Optional[int],\n        out_channels: Optional[int] = None,\n        scale_factor: int = 2,\n        conv_block: Optional[Union[nn.Module, str]] = \"default\",\n        apply_pad_pool: bool = True,\n        bias: bool = True,\n        dimensions: Optional[int] = None,\n    ) -> None:\n        \"\"\"\n        Args:\n            spatial_dims: number of spatial dimensions of the input image.\n            in_channels: number of channels of the input image.\n            out_channels: optional number of channels of the output image.\n            scale_factor: multiplier for spatial size. Defaults to 2.\n            conv_block: a conv block to extract feature maps before upsampling. Defaults to None.\n\n                - When ``conv_block`` is ``\"default\"``, one reserved conv layer will be utilized.\n                - When ``conv_block`` is an ``nn.module``,\n                  please ensure the output number of channels is divisible ``(scale_factor ** dimensions)``.\n\n            apply_pad_pool: if True the upsampled tensor is padded then average pooling is applied with a kernel the\n                size of `scale_factor` with a stride of 1. This implements the nearest neighbour resize convolution\n                component of subpixel convolutions described in Aitken et al.\n            bias: whether to have a bias term in the default conv_block. Defaults to True.\n\n        .. deprecated:: 0.6.0\n            ``dimensions`` is deprecated, use ``spatial_dims`` instead.\n        \"\"\"\n        super().__init__()\n\n        if scale_factor <= 0:\n            raise ValueError(f\"The `scale_factor` multiplier must be an integer greater than 0, got {scale_factor}.\")\n\n        self.dimensions = spatial_dims if dimensions is None else dimensions\n        self.scale_factor = scale_factor\n\n        if conv_block == \"default\":\n            out_channels = out_channels or in_channels\n            if not out_channels:\n                raise ValueError(\"in_channels need to be specified.\")\n            conv_out_channels = out_channels * (scale_factor ** self.dimensions)\n            self.conv_block = Conv[Conv.CONV, self.dimensions](\n                in_channels=in_channels, out_channels=conv_out_channels, kernel_size=3, stride=1, padding=1, bias=bias\n            )\n\n            icnr_init(self.conv_block, self.scale_factor)\n        elif conv_block is None:\n            self.conv_block = nn.Identity()\n        else:\n            self.conv_block = conv_block\n\n        self.pad_pool: nn.Module = nn.Identity()\n\n        if apply_pad_pool:\n            pool_type = Pool[Pool.AVG, self.dimensions]\n            pad_type = Pad[Pad.CONSTANTPAD, self.dimensions]\n\n            self.pad_pool = nn.Sequential(\n                pad_type(padding=(self.scale_factor - 1, 0) * self.dimensions, value=0.0),\n                pool_type(kernel_size=self.scale_factor, stride=1),\n            )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/upsample.py_SubpixelUpsample.forward_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/upsample.py_SubpixelUpsample.forward_", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/upsample.py", "file_name": "upsample.py", "file_type": "text/x-python", "category": "implementation", "start_line": 213, "end_line": 232, "span_ids": ["SubpixelUpsample.forward", "impl:3"], "tokens": 183}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SubpixelUpsample(nn.Module):\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        \"\"\"\n        Args:\n            x: Tensor in shape (batch, channel, spatial_1[, spatial_2, ...).\n        \"\"\"\n        x = self.conv_block(x)\n        if x.shape[1] % (self.scale_factor ** self.dimensions) != 0:\n            raise ValueError(\n                f\"Number of channels after `conv_block` ({x.shape[1]}) must be evenly \"\n                \"divisible by scale_factor ** dimensions \"\n                f\"({self.scale_factor}^{self.dimensions}={self.scale_factor**self.dimensions}).\"\n            )\n        x = pixelshuffle(x, self.dimensions, self.scale_factor)\n        x = self.pad_pool(x)\n        return x\n\n\nUpsample = UpSample\nSubpixelupsample = SubpixelUpSample = SubpixelUpsample", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/convutils.py_from_typing_import_List__same_padding.return.padding_if_len_padding_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/convutils.py_from_typing_import_List__same_padding.return.padding_if_len_padding_", "embedding": null, "metadata": {"file_path": "monai/networks/layers/convutils.py", "file_name": "convutils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 43, "span_ids": ["same_padding", "docstring"], "tokens": 295}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import List, Optional, Sequence, Tuple, Union\n\nimport numpy as np\nimport torch\n\n__all__ = [\"same_padding\", \"stride_minus_kernel_padding\", \"calculate_out_shape\", \"gaussian_1d\", \"polyval\"]\n\n\ndef same_padding(\n    kernel_size: Union[Sequence[int], int], dilation: Union[Sequence[int], int] = 1\n) -> Union[Tuple[int, ...], int]:\n    \"\"\"\n    Return the padding value needed to ensure a convolution using the given kernel size produces an output of the same\n    shape as the input for a stride of 1, otherwise ensure a shape of the input divided by the stride rounded down.\n\n    Raises:\n        NotImplementedError: When ``np.any((kernel_size - 1) * dilation % 2 == 1)``.\n\n    \"\"\"\n\n    kernel_size_np = np.atleast_1d(kernel_size)\n    dilation_np = np.atleast_1d(dilation)\n\n    if np.any((kernel_size_np - 1) * dilation % 2 == 1):\n        raise NotImplementedError(\n            f\"Same padding not available for kernel_size={kernel_size_np} and dilation={dilation_np}.\"\n        )\n\n    padding_np = (kernel_size_np - 1) / 2 * dilation_np\n    padding = tuple(int(p) for p in padding_np)\n\n    return padding if len(padding) > 1 else padding[0]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/convutils.py_stride_minus_kernel_padding_stride_minus_kernel_padding.return.out_padding_if_len_out_pa": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/convutils.py_stride_minus_kernel_padding_stride_minus_kernel_padding.return.out_padding_if_len_out_pa", "embedding": null, "metadata": {"file_path": "monai/networks/layers/convutils.py", "file_name": "convutils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 46, "end_line": 55, "span_ids": ["stride_minus_kernel_padding"], "tokens": 109}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def stride_minus_kernel_padding(\n    kernel_size: Union[Sequence[int], int], stride: Union[Sequence[int], int]\n) -> Union[Tuple[int, ...], int]:\n    kernel_size_np = np.atleast_1d(kernel_size)\n    stride_np = np.atleast_1d(stride)\n\n    out_padding_np = stride_np - kernel_size_np\n    out_padding = tuple(int(p) for p in out_padding_np)\n\n    return out_padding if len(out_padding) > 1 else out_padding[0]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/convutils.py_gaussian_1d_gaussian_1d._type_ignore": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/convutils.py_gaussian_1d_gaussian_1d._type_ignore", "embedding": null, "metadata": {"file_path": "monai/networks/layers/convutils.py", "file_name": "convutils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 81, "end_line": 134, "span_ids": ["gaussian_1d"], "tokens": 608}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def gaussian_1d(\n    sigma: torch.Tensor, truncated: float = 4.0, approx: str = \"erf\", normalize: bool = False\n) -> torch.Tensor:\n    \"\"\"\n    one dimensional Gaussian kernel.\n\n    Args:\n        sigma: std of the kernel\n        truncated: tail length\n        approx: discrete Gaussian kernel type, available options are \"erf\", \"sampled\", and \"scalespace\".\n\n            - ``erf`` approximation interpolates the error function;\n            - ``sampled`` uses a sampled Gaussian kernel;\n            - ``scalespace`` corresponds to\n              https://en.wikipedia.org/wiki/Scale_space_implementation#The_discrete_Gaussian_kernel\n              based on the modified Bessel functions.\n\n        normalize: whether to normalize the kernel with `kernel.sum()`.\n\n    Raises:\n        ValueError: When ``truncated`` is non-positive.\n\n    Returns:\n        1D torch tensor\n\n    \"\"\"\n    sigma = torch.as_tensor(sigma, dtype=torch.float, device=sigma.device if isinstance(sigma, torch.Tensor) else None)\n    device = sigma.device\n    if truncated <= 0.0:\n        raise ValueError(f\"truncated must be positive, got {truncated}.\")\n    tail = int(max(float(sigma) * truncated, 0.5) + 0.5)\n    if approx.lower() == \"erf\":\n        x = torch.arange(-tail, tail + 1, dtype=torch.float, device=device)\n        t = 0.70710678 / torch.abs(sigma)\n        out = 0.5 * ((t * (x + 0.5)).erf() - (t * (x - 0.5)).erf())\n        out = out.clamp(min=0)\n    elif approx.lower() == \"sampled\":\n        x = torch.arange(-tail, tail + 1, dtype=torch.float, device=sigma.device)\n        out = torch.exp(-0.5 / (sigma * sigma) * x ** 2)\n        if not normalize:  # compute the normalizer\n            out = out / (2.5066282 * sigma)\n    elif approx.lower() == \"scalespace\":\n        sigma2 = sigma * sigma\n        out_pos: List[Optional[torch.Tensor]] = [None] * (tail + 1)\n        out_pos[0] = _modified_bessel_0(sigma2)\n        out_pos[1] = _modified_bessel_1(sigma2)\n        for k in range(2, len(out_pos)):\n            out_pos[k] = _modified_bessel_i(k, sigma2)\n        out = out_pos[:0:-1]\n        out.extend(out_pos)\n        out = torch.stack(out) * torch.exp(-sigma2)\n    else:\n        raise NotImplementedError(f\"Unsupported option: approx='{approx}'.\")\n    return out / out.sum() if normalize else out  # type: ignore", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/convutils.py_polyval_polyval._type_ignore": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/convutils.py_polyval_polyval._type_ignore", "embedding": null, "metadata": {"file_path": "monai/networks/layers/convutils.py", "file_name": "convutils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 137, "end_line": 160, "span_ids": ["polyval"], "tokens": 224}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def polyval(coef, x) -> torch.Tensor:\n    \"\"\"\n    Evaluates the polynomial defined by `coef` at `x`.\n\n    For a 1D sequence of coef (length n), evaluate::\n\n        y = coef[n-1] + x * (coef[n-2] + ... + x * (coef[1] + x * coef[0]))\n\n    Args:\n        coef: a sequence of floats representing the coefficients of the polynomial\n        x: float or a sequence of floats representing the variable of the polynomial\n\n    Returns:\n        1D torch tensor\n    \"\"\"\n    device = x.device if isinstance(x, torch.Tensor) else None\n    coef = torch.as_tensor(coef, dtype=torch.float, device=device)\n    if coef.ndim == 0 or (len(coef) < 1):\n        return torch.zeros(x.shape)\n    x = torch.as_tensor(x, dtype=torch.float, device=device)\n    ans = coef[0]\n    for c in coef[1:]:\n        ans = ans * x + c\n    return ans  # type: ignore", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/convutils.py__modified_bessel_0__modified_bessel_0.return.polyval__coef_y_torch": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/convutils.py__modified_bessel_0__modified_bessel_0.return.polyval__coef_y_torch", "embedding": null, "metadata": {"file_path": "monai/networks/layers/convutils.py", "file_name": "convutils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 163, "end_line": 181, "span_ids": ["_modified_bessel_0"], "tokens": 258}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _modified_bessel_0(x: torch.Tensor) -> torch.Tensor:\n    x = torch.as_tensor(x, dtype=torch.float, device=x.device if isinstance(x, torch.Tensor) else None)\n    if torch.abs(x) < 3.75:\n        y = x * x / 14.0625\n        return polyval([0.45813e-2, 0.360768e-1, 0.2659732, 1.2067492, 3.0899424, 3.5156229, 1.0], y)\n    ax = torch.abs(x)\n    y = 3.75 / ax\n    _coef = [\n        0.392377e-2,\n        -0.1647633e-1,\n        0.2635537e-1,\n        -0.2057706e-1,\n        0.916281e-2,\n        -0.157565e-2,\n        0.225319e-2,\n        0.1328592e-1,\n        0.39894228,\n    ]\n    return polyval(_coef, y) * torch.exp(ax) / torch.sqrt(ax)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/convutils.py__modified_bessel_1__modified_bessel_1.return._ans_if_x_0_0_else_ans": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/convutils.py__modified_bessel_1__modified_bessel_1.return._ans_if_x_0_0_else_ans", "embedding": null, "metadata": {"file_path": "monai/networks/layers/convutils.py", "file_name": "convutils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 184, "end_line": 204, "span_ids": ["_modified_bessel_1"], "tokens": 288}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _modified_bessel_1(x: torch.Tensor) -> torch.Tensor:\n    x = torch.as_tensor(x, dtype=torch.float, device=x.device if isinstance(x, torch.Tensor) else None)\n    if torch.abs(x) < 3.75:\n        y = x * x / 14.0625\n        _coef = [0.32411e-3, 0.301532e-2, 0.2658733e-1, 0.15084934, 0.51498869, 0.87890594, 0.5]\n        return torch.abs(x) * polyval(_coef, y)\n    ax = torch.abs(x)\n    y = 3.75 / ax\n    _coef = [\n        -0.420059e-2,\n        0.1787654e-1,\n        -0.2895312e-1,\n        0.2282967e-1,\n        -0.1031555e-1,\n        0.163801e-2,\n        -0.362018e-2,\n        -0.3988024e-1,\n        0.39894228,\n    ]\n    ans = polyval(_coef, y) * torch.exp(ax) / torch.sqrt(ax)\n    return -ans if x < 0.0 else ans", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/convutils.py__modified_bessel_i_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/convutils.py__modified_bessel_i_", "embedding": null, "metadata": {"file_path": "monai/networks/layers/convutils.py", "file_name": "convutils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 207, "end_line": 229, "span_ids": ["_modified_bessel_i"], "tokens": 300}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _modified_bessel_i(n: int, x: torch.Tensor) -> torch.Tensor:\n    if n < 2:\n        raise ValueError(f\"n must be greater than 1, got n={n}.\")\n    x = torch.as_tensor(x, dtype=torch.float, device=x.device if isinstance(x, torch.Tensor) else None)\n    if x == 0.0:\n        return x\n    device = x.device\n    tox = 2.0 / torch.abs(x)\n    ans, bip, bi = torch.tensor(0.0, device=device), torch.tensor(0.0, device=device), torch.tensor(1.0, device=device)\n    m = int(2 * (n + np.floor(np.sqrt(40.0 * n))))\n    for j in range(m, 0, -1):\n        bim = bip + float(j) * tox * bi\n        bip = bi\n        bi = bim\n        if abs(bi) > 1.0e10:\n            ans = ans * 1.0e-10\n            bi = bi * 1.0e-10\n            bip = bip * 1.0e-10\n        if j == n:\n            ans = bip\n    ans = ans * _modified_bessel_0(x) / bi\n    return -ans if x < 0.0 and (n % 2) == 1 else ans", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/simplelayers.py_GaussianFilter_GaussianFilter.forward.return.separable_filtering_x_x_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/simplelayers.py_GaussianFilter_GaussianFilter.forward.return.separable_filtering_x_x_", "embedding": null, "metadata": {"file_path": "monai/networks/layers/simplelayers.py", "file_name": "simplelayers.py", "file_type": "text/x-python", "category": "implementation", "start_line": 369, "end_line": 421, "span_ids": ["GaussianFilter", "GaussianFilter.__init__", "GaussianFilter.forward"], "tokens": 515}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GaussianFilter(nn.Module):\n    def __init__(\n        self,\n        spatial_dims: int,\n        sigma: Union[Sequence[float], float, Sequence[torch.Tensor], torch.Tensor],\n        truncated: float = 4.0,\n        approx: str = \"erf\",\n        requires_grad: bool = False,\n    ) -> None:\n        \"\"\"\n        Args:\n            spatial_dims: number of spatial dimensions of the input image.\n                must have shape (Batch, channels, H[, W, ...]).\n            sigma: std. could be a single value, or `spatial_dims` number of values.\n            truncated: spreads how many stds.\n            approx: discrete Gaussian kernel type, available options are \"erf\", \"sampled\", and \"scalespace\".\n\n                - ``erf`` approximation interpolates the error function;\n                - ``sampled`` uses a sampled Gaussian kernel;\n                - ``scalespace`` corresponds to\n                  https://en.wikipedia.org/wiki/Scale_space_implementation#The_discrete_Gaussian_kernel\n                  based on the modified Bessel functions.\n\n            requires_grad: whether to store the gradients for sigma.\n                if True, `sigma` will be the initial value of the parameters of this module\n                (for example `parameters()` iterator could be used to get the parameters);\n                otherwise this module will fix the kernels using `sigma` as the std.\n        \"\"\"\n        if issequenceiterable(sigma):\n            if len(sigma) != spatial_dims:  # type: ignore\n                raise ValueError\n        else:\n            sigma = [deepcopy(sigma) for _ in range(spatial_dims)]  # type: ignore\n        super().__init__()\n        self.sigma = [\n            torch.nn.Parameter(\n                torch.as_tensor(s, dtype=torch.float, device=s.device if isinstance(s, torch.Tensor) else None),\n                requires_grad=requires_grad,\n            )\n            for s in sigma  # type: ignore\n        ]\n        self.truncated = truncated\n        self.approx = approx\n        for idx, param in enumerate(self.sigma):\n            self.register_parameter(f\"kernel_sigma_{idx}\", param)\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        \"\"\"\n        Args:\n            x: in shape [Batch, chns, H, W, D].\n        \"\"\"\n        _kernel = [gaussian_1d(s, truncated=self.truncated, approx=self.approx) for s in self.sigma]\n        return separable_filtering(x=x, kernels=_kernel)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/spatial_transforms.py_grid_count_grid_count.return._GridCount_apply_grid_sh": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/spatial_transforms.py_grid_count_grid_count.return._GridCount_apply_grid_sh", "embedding": null, "metadata": {"file_path": "monai/networks/layers/spatial_transforms.py", "file_name": "spatial_transforms.py", "file_type": "text/x-python", "category": "implementation", "start_line": 240, "end_line": 313, "span_ids": ["grid_count"], "tokens": 822}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def grid_count(grid: torch.Tensor, shape=None, interpolation=\"linear\", bound=\"zero\", extrapolate: bool = True):\n    \"\"\"\n    Splatting weights with respect to a deformation field (pull adjoint).\n\n    This function is equivalent to applying grid_push to an image of ones.\n\n    `interpolation` can be an int, a string or an InterpolationType.\n    Possible values are::\n\n        - 0 or 'nearest'    or InterpolationType.nearest\n        - 1 or 'linear'     or InterpolationType.linear\n        - 2 or 'quadratic'  or InterpolationType.quadratic\n        - 3 or 'cubic'      or InterpolationType.cubic\n        - 4 or 'fourth'     or InterpolationType.fourth\n        - 5 or 'fifth'      or InterpolationType.fifth\n        - 6 or 'sixth'      or InterpolationType.sixth\n        - 7 or 'seventh'    or InterpolationType.seventh\n\n    A list of values can be provided, in the order [W, H, D],\n    to specify dimension-specific interpolation orders.\n\n    `bound` can be an int, a string or a BoundType.\n    Possible values are::\n\n        - 0 or 'replicate' or 'nearest'      or BoundType.replicate\n        - 1 or 'dct1'      or 'mirror'       or BoundType.dct1\n        - 2 or 'dct2'      or 'reflect'      or BoundType.dct2\n        - 3 or 'dst1'      or 'antimirror'   or BoundType.dst1\n        - 4 or 'dst2'      or 'antireflect'  or BoundType.dst2\n        - 5 or 'dft'       or 'wrap'         or BoundType.dft\n        - 7 or 'zero'                        or BoundType.zero\n\n    A list of values can be provided, in the order [W, H, D],\n    to specify dimension-specific boundary conditions.\n    `sliding` is a specific condition than only applies to flow fields\n    (with as many channels as dimensions). It cannot be dimension-specific.\n    Note that:\n\n        - `dft` corresponds to circular padding\n        - `dct2` corresponds to Neumann boundary conditions (symmetric)\n        - `dst2` corresponds to Dirichlet boundary conditions (antisymmetric)\n\n    See Also:\n\n        - https://en.wikipedia.org/wiki/Discrete_cosine_transform\n        - https://en.wikipedia.org/wiki/Discrete_sine_transform\n        - ``help(monai._C.BoundType)``\n        - ``help(monai._C.InterpolationType)``\n\n    Args:\n        grid: Deformation field `(B, Wi, Hi, Di, 2|3)`.\n        shape: shape of the source image.\n        interpolation (int or list[int] , optional): Interpolation order.\n            Defaults to `'linear'`.\n        bound (BoundType, or list[BoundType], optional): Boundary conditions.\n            Defaults to `'zero'`.\n        extrapolate (bool, optional): Extrapolate out-of-bound data.\n            Defaults to `True`.\n\n    Returns:\n        output (torch.Tensor): Splat weights `(B, 1, Wo, Ho, Do)`.\n\n    \"\"\"\n    # Convert parameters\n    bound = [_C.BoundType.__members__[b] if isinstance(b, str) else _C.BoundType(b) for b in ensure_tuple(bound)]\n    interpolation = [\n        _C.InterpolationType.__members__[i] if isinstance(i, str) else _C.InterpolationType(i)\n        for i in ensure_tuple(interpolation)\n    ]\n\n    if shape is None:\n        shape = tuple(grid.shape[2:])\n\n    return _GridCount.apply(grid, shape, interpolation, bound, extrapolate)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/spatial_transforms.py_grid_grad_grid_grad.return._GridGrad_apply_input_gr": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/spatial_transforms.py_grid_grad_grid_grad.return._GridGrad_apply_input_gr", "embedding": null, "metadata": {"file_path": "monai/networks/layers/spatial_transforms.py", "file_name": "spatial_transforms.py", "file_type": "text/x-python", "category": "implementation", "start_line": 340, "end_line": 408, "span_ids": ["grid_grad"], "tokens": 798}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def grid_grad(input: torch.Tensor, grid: torch.Tensor, interpolation=\"linear\", bound=\"zero\", extrapolate: bool = True):\n    \"\"\"\n    Sample an image with respect to a deformation field.\n\n    `interpolation` can be an int, a string or an InterpolationType.\n    Possible values are::\n\n        - 0 or 'nearest'    or InterpolationType.nearest\n        - 1 or 'linear'     or InterpolationType.linear\n        - 2 or 'quadratic'  or InterpolationType.quadratic\n        - 3 or 'cubic'      or InterpolationType.cubic\n        - 4 or 'fourth'     or InterpolationType.fourth\n        - 5 or 'fifth'      or InterpolationType.fifth\n        - 6 or 'sixth'      or InterpolationType.sixth\n        - 7 or 'seventh'    or InterpolationType.seventh\n\n    A list of values can be provided, in the order [W, H, D],\n    to specify dimension-specific interpolation orders.\n\n    `bound` can be an int, a string or a BoundType.\n    Possible values are::\n\n        - 0 or 'replicate' or 'nearest'      or BoundType.replicate\n        - 1 or 'dct1'      or 'mirror'       or BoundType.dct1\n        - 2 or 'dct2'      or 'reflect'      or BoundType.dct2\n        - 3 or 'dst1'      or 'antimirror'   or BoundType.dst1\n        - 4 or 'dst2'      or 'antireflect'  or BoundType.dst2\n        - 5 or 'dft'       or 'wrap'         or BoundType.dft\n        - 7 or 'zero'                        or BoundType.zero\n\n    A list of values can be provided, in the order [W, H, D],\n    to specify dimension-specific boundary conditions.\n    `sliding` is a specific condition than only applies to flow fields\n    (with as many channels as dimensions). It cannot be dimension-specific.\n    Note that:\n\n        - `dft` corresponds to circular padding\n        - `dct2` corresponds to Neumann boundary conditions (symmetric)\n        - `dst2` corresponds to Dirichlet boundary conditions (antisymmetric)\n\n    See Also:\n\n        - https://en.wikipedia.org/wiki/Discrete_cosine_transform\n        - https://en.wikipedia.org/wiki/Discrete_sine_transform\n        - ``help(monai._C.BoundType)``\n        - ``help(monai._C.InterpolationType)``\n\n\n    Args:\n        input: Input image. `(B, C, Wi, Hi, Di)`.\n        grid: Deformation field. `(B, Wo, Ho, Do, 2|3)`.\n        interpolation (int or list[int] , optional): Interpolation order.\n            Defaults to `'linear'`.\n        bound (BoundType, or list[BoundType], optional): Boundary conditions.\n            Defaults to `'zero'`.\n        extrapolate: Extrapolate out-of-bound data. Defaults to `True`.\n\n    Returns:\n        output (torch.Tensor): Sampled gradients (B, C, Wo, Ho, Do, 1|2|3).\n\n    \"\"\"\n    # Convert parameters\n    bound = [_C.BoundType.__members__[b] if isinstance(b, str) else _C.BoundType(b) for b in ensure_tuple(bound)]\n    interpolation = [\n        _C.InterpolationType.__members__[i] if isinstance(i, str) else _C.InterpolationType(i)\n        for i in ensure_tuple(interpolation)\n    ]\n\n    return _GridGrad.apply(input, grid, interpolation, bound, extrapolate)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/spatial_transforms.py_AffineTransform_AffineTransform.__init__.self.reverse_indexing.reverse_indexing": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/spatial_transforms.py_AffineTransform_AffineTransform.__init__.self.reverse_indexing.reverse_indexing", "embedding": null, "metadata": {"file_path": "monai/networks/layers/spatial_transforms.py", "file_name": "spatial_transforms.py", "file_type": "text/x-python", "category": "implementation", "start_line": 414, "end_line": 464, "span_ids": ["AffineTransform.__init__", "AffineTransform"], "tokens": 706}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AffineTransform(nn.Module):\n    def __init__(\n        self,\n        spatial_size: Optional[Union[Sequence[int], int]] = None,\n        normalized: bool = False,\n        mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,\n        padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.ZEROS,\n        align_corners: bool = False,\n        reverse_indexing: bool = True,\n    ) -> None:\n        \"\"\"\n        Apply affine transformations with a batch of affine matrices.\n\n        When `normalized=False` and `reverse_indexing=True`,\n        it does the commonly used resampling in the 'pull' direction\n        following the ``scipy.ndimage.affine_transform`` convention.\n        In this case `theta` is equivalent to (ndim+1, ndim+1) input ``matrix`` of ``scipy.ndimage.affine_transform``,\n        operates on homogeneous coordinates.\n        See also: https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.affine_transform.html\n\n        When `normalized=True` and `reverse_indexing=False`,\n        it applies `theta` to the normalized coordinates (coords. in the range of [-1, 1]) directly.\n        This is often used with `align_corners=False` to achieve resolution-agnostic resampling,\n        thus useful as a part of trainable modules such as the spatial transformer networks.\n        See also: https://pytorch.org/tutorials/intermediate/spatial_transformer_tutorial.html\n\n        Args:\n            spatial_size: output spatial shape, the full output shape will be\n                `[N, C, *spatial_size]` where N and C are inferred from the `src` input of `self.forward`.\n            normalized: indicating whether the provided affine matrix `theta` is defined\n                for the normalized coordinates. If `normalized=False`, `theta` will be converted\n                to operate on normalized coordinates as pytorch affine_grid works with the normalized\n                coordinates.\n            mode: {``\"bilinear\"``, ``\"nearest\"``}\n                Interpolation mode to calculate output values. Defaults to ``\"bilinear\"``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            padding_mode: {``\"zeros\"``, ``\"border\"``, ``\"reflection\"``}\n                Padding mode for outside grid values. Defaults to ``\"zeros\"``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            align_corners: see also https://pytorch.org/docs/stable/nn.functional.html#grid-sample.\n            reverse_indexing: whether to reverse the spatial indexing of image and coordinates.\n                set to `False` if `theta` follows pytorch's default \"D, H, W\" convention.\n                set to `True` if `theta` follows `scipy.ndimage` default \"i, j, k\" convention.\n        \"\"\"\n        super().__init__()\n        self.spatial_size = ensure_tuple(spatial_size) if spatial_size is not None else None\n        self.normalized = normalized\n        self.mode: GridSampleMode = look_up_option(mode, GridSampleMode)\n        self.padding_mode: GridSamplePadMode = look_up_option(padding_mode, GridSamplePadMode)\n        self.align_corners = align_corners\n        self.reverse_indexing = reverse_indexing", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/ahnet.py_AHNet.__init___AHNet.__init__.if_pretrained_.self_copy_from_net2d_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/ahnet.py_AHNet.__init___AHNet.__init__.if_pretrained_.self_copy_from_net2d_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/ahnet.py", "file_name": "ahnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 333, "end_line": 438, "span_ids": ["AHNet.__init__"], "tokens": 1367}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AHNet(nn.Module):\n\n    def __init__(\n        self,\n        layers: tuple = (3, 4, 6, 3),\n        spatial_dims: int = 3,\n        in_channels: int = 1,\n        out_channels: int = 1,\n        psp_block_num: int = 4,\n        upsample_mode: str = \"transpose\",\n        pretrained: bool = False,\n        progress: bool = True,\n    ):\n        self.inplanes = 64\n        super().__init__()\n\n        conv_type = Conv[Conv.CONV, spatial_dims]\n        conv_trans_type = Conv[Conv.CONVTRANS, spatial_dims]\n        norm_type = Norm[Norm.BATCH, spatial_dims]\n        pool_type: Type[Union[nn.MaxPool2d, nn.MaxPool3d]] = Pool[Pool.MAX, spatial_dims]\n        relu_type: Type[nn.ReLU] = Act[Act.RELU]\n        conv2d_type: Type[nn.Conv2d] = Conv[Conv.CONV, 2]\n        norm2d_type: Type[nn.BatchNorm2d] = Norm[Norm.BATCH, 2]\n\n        self.conv2d_type = conv2d_type\n        self.norm2d_type = norm2d_type\n        self.conv_type = conv_type\n        self.norm_type = norm_type\n        self.relu_type = relu_type\n        self.pool_type = pool_type\n        self.spatial_dims = spatial_dims\n        self.psp_block_num = psp_block_num\n        self.psp = None\n\n        if spatial_dims not in [2, 3]:\n            raise AssertionError(\"spatial_dims can only be 2 or 3.\")\n        if psp_block_num not in [0, 1, 2, 3, 4]:\n            raise AssertionError(\"psp_block_num should be an integer that belongs to [0, 4].\")\n\n        self.conv1 = conv_type(\n            in_channels,\n            64,\n            kernel_size=(7, 7, 3)[-spatial_dims:],\n            stride=(2, 2, 1)[-spatial_dims:],\n            padding=(3, 3, 1)[-spatial_dims:],\n            bias=False,\n        )\n        self.pool1 = pool_type(kernel_size=(1, 1, 2)[-spatial_dims:], stride=(1, 1, 2)[-spatial_dims:])\n        self.bn0 = norm_type(64)\n        self.relu = relu_type(inplace=True)\n        if upsample_mode in [\"transpose\", \"nearest\"]:\n            # To maintain the determinism, the value of kernel_size and stride should be the same.\n            # (you can check this link for reference: https://github.com/Project-MONAI/MONAI/pull/815 )\n            self.maxpool = pool_type(kernel_size=(2, 2, 2)[-spatial_dims:], stride=2)\n        else:\n            self.maxpool = pool_type(kernel_size=(3, 3, 3)[-spatial_dims:], stride=2, padding=1)\n\n        self.layer1 = self._make_layer(Bottleneck3x3x1, 64, layers[0], stride=1)\n        self.layer2 = self._make_layer(Bottleneck3x3x1, 128, layers[1], stride=2)\n        self.layer3 = self._make_layer(Bottleneck3x3x1, 256, layers[2], stride=2)\n        self.layer4 = self._make_layer(Bottleneck3x3x1, 512, layers[3], stride=2)\n\n        # Make the 3D dense decoder layers\n        densegrowth = 20\n        densebn = 4\n        ndenselayer = 3\n\n        num_init_features = 64\n        noutres1 = 256\n        noutres2 = 512\n        noutres3 = 1024\n        noutres4 = 2048\n\n        self.up0 = UpTransition(spatial_dims, noutres4, noutres3, upsample_mode)\n        self.dense0 = DenseBlock(spatial_dims, ndenselayer, noutres3, densebn, densegrowth, 0.0)\n        noutdense = noutres3 + ndenselayer * densegrowth\n\n        self.up1 = UpTransition(spatial_dims, noutdense, noutres2, upsample_mode)\n        self.dense1 = DenseBlock(spatial_dims, ndenselayer, noutres2, densebn, densegrowth, 0.0)\n        noutdense1 = noutres2 + ndenselayer * densegrowth\n\n        self.up2 = UpTransition(spatial_dims, noutdense1, noutres1, upsample_mode)\n        self.dense2 = DenseBlock(spatial_dims, ndenselayer, noutres1, densebn, densegrowth, 0.0)\n        noutdense2 = noutres1 + ndenselayer * densegrowth\n\n        self.trans1 = Projection(spatial_dims, noutdense2, num_init_features)\n        self.dense3 = DenseBlock(spatial_dims, ndenselayer, num_init_features, densebn, densegrowth, 0.0)\n        noutdense3 = num_init_features + densegrowth * ndenselayer\n\n        self.up3 = UpTransition(spatial_dims, noutdense3, num_init_features, upsample_mode)\n        self.dense4 = DenseBlock(spatial_dims, ndenselayer, num_init_features, densebn, densegrowth, 0.0)\n        noutdense4 = num_init_features + densegrowth * ndenselayer\n\n        self.psp = PSP(spatial_dims, psp_block_num, noutdense4, upsample_mode)\n        self.final = Final(spatial_dims, psp_block_num + noutdense4, out_channels, upsample_mode)\n\n        # Initialise parameters\n        for m in self.modules():\n            if isinstance(m, (conv_type, conv_trans_type)):\n                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\n                m.weight.data.normal_(0, math.sqrt(2.0 / n))\n            elif isinstance(m, norm_type):\n                m.weight.data.fill_(1)\n                m.bias.data.zero_()\n\n        if pretrained:\n            net2d = FCN(pretrained=True, progress=progress)\n            self.copy_from(net2d)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dynunet.py_DynUNet.get_module_list_DynUNet.get_module_list.return.nn_ModuleList_layers_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dynunet.py_DynUNet.get_module_list_DynUNet.get_module_list.return.nn_ModuleList_layers_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/dynunet.py", "file_name": "dynunet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 291, "end_line": 334, "span_ids": ["DynUNet.get_module_list"], "tokens": 361}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DynUNet(nn.Module):\n\n    def get_module_list(\n        self,\n        in_channels: List[int],\n        out_channels: List[int],\n        kernel_size: Sequence[Union[Sequence[int], int]],\n        strides: Sequence[Union[Sequence[int], int]],\n        conv_block: nn.Module,\n        upsample_kernel_size: Optional[Sequence[Union[Sequence[int], int]]] = None,\n        trans_bias: bool = False,\n    ):\n        layers = []\n        if upsample_kernel_size is not None:\n            for in_c, out_c, kernel, stride, up_kernel in zip(\n                in_channels, out_channels, kernel_size, strides, upsample_kernel_size\n            ):\n                params = {\n                    \"spatial_dims\": self.spatial_dims,\n                    \"in_channels\": in_c,\n                    \"out_channels\": out_c,\n                    \"kernel_size\": kernel,\n                    \"stride\": stride,\n                    \"norm_name\": self.norm_name,\n                    \"act_name\": self.act_name,\n                    \"dropout\": self.dropout,\n                    \"upsample_kernel_size\": up_kernel,\n                    \"trans_bias\": trans_bias,\n                }\n                layer = conv_block(**params)\n                layers.append(layer)\n        else:\n            for in_c, out_c, kernel, stride in zip(in_channels, out_channels, kernel_size, strides):\n                params = {\n                    \"spatial_dims\": self.spatial_dims,\n                    \"in_channels\": in_c,\n                    \"out_channels\": out_c,\n                    \"kernel_size\": kernel,\n                    \"stride\": stride,\n                    \"norm_name\": self.norm_name,\n                    \"act_name\": self.act_name,\n                    \"dropout\": self.dropout,\n                }\n                layer = conv_block(**params)\n                layers.append(layer)\n        return nn.ModuleList(layers)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dynunet.py_DynUNet.get_deep_supervision_heads_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dynunet.py_DynUNet.get_deep_supervision_heads_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/dynunet.py", "file_name": "dynunet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 356, "end_line": 368, "span_ids": ["DynUNet.get_deep_supervision_heads", "DynUNet.initialize_weights", "impl:3"], "tokens": 133}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DynUNet(nn.Module):\n\n    def get_deep_supervision_heads(self):\n        return nn.ModuleList([self.get_output_block(i + 1) for i in range(self.deep_supr_num)])\n\n    @staticmethod\n    def initialize_weights(module):\n        if isinstance(module, (nn.Conv3d, nn.Conv2d, nn.ConvTranspose3d, nn.ConvTranspose2d)):\n            module.weight = nn.init.kaiming_normal_(module.weight, a=0.01)\n            if module.bias is not None:\n                module.bias = nn.init.constant_(module.bias, 0)\n\n\nDynUnet = Dynunet = DynUNet", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/optimizers/novograd.py_from_typing_import_Callab_Novograd.__setstate__.for_group_in_self_param_g.group_setdefault_amsgrad": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/optimizers/novograd.py_from_typing_import_Callab_Novograd.__setstate__.for_group_in_self_param_g.group_setdefault_amsgrad", "embedding": null, "metadata": {"file_path": "monai/optimizers/novograd.py", "file_name": "novograd.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 66, "span_ids": ["Novograd", "Novograd.__setstate__", "Novograd.__init__", "docstring"], "tokens": 631}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Callable, Iterable, Optional, Tuple\n\nimport torch\nfrom torch.optim import Optimizer\n\n\nclass Novograd(Optimizer):\n    \"\"\"\n    Novograd based on `Stochastic Gradient Methods with Layer-wise Adaptive Moments for Training of Deep Networks\n    <https://arxiv.org/pdf/1905.11286.pdf>`_.\n    The code is adapted from the implementations in `Jasper for PyTorch\n    <https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechRecognition/Jasper/common/optimizers.py>`_,\n    and `OpenSeq2Seq <https://github.com/NVIDIA/OpenSeq2Seq/blob/master/open_seq2seq/optimizers/novograd.py>`_.\n\n    Args:\n        params: iterable of parameters to optimize or dicts defining parameter groups.\n        lr: learning rate. Defaults to 1e-3.\n        betas: coefficients used for computing running averages of gradient and its square. Defaults to (0.9, 0.98).\n        eps: term added to the denominator to improve numerical stability. Defaults to 1e-8.\n        weight_decay: weight decay (L2 penalty). Defaults to 0.\n        grad_averaging: gradient averaging. Defaults to ``False``.\n        amsgrad: whether to use the AMSGrad variant of this algorithm from the paper\n            `On the Convergence of Adam and Beyond <https://arxiv.org/pdf/1904.09237.pdf>`_. Defaults to ``False``.\n    \"\"\"\n\n    def __init__(\n        self,\n        params: Iterable,\n        lr: float = 1e-3,\n        betas: Tuple[float, float] = (0.9, 0.98),\n        eps: float = 1e-8,\n        weight_decay: float = 0,\n        grad_averaging: bool = False,\n        amsgrad: bool = False,\n    ):\n        if 0.0 > lr:\n            raise ValueError(f\"Invalid learning rate: {lr}\")\n        if 0.0 > eps:\n            raise ValueError(f\"Invalid epsilon value: {eps}\")\n        if not 0.0 <= betas[0] < 1.0:\n            raise ValueError(f\"Invalid beta parameter at index 0: {betas[0]}\")\n        if not 0.0 <= betas[1] < 1.0:\n            raise ValueError(f\"Invalid beta parameter at index 1: {betas[1]}\")\n        if 0.0 > weight_decay:\n            raise ValueError(f\"Invalid weight_decay value: {weight_decay}\")\n        defaults = dict(\n            lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, grad_averaging=grad_averaging, amsgrad=amsgrad\n        )\n\n        super().__init__(params, defaults)\n\n    def __setstate__(self, state):\n        super().__setstate__(state)\n        for group in self.param_groups:\n            group.setdefault(\"amsgrad\", False)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/optimizers/novograd.py_Novograd.step_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/optimizers/novograd.py_Novograd.step_", "embedding": null, "metadata": {"file_path": "monai/optimizers/novograd.py", "file_name": "novograd.py", "file_type": "text/x-python", "category": "implementation", "start_line": 73, "end_line": 137, "span_ids": ["Novograd.step"], "tokens": 542}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Novograd(Optimizer):\n\n    def step(self, closure: Optional[Callable] = None):\n        \"\"\"Performs a single optimization step.\n\n        Arguments:\n            closure: A closure that reevaluates the model and returns the loss. Defaults to ``None``.\n        \"\"\"\n        loss = None\n        if closure is not None:\n            loss = closure()\n\n        for group in self.param_groups:\n            for p in group[\"params\"]:\n                if p.grad is None:\n                    continue\n                grad = p.grad.data\n                if grad.is_sparse:\n                    raise RuntimeError(\"Sparse gradients are not supported.\")\n                amsgrad = group[\"amsgrad\"]\n\n                state = self.state[p]\n\n                # State initialization\n                if len(state) == 0:\n                    state[\"step\"] = 0\n                    # Exponential moving average of gradient values\n                    state[\"exp_avg\"] = torch.zeros_like(p.data)\n                    # Exponential moving average of squared gradient values\n                    state[\"exp_avg_sq\"] = torch.zeros([]).to(state[\"exp_avg\"].device)\n                    if amsgrad:\n                        # Maintains max of all exp. moving avg. of sq. grad. values\n                        state[\"max_exp_avg_sq\"] = torch.zeros([]).to(state[\"exp_avg\"].device)\n\n                exp_avg, exp_avg_sq = state[\"exp_avg\"], state[\"exp_avg_sq\"]\n                if amsgrad:\n                    max_exp_avg_sq = state[\"max_exp_avg_sq\"]\n                beta1, beta2 = group[\"betas\"]\n\n                state[\"step\"] += 1\n\n                norm = torch.sum(torch.pow(grad, 2))\n\n                if exp_avg_sq == 0:\n                    exp_avg_sq.copy_(norm)\n                else:\n                    exp_avg_sq.mul_(beta2).add_(norm, alpha=1 - beta2)\n\n                if amsgrad:\n                    # Maintains the maximum of all 2nd moment running avg. till now\n                    torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq)\n                    # Use the max. for normalizing running avg. of gradient\n                    denom = max_exp_avg_sq.sqrt().add_(group[\"eps\"])\n                else:\n                    denom = exp_avg_sq.sqrt().add_(group[\"eps\"])\n\n                grad.div_(denom)\n                if group[\"weight_decay\"] != 0:\n                    grad.add_(p.data, alpha=group[\"weight_decay\"])\n                if group[\"grad_averaging\"]:\n                    grad.mul_(1 - beta1)\n                exp_avg.mul_(beta1).add_(grad)\n\n                p.data.add_(exp_avg, alpha=-group[\"lr\"])\n\n        return loss", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_RandCropByPosNegLabel_RandCropByPosNegLabel._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_RandCropByPosNegLabel_RandCropByPosNegLabel._", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 835, "end_line": 886, "span_ids": ["RandCropByPosNegLabel"], "tokens": 894}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandCropByPosNegLabel(Randomizable, Transform):\n    \"\"\"\n    Crop random fixed sized regions with the center being a foreground or background voxel\n    based on the Pos Neg Ratio.\n    And will return a list of arrays for all the cropped images.\n    For example, crop two (3 x 3) arrays from (5 x 5) array with pos/neg=1::\n\n        [[[0, 0, 0, 0, 0],\n          [0, 1, 2, 1, 0],            [[0, 1, 2],     [[2, 1, 0],\n          [0, 1, 3, 0, 0],     -->     [0, 1, 3],      [3, 0, 0],\n          [0, 0, 0, 0, 0],             [0, 0, 0]]      [0, 0, 0]]\n          [0, 0, 0, 0, 0]]]\n\n    If a dimension of the expected spatial size is bigger than the input image size,\n    will not crop that dimension. So the cropped result may be smaller than expected size, and the cropped\n    results of several images may not have exactly same shape.\n\n    Args:\n        spatial_size: the spatial size of the crop region e.g. [224, 224, 128].\n            if a dimension of ROI size is bigger than image size, will not crop that dimension of the image.\n            if its components have non-positive values, the corresponding size of `label` will be used.\n            for example: if the spatial size of input data is [40, 40, 40] and `spatial_size=[32, 64, -1]`,\n            the spatial size of output data will be [32, 40, 40].\n        label: the label image that is used for finding foreground/background, if None, must set at\n            `self.__call__`.  Non-zero indicates foreground, zero indicates background.\n        pos: used with `neg` together to calculate the ratio ``pos / (pos + neg)`` for the probability\n            to pick a foreground voxel as a center rather than a background voxel.\n        neg: used with `pos` together to calculate the ratio ``pos / (pos + neg)`` for the probability\n            to pick a foreground voxel as a center rather than a background voxel.\n        num_samples: number of samples (crop regions) to take in each list.\n        image: optional image data to help select valid area, can be same as `img` or another image array.\n            if not None, use ``label == 0 & image > image_threshold`` to select the negative\n            sample (background) center. So the crop center will only come from the valid image areas.\n        image_threshold: if enabled `image`, use ``image > image_threshold`` to determine\n            the valid image content areas.\n        fg_indices: if provided pre-computed foreground indices of `label`, will ignore above `image` and\n            `image_threshold`, and randomly select crop centers based on them, need to provide `fg_indices`\n            and `bg_indices` together, expect to be 1 dim array of spatial indices after flattening.\n            a typical usage is to call `FgBgToIndices` transform first and cache the results.\n        bg_indices: if provided pre-computed background indices of `label`, will ignore above `image` and\n            `image_threshold`, and randomly select crop centers based on them, need to provide `fg_indices`\n            and `bg_indices` together, expect to be 1 dim array of spatial indices after flattening.\n            a typical usage is to call `FgBgToIndices` transform first and cache the results.\n        allow_smaller: if `False`, an exception will be raised if the image is smaller than\n            the requested ROI in any dimension. If `True`, any smaller dimensions will be set to\n            match the cropped size (i.e., no cropping in that dimension).\n\n    Raises:\n        ValueError: When ``pos`` or ``neg`` are negative.\n        ValueError: When ``pos=0`` and ``neg=0``. Incompatible values.\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_RandCropByPosNegLabel.randomize_RandCropByPosNegLabel.randomize.self.centers.generate_pos_neg_label_cr": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_RandCropByPosNegLabel.randomize_RandCropByPosNegLabel.randomize.self.centers.generate_pos_neg_label_cr", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 918, "end_line": 944, "span_ids": ["RandCropByPosNegLabel.randomize"], "tokens": 236}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandCropByPosNegLabel(Randomizable, Transform):\n\n    def randomize(\n        self,\n        label: NdarrayOrTensor,\n        fg_indices: Optional[NdarrayOrTensor] = None,\n        bg_indices: Optional[NdarrayOrTensor] = None,\n        image: Optional[NdarrayOrTensor] = None,\n    ) -> None:\n        self.spatial_size = fall_back_tuple(self.spatial_size, default=label.shape[1:])\n        if fg_indices is None or bg_indices is None:\n            if self.fg_indices is not None and self.bg_indices is not None:\n                fg_indices_ = self.fg_indices\n                bg_indices_ = self.bg_indices\n            else:\n                fg_indices_, bg_indices_ = map_binary_to_indices(label, image, self.image_threshold)\n        else:\n            fg_indices_ = fg_indices\n            bg_indices_ = bg_indices\n        self.centers = generate_pos_neg_label_crop_centers(\n            self.spatial_size,\n            self.num_samples,\n            self.pos_ratio,\n            label.shape[1:],\n            fg_indices_,\n            bg_indices_,\n            self.R,\n            self.allow_smaller,\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_RandCropByPosNegLabel.__call___RandCropByPosNegLabel.__call__.return.results": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_RandCropByPosNegLabel.__call___RandCropByPosNegLabel.__call__.return.results", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 946, "end_line": 982, "span_ids": ["RandCropByPosNegLabel.__call__"], "tokens": 387}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandCropByPosNegLabel(Randomizable, Transform):\n\n    def __call__(\n        self,\n        img: NdarrayOrTensor,\n        label: Optional[NdarrayOrTensor] = None,\n        image: Optional[NdarrayOrTensor] = None,\n        fg_indices: Optional[NdarrayOrTensor] = None,\n        bg_indices: Optional[NdarrayOrTensor] = None,\n    ) -> List[NdarrayOrTensor]:\n        \"\"\"\n        Args:\n            img: input data to crop samples from based on the pos/neg ratio of `label` and `image`.\n                Assumes `img` is a channel-first array.\n            label: the label image that is used for finding foreground/background, if None, use `self.label`.\n            image: optional image data to help select valid area, can be same as `img` or another image array.\n                use ``label == 0 & image > image_threshold`` to select the negative sample(background) center.\n                so the crop center will only exist on valid image area. if None, use `self.image`.\n            fg_indices: foreground indices to randomly select crop centers,\n                need to provide `fg_indices` and `bg_indices` together.\n            bg_indices: background indices to randomly select crop centers,\n                need to provide `fg_indices` and `bg_indices` together.\n\n        \"\"\"\n        if label is None:\n            label = self.label\n        if label is None:\n            raise ValueError(\"label should be provided.\")\n        if image is None:\n            image = self.image\n\n        self.randomize(label, fg_indices, bg_indices, image)\n        results: List[NdarrayOrTensor] = []\n        if self.centers is not None:\n            for center in self.centers:\n                cropper = SpatialCrop(roi_center=center, roi_size=self.spatial_size)\n                results.append(cropper(img))\n\n        return results", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_ResizeWithPadOrCrop_ResizeWithPadOrCrop.__init__.self.cropper.CenterSpatialCrop_roi_siz": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_ResizeWithPadOrCrop_ResizeWithPadOrCrop.__init__.self.cropper.CenterSpatialCrop_roi_siz", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1125, "end_line": 1156, "span_ids": ["ResizeWithPadOrCrop", "ResizeWithPadOrCrop.__init__"], "tokens": 434}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ResizeWithPadOrCrop(Transform):\n    \"\"\"\n    Resize an image to a target spatial size by either centrally cropping the image or\n    padding it evenly with a user-specified mode.\n    When the dimension is smaller than the target size, do symmetric padding along that dim.\n    When the dimension is larger than the target size, do central cropping along that dim.\n\n    Args:\n        spatial_size: the spatial size of output data after padding or crop.\n            If has non-positive values, the corresponding size of input image will be used (no padding).\n        mode: {``\"constant\"``, ``\"edge\"``, ``\"linear_ramp\"``, ``\"maximum\"``, ``\"mean\"``,\n            ``\"median\"``, ``\"minimum\"``, ``\"reflect\"``, ``\"symmetric\"``, ``\"wrap\"``, ``\"empty\"``}\n            One of the listed string values or a user supplied function for padding. Defaults to ``\"constant\"``.\n            See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html\n        method: {``\"symmetric\"``, ``\"end\"``}\n            Pad image symmetrically on every side or only pad at the end sides. Defaults to ``\"symmetric\"``.\n        np_kwargs: other args for `np.pad` API, note that `np.pad` treats channel dimension as the first dimension.\n            more details: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html\n\n    \"\"\"\n\n    backend = list(set(SpatialPad.backend) & set(CenterSpatialCrop.backend))\n\n    def __init__(\n        self,\n        spatial_size: Union[Sequence[int], int],\n        mode: Union[NumpyPadMode, str] = NumpyPadMode.CONSTANT,\n        method: Union[Method, str] = Method.SYMMETRIC,\n        **np_kwargs,\n    ):\n        self.padder = SpatialPad(spatial_size=spatial_size, method=method, mode=mode, **np_kwargs)\n        self.cropper = CenterSpatialCrop(roi_size=spatial_size)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_RandCropByPosNegLabeld_RandCropByPosNegLabeld._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_RandCropByPosNegLabeld_RandCropByPosNegLabeld._", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1017, "end_line": 1074, "span_ids": ["RandCropByPosNegLabeld"], "tokens": 957}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandCropByPosNegLabeld(Randomizable, MapTransform, InvertibleTransform):\n    \"\"\"\n    Dictionary-based version :py:class:`monai.transforms.RandCropByPosNegLabel`.\n    Crop random fixed sized regions with the center being a foreground or background voxel\n    based on the Pos Neg Ratio.\n    Suppose all the expected fields specified by `keys` have same shape,\n    and add `patch_index` to the corresponding meta data.\n    And will return a list of dictionaries for all the cropped images.\n\n    If a dimension of the expected spatial size is bigger than the input image size,\n    will not crop that dimension. So the cropped result may be smaller than the expected size,\n    and the cropped results of several images may not have exactly the same shape.\n\n    Args:\n        keys: keys of the corresponding items to be transformed.\n            See also: :py:class:`monai.transforms.compose.MapTransform`\n        label_key: name of key for label image, this will be used for finding foreground/background.\n        spatial_size: the spatial size of the crop region e.g. [224, 224, 128].\n            if a dimension of ROI size is bigger than image size, will not crop that dimension of the image.\n            if its components have non-positive values, the corresponding size of `data[label_key]` will be used.\n            for example: if the spatial size of input data is [40, 40, 40] and `spatial_size=[32, 64, -1]`,\n            the spatial size of output data will be [32, 40, 40].\n        pos: used with `neg` together to calculate the ratio ``pos / (pos + neg)`` for the probability\n            to pick a foreground voxel as a center rather than a background voxel.\n        neg: used with `pos` together to calculate the ratio ``pos / (pos + neg)`` for the probability\n            to pick a foreground voxel as a center rather than a background voxel.\n        num_samples: number of samples (crop regions) to take in each list.\n        image_key: if image_key is not None, use ``label == 0 & image > image_threshold`` to select\n            the negative sample(background) center. so the crop center will only exist on valid image area.\n        image_threshold: if enabled image_key, use ``image > image_threshold`` to determine\n            the valid image content area.\n        fg_indices_key: if provided pre-computed foreground indices of `label`, will ignore above `image_key` and\n            `image_threshold`, and randomly select crop centers based on them, need to provide `fg_indices_key`\n            and `bg_indices_key` together, expect to be 1 dim array of spatial indices after flattening.\n            a typical usage is to call `FgBgToIndicesd` transform first and cache the results.\n        bg_indices_key: if provided pre-computed background indices of `label`, will ignore above `image_key` and\n            `image_threshold`, and randomly select crop centers based on them, need to provide `fg_indices_key`\n            and `bg_indices_key` together, expect to be 1 dim array of spatial indices after flattening.\n            a typical usage is to call `FgBgToIndicesd` transform first and cache the results.\n        meta_keys: explicitly indicate the key of the corresponding meta data dictionary.\n            used to add `patch_index` to the meta dict.\n            for example, for data with key `image`, the metadata by default is in `image_meta_dict`.\n            the meta data is a dictionary object which contains: filename, original_shape, etc.\n            it can be a sequence of string, map to the `keys`.\n            if None, will try to construct meta_keys by `key_{meta_key_postfix}`.\n        meta_key_postfix: if meta_keys is None, use `key_{postfix}` to to fetch the meta data according\n            to the key data, default is `meta_dict`, the meta data is a dictionary object.\n            used to add `patch_index` to the meta dict.\n        allow_smaller: if `False`, an exception will be raised if the image is smaller than\n            the requested ROI in any dimension. If `True`, any smaller dimensions will be set to\n            match the cropped size (i.e., no cropping in that dimension).\n        allow_missing_keys: don't raise exception if key is missing.\n\n    Raises:\n        ValueError: When ``pos`` or ``neg`` are negative.\n        ValueError: When ``pos=0`` and ``neg=0``. Incompatible values.\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_RandCropByPosNegLabeld.randomize_RandCropByPosNegLabeld.randomize.self.centers.generate_pos_neg_label_cr": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_RandCropByPosNegLabeld.randomize_RandCropByPosNegLabeld.randomize.self.centers.generate_pos_neg_label_cr", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1115, "end_line": 1137, "span_ids": ["RandCropByPosNegLabeld.randomize"], "tokens": 204}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandCropByPosNegLabeld(Randomizable, MapTransform, InvertibleTransform):\n\n    def randomize(\n        self,\n        label: NdarrayOrTensor,\n        fg_indices: Optional[NdarrayOrTensor] = None,\n        bg_indices: Optional[NdarrayOrTensor] = None,\n        image: Optional[NdarrayOrTensor] = None,\n    ) -> None:\n        self.spatial_size = fall_back_tuple(self.spatial_size, default=label.shape[1:])\n        if fg_indices is None or bg_indices is None:\n            fg_indices_, bg_indices_ = map_binary_to_indices(label, image, self.image_threshold)\n        else:\n            fg_indices_ = fg_indices\n            bg_indices_ = bg_indices\n        self.centers = generate_pos_neg_label_crop_centers(\n            self.spatial_size,\n            self.num_samples,\n            self.pos_ratio,\n            label.shape[1:],\n            fg_indices_,\n            bg_indices_,\n            self.R,\n            self.allow_smaller,\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_NormalizeIntensity._normalize_NormalizeIntensity._normalize.return.img": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_NormalizeIntensity._normalize_NormalizeIntensity._normalize.return.img", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 634, "end_line": 662, "span_ids": ["NormalizeIntensity._normalize"], "tokens": 283}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class NormalizeIntensity(Transform):\n\n    def _normalize(self, img: NdarrayOrTensor, sub=None, div=None) -> NdarrayOrTensor:\n        img, *_ = convert_data_type(img, dtype=torch.float32)\n\n        if self.nonzero:\n            slices = img != 0\n        else:\n            if isinstance(img, np.ndarray):\n                slices = np.ones_like(img, dtype=bool)\n            else:\n                slices = torch.ones_like(img, dtype=torch.bool)\n        if not slices.any():\n            return img\n\n        _sub = sub if sub is not None else self._mean(img[slices])\n        if isinstance(_sub, (torch.Tensor, np.ndarray)):\n            _sub, *_ = convert_to_dst_type(_sub, img)\n            _sub = _sub[slices]\n\n        _div = div if div is not None else self._std(img[slices])\n        if np.isscalar(_div):\n            if _div == 0.0:\n                _div = 1.0\n        elif isinstance(_div, (torch.Tensor, np.ndarray)):\n            _div, *_ = convert_to_dst_type(_div, img)\n            _div = _div[slices]\n            _div[_div == 0.0] = 1.0\n\n        img[slices] = (img[slices] - _sub) / _div\n        return img", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_GaussianSharpen_GaussianSharpen.__init__.self.approx.approx": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_GaussianSharpen_GaussianSharpen.__init__.self.approx.approx", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1158, "end_line": 1197, "span_ids": ["GaussianSharpen", "GaussianSharpen.__init__"], "tokens": 419}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GaussianSharpen(Transform):\n    \"\"\"\n    Sharpen images using the Gaussian Blur filter.\n    Referring to: http://scipy-lectures.org/advanced/image_processing/auto_examples/plot_sharpen.html.\n    The algorithm is shown as below\n\n    .. code-block:: python\n\n        blurred_f = gaussian_filter(img, sigma1)\n        filter_blurred_f = gaussian_filter(blurred_f, sigma2)\n        img = blurred_f + alpha * (blurred_f - filter_blurred_f)\n\n    A set of default values `sigma1=3.0`, `sigma2=1.0` and `alpha=30.0` is provide for reference.\n\n    Args:\n        sigma1: sigma parameter for the first gaussian kernel. if a list of values, must match the count\n            of spatial dimensions of input data, and apply every value in the list to 1 spatial dimension.\n            if only 1 value provided, use it for all spatial dimensions.\n        sigma2: sigma parameter for the second gaussian kernel. if a list of values, must match the count\n            of spatial dimensions of input data, and apply every value in the list to 1 spatial dimension.\n            if only 1 value provided, use it for all spatial dimensions.\n        alpha: weight parameter to compute the final result.\n        approx: discrete Gaussian kernel type, available options are \"erf\", \"sampled\", and \"scalespace\".\n            see also :py:meth:`monai.networks.layers.GaussianFilter`.\n\n    \"\"\"\n\n    backend = [TransformBackends.TORCH]\n\n    def __init__(\n        self,\n        sigma1: Union[Sequence[float], float] = 3.0,\n        sigma2: Union[Sequence[float], float] = 1.0,\n        alpha: float = 30.0,\n        approx: str = \"erf\",\n    ) -> None:\n        self.sigma1 = sigma1\n        self.sigma2 = sigma2\n        self.alpha = alpha\n        self.approx = approx", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandGaussianSharpen.__call___RandGaussianSharpen.__call__.return.GaussianSharpen_sigma1_si": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandGaussianSharpen.__call___RandGaussianSharpen.__call__.return.GaussianSharpen_sigma1_si", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1282, "end_line": 1293, "span_ids": ["RandGaussianSharpen.__call__"], "tokens": 184}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandGaussianSharpen(RandomizableTransform):\n\n    def __call__(self, img: NdarrayOrTensor, randomize: bool = True) -> NdarrayOrTensor:\n        if randomize:\n            self.randomize()\n\n        if not self._do_transform:\n            return img\n\n        if self.x2 is None or self.y2 is None or self.z2 is None or self.a is None:\n            raise RuntimeError(\"please call the `randomize()` function first.\")\n        sigma1 = ensure_tuple_size(tup=(self.x1, self.y1, self.z1), dim=img.ndim - 1)\n        sigma2 = ensure_tuple_size(tup=(self.x2, self.y2, self.z2), dim=img.ndim - 1)\n        return GaussianSharpen(sigma1=sigma1, sigma2=sigma2, alpha=self.a, approx=self.approx)(img)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandHistogramShift.randomize_RandHistogramShift.randomize.for_i_in_range_1_num_con.self_floating_control_poi": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandHistogramShift.randomize_RandHistogramShift.randomize.for_i_in_range_1_num_con.self_floating_control_poi", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1325, "end_line": 1335, "span_ids": ["RandHistogramShift.randomize"], "tokens": 153}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandHistogramShift(RandomizableTransform):\n\n    def randomize(self, data: Optional[Any] = None) -> None:\n        super().randomize(None)\n        if not self._do_transform:\n            return None\n        num_control_point = self.R.randint(self.num_control_points[0], self.num_control_points[1] + 1)\n        self.reference_control_points = np.linspace(0, 1, num_control_point)\n        self.floating_control_points = np.copy(self.reference_control_points)\n        for i in range(1, num_control_point - 1):\n            self.floating_control_points[i] = self.R.uniform(\n                self.floating_control_points[i - 1], self.floating_control_points[i + 1]\n            )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/io/array.py_LoadImage.__call___LoadImage.__call__.return.img_array_meta_data": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/io/array.py_LoadImage.__call___LoadImage.__call__.return.img_array_meta_data", "embedding": null, "metadata": {"file_path": "monai/transforms/io/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 170, "end_line": 225, "span_ids": ["LoadImage.__call__"], "tokens": 560}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LoadImage(Transform):\n\n    def __call__(self, filename: Union[Sequence[PathLike], PathLike], reader: Optional[ImageReader] = None):\n        \"\"\"\n        Load image file and meta data from the given filename(s).\n        If `reader` is not specified, this class automatically chooses readers based on the\n        reversed order of registered readers `self.readers`.\n\n        Args:\n            filename: path file or file-like object or a list of files.\n                will save the filename to meta_data with key `filename_or_obj`.\n                if provided a list of files, use the filename of first file to save,\n                and will stack them together as multi-channels data.\n                if provided directory path instead of file path, will treat it as\n                DICOM images series and read.\n            reader: runtime reader to load image file and meta data.\n\n        \"\"\"\n        filename = tuple(f\"{Path(s).expanduser()}\" for s in ensure_tuple(filename))  # allow Path objects\n        img = None\n        if reader is not None:\n            img = reader.read(filename)  # runtime specified reader\n        else:\n            for reader in self.readers[::-1]:\n                if self.auto_select:  # rely on the filename extension to choose the reader\n                    if reader.verify_suffix(filename):\n                        img = reader.read(filename)\n                        break\n                else:  # try the user designated readers\n                    try:\n                        img = reader.read(filename)\n                    except Exception as e:\n                        logging.getLogger(self.__class__.__name__).debug(\n                            f\"{reader.__class__.__name__}: unable to load {filename}.\\n\" f\"Error: {e}\"\n                        )\n                    else:\n                        break\n\n        if img is None or reader is None:\n            if isinstance(filename, tuple) and len(filename) == 1:\n                filename = filename[0]\n            raise RuntimeError(\n                f\"cannot find a suitable reader for file: {filename}.\\n\"\n                \"    Please install the reader libraries, see also the installation instructions:\\n\"\n                \"    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies.\\n\"\n                f\"   The current registered: {self.readers}.\\n\"\n            )\n\n        img_array, meta_data = reader.get_data(img)\n        img_array = img_array.astype(self.dtype, copy=False)\n\n        if self.image_only:\n            return img_array\n        meta_data[Key.FILENAME_OR_OBJ] = f\"{ensure_tuple(filename)[0]}\"  # Path obj should be strings for data loader\n        # make sure all elements in metadata are little endian\n        meta_data = switch_endianness(meta_data, \"<\")\n\n        return img_array, meta_data", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/io/dictionary.py_LoadImaged.__call___LoadImaged.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/io/dictionary.py_LoadImaged.__call___LoadImaged.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/io/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 105, "end_line": 128, "span_ids": ["LoadImaged.__call__"], "tokens": 264}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LoadImaged(MapTransform):\n\n    def __call__(self, data, reader: Optional[ImageReader] = None):\n        \"\"\"\n        Raises:\n            KeyError: When not ``self.overwriting`` and key already exists in ``data``.\n\n        \"\"\"\n        d = dict(data)\n        for key, meta_key, meta_key_postfix in self.key_iterator(d, self.meta_keys, self.meta_key_postfix):\n            data = self._loader(d[key], reader)\n            if self._loader.image_only:\n                if not isinstance(data, np.ndarray):\n                    raise ValueError(\"loader must return a numpy array (because image_only=True was used).\")\n                d[key] = data\n            else:\n                if not isinstance(data, (tuple, list)):\n                    raise ValueError(\"loader must return a tuple or list (because image_only=False was used).\")\n                d[key] = data[0]\n                if not isinstance(data[1], dict):\n                    raise ValueError(\"metadata must be a dict.\")\n                meta_key = meta_key or f\"{key}_{meta_key_postfix}\"\n                if meta_key in d and not self.overwriting:\n                    raise KeyError(f\"Meta data with key {meta_key} already exists and overwriting=False.\")\n                d[meta_key] = data[1]\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_FgBgToIndices_FgBgToIndices.__init__.self.output_shape.output_shape": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_FgBgToIndices_FgBgToIndices.__init__.self.output_shape.output_shape", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 773, "end_line": 792, "span_ids": ["FgBgToIndices", "FgBgToIndices.__init__"], "tokens": 219}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class FgBgToIndices(Transform):\n    \"\"\"\n    Compute foreground and background of the input label data, return the indices.\n    If no output_shape specified, output data will be 1 dim indices after flattening.\n    This transform can help pre-compute foreground and background regions for other transforms.\n    A typical usage is to randomly select foreground and background to crop.\n    The main logic is based on :py:class:`monai.transforms.utils.map_binary_to_indices`.\n\n    Args:\n        image_threshold: if enabled `image` at runtime, use ``image > image_threshold`` to\n            determine the valid image content area and select background only in this area.\n        output_shape: expected shape of output indices. if not None, unravel indices to specified shape.\n\n    \"\"\"\n\n    backend = [TransformBackends.NUMPY, TransformBackends.TORCH]\n\n    def __init__(self, image_threshold: float = 0.0, output_shape: Optional[Sequence[int]] = None) -> None:\n        self.image_threshold = image_threshold\n        self.output_shape = output_shape", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_FgBgToIndicesd_FgBgToIndicesd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_FgBgToIndicesd_FgBgToIndicesd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1092, "end_line": 1136, "span_ids": ["FgBgToIndicesd", "FgBgToIndicesd.__init__", "FgBgToIndicesd.__call__"], "tokens": 506}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class FgBgToIndicesd(MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.FgBgToIndices`.\n\n    Args:\n        keys: keys of the corresponding items to be transformed.\n            See also: :py:class:`monai.transforms.compose.MapTransform`\n        fg_postfix: postfix to save the computed foreground indices in dict.\n            for example, if computed on `label` and `postfix = \"_fg_indices\"`, the key will be `label_fg_indices`.\n        bg_postfix: postfix to save the computed background indices in dict.\n            for example, if computed on `label` and `postfix = \"_bg_indices\"`, the key will be `label_bg_indices`.\n        image_key: if image_key is not None, use ``label == 0 & image > image_threshold`` to determine\n            the negative sample(background). so the output items will not map to all the voxels in the label.\n        image_threshold: if enabled image_key, use ``image > image_threshold`` to determine\n            the valid image content area and select background only in this area.\n        output_shape: expected shape of output indices. if not None, unravel indices to specified shape.\n        allow_missing_keys: don't raise exception if key is missing.\n\n    \"\"\"\n\n    backend = FgBgToIndices.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        fg_postfix: str = \"_fg_indices\",\n        bg_postfix: str = \"_bg_indices\",\n        image_key: Optional[str] = None,\n        image_threshold: float = 0.0,\n        output_shape: Optional[Sequence[int]] = None,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        super().__init__(keys, allow_missing_keys)\n        self.fg_postfix = fg_postfix\n        self.bg_postfix = bg_postfix\n        self.image_key = image_key\n        self.converter = FgBgToIndices(image_threshold, output_shape)\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        image = d[self.image_key] if self.image_key else None\n        for key in self.key_iterator(d):\n            d[str(key) + self.fg_postfix], d[str(key) + self.bg_postfix] = self.converter(d[key], image)\n\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_map_binary_to_indices_map_binary_to_indices.return.fg_indices_bg_indices": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_map_binary_to_indices_map_binary_to_indices.return.fg_indices_bg_indices", "embedding": null, "metadata": {"file_path": "monai/transforms/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 268, "end_line": 300, "span_ids": ["map_binary_to_indices"], "tokens": 456}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def map_binary_to_indices(\n    label: NdarrayOrTensor, image: Optional[NdarrayOrTensor] = None, image_threshold: float = 0.0\n) -> Tuple[NdarrayOrTensor, NdarrayOrTensor]:\n    \"\"\"\n    Compute the foreground and background of input label data, return the indices after fattening.\n    For example:\n    ``label = np.array([[[0, 1, 1], [1, 0, 1], [1, 1, 0]]])``\n    ``foreground indices = np.array([1, 2, 3, 5, 6, 7])`` and ``background indices = np.array([0, 4, 8])``\n\n    Args:\n        label: use the label data to get the foreground/background information.\n        image: if image is not None, use ``label = 0 & image > image_threshold``\n            to define background. so the output items will not map to all the voxels in the label.\n        image_threshold: if enabled `image`, use ``image > image_threshold`` to\n            determine the valid image content area and select background only in this area.\n    \"\"\"\n\n    # Prepare fg/bg indices\n    if label.shape[0] > 1:\n        label = label[1:]  # for One-Hot format data, remove the background channel\n    label_flat = ravel(any_np_pt(label, 0))  # in case label has multiple dimensions\n    fg_indices = nonzero(label_flat)\n    if image is not None:\n        img_flat = ravel(any_np_pt(image > image_threshold, 0))\n        img_flat, *_ = convert_to_dst_type(img_flat, label, dtype=img_flat.dtype)\n        bg_indices = nonzero(img_flat & ~label_flat)\n    else:\n        bg_indices = nonzero(~label_flat)\n\n    # no need to save the indices in GPU, otherwise, still need to move to CPU at runtime when crop by indices\n    fg_indices, *_ = convert_data_type(fg_indices, device=torch.device(\"cpu\"))\n    bg_indices, *_ = convert_data_type(bg_indices, device=torch.device(\"cpu\"))\n    return fg_indices, bg_indices", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_generate_pos_neg_label_crop_centers_generate_pos_neg_label_crop_centers.return.centers": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_generate_pos_neg_label_crop_centers_generate_pos_neg_label_crop_centers.return.centers", "embedding": null, "metadata": {"file_path": "monai/transforms/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 445, "end_line": 500, "span_ids": ["generate_pos_neg_label_crop_centers"], "tokens": 585}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def generate_pos_neg_label_crop_centers(\n    spatial_size: Union[Sequence[int], int],\n    num_samples: int,\n    pos_ratio: float,\n    label_spatial_shape: Sequence[int],\n    fg_indices: NdarrayOrTensor,\n    bg_indices: NdarrayOrTensor,\n    rand_state: Optional[np.random.RandomState] = None,\n    allow_smaller: bool = False,\n) -> List[List[int]]:\n    \"\"\"\n    Generate valid sample locations based on the label with option for specifying foreground ratio\n    Valid: samples sitting entirely within image, expected input shape: [C, H, W, D] or [C, H, W]\n\n    Args:\n        spatial_size: spatial size of the ROIs to be sampled.\n        num_samples: total sample centers to be generated.\n        pos_ratio: ratio of total locations generated that have center being foreground.\n        label_spatial_shape: spatial shape of the original label data to unravel selected centers.\n        fg_indices: pre-computed foreground indices in 1 dimension.\n        bg_indices: pre-computed background indices in 1 dimension.\n        rand_state: numpy randomState object to align with other modules.\n        allow_smaller: if `False`, an exception will be raised if the image is smaller than\n            the requested ROI in any dimension. If `True`, any smaller dimensions will be set to\n            match the cropped size (i.e., no cropping in that dimension).\n\n    Raises:\n        ValueError: When the proposed roi is larger than the image.\n        ValueError: When the foreground and background indices lengths are 0.\n\n    \"\"\"\n    if rand_state is None:\n        rand_state = np.random.random.__self__  # type: ignore\n\n    centers = []\n    fg_indices = np.asarray(fg_indices) if isinstance(fg_indices, Sequence) else fg_indices\n    bg_indices = np.asarray(bg_indices) if isinstance(bg_indices, Sequence) else bg_indices\n    if len(fg_indices) == 0 and len(bg_indices) == 0:\n        raise ValueError(\"No sampling location available.\")\n\n    if len(fg_indices) == 0 or len(bg_indices) == 0:\n        warnings.warn(\n            f\"N foreground {len(fg_indices)}, N  background {len(bg_indices)},\"\n            \"unable to generate class balanced samples.\"\n        )\n        pos_ratio = 0 if fg_indices.size == 0 else 1\n\n    for _ in range(num_samples):\n        indices_to_use = fg_indices if rand_state.rand() < pos_ratio else bg_indices\n        random_int = rand_state.randint(len(indices_to_use))\n        idx = indices_to_use[random_int]\n        center = unravel_index(idx, label_spatial_shape)\n        # shift center to range of valid centers\n        centers.append(correct_crop_centers(center, spatial_size, label_spatial_shape, allow_smaller))\n\n    return centers", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/module.py_load_submodules_load_submodules.return.submodules_err_mod": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/module.py_load_submodules_load_submodules.return.submodules_err_mod", "embedding": null, "metadata": {"file_path": "monai/utils/module.py", "file_name": "module.py", "file_type": "text/x-python", "category": "implementation", "start_line": 64, "end_line": 82, "span_ids": ["load_submodules"], "tokens": 216}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def load_submodules(basemod, load_all: bool = True, exclude_pattern: str = \"(.*[tT]est.*)|(_.*)\"):\n    \"\"\"\n    Traverse the source of the module structure starting with module `basemod`, loading all packages plus all files if\n    `load_all` is True, excluding anything whose name matches `exclude_pattern`.\n    \"\"\"\n    submodules = []\n    err_mod: List[str] = []\n    for importer, name, is_pkg in walk_packages(\n        basemod.__path__, prefix=basemod.__name__ + \".\", onerror=err_mod.append\n    ):\n        if (is_pkg or load_all) and name not in sys.modules and match(exclude_pattern, name) is None:\n            try:\n                mod = import_module(name)\n                importer.find_module(name).load_module(name)  # type: ignore\n                submodules.append(mod)\n            except OptionalImportError:\n                pass  # could not import the optional deps., they are ignored\n\n    return submodules, err_mod", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/setup.py_glob_try_.finally_.print_f_BUILD_MONAI_CPP_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/setup.py_glob_try_.finally_.print_f_BUILD_MONAI_CPP_", "embedding": null, "metadata": {"file_path": "setup.py", "file_name": "setup.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 51, "span_ids": ["docstring"], "tokens": 356}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import glob\nimport os\nimport re\nimport sys\nimport warnings\n\nimport pkg_resources\nfrom setuptools import find_packages, setup\n\nimport versioneer\n\n# TODO: debug mode -g -O0, compile test cases\n\nRUN_BUILD = os.getenv(\"BUILD_MONAI\", \"0\") == \"1\"\nFORCE_CUDA = os.getenv(\"FORCE_CUDA\", \"0\") == \"1\"  # flag ignored if BUILD_MONAI is False\n\nBUILD_CPP = BUILD_CUDA = False\nTORCH_VERSION = 0\ntry:\n    import torch\n\n    print(f\"setup.py with torch {torch.__version__}\")\n    from torch.utils.cpp_extension import BuildExtension, CppExtension\n\n    BUILD_CPP = True\n    from torch.utils.cpp_extension import CUDA_HOME, CUDAExtension\n\n    BUILD_CUDA = (CUDA_HOME is not None) if torch.cuda.is_available() else FORCE_CUDA\n\n    _pt_version = pkg_resources.parse_version(torch.__version__).release  # type: ignore[attr-defined]\n    if _pt_version is None or len(_pt_version) < 3:\n        raise AssertionError(\"unknown torch version\")\n    TORCH_VERSION = int(_pt_version[0]) * 10000 + int(_pt_version[1]) * 100 + int(_pt_version[2])\nexcept (ImportError, TypeError, AssertionError, AttributeError) as e:\n    warnings.warn(f\"extension build skipped: {e}\")\nfinally:\n    if not RUN_BUILD:\n        BUILD_CPP = BUILD_CUDA = False\n        print(\"Please set environment variable `BUILD_MONAI=1` to enable Cpp/CUDA extension build.\")\n    print(f\"BUILD_MONAI_CPP={BUILD_CPP}, BUILD_MONAI_CUDA={BUILD_CUDA}, TORCH_VERSION={TORCH_VERSION}.\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/setup.py_torch_parallel_backend_omp_flags.return._fopenmp_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/setup.py_torch_parallel_backend_omp_flags.return._fopenmp_", "embedding": null, "metadata": {"file_path": "setup.py", "file_name": "setup.py", "file_type": "text/x-python", "category": "implementation", "start_line": 54, "end_line": 78, "span_ids": ["torch_parallel_backend", "omp_flags"], "tokens": 207}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def torch_parallel_backend():\n    try:\n        match = re.search(\"^ATen parallel backend: (?P<backend>.*)$\", torch._C._parallel_info(), re.MULTILINE)\n        if match is None:\n            return None\n        backend = match.group(\"backend\")\n        if backend == \"OpenMP\":\n            return \"AT_PARALLEL_OPENMP\"\n        if backend == \"native thread pool\":\n            return \"AT_PARALLEL_NATIVE\"\n        if backend == \"native thread pool and TBB\":\n            return \"AT_PARALLEL_NATIVE_TBB\"\n    except (NameError, AttributeError):  # no torch or no binaries\n        warnings.warn(\"Could not determine torch parallel_info.\")\n    return None\n\n\ndef omp_flags():\n    if sys.platform == \"win32\":\n        return [\"/openmp\"]\n    if sys.platform == \"darwin\":\n        # https://stackoverflow.com/questions/37362414/\n        # return [\"-fopenmp=libiomp5\"]\n        return []\n    return [\"-fopenmp\"]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/clang_format_utils.py_os_CLANG_FORMAT_PATH.os_path_join_CLANG_FORMAT": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/clang_format_utils.py_os_CLANG_FORMAT_PATH.os_path_join_CLANG_FORMAT", "embedding": null, "metadata": {"file_path": "tests/clang_format_utils.py", "file_name": "clang_format_utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 15, "end_line": 43, "span_ids": ["impl:11", "docstring"], "tokens": 354}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport platform\nimport stat\nimport sys\n\nfrom monai.apps.utils import download_url\n\n# String representing the host platform (e.g. Linux, Darwin).\nHOST_PLATFORM = platform.system()\n\n# MONAI directory root, derived from the location of this file.\nMONAI_ROOT = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))\n\n# This dictionary maps each platform to the S3 object URL for its clang-format binary.\nPLATFORM_TO_CF_URL = {\n    \"Darwin\": \"https://oss-clang-format.s3.us-east-2.amazonaws.com/mac/clang-format-mojave\",\n    \"Linux\": \"https://oss-clang-format.s3.us-east-2.amazonaws.com/linux64/clang-format-linux64\",\n}\n\n# This dictionary maps each platform to a relative path to a file containing its reference hash.\n# github/pytorch/pytorch/tree/63d62d3e44a0a4ec09d94f30381d49b78cc5b095/tools/clang_format_hash\nPLATFORM_TO_HASH = {\n    \"Darwin\": \"1485a242a96c737ba7cdd9f259114f2201accdb46d87ac7a8650b1a814cd4d4d\",\n    \"Linux\": \"e1c8b97b919541a99e0a355df5c3f9e8abebc64259dbee6f8c68e1ef90582856\",\n}\n\n# Directory and file paths for the clang-format binary.\nCLANG_FORMAT_DIR = os.path.join(MONAI_ROOT, \".clang-format-bin\")\nCLANG_FORMAT_PATH = os.path.join(CLANG_FORMAT_DIR, \"clang-format\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/clang_format_utils.py_get_and_check_clang_format_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/clang_format_utils.py_get_and_check_clang_format_", "embedding": null, "metadata": {"file_path": "tests/clang_format_utils.py", "file_name": "clang_format_utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 46, "end_line": 80, "span_ids": ["get_and_check_clang_format", "impl:13"], "tokens": 283}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_and_check_clang_format():\n    \"\"\"\n    Download a platform-appropriate clang-format binary if one doesn't already exist at the expected location and verify\n    that it is the right binary by checking its SHA1 hash against the expected hash.\n    \"\"\"\n    # If the host platform is not in PLATFORM_TO_HASH, it is unsupported.\n    if HOST_PLATFORM not in PLATFORM_TO_HASH:\n        print(f\"Unsupported platform: {HOST_PLATFORM}\")\n        return False\n    if HOST_PLATFORM not in PLATFORM_TO_CF_URL:\n        print(f\"Unsupported platform: {HOST_PLATFORM}\")\n        return False\n\n    try:\n        download_url(\n            PLATFORM_TO_CF_URL[HOST_PLATFORM], CLANG_FORMAT_PATH, PLATFORM_TO_HASH[HOST_PLATFORM], hash_type=\"sha256\"\n        )\n    except Exception as e:\n        print(f\"Download {CLANG_FORMAT_PATH} failed: {e}\")\n        print(f\"Please remove {CLANG_FORMAT_PATH} and retry.\")\n        return False\n\n    # Make sure the binary is executable.\n    mode = os.stat(CLANG_FORMAT_PATH).st_mode\n    mode |= stat.S_IXUSR\n    os.chmod(CLANG_FORMAT_PATH, mode)\n    print(f\"Using clang-format located at {CLANG_FORMAT_PATH}\")\n\n    return True\n\n\nif __name__ == \"__main__\":\n    ok = get_and_check_clang_format()\n    sys.exit(int(not ok))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/min_tests.py_if___name_____main____": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/min_tests.py_if___name_____main____", "embedding": null, "metadata": {"file_path": "tests/min_tests.py", "file_name": "min_tests.py", "file_type": "text/x-python", "category": "implementation", "start_line": 141, "end_line": 156, "span_ids": ["impl"], "tokens": 125}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "if __name__ == \"__main__\":\n\n    # testing import submodules\n    from monai.utils.module import load_submodules\n\n    _, err_mod = load_submodules(sys.modules[\"monai\"], True)\n    if err_mod:\n        print(err_mod)\n        # expecting that only engines and handlers are not imported\n        assert sorted(err_mod) == [\"monai.engines\", \"monai.handlers\"]\n\n    # testing all modules\n    test_runner = unittest.TextTestRunner(stream=sys.stdout, verbosity=2)\n    result = test_runner.run(run_testsuit())\n    sys.exit(int(not result.wasSuccessful()))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_check_hash.py_os_TEST_CASE_5._b4dc3c246b298eae37cefdf": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_check_hash.py_os_TEST_CASE_5._b4dc3c246b298eae37cefdf", "embedding": null, "metadata": {"file_path": "tests/test_check_hash.py", "file_name": "test_check_hash.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 29, "span_ids": ["docstring"], "tokens": 139}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport tempfile\nimport unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.apps import check_hash\n\nTEST_CASE_1 = [\"b94716452086a054208395e8c9d1ae2a\", \"md5\", True]\n\nTEST_CASE_2 = [\"abcdefg\", \"md5\", False]\n\nTEST_CASE_3 = [None, \"md5\", True]\n\nTEST_CASE_4 = [None, \"sha1\", True]\n\nTEST_CASE_5 = [\"b4dc3c246b298eae37cefdfdd2a50b091ffd5e69\", \"sha1\", True]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_check_hash.py_TestCheckMD5_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_check_hash.py_TestCheckMD5_", "embedding": null, "metadata": {"file_path": "tests/test_check_hash.py", "file_name": "test_check_hash.py", "file_type": "text/x-python", "category": "test", "start_line": 32, "end_line": 51, "span_ids": ["TestCheckMD5", "impl:11", "TestCheckMD5.test_result", "TestCheckMD5.test_hash_type_error"], "tokens": 175}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestCheckMD5(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5])\n    def test_result(self, md5_value, t, expected_result):\n        test_image = np.ones((5, 5, 3))\n        with tempfile.TemporaryDirectory() as tempdir:\n            filename = os.path.join(tempdir, \"test_file.png\")\n            test_image.tofile(filename)\n\n            result = check_hash(filename, md5_value, hash_type=t)\n            self.assertTrue(result == expected_result)\n\n    def test_hash_type_error(self):\n        with self.assertRaises(ValueError):\n            with tempfile.TemporaryDirectory() as tempdir:\n                check_hash(tempdir, \"test_hash\", \"test_type\")\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compose.py_sys_TestCompose.test_dict_compose.self_assertDictEqual_c_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compose.py_sys_TestCompose.test_dict_compose.self_assertDictEqual_c_", "embedding": null, "metadata": {"file_path": "tests/test_compose.py", "file_name": "test_compose.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 58, "span_ids": ["TestCompose", "_RandXform.randomize", "TestCompose.test_empty_compose", "_RandXform", "TestCompose.test_non_dict_compose", "docstring", "TestCompose.test_dict_compose", "_RandXform.__call__"], "tokens": 281}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import sys\nimport unittest\n\nfrom monai.data import DataLoader, Dataset\nfrom monai.transforms import AddChannel, Compose\nfrom monai.transforms.transform import Randomizable\nfrom monai.utils import set_determinism\n\n\nclass _RandXform(Randomizable):\n    def randomize(self):\n        self.val = self.R.random_sample()\n\n    def __call__(self, __unused):\n        self.randomize()\n        return self.val\n\n\nclass TestCompose(unittest.TestCase):\n    def test_empty_compose(self):\n        c = Compose()\n        i = 1\n        self.assertEqual(c(i), 1)\n\n    def test_non_dict_compose(self):\n        def a(i):\n            return i + \"a\"\n\n        def b(i):\n            return i + \"b\"\n\n        c = Compose([a, b, a, b])\n        self.assertEqual(c(\"\"), \"abab\")\n\n    def test_dict_compose(self):\n        def a(d):\n            d = dict(d)\n            d[\"a\"] += 1\n            return d\n\n        def b(d):\n            d = dict(d)\n            d[\"b\"] += 1\n            return d\n\n        c = Compose([a, b, a, b, a])\n        self.assertDictEqual(c({\"a\": 0, \"b\": 0}), {\"a\": 3, \"b\": 2})", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compose.py_TestCompose.test_random_compose_TestCompose.test_err_msg.with_self_assertRaisesReg.transforms_42_1_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compose.py_TestCompose.test_random_compose_TestCompose.test_err_msg.with_self_assertRaisesReg.transforms_42_1_", "embedding": null, "metadata": {"file_path": "tests/test_compose.py", "file_name": "test_compose.py", "file_type": "text/x-python", "category": "test", "start_line": 82, "end_line": 116, "span_ids": ["TestCompose.test_err_msg", "TestCompose.test_randomize_warn", "TestCompose.test_random_compose"], "tokens": 250}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestCompose(unittest.TestCase):\n\n    def test_random_compose(self):\n        class _Acc(Randomizable):\n            self.rand = 0.0\n\n            def randomize(self, data=None):\n                self.rand = self.R.rand()\n\n            def __call__(self, data):\n                self.randomize()\n                return self.rand + data\n\n        c = Compose([_Acc(), _Acc()])\n        self.assertNotAlmostEqual(c(0), c(0))\n        c.set_random_state(123)\n        self.assertAlmostEqual(c(1), 1.61381597)\n        c.set_random_state(223)\n        c.randomize()\n        self.assertAlmostEqual(c(1), 1.90734751)\n\n    def test_randomize_warn(self):\n        class _RandomClass(Randomizable):\n            def randomize(self, foo1, foo2):\n                pass\n\n            def __call__(self, data):\n                pass\n\n        c = Compose([_RandomClass(), _RandomClass()])\n        with self.assertWarns(Warning):\n            c.randomize()\n\n    def test_err_msg(self):\n        transforms = Compose([abs, AddChannel(), round])\n        with self.assertRaisesRegex(Exception, \"AddChannel\"):\n            transforms(42.1)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compose.py_TestCompose.test_data_loader_TestCompose.test_data_loader.set_determinism_None_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compose.py_TestCompose.test_data_loader_TestCompose.test_data_loader.set_determinism_None_", "embedding": null, "metadata": {"file_path": "tests/test_compose.py", "file_name": "test_compose.py", "file_type": "text/x-python", "category": "test", "start_line": 114, "end_line": 135, "span_ids": ["TestCompose.test_data_loader"], "tokens": 231}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestCompose(unittest.TestCase):\n\n    def test_data_loader(self):\n        xform_1 = Compose([_RandXform()])\n        train_ds = Dataset([1], transform=xform_1)\n\n        xform_1.set_random_state(123)\n        out_1 = train_ds[0]\n        self.assertAlmostEqual(out_1, 0.2045649)\n\n        set_determinism(seed=123)\n        train_loader = DataLoader(train_ds, num_workers=0)\n        out_1 = next(iter(train_loader))\n        self.assertAlmostEqual(out_1.cpu().item(), 0.84291356)\n\n        if sys.platform != \"win32\":  # skip multi-worker tests on win32\n            train_loader = DataLoader(train_ds, num_workers=1)\n            out_1 = next(iter(train_loader))\n            self.assertAlmostEqual(out_1.cpu().item(), 0.180814653)\n\n            train_loader = DataLoader(train_ds, num_workers=2)\n            out_1 = next(iter(train_loader))\n            self.assertAlmostEqual(out_1.cpu().item(), 0.04293707)\n        set_determinism(None)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_convolutions.py_unittest_TestConvolution2D.test_transpose2.self_assertEqual_out_shap": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_convolutions.py_unittest_TestConvolution2D.test_transpose2.self_assertEqual_out_shap", "embedding": null, "metadata": {"file_path": "tests/test_convolutions.py", "file_name": "test_convolutions.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 66, "span_ids": ["TestConvolution2D.test_conv1", "TestConvolution2D.test_conv_only1", "TestConvolution2D.test_transpose2", "TestConvolution2D.test_dilation1", "TestConvolution2D.test_dropout1", "TestConvolution2D", "TestConvolution2D.test_transpose1", "docstring", "TestConvolution2D.test_stride1", "TestConvolution2D.test_conv1_no_acti"], "tokens": 628}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nfrom monai.networks.blocks import Convolution, ResidualUnit\nfrom tests.utils import TorchImageTestCase2D, TorchImageTestCase3D\n\n\nclass TestConvolution2D(TorchImageTestCase2D):\n    def test_conv1(self):\n        conv = Convolution(2, self.input_channels, self.output_channels)\n        out = conv(self.imt)\n        expected_shape = (1, self.output_channels, self.im_shape[0], self.im_shape[1])\n        self.assertEqual(out.shape, expected_shape)\n\n    def test_conv1_no_acti(self):\n        conv = Convolution(2, self.input_channels, self.output_channels, act=None)\n        out = conv(self.imt)\n        expected_shape = (1, self.output_channels, self.im_shape[0], self.im_shape[1])\n        self.assertEqual(out.shape, expected_shape)\n\n    def test_conv_only1(self):\n        conv = Convolution(2, self.input_channels, self.output_channels, conv_only=True)\n        out = conv(self.imt)\n        expected_shape = (1, self.output_channels, self.im_shape[0], self.im_shape[1])\n        self.assertEqual(out.shape, expected_shape)\n\n    def test_stride1(self):\n        for strides in [2, [2, 2], (2, 2)]:\n            conv = Convolution(2, self.input_channels, self.output_channels, strides=strides)\n            out = conv(self.imt)\n            expected_shape = (1, self.output_channels, self.im_shape[0] // 2, self.im_shape[1] // 2)\n            self.assertEqual(out.shape, expected_shape)\n\n    def test_dilation1(self):\n        conv = Convolution(2, self.input_channels, self.output_channels, dilation=3)\n        out = conv(self.imt)\n        expected_shape = (1, self.output_channels, self.im_shape[0], self.im_shape[1])\n        self.assertEqual(out.shape, expected_shape)\n\n    def test_dropout1(self):\n        conv = Convolution(2, self.input_channels, self.output_channels, dropout=0.15)\n        out = conv(self.imt)\n        expected_shape = (1, self.output_channels, self.im_shape[0], self.im_shape[1])\n        self.assertEqual(out.shape, expected_shape)\n\n    def test_transpose1(self):\n        conv = Convolution(2, self.input_channels, self.output_channels, is_transposed=True)\n        out = conv(self.imt)\n        expected_shape = (1, self.output_channels, self.im_shape[0], self.im_shape[1])\n        self.assertEqual(out.shape, expected_shape)\n\n    def test_transpose2(self):\n        conv = Convolution(2, self.input_channels, self.output_channels, strides=2, is_transposed=True)\n        out = conv(self.imt)\n        expected_shape = (1, self.output_channels, self.im_shape[0] * 2, self.im_shape[1] * 2)\n        self.assertEqual(out.shape, expected_shape)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_convolutions.py_TestConvolution3D_TestConvolution3D.test_conv_only1.self_assertEqual_out_shap": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_convolutions.py_TestConvolution3D_TestConvolution3D.test_conv_only1.self_assertEqual_out_shap", "embedding": null, "metadata": {"file_path": "tests/test_convolutions.py", "file_name": "test_convolutions.py", "file_type": "text/x-python", "category": "test", "start_line": 69, "end_line": 86, "span_ids": ["TestConvolution3D", "TestConvolution3D.test_conv1_no_acti", "TestConvolution3D.test_conv1", "TestConvolution3D.test_conv_only1"], "tokens": 252}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestConvolution3D(TorchImageTestCase3D):\n    def test_conv1(self):\n        conv = Convolution(3, self.input_channels, self.output_channels, dropout=0.1, adn_ordering=\"DAN\")\n        out = conv(self.imt)\n        expected_shape = (1, self.output_channels, self.im_shape[1], self.im_shape[0], self.im_shape[2])\n        self.assertEqual(out.shape, expected_shape)\n\n    def test_conv1_no_acti(self):\n        conv = Convolution(3, self.input_channels, self.output_channels, act=None, adn_ordering=\"AND\")\n        out = conv(self.imt)\n        expected_shape = (1, self.output_channels, self.im_shape[1], self.im_shape[0], self.im_shape[2])\n        self.assertEqual(out.shape, expected_shape)\n\n    def test_conv_only1(self):\n        conv = Convolution(3, self.input_channels, self.output_channels, conv_only=True)\n        out = conv(self.imt)\n        expected_shape = (1, self.output_channels, self.im_shape[1], self.im_shape[0], self.im_shape[2])\n        self.assertEqual(out.shape, expected_shape)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_convolutions.py_TestConvolution3D.test_stride1_TestConvolution3D.test_stride1.for_strides_in_2_2_2_.self_assertEqual_out_shap": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_convolutions.py_TestConvolution3D.test_stride1_TestConvolution3D.test_stride1.for_strides_in_2_2_2_.self_assertEqual_out_shap", "embedding": null, "metadata": {"file_path": "tests/test_convolutions.py", "file_name": "test_convolutions.py", "file_type": "text/x-python", "category": "test", "start_line": 88, "end_line": 99, "span_ids": ["TestConvolution3D.test_stride1"], "tokens": 131}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestConvolution3D(TorchImageTestCase3D):\n\n    def test_stride1(self):\n        for strides in [2, (2, 2, 2), [2, 2, 2]]:\n            conv = Convolution(3, self.input_channels, self.output_channels, strides=strides)\n            out = conv(self.imt)\n            expected_shape = (\n                1,\n                self.output_channels,\n                self.im_shape[1] // 2,\n                self.im_shape[0] // 2,\n                self.im_shape[2] // 2,\n            )\n            self.assertEqual(out.shape, expected_shape)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_convolutions.py_TestConvolution3D.test_dilation1_TestConvolution3D.test_transpose2.self_assertEqual_out_shap": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_convolutions.py_TestConvolution3D.test_dilation1_TestConvolution3D.test_transpose2.self_assertEqual_out_shap", "embedding": null, "metadata": {"file_path": "tests/test_convolutions.py", "file_name": "test_convolutions.py", "file_type": "text/x-python", "category": "test", "start_line": 101, "end_line": 123, "span_ids": ["TestConvolution3D.test_transpose1", "TestConvolution3D.test_dropout1", "TestConvolution3D.test_dilation1", "TestConvolution3D.test_transpose2"], "tokens": 328}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestConvolution3D(TorchImageTestCase3D):\n\n    def test_dilation1(self):\n        conv = Convolution(3, self.input_channels, self.output_channels, dilation=3)\n        out = conv(self.imt)\n        expected_shape = (1, self.output_channels, self.im_shape[1], self.im_shape[0], self.im_shape[2])\n        self.assertEqual(out.shape, expected_shape)\n\n    def test_dropout1(self):\n        conv = Convolution(3, self.input_channels, self.output_channels, dropout=0.15)\n        out = conv(self.imt)\n        expected_shape = (1, self.output_channels, self.im_shape[1], self.im_shape[0], self.im_shape[2])\n        self.assertEqual(out.shape, expected_shape)\n\n    def test_transpose1(self):\n        conv = Convolution(3, self.input_channels, self.output_channels, is_transposed=True)\n        out = conv(self.imt)\n        expected_shape = (1, self.output_channels, self.im_shape[1], self.im_shape[0], self.im_shape[2])\n        self.assertEqual(out.shape, expected_shape)\n\n    def test_transpose2(self):\n        conv = Convolution(3, self.input_channels, self.output_channels, strides=2, is_transposed=True)\n        out = conv(self.imt)\n        expected_shape = (1, self.output_channels, self.im_shape[1] * 2, self.im_shape[0] * 2, self.im_shape[2] * 2)\n        self.assertEqual(out.shape, expected_shape)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dynunet.py_unittest_TEST_CASE_DEEP_SUPERVISION._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dynunet.py_unittest_TEST_CASE_DEEP_SUPERVISION._", "embedding": null, "metadata": {"file_path": "tests/test_dynunet.py", "file_name": "test_dynunet.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 81, "span_ids": ["docstring:11", "docstring"], "tokens": 661}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom typing import Any, Sequence, Union\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks import eval_mode\nfrom monai.networks.nets import DynUNet\nfrom tests.utils import test_script_save\n\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\nstrides: Sequence[Union[Sequence[int], int]]\nkernel_size: Sequence[Any]\nexpected_shape: Sequence[Any]\n\nTEST_CASE_DYNUNET_2D = []\nout_channels = 2\nin_size = 64\nspatial_dims = 2\nfor kernel_size in [(3, 3, 3, 1), ((3, 1), 1, (3, 3), (1, 1))]:\n    for strides in [(1, 1, 1, 1), (2, 2, 2, 1)]:\n        expected_shape = (1, out_channels, *[in_size // strides[0]] * spatial_dims)\n        for in_channels in [2, 3]:\n            for res_block in [True, False]:\n                test_case = [\n                    {\n                        \"spatial_dims\": spatial_dims,\n                        \"in_channels\": in_channels,\n                        \"out_channels\": out_channels,\n                        \"kernel_size\": kernel_size,\n                        \"strides\": strides,\n                        \"upsample_kernel_size\": strides[1:],\n                        \"norm_name\": \"batch\",\n                        \"act_name\": (\"leakyrelu\", {\"inplace\": True, \"negative_slope\": 0.2}),\n                        \"deep_supervision\": False,\n                        \"res_block\": res_block,\n                        \"dropout\": None,\n                    },\n                    (1, in_channels, in_size, in_size),\n                    expected_shape,\n                ]\n                TEST_CASE_DYNUNET_2D.append(test_case)\n\nTEST_CASE_DYNUNET_3D = []  # in 3d cases, also test anisotropic kernel/strides\nin_channels = 1\nin_size = 64\nfor out_channels in [2, 3]:\n    expected_shape = (1, out_channels, 64, 32, 64)\n    for res_block in [True, False]:\n        test_case = [\n            {\n                \"spatial_dims\": 3,\n                \"in_channels\": in_channels,\n                \"out_channels\": out_channels,\n                \"kernel_size\": (3, (1, 1, 3), 3, 3),\n                \"strides\": ((1, 2, 1), 2, 2, 1),\n                \"upsample_kernel_size\": (2, 2, 1),\n                \"filters\": (64, 96, 128, 192),\n                \"norm_name\": (\"INSTANCE\", {\"affine\": True}),\n                \"deep_supervision\": True,\n                \"res_block\": res_block,\n                \"dropout\": (\"alphadropout\", {\"p\": 0.25}),\n            },\n            (1, in_channels, in_size, in_size, in_size),\n            expected_shape,\n        ]\n        TEST_CASE_DYNUNET_3D.append(test_case)\n\nTEST_CASE_DEEP_SUPERVISION = []", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gaussian.py_unittest_TEST_CASES_NORM_F": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gaussian.py_unittest_TEST_CASES_NORM_F", "embedding": null, "metadata": {"file_path": "tests/test_gaussian.py", "file_name": "test_gaussian.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 221, "span_ids": ["docstring"], "tokens": 43}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks.layers.convutils import gaussian_1d\n\nTEST_CASES_NORM_F =\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gaussian.py_TestGaussian1d_TestGaussian1d.test_gaussian.None_2": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gaussian.py_TestGaussian1d_TestGaussian1d.test_gaussian.None_2", "embedding": null, "metadata": {"file_path": "tests/test_gaussian.py", "file_name": "test_gaussian.py", "file_type": "text/x-python", "category": "test", "start_line": 224, "end_line": 245, "span_ids": ["TestGaussian1d.test_gaussian", "TestGaussian1d"], "tokens": 238}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestGaussian1d(unittest.TestCase):\n    def test_gaussian(self):\n        np.testing.assert_allclose(\n            gaussian_1d(0.5, 8),\n            torch.tensor(\n                [\n                    0.0000e00,\n                    2.9802e-07,\n                    1.3496e-03,\n                    1.5731e-01,\n                    6.8269e-01,\n                    1.5731e-01,\n                    1.3496e-03,\n                    2.9802e-07,\n                    0.0000e00,\n                ]\n            ),\n            rtol=1e-4,\n        )\n\n        np.testing.assert_allclose(gaussian_1d(1, 1), torch.tensor([0.24173, 0.382925, 0.24173]), rtol=1e-4)\n        np.testing.assert_allclose(gaussian_1d(1, 1, normalize=True), torch.tensor([0.2790, 0.4420, 0.2790]), rtol=1e-4)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gaussian.py_TestGaussian1d.test_scalespace_gaussian_TestGaussian1d.test_scalespace_gaussian.None_3": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gaussian.py_TestGaussian1d.test_scalespace_gaussian_TestGaussian1d.test_scalespace_gaussian.None_3", "embedding": null, "metadata": {"file_path": "tests/test_gaussian.py", "file_name": "test_gaussian.py", "file_type": "text/x-python", "category": "test", "start_line": 247, "end_line": 292, "span_ids": ["TestGaussian1d.test_scalespace_gaussian"], "tokens": 385}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestGaussian1d(unittest.TestCase):\n\n    def test_scalespace_gaussian(self):\n        np.testing.assert_allclose(\n            gaussian_1d(0.5, 8, \"scalespace\"),\n            torch.tensor(\n                [\n                    7.9472e-06,\n                    2.5451e-04,\n                    6.1161e-03,\n                    9.8113e-02,\n                    7.9102e-01,\n                    9.8113e-02,\n                    6.1161e-03,\n                    2.5451e-04,\n                    7.9472e-06,\n                ]\n            ),\n            rtol=1e-4,\n        )\n\n        np.testing.assert_allclose(\n            gaussian_1d(1, 1, \"scalespace\"), torch.tensor([0.20791, 0.46576, 0.20791]), rtol=1e-3\n        )\n\n        np.testing.assert_allclose(\n            gaussian_1d(1, 1, \"scalespace\", normalize=True), torch.tensor([0.2358, 0.5283, 0.2358]), rtol=1e-3\n        )\n\n        np.testing.assert_allclose(\n            gaussian_1d(5, 1, \"scalespace\"),\n            torch.tensor(\n                [\n                    0.048225,\n                    0.057891,\n                    0.06675,\n                    0.073911,\n                    0.078576,\n                    0.080197,\n                    0.078576,\n                    0.073911,\n                    0.06675,\n                    0.057891,\n                    0.048225,\n                ]\n            ),\n            rtol=1e-3,\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gaussian.py_TestGaussian1d.test_norm_false_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gaussian.py_TestGaussian1d.test_norm_false_", "embedding": null, "metadata": {"file_path": "tests/test_gaussian.py", "file_name": "test_gaussian.py", "file_type": "text/x-python", "category": "test", "start_line": 306, "end_line": 327, "span_ids": ["TestGaussian1d.test_norm_false", "impl:3", "TestGaussian1d.test_wrong_sigma"], "tokens": 250}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestGaussian1d(unittest.TestCase):\n\n    @parameterized.expand(TEST_CASES_NORM_F)\n    def test_norm_false(self, variance, expected):\n        extent = 6\n        atol = 1e-4\n        sigma = np.sqrt(variance)\n        k_erf = gaussian_1d(sigma, truncated=extent / sigma, approx=\"erf\", normalize=False).numpy()\n        k_sampled = gaussian_1d(sigma, truncated=extent / sigma, approx=\"sampled\").numpy()\n        k_scalespace = gaussian_1d(sigma, truncated=extent / sigma, approx=\"scalespace\").numpy()\n        np.testing.assert_allclose(k_erf, expected[0], atol=atol)\n        np.testing.assert_allclose(k_sampled, expected[1], atol=atol)\n        np.testing.assert_allclose(k_scalespace, expected[2], atol=atol)\n\n    def test_wrong_sigma(self):\n        with self.assertRaises(ValueError):\n            gaussian_1d(1, -10)\n        with self.assertRaises(NotImplementedError):\n            gaussian_1d(1, 10, \"wrong_arg\")\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gaussian_filter.py_GaussianFilterTestCase_GaussianFilterTestCase.test_1d.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gaussian_filter.py_GaussianFilterTestCase_GaussianFilterTestCase.test_1d.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_gaussian_filter.py", "file_name": "test_gaussian_filter.py", "file_type": "text/x-python", "category": "test", "start_line": 86, "end_line": 109, "span_ids": ["GaussianFilterTestCase.test_1d", "GaussianFilterTestCase"], "tokens": 190}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GaussianFilterTestCase(unittest.TestCase):\n    def test_1d(self):\n        a = torch.ones(1, 8, 10)\n        g = GaussianFilter(1, 3, 3).to(torch.device(\"cpu:0\"))\n        expected = np.array(\n            [\n                [\n                    [\n                        0.5654129,\n                        0.68915915,\n                        0.79146194,\n                        0.8631974,\n                        0.8998163,\n                        0.8998163,\n                        0.8631973,\n                        0.79146194,\n                        0.6891592,\n                        0.5654129,\n                    ]\n                ]\n            ]\n        )\n        expected = np.tile(expected, (1, 8, 1))\n        np.testing.assert_allclose(g(a).cpu().numpy(), expected, rtol=1e-5)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_hausdorff_distance.py_unittest_create_spherical_seg_3d.return.image": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_hausdorff_distance.py_unittest_create_spherical_seg_3d.return.image", "embedding": null, "metadata": {"file_path": "tests/test_hausdorff_distance.py", "file_name": "test_hausdorff_distance.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 46, "span_ids": ["create_spherical_seg_3d", "docstring"], "tokens": 319}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom typing import Tuple\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.metrics import HausdorffDistanceMetric\n\n\ndef create_spherical_seg_3d(\n    radius: float = 20.0, centre: Tuple[int, int, int] = (49, 49, 49), im_shape: Tuple[int, int, int] = (99, 99, 99)\n) -> np.ndarray:\n    \"\"\"\n    Return a 3D image with a sphere inside. Voxel values will be\n    1 inside the sphere, and 0 elsewhere.\n\n    Args:\n        radius: radius of sphere (in terms of number of voxels, can be partial)\n        centre: location of sphere centre.\n        im_shape: shape of image to create\n\n    See also:\n        :py:meth:`~create_test_image_3d`\n    \"\"\"\n    # Create image\n    image = np.zeros(im_shape, dtype=np.int32)\n    spy, spx, spz = np.ogrid[\n        -centre[0] : im_shape[0] - centre[0], -centre[1] : im_shape[1] - centre[1], -centre[2] : im_shape[2] - centre[2]\n    ]\n    circle = (spx * spx + spy * spy + spz * spz) <= radius * radius\n\n    image[circle] = 1\n    image[~circle] = 0\n    return image", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_is_supported_format.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_is_supported_format.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_is_supported_format.py", "file_name": "test_is_supported_format.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 39, "span_ids": ["impl:13", "TestIsSupportedFormat.test_value", "TestIsSupportedFormat", "docstring"], "tokens": 295}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nfrom parameterized import parameterized\n\nfrom monai.data import is_supported_format\n\nTEST_CASE_1 = [{\"filename\": \"testfile.nii.gz\", \"suffixes\": [\"nii\", \"nii.gz\"]}, True]\n\nTEST_CASE_2 = [{\"filename\": \"./testfile.nii.gz\", \"suffixes\": [\"nii\", \"nii.gz\"]}, True]\n\nTEST_CASE_3 = [{\"filename\": \"./test.data/file.nii.gz\", \"suffixes\": [\"nii\", \"nii.gz\"]}, True]\n\nTEST_CASE_4 = [{\"filename\": \"./test.data/file.nii\", \"suffixes\": [\"nii\", \"nii.gz\"]}, True]\n\nTEST_CASE_5 = [{\"filename\": \"C:\\\\documents\\\\testfile.nii.gz\", \"suffixes\": [\"nii\", \"nii.gz\"]}, True]\n\nTEST_CASE_6 = [{\"filename\": \"1.3.12.2.1107.5.4.4.145.nii.gz\", \"suffixes\": [\"nii.gz\"]}, True]\n\n\nclass TestIsSupportedFormat(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5, TEST_CASE_6])\n    def test_value(self, input_param, result):\n        self.assertEqual(is_supported_format(**input_param), result)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_decathlon_datalist.py_json_TestLoadDecathlonDatalist.test_seg_values.with_tempfile_TemporaryDi.None_1": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_decathlon_datalist.py_json_TestLoadDecathlonDatalist.test_seg_values.with_tempfile_TemporaryDi.None_1", "embedding": null, "metadata": {"file_path": "tests/test_load_decathlon_datalist.py", "file_name": "test_load_decathlon_datalist.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 40, "span_ids": ["TestLoadDecathlonDatalist", "TestLoadDecathlonDatalist.test_seg_values", "docstring"], "tokens": 294}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import json\nimport os\nimport tempfile\nimport unittest\nfrom pathlib import Path\n\nfrom monai.data import load_decathlon_datalist\n\n\nclass TestLoadDecathlonDatalist(unittest.TestCase):\n    def test_seg_values(self):\n        with tempfile.TemporaryDirectory() as tempdir:\n            test_data = {\n                \"name\": \"Spleen\",\n                \"description\": \"Spleen Segmentation\",\n                \"labels\": {\"0\": \"background\", \"1\": \"spleen\"},\n                \"training\": [\n                    {\"image\": \"spleen_19.nii.gz\", \"label\": \"spleen_19.nii.gz\"},\n                    {\"image\": \"spleen_31.nii.gz\", \"label\": \"spleen_31.nii.gz\"},\n                ],\n                \"test\": [\"spleen_15.nii.gz\", \"spleen_23.nii.gz\"],\n            }\n            json_str = json.dumps(test_data)\n            file_path = os.path.join(tempdir, \"test_data.json\")\n            with open(file_path, \"w\") as json_file:\n                json_file.write(json_str)\n            result = load_decathlon_datalist(file_path, True, \"training\", tempdir)\n            self.assertEqual(result[0][\"image\"], os.path.join(tempdir, \"spleen_19.nii.gz\"))\n            self.assertEqual(result[0][\"label\"], os.path.join(tempdir, \"spleen_19.nii.gz\"))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_decathlon_datalist.py_TestLoadDecathlonDatalist.test_cls_values_TestLoadDecathlonDatalist.test_cls_values.with_tempfile_TemporaryDi.None_1": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_decathlon_datalist.py_TestLoadDecathlonDatalist.test_cls_values_TestLoadDecathlonDatalist.test_cls_values.with_tempfile_TemporaryDi.None_1", "embedding": null, "metadata": {"file_path": "tests/test_load_decathlon_datalist.py", "file_name": "test_load_decathlon_datalist.py", "file_type": "text/x-python", "category": "test", "start_line": 41, "end_line": 56, "span_ids": ["TestLoadDecathlonDatalist.test_cls_values"], "tokens": 225}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestLoadDecathlonDatalist(unittest.TestCase):\n\n    def test_cls_values(self):\n        with tempfile.TemporaryDirectory() as tempdir:\n            test_data = {\n                \"name\": \"ChestXRay\",\n                \"description\": \"Chest X-ray classification\",\n                \"labels\": {\"0\": \"background\", \"1\": \"chest\"},\n                \"training\": [{\"image\": \"chest_19.nii.gz\", \"label\": 0}, {\"image\": \"chest_31.nii.gz\", \"label\": 1}],\n                \"test\": [\"chest_15.nii.gz\", \"chest_23.nii.gz\"],\n            }\n            json_str = json.dumps(test_data)\n            file_path = os.path.join(tempdir, \"test_data.json\")\n            with open(file_path, \"w\") as json_file:\n                json_file.write(json_str)\n            result = load_decathlon_datalist(file_path, False, \"training\", tempdir)\n            self.assertEqual(result[0][\"image\"], os.path.join(tempdir, \"chest_19.nii.gz\"))\n            self.assertEqual(result[0][\"label\"], 0)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_decathlon_datalist.py_TestLoadDecathlonDatalist.test_seg_no_basedir_TestLoadDecathlonDatalist.test_seg_no_basedir.with_tempfile_TemporaryDi.None_1": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_decathlon_datalist.py_TestLoadDecathlonDatalist.test_seg_no_basedir_TestLoadDecathlonDatalist.test_seg_no_basedir.with_tempfile_TemporaryDi.None_1", "embedding": null, "metadata": {"file_path": "tests/test_load_decathlon_datalist.py", "file_name": "test_load_decathlon_datalist.py", "file_type": "text/x-python", "category": "test", "start_line": 58, "end_line": 82, "span_ids": ["TestLoadDecathlonDatalist.test_seg_no_basedir"], "tokens": 314}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestLoadDecathlonDatalist(unittest.TestCase):\n\n    def test_seg_no_basedir(self):\n        with tempfile.TemporaryDirectory() as tempdir:\n            test_data = {\n                \"name\": \"Spleen\",\n                \"description\": \"Spleen Segmentation\",\n                \"labels\": {\"0\": \"background\", \"1\": \"spleen\"},\n                \"training\": [\n                    {\n                        \"image\": os.path.join(tempdir, \"spleen_19.nii.gz\"),\n                        \"label\": os.path.join(tempdir, \"spleen_19.nii.gz\"),\n                    },\n                    {\n                        \"image\": os.path.join(tempdir, \"spleen_31.nii.gz\"),\n                        \"label\": os.path.join(tempdir, \"spleen_31.nii.gz\"),\n                    },\n                ],\n                \"test\": [os.path.join(tempdir, \"spleen_15.nii.gz\"), os.path.join(tempdir, \"spleen_23.nii.gz\")],\n            }\n            json_str = json.dumps(test_data)\n            file_path = os.path.join(tempdir, \"test_data.json\")\n            with open(file_path, \"w\") as json_file:\n                json_file.write(json_str)\n            result = load_decathlon_datalist(file_path, True, \"training\", None)\n            self.assertEqual(result[0][\"image\"], os.path.join(tempdir, \"spleen_19.nii.gz\"))\n            self.assertEqual(result[0][\"label\"], os.path.join(tempdir, \"spleen_19.nii.gz\"))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_image.py_TestLoadImage.test_itk_reader_TestLoadImage.test_itk_reader.with_tempfile_TemporaryDi.self_assertTupleEqual_res": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_image.py_TestLoadImage.test_itk_reader_TestLoadImage.test_itk_reader.with_tempfile_TemporaryDi.self_assertTupleEqual_res", "embedding": null, "metadata": {"file_path": "tests/test_load_image.py", "file_name": "test_load_image.py", "file_type": "text/x-python", "category": "test", "start_line": 122, "end_line": 139, "span_ids": ["TestLoadImage.test_itk_reader"], "tokens": 238}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestLoadImage(unittest.TestCase):\n\n    @parameterized.expand([TEST_CASE_6, TEST_CASE_7, TEST_CASE_8, TEST_CASE_9])\n    def test_itk_reader(self, input_param, filenames, expected_shape):\n        test_image = np.random.rand(128, 128, 128)\n        with tempfile.TemporaryDirectory() as tempdir:\n            for i, name in enumerate(filenames):\n                filenames[i] = os.path.join(tempdir, name)\n                itk_np_view = itk.image_view_from_array(test_image)\n                itk.imwrite(itk_np_view, filenames[i])\n            result = LoadImage(**input_param)(filenames)\n\n            if isinstance(result, tuple):\n                result, header = result\n                self.assertTrue(\"affine\" in header)\n                self.assertEqual(header[\"filename_or_obj\"], os.path.join(tempdir, \"test_image.nii.gz\"))\n                np_diag = np.diag([-1, -1, 1, 1])\n                np.testing.assert_allclose(header[\"affine\"], np_diag)\n                np.testing.assert_allclose(header[\"original_affine\"], np_diag)\n            self.assertTupleEqual(result.shape, expected_shape)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_image.py_TestLoadImage.test_register_TestLoadImage.test_register.with_tempfile_TemporaryDi.self_assertTupleEqual_res": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_image.py_TestLoadImage.test_register_TestLoadImage.test_register.with_tempfile_TemporaryDi.self_assertTupleEqual_res", "embedding": null, "metadata": {"file_path": "tests/test_load_image.py", "file_name": "test_load_image.py", "file_type": "text/x-python", "category": "test", "start_line": 199, "end_line": 211, "span_ids": ["TestLoadImage.test_register"], "tokens": 141}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestLoadImage(unittest.TestCase):\n\n    def test_register(self):\n        spatial_size = (32, 64, 128)\n        test_image = np.random.rand(*spatial_size)\n        with tempfile.TemporaryDirectory() as tempdir:\n            filename = os.path.join(tempdir, \"test_image.nii.gz\")\n            itk_np_view = itk.image_view_from_array(test_image)\n            itk.imwrite(itk_np_view, filename)\n\n            loader = LoadImage(image_only=False)\n            loader.register(ITKReader())\n            result, header = loader(filename)\n            self.assertTupleEqual(tuple(header[\"spatial_shape\"]), spatial_size[::-1])\n            self.assertTupleEqual(result.shape, spatial_size[::-1])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_map_binary_to_indices.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_map_binary_to_indices.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_map_binary_to_indices.py", "file_name": "test_map_binary_to_indices.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 74, "span_ids": ["impl:8", "TestMapBinaryToIndices.test_type_shape", "TestMapBinaryToIndices", "docstring"], "tokens": 616}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import map_binary_to_indices\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    TESTS.append(\n        [\n            {\"label\": p(np.array([[[0, 1, 1], [1, 0, 1], [1, 1, 0]]])), \"image\": None, \"image_threshold\": 0.0},\n            np.array([1, 2, 3, 5, 6, 7]),\n            np.array([0, 4, 8]),\n        ]\n    )\n    TESTS.append(\n        [\n            {\n                \"label\": p(np.array([[[0, 1, 1], [1, 0, 1], [1, 1, 0]]])),\n                \"image\": p(np.array([[[1, 1, 1], [1, 0, 1], [1, 1, 1]]])),\n                \"image_threshold\": 0.0,\n            },\n            np.array([1, 2, 3, 5, 6, 7]),\n            np.array([0, 8]),\n        ]\n    )\n    TESTS.append(\n        [\n            {\n                \"label\": p(np.array([[[0, 1, 1], [1, 0, 1], [1, 1, 0]]])),\n                \"image\": p(np.array([[[3, 3, 3], [3, 1, 3], [3, 3, 3]]])),\n                \"image_threshold\": 1.0,\n            },\n            np.array([1, 2, 3, 5, 6, 7]),\n            np.array([0, 8]),\n        ]\n    )\n    TESTS.append(\n        [\n            {\n                \"label\": p(np.array([[[0, 1, 2], [3, 0, 4], [5, 6, 0]]])),\n                \"image\": p(np.array([[[3, 3, 3], [3, 1, 3], [3, 3, 3]]])),\n                \"image_threshold\": 1.0,\n            },\n            np.array([1, 2, 3, 5, 6, 7]),\n            np.array([0, 8]),\n        ]\n    )\n\n\nclass TestMapBinaryToIndices(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_type_shape(self, input_data, expected_fg, expected_bg):\n        fg_indices, bg_indices = map_binary_to_indices(**input_data)\n        assert_allclose(fg_indices, expected_fg, type_test=False)\n        assert_allclose(bg_indices, expected_bg, type_test=False)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_normalize_intensity.py_TestNormalizeIntensity.test_value_errors_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_normalize_intensity.py_TestNormalizeIntensity.test_value_errors_", "embedding": null, "metadata": {"file_path": "tests/test_normalize_intensity.py", "file_name": "test_normalize_intensity.py", "file_type": "text/x-python", "category": "test", "start_line": 111, "end_line": 124, "span_ids": ["TestNormalizeIntensity.test_value_errors", "impl:16"], "tokens": 174}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestNormalizeIntensity(NumpyImageTestCase2D):\n\n    @parameterized.expand([[p] for p in TEST_NDARRAYS])\n    def test_value_errors(self, im_type):\n        input_data = im_type(np.array([[0.0, 3.0, 0.0, 4.0], [0.0, 4.0, 0.0, 5.0]]))\n        normalizer = NormalizeIntensity(nonzero=True, channel_wise=True, subtrahend=[1])\n        with self.assertRaises(ValueError):\n            normalizer(input_data)\n        normalizer = NormalizeIntensity(nonzero=True, channel_wise=True, subtrahend=[1, 2], divisor=[1])\n        with self.assertRaises(ValueError):\n            normalizer(input_data)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_optim_novograd.py_unittest_build_test_cases.return.test_cases": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_optim_novograd.py_unittest_build_test_cases.return.test_cases", "embedding": null, "metadata": {"file_path": "tests/test_optim_novograd.py", "file_name": "test_optim_novograd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 38, "span_ids": ["build_test_cases", "docstring"], "tokens": 224}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\nfrom torch.autograd import Variable\n\nfrom monai.optimizers import Novograd\n\n\ndef build_test_cases(data):\n    [weight, bias, input] = data\n    weight = Variable(weight, requires_grad=True)\n    bias = Variable(bias, requires_grad=True)\n    input = Variable(input)\n\n    default_params = {\"lr\": 1e-3, \"amsgrad\": False, \"grad_averaging\": False, \"weight_decay\": 0}\n\n    test_case_same_param = [{\"params\": [weight, bias]}]\n    test_case_diff_param = [\n        {\"params\": [weight]},\n        {\"params\": [bias], \"lr\": 1e-2, \"amsgrad\": True, \"grad_averaging\": True, \"weight_decay\": 0.1},\n    ]\n\n    test_cases = []\n    test_cases.append([test_case_same_param, default_params, weight, bias, input])\n    test_cases.append([test_case_diff_param, default_params, weight, bias, input])\n    return test_cases", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_optim_novograd.py_TEST_CASES_ALL_if_torch_cuda_device_coun.TEST_CASES_ALL_build_t": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_optim_novograd.py_TEST_CASES_ALL_if_torch_cuda_device_coun.TEST_CASES_ALL_build_t", "embedding": null, "metadata": {"file_path": "tests/test_optim_novograd.py", "file_name": "test_optim_novograd.py", "file_type": "text/x-python", "category": "test", "start_line": 41, "end_line": 54, "span_ids": ["impl"], "tokens": 193}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "TEST_CASES_ALL = build_test_cases([torch.randn(10, 5), torch.randn(10), torch.randn(5)])  # normal parameters\n\nTEST_CASES_ALL += build_test_cases(  # non-contiguous parameters\n    [torch.randn(10, 5, 2)[..., 0], torch.randn(10, 2)[..., 0], torch.randn(5)]\n)\n\nif torch.cuda.is_available():\n    TEST_CASES_ALL += build_test_cases(  # gpu parameters\n        [torch.randn(10, 5).cuda(), torch.randn(10).cuda(), torch.randn(5).cuda()]\n    )\nif torch.cuda.device_count() > 1:\n    TEST_CASES_ALL += build_test_cases(  # multi-gpu parameters\n        [torch.randn(10, 5).cuda(0), torch.randn(10).cuda(1), torch.randn(5).cuda(0)]\n    )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_optim_novograd.py_TestNovograd_TestNovograd.test_step.self_assertLess_fn_item": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_optim_novograd.py_TestNovograd_TestNovograd.test_step.self_assertLess_fn_item", "embedding": null, "metadata": {"file_path": "tests/test_optim_novograd.py", "file_name": "test_optim_novograd.py", "file_type": "text/x-python", "category": "test", "start_line": 57, "end_line": 80, "span_ids": ["TestNovograd.test_step", "TestNovograd"], "tokens": 189}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestNovograd(unittest.TestCase):\n    \"\"\"\n    This class takes `Pytorch's test_optim function:\n    https://github.com/pytorch/pytorch/blob/v1.9.0/test/test_optim.py for reference.\n\n    \"\"\"\n\n    @parameterized.expand(TEST_CASES_ALL)\n    def test_step(self, specify_param, default_param, weight, bias, input):\n        optimizer = Novograd(specify_param, **default_param)\n\n        def fn():\n            optimizer.zero_grad()\n            y = weight.mv(input)\n            if y.is_cuda and bias.is_cuda and y.get_device() != bias.get_device():\n                y = y.cuda(bias.get_device())\n            loss = (y + bias).pow(2).sum()\n            loss.backward()\n            return loss\n\n        initial_value = fn().item()\n        for _ in range(100):\n            optimizer.step(fn)\n        self.assertLess(fn().item(), initial_value)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_optim_novograd.py_TestNovograd.test_ill_arg_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_optim_novograd.py_TestNovograd.test_ill_arg_", "embedding": null, "metadata": {"file_path": "tests/test_optim_novograd.py", "file_name": "test_optim_novograd.py", "file_type": "text/x-python", "category": "test", "start_line": 99, "end_line": 115, "span_ids": ["TestNovograd.test_ill_arg", "impl:8"], "tokens": 191}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestNovograd(unittest.TestCase):\n\n    def test_ill_arg(self):\n        param = {\"params\": [Variable(torch.randn(10), requires_grad=True)]}\n        with self.assertRaisesRegex(ValueError, \"Invalid learning rate: -1\"):\n            Novograd(param, lr=-1)\n        with self.assertRaisesRegex(ValueError, \"Invalid epsilon value: -1\"):\n            Novograd(param, eps=-1)\n        with self.assertRaisesRegex(ValueError, \"Invalid beta parameter at index 0: 1.0\"):\n            Novograd(param, betas=(1.0, 0.98))\n        with self.assertRaisesRegex(ValueError, \"Invalid beta parameter at index 1: -1\"):\n            Novograd(param, betas=(0.9, -1))\n        with self.assertRaisesRegex(ValueError, \"Invalid weight_decay value: -1\"):\n            Novograd(param, weight_decay=-1)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_parallel_execution.py_unittest_TestParallelExecution.test_single_gpu.trainer_run_fake_data_str": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_parallel_execution.py_unittest_TestParallelExecution.test_single_gpu.trainer_run_fake_data_str", "embedding": null, "metadata": {"file_path": "tests/test_parallel_execution.py", "file_name": "test_parallel_execution.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 41, "span_ids": ["TestParallelExecution.test_single_gpu", "fake_loss", "TestParallelExecution", "docstring", "fake_data_stream"], "tokens": 219}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nimport warnings\n\nimport torch\n\nfrom monai.engines import create_multigpu_supervised_trainer\nfrom tests.utils import skip_if_no_cuda\n\n\ndef fake_loss(y_pred, y):\n    return (y_pred[0] + y).sum()\n\n\ndef fake_data_stream():\n    while True:\n        yield torch.rand((10, 1, 64, 64)), torch.rand((10, 1, 64, 64))\n\n\nclass TestParallelExecution(unittest.TestCase):\n    \"\"\"\n    Tests single GPU, multi GPU, and CPU execution with the Ignite supervised trainer.\n    \"\"\"\n\n    @skip_if_no_cuda\n    def test_single_gpu(self):\n        device = torch.device(\"cuda:0\")\n        net = torch.nn.Conv2d(1, 1, 3, padding=1).to(device)\n        opt = torch.optim.Adam(net.parameters(), 1e-3)\n        trainer = create_multigpu_supervised_trainer(net, opt, fake_loss, [device])\n        trainer.run(fake_data_stream(), 2, 2)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_parallel_execution.py_TestParallelExecution.test_multi_gpu_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_parallel_execution.py_TestParallelExecution.test_multi_gpu_", "embedding": null, "metadata": {"file_path": "tests/test_parallel_execution.py", "file_name": "test_parallel_execution.py", "file_type": "text/x-python", "category": "test", "start_line": 43, "end_line": 65, "span_ids": ["TestParallelExecution.test_multi_gpu", "TestParallelExecution.test_cpu", "impl"], "tokens": 210}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestParallelExecution(unittest.TestCase):\n\n    @skip_if_no_cuda\n    def test_multi_gpu(self):\n        device = torch.device(\"cuda\")\n        net = torch.nn.Conv2d(1, 1, 3, padding=1).to(device)\n        opt = torch.optim.Adam(net.parameters(), 1e-3)\n\n        with warnings.catch_warnings():\n            warnings.simplefilter(\"ignore\")  # ignore warnings about imbalanced GPU memory\n\n            trainer = create_multigpu_supervised_trainer(net, opt, fake_loss, None)\n\n        trainer.run(fake_data_stream(), 2, 2)\n\n    def test_cpu(self):\n        net = torch.nn.Conv2d(1, 1, 3, padding=1)\n        opt = torch.optim.Adam(net.parameters(), 1e-3)\n        trainer = create_multigpu_supervised_trainer(net, opt, fake_loss, [])\n        trainer.run(fake_data_stream(), 2, 2)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_patch_dataset.py_sys_TestPatchDataset.test_shape.self_assertEqual_output_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_patch_dataset.py_sys_TestPatchDataset.test_shape.self_assertEqual_output_", "embedding": null, "metadata": {"file_path": "tests/test_patch_dataset.py", "file_name": "test_patch_dataset.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 39, "span_ids": ["TestPatchDataset", "TestPatchDataset.test_shape", "identity", "docstring"], "tokens": 199}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import sys\nimport unittest\n\nimport numpy as np\n\nfrom monai.data import DataLoader, Dataset, PatchDataset\nfrom monai.transforms import RandShiftIntensity, RandSpatialCropSamples\nfrom monai.utils import set_determinism\n\n\ndef identity(x):\n    # simple transform that returns the input itself\n    return x\n\n\nclass TestPatchDataset(unittest.TestCase):\n    def test_shape(self):\n        test_dataset = [\"vwxyz\", \"hello\", \"world\"]\n        n_per_image = len(test_dataset[0])\n\n        result = PatchDataset(dataset=test_dataset, patch_func=identity, samples_per_image=n_per_image)\n\n        output = []\n        n_workers = 0 if sys.platform == \"win32\" else 2\n        for item in DataLoader(result, batch_size=3, num_workers=n_workers):\n            output.append(\"\".join(item))\n        expected = [\"vwx\", \"yzh\", \"ell\", \"owo\", \"rld\"]\n        self.assertEqual(output, expected)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_patch_dataset.py_TestPatchDataset.test_loading_array_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_patch_dataset.py_TestPatchDataset.test_loading_array_", "embedding": null, "metadata": {"file_path": "tests/test_patch_dataset.py", "file_name": "test_patch_dataset.py", "file_type": "text/x-python", "category": "test", "start_line": 41, "end_line": 87, "span_ids": ["TestPatchDataset.test_loading_array", "impl"], "tokens": 500}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestPatchDataset(unittest.TestCase):\n\n    def test_loading_array(self):\n        set_determinism(seed=1234)\n        # image dataset\n        images = [np.arange(16, dtype=float).reshape(1, 4, 4), np.arange(16, dtype=float).reshape(1, 4, 4)]\n        # image patch sampler\n        n_samples = 8\n        sampler = RandSpatialCropSamples(roi_size=(3, 3), num_samples=n_samples, random_center=True, random_size=False)\n\n        # image level\n        patch_intensity = RandShiftIntensity(offsets=1.0, prob=1.0)\n        image_ds = Dataset(images, transform=patch_intensity)\n        # patch level\n        ds = PatchDataset(dataset=image_ds, patch_func=sampler, samples_per_image=n_samples, transform=patch_intensity)\n\n        np.testing.assert_equal(len(ds), n_samples * len(images))\n        # use the patch dataset, length: len(images) x samplers_per_image\n        for item in DataLoader(ds, batch_size=2, shuffle=False, num_workers=0):\n            np.testing.assert_equal(tuple(item.shape), (2, 1, 3, 3))\n        np.testing.assert_allclose(\n            item[0],\n            np.array(\n                [[[1.338681, 2.338681, 3.338681], [5.338681, 6.338681, 7.338681], [9.338681, 10.338681, 11.338681]]]\n            ),\n            rtol=1e-5,\n        )\n        if sys.platform != \"win32\":\n            for item in DataLoader(ds, batch_size=2, shuffle=False, num_workers=2):\n                np.testing.assert_equal(tuple(item.shape), (2, 1, 3, 3))\n            np.testing.assert_allclose(\n                item[0],\n                np.array(\n                    [\n                        [\n                            [4.957847, 5.957847, 6.957847],\n                            [8.957847, 9.957847, 10.957847],\n                            [12.957847, 13.957847, 14.957847],\n                        ]\n                    ]\n                ),\n                rtol=1e-5,\n            )\n        set_determinism(seed=None)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_pil_reader.py_os_TEST_CASE_7._128_128_3_test_im": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_pil_reader.py_os_TEST_CASE_7._128_128_3_test_im", "embedding": null, "metadata": {"file_path": "tests/test_pil_reader.py", "file_name": "test_pil_reader.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 34, "span_ids": ["docstring"], "tokens": 276}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport tempfile\nimport unittest\n\nimport numpy as np\nfrom parameterized import parameterized\nfrom PIL import Image\n\nfrom monai.data import PILReader\n\nTEST_CASE_1 = [(128, 128), [\"test_image.png\"], (128, 128), (128, 128)]\n\nTEST_CASE_2 = [(128, 128, 3), [\"test_image.png\"], (128, 128, 3), (128, 128)]\n\nTEST_CASE_3 = [(128, 128, 4), [\"test_image.png\"], (128, 128, 4), (128, 128)]\n\nTEST_CASE_4 = [(128, 128), [\"test_image1.png\", \"test_image2.png\", \"test_image3.png\"], (3, 128, 128), (128, 128)]\n\nTEST_CASE_5 = [(128, 128, 3), [\"test_image.jpg\"], (128, 128, 3), (128, 128)]\n\nTEST_CASE_6 = [(128, 128, 3), [\"test_image.bmp\"], (128, 128, 3), (128, 128)]\n\nTEST_CASE_7 = [(128, 128, 3), [\"test_image.png\"], (128, 128, 2), (128, 128)]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_pil_reader.py_TestPNGReader_TestPNGReader.test_shape_value.if_result_0_shape_tes.else_.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_pil_reader.py_TestPNGReader_TestPNGReader.test_shape_value.if_result_0_shape_tes.else_.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_pil_reader.py", "file_name": "test_pil_reader.py", "file_type": "text/x-python", "category": "test", "start_line": 37, "end_line": 56, "span_ids": ["TestPNGReader", "TestPNGReader.test_shape_value"], "tokens": 277}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestPNGReader(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5, TEST_CASE_6])\n    def test_shape_value(self, data_shape, filenames, expected_shape, meta_shape):\n        test_image = np.random.randint(0, 256, size=data_shape)\n        with tempfile.TemporaryDirectory() as tempdir:\n            for i, name in enumerate(filenames):\n                filenames[i] = os.path.join(tempdir, name)\n                Image.fromarray(test_image.astype(\"uint8\")).save(filenames[i])\n            reader = PILReader(mode=\"r\")\n            result = reader.get_data(reader.read(filenames))\n            # load image by PIL and compare the result\n            test_image = np.asarray(Image.open(filenames[0]))\n\n        self.assertTupleEqual(tuple(result[1][\"spatial_shape\"]), meta_shape)\n        self.assertTupleEqual(result[0].shape, expected_shape)\n        test_image = np.moveaxis(test_image, 0, 1)\n        if result[0].shape == test_image.shape:\n            np.testing.assert_allclose(result[0], test_image)\n        else:\n            np.testing.assert_allclose(result[0], np.tile(test_image, [result[0].shape[0], 1, 1]))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_pil_reader.py_TestPNGReader.test_converter_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_pil_reader.py_TestPNGReader.test_converter_", "embedding": null, "metadata": {"file_path": "tests/test_pil_reader.py", "file_name": "test_pil_reader.py", "file_type": "text/x-python", "category": "test", "start_line": 58, "end_line": 78, "span_ids": ["TestPNGReader.test_converter", "impl:15"], "tokens": 232}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestPNGReader(unittest.TestCase):\n\n    @parameterized.expand([TEST_CASE_7])\n    def test_converter(self, data_shape, filenames, expected_shape, meta_shape):\n        test_image = np.random.randint(0, 256, size=data_shape)\n        with tempfile.TemporaryDirectory() as tempdir:\n            for i, name in enumerate(filenames):\n                filenames[i] = os.path.join(tempdir, name)\n                Image.fromarray(test_image.astype(\"uint8\")).save(filenames[i])\n            reader = PILReader(converter=lambda image: image.convert(\"LA\"))\n            result = reader.get_data(reader.read(filenames, mode=\"r\"))\n            # load image by PIL and compare the result\n            test_image = np.asarray(Image.open(filenames[0]).convert(\"LA\"))\n\n        self.assertTupleEqual(tuple(result[1][\"spatial_shape\"]), meta_shape)\n        self.assertTupleEqual(result[0].shape, expected_shape)\n        test_image = np.moveaxis(test_image, 0, 1)\n        np.testing.assert_allclose(result[0], test_image)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_polyval.py_unittest_TEST_CASES._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_polyval.py_unittest_TEST_CASES._", "embedding": null, "metadata": {"file_path": "tests/test_polyval.py", "file_name": "test_polyval.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 28, "span_ids": ["docstring"], "tokens": 220}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks.layers import polyval\n\nTEST_CASES = [\n    [[1.0, 2.5, -4.2], 5.0, 33.3],\n    [[2, 1, 0], 3.0, 21],\n    [[2, 1, 0], [3.0, 3.0], [21, 21]],\n    [torch.as_tensor([2, 1, 0]), [3.0, 3.0], [21, 21]],\n    [torch.as_tensor([2, 1, 0]), torch.as_tensor([3.0, 3.0]), [21, 21]],\n    [torch.as_tensor([2, 1, 0]), np.array([3.0, 3.0]), [21, 21]],\n    [[], np.array([3.0, 3.0]), [0, 0]],\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_polyval.py_TestPolyval_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_polyval.py_TestPolyval_", "embedding": null, "metadata": {"file_path": "tests/test_polyval.py", "file_name": "test_polyval.py", "file_type": "text/x-python", "category": "test", "start_line": 31, "end_line": 53, "span_ids": ["impl:3", "TestPolyval", "TestPolyval.test_floats", "TestPolyval.test_gpu"], "tokens": 211}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestPolyval(unittest.TestCase):\n    @parameterized.expand(TEST_CASES)\n    def test_floats(self, coef, x, expected):\n        result = polyval(coef, x)\n        np.testing.assert_allclose(result.cpu().numpy(), expected)\n\n    @parameterized.expand(TEST_CASES)\n    def test_gpu(self, coef, x, expected):\n        device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n        x = torch.as_tensor(x, dtype=torch.float, device=device)\n        x.requires_grad = True\n        coef = torch.as_tensor(coef, dtype=torch.float, device=device)\n        coef.requires_grad = True\n        result = polyval(coef, x)\n        if coef.shape[0] > 0:  # empty coef doesn't have grad\n            result.mean().backward()\n            np.testing.assert_allclose(coef.grad.shape, coef.shape)\n        np.testing.assert_allclose(result.cpu().detach().numpy(), expected)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_histogram_shift.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_histogram_shift.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_rand_histogram_shift.py", "file_name": "test_rand_histogram_shift.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 56, "span_ids": ["TestRandHistogramShift", "impl:7", "TestRandHistogramShift.test_rand_histogram_shift", "docstring"], "tokens": 456}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import RandHistogramShift\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    TESTS.append(\n        [\n            {\"num_control_points\": 5, \"prob\": 0.0},\n            {\"img\": p(np.arange(8).reshape((1, 2, 2, 2)))},\n            np.arange(8).reshape((1, 2, 2, 2)),\n        ]\n    )\n    TESTS.append(\n        [\n            {\"num_control_points\": 5, \"prob\": 0.9},\n            {\"img\": p(np.arange(8).reshape((1, 2, 2, 2)).astype(np.float32))},\n            np.array([[[[0.0, 0.57227867], [1.1391707, 1.68990281]], [[2.75833219, 4.34445884], [5.70913743, 7.0]]]]),\n        ]\n    )\n    TESTS.append(\n        [\n            {\"num_control_points\": (5, 20), \"prob\": 0.9},\n            {\"img\": p(np.arange(8).reshape((1, 2, 2, 2)).astype(np.float32))},\n            np.array([[[[0.0, 1.17472492], [2.21553091, 2.88292011]], [[3.98407301, 5.01302123], [6.09275004, 7.0]]]]),\n        ]\n    )\n\n\nclass TestRandHistogramShift(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_rand_histogram_shift(self, input_param, input_data, expected_val):\n        g = RandHistogramShift(**input_param)\n        g.set_random_state(123)\n        result = g(**input_data)\n        assert_allclose(result, expected_val, rtol=1e-4, atol=1e-4, type_test=False)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_histogram_shiftd.py_TestRandHistogramShiftD_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_histogram_shiftd.py_TestRandHistogramShiftD_", "embedding": null, "metadata": {"file_path": "tests/test_rand_histogram_shiftd.py", "file_name": "test_rand_histogram_shiftd.py", "file_type": "text/x-python", "category": "test", "start_line": 61, "end_line": 75, "span_ids": ["TestRandHistogramShiftD.test_rand_histogram_shiftd", "TestRandHistogramShiftD", "impl:7"], "tokens": 132}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandHistogramShiftD(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_rand_histogram_shiftd(self, input_param, input_data, expected_val):\n        g = RandHistogramShiftd(**input_param)\n        g.set_random_state(123)\n        res = g(input_data)\n        for key in res:\n            result = res[key]\n            expected = expected_val[key] if isinstance(expected_val, dict) else expected_val\n            assert_allclose(result, expected, rtol=1e-4, atol=1e-4, type_test=False)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_spatial_crop_samples.py_unittest_TEST_CASE_1._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_spatial_crop_samples.py_unittest_TEST_CASE_1._", "embedding": null, "metadata": {"file_path": "tests/test_rand_spatial_crop_samples.py", "file_name": "test_rand_spatial_crop_samples.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 43, "span_ids": ["docstring"], "tokens": 421}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import RandSpatialCropSamples\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTEST_CASE_1 = [\n    {\"roi_size\": [3, 3, 3], \"num_samples\": 4, \"random_center\": True, \"random_size\": False},\n    np.arange(192).reshape(3, 4, 4, 4),\n    [(3, 3, 3, 3), (3, 3, 3, 3), (3, 3, 3, 3), (3, 3, 3, 3)],\n    np.array(\n        [\n            [\n                [[21, 22, 23], [25, 26, 27], [29, 30, 31]],\n                [[37, 38, 39], [41, 42, 43], [45, 46, 47]],\n                [[53, 54, 55], [57, 58, 59], [61, 62, 63]],\n            ],\n            [\n                [[85, 86, 87], [89, 90, 91], [93, 94, 95]],\n                [[101, 102, 103], [105, 106, 107], [109, 110, 111]],\n                [[117, 118, 119], [121, 122, 123], [125, 126, 127]],\n            ],\n            [\n                [[149, 150, 151], [153, 154, 155], [157, 158, 159]],\n                [[165, 166, 167], [169, 170, 171], [173, 174, 175]],\n                [[181, 182, 183], [185, 186, 187], [189, 190, 191]],\n            ],\n        ]\n    ),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_spatial_crop_samples.py_TEST_CASE_2_TEST_CASE_2._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_spatial_crop_samples.py_TEST_CASE_2_TEST_CASE_2._", "embedding": null, "metadata": {"file_path": "tests/test_rand_spatial_crop_samples.py", "file_name": "test_rand_spatial_crop_samples.py", "file_type": "text/x-python", "category": "test", "start_line": 44, "end_line": 67, "span_ids": ["impl:3"], "tokens": 430}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "TEST_CASE_2 = [\n    {\"roi_size\": [3, 3, 3], \"num_samples\": 8, \"random_center\": False, \"random_size\": True},\n    np.arange(192).reshape(3, 4, 4, 4),\n    [(3, 4, 4, 3), (3, 4, 3, 3), (3, 3, 4, 4), (3, 4, 4, 4), (3, 3, 3, 4), (3, 3, 3, 3), (3, 3, 3, 3), (3, 3, 3, 3)],\n    np.array(\n        [\n            [\n                [[21, 22, 23], [25, 26, 27], [29, 30, 31]],\n                [[37, 38, 39], [41, 42, 43], [45, 46, 47]],\n                [[53, 54, 55], [57, 58, 59], [61, 62, 63]],\n            ],\n            [\n                [[85, 86, 87], [89, 90, 91], [93, 94, 95]],\n                [[101, 102, 103], [105, 106, 107], [109, 110, 111]],\n                [[117, 118, 119], [121, 122, 123], [125, 126, 127]],\n            ],\n            [\n                [[149, 150, 151], [153, 154, 155], [157, 158, 159]],\n                [[165, 166, 167], [169, 170, 171], [173, 174, 175]],\n                [[181, 182, 183], [185, 186, 187], [189, 190, 191]],\n            ],\n        ]\n    ),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_spatial_crop_samples.py_TestRandSpatialCropSamples_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_spatial_crop_samples.py_TestRandSpatialCropSamples_", "embedding": null, "metadata": {"file_path": "tests/test_rand_spatial_crop_samples.py", "file_name": "test_rand_spatial_crop_samples.py", "file_type": "text/x-python", "category": "test", "start_line": 71, "end_line": 87, "span_ids": ["impl:5", "TestRandSpatialCropSamples", "TestRandSpatialCropSamples.test_shape"], "tokens": 149}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandSpatialCropSamples(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2])\n    def test_shape(self, input_param, input_data, expected_shape, expected_last_item):\n        for p in TEST_NDARRAYS:\n            xform = RandSpatialCropSamples(**input_param)\n            xform.set_random_state(1234)\n            result = xform(p(input_data))\n\n            np.testing.assert_equal(len(result), input_param[\"num_samples\"])\n            for item, expected in zip(result, expected_shape):\n                self.assertTupleEqual(item.shape, expected)\n            assert_allclose(result[-1], expected_last_item, type_test=False)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_sliding_window_inference.py_unittest_TEST_CASES._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_sliding_window_inference.py_unittest_TEST_CASES._", "embedding": null, "metadata": {"file_path": "tests/test_sliding_window_inference.py", "file_name": "test_sliding_window_inference.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 39, "span_ids": ["docstring"], "tokens": 919}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.inferers import SlidingWindowInferer, sliding_window_inference\nfrom tests.utils import skip_if_no_cuda\n\nTEST_CASES = [\n    [(2, 3, 16), (4,), 3, 0.25, \"constant\", torch.device(\"cpu:0\")],  # 1D small roi\n    [(2, 3, 16, 15, 7, 9), 4, 3, 0.25, \"constant\", torch.device(\"cpu:0\")],  # 4D small roi\n    [(1, 3, 16, 15, 7), (4, -1, 7), 3, 0.25, \"constant\", torch.device(\"cpu:0\")],  # 3D small roi\n    [(2, 3, 16, 15, 7), (4, -1, 7), 3, 0.25, \"constant\", torch.device(\"cpu:0\")],  # 3D small roi\n    [(3, 3, 16, 15, 7), (4, -1, 7), 3, 0.25, \"constant\", torch.device(\"cpu:0\")],  # 3D small roi\n    [(2, 3, 16, 15, 7), (4, -1, 7), 3, 0.25, \"constant\", torch.device(\"cpu:0\")],  # 3D small roi\n    [(1, 3, 16, 15, 7), (4, 10, 7), 3, 0.25, \"constant\", torch.device(\"cpu:0\")],  # 3D small roi\n    [(1, 3, 16, 15, 7), (20, 22, 23), 10, 0.25, \"constant\", torch.device(\"cpu:0\")],  # 3D large roi\n    [(2, 3, 15, 7), (2, 6), 1000, 0.25, \"constant\", torch.device(\"cpu:0\")],  # 2D small roi, large batch\n    [(1, 3, 16, 7), (80, 50), 7, 0.25, \"constant\", torch.device(\"cpu:0\")],  # 2D large roi\n    [(1, 3, 16, 15, 7), (20, 22, 23), 10, 0.5, \"constant\", torch.device(\"cpu:0\")],  # 3D large overlap\n    [(1, 3, 16, 7), (80, 50), 7, 0.5, \"gaussian\", torch.device(\"cpu:0\")],  # 2D large overlap, gaussian\n    [(1, 3, 16, 15, 7), (4, 10, 7), 3, 0.25, \"gaussian\", torch.device(\"cpu:0\")],  # 3D small roi, gaussian\n    [(3, 3, 16, 15, 7), (4, 10, 7), 3, 0.25, \"gaussian\", torch.device(\"cpu:0\")],  # 3D small roi, gaussian\n    [(1, 3, 16, 15, 7), (4, 10, 7), 3, 0.25, \"gaussian\", torch.device(\"cuda:0\")],  # test inference on gpu if availabe\n    [(1, 3, 16, 15, 7), (4, 1, 7), 3, 0.25, \"constant\", torch.device(\"cpu:0\")],  # 3D small roi\n    [(5, 3, 16, 15, 7), (4, 1, 7), 3, 0.25, \"constant\", torch.device(\"cpu:0\")],  # 3D small roi\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_sliding_window_inference.py_TestSlidingWindowInference_TestSlidingWindowInference.test_sliding_window_default.None_3": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_sliding_window_inference.py_TestSlidingWindowInference_TestSlidingWindowInference.test_sliding_window_default.None_3", "embedding": null, "metadata": {"file_path": "tests/test_sliding_window_inference.py", "file_name": "test_sliding_window_inference.py", "file_type": "text/x-python", "category": "test", "start_line": 48, "end_line": 72, "span_ids": ["TestSlidingWindowInference", "TestSlidingWindowInference.test_sliding_window_default"], "tokens": 292}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestSlidingWindowInference(unittest.TestCase):\n    @parameterized.expand(TEST_CASES)\n    def test_sliding_window_default(self, image_shape, roi_shape, sw_batch_size, overlap, mode, device):\n        n_total = np.prod(image_shape)\n        if mode == \"constant\":\n            inputs = torch.arange(n_total, dtype=torch.float).reshape(*image_shape)\n        else:\n            inputs = torch.ones(*image_shape, dtype=torch.float)\n        if device.type == \"cuda\" and not torch.cuda.is_available():\n            device = torch.device(\"cpu:0\")\n\n        def compute(data):\n            return data + 1\n\n        if mode == \"constant\":\n            expected_val = np.arange(n_total, dtype=np.float32).reshape(*image_shape) + 1.0\n        else:\n            expected_val = np.ones(image_shape, dtype=np.float32) + 1.0\n        result = sliding_window_inference(inputs.to(device), roi_shape, sw_batch_size, compute, overlap, mode=mode)\n        np.testing.assert_string_equal(device.type, result.device.type)\n        np.testing.assert_allclose(result.cpu().numpy(), expected_val)\n\n        result = SlidingWindowInferer(roi_shape, sw_batch_size, overlap, mode)(inputs.to(device), compute)\n        np.testing.assert_string_equal(device.type, result.device.type)\n        np.testing.assert_allclose(result.cpu().numpy(), expected_val)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_sliding_window_inference.py_TestSlidingWindowInference.test_default_device_TestSlidingWindowInference.test_default_device.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_sliding_window_inference.py_TestSlidingWindowInference.test_default_device_TestSlidingWindowInference.test_default_device.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_sliding_window_inference.py", "file_name": "test_sliding_window_inference.py", "file_type": "text/x-python", "category": "test", "start_line": 74, "end_line": 86, "span_ids": ["TestSlidingWindowInference.test_default_device"], "tokens": 168}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestSlidingWindowInference(unittest.TestCase):\n\n    def test_default_device(self):\n        device = \"cuda\" if torch.cuda.is_available() else \"cpu:0\"\n        inputs = torch.ones((1, 3, 16, 15, 7)).to(device=device)\n        roi_shape = (4, 10, 7)\n        sw_batch_size = 10\n\n        def compute(data):\n            return data + 1\n\n        result = sliding_window_inference(inputs, roi_shape, sw_batch_size, compute)\n        np.testing.assert_string_equal(inputs.device.type, result.device.type)\n        expected_val = np.ones((1, 3, 16, 15, 7), dtype=np.float32) + 1\n        np.testing.assert_allclose(result.cpu().numpy(), expected_val)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_sliding_window_inference.py_TestSlidingWindowInference.test_sigma_TestSlidingWindowInference.test_sigma.result_6.sliding_window_inference_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_sliding_window_inference.py_TestSlidingWindowInference.test_sigma_TestSlidingWindowInference.test_sigma.result_6.sliding_window_inference_", "embedding": null, "metadata": {"file_path": "tests/test_sliding_window_inference.py", "file_name": "test_sliding_window_inference.py", "file_type": "text/x-python", "category": "test", "start_line": 88, "end_line": 139, "span_ids": ["TestSlidingWindowInference.test_sigma"], "tokens": 573}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestSlidingWindowInference(unittest.TestCase):\n\n    def test_sigma(self):\n        device = \"cuda\" if torch.cuda.is_available() else \"cpu:0\"\n        inputs = torch.ones((1, 1, 7, 7)).to(device=device)\n        roi_shape = (3, 3)\n        sw_batch_size = 10\n\n        class _Pred:\n            add = 1\n\n            def compute(self, data):\n                self.add += 1\n                return data + self.add\n\n        result = sliding_window_inference(\n            inputs,\n            roi_shape,\n            sw_batch_size,\n            _Pred().compute,\n            overlap=0.5,\n            padding_mode=\"constant\",\n            cval=-1,\n            mode=\"constant\",\n            sigma_scale=1.0,\n        )\n\n        expected = np.array(\n            [\n                [\n                    [\n                        [3.0000, 3.0000, 3.0000, 3.0000, 3.0000, 3.0000, 3.0000],\n                        [3.0000, 3.0000, 3.0000, 3.0000, 3.0000, 3.0000, 3.0000],\n                        [3.3333, 3.3333, 3.3333, 3.3333, 3.3333, 3.3333, 3.3333],\n                        [3.6667, 3.6667, 3.6667, 3.6667, 3.6667, 3.6667, 3.6667],\n                        [4.3333, 4.3333, 4.3333, 4.3333, 4.3333, 4.3333, 4.3333],\n                        [4.5000, 4.5000, 4.5000, 4.5000, 4.5000, 4.5000, 4.5000],\n                        [5.0000, 5.0000, 5.0000, 5.0000, 5.0000, 5.0000, 5.0000],\n                    ]\n                ]\n            ]\n        )\n        np.testing.assert_allclose(result.cpu().numpy(), expected, rtol=1e-4)\n        result = sliding_window_inference(\n            inputs,\n            roi_shape,\n            sw_batch_size,\n            _Pred().compute,\n            overlap=0.5,\n            padding_mode=\"constant\",\n            cval=-1,\n            mode=\"gaussian\",\n            sigma_scale=1.0,\n        )\n        # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_sliding_window_inference.py_TestSlidingWindowInference.test_sigma.expected_7_TestSlidingWindowInference.test_sigma.None_3": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_sliding_window_inference.py_TestSlidingWindowInference.test_sigma.expected_7_TestSlidingWindowInference.test_sigma.None_3", "embedding": null, "metadata": {"file_path": "tests/test_sliding_window_inference.py", "file_name": "test_sliding_window_inference.py", "file_type": "text/x-python", "category": "test", "start_line": 140, "end_line": 165, "span_ids": ["TestSlidingWindowInference.test_sigma"], "tokens": 506}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestSlidingWindowInference(unittest.TestCase):\n\n    def test_sigma(self):\n        # ... other code\n        expected = np.array(\n            [\n                [\n                    [\n                        [3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0],\n                        [3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0],\n                        [3.3271625, 3.3271623, 3.3271623, 3.3271623, 3.3271623, 3.3271623, 3.3271625],\n                        [3.6728377, 3.6728377, 3.6728377, 3.6728377, 3.6728377, 3.6728377, 3.6728377],\n                        [4.3271623, 4.3271623, 4.3271627, 4.3271627, 4.3271627, 4.3271623, 4.3271623],\n                        [4.513757, 4.513757, 4.513757, 4.513757, 4.513757, 4.513757, 4.513757],\n                        [4.9999995, 5.0, 5.0, 5.0, 5.0, 5.0, 4.9999995],\n                    ]\n                ]\n            ]\n        )\n        np.testing.assert_allclose(result.cpu().numpy(), expected, rtol=1e-4)\n\n        result = SlidingWindowInferer(roi_shape, sw_batch_size, overlap=0.5, mode=\"gaussian\", sigma_scale=1.0)(\n            inputs, _Pred().compute\n        )\n        np.testing.assert_allclose(result.cpu().numpy(), expected, rtol=1e-4)\n\n        result = SlidingWindowInferer(roi_shape, sw_batch_size, overlap=0.5, mode=\"gaussian\", sigma_scale=[1.0, 1.0])(\n            inputs, _Pred().compute\n        )\n        np.testing.assert_allclose(result.cpu().numpy(), expected, rtol=1e-4)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_surface_distance.py_unittest_create_spherical_seg_3d.return.image": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_surface_distance.py_unittest_create_spherical_seg_3d.return.image", "embedding": null, "metadata": {"file_path": "tests/test_surface_distance.py", "file_name": "test_surface_distance.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 46, "span_ids": ["create_spherical_seg_3d", "docstring"], "tokens": 316}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom typing import Tuple\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.metrics import SurfaceDistanceMetric\n\n\ndef create_spherical_seg_3d(\n    radius: float = 20.0, centre: Tuple[int, int, int] = (49, 49, 49), im_shape: Tuple[int, int, int] = (99, 99, 99)\n) -> np.ndarray:\n    \"\"\"\n    Return a 3D image with a sphere inside. Voxel values will be\n    1 inside the sphere, and 0 elsewhere.\n\n    Args:\n        radius: radius of sphere (in terms of number of voxels, can be partial)\n        centre: location of sphere centre.\n        im_shape: shape of image to create\n\n    See also:\n        :py:meth:`~create_test_image_3d`\n    \"\"\"\n    # Create image\n    image = np.zeros(im_shape, dtype=np.int32)\n    spy, spx, spz = np.ogrid[\n        -centre[0] : im_shape[0] - centre[0], -centre[1] : im_shape[1] - centre[1], -centre[2] : im_shape[2] - centre[2]\n    ]\n    circle = (spx * spx + spy * spy + spz * spz) <= radius * radius\n\n    image[circle] = 1\n    image[~circle] = 0\n    return image", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_zoom.py_TestZoom.test_padding_mode_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_zoom.py_TestZoom.test_padding_mode_", "embedding": null, "metadata": {"file_path": "tests/test_zoom.py", "file_name": "test_zoom.py", "file_type": "text/x-python", "category": "test", "start_line": 59, "end_line": 70, "span_ids": ["TestZoom.test_padding_mode", "impl:5"], "tokens": 258}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestZoom(NumpyImageTestCase2D):\n\n    def test_padding_mode(self):\n        for p in TEST_NDARRAYS:\n            zoom_fn = Zoom(zoom=0.5, mode=\"nearest\", padding_mode=\"constant\", keep_size=True)\n            test_data = p([[[1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0]]])\n            zoomed = zoom_fn(test_data)\n            expected = p([[[0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0], [0.0, 0.0, 0.0, 0.0]]])\n            torch.testing.assert_allclose(zoomed, expected)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/_version.py_render_pep440_pre_render_pep440_pre.return.rendered": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/_version.py_render_pep440_pre_render_pep440_pre.return.rendered", "embedding": null, "metadata": {"file_path": "monai/_version.py", "file_name": "_version.py", "file_type": "text/x-python", "category": "implementation", "start_line": 345, "end_line": 358, "span_ids": ["render_pep440_pre"], "tokens": 107}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def render_pep440_pre(pieces):\n    \"\"\"TAG[.post0.devDISTANCE] -- No -dirty.\n\n    Exceptions:\n    1: no tags. 0.post0.devDISTANCE\n    \"\"\"\n    if pieces[\"closest-tag\"]:\n        rendered = pieces[\"closest-tag\"]\n        if pieces[\"distance\"]:\n            rendered += \".post0.dev%d\" % pieces[\"distance\"]\n    else:\n        # exception #1\n        rendered = \"0.post0.dev%d\" % pieces[\"distance\"]\n    return rendered", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/_version.py_render_pep440_post_render_pep440_post.return.rendered": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/_version.py_render_pep440_post_render_pep440_post.return.rendered", "embedding": null, "metadata": {"file_path": "monai/_version.py", "file_name": "_version.py", "file_type": "text/x-python", "category": "implementation", "start_line": 361, "end_line": 385, "span_ids": ["render_pep440_post"], "tokens": 217}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def render_pep440_post(pieces):\n    \"\"\"TAG[.postDISTANCE[.dev0]+gHEX] .\n\n    The \".dev0\" means dirty. Note that .dev0 sorts backwards\n    (a dirty tree will appear \"older\" than the corresponding clean one),\n    but you shouldn't be releasing software with -dirty anyways.\n\n    Exceptions:\n    1: no tags. 0.postDISTANCE[.dev0]\n    \"\"\"\n    if pieces[\"closest-tag\"]:\n        rendered = pieces[\"closest-tag\"]\n        if pieces[\"distance\"] or pieces[\"dirty\"]:\n            rendered += \".post%d\" % pieces[\"distance\"]\n            if pieces[\"dirty\"]:\n                rendered += \".dev0\"\n            rendered += plus_or_dot(pieces)\n            rendered += \"g%s\" % pieces[\"short\"]\n    else:\n        # exception #1\n        rendered = \"0.post%d\" % pieces[\"distance\"]\n        if pieces[\"dirty\"]:\n            rendered += \".dev0\"\n        rendered += \"+g%s\" % pieces[\"short\"]\n    return rendered", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/__init__.py_CrossValidation_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/__init__.py_CrossValidation_", "embedding": null, "metadata": {"file_path": "monai/apps/__init__.py", "file_name": "__init__.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 15, "span_ids": ["docstring"], "tokens": 68}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from .datasets import CrossValidation, DecathlonDataset, MedNISTDataset\nfrom .mmars import MODEL_DESC, RemoteMMARKeys, download_mmar, get_model_spec, load_from_mmar\nfrom .utils import SUPPORTED_HASH_TYPES, check_hash, download_and_extract, download_url, extractall, get_logger, logger", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/datasets.py_DecathlonDataset.__init___DecathlonDataset._split_datalist.return._datalist_i_for_i_in_sel": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/datasets.py_DecathlonDataset.__init___DecathlonDataset._split_datalist.return._datalist_i_for_i_in_sel", "embedding": null, "metadata": {"file_path": "monai/apps/datasets.py", "file_name": "datasets.py", "file_type": "text/x-python", "category": "implementation", "start_line": 260, "end_line": 369, "span_ids": ["DecathlonDataset._split_datalist", "DecathlonDataset.get_properties", "DecathlonDataset.__init__", "DecathlonDataset._generate_data_list", "DecathlonDataset.randomize", "DecathlonDataset.get_indices"], "tokens": 834}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DecathlonDataset(Randomizable, CacheDataset):\n\n    def __init__(\n        self,\n        root_dir: PathLike,\n        task: str,\n        section: str,\n        transform: Union[Sequence[Callable], Callable] = (),\n        download: bool = False,\n        seed: int = 0,\n        val_frac: float = 0.2,\n        cache_num: int = sys.maxsize,\n        cache_rate: float = 1.0,\n        num_workers: int = 0,\n        progress: bool = True,\n        copy_cache: bool = True,\n    ) -> None:\n        root_dir = Path(root_dir)\n        if not root_dir.is_dir():\n            raise ValueError(\"Root directory root_dir must be a directory.\")\n        self.section = section\n        self.val_frac = val_frac\n        self.set_random_state(seed=seed)\n        if task not in self.resource:\n            raise ValueError(f\"Unsupported task: {task}, available options are: {list(self.resource.keys())}.\")\n        dataset_dir = root_dir / task\n        tarfile_name = f\"{dataset_dir}.tar\"\n        if download:\n            download_and_extract(\n                url=self.resource[task],\n                filepath=tarfile_name,\n                output_dir=root_dir,\n                hash_val=self.md5[task],\n                hash_type=\"md5\",\n                progress=progress,\n            )\n\n        if not dataset_dir.exists():\n            raise RuntimeError(\n                f\"Cannot find dataset directory: {dataset_dir}, please use download=True to download it.\"\n            )\n        self.indices: np.ndarray = np.array([])\n        data = self._generate_data_list(dataset_dir)\n        # as `release` key has typo in Task04 config file, ignore it.\n        property_keys = [\n            \"name\",\n            \"description\",\n            \"reference\",\n            \"licence\",\n            \"tensorImageSize\",\n            \"modality\",\n            \"labels\",\n            \"numTraining\",\n            \"numTest\",\n        ]\n        self._properties = load_decathlon_properties(dataset_dir / \"dataset.json\", property_keys)\n        if transform == ():\n            transform = LoadImaged([\"image\", \"label\"])\n        CacheDataset.__init__(\n            self,\n            data=data,\n            transform=transform,\n            cache_num=cache_num,\n            cache_rate=cache_rate,\n            num_workers=num_workers,\n            progress=progress,\n            copy_cache=copy_cache,\n        )\n\n    def get_indices(self) -> np.ndarray:\n        \"\"\"\n        Get the indices of datalist used in this dataset.\n\n        \"\"\"\n        return self.indices\n\n    def randomize(self, data: np.ndarray) -> None:\n        self.R.shuffle(data)\n\n    def get_properties(self, keys: Optional[Union[Sequence[str], str]] = None):\n        \"\"\"\n        Get the loaded properties of dataset with specified keys.\n        If no keys specified, return all the loaded properties.\n\n        \"\"\"\n        if keys is None:\n            return self._properties\n        if self._properties is not None:\n            return {key: self._properties[key] for key in ensure_tuple(keys)}\n        return {}\n\n    def _generate_data_list(self, dataset_dir: PathLike) -> List[Dict]:\n        # the types of the item in data list should be compatible with the dataloader\n        dataset_dir = Path(dataset_dir)\n        section = \"training\" if self.section in [\"training\", \"validation\"] else \"test\"\n        datalist = load_decathlon_datalist(dataset_dir / \"dataset.json\", True, section)\n        return self._split_datalist(datalist)\n\n    def _split_datalist(self, datalist: List[Dict]) -> List[Dict]:\n        if self.section == \"test\":\n            return datalist\n        length = len(datalist)\n        indices = np.arange(length)\n        self.randomize(indices)\n\n        val_length = int(length * self.val_frac)\n        if self.section == \"training\":\n            self.indices = indices[val_length:]\n        else:\n            self.indices = indices[:val_length]\n\n        return [datalist[i] for i in self.indices]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/datasets.py_CrossValidation_CrossValidation.__init__.self.dataset_params.dataset_params": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/datasets.py_CrossValidation_CrossValidation.__init__.self.dataset_params.dataset_params", "embedding": null, "metadata": {"file_path": "monai/apps/datasets.py", "file_name": "datasets.py", "file_type": "text/x-python", "category": "implementation", "start_line": 328, "end_line": 372, "span_ids": ["CrossValidation.__init__", "CrossValidation"], "tokens": 395}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CrossValidation:\n    \"\"\"\n    Cross validation dataset based on the general dataset which must have `_split_datalist` API.\n\n    Args:\n        dataset_cls: dataset class to be used to create the cross validation partitions.\n            It must have `_split_datalist` API.\n        nfolds: number of folds to split the data for cross validation.\n        seed: random seed to randomly shuffle the datalist before splitting into N folds, default is 0.\n        dataset_params: other additional parameters for the dataset_cls base class.\n\n    Example of 5 folds cross validation training::\n\n        cvdataset = CrossValidation(\n            dataset_cls=DecathlonDataset,\n            nfolds=5,\n            seed=12345,\n            root_dir=\"./\",\n            task=\"Task09_Spleen\",\n            section=\"training\",\n            transform=train_transform,\n            download=True,\n        )\n        dataset_fold0_train = cvdataset.get_dataset(folds=[1, 2, 3, 4])\n        dataset_fold0_val = cvdataset.get_dataset(folds=0, transform=val_transform, download=False)\n        # execute training for fold 0 ...\n\n        dataset_fold1_train = cvdataset.get_dataset(folds=[0, 2, 3, 4])\n        dataset_fold1_val = cvdataset.get_dataset(folds=1, transform=val_transform, download=False)\n        # execute training for fold 1 ...\n\n        ...\n\n        dataset_fold4_train = ...\n        # execute training for fold 4 ...\n\n    \"\"\"\n\n    def __init__(self, dataset_cls, nfolds: int = 5, seed: int = 0, **dataset_params) -> None:\n        if not hasattr(dataset_cls, \"_split_datalist\"):\n            raise ValueError(\"dataset class must have _split_datalist API.\")\n        self.dataset_cls = dataset_cls\n        self.nfolds = nfolds\n        self.seed = seed\n        self.dataset_params = dataset_params", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/datasets.py_CrossValidation.get_dataset_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/datasets.py_CrossValidation.get_dataset_", "embedding": null, "metadata": {"file_path": "monai/apps/datasets.py", "file_name": "datasets.py", "file_type": "text/x-python", "category": "implementation", "start_line": 374, "end_line": 395, "span_ids": ["CrossValidation.get_dataset"], "tokens": 211}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CrossValidation:\n\n    def get_dataset(self, folds: Union[Sequence[int], int], **dataset_params):\n        \"\"\"\n        Generate dataset based on the specified fold indice in the cross validation group.\n\n        Args:\n            folds: index of folds for training or validation, if a list of values, concatenate the data.\n            dataset_params: other additional parameters for the dataset_cls base class, will override\n                the same parameters in `self.dataset_params`.\n\n        \"\"\"\n        nfolds = self.nfolds\n        seed = self.seed\n        dataset_params_ = dict(self.dataset_params)\n        dataset_params_.update(dataset_params)\n\n        class _NsplitsDataset(self.dataset_cls):  # type: ignore\n            def _split_datalist(self, datalist: List[Dict]) -> List[Dict]:\n                data = partition_dataset(data=datalist, num_partitions=nfolds, shuffle=True, seed=seed)\n                return select_cross_validation_folds(partitions=data, folds=folds)\n\n        return _NsplitsDataset(**dataset_params_)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/__init__.py__": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/__init__.py__", "embedding": null, "metadata": {"file_path": "monai/apps/deepgrow/__init__.py", "file_name": "__init__.py", "file_type": "text/x-python", "category": "implementation", "start_line": 11, "end_line": 11, "span_ids": [], "tokens": 0}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/dataset.py_logging_create_dataset.new_datalist._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/dataset.py_logging_create_dataset.new_datalist._", "embedding": null, "metadata": {"file_path": "monai/apps/deepgrow/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 86, "span_ids": ["create_dataset", "docstring"], "tokens": 626}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import logging\nimport os\nfrom typing import Dict, List\n\nimport numpy as np\n\nfrom monai.transforms import AsChannelFirstd, Compose, LoadImaged, Orientationd, Spacingd\nfrom monai.utils import GridSampleMode\n\n\ndef create_dataset(\n    datalist,\n    output_dir: str,\n    dimension: int,\n    pixdim,\n    image_key: str = \"image\",\n    label_key: str = \"label\",\n    base_dir=None,\n    limit: int = 0,\n    relative_path: bool = False,\n    transforms=None,\n) -> List[Dict]:\n    \"\"\"\n    Utility to pre-process and create dataset list for Deepgrow training over on existing one.\n    The input data list is normally a list of images and labels (3D volume) that needs pre-processing\n    for Deepgrow training pipeline.\n\n    Args:\n        datalist: A list of data dictionary. Each entry should at least contain 'image_key': <image filename>.\n            For example, typical input data can be a list of dictionaries::\n\n                [{'image': <image filename>, 'label': <label filename>}]\n\n        output_dir: target directory to store the training data for Deepgrow Training\n        pixdim: output voxel spacing.\n        dimension: dimension for Deepgrow training.  It can be 2 or 3.\n        image_key: image key in input datalist. Defaults to 'image'.\n        label_key: label key in input datalist. Defaults to 'label'.\n        base_dir: base directory in case related path is used for the keys in datalist.  Defaults to None.\n        limit: limit number of inputs for pre-processing.  Defaults to 0 (no limit).\n        relative_path: output keys values should be based on relative path.  Defaults to False.\n        transforms: explicit transforms to execute operations on input data.\n\n    Raises:\n        ValueError: When ``dimension`` is not one of [2, 3]\n        ValueError: When ``datalist`` is Empty\n\n    Returns:\n        A new datalist that contains path to the images/labels after pre-processing.\n\n    Example::\n\n        datalist = create_dataset(\n            datalist=[{'image': 'img1.nii', 'label': 'label1.nii'}],\n            base_dir=None,\n            output_dir=output_2d,\n            dimension=2,\n            image_key='image',\n            label_key='label',\n            pixdim=(1.0, 1.0),\n            limit=0,\n            relative_path=True\n        )\n\n        print(datalist[0][\"image\"], datalist[0][\"label\"])\n    \"\"\"\n\n    if dimension not in [2, 3]:\n        raise ValueError(\"Dimension can be only 2 or 3 as Deepgrow supports only 2D/3D Training\")\n\n    if not len(datalist):\n        raise ValueError(\"Input datalist is empty\")\n\n    transforms = _default_transforms(image_key, label_key, pixdim) if transforms is None else transforms\n    new_datalist = []\n    # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/dataset.py_create_dataset.for_idx_in_range_len_data_create_dataset.return.new_datalist": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/dataset.py_create_dataset.for_idx_in_range_len_data_create_dataset.return.new_datalist", "embedding": null, "metadata": {"file_path": "monai/apps/deepgrow/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 87, "end_line": 119, "span_ids": ["create_dataset"], "tokens": 323}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def create_dataset(\n    datalist,\n    output_dir: str,\n    dimension: int,\n    pixdim,\n    image_key: str = \"image\",\n    label_key: str = \"label\",\n    base_dir=None,\n    limit: int = 0,\n    relative_path: bool = False,\n    transforms=None,\n) -> List[Dict]:\n    # ... other code\n    for idx in range(len(datalist)):\n        if limit and idx >= limit:\n            break\n\n        image = datalist[idx][image_key]\n        label = datalist[idx].get(label_key, None)\n        if base_dir:\n            image = os.path.join(base_dir, image)\n            label = os.path.join(base_dir, label) if label else None\n\n        image = os.path.abspath(image)\n        label = os.path.abspath(label) if label else None\n\n        logging.info(f\"Image: {image}; Label: {label if label else None}\")\n        data = transforms({image_key: image, label_key: label})\n        if dimension == 2:\n            data = _save_data_2d(\n                vol_idx=idx,\n                vol_image=data[image_key],\n                vol_label=data[label_key],\n                dataset_dir=output_dir,\n                relative_path=relative_path,\n            )\n        else:\n            data = _save_data_3d(\n                vol_idx=idx,\n                vol_image=data[image_key],\n                vol_label=data[label_key],\n                dataset_dir=output_dir,\n                relative_path=relative_path,\n            )\n        new_datalist.extend(data)\n    return new_datalist", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/dataset.py__default_transforms__default_transforms.return.Compose_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/dataset.py__default_transforms__default_transforms.return.Compose_", "embedding": null, "metadata": {"file_path": "monai/apps/deepgrow/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 122, "end_line": 132, "span_ids": ["_default_transforms"], "tokens": 127}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _default_transforms(image_key, label_key, pixdim):\n    keys = [image_key] if label_key is None else [image_key, label_key]\n    mode = [GridSampleMode.BILINEAR, GridSampleMode.NEAREST] if len(keys) == 2 else [GridSampleMode.BILINEAR]\n    return Compose(\n        [\n            LoadImaged(keys=keys),\n            AsChannelFirstd(keys=keys),\n            Spacingd(keys=keys, pixdim=pixdim, mode=mode),\n            Orientationd(keys=keys, axcodes=\"RAS\"),\n        ]\n    )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/dataset.py__save_data_2d__save_data_2d.return.data_list": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/dataset.py__save_data_2d__save_data_2d.return.data_list", "embedding": null, "metadata": {"file_path": "monai/apps/deepgrow/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 135, "end_line": 208, "span_ids": ["_save_data_2d"], "tokens": 619}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _save_data_2d(vol_idx, vol_image, vol_label, dataset_dir, relative_path):\n    data_list = []\n\n    if len(vol_image.shape) == 4:\n        logging.info(\n            \"4D-Image, pick only first series; Image: {}; Label: {}\".format(\n                vol_image.shape, vol_label.shape if vol_label is not None else None\n            )\n        )\n        vol_image = vol_image[0]\n        vol_image = np.moveaxis(vol_image, -1, 0)\n\n    image_count = 0\n    label_count = 0\n    unique_labels_count = 0\n    for sid in range(vol_image.shape[0]):\n        image = vol_image[sid, ...]\n        label = vol_label[sid, ...] if vol_label is not None else None\n\n        if vol_label is not None and np.sum(label) == 0:\n            continue\n\n        image_file_prefix = f\"vol_idx_{vol_idx:0>4d}_slice_{sid:0>3d}\"\n        image_file = os.path.join(dataset_dir, \"images\", image_file_prefix)\n        image_file += \".npy\"\n\n        os.makedirs(os.path.join(dataset_dir, \"images\"), exist_ok=True)\n        np.save(image_file, image)\n        image_count += 1\n\n        # Test Data\n        if vol_label is None:\n            data_list.append(\n                {\"image\": image_file.replace(dataset_dir + os.pathsep, \"\") if relative_path else image_file}\n            )\n            continue\n\n        # For all Labels\n        unique_labels = np.unique(label.flatten())\n        unique_labels = unique_labels[unique_labels != 0]\n        unique_labels_count = max(unique_labels_count, len(unique_labels))\n\n        for idx in unique_labels:\n            label_file_prefix = f\"{image_file_prefix}_region_{int(idx):0>2d}\"\n            label_file = os.path.join(dataset_dir, \"labels\", label_file_prefix)\n            label_file += \".npy\"\n\n            os.makedirs(os.path.join(dataset_dir, \"labels\"), exist_ok=True)\n            curr_label = (label == idx).astype(np.float32)\n            np.save(label_file, curr_label)\n\n            label_count += 1\n            data_list.append(\n                {\n                    \"image\": image_file.replace(dataset_dir + os.pathsep, \"\") if relative_path else image_file,\n                    \"label\": label_file.replace(dataset_dir + os.pathsep, \"\") if relative_path else label_file,\n                    \"region\": int(idx),\n                }\n            )\n\n    if unique_labels_count >= 20:\n        logging.warning(f\"Unique labels {unique_labels_count} exceeds 20. Please check if this is correct.\")\n\n    logging.info(\n        \"{} => Image Shape: {} => {}; Label Shape: {} => {}; Unique Labels: {}\".format(\n            vol_idx,\n            vol_image.shape,\n            image_count,\n            vol_label.shape if vol_label is not None else None,\n            label_count,\n            unique_labels_count,\n        )\n    )\n    return data_list", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/dataset.py__save_data_3d_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/dataset.py__save_data_3d_", "embedding": null, "metadata": {"file_path": "monai/apps/deepgrow/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 211, "end_line": 276, "span_ids": ["_save_data_3d"], "tokens": 550}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _save_data_3d(vol_idx, vol_image, vol_label, dataset_dir, relative_path):\n    data_list = []\n\n    if len(vol_image.shape) == 4:\n        logging.info(\n            \"4D-Image, pick only first series; Image: {}; Label: {}\".format(\n                vol_image.shape, vol_label.shape if vol_label is not None else None\n            )\n        )\n        vol_image = vol_image[0]\n        vol_image = np.moveaxis(vol_image, -1, 0)\n\n    image_count = 0\n    label_count = 0\n    unique_labels_count = 0\n\n    image_file_prefix = f\"vol_idx_{vol_idx:0>4d}\"\n    image_file = os.path.join(dataset_dir, \"images\", image_file_prefix)\n    image_file += \".npy\"\n\n    os.makedirs(os.path.join(dataset_dir, \"images\"), exist_ok=True)\n    np.save(image_file, vol_image)\n    image_count += 1\n\n    # Test Data\n    if vol_label is None:\n        data_list.append({\"image\": image_file.replace(dataset_dir + os.pathsep, \"\") if relative_path else image_file})\n    else:\n        # For all Labels\n        unique_labels = np.unique(vol_label.flatten())\n        unique_labels = unique_labels[unique_labels != 0]\n        unique_labels_count = max(unique_labels_count, len(unique_labels))\n\n        for idx in unique_labels:\n            label_file_prefix = f\"{image_file_prefix}_region_{int(idx):0>2d}\"\n            label_file = os.path.join(dataset_dir, \"labels\", label_file_prefix)\n            label_file += \".npy\"\n\n            curr_label = (vol_label == idx).astype(np.float32)\n            os.makedirs(os.path.join(dataset_dir, \"labels\"), exist_ok=True)\n            np.save(label_file, curr_label)\n\n            label_count += 1\n            data_list.append(\n                {\n                    \"image\": image_file.replace(dataset_dir + os.pathsep, \"\") if relative_path else image_file,\n                    \"label\": label_file.replace(dataset_dir + os.pathsep, \"\") if relative_path else label_file,\n                    \"region\": int(idx),\n                }\n            )\n\n    if unique_labels_count >= 20:\n        logging.warning(f\"Unique labels {unique_labels_count} exceeds 20. Please check if this is correct.\")\n\n    logging.info(\n        \"{} => Image Shape: {} => {}; Label Shape: {} => {}; Unique Labels: {}\".format(\n            vol_idx,\n            vol_image.shape,\n            image_count,\n            vol_label.shape if vol_label is not None else None,\n            label_count,\n            unique_labels_count,\n        )\n    )\n    return data_list", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/interaction.py_Interaction.__call___": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/interaction.py_Interaction.__call___", "embedding": null, "metadata": {"file_path": "monai/apps/deepgrow/interaction.py", "file_name": "interaction.py", "file_type": "text/x-python", "category": "implementation", "start_line": 54, "end_line": 88, "span_ids": ["Interaction.__call__"], "tokens": 301}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Interaction:\n\n    def __call__(self, engine: Union[SupervisedTrainer, SupervisedEvaluator], batchdata: Dict[str, torch.Tensor]):\n        if batchdata is None:\n            raise ValueError(\"Must provide batch data for current iteration.\")\n\n        for j in range(self.max_interactions):\n            inputs, _ = engine.prepare_batch(batchdata)\n            inputs = inputs.to(engine.state.device)\n\n            engine.fire_event(IterationEvents.INNER_ITERATION_STARTED)\n\n            engine.network.eval()\n            with torch.no_grad():\n                if engine.amp:\n                    with torch.cuda.amp.autocast():\n                        predictions = engine.inferer(inputs, engine.network)\n                else:\n                    predictions = engine.inferer(inputs, engine.network)\n\n            engine.fire_event(IterationEvents.INNER_ITERATION_COMPLETED)\n\n            batchdata.update({CommonKeys.PRED: predictions})\n\n            # decollate batch data to execute click transforms\n            batchdata_list = decollate_batch(batchdata, detach=True)\n            for i in range(len(batchdata_list)):\n                batchdata_list[i][self.key_probability] = (\n                    (1.0 - ((1.0 / self.max_interactions) * j)) if self.train else 1.0\n                )\n                batchdata_list[i] = self.transforms(batchdata_list[i])\n\n            # collate list into a batch for next round interaction\n            batchdata = list_data_collate(batchdata_list)\n\n        return engine._iteration(engine, batchdata)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py__Transforms_to_support_T_FindAllValidSlicesd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py__Transforms_to_support_T_FindAllValidSlicesd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/apps/deepgrow/transforms.py", "file_name": "transforms.py", "file_type": "text/x-python", "category": "implementation", "start_line": 27, "end_line": 61, "span_ids": ["FindAllValidSlicesd.__init__", "docstring", "FindAllValidSlicesd._apply", "FindAllValidSlicesd.__call__", "FindAllValidSlicesd"], "tokens": 284}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "# Transforms to support Training for Deepgrow models\nclass FindAllValidSlicesd(Transform):\n    \"\"\"\n    Find/List all valid slices in the label.\n    Label is assumed to be a 4D Volume with shape CDHW, where C=1.\n\n    Args:\n        label: key to the label source.\n        sids: key to store slices indices having valid label map.\n    \"\"\"\n\n    def __init__(self, label: str = \"label\", sids: str = \"sids\"):\n        self.label = label\n        self.sids = sids\n\n    def _apply(self, label):\n        sids = []\n        for sid in range(label.shape[1]):  # Assume channel is first\n            if np.sum(label[0][sid]) != 0:\n                sids.append(sid)\n        return np.asarray(sids)\n\n    def __call__(self, data):\n        d: Dict = dict(data)\n        label = d[self.label]\n        if label.shape[0] != 1:\n            raise ValueError(\"Only supports single channel labels!\")\n\n        if len(label.shape) != 4:  # only for 3D\n            raise ValueError(\"Only supports label with shape CDHW!\")\n\n        sids = self._apply(label)\n        if sids is not None and len(sids):\n            d[self.sids] = sids\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_AddInitialSeedPointd_AddInitialSeedPointd.randomize.self.sid.sid": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_AddInitialSeedPointd_AddInitialSeedPointd.randomize.self.sid.sid", "embedding": null, "metadata": {"file_path": "monai/apps/deepgrow/transforms.py", "file_name": "transforms.py", "file_type": "text/x-python", "category": "implementation", "start_line": 64, "end_line": 104, "span_ids": ["AddInitialSeedPointd", "AddInitialSeedPointd.__init__", "AddInitialSeedPointd.randomize"], "tokens": 353}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AddInitialSeedPointd(Randomizable, Transform):\n    \"\"\"\n    Add random guidance as initial seed point for a given label.\n\n    Note that the label is of size (C, D, H, W) or (C, H, W)\n\n    The guidance is of size (2, N, # of dims) where N is number of guidance added.\n    # of dims = 4 when C, D, H, W; # of dims = 3 when (C, H, W)\n\n    Args:\n        label: label source.\n        guidance: key to store guidance.\n        sids: key that represents list of valid slice indices for the given label.\n        sid: key that represents the slice to add initial seed point.  If not present, random sid will be chosen.\n        connected_regions: maximum connected regions to use for adding initial points.\n    \"\"\"\n\n    def __init__(\n        self,\n        label: str = \"label\",\n        guidance: str = \"guidance\",\n        sids: str = \"sids\",\n        sid: str = \"sid\",\n        connected_regions: int = 5,\n    ):\n        self.label = label\n        self.sids_key = sids\n        self.sid_key = sid\n        self.sid = None\n        self.guidance = guidance\n        self.connected_regions = connected_regions\n\n    def randomize(self, data):\n        sid = data.get(self.sid_key, None)\n        sids = data.get(self.sids_key, None)\n        if sids is not None:\n            if sid is None or sid not in sids:\n                sid = self.R.choice(sids, replace=False)\n        else:\n            sid = None\n        self.sid = sid", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_AddInitialSeedPointd._apply_AddInitialSeedPointd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_AddInitialSeedPointd._apply_AddInitialSeedPointd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/apps/deepgrow/transforms.py", "file_name": "transforms.py", "file_type": "text/x-python", "category": "implementation", "start_line": 107, "end_line": 149, "span_ids": ["AddInitialSeedPointd.__call__", "AddInitialSeedPointd._apply"], "tokens": 447}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AddInitialSeedPointd(Randomizable, Transform):\n\n    def _apply(self, label, sid):\n        dimensions = 3 if len(label.shape) > 3 else 2\n        default_guidance = [-1] * (dimensions + 1)\n\n        dims = dimensions\n        if sid is not None and dimensions == 3:\n            dims = 2\n            label = label[0][sid][np.newaxis]  # Assume channel is first\n\n        label = (label > 0.5).astype(np.float32)\n        blobs_labels = measure.label(label.astype(int), background=0) if dims == 2 else label\n        if np.max(blobs_labels) <= 0:\n            raise AssertionError(\"Not a valid Label\")\n\n        pos_guidance = []\n        for ridx in range(1, 2 if dims == 3 else self.connected_regions + 1):\n            if dims == 2:\n                label = (blobs_labels == ridx).astype(np.float32)\n                if np.sum(label) == 0:\n                    pos_guidance.append(default_guidance)\n                    continue\n\n            distance = distance_transform_cdt(label).flatten()\n            probability = np.exp(distance) - 1.0\n\n            idx = np.where(label.flatten() > 0)[0]\n            seed = self.R.choice(idx, size=1, p=probability[idx] / np.sum(probability[idx]))\n            dst = distance[seed]\n\n            g = np.asarray(np.unravel_index(seed, label.shape)).transpose().tolist()[0]\n            g[0] = dst[0]  # for debug\n            if dimensions == 2 or dims == 3:\n                pos_guidance.append(g)\n            else:\n                pos_guidance.append([g[0], sid, g[-2], g[-1]])\n\n        return np.asarray([pos_guidance, [default_guidance] * len(pos_guidance)])\n\n    def __call__(self, data):\n        d = dict(data)\n        self.randomize(data)\n        d[self.guidance] = json.dumps(self._apply(d[self.label], self.sid).astype(int, copy=False).tolist())\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_FindDiscrepancyRegionsd_FindDiscrepancyRegionsd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_FindDiscrepancyRegionsd_FindDiscrepancyRegionsd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/apps/deepgrow/transforms.py", "file_name": "transforms.py", "file_type": "text/x-python", "category": "implementation", "start_line": 220, "end_line": 255, "span_ids": ["FindDiscrepancyRegionsd.__call__", "FindDiscrepancyRegionsd.__init__", "FindDiscrepancyRegionsd", "FindDiscrepancyRegionsd.disparity", "FindDiscrepancyRegionsd._apply"], "tokens": 273}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class FindDiscrepancyRegionsd(Transform):\n    \"\"\"\n    Find discrepancy between prediction and actual during click interactions during training.\n\n    Args:\n        label: key to label source.\n        pred: key to prediction source.\n        discrepancy: key to store discrepancies found between label and prediction.\n\n    \"\"\"\n\n    def __init__(self, label: str = \"label\", pred: str = \"pred\", discrepancy: str = \"discrepancy\"):\n        self.label = label\n        self.pred = pred\n        self.discrepancy = discrepancy\n\n    @staticmethod\n    def disparity(label, pred):\n        label = (label > 0.5).astype(np.float32)\n        pred = (pred > 0.5).astype(np.float32)\n        disparity = label - pred\n\n        pos_disparity = (disparity > 0).astype(np.float32)\n        neg_disparity = (disparity < 0).astype(np.float32)\n        return [pos_disparity, neg_disparity]\n\n    def _apply(self, label, pred):\n        return self.disparity(label, pred)\n\n    def __call__(self, data):\n        d = dict(data)\n        label = d[self.label]\n        pred = d[self.pred]\n\n        d[self.discrepancy] = self._apply(label, pred)\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_AddRandomGuidanced.find_guidance_AddRandomGuidanced.find_guidance.return.None": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_AddRandomGuidanced.find_guidance_AddRandomGuidanced.find_guidance.return.None", "embedding": null, "metadata": {"file_path": "monai/apps/deepgrow/transforms.py", "file_name": "transforms.py", "file_type": "text/x-python", "category": "implementation", "start_line": 337, "end_line": 349, "span_ids": ["AddRandomGuidanced.find_guidance"], "tokens": 148}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AddRandomGuidanced(Randomizable, Transform):\n\n    def find_guidance(self, discrepancy):\n        distance = distance_transform_cdt(discrepancy).flatten()\n        probability = np.exp(distance) - 1.0\n        idx = np.where(discrepancy.flatten() > 0)[0]\n\n        if np.sum(discrepancy > 0) > 0:\n            seed = self.R.choice(idx, size=1, p=probability[idx] / np.sum(probability[idx]))\n            dst = distance[seed]\n\n            g = np.asarray(np.unravel_index(seed, discrepancy.shape)).transpose().tolist()[0]\n            g[0] = dst[0]\n            return g\n        return None", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_AddRandomGuidanced.add_guidance_AddRandomGuidanced.add_guidance.return.None_None": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_AddRandomGuidanced.add_guidance_AddRandomGuidanced.add_guidance.return.None_None", "embedding": null, "metadata": {"file_path": "monai/apps/deepgrow/transforms.py", "file_name": "transforms.py", "file_type": "text/x-python", "category": "implementation", "start_line": 351, "end_line": 367, "span_ids": ["AddRandomGuidanced.add_guidance"], "tokens": 152}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AddRandomGuidanced(Randomizable, Transform):\n\n    def add_guidance(self, discrepancy, will_interact):\n        if not will_interact:\n            return None, None\n\n        pos_discr = discrepancy[0]\n        neg_discr = discrepancy[1]\n\n        can_be_positive = np.sum(pos_discr) > 0\n        can_be_negative = np.sum(neg_discr) > 0\n        correct_pos = np.sum(pos_discr) >= np.sum(neg_discr)\n\n        if correct_pos and can_be_positive:\n            return self.find_guidance(pos_discr), None\n\n        if not correct_pos and can_be_negative:\n            return None, self.find_guidance(neg_discr)\n        return None, None", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_AddRandomGuidanced._apply_AddRandomGuidanced.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_AddRandomGuidanced._apply_AddRandomGuidanced.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/apps/deepgrow/transforms.py", "file_name": "transforms.py", "file_type": "text/x-python", "category": "implementation", "start_line": 315, "end_line": 335, "span_ids": ["AddRandomGuidanced.__call__", "AddRandomGuidanced._apply"], "tokens": 198}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AddRandomGuidanced(Randomizable, Transform):\n\n    def _apply(self, guidance, discrepancy):\n        guidance = guidance.tolist() if isinstance(guidance, np.ndarray) else guidance\n        guidance = json.loads(guidance) if isinstance(guidance, str) else guidance\n        pos, neg = self.add_guidance(discrepancy, self._will_interact)\n        if pos:\n            guidance[0].append(pos)\n            guidance[1].append([-1] * len(pos))\n        if neg:\n            guidance[0].append([-1] * len(neg))\n            guidance[1].append(neg)\n\n        return json.dumps(np.asarray(guidance, dtype=int).tolist())\n\n    def __call__(self, data):\n        d = dict(data)\n        guidance = d[self.guidance]\n        discrepancy = d[self.discrepancy]\n\n        self.randomize(data)\n        d[self.guidance] = self._apply(guidance, discrepancy)\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_SpatialCropForegroundd_SpatialCropForegroundd._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_SpatialCropForegroundd_SpatialCropForegroundd._", "embedding": null, "metadata": {"file_path": "monai/apps/deepgrow/transforms.py", "file_name": "transforms.py", "file_type": "text/x-python", "category": "implementation", "start_line": 400, "end_line": 445, "span_ids": ["SpatialCropForegroundd"], "tokens": 672}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SpatialCropForegroundd(MapTransform):\n    \"\"\"\n    Crop only the foreground object of the expected images.\n\n    Difference VS :py:class:`monai.transforms.CropForegroundd`:\n\n      1. If the bounding box is smaller than spatial size in all dimensions then this transform will crop the\n         object using box's center and spatial_size.\n\n      2. This transform will set \"start_coord_key\", \"end_coord_key\", \"original_shape_key\" and \"cropped_shape_key\"\n         in data[{key}_{meta_key_postfix}]\n\n    The typical usage is to help training and evaluation if the valid part is small in the whole medical image.\n    The valid part can be determined by any field in the data with `source_key`, for example:\n\n    - Select values > 0 in image field as the foreground and crop on all fields specified by `keys`.\n    - Select label = 3 in label field as the foreground to crop on all fields specified by `keys`.\n    - Select label > 0 in the third channel of a One-Hot label field as the foreground to crop all `keys` fields.\n\n    Users can define arbitrary function to select expected foreground from the whole source image or specified\n    channels. And it can also add margin to every dim of the bounding box of foreground object.\n\n    Args:\n        keys: keys of the corresponding items to be transformed.\n            See also: :py:class:`monai.transforms.MapTransform`\n        source_key: data source to generate the bounding box of foreground, can be image or label, etc.\n        spatial_size: minimal spatial size of the image patch e.g. [128, 128, 128] to fit in.\n        select_fn: function to select expected foreground, default is to select values > 0.\n        channel_indices: if defined, select foreground only on the specified channels\n            of image. if None, select foreground on the whole image.\n        margin: add margin value to spatial dims of the bounding box, if only 1 value provided, use it for all dims.\n        meta_keys: explicitly indicate the key of the corresponding meta data dictionary.\n            for example, for data with key `image`, the metadata by default is in `image_meta_dict`.\n            the meta data is a dictionary object which contains: filename, original_shape, etc.\n            it can be a sequence of string, map to the `keys`.\n            if None, will try to construct meta_keys by `key_{meta_key_postfix}`.\n        meta_key_postfix: if meta_keys is None, use `{key}_{meta_key_postfix}` to to fetch/store the meta data according\n            to the key data, default is `meta_dict`, the meta data is a dictionary object.\n            For example, to handle key `image`,  read/write affine matrices from the\n            metadata `image_meta_dict` dictionary's `affine` field.\n        start_coord_key: key to record the start coordinate of spatial bounding box for foreground.\n        end_coord_key: key to record the end coordinate of spatial bounding box for foreground.\n        original_shape_key: key to record original shape for foreground.\n        cropped_shape_key: key to record cropped shape for foreground.\n        allow_missing_keys: don't raise exception if key is missing.\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_SpatialCropForegroundd.__init___SpatialCropForegroundd.__init__.self.cropped_shape_key.cropped_shape_key": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_SpatialCropForegroundd.__init___SpatialCropForegroundd.__init__.self.cropped_shape_key.cropped_shape_key", "embedding": null, "metadata": {"file_path": "monai/apps/deepgrow/transforms.py", "file_name": "transforms.py", "file_type": "text/x-python", "category": "implementation", "start_line": 393, "end_line": 423, "span_ids": ["SpatialCropForegroundd.__init__"], "tokens": 313}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SpatialCropForegroundd(MapTransform):\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        source_key: str,\n        spatial_size: Union[Sequence[int], np.ndarray],\n        select_fn: Callable = is_positive,\n        channel_indices: Optional[IndexSelection] = None,\n        margin: int = 0,\n        meta_keys: Optional[KeysCollection] = None,\n        meta_key_postfix=\"meta_dict\",\n        start_coord_key: str = \"foreground_start_coord\",\n        end_coord_key: str = \"foreground_end_coord\",\n        original_shape_key: str = \"foreground_original_shape\",\n        cropped_shape_key: str = \"foreground_cropped_shape\",\n        allow_missing_keys: bool = False,\n    ) -> None:\n        super().__init__(keys, allow_missing_keys)\n\n        self.source_key = source_key\n        self.spatial_size = list(spatial_size)\n        self.select_fn = select_fn\n        self.channel_indices = channel_indices\n        self.margin = margin\n        self.meta_keys = ensure_tuple_rep(None, len(self.keys)) if meta_keys is None else ensure_tuple(meta_keys)\n        if len(self.keys) != len(self.meta_keys):\n            raise ValueError(\"meta_keys should have the same length as keys.\")\n        self.meta_key_postfix = ensure_tuple_rep(meta_key_postfix, len(self.keys))\n        self.start_coord_key = start_coord_key\n        self.end_coord_key = end_coord_key\n        self.original_shape_key = original_shape_key\n        self.cropped_shape_key = cropped_shape_key", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_SpatialCropForegroundd.__call___SpatialCropForegroundd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_SpatialCropForegroundd.__call___SpatialCropForegroundd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/apps/deepgrow/transforms.py", "file_name": "transforms.py", "file_type": "text/x-python", "category": "implementation", "start_line": 417, "end_line": 442, "span_ids": ["SpatialCropForegroundd.__call__"], "tokens": 308}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SpatialCropForegroundd(MapTransform):\n\n    def __call__(self, data):\n        d = dict(data)\n        box_start, box_end = generate_spatial_bounding_box(\n            d[self.source_key], self.select_fn, self.channel_indices, self.margin\n        )\n\n        center = list(np.mean([box_start, box_end], axis=0).astype(int, copy=False))\n        current_size = list(np.subtract(box_end, box_start).astype(int, copy=False))\n\n        if np.all(np.less(current_size, self.spatial_size)):\n            cropper = SpatialCrop(roi_center=center, roi_size=self.spatial_size)\n            box_start = np.array([s.start for s in cropper.slices])\n            box_end = np.array([s.stop for s in cropper.slices])\n        else:\n            cropper = SpatialCrop(roi_start=box_start, roi_end=box_end)\n\n        for key, meta_key, meta_key_postfix in self.key_iterator(d, self.meta_keys, self.meta_key_postfix):\n            meta_key = meta_key or f\"{key}_{meta_key_postfix}\"\n            d[meta_key][self.start_coord_key] = box_start\n            d[meta_key][self.end_coord_key] = box_end\n            d[meta_key][self.original_shape_key] = d[key].shape\n\n            image = cropper(d[key])\n            d[meta_key][self.cropped_shape_key] = image.shape\n            d[key] = image\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py__Transforms_to_support_I_AddGuidanceFromPointsd.__init__.self.meta_key_postfix.meta_key_postfix": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py__Transforms_to_support_I_AddGuidanceFromPointsd.__init__.self.meta_key_postfix.meta_key_postfix", "embedding": null, "metadata": {"file_path": "monai/apps/deepgrow/transforms.py", "file_name": "transforms.py", "file_type": "text/x-python", "category": "implementation", "start_line": 445, "end_line": 508, "span_ids": ["AddGuidanceFromPointsd.__init__", "AddGuidanceFromPointsd", "SpatialCropForegroundd.__call__"], "tokens": 659}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "# Transforms to support Inference for Deepgrow models\nclass AddGuidanceFromPointsd(Transform):\n    \"\"\"\n    Add guidance based on user clicks.\n\n    We assume the input is loaded by LoadImaged and has the shape of (H, W, D) originally.\n    Clicks always specify the coordinates in (H, W, D)\n\n    If depth_first is True:\n\n        Input is now of shape (D, H, W), will return guidance that specifies the coordinates in (D, H, W)\n\n    else:\n\n        Input is now of shape (H, W, D), will return guidance that specifies the coordinates in (H, W, D)\n\n    Args:\n        ref_image: key to reference image to fetch current and original image details.\n        guidance: output key to store guidance.\n        foreground: key that represents user foreground (+ve) clicks.\n        background: key that represents user background (-ve) clicks.\n        axis: axis that represents slices in 3D volume. (axis to Depth)\n        depth_first: if depth (slices) is positioned at first dimension.\n        spatial_dims: dimensions based on model used for deepgrow (2D vs 3D).\n        slice_key: key that represents applicable slice to add guidance.\n        meta_keys: explicitly indicate the key of the meta data dictionary of `ref_image`.\n            for example, for data with key `image`, the metadata by default is in `image_meta_dict`.\n            the meta data is a dictionary object which contains: filename, original_shape, etc.\n            if None, will try to construct meta_keys by `{ref_image}_{meta_key_postfix}`.\n        meta_key_postfix: if meta_key is None, use `{ref_image}_{meta_key_postfix}` to to fetch the meta data according\n            to the key data, default is `meta_dict`, the meta data is a dictionary object.\n            For example, to handle key `image`,  read/write affine matrices from the\n            metadata `image_meta_dict` dictionary's `affine` field.\n\n    .. deprecated:: 0.6.0\n        ``dimensions`` is deprecated, use ``spatial_dims`` instead.\n\n    \"\"\"\n\n    @deprecated_arg(name=\"dimensions\", since=\"0.6\", msg_suffix=\"Please use `spatial_dims` instead.\")\n    def __init__(\n        self,\n        ref_image,\n        guidance: str = \"guidance\",\n        foreground: str = \"foreground\",\n        background: str = \"background\",\n        axis: int = 0,\n        depth_first: bool = True,\n        spatial_dims: int = 2,\n        slice_key: str = \"slice\",\n        meta_keys: Optional[str] = None,\n        meta_key_postfix: str = \"meta_dict\",\n        dimensions: Optional[int] = None,\n    ):\n        self.ref_image = ref_image\n        self.guidance = guidance\n        self.foreground = foreground\n        self.background = background\n        self.axis = axis\n        self.depth_first = depth_first\n        self.dimensions = spatial_dims if dimensions is None else dimensions\n        self.slice = slice_key\n        self.meta_keys = meta_keys\n        self.meta_key_postfix = meta_key_postfix", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_AddGuidanceFromPointsd._apply_AddGuidanceFromPointsd._apply.return.guidance": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_AddGuidanceFromPointsd._apply_AddGuidanceFromPointsd._apply.return.guidance", "embedding": null, "metadata": {"file_path": "monai/apps/deepgrow/transforms.py", "file_name": "transforms.py", "file_type": "text/x-python", "category": "implementation", "start_line": 510, "end_line": 535, "span_ids": ["AddGuidanceFromPointsd._apply"], "tokens": 284}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AddGuidanceFromPointsd(Transform):\n\n    def _apply(self, pos_clicks, neg_clicks, factor, slice_num):\n        pos = neg = []\n\n        if self.dimensions == 2:\n            points = list(pos_clicks)\n            points.extend(neg_clicks)\n            points = np.array(points)\n\n            slices = list(np.unique(points[:, self.axis]))\n            slice_idx = slices[0] if slice_num is None else next(x for x in slices if x == slice_num)\n\n            if len(pos_clicks):\n                pos_clicks = np.array(pos_clicks)\n                pos = (pos_clicks[np.where(pos_clicks[:, self.axis] == slice_idx)] * factor)[:, 1:].astype(int).tolist()\n            if len(neg_clicks):\n                neg_clicks = np.array(neg_clicks)\n                neg = (neg_clicks[np.where(neg_clicks[:, self.axis] == slice_idx)] * factor)[:, 1:].astype(int).tolist()\n\n            guidance = [pos, neg, slice_idx]\n        else:\n            if len(pos_clicks):\n                pos = np.multiply(pos_clicks, factor).astype(int, copy=False).tolist()\n            if len(neg_clicks):\n                neg = np.multiply(neg_clicks, factor).astype(int, copy=False).tolist()\n            guidance = [pos, neg]\n        return guidance", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_AddGuidanceFromPointsd.__call___AddGuidanceFromPointsd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_AddGuidanceFromPointsd.__call___AddGuidanceFromPointsd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/apps/deepgrow/transforms.py", "file_name": "transforms.py", "file_type": "text/x-python", "category": "implementation", "start_line": 537, "end_line": 564, "span_ids": ["AddGuidanceFromPointsd.__call__"], "tokens": 308}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AddGuidanceFromPointsd(Transform):\n\n    def __call__(self, data):\n        d = dict(data)\n        meta_dict_key = self.meta_keys or f\"{self.ref_image}_{self.meta_key_postfix}\"\n        if meta_dict_key not in d:\n            raise RuntimeError(f\"Missing meta_dict {meta_dict_key} in data!\")\n        if \"spatial_shape\" not in d[meta_dict_key]:\n            raise RuntimeError('Missing \"spatial_shape\" in meta_dict!')\n        original_shape = d[meta_dict_key][\"spatial_shape\"]\n        current_shape = list(d[self.ref_image].shape)\n\n        if self.depth_first:\n            if self.axis != 0:\n                raise RuntimeError(\"Depth first means the depth axis should be 0.\")\n            # in here we assume the depth dimension was in the last dimension of \"original_shape\"\n            original_shape = np.roll(original_shape, 1)\n\n        factor = np.array(current_shape) / original_shape\n\n        fg_bg_clicks = []\n        for key in [self.foreground, self.background]:\n            clicks = d[key]\n            clicks = list(np.array(clicks, dtype=int))\n            if self.depth_first:\n                for i in range(len(clicks)):\n                    clicks[i] = list(np.roll(clicks[i], 1))\n            fg_bg_clicks.append(clicks)\n        d[self.guidance] = self._apply(fg_bg_clicks[0], fg_bg_clicks[1], factor, d.get(self.slice))\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_SpatialCropGuidanced_SpatialCropGuidanced.__init__.self.cropped_shape_key.cropped_shape_key": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_SpatialCropGuidanced_SpatialCropGuidanced.__init__.self.cropped_shape_key.cropped_shape_key", "embedding": null, "metadata": {"file_path": "monai/apps/deepgrow/transforms.py", "file_name": "transforms.py", "file_type": "text/x-python", "category": "implementation", "start_line": 623, "end_line": 682, "span_ids": ["SpatialCropGuidanced.__init__", "SpatialCropGuidanced"], "tokens": 705}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SpatialCropGuidanced(MapTransform):\n    \"\"\"\n    Crop image based on guidance with minimal spatial size.\n\n    - If the bounding box is smaller than spatial size in all dimensions then this transform will crop the\n      object using box's center and spatial_size.\n\n    - This transform will set \"start_coord_key\", \"end_coord_key\", \"original_shape_key\" and \"cropped_shape_key\"\n      in data[{key}_{meta_key_postfix}]\n\n    Input data is of shape (C, spatial_1, [spatial_2, ...])\n\n    Args:\n        keys: keys of the corresponding items to be transformed.\n        guidance: key to the guidance. It is used to generate the bounding box of foreground\n        spatial_size: minimal spatial size of the image patch e.g. [128, 128, 128] to fit in.\n        margin: add margin value to spatial dims of the bounding box, if only 1 value provided, use it for all dims.\n        meta_keys: explicitly indicate the key of the corresponding meta data dictionary.\n            for example, for data with key `image`, the metadata by default is in `image_meta_dict`.\n            the meta data is a dictionary object which contains: filename, original_shape, etc.\n            it can be a sequence of string, map to the `keys`.\n            if None, will try to construct meta_keys by `key_{meta_key_postfix}`.\n        meta_key_postfix: if meta_keys is None, use `key_{postfix}` to to fetch the meta data according\n            to the key data, default is `meta_dict`, the meta data is a dictionary object.\n            For example, to handle key `image`,  read/write affine matrices from the\n            metadata `image_meta_dict` dictionary's `affine` field.\n        start_coord_key: key to record the start coordinate of spatial bounding box for foreground.\n        end_coord_key: key to record the end coordinate of spatial bounding box for foreground.\n        original_shape_key: key to record original shape for foreground.\n        cropped_shape_key: key to record cropped shape for foreground.\n        allow_missing_keys: don't raise exception if key is missing.\n    \"\"\"\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        guidance: str,\n        spatial_size,\n        margin=20,\n        meta_keys: Optional[KeysCollection] = None,\n        meta_key_postfix=\"meta_dict\",\n        start_coord_key: str = \"foreground_start_coord\",\n        end_coord_key: str = \"foreground_end_coord\",\n        original_shape_key: str = \"foreground_original_shape\",\n        cropped_shape_key: str = \"foreground_cropped_shape\",\n        allow_missing_keys: bool = False,\n    ) -> None:\n        super().__init__(keys, allow_missing_keys)\n\n        self.guidance = guidance\n        self.spatial_size = list(spatial_size)\n        self.margin = margin\n        self.meta_keys = ensure_tuple_rep(None, len(self.keys)) if meta_keys is None else ensure_tuple(meta_keys)\n        if len(self.keys) != len(self.meta_keys):\n            raise ValueError(\"meta_keys should have the same length as keys.\")\n        self.meta_key_postfix = ensure_tuple_rep(meta_key_postfix, len(self.keys))\n        self.start_coord_key = start_coord_key\n        self.end_coord_key = end_coord_key\n        self.original_shape_key = original_shape_key\n        self.cropped_shape_key = cropped_shape_key", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_SpatialCropGuidanced.bounding_box_SpatialCropGuidanced.bounding_box.return.box_start_box_end": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_SpatialCropGuidanced.bounding_box_SpatialCropGuidanced.bounding_box.return.box_start_box_end", "embedding": null, "metadata": {"file_path": "monai/apps/deepgrow/transforms.py", "file_name": "transforms.py", "file_type": "text/x-python", "category": "implementation", "start_line": 660, "end_line": 675, "span_ids": ["SpatialCropGuidanced.bounding_box"], "tokens": 162}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SpatialCropGuidanced(MapTransform):\n\n    def bounding_box(self, points, img_shape):\n        ndim = len(img_shape)\n        margin = ensure_tuple_rep(self.margin, ndim)\n        for m in margin:\n            if m < 0:\n                raise ValueError(\"margin value should not be negative number.\")\n\n        box_start = [0] * ndim\n        box_end = [0] * ndim\n\n        for di in range(ndim):\n            dt = points[..., di]\n            min_d = max(min(dt - margin[di]), 0)\n            max_d = min(img_shape[di], max(dt + margin[di] + 1))\n            box_start[di], box_end[di] = min_d, max_d\n        return box_start, box_end", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_SpatialCropGuidanced.__call___SpatialCropGuidanced.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_SpatialCropGuidanced.__call___SpatialCropGuidanced.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/apps/deepgrow/transforms.py", "file_name": "transforms.py", "file_type": "text/x-python", "category": "implementation", "start_line": 645, "end_line": 694, "span_ids": ["SpatialCropGuidanced.__call__"], "tokens": 632}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SpatialCropGuidanced(MapTransform):\n\n    def __call__(self, data):\n        d: Dict = dict(data)\n        first_key: Union[Hashable, List] = self.first_key(d)\n        if first_key == []:\n            return d\n\n        guidance = d[self.guidance]\n        original_spatial_shape = d[first_key].shape[1:]  # type: ignore\n        box_start, box_end = self.bounding_box(np.array(guidance[0] + guidance[1]), original_spatial_shape)\n        center = list(np.mean([box_start, box_end], axis=0).astype(int, copy=False))\n        spatial_size = self.spatial_size\n\n        box_size = list(np.subtract(box_end, box_start).astype(int, copy=False))\n        spatial_size = spatial_size[-len(box_size) :]\n\n        if len(spatial_size) < len(box_size):\n            # If the data is in 3D and spatial_size is specified as 2D [256,256]\n            # Then we will get all slices in such case\n            diff = len(box_size) - len(spatial_size)\n            spatial_size = list(original_spatial_shape[1 : (1 + diff)]) + spatial_size\n\n        if np.all(np.less(box_size, spatial_size)):\n            if len(center) == 3:\n                # 3D Deepgrow: set center to be middle of the depth dimension (D)\n                center[0] = spatial_size[0] // 2\n            cropper = SpatialCrop(roi_center=center, roi_size=spatial_size)\n        else:\n            cropper = SpatialCrop(roi_start=box_start, roi_end=box_end)\n\n        # update bounding box in case it was corrected by the SpatialCrop constructor\n        box_start = np.array([s.start for s in cropper.slices])\n        box_end = np.array([s.stop for s in cropper.slices])\n        for key, meta_key, meta_key_postfix in self.key_iterator(d, self.meta_keys, self.meta_key_postfix):\n            if not np.array_equal(d[key].shape[1:], original_spatial_shape):\n                raise RuntimeError(\"All the image specified in keys should have same spatial shape\")\n            meta_key = meta_key or f\"{key}_{meta_key_postfix}\"\n            d[meta_key][self.start_coord_key] = box_start\n            d[meta_key][self.end_coord_key] = box_end\n            d[meta_key][self.original_shape_key] = d[key].shape\n\n            image = cropper(d[key])\n            d[meta_key][self.cropped_shape_key] = image.shape\n            d[key] = image\n\n        pos_clicks, neg_clicks = guidance[0], guidance[1]\n        pos = np.subtract(pos_clicks, box_start).tolist() if len(pos_clicks) else []\n        neg = np.subtract(neg_clicks, box_start).tolist() if len(neg_clicks) else []\n\n        d[self.guidance] = [pos, neg]\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_ResizeGuidanced_ResizeGuidanced.__init__.self.cropped_shape_key.cropped_shape_key": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_ResizeGuidanced_ResizeGuidanced.__init__.self.cropped_shape_key.cropped_shape_key", "embedding": null, "metadata": {"file_path": "monai/apps/deepgrow/transforms.py", "file_name": "transforms.py", "file_type": "text/x-python", "category": "implementation", "start_line": 749, "end_line": 782, "span_ids": ["ResizeGuidanced.__init__", "ResizeGuidanced"], "tokens": 356}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ResizeGuidanced(Transform):\n    \"\"\"\n    Resize the guidance based on cropped vs resized image.\n\n    This transform assumes that the images have been cropped and resized. And the shape after cropped is store inside\n    the meta dict of ref image.\n\n    Args:\n        guidance: key to guidance\n        ref_image: key to reference image to fetch current and original image details\n        meta_keys: explicitly indicate the key of the meta data dictionary of `ref_image`.\n            for example, for data with key `image`, the metadata by default is in `image_meta_dict`.\n            the meta data is a dictionary object which contains: filename, original_shape, etc.\n            if None, will try to construct meta_keys by `{ref_image}_{meta_key_postfix}`.\n        meta_key_postfix: if meta_key is None, use `{ref_image}_{meta_key_postfix}` to to fetch the meta data according\n            to the key data, default is `meta_dict`, the meta data is a dictionary object.\n            For example, to handle key `image`,  read/write affine matrices from the\n            metadata `image_meta_dict` dictionary's `affine` field.\n        cropped_shape_key: key that records cropped shape for foreground.\n    \"\"\"\n\n    def __init__(\n        self,\n        guidance: str,\n        ref_image: str,\n        meta_keys: Optional[str] = None,\n        meta_key_postfix: str = \"meta_dict\",\n        cropped_shape_key: str = \"foreground_cropped_shape\",\n    ) -> None:\n        self.guidance = guidance\n        self.ref_image = ref_image\n        self.meta_keys = meta_keys\n        self.meta_key_postfix = meta_key_postfix\n        self.cropped_shape_key = cropped_shape_key", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_ResizeGuidanced.__call___ResizeGuidanced.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_ResizeGuidanced.__call___ResizeGuidanced.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/apps/deepgrow/transforms.py", "file_name": "transforms.py", "file_type": "text/x-python", "category": "implementation", "start_line": 732, "end_line": 745, "span_ids": ["ResizeGuidanced.__call__"], "tokens": 179}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ResizeGuidanced(Transform):\n\n    def __call__(self, data):\n        d = dict(data)\n        guidance = d[self.guidance]\n        meta_dict: Dict = d[self.meta_keys or f\"{self.ref_image}_{self.meta_key_postfix}\"]\n        current_shape = d[self.ref_image].shape[1:]\n        cropped_shape = meta_dict[self.cropped_shape_key][1:]\n        factor = np.divide(current_shape, cropped_shape)\n\n        pos_clicks, neg_clicks = guidance[0], guidance[1]\n        pos = np.multiply(pos_clicks, factor).astype(int, copy=False).tolist() if len(pos_clicks) else []\n        neg = np.multiply(neg_clicks, factor).astype(int, copy=False).tolist() if len(neg_clicks) else []\n\n        d[self.guidance] = [pos, neg]\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_RestoreLabeld_RestoreLabeld._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_RestoreLabeld_RestoreLabeld._", "embedding": null, "metadata": {"file_path": "monai/apps/deepgrow/transforms.py", "file_name": "transforms.py", "file_type": "text/x-python", "category": "implementation", "start_line": 800, "end_line": 848, "span_ids": ["RestoreLabeld"], "tokens": 637}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RestoreLabeld(MapTransform):\n    \"\"\"\n    Restores label based on the ref image.\n\n    The ref_image is assumed that it went through the following transforms:\n\n        1. Fetch2DSliced (If 2D)\n        2. Spacingd\n        3. SpatialCropGuidanced\n        4. Resized\n\n    And its shape is assumed to be (C, D, H, W)\n\n    This transform tries to undo these operation so that the result label can be overlapped with original volume.\n    It does the following operation:\n\n        1. Undo Resized\n        2. Undo SpatialCropGuidanced\n        3. Undo Spacingd\n        4. Undo Fetch2DSliced\n\n    The resulting label is of shape (D, H, W)\n\n    Args:\n        keys: keys of the corresponding items to be transformed.\n        ref_image: reference image to fetch current and original image details\n        slice_only: apply only to an applicable slice, in case of 2D model/prediction\n        mode: {``\"constant\"``, ``\"edge\"``, ``\"linear_ramp\"``, ``\"maximum\"``, ``\"mean\"``,\n            ``\"median\"``, ``\"minimum\"``, ``\"reflect\"``, ``\"symmetric\"``, ``\"wrap\"``, ``\"empty\"``}\n            One of the listed string values or a user supplied function for padding. Defaults to ``\"constant\"``.\n            See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html\n        align_corners: Geometrically, we consider the pixels of the input as squares rather than points.\n            See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            It also can be a sequence of bool, each element corresponds to a key in ``keys``.\n        meta_keys: explicitly indicate the key of the corresponding meta data dictionary.\n            for example, for data with key `image`, the metadata by default is in `image_meta_dict`.\n            the meta data is a dictionary object which contains: filename, original_shape, etc.\n            it can be a sequence of string, map to the `keys`.\n            if None, will try to construct meta_keys by `key_{meta_key_postfix}`.\n        meta_key_postfix: if meta_key is None, use `key_{meta_key_postfix} to to fetch the meta data according\n            to the key data, default is `meta_dict`, the meta data is a dictionary object.\n            For example, to handle key `image`,  read/write affine matrices from the\n            metadata `image_meta_dict` dictionary's `affine` field.\n        start_coord_key: key that records the start coordinate of spatial bounding box for foreground.\n        end_coord_key: key that records the end coordinate of spatial bounding box for foreground.\n        original_shape_key: key that records original shape for foreground.\n        cropped_shape_key: key that records cropped shape for foreground.\n        allow_missing_keys: don't raise exception if key is missing.\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_RestoreLabeld.__init___RestoreLabeld.__init__.self.cropped_shape_key.cropped_shape_key": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_RestoreLabeld.__init___RestoreLabeld.__init__.self.cropped_shape_key.cropped_shape_key", "embedding": null, "metadata": {"file_path": "monai/apps/deepgrow/transforms.py", "file_name": "transforms.py", "file_type": "text/x-python", "category": "implementation", "start_line": 850, "end_line": 877, "span_ids": ["RestoreLabeld.__init__"], "tokens": 327}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RestoreLabeld(MapTransform):\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        ref_image: str,\n        slice_only: bool = False,\n        mode: Union[Sequence[Union[InterpolateMode, str]], InterpolateMode, str] = InterpolateMode.NEAREST,\n        align_corners: Union[Sequence[Optional[bool]], Optional[bool]] = None,\n        meta_keys: Optional[str] = None,\n        meta_key_postfix: str = \"meta_dict\",\n        start_coord_key: str = \"foreground_start_coord\",\n        end_coord_key: str = \"foreground_end_coord\",\n        original_shape_key: str = \"foreground_original_shape\",\n        cropped_shape_key: str = \"foreground_cropped_shape\",\n        allow_missing_keys: bool = False,\n    ) -> None:\n        super().__init__(keys, allow_missing_keys)\n        self.ref_image = ref_image\n        self.slice_only = slice_only\n        self.mode = ensure_tuple_rep(mode, len(self.keys))\n        self.align_corners = ensure_tuple_rep(align_corners, len(self.keys))\n        self.meta_keys = ensure_tuple_rep(None, len(self.keys)) if meta_keys is None else ensure_tuple(meta_keys)\n        if len(self.keys) != len(self.meta_keys):\n            raise ValueError(\"meta_keys should have the same length as keys.\")\n        self.meta_key_postfix = meta_key_postfix\n        self.start_coord_key = start_coord_key\n        self.end_coord_key = end_coord_key\n        self.original_shape_key = original_shape_key\n        self.cropped_shape_key = cropped_shape_key", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_RestoreLabeld.__call___RestoreLabeld.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_RestoreLabeld.__call___RestoreLabeld.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/apps/deepgrow/transforms.py", "file_name": "transforms.py", "file_type": "text/x-python", "category": "implementation", "start_line": 879, "end_line": 931, "span_ids": ["RestoreLabeld.__call__"], "tokens": 530}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RestoreLabeld(MapTransform):\n\n    def __call__(self, data):\n        d = dict(data)\n        meta_dict: Dict = d[f\"{self.ref_image}_{self.meta_key_postfix}\"]\n\n        for key, mode, align_corners, meta_key in self.key_iterator(d, self.mode, self.align_corners, self.meta_keys):\n            image = d[key]\n\n            # Undo Resize\n            current_shape = image.shape\n            cropped_shape = meta_dict[self.cropped_shape_key]\n            if np.any(np.not_equal(current_shape, cropped_shape)):\n                resizer = Resize(spatial_size=cropped_shape[1:], mode=mode)\n                image = resizer(image, mode=mode, align_corners=align_corners)\n\n            # Undo Crop\n            original_shape = meta_dict[self.original_shape_key]\n            result = np.zeros(original_shape, dtype=np.float32)\n            box_start = meta_dict[self.start_coord_key]\n            box_end = meta_dict[self.end_coord_key]\n\n            spatial_dims = min(len(box_start), len(image.shape[1:]))\n            slices = [slice(None)] + [slice(s, e) for s, e in zip(box_start[:spatial_dims], box_end[:spatial_dims])]\n            slices = tuple(slices)\n            result[slices] = image\n\n            # Undo Spacing\n            current_size = result.shape[1:]\n            # change spatial_shape from HWD to DHW\n            spatial_shape = list(np.roll(meta_dict[\"spatial_shape\"], 1))\n            spatial_size = spatial_shape[-len(current_size) :]\n\n            if np.any(np.not_equal(current_size, spatial_size)):\n                resizer = Resize(spatial_size=spatial_size, mode=mode)\n                result = resizer(result, mode=mode, align_corners=align_corners)\n\n            # Undo Slicing\n            slice_idx = meta_dict.get(\"slice_idx\")\n            if slice_idx is None or self.slice_only:\n                final_result = result if len(result.shape) <= 3 else result[0]\n            else:\n                slice_idx = meta_dict[\"slice_idx\"][0]\n                final_result = np.zeros(tuple(spatial_shape))\n                final_result[slice_idx] = result\n            d[key] = final_result\n\n            meta_key = meta_key or f\"{key}_{self.meta_key_postfix}\"\n            meta = d.get(meta_key)\n            if meta is None:\n                meta = dict()\n                d[meta_key] = meta\n            meta[\"slice_idx\"] = slice_idx\n            meta[\"affine\"] = meta_dict[\"original_affine\"]\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/__init__.py_MaskedInferenceWSIDataset_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/__init__.py_MaskedInferenceWSIDataset_", "embedding": null, "metadata": {"file_path": "monai/apps/pathology/__init__.py", "file_name": "__init__.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 25, "span_ids": ["docstring"], "tokens": 130}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from .data import MaskedInferenceWSIDataset, PatchWSIDataset, SmartCachePatchWSIDataset\nfrom .handlers import ProbMapProducer\nfrom .metrics import LesionFROC\nfrom .transforms.stain.array import ExtractHEStains, NormalizeHEStains\nfrom .transforms.stain.dictionary import (\n    ExtractHEStainsd,\n    ExtractHEStainsD,\n    ExtractHEStainsDict,\n    NormalizeHEStainsd,\n    NormalizeHEStainsD,\n    NormalizeHEStainsDict,\n)\nfrom .utils import PathologyProbNMS, compute_isolated_tumor_cells, compute_multi_instance_mask", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/utils.py_from_typing_import_List__compute_multi_instance_mask.return.multi_instance_mask": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/utils.py_from_typing_import_List__compute_multi_instance_mask.return.multi_instance_mask", "embedding": null, "metadata": {"file_path": "monai/apps/pathology/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 40, "span_ids": ["compute_multi_instance_mask", "docstring"], "tokens": 178}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import List, Union\n\nimport numpy as np\nimport torch\n\nfrom monai.transforms.post.array import ProbNMS\nfrom monai.utils import optional_import\n\nmeasure, _ = optional_import(\"skimage.measure\")\nndimage, _ = optional_import(\"scipy.ndimage\")\n\n\ndef compute_multi_instance_mask(mask: np.ndarray, threshold: float):\n    \"\"\"\n    This method computes the segmentation mask according to the binary tumor mask.\n\n    Args:\n        mask: the binary mask array\n        threshold: the threshold to fill holes\n    \"\"\"\n\n    neg = 255 - mask * 255\n    distance = ndimage.morphology.distance_transform_edt(neg)\n    binary = distance < threshold\n\n    filled_image = ndimage.morphology.binary_fill_holes(binary)\n    multi_instance_mask = measure.label(filled_image, connectivity=2)\n\n    return multi_instance_mask", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/utils.py_compute_isolated_tumor_cells_compute_isolated_tumor_cells.return.itc_list": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/utils.py_compute_isolated_tumor_cells_compute_isolated_tumor_cells.return.itc_list", "embedding": null, "metadata": {"file_path": "monai/apps/pathology/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 43, "end_line": 56, "span_ids": ["compute_isolated_tumor_cells"], "tokens": 152}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def compute_isolated_tumor_cells(tumor_mask: np.ndarray, threshold: float) -> List[int]:\n    \"\"\"\n    This method computes identifies Isolated Tumor Cells (ITC) and return their labels.\n\n    Args:\n        tumor_mask: the tumor mask.\n        threshold: the threshold (at the mask level) to define an isolated tumor cell (ITC).\n            A region with the longest diameter less than this threshold is considered as an ITC.\n    \"\"\"\n    max_label = np.amax(tumor_mask)\n    properties = measure.regionprops(tumor_mask, coordinates=\"rc\")\n    itc_list = [i + 1 for i in range(max_label) if properties[i].major_axis_length < threshold]\n\n    return itc_list", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/utils.py_PathologyProbNMS_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/utils.py_PathologyProbNMS_", "embedding": null, "metadata": {"file_path": "monai/apps/pathology/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 59, "end_line": 79, "span_ids": ["PathologyProbNMS", "PathologyProbNMS.__call__"], "tokens": 191}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class PathologyProbNMS(ProbNMS):\n    \"\"\"\n    This class extends monai.utils.ProbNMS and add the `resolution` option for\n    Pathology.\n    \"\"\"\n\n    def __call__(self, probs_map: Union[np.ndarray, torch.Tensor], resolution_level: int = 0):\n        \"\"\"\n        probs_map: the input probabilities map, it must have shape (H[, W, ...]).\n        resolution_level: the level at which the probabilities map is made.\n        \"\"\"\n        resolution = pow(2, resolution_level)\n        org_outputs = ProbNMS.__call__(self, probs_map)\n        outputs = []\n        for org_output in org_outputs:\n            prob = org_output[0]\n            coord = np.asarray(org_output[1:])\n            coord_wsi = ((coord + 0.5) * resolution).astype(int)\n            outputs.append([prob] + list(coord_wsi))\n        return outputs", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/utils.py_download_url_download_url.if_not_check_hash_filepat.raise_RuntimeError_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/utils.py_download_url_download_url.if_not_check_hash_filepat.raise_RuntimeError_", "embedding": null, "metadata": {"file_path": "monai/apps/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 148, "end_line": 207, "span_ids": ["download_url"], "tokens": 594}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def download_url(\n    url: str, filepath: PathLike = \"\", hash_val: Optional[str] = None, hash_type: str = \"md5\", progress: bool = True\n) -> None:\n    \"\"\"\n    Download file from specified URL link, support process bar and hash check.\n\n    Args:\n        url: source URL link to download file.\n        filepath: target filepath to save the downloaded file (including the filename).\n            If undefined, `os.path.basename(url)` will be used.\n        hash_val: expected hash value to validate the downloaded file.\n            if None, skip hash validation.\n        hash_type: 'md5' or 'sha1', defaults to 'md5'.\n        progress: whether to display a progress bar.\n\n    Raises:\n        RuntimeError: When the hash validation of the ``filepath`` existing file fails.\n        RuntimeError: When a network issue or denied permission prevents the\n            file download from ``url`` to ``filepath``.\n        URLError: See urllib.request.urlretrieve.\n        HTTPError: See urllib.request.urlretrieve.\n        ContentTooShortError: See urllib.request.urlretrieve.\n        IOError: See urllib.request.urlretrieve.\n        RuntimeError: When the hash validation of the ``url`` downloaded file fails.\n\n    \"\"\"\n    if not filepath:\n        filepath = Path(\".\", _basename(url)).resolve()\n        logger.info(f\"Default downloading to '{filepath}'\")\n    filepath = Path(filepath)\n    if filepath.exists():\n        if not check_hash(filepath, hash_val, hash_type):\n            raise RuntimeError(\n                f\"{hash_type} check of existing file failed: filepath={filepath}, expected {hash_type}={hash_val}.\"\n            )\n        logger.info(f\"File exists: {filepath}, skipped downloading.\")\n        return\n\n    with tempfile.TemporaryDirectory() as tmp_dir:\n        tmp_name = Path(tmp_dir, _basename(filepath))\n        if url.startswith(\"https://drive.google.com\"):\n            if not has_gdown:\n                raise RuntimeError(\"To download files from Google Drive, please install the gdown dependency.\")\n            gdown.download(url, f\"{tmp_name}\", quiet=not progress)\n        else:\n            _download_with_progress(url, tmp_name, progress=progress)\n        if not tmp_name.exists():\n            raise RuntimeError(\n                f\"Download of file from {url} to {filepath} failed due to network issue or denied permission.\"\n            )\n        file_dir = filepath.parent\n        if file_dir:\n            os.makedirs(file_dir, exist_ok=True)\n        shutil.move(f\"{tmp_name}\", f\"{filepath}\")  # copy the downloaded to a user-specified cache.\n        logger.info(f\"Downloaded: {filepath}\")\n    if not check_hash(filepath, hash_val, hash_type):\n        raise RuntimeError(\n            f\"{hash_type} check of downloaded file failed: URL={url}, \"\n            f\"filepath={filepath}, expected {hash_type}={hash_val}.\"\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/utils.py_download_and_extract_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/utils.py_download_and_extract_", "embedding": null, "metadata": {"file_path": "monai/apps/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 269, "end_line": 302, "span_ids": ["download_and_extract"], "tokens": 389}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def download_and_extract(\n    url: str,\n    filepath: PathLike = \"\",\n    output_dir: PathLike = \".\",\n    hash_val: Optional[str] = None,\n    hash_type: str = \"md5\",\n    file_type: str = \"\",\n    has_base: bool = True,\n    progress: bool = True,\n) -> None:\n    \"\"\"\n    Download file from URL and extract it to the output directory.\n\n    Args:\n        url: source URL link to download file.\n        filepath: the file path of the downloaded compressed file.\n            use this option to keep the directly downloaded compressed file, to avoid further repeated downloads.\n        output_dir: target directory to save extracted files.\n            default is the current directory.\n        hash_val: expected hash value to validate the downloaded file.\n            if None, skip hash validation.\n        hash_type: 'md5' or 'sha1', defaults to 'md5'.\n        file_type: string of file type for decompressing. Leave it empty to infer the type from url's base file name.\n        has_base: whether the extracted files have a base folder. This flag is used when checking if the existing\n            folder is a result of `extractall`, if it is, the extraction is skipped. For example, if A.zip is unzipped\n            to folder structure `A/*.png`, this flag should be True; if B.zip is unzipped to `*.png`, this flag should\n            be False.\n        progress: whether to display progress bar.\n    \"\"\"\n    with tempfile.TemporaryDirectory() as tmp_dir:\n        filename = filepath or Path(tmp_dir, _basename(url)).resolve()\n        download_url(url=url, filepath=filename, hash_val=hash_val, hash_type=hash_type, progress=progress)\n        extractall(filepath=filename, output_dir=output_dir, file_type=file_type, has_base=has_base)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/config/__init__.py_USE_COMPILED_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/config/__init__.py_USE_COMPILED_", "embedding": null, "metadata": {"file_path": "monai/config/__init__.py", "file_name": "__init__.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 33, "span_ids": ["docstring"], "tokens": 97}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from .deviceconfig import (\n    USE_COMPILED,\n    IgniteInfo,\n    get_config_values,\n    get_gpu_info,\n    get_optional_config_values,\n    get_system_info,\n    print_config,\n    print_debug_info,\n    print_gpu_info,\n    print_system_info,\n)\nfrom .type_definitions import (\n    DtypeLike,\n    IndexSelection,\n    KeysCollection,\n    NdarrayOrTensor,\n    NdarrayTensor,\n    PathLike,\n    TensorOrList,\n)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/config/deviceconfig.py_print_config__dict_append.try_.except_BaseException_.in_dict_key_UNKNOWN_f": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/config/deviceconfig.py_print_config__dict_append.try_.except_BaseException_.in_dict_key_UNKNOWN_f", "embedding": null, "metadata": {"file_path": "monai/config/deviceconfig.py", "file_name": "deviceconfig.py", "file_type": "text/x-python", "category": "implementation", "start_line": 82, "end_line": 110, "span_ids": ["_dict_append", "print_config"], "tokens": 274}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def print_config(file=sys.stdout):\n    \"\"\"\n    Print the package versions to `file`.\n\n    Args:\n        file: `print()` text stream file. Defaults to `sys.stdout`.\n    \"\"\"\n    for k, v in get_config_values().items():\n        print(f\"{k} version: {v}\", file=file, flush=True)\n    print(f\"MONAI flags: HAS_EXT = {HAS_EXT}, USE_COMPILED = {USE_COMPILED}\")\n    print(f\"MONAI rev id: {monai.__revision_id__}\")\n    print(f\"MONAI __file__: {monai.__file__}\")\n\n    print(\"\\nOptional dependencies:\", file=file, flush=True)\n    for k, v in get_optional_config_values().items():\n        print(f\"{k} version: {v}\", file=file, flush=True)\n    print(\"\\nFor details about installing the optional dependencies, please visit:\", file=file, flush=True)\n    print(\n        \"    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\\n\",\n        file=file,\n        flush=True,\n    )\n\n\ndef _dict_append(in_dict, key, fn):\n    try:\n        in_dict[key] = fn() if callable(fn) else fn\n    except BaseException:\n        in_dict[key] = \"UNKNOWN for given OS\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/config/deviceconfig.py_get_system_info_get_system_info.return.output": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/config/deviceconfig.py_get_system_info_get_system_info.return.output", "embedding": null, "metadata": {"file_path": "monai/config/deviceconfig.py", "file_name": "deviceconfig.py", "file_type": "text/x-python", "category": "implementation", "start_line": 112, "end_line": 167, "span_ids": ["get_system_info"], "tokens": 668}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_system_info() -> OrderedDict:\n    \"\"\"\n    Get system info as an ordered dictionary.\n    \"\"\"\n    output: OrderedDict = OrderedDict()\n\n    _dict_append(output, \"System\", platform.system)\n    if output[\"System\"] == \"Windows\":\n        _dict_append(output, \"Win32 version\", platform.win32_ver)\n        if hasattr(platform, \"win32_edition\"):\n            _dict_append(output, \"Win32 edition\", platform.win32_edition)  # type:ignore[attr-defined]\n    elif output[\"System\"] == \"Darwin\":\n        _dict_append(output, \"Mac version\", lambda: platform.mac_ver()[0])\n    else:\n        with open(\"/etc/os-release\") as rel_f:\n            linux_ver = re.search(r'PRETTY_NAME=\"(.*)\"', rel_f.read())\n        if linux_ver:\n            _dict_append(output, \"Linux version\", lambda: linux_ver.group(1))\n\n    _dict_append(output, \"Platform\", platform.platform)\n    _dict_append(output, \"Processor\", platform.processor)\n    _dict_append(output, \"Machine\", platform.machine)\n    _dict_append(output, \"Python version\", platform.python_version)\n\n    if not has_psutil:\n        _dict_append(output, \"`psutil` missing\", lambda: \"run `pip install monai[psutil]`\")\n    else:\n        p = psutil.Process()\n        with p.oneshot():\n            _dict_append(output, \"Process name\", p.name)\n            _dict_append(output, \"Command\", p.cmdline)\n            _dict_append(output, \"Open files\", p.open_files)\n            _dict_append(output, \"Num physical CPUs\", lambda: psutil.cpu_count(logical=False))\n            _dict_append(output, \"Num logical CPUs\", lambda: psutil.cpu_count(logical=True))\n            _dict_append(output, \"Num usable CPUs\", lambda: len(psutil.Process().cpu_affinity()))\n            _dict_append(output, \"CPU usage (%)\", lambda: psutil.cpu_percent(percpu=True))\n            _dict_append(output, \"CPU freq. (MHz)\", lambda: round(psutil.cpu_freq(percpu=False)[0]))\n            _dict_append(\n                output,\n                \"Load avg. in last 1, 5, 15 mins (%)\",\n                lambda: [round(x / psutil.cpu_count() * 100, 1) for x in psutil.getloadavg()],\n            )\n            _dict_append(output, \"Disk usage (%)\", lambda: psutil.disk_usage(os.getcwd()).percent)\n            _dict_append(\n                output,\n                \"Avg. sensor temp. (Celsius)\",\n                lambda: np.round(\n                    np.mean([item.current for sublist in psutil.sensors_temperatures().values() for item in sublist], 1)\n                ),\n            )\n            mem = psutil.virtual_memory()\n            _dict_append(output, \"Total physical memory (GB)\", lambda: round(mem.total / 1024 ** 3, 1))\n            _dict_append(output, \"Available memory (GB)\", lambda: round(mem.available / 1024 ** 3, 1))\n            _dict_append(output, \"Used memory (GB)\", lambda: round(mem.used / 1024 ** 3, 1))\n\n    return output", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/config/deviceconfig.py_print_system_info_print_system_info.if_not_has_psutil_.else_.for_k_v_in_get_system_in.print_f_k_v_file_f": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/config/deviceconfig.py_print_system_info_print_system_info.if_not_has_psutil_.else_.for_k_v_in_get_system_in.print_f_k_v_file_f", "embedding": null, "metadata": {"file_path": "monai/config/deviceconfig.py", "file_name": "deviceconfig.py", "file_type": "text/x-python", "category": "implementation", "start_line": 178, "end_line": 189, "span_ids": ["print_system_info"], "tokens": 108}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def print_system_info(file=sys.stdout) -> None:\n    \"\"\"\n    Print system info to `file`. Requires the optional library, `psutil`.\n\n    Args:\n        file: `print()` text stream file. Defaults to `sys.stdout`.\n    \"\"\"\n    if not has_psutil:\n        print(\"`psutil` required for `print_system_info`\", file=file, flush=True)\n    else:\n        for k, v in get_system_info().items():\n            print(f\"{k}: {v}\", file=file, flush=True)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/config/deviceconfig.py_get_gpu_info_get_gpu_info.return.output": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/config/deviceconfig.py_get_gpu_info_get_gpu_info.return.output", "embedding": null, "metadata": {"file_path": "monai/config/deviceconfig.py", "file_name": "deviceconfig.py", "file_type": "text/x-python", "category": "implementation", "start_line": 184, "end_line": 214, "span_ids": ["get_gpu_info"], "tokens": 369}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_gpu_info() -> OrderedDict:\n\n    output: OrderedDict = OrderedDict()\n\n    num_gpus = torch.cuda.device_count()\n    _dict_append(output, \"Num GPUs\", lambda: num_gpus)\n\n    _dict_append(output, \"Has CUDA\", lambda: bool(torch.cuda.is_available()))\n\n    if output[\"Has CUDA\"]:\n        _dict_append(output, \"CUDA version\", lambda: torch.version.cuda)\n    cudnn_ver = torch.backends.cudnn.version()\n    _dict_append(output, \"cuDNN enabled\", lambda: bool(cudnn_ver))\n\n    if cudnn_ver:\n        _dict_append(output, \"cuDNN version\", lambda: cudnn_ver)\n\n    if num_gpus > 0:\n        _dict_append(output, \"Current device\", torch.cuda.current_device)\n        _dict_append(output, \"Library compiled for CUDA architectures\", torch.cuda.get_arch_list)\n\n    for gpu in range(num_gpus):\n        gpu_info = torch.cuda.get_device_properties(gpu)\n        _dict_append(output, f\"GPU {gpu} Name\", lambda: gpu_info.name)\n        _dict_append(output, f\"GPU {gpu} Is integrated\", lambda: bool(gpu_info.is_integrated))\n        _dict_append(output, f\"GPU {gpu} Is multi GPU board\", lambda: bool(gpu_info.is_multi_gpu_board))\n        _dict_append(output, f\"GPU {gpu} Multi processor count\", lambda: gpu_info.multi_processor_count)\n        _dict_append(output, f\"GPU {gpu} Total memory (GB)\", lambda: round(gpu_info.total_memory / 1024 ** 3, 1))\n        _dict_append(output, f\"GPU {gpu} CUDA capability (maj.min)\", lambda: f\"{gpu_info.major}.{gpu_info.minor}\")\n\n    return output", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/config/deviceconfig.py_print_gpu_info_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/config/deviceconfig.py_print_gpu_info_", "embedding": null, "metadata": {"file_path": "monai/config/deviceconfig.py", "file_name": "deviceconfig.py", "file_type": "text/x-python", "category": "implementation", "start_line": 218, "end_line": 261, "span_ids": ["print_gpu_info", "print_debug_info", "impl:14", "IgniteInfo"], "tokens": 276}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def print_gpu_info(file=sys.stdout) -> None:\n    \"\"\"\n    Print GPU info to `file`.\n\n    Args:\n        file: `print()` text stream file. Defaults to `sys.stdout`.\n    \"\"\"\n    for k, v in get_gpu_info().items():\n        print(f\"{k}: {v}\", file=file, flush=True)\n\n\ndef print_debug_info(file=sys.stdout) -> None:\n    \"\"\"\n    Print config (installed dependencies, etc.) and system info for debugging.\n\n    Args:\n        file: `print()` text stream file. Defaults to `sys.stdout`.\n    \"\"\"\n    print(\"================================\", file=file, flush=True)\n    print(\"Printing MONAI config...\", file=file, flush=True)\n    print(\"================================\", file=file, flush=True)\n    print_config(file)\n    print(\"\\n================================\", file=file, flush=True)\n    print(\"Printing system config...\")\n    print(\"================================\", file=file, flush=True)\n    print_system_info(file)\n    print(\"\\n================================\", file=file, flush=True)\n    print(\"Printing GPU config...\")\n    print(\"================================\", file=file, flush=True)\n    print_gpu_info(file)\n\n\nclass IgniteInfo:\n    \"\"\"\n    Config information of the PyTorch ignite package.\n\n    \"\"\"\n\n    OPT_IMPORT_VERSION = \"0.4.4\"\n\n\nif __name__ == \"__main__\":\n    print_debug_info()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataloader.py_torch_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataloader.py_torch_", "embedding": null, "metadata": {"file_path": "monai/data/dataloader.py", "file_name": "dataloader.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 85, "span_ids": ["DataLoader.__init__", "DataLoader", "docstring"], "tokens": 644}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import torch\nfrom torch.utils.data import DataLoader as _TorchDataLoader\nfrom torch.utils.data import Dataset\n\nfrom monai.data.utils import list_data_collate, set_rnd, worker_init_fn\n\n__all__ = [\"DataLoader\"]\n\n\nclass DataLoader(_TorchDataLoader):\n    \"\"\"\n    Provides an iterable over the given `dataset`.  It inherits the PyTorch\n    DataLoader and adds enhanced `collate_fn` and `worker_fn` by default.\n\n    Although this class could be configured to be the same as\n    `torch.utils.data.DataLoader`, its default configuration is\n    recommended, mainly for the following extra features:\n\n        - It handles MONAI randomizable objects with appropriate random state\n          managements for deterministic behaviour.\n        - It is aware of the patch-based transform (such as\n          :py:class:`monai.transforms.RandSpatialCropSamplesDict`) samples for\n          preprocessing with enhanced data collating behaviour.\n          See: :py:class:`monai.transforms.Compose`.\n\n    For more details about :py:class:`torch.utils.data.DataLoader`, please see:\n    https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader.\n\n    For example, to construct a randomized dataset and iterate with the data loader:\n\n    .. code-block:: python\n\n        import torch\n\n        from monai.data import DataLoader\n        from monai.transforms import Randomizable\n\n\n        class RandomDataset(torch.utils.data.Dataset, Randomizable):\n            def __getitem__(self, index):\n                return self.R.randint(0, 1000, (1,))\n\n            def __len__(self):\n                return 16\n\n\n        dataset = RandomDataset()\n        dataloader = DataLoader(dataset, batch_size=2, num_workers=4)\n        for epoch in range(2):\n            for i, batch in enumerate(dataloader):\n                print(epoch, i, batch.data.numpy().flatten().tolist())\n\n    Args:\n        dataset: dataset from which to load the data.\n        num_workers: how many subprocesses to use for data\n            loading. ``0`` means that the data will be loaded in the main process.\n            (default: ``0``)\n        kwargs: other parameters for PyTorch DataLoader.\n    \"\"\"\n\n    def __init__(self, dataset: Dataset, num_workers: int = 0, **kwargs) -> None:\n        if num_workers == 0:\n            # when num_workers > 0, random states are determined by worker_init_fn\n            # this is to make the behavior consistent when num_workers == 0\n            # torch.int64 doesn't work well on some versions of windows\n            _seed = torch.empty((), dtype=torch.int32).random_(generator=None).item()\n            set_rnd(dataset, int(_seed))\n        if \"collate_fn\" not in kwargs:\n            kwargs.update({\"collate_fn\": list_data_collate})\n        if \"worker_init_fn\" not in kwargs:\n            kwargs.update({\"worker_init_fn\": worker_init_fn})\n\n        super().__init__(dataset=dataset, num_workers=num_workers, **kwargs)  # type: ignore[call-overload]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_Dataset_Dataset._transform.return.apply_transform_self_tran": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_Dataset_Dataset._transform.return.apply_transform_self_tran", "embedding": null, "metadata": {"file_path": "monai/data/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 44, "end_line": 78, "span_ids": ["Dataset.__len__", "Dataset.__init__", "Dataset", "Dataset._transform"], "tokens": 327}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(_TorchDataset):\n    \"\"\"\n    A generic dataset with a length property and an optional callable data transform\n    when fetching a data sample.\n    If passing slicing indices, will return a PyTorch Subset, for example: `data: Subset = dataset[1:4]`,\n    for more details, please check: https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset\n\n    For example, typical input data can be a list of dictionaries::\n\n        [{                            {                            {\n             'img': 'image1.nii.gz',      'img': 'image2.nii.gz',      'img': 'image3.nii.gz',\n             'seg': 'label1.nii.gz',      'seg': 'label2.nii.gz',      'seg': 'label3.nii.gz',\n             'extra': 123                 'extra': 456                 'extra': 789\n         },                           },                           }]\n    \"\"\"\n\n    def __init__(self, data: Sequence, transform: Optional[Callable] = None) -> None:\n        \"\"\"\n        Args:\n            data: input data to load and transform to generate dataset for model.\n            transform: a callable data transform on input data.\n\n        \"\"\"\n        self.data = data\n        self.transform = transform\n\n    def __len__(self) -> int:\n        return len(self.data)\n\n    def _transform(self, index: int):\n        \"\"\"\n        Fetch single data item from `self.data`.\n        \"\"\"\n        data_i = self.data[index]\n        return apply_transform(self.transform, data_i) if self.transform is not None else data_i", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_Dataset.__getitem___Dataset.__getitem__.return.self__transform_index_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_Dataset.__getitem___Dataset.__getitem__.return.self__transform_index_", "embedding": null, "metadata": {"file_path": "monai/data/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 80, "end_line": 92, "span_ids": ["Dataset.__getitem__"], "tokens": 133}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Dataset(_TorchDataset):\n\n    def __getitem__(self, index: Union[int, slice, Sequence[int]]):\n        \"\"\"\n        Returns a `Subset` if `index` is a slice or Sequence, a data item otherwise.\n        \"\"\"\n        if isinstance(index, slice):\n            # dataset[:42]\n            start, stop, step = index.indices(len(self))\n            indices = range(start, stop, step)\n            return Subset(dataset=self, indices=indices)\n        if isinstance(index, collections.abc.Sequence):\n            # dataset[[1, 3, 4]]\n            return Subset(dataset=self, indices=index)\n        return self._transform(index)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_PersistentDataset_PersistentDataset._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_PersistentDataset_PersistentDataset._", "embedding": null, "metadata": {"file_path": "monai/data/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 98, "end_line": 146, "span_ids": ["PersistentDataset"], "tokens": 624}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class PersistentDataset(Dataset):\n    \"\"\"\n    Persistent storage of pre-computed values to efficiently manage larger than memory dictionary format data,\n    it can operate transforms for specific fields.  Results from the non-random transform components are computed\n    when first used, and stored in the `cache_dir` for rapid retrieval on subsequent uses.\n    If passing slicing indices, will return a PyTorch Subset, for example: `data: Subset = dataset[1:4]`,\n    for more details, please check: https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset\n\n    The transforms which are supposed to be cached must implement the `monai.transforms.Transform`\n    interface and should not be `Randomizable`. This dataset will cache the outcomes before the first\n    `Randomizable` `Transform` within a `Compose` instance.\n\n    For example, typical input data can be a list of dictionaries::\n\n        [{                            {                            {\n            'image': 'image1.nii.gz',    'image': 'image2.nii.gz',    'image': 'image3.nii.gz',\n            'label': 'label1.nii.gz',    'label': 'label2.nii.gz',    'label': 'label3.nii.gz',\n            'extra': 123                 'extra': 456                 'extra': 789\n        },                           },                           }]\n\n    For a composite transform like\n\n    .. code-block:: python\n\n        [ LoadImaged(keys=['image', 'label']),\n        Orientationd(keys=['image', 'label'], axcodes='RAS'),\n        ScaleIntensityRanged(keys=['image'], a_min=-57, a_max=164, b_min=0.0, b_max=1.0, clip=True),\n        RandCropByPosNegLabeld(keys=['image', 'label'], label_key='label', spatial_size=(96, 96, 96),\n                                pos=1, neg=1, num_samples=4, image_key='image', image_threshold=0),\n        ToTensord(keys=['image', 'label'])]\n\n    Upon first use a filename based dataset will be processed by the transform for the\n    [LoadImaged, Orientationd, ScaleIntensityRanged] and the resulting tensor written to\n    the `cache_dir` before applying the remaining random dependant transforms\n    [RandCropByPosNegLabeld, ToTensord] elements for use in the analysis.\n\n    Subsequent uses of a dataset directly read pre-processed results from `cache_dir`\n    followed by applying the random dependant parts of transform processing.\n\n    During training call `set_data()` to update input data and recompute cache content.\n\n    Note:\n        The input data must be a list of file paths and will hash them as cache keys.\n\n        When loading persistent cache content, it can't guarantee the cached data matches current\n        transform chain, so please make sure to use exactly the same non-random transforms and the\n        args as the cache content, otherwise, it may cause unexpected errors.\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_PersistentDataset._pre_transform_PersistentDataset._pre_transform.return.item_transformed": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_PersistentDataset._pre_transform_PersistentDataset._pre_transform.return.item_transformed", "embedding": null, "metadata": {"file_path": "monai/data/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 176, "end_line": 195, "span_ids": ["PersistentDataset._pre_transform"], "tokens": 172}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class PersistentDataset(Dataset):\n\n    def _pre_transform(self, item_transformed):\n        \"\"\"\n        Process the data from original state up to the first random element.\n\n        Args:\n            item_transformed: The data to be transformed\n\n        Returns:\n            the transformed element up to the first identified\n            random transform object\n\n        \"\"\"\n        for _transform in self.transform.transforms:  # type:ignore\n            # execute all the deterministic transforms\n            if isinstance(_transform, Randomizable) or not isinstance(_transform, Transform):\n                break\n            # this is to be consistent with CacheDataset even though it's not in a multi-thread situation.\n            _xform = deepcopy(_transform) if isinstance(_transform, ThreadUnsafe) else _transform\n            item_transformed = apply_transform(_xform, item_transformed)\n        return item_transformed", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_PersistentDataset._post_transform_PersistentDataset._post_transform.return.item_transformed": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_PersistentDataset._post_transform_PersistentDataset._post_transform.return.item_transformed", "embedding": null, "metadata": {"file_path": "monai/data/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 189, "end_line": 211, "span_ids": ["PersistentDataset._post_transform"], "tokens": 175}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class PersistentDataset(Dataset):\n\n    def _post_transform(self, item_transformed):\n        \"\"\"\n        Process the data from before the first random transform to the final state ready for evaluation.\n\n        Args:\n            item_transformed: The data to be transformed (already processed up to the first random transform)\n\n        Returns:\n            the transformed element through the random transforms\n\n        \"\"\"\n        if not isinstance(self.transform, Compose):\n            raise ValueError(\"transform must be an instance of monai.transforms.Compose.\")\n        start_post_randomize_run = False\n        for _transform in self.transform.transforms:\n            if (\n                start_post_randomize_run\n                or isinstance(_transform, Randomizable)\n                or not isinstance(_transform, Transform)\n            ):\n                start_post_randomize_run = True\n                item_transformed = apply_transform(_transform, item_transformed)\n        return item_transformed", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_PersistentDataset._cachecheck_PersistentDataset._transform.return.self__post_transform_pre_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_PersistentDataset._cachecheck_PersistentDataset._transform.return.self__post_transform_pre_", "embedding": null, "metadata": {"file_path": "monai/data/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 305, "end_line": 361, "span_ids": ["PersistentDataset._cachecheck", "PersistentDataset._transform"], "tokens": 544}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class PersistentDataset(Dataset):\n\n    def _cachecheck(self, item_transformed):\n        \"\"\"\n        A function to cache the expensive input data transform operations\n        so that huge data sets (larger than computer memory) can be processed\n        on the fly as needed, and intermediate results written to disk for\n        future use.\n\n        Args:\n            item_transformed: The current data element to be mutated into transformed representation\n\n        Returns:\n            The transformed data_element, either from cache, or explicitly computing it.\n\n        Warning:\n            The current implementation does not encode transform information as part of the\n            hashing mechanism used for generating cache names.  If the transforms applied are\n            changed in any way, the objects in the cache dir will be invalid.  The hash for the\n            cache is ONLY dependant on the input filename paths.\n\n        \"\"\"\n        hashfile = None\n        if self.cache_dir is not None:\n            data_item_md5 = self.hash_func(item_transformed).decode(\"utf-8\")\n            hashfile = self.cache_dir / f\"{data_item_md5}.pt\"\n\n        if hashfile is not None and hashfile.is_file():  # cache hit\n            try:\n                return torch.load(hashfile)\n            except PermissionError as e:\n                if sys.platform != \"win32\":\n                    raise e\n\n        _item_transformed = self._pre_transform(deepcopy(item_transformed))  # keep the original hashed\n        if hashfile is not None:\n            # NOTE: Writing to a temporary directory and then using a nearly atomic rename operation\n            #       to make the cache more robust to manual killing of parent process\n            #       which may leave partially written cache files in an incomplete state\n            with tempfile.TemporaryDirectory() as tmpdirname:\n                temp_hash_file = Path(tmpdirname) / hashfile.name\n                torch.save(\n                    obj=_item_transformed,\n                    f=temp_hash_file,\n                    pickle_module=look_up_option(self.pickle_module, SUPPORTED_PICKLE_MOD),\n                    pickle_protocol=self.pickle_protocol,\n                )\n                if temp_hash_file.is_file() and not hashfile.is_file():\n                    # On Unix, if target exists and is a file, it will be replaced silently if the user has permission.\n                    # for more details: https://docs.python.org/3/library/shutil.html#shutil.move.\n                    try:\n                        shutil.move(temp_hash_file, hashfile)\n                    except FileExistsError:\n                        pass\n        return _item_transformed\n\n    def _transform(self, index: int):\n        pre_random_item = self._cachecheck(self.data[index])\n        return self._post_transform(pre_random_item)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_CacheNTransDataset_CacheNTransDataset.__init__.self.cache_n_trans.cache_n_trans": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_CacheNTransDataset_CacheNTransDataset.__init__.self.cache_n_trans.cache_n_trans", "embedding": null, "metadata": {"file_path": "monai/data/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 364, "end_line": 417, "span_ids": ["CacheNTransDataset.__init__", "CacheNTransDataset"], "tokens": 568}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CacheNTransDataset(PersistentDataset):\n    \"\"\"\n    Extension of `PersistentDataset`, tt can also cache the result of first N transforms, no matter it's random or not.\n\n    \"\"\"\n\n    def __init__(\n        self,\n        data: Sequence,\n        transform: Union[Sequence[Callable], Callable],\n        cache_n_trans: int,\n        cache_dir: Optional[Union[Path, str]],\n        hash_func: Callable[..., bytes] = pickle_hashing,\n        pickle_module: str = \"pickle\",\n        pickle_protocol: int = DEFAULT_PROTOCOL,\n    ) -> None:\n        \"\"\"\n        Args:\n            data: input data file paths to load and transform to generate dataset for model.\n                `PersistentDataset` expects input data to be a list of serializable\n                and hashes them as cache keys using `hash_func`.\n            transform: transforms to execute operations on input data.\n            cache_n_trans: cache the result of first N transforms.\n            cache_dir: If specified, this is the location for persistent storage\n                of pre-computed transformed data tensors. The cache_dir is computed once, and\n                persists on disk until explicitly removed.  Different runs, programs, experiments\n                may share a common cache dir provided that the transforms pre-processing is consistent.\n                If `cache_dir` doesn't exist, will automatically create it.\n                If `cache_dir` is `None`, there is effectively no caching.\n            hash_func: a callable to compute hash from data items to be cached.\n                defaults to `monai.data.utils.pickle_hashing`.\n            pickle_module: string representing the module used for pickling metadata and objects,\n                default to `\"pickle\"`. due to the pickle limitation in multi-processing of Dataloader,\n                we can't use `pickle` as arg directly, so here we use a string name instead.\n                if want to use other pickle module at runtime, just register like:\n                >>> from monai.data import utils\n                >>> utils.SUPPORTED_PICKLE_MOD[\"test\"] = other_pickle\n                this arg is used by `torch.save`, for more details, please check:\n                https://pytorch.org/docs/stable/generated/torch.save.html#torch.save,\n                and ``monai.data.utils.SUPPORTED_PICKLE_MOD``.\n            pickle_protocol: can be specified to override the default protocol, default to `2`.\n                this arg is used by `torch.save`, for more details, please check:\n                https://pytorch.org/docs/stable/generated/torch.save.html#torch.save.\n\n        \"\"\"\n        super().__init__(\n            data=data,\n            transform=transform,\n            cache_dir=cache_dir,\n            hash_func=hash_func,\n            pickle_module=pickle_module,\n            pickle_protocol=pickle_protocol,\n        )\n        self.cache_n_trans = cache_n_trans", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_CacheNTransDataset._pre_transform_CacheNTransDataset._pre_transform.return.item_transformed": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_CacheNTransDataset._pre_transform_CacheNTransDataset._pre_transform.return.item_transformed", "embedding": null, "metadata": {"file_path": "monai/data/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 311, "end_line": 328, "span_ids": ["CacheNTransDataset._pre_transform"], "tokens": 155}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CacheNTransDataset(PersistentDataset):\n\n    def _pre_transform(self, item_transformed):\n        \"\"\"\n        Process the data from original state up to the N element.\n\n        Args:\n            item_transformed: The data to be transformed\n\n        Returns:\n            the transformed element up to the N transform object\n        \"\"\"\n        if not isinstance(self.transform, Compose):\n            raise ValueError(\"transform must be an instance of monai.transforms.Compose.\")\n        for i, _transform in enumerate(self.transform.transforms):\n            if i == self.cache_n_trans:\n                break\n            _xform = deepcopy(_transform) if isinstance(_transform, ThreadUnsafe) else _transform\n            item_transformed = apply_transform(_xform, item_transformed)\n        return item_transformed", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_CacheNTransDataset._post_transform_CacheNTransDataset._post_transform.return.item_transformed": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_CacheNTransDataset._post_transform_CacheNTransDataset._post_transform.return.item_transformed", "embedding": null, "metadata": {"file_path": "monai/data/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 307, "end_line": 322, "span_ids": ["CacheNTransDataset._post_transform"], "tokens": 141}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CacheNTransDataset(PersistentDataset):\n\n    def _post_transform(self, item_transformed):\n        \"\"\"\n        Process the data from before the N + 1 transform to the final state ready for evaluation.\n\n        Args:\n            item_transformed: The data to be transformed (already processed up to the first N transform)\n\n        Returns:\n            the final transformed result\n        \"\"\"\n        if not isinstance(self.transform, Compose):\n            raise ValueError(\"transform must be an instance of monai.transforms.Compose.\")\n        for i, _transform in enumerate(self.transform.transforms):\n            if i >= self.cache_n_trans:\n                item_transformed = apply_transform(_transform, item_transformed)\n        return item_transformed", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_LMDBDataset._fill_cache_start_reader_LMDBDataset._fill_cache_start_reader.return.lmdb_open_path_f_self_db": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_LMDBDataset._fill_cache_start_reader_LMDBDataset._fill_cache_start_reader.return.lmdb_open_path_f_self_db", "embedding": null, "metadata": {"file_path": "monai/data/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 433, "end_line": 487, "span_ids": ["LMDBDataset._fill_cache_start_reader"], "tokens": 601}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LMDBDataset(PersistentDataset):\n\n    def _fill_cache_start_reader(self, show_progress=True):\n        \"\"\"\n        Check the LMDB cache and write the cache if needed. py-lmdb doesn't have a good support for concurrent write.\n        This method can be used with multiple processes, but it may have a negative impact on the performance.\n\n        Args:\n            show_progress: whether to show the progress bar if possible.\n        \"\"\"\n        # create cache\n        self.lmdb_kwargs[\"readonly\"] = False\n        env = lmdb.open(path=f\"{self.db_file}\", subdir=False, **self.lmdb_kwargs)\n        if show_progress and not has_tqdm:\n            warnings.warn(\"LMDBDataset: tqdm is not installed. not displaying the caching progress.\")\n        with env.begin(write=False) as search_txn:\n            for item in tqdm(self.data) if has_tqdm and show_progress else self.data:\n                key = self.hash_func(item)\n                done, retry, val = False, 5, None\n                while not done and retry > 0:\n                    try:\n                        with search_txn.cursor() as cursor:\n                            done = cursor.set_key(key)\n                        if done:\n                            continue\n                        if val is None:\n                            val = self._pre_transform(deepcopy(item))  # keep the original hashed\n                            val = pickle.dumps(val, protocol=self.pickle_protocol)\n                        with env.begin(write=True) as txn:\n                            txn.put(key, val)\n                        done = True\n                    except lmdb.MapFullError:\n                        done, retry = False, retry - 1\n                        size = env.info()[\"map_size\"]\n                        new_size = size * 2\n                        warnings.warn(\n                            f\"Resizing the cache database from {int(size) >> 20}MB\" f\" to {int(new_size) >> 20}MB.\"\n                        )\n                        env.set_mapsize(new_size)\n                    except lmdb.MapResizedError:\n                        # the mapsize is increased by another process\n                        # set_mapsize with a size of 0 to adopt the new size\n                        env.set_mapsize(0)\n                if not done:  # still has the map full error\n                    size = env.info()[\"map_size\"]\n                    env.close()\n                    raise ValueError(f\"LMDB map size reached, increase size above current size of {size}.\")\n        size = env.info()[\"map_size\"]\n        env.close()\n        # read-only database env\n        self.lmdb_kwargs[\"readonly\"] = True\n        self.lmdb_kwargs[\"map_size\"] = size\n        if self.lmdb_kwargs.get(\"lock\", None) is None:\n            self.lmdb_kwargs[\"lock\"] = False\n        if self.lmdb_kwargs.get(\"readahead\", None) is None:\n            self.lmdb_kwargs[\"readahead\"] = False\n        return lmdb.open(path=f\"{self.db_file}\", subdir=False, **self.lmdb_kwargs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_LMDBDataset._cachecheck_LMDBDataset.info.return.out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_LMDBDataset._cachecheck_LMDBDataset.info.return.out", "embedding": null, "metadata": {"file_path": "monai/data/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 489, "end_line": 517, "span_ids": ["LMDBDataset.info", "LMDBDataset._cachecheck"], "tokens": 250}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LMDBDataset(PersistentDataset):\n\n    def _cachecheck(self, item_transformed):\n        \"\"\"\n        if the item is not found in the lmdb file, resolves to the persistent cache default behaviour.\n\n        \"\"\"\n        if self._read_env is None:\n            # this runs on multiple processes, each one should have its own env.\n            self._read_env = self._fill_cache_start_reader(show_progress=False)\n        with self._read_env.begin(write=False) as txn:\n            data = txn.get(self.hash_func(item_transformed))\n        if data is None:\n            warnings.warn(\"LMDBDataset: cache key not found, running fallback caching.\")\n            return super()._cachecheck(item_transformed)\n        try:\n            return pickle.loads(data)\n        except Exception as err:\n            raise RuntimeError(\"Invalid cache value, corrupted lmdb file?\") from err\n\n    def info(self):\n        \"\"\"\n        Returns: dataset info dictionary.\n\n        \"\"\"\n        if self._read_env is None:\n            self._read_env = self._fill_cache_start_reader()\n        out = dict(self._read_env.info())\n        out[\"size\"] = len(self.data)\n        out[\"filename\"] = f\"{self.db_file.absolute()}\"\n        return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_CacheDataset._fill_cache_CacheDataset._fill_cache.with_ThreadPool_self_num_.return.list_p_imap_self__load_ca": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_CacheDataset._fill_cache_CacheDataset._fill_cache.with_ThreadPool_self_num_.return.list_p_imap_self__load_ca", "embedding": null, "metadata": {"file_path": "monai/data/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 523, "end_line": 537, "span_ids": ["CacheDataset._fill_cache"], "tokens": 133}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CacheDataset(Dataset):\n\n    def _fill_cache(self) -> List:\n        if self.cache_num <= 0:\n            return []\n        if self.progress and not has_tqdm:\n            warnings.warn(\"tqdm is not installed, will not show the caching progress bar.\")\n        with ThreadPool(self.num_workers) as p:\n            if self.progress and has_tqdm:\n                return list(\n                    tqdm(\n                        p.imap(self._load_cache_item, range(self.cache_num)),\n                        total=self.cache_num,\n                        desc=\"Loading dataset\",\n                    )\n                )\n            return list(p.imap(self._load_cache_item, range(self.cache_num)))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_CacheDataset._load_cache_item_CacheDataset._load_cache_item.return.item": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_CacheDataset._load_cache_item_CacheDataset._load_cache_item.return.item", "embedding": null, "metadata": {"file_path": "monai/data/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 574, "end_line": 586, "span_ids": ["CacheDataset._load_cache_item"], "tokens": 121}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CacheDataset(Dataset):\n\n    def _load_cache_item(self, idx: int):\n        \"\"\"\n        Args:\n            idx: the index of the input data sequence.\n        \"\"\"\n        item = self.data[idx]\n        for _transform in self.transform.transforms:  # type:ignore\n            # execute all the deterministic transforms\n            if isinstance(_transform, Randomizable) or not isinstance(_transform, Transform):\n                break\n            _xform = deepcopy(_transform) if isinstance(_transform, ThreadUnsafe) else _transform\n            item = apply_transform(_xform, item)\n        return item", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_CacheDataset._transform_CacheDataset._transform.return.data": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_CacheDataset._transform_CacheDataset._transform.return.data", "embedding": null, "metadata": {"file_path": "monai/data/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 650, "end_line": 669, "span_ids": ["CacheDataset._transform"], "tokens": 208}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CacheDataset(Dataset):\n\n    def _transform(self, index: int):\n        if index % len(self) >= self.cache_num:  # support negative index\n            # no cache for this index, execute all the transforms directly\n            return super()._transform(index)\n        # load data from cache and execute from the first random transform\n        start_run = False\n        if self._cache is None:\n            self._cache = self._fill_cache()\n        data = self._cache[index]\n        if not isinstance(self.transform, Compose):\n            raise ValueError(\"transform must be an instance of monai.transforms.Compose.\")\n        for _transform in self.transform.transforms:\n            if start_run or isinstance(_transform, Randomizable) or not isinstance(_transform, Transform):\n                # only need to deep copy data on first non-deterministic transform\n                if not start_run:\n                    start_run = True\n                    if self.copy_cache:\n                        data = deepcopy(data)\n                data = apply_transform(_transform, data)\n        return data", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_SmartCacheDataset._try_update_cache_SmartCacheDataset._try_update_cache.with_self__update_lock_.return.True": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_SmartCacheDataset._try_update_cache_SmartCacheDataset._try_update_cache.with_self__update_lock_.return.True", "embedding": null, "metadata": {"file_path": "monai/data/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 755, "end_line": 776, "span_ids": ["SmartCacheDataset._try_update_cache"], "tokens": 141}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SmartCacheDataset(Randomizable, CacheDataset):\n\n    def _try_update_cache(self):\n        \"\"\"\n        Update the cache items with new replacement for current epoch.\n\n        \"\"\"\n        with self._update_lock:\n            if not self._replace_done:\n                return False\n\n            del self._cache[: self._replace_num]\n            self._cache.extend(self._replacements)\n\n            self._start_pos += self._replace_num\n            if self._start_pos >= self._total_num:\n                self._start_pos -= self._total_num\n\n            self._compute_data_idx()\n\n            # ready for next round\n            self._round += 1\n            self._replace_done = False\n            return True", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_ZipDataset_ZipDataset._transform.return.tuple_data_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_ZipDataset_ZipDataset._transform.return.tuple_data_", "embedding": null, "metadata": {"file_path": "monai/data/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 961, "end_line": 1005, "span_ids": ["ZipDataset", "ZipDataset.__init__", "ZipDataset._transform", "ZipDataset.__len__"], "tokens": 433}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ZipDataset(Dataset):\n    \"\"\"\n    Zip several PyTorch datasets and output data(with the same index) together in a tuple.\n    If the output of single dataset is already a tuple, flatten it and extend to the result.\n    For example: if datasetA returns (img, imgmeta), datasetB returns (seg, segmeta),\n    finally return (img, imgmeta, seg, segmeta).\n    And if the datasets don't have same length, use the minimum length of them as the length\n    of ZipDataset.\n    If passing slicing indices, will return a PyTorch Subset, for example: `data: Subset = dataset[1:4]`,\n    for more details, please check: https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset\n\n    Examples::\n\n        >>> zip_data = ZipDataset([[1, 2, 3], [4, 5]])\n        >>> print(len(zip_data))\n        2\n        >>> for item in zip_data:\n        >>>    print(item)\n        [1, 4]\n        [2, 5]\n\n    \"\"\"\n\n    def __init__(self, datasets: Sequence, transform: Optional[Callable] = None) -> None:\n        \"\"\"\n        Args:\n            datasets: list of datasets to zip together.\n            transform: a callable data transform operates on the zipped item from `datasets`.\n        \"\"\"\n        super().__init__(list(datasets), transform=transform)\n\n    def __len__(self) -> int:\n        return min(len(dataset) for dataset in self.data)\n\n    def _transform(self, index: int):\n        def to_list(x):\n            return list(x) if isinstance(x, (tuple, list)) else [x]\n\n        data = []\n        for dataset in self.data:\n            data.extend(to_list(dataset[index]))\n        if self.transform is not None:\n            data = apply_transform(self.transform, data, map_items=False)  # transform the list data\n        # use tuple instead of list as the default collate_fn callback of MONAI DataLoader flattens nested lists\n        return tuple(data)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_ArrayDataset_ArrayDataset.__getitem__.return.self_dataset_index_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_ArrayDataset_ArrayDataset.__getitem__.return.self_dataset_index_", "embedding": null, "metadata": {"file_path": "monai/data/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 878, "end_line": 974, "span_ids": ["ArrayDataset.__len__", "ArrayDataset.randomize", "ArrayDataset.__getitem__", "ArrayDataset", "ArrayDataset.__init__"], "tokens": 844}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ArrayDataset(Randomizable, _TorchDataset):\n    \"\"\"\n    Dataset for segmentation and classification tasks based on array format input data and transforms.\n    It ensures the same random seeds in the randomized transforms defined for image, segmentation and label.\n    The `transform` can be :py:class:`monai.transforms.Compose` or any other callable object.\n    For example:\n    If train based on Nifti format images without metadata, all transforms can be composed::\n\n        img_transform = Compose(\n            [\n                LoadImage(image_only=True),\n                AddChannel(),\n                RandAdjustContrast()\n            ]\n        )\n        ArrayDataset(img_file_list, img_transform=img_transform)\n\n    If training based on images and the metadata, the array transforms can not be composed\n    because several transforms receives multiple parameters or return multiple values. Then Users need\n    to define their own callable method to parse metadata from `LoadImage` or set `affine` matrix\n    to `Spacing` transform::\n\n        class TestCompose(Compose):\n            def __call__(self, input_):\n                img, metadata = self.transforms[0](input_)\n                img = self.transforms[1](img)\n                img, _, _ = self.transforms[2](img, metadata[\"affine\"])\n                return self.transforms[3](img), metadata\n        img_transform = TestCompose(\n            [\n                LoadImage(image_only=False),\n                AddChannel(),\n                Spacing(pixdim=(1.5, 1.5, 3.0)),\n                RandAdjustContrast()\n            ]\n        )\n        ArrayDataset(img_file_list, img_transform=img_transform)\n\n    Examples::\n\n        >>> ds = ArrayDataset([1, 2, 3, 4], lambda x: x + 0.1)\n        >>> print(ds[0])\n        1.1\n\n        >>> ds = ArrayDataset(img=[1, 2, 3, 4], seg=[5, 6, 7, 8])\n        >>> print(ds[0])\n        [1, 5]\n\n    \"\"\"\n\n    def __init__(\n        self,\n        img: Sequence,\n        img_transform: Optional[Callable] = None,\n        seg: Optional[Sequence] = None,\n        seg_transform: Optional[Callable] = None,\n        labels: Optional[Sequence] = None,\n        label_transform: Optional[Callable] = None,\n    ) -> None:\n        \"\"\"\n        Initializes the dataset with the filename lists. The transform `img_transform` is applied\n        to the images and `seg_transform` to the segmentations.\n\n        Args:\n            img: sequence of images.\n            img_transform: transform to apply to each element in `img`.\n            seg: sequence of segmentations.\n            seg_transform: transform to apply to each element in `seg`.\n            labels: sequence of labels.\n            label_transform: transform to apply to each element in `labels`.\n\n        \"\"\"\n        items = [(img, img_transform), (seg, seg_transform), (labels, label_transform)]\n        self.set_random_state(seed=get_seed())\n        datasets = [Dataset(x[0], x[1]) for x in items if x[0] is not None]\n        self.dataset = datasets[0] if len(datasets) == 1 else ZipDataset(datasets)\n\n        self._seed = 0  # transform synchronization seed\n\n    def __len__(self) -> int:\n        return len(self.dataset)\n\n    def randomize(self, data: Optional[Any] = None) -> None:\n        self._seed = self.R.randint(MAX_SEED, dtype=\"uint32\")\n\n    def __getitem__(self, index: int):\n        self.randomize()\n        if isinstance(self.dataset, ZipDataset):\n            # set transforms of each zip component\n            for dataset in self.dataset.data:\n                transform = getattr(dataset, \"transform\", None)\n                if isinstance(transform, Randomizable):\n                    transform.set_random_state(seed=self._seed)\n        transform = getattr(self.dataset, \"transform\", None)\n        if isinstance(transform, Randomizable):\n            transform.set_random_state(seed=self._seed)\n        return self.dataset[index]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/grid_dataset.py_from_typing_import_Callab_PatchIter.__call__.yield_from_iter_patch_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/grid_dataset.py_from_typing_import_Callab_PatchIter.__call__.yield_from_iter_patch_", "embedding": null, "metadata": {"file_path": "monai/data/grid_dataset.py", "file_name": "grid_dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 75, "span_ids": ["PatchIter.__init__", "PatchIter", "PatchIter.__call__", "docstring"], "tokens": 592}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Callable, Dict, Optional, Sequence, Union\n\nimport numpy as np\nimport torch\nfrom torch.utils.data import IterableDataset\n\nfrom monai.data.dataset import Dataset\nfrom monai.data.utils import iter_patch\nfrom monai.transforms import apply_transform\nfrom monai.utils import NumpyPadMode, ensure_tuple, look_up_option\n\n__all__ = [\"PatchDataset\", \"GridPatchDataset\", \"PatchIter\"]\n\n\nclass PatchIter:\n    \"\"\"\n    A class to return a patch generator with predefined properties such as `patch_size`.\n    Typically used with :py:class:`monai.data.GridPatchDataset`.\n    \"\"\"\n\n    def __init__(\n        self,\n        patch_size: Sequence[int],\n        start_pos: Sequence[int] = (),\n        mode: Union[NumpyPadMode, str] = NumpyPadMode.WRAP,\n        **pad_opts: Dict,\n    ):\n        \"\"\"\n\n        Args:\n            patch_size: size of patches to generate slices for, 0/None selects whole dimension\n            start_pos: starting position in the array, default is 0 for each dimension\n            mode: {``\"constant\"``, ``\"edge\"``, ``\"linear_ramp\"``, ``\"maximum\"``, ``\"mean\"``,\n                ``\"median\"``, ``\"minimum\"``, ``\"reflect\"``, ``\"symmetric\"``, ``\"wrap\"``, ``\"empty\"``}\n                One of the listed string values or a user supplied function. Defaults to ``\"wrap\"``.\n                See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html\n            pad_opts: padding options, see numpy.pad\n\n        Note:\n            The `patch_size` is the size of the\n            patch to sample from the input arrays. It is assumed the arrays first dimension is the channel dimension which\n            will be yielded in its entirety so this should not be specified in `patch_size`. For example, for an input 3D\n            array with 1 channel of size (1, 20, 20, 20) a regular grid sampling of eight patches (1, 10, 10, 10) would be\n            specified by a `patch_size` of (10, 10, 10).\n\n        \"\"\"\n        self.patch_size = (None,) + tuple(patch_size)\n        self.start_pos = ensure_tuple(start_pos)\n        self.mode: NumpyPadMode = look_up_option(mode, NumpyPadMode)\n        self.pad_opts = pad_opts\n\n    def __call__(self, array):\n        \"\"\"\n        Args:\n            array: the image to generate patches from.\n        \"\"\"\n        yield from iter_patch(\n            array,\n            patch_size=self.patch_size,  # expand to have the channel dim\n            start_pos=self.start_pos,\n            copy_back=False,\n            mode=self.mode,\n            **self.pad_opts,\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/grid_dataset.py_GridPatchDataset_GridPatchDataset.__init__.self.with_coordinates.with_coordinates": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/grid_dataset.py_GridPatchDataset_GridPatchDataset.__init__.self.with_coordinates.with_coordinates", "embedding": null, "metadata": {"file_path": "monai/data/grid_dataset.py", "file_name": "grid_dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 78, "end_line": 136, "span_ids": ["GridPatchDataset.__init__", "GridPatchDataset"], "tokens": 521}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GridPatchDataset(IterableDataset):\n    \"\"\"\n    Yields patches from images read from an image dataset.\n    Typically used with `PatchIter` so that the patches are chosen in a contiguous grid sampling scheme.\n\n     .. code-block:: python\n\n        import numpy as np\n\n        from monai.data import GridPatchDataset, DataLoader, PatchIter\n        from monai.transforms import RandShiftIntensity\n\n        # image-level dataset\n        images = [np.arange(16, dtype=float).reshape(1, 4, 4),\n                  np.arange(16, dtype=float).reshape(1, 4, 4)]\n        # image-level patch generator, \"grid sampling\"\n        patch_iter = PatchIter(patch_size=(2, 2), start_pos=(0, 0))\n        # patch-level intensity shifts\n        patch_intensity = RandShiftIntensity(offsets=1.0, prob=1.0)\n\n        # construct the dataset\n        ds = GridPatchDataset(dataset=images,\n                              patch_iter=patch_iter,\n                              transform=patch_intensity)\n        # use the grid patch dataset\n        for item in DataLoader(ds, batch_size=2, num_workers=2):\n            print(\"patch size:\", item[0].shape)\n            print(\"coordinates:\", item[1])\n\n        # >>> patch size: torch.Size([2, 1, 2, 2])\n        #     coordinates: tensor([[[0, 1], [0, 2], [0, 2]],\n        #                          [[0, 1], [2, 4], [0, 2]]])\n\n    \"\"\"\n\n    def __init__(\n        self,\n        dataset: Sequence,\n        patch_iter: Callable,\n        transform: Optional[Callable] = None,\n        with_coordinates: bool = True,\n    ) -> None:\n        \"\"\"\n        Initializes this dataset in terms of the image dataset, patch generator, and an optional transform.\n\n        Args:\n            dataset: the dataset to read image data from.\n            patch_iter: converts an input image (item from dataset) into a iterable of image patches.\n                `patch_iter(dataset[idx])` must yield a tuple: (patches, coordinates).\n                see also: :py:class:`monai.data.PatchIter`.\n            transform: a callable data transform operates on the patches.\n            with_coordinates: whether to yield the coordinates of each patch, default to `True`.\n\n        \"\"\"\n\n        self.dataset = dataset\n        self.patch_iter = patch_iter\n        self.transform = transform\n        self.with_coordinates = with_coordinates", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/grid_dataset.py_GridPatchDataset.__iter___GridPatchDataset.__iter__.for_index_in_range_iter_s.if_not_self_with_coordina.else_.for_patch_slices___in_.yield_out_patch_slices": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/grid_dataset.py_GridPatchDataset.__iter___GridPatchDataset.__iter__.for_index_in_range_iter_s.if_not_self_with_coordina.else_.for_patch_slices___in_.yield_out_patch_slices", "embedding": null, "metadata": {"file_path": "monai/data/grid_dataset.py", "file_name": "grid_dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 138, "end_line": 165, "span_ids": ["GridPatchDataset.__iter__"], "tokens": 283}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GridPatchDataset(IterableDataset):\n\n    def __iter__(self):\n        worker_info = torch.utils.data.get_worker_info()\n        iter_start, iter_end = 0, 1\n        try:\n            iter_end = len(self.dataset)  # TODO: support iterable self.dataset\n        except TypeError:\n            raise NotImplementedError(\"image dataset must implement `len()`.\") from None\n\n        if worker_info is not None:\n            # split workload\n            per_worker = int(np.ceil((iter_end - iter_start) / float(worker_info.num_workers)))\n            iter_start += worker_info.id * per_worker\n            iter_end = min(iter_start + per_worker, iter_end)\n\n        for index in range(iter_start, iter_end):\n            image = self.dataset[index]\n            if not self.with_coordinates:\n                for patch, *_ in self.patch_iter(image):  # patch_iter to yield at least 1 item: patch\n                    out_patch = (\n                        patch if self.transform is None else apply_transform(self.transform, patch, map_items=False)\n                    )\n                    yield out_patch\n            else:\n                for patch, slices, *_ in self.patch_iter(image):  # patch_iter to yield at least 2 items: patch, coords\n                    out_patch = (\n                        patch if self.transform is None else apply_transform(self.transform, patch, map_items=False)\n                    )\n                    yield out_patch, slices", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/grid_dataset.py_PatchDataset._transform_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/grid_dataset.py_PatchDataset._transform_", "embedding": null, "metadata": {"file_path": "monai/data/grid_dataset.py", "file_name": "grid_dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 230, "end_line": 243, "span_ids": ["PatchDataset._transform"], "tokens": 146}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class PatchDataset(Dataset):\n\n    def _transform(self, index: int):\n        image_id = int(index / self.samples_per_image)\n        image = self.data[image_id]\n        patches = self.patch_func(image)\n        if len(patches) != self.samples_per_image:\n            raise RuntimeWarning(\n                f\"`patch_func` must return a sequence of length: samples_per_image={self.samples_per_image}.\"\n            )\n        patch_id = (index - image_id * self.samples_per_image) * (-1 if index < 0 else 1)\n        patch = patches[patch_id]\n        if self.transform is not None:\n            patch = apply_transform(self.transform, patch, map_items=False)\n        return patch", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_dataset.py_from_typing_import_Any_C_ImageDataset.randomize.self._seed.self_R_randint_MAX_SEED_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_dataset.py_from_typing_import_Any_C_ImageDataset.randomize.self._seed.self_R_randint_MAX_SEED_", "embedding": null, "metadata": {"file_path": "monai/data/image_dataset.py", "file_name": "image_dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 94, "span_ids": ["ImageDataset", "ImageDataset.__init__", "ImageDataset.randomize", "docstring", "ImageDataset.__len__"], "tokens": 807}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Any, Callable, Optional, Sequence, Union\n\nimport numpy as np\nfrom torch.utils.data import Dataset\n\nfrom monai.config import DtypeLike\nfrom monai.data.image_reader import ImageReader\nfrom monai.transforms import LoadImage, Randomizable, apply_transform\nfrom monai.utils import MAX_SEED, get_seed\n\n\nclass ImageDataset(Dataset, Randomizable):\n    \"\"\"\n    Loads image/segmentation pairs of files from the given filename lists. Transformations can be specified\n    for the image and segmentation arrays separately.\n    The difference between this dataset and `ArrayDataset` is that this dataset can apply transform chain to images\n    and segs and return both the images and metadata, and no need to specify transform to load images from files.\n    For more information, please see the image_dataset demo in the MONAI tutorial repo,\n    https://github.com/Project-MONAI/tutorials/blob/master/modules/image_dataset.ipynb\n    \"\"\"\n\n    def __init__(\n        self,\n        image_files: Sequence[str],\n        seg_files: Optional[Sequence[str]] = None,\n        labels: Optional[Sequence[float]] = None,\n        transform: Optional[Callable] = None,\n        seg_transform: Optional[Callable] = None,\n        image_only: bool = True,\n        transform_with_metadata: bool = False,\n        dtype: DtypeLike = np.float32,\n        reader: Optional[Union[ImageReader, str]] = None,\n        *args,\n        **kwargs,\n    ) -> None:\n        \"\"\"\n        Initializes the dataset with the image and segmentation filename lists. The transform `transform` is applied\n        to the images and `seg_transform` to the segmentations.\n\n        Args:\n            image_files: list of image filenames\n            seg_files: if in segmentation task, list of segmentation filenames\n            labels: if in classification task, list of classification labels\n            transform: transform to apply to image arrays\n            seg_transform: transform to apply to segmentation arrays\n            image_only: if True return only the image volume, otherwise, return image volume and the metadata\n            transform_with_metadata: if True, the metadata will be passed to the transforms whenever possible.\n            dtype: if not None convert the loaded image to this data type\n            reader: register reader to load image file and meta data, if None, will use the default readers.\n                If a string of reader name provided, will construct a reader object with the `*args` and `**kwargs`\n                parameters, supported reader name: \"NibabelReader\", \"PILReader\", \"ITKReader\", \"NumpyReader\"\n            args: additional parameters for reader if providing a reader name\n            kwargs: additional parameters for reader if providing a reader name\n\n        Raises:\n            ValueError: When ``seg_files`` length differs from ``image_files``\n\n        \"\"\"\n\n        if seg_files is not None and len(image_files) != len(seg_files):\n            raise ValueError(\n                \"Must have same the number of segmentation as image files: \"\n                f\"images={len(image_files)}, segmentations={len(seg_files)}.\"\n            )\n\n        self.image_files = image_files\n        self.seg_files = seg_files\n        self.labels = labels\n        self.transform = transform\n        self.seg_transform = seg_transform\n        if image_only and transform_with_metadata:\n            raise ValueError(\"transform_with_metadata=True requires image_only=False.\")\n        self.image_only = image_only\n        self.transform_with_metadata = transform_with_metadata\n        self.loader = LoadImage(reader, image_only, dtype, *args, **kwargs)\n        self.set_random_state(seed=get_seed())\n        self._seed = 0  # transform synchronization seed\n\n    def __len__(self) -> int:\n        return len(self.image_files)\n\n    def randomize(self, data: Optional[Any] = None) -> None:\n        self._seed = self.R.randint(MAX_SEED, dtype=\"uint32\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_dataset.py_ImageDataset.__getitem___": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_dataset.py_ImageDataset.__getitem___", "embedding": null, "metadata": {"file_path": "monai/data/image_dataset.py", "file_name": "image_dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 96, "end_line": 148, "span_ids": ["ImageDataset.__getitem__"], "tokens": 440}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ImageDataset(Dataset, Randomizable):\n\n    def __getitem__(self, index: int):\n        self.randomize()\n        meta_data, seg_meta_data, seg, label = None, None, None, None\n\n        # load data and optionally meta\n        if self.image_only:\n            img = self.loader(self.image_files[index])\n            if self.seg_files is not None:\n                seg = self.loader(self.seg_files[index])\n        else:\n            img, meta_data = self.loader(self.image_files[index])\n            if self.seg_files is not None:\n                seg, seg_meta_data = self.loader(self.seg_files[index])\n\n        # apply the transforms\n        if self.transform is not None:\n            if isinstance(self.transform, Randomizable):\n                self.transform.set_random_state(seed=self._seed)\n\n            if self.transform_with_metadata:\n                img, meta_data = apply_transform(self.transform, (img, meta_data), map_items=False, unpack_items=True)\n            else:\n                img = apply_transform(self.transform, img, map_items=False)\n\n        if self.seg_transform is not None:\n            if isinstance(self.seg_transform, Randomizable):\n                self.seg_transform.set_random_state(seed=self._seed)\n\n            if self.transform_with_metadata:\n                seg, seg_meta_data = apply_transform(\n                    self.seg_transform, (seg, seg_meta_data), map_items=False, unpack_items=True\n                )\n            else:\n                seg = apply_transform(self.seg_transform, seg, map_items=False)\n\n        if self.labels is not None:\n            label = self.labels[index]\n\n        # construct outputs\n        data = [img]\n        if seg is not None:\n            data.append(seg)\n        if label is not None:\n            data.append(label)\n        if not self.image_only and meta_data is not None:\n            data.append(meta_data)\n        if not self.image_only and seg_meta_data is not None:\n            data.append(seg_meta_data)\n        if len(data) == 1:\n            return data[0]\n        # use tuple instead of list as the default collate_fn callback of MONAI DataLoader flattens nested lists\n        return tuple(data)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_ITKReader_ITKReader.verify_suffix.return.has_itk": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_ITKReader_ITKReader.verify_suffix.return.has_itk", "embedding": null, "metadata": {"file_path": "monai/data/image_reader.py", "file_name": "image_reader.py", "file_type": "text/x-python", "category": "implementation", "start_line": 139, "end_line": 185, "span_ids": ["ITKReader", "ITKReader.verify_suffix", "ITKReader.__init__"], "tokens": 489}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@require_pkg(pkg_name=\"itk\")\nclass ITKReader(ImageReader):\n    \"\"\"\n    Load medical images based on ITK library.\n    All the supported image formats can be found at:\n    https://github.com/InsightSoftwareConsortium/ITK/tree/master/Modules/IO\n    The loaded data array will be in C order, for example, a 3D image NumPy\n    array index order will be `CDWH`.\n\n    Args:\n        channel_dim: the channel dimension of the input image, default is None.\n            This is used to set original_channel_dim in the meta data, EnsureChannelFirstD reads this field.\n            If None, original_channel_dim will be either `no_channel` or `-1`.\n\n                - Nifti file is usually \"channel last\", so there is no need to specify this argument.\n                - PNG file usually has `GetNumberOfComponentsPerPixel()==3`, so there is no need to specify this argument.\n\n        series_name: the name of the DICOM series if there are multiple ones.\n            used when loading DICOM series.\n        reverse_indexing: whether to use a reversed spatial indexing convention for the returned data array.\n            If ``False``, the spatial indexing follows the numpy convention;\n            otherwise, the spatial indexing convention is reversed to be compatible with ITK. Default is ``False``.\n            This option does not affect the metadata.\n        kwargs: additional args for `itk.imread` API. more details about available args:\n            https://github.com/InsightSoftwareConsortium/ITK/blob/master/Wrapping/Generators/Python/itk/support/extras.py\n\n    \"\"\"\n\n    def __init__(\n        self, channel_dim: Optional[int] = None, series_name: str = \"\", reverse_indexing: bool = False, **kwargs\n    ):\n        super().__init__()\n        self.kwargs = kwargs\n        self.channel_dim = channel_dim\n        self.series_name = series_name\n        self.reverse_indexing = reverse_indexing\n\n    def verify_suffix(self, filename: Union[Sequence[PathLike], PathLike]) -> bool:\n        \"\"\"\n        Verify whether the specified file or files format is supported by ITK reader.\n\n        Args:\n            filename: file name or a list of file names to read.\n                if a list of files, verify all the suffixes.\n\n        \"\"\"\n        return has_itk", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_ITKReader.get_data_ITKReader.get_data.return._stack_images_img_array_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_ITKReader.get_data_ITKReader.get_data.return._stack_images_img_array_", "embedding": null, "metadata": {"file_path": "monai/data/image_reader.py", "file_name": "image_reader.py", "file_type": "text/x-python", "category": "implementation", "start_line": 219, "end_line": 247, "span_ids": ["ITKReader.get_data"], "tokens": 338}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@require_pkg(pkg_name=\"itk\")\nclass ITKReader(ImageReader):\n\n    def get_data(self, img):\n        \"\"\"\n        Extract data array and meta data from loaded image and return them.\n        This function returns two objects, first is numpy array of image data, second is dict of meta data.\n        It constructs `affine`, `original_affine`, and `spatial_shape` and stores them in meta dict.\n        When loading a list of files, they are stacked together at a new dimension as the first dimension,\n        and the meta data of the first image is used to represent the output meta data.\n\n        Args:\n            img: an ITK image object loaded from an image file or a list of ITK image objects.\n\n        \"\"\"\n        img_array: List[np.ndarray] = []\n        compatible_meta: Dict = {}\n\n        for i in ensure_tuple(img):\n            data = self._get_array_data(i)\n            img_array.append(data)\n            header = self._get_meta_dict(i)\n            header[\"original_affine\"] = self._get_affine(i)\n            header[\"affine\"] = header[\"original_affine\"].copy()\n            header[\"spatial_shape\"] = self._get_spatial_shape(i)\n            if self.channel_dim is None:  # default to \"no_channel\" or -1\n                header[\"original_channel_dim\"] = \"no_channel\" if len(data.shape) == len(header[\"spatial_shape\"]) else -1\n            else:\n                header[\"original_channel_dim\"] = self.channel_dim\n            _copy_compatible_dict(header, compatible_meta)\n\n        return _stack_images(img_array, compatible_meta), compatible_meta", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_NibabelReader_NibabelReader.verify_suffix.return.has_nib_and_is_supported_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_NibabelReader_NibabelReader.verify_suffix.return.has_nib_and_is_supported_", "embedding": null, "metadata": {"file_path": "monai/data/image_reader.py", "file_name": "image_reader.py", "file_type": "text/x-python", "category": "implementation", "start_line": 326, "end_line": 362, "span_ids": ["NibabelReader.__init__", "NibabelReader.verify_suffix", "NibabelReader"], "tokens": 331}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@require_pkg(pkg_name=\"nibabel\")\nclass NibabelReader(ImageReader):\n    \"\"\"\n    Load NIfTI format images based on Nibabel library.\n\n    Args:\n        as_closest_canonical: if True, load the image as closest to canonical axis format.\n        squeeze_non_spatial_dims: if True, non-spatial singletons will be squeezed, e.g. (256,256,1,3) -> (256,256,3)\n        kwargs: additional args for `nibabel.load` API. more details about available args:\n            https://github.com/nipy/nibabel/blob/master/nibabel/loadsave.py\n\n    \"\"\"\n\n    def __init__(\n        self,\n        as_closest_canonical: bool = False,\n        squeeze_non_spatial_dims: bool = False,\n        dtype: DtypeLike = np.float32,\n        **kwargs,\n    ):\n        super().__init__()\n        self.as_closest_canonical = as_closest_canonical\n        self.squeeze_non_spatial_dims = squeeze_non_spatial_dims\n        self.dtype = dtype\n        self.kwargs = kwargs\n\n    def verify_suffix(self, filename: Union[Sequence[PathLike], PathLike]) -> bool:\n        \"\"\"\n        Verify whether the specified file or files format is supported by Nibabel reader.\n\n        Args:\n            filename: file name or a list of file names to read.\n                if a list of files, verify all the suffixes.\n\n        \"\"\"\n        suffixes: Sequence[str] = [\"nii\", \"nii.gz\"]\n        return has_nib and is_supported_format(filename, suffixes)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_NibabelReader.get_data_NibabelReader.get_data.return._stack_images_img_array_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_NibabelReader.get_data_NibabelReader.get_data.return._stack_images_img_array_", "embedding": null, "metadata": {"file_path": "monai/data/image_reader.py", "file_name": "image_reader.py", "file_type": "text/x-python", "category": "implementation", "start_line": 387, "end_line": 420, "span_ids": ["NibabelReader.get_data"], "tokens": 409}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@require_pkg(pkg_name=\"nibabel\")\nclass NibabelReader(ImageReader):\n\n    def get_data(self, img):\n        \"\"\"\n        Extract data array and meta data from loaded image and return them.\n        This function returns two objects, first is numpy array of image data, second is dict of meta data.\n        It constructs `affine`, `original_affine`, and `spatial_shape` and stores them in meta dict.\n        When loading a list of files, they are stacked together at a new dimension as the first dimension,\n        and the meta data of the first image is used to present the output meta data.\n\n        Args:\n            img: a Nibabel image object loaded from an image file or a list of Nibabel image objects.\n\n        \"\"\"\n        img_array: List[np.ndarray] = []\n        compatible_meta: Dict = {}\n\n        for i in ensure_tuple(img):\n            header = self._get_meta_dict(i)\n            header[\"affine\"] = self._get_affine(i)\n            header[\"original_affine\"] = self._get_affine(i)\n            header[\"as_closest_canonical\"] = self.as_closest_canonical\n            if self.as_closest_canonical:\n                i = nib.as_closest_canonical(i)\n                header[\"affine\"] = self._get_affine(i)\n            header[\"spatial_shape\"] = self._get_spatial_shape(i)\n            data = self._get_array_data(i)\n            if self.squeeze_non_spatial_dims:\n                for d in range(len(data.shape), len(header[\"spatial_shape\"]), -1):\n                    if data.shape[d - 1] == 1:\n                        data = data.squeeze(axis=d - 1)\n            img_array.append(data)\n            header[\"original_channel_dim\"] = \"no_channel\" if len(data.shape) == len(header[\"spatial_shape\"]) else -1\n            _copy_compatible_dict(header, compatible_meta)\n\n        return _stack_images(img_array, compatible_meta), compatible_meta", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_NumpyReader.get_data_NumpyReader.get_data.return._stack_images_img_array_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_NumpyReader.get_data_NumpyReader.get_data.return._stack_images_img_array_", "embedding": null, "metadata": {"file_path": "monai/data/image_reader.py", "file_name": "image_reader.py", "file_type": "text/x-python", "category": "implementation", "start_line": 547, "end_line": 576, "span_ids": ["NumpyReader.get_data"], "tokens": 315}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class NumpyReader(ImageReader):\n\n    def get_data(self, img):\n        \"\"\"\n        Extract data array and meta data from loaded image and return them.\n        This function returns two objects, first is numpy array of image data, second is dict of meta data.\n        It constructs `affine`, `original_affine`, and `spatial_shape` and stores them in meta dict.\n        When loading a list of files, they are stacked together at a new dimension as the first dimension,\n        and the meta data of the first image is used to represent the output meta data.\n\n        Args:\n            img: a Numpy array loaded from a file or a list of Numpy arrays.\n\n        \"\"\"\n        img_array: List[np.ndarray] = []\n        compatible_meta: Dict = {}\n        if isinstance(img, np.ndarray):\n            img = (img,)\n\n        for i in ensure_tuple(img):\n            header = {}\n            if isinstance(i, np.ndarray):\n                # if `channel_dim` is None, can not detect the channel dim, use all the dims as spatial_shape\n                spatial_shape = np.asarray(i.shape)\n                if isinstance(self.channel_dim, int):\n                    spatial_shape = np.delete(spatial_shape, self.channel_dim)\n                header[\"spatial_shape\"] = spatial_shape\n            img_array.append(i)\n            header[\"original_channel_dim\"] = self.channel_dim if isinstance(self.channel_dim, int) else \"no_channel\"\n            _copy_compatible_dict(header, compatible_meta)\n\n        return _stack_images(img_array, compatible_meta), compatible_meta", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_PILReader_PILReader.verify_suffix.return.has_pil_and_is_supported_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_PILReader_PILReader.verify_suffix.return.has_pil_and_is_supported_", "embedding": null, "metadata": {"file_path": "monai/data/image_reader.py", "file_name": "image_reader.py", "file_type": "text/x-python", "category": "implementation", "start_line": 580, "end_line": 606, "span_ids": ["PILReader.__init__", "PILReader", "PILReader.verify_suffix"], "tokens": 264}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@require_pkg(pkg_name=\"PIL\")\nclass PILReader(ImageReader):\n    \"\"\"\n    Load common 2D image format (supports PNG, JPG, BMP) file or files from provided path.\n\n    Args:\n        converter: additional function to convert the image data after `read()`.\n            for example, use `converter=lambda image: image.convert(\"LA\")` to convert image format.\n        kwargs: additional args for `Image.open` API in `read()`, mode details about available args:\n            https://pillow.readthedocs.io/en/stable/reference/Image.html#PIL.Image.open\n    \"\"\"\n\n    def __init__(self, converter: Optional[Callable] = None, **kwargs):\n        super().__init__()\n        self.converter = converter\n        self.kwargs = kwargs\n\n    def verify_suffix(self, filename: Union[Sequence[PathLike], PathLike]) -> bool:\n        \"\"\"\n        Verify whether the specified file or files format is supported by PIL reader.\n\n        Args:\n            filename: file name or a list of file names to read.\n                if a list of files, verify all the suffixes.\n        \"\"\"\n        suffixes: Sequence[str] = [\"png\", \"jpg\", \"jpeg\", \"bmp\"]\n        return has_pil and is_supported_format(filename, suffixes)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_PILReader.get_data_PILReader.get_data.return._stack_images_img_array_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_PILReader.get_data_PILReader.get_data.return._stack_images_img_array_", "embedding": null, "metadata": {"file_path": "monai/data/image_reader.py", "file_name": "image_reader.py", "file_type": "text/x-python", "category": "implementation", "start_line": 633, "end_line": 658, "span_ids": ["PILReader.get_data"], "tokens": 299}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@require_pkg(pkg_name=\"PIL\")\nclass PILReader(ImageReader):\n\n    def get_data(self, img):\n        \"\"\"\n        Extract data array and meta data from loaded image and return them.\n        This function returns two objects, first is numpy array of image data, second is dict of meta data.\n        It computes `spatial_shape` and stores it in meta dict.\n        When loading a list of files, they are stacked together at a new dimension as the first dimension,\n        and the meta data of the first image is used to represent the output meta data.\n        Note that it will switch axis 0 and 1 after loading the array because the `HW` definition in PIL\n        is different from other common medical packages.\n\n        Args:\n            img: a PIL Image object loaded from a file or a list of PIL Image objects.\n\n        \"\"\"\n        img_array: List[np.ndarray] = []\n        compatible_meta: Dict = {}\n\n        for i in ensure_tuple(img):\n            header = self._get_meta_dict(i)\n            header[\"spatial_shape\"] = self._get_spatial_shape(i)\n            data = np.moveaxis(np.asarray(i), 0, 1)\n            img_array.append(data)\n            header[\"original_channel_dim\"] = \"no_channel\" if len(data.shape) == len(header[\"spatial_shape\"]) else -1\n            _copy_compatible_dict(header, compatible_meta)\n\n        return _stack_images(img_array, compatible_meta), compatible_meta", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_PILReader._get_meta_dict_PILReader._get_spatial_shape.return.np_asarray_img_width_im": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_PILReader._get_meta_dict_PILReader._get_spatial_shape.return.np_asarray_img_width_im", "embedding": null, "metadata": {"file_path": "monai/data/image_reader.py", "file_name": "image_reader.py", "file_type": "text/x-python", "category": "implementation", "start_line": 660, "end_line": 675, "span_ids": ["PILReader._get_meta_dict", "PILReader._get_spatial_shape"], "tokens": 149}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@require_pkg(pkg_name=\"PIL\")\nclass PILReader(ImageReader):\n\n    def _get_meta_dict(self, img) -> Dict:\n        \"\"\"\n        Get the all the meta data of the image and convert to dict type.\n        Args:\n            img: a PIL Image object loaded from an image file.\n\n        \"\"\"\n        return {\"format\": img.format, \"mode\": img.mode, \"width\": img.width, \"height\": img.height}\n\n    def _get_spatial_shape(self, img):\n        \"\"\"\n        Get the spatial shape of image data, it doesn't contain the channel dim.\n        Args:\n            img: a PIL Image object loaded from an image file.\n        \"\"\"\n        return np.asarray((img.width, img.height))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_WSIReader_WSIReader.verify_suffix.return.is_supported_format_filen": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_WSIReader_WSIReader.verify_suffix.return.is_supported_format_filen", "embedding": null, "metadata": {"file_path": "monai/data/image_reader.py", "file_name": "image_reader.py", "file_type": "text/x-python", "category": "implementation", "start_line": 688, "end_line": 729, "span_ids": ["WSIReader.__init__", "WSIReader._set_reader", "WSIReader", "WSIReader.verify_suffix"], "tokens": 391}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class WSIReader(ImageReader):\n    \"\"\"\n    Read whole slide images and extract patches.\n\n    Args:\n        backend: backend library to load the images, available options: \"cuCIM\", \"OpenSlide\" and \"TiffFile\".\n        level: the whole slide image level at which the image is extracted. (default=0)\n            This is overridden if the level argument is provided in `get_data`.\n\n    Note:\n        While \"cuCIM\" and \"OpenSlide\" backends both can load patches from large whole slide images\n        without loading the entire image into memory, \"TiffFile\" backend needs to load the entire image into memory\n        before extracting any patch; thus, memory consideration is needed when using \"TiffFile\" backend for\n        patch extraction.\n    \"\"\"\n\n    def __init__(self, backend: str = \"OpenSlide\", level: int = 0):\n        super().__init__()\n        self.backend = backend.lower()\n        func = require_pkg(self.backend)(self._set_reader)\n        self.wsi_reader = func(self.backend)\n        self.level = level\n\n    @staticmethod\n    def _set_reader(backend: str):\n        if backend == \"openslide\":\n            return OpenSlide\n        if backend == \"cucim\":\n            return CuImage\n        if backend == \"tifffile\":\n            return TiffFile\n        raise ValueError(\"`backend` should be 'cuCIM', 'OpenSlide' or 'TiffFile'.\")\n\n    def verify_suffix(self, filename: Union[Sequence[PathLike], PathLike]) -> bool:\n        \"\"\"\n        Verify whether the specified file or files format is supported by WSI reader.\n\n        Args:\n            filename: file name or a list of file names to read.\n                if a list of files, verify all the suffixes.\n        \"\"\"\n        return is_supported_format(filename, [\"tif\", \"tiff\"])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_WSIReader.read_WSIReader.read.return.img__if_len_filenames_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_WSIReader.read_WSIReader.read.return.img__if_len_filenames_", "embedding": null, "metadata": {"file_path": "monai/data/image_reader.py", "file_name": "image_reader.py", "file_type": "text/x-python", "category": "implementation", "start_line": 722, "end_line": 742, "span_ids": ["WSIReader.read"], "tokens": 159}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class WSIReader(ImageReader):\n\n    def read(self, data: Union[Sequence[PathLike], PathLike, np.ndarray], **kwargs):\n        \"\"\"\n        Read image data from given file or list of files.\n\n        Args:\n            data: file name or a list of file names to read.\n\n        Returns:\n            image object or list of image objects\n\n        \"\"\"\n        img_: List = []\n\n        filenames: Sequence[PathLike] = ensure_tuple(data)\n        for name in filenames:\n            img = self.wsi_reader(name)\n            if self.backend == \"openslide\":\n                img.shape = (img.dimensions[1], img.dimensions[0], 3)\n            img_.append(img)\n\n        return img_ if len(filenames) > 1 else img_[0]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_WSIReader.get_data_WSIReader.get_data.return.patches_metadata": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_WSIReader.get_data_WSIReader.get_data.return.patches_metadata", "embedding": null, "metadata": {"file_path": "monai/data/image_reader.py", "file_name": "image_reader.py", "file_type": "text/x-python", "category": "implementation", "start_line": 753, "end_line": 801, "span_ids": ["WSIReader.get_data"], "tokens": 471}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class WSIReader(ImageReader):\n\n    def get_data(\n        self,\n        img,\n        location: Tuple[int, int] = (0, 0),\n        size: Optional[Tuple[int, int]] = None,\n        level: Optional[int] = None,\n        dtype: DtypeLike = np.uint8,\n        grid_shape: Tuple[int, int] = (1, 1),\n        patch_size: Optional[Union[int, Tuple[int, int]]] = None,\n    ):\n        \"\"\"\n        Extract regions as numpy array from WSI image and return them.\n\n        Args:\n            img: a WSIReader image object loaded from a file, or list of CuImage objects\n            location: (x_min, y_min) tuple giving the top left pixel in the level 0 reference frame,\n            or list of tuples (default=(0, 0))\n            size: (height, width) tuple giving the region size, or list of tuples (default to full image size)\n            This is the size of image at the given level (`level`)\n            level: the level number, or list of level numbers (default=0)\n            dtype: the data type of output image\n            grid_shape: (row, columns) tuple define a grid to extract patches on that\n            patch_size: (height, width) the size of extracted patches at the given level\n        \"\"\"\n        # Verify inputs\n        if level is None:\n            level = self._check_level(img, level)\n\n        # Extract a region or the entire image\n        region = self._extract_region(img, location=location, size=size, level=level, dtype=dtype)\n\n        # Add necessary metadata\n        metadata: Dict = {}\n        metadata[\"spatial_shape\"] = np.asarray(region.shape[:-1])\n        metadata[\"original_channel_dim\"] = -1\n\n        # Make it channel first\n        region = EnsureChannelFirst()(region, metadata)\n\n        # Split into patches\n        if patch_size is None:\n            patches = region\n        else:\n            tuple_patch_size = ensure_tuple_rep(patch_size, 2)\n            patches = self._extract_patches(\n                region, patch_size=tuple_patch_size, grid_shape=grid_shape, dtype=dtype  # type: ignore\n            )\n\n        return patches, metadata", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_WSIReader._extract_region_WSIReader._extract_region.return.region": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_WSIReader._extract_region_WSIReader._extract_region.return.region", "embedding": null, "metadata": {"file_path": "monai/data/image_reader.py", "file_name": "image_reader.py", "file_type": "text/x-python", "category": "implementation", "start_line": 842, "end_line": 872, "span_ids": ["WSIReader._extract_region"], "tokens": 312}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class WSIReader(ImageReader):\n\n    def _extract_region(\n        self,\n        img_obj,\n        size: Optional[Tuple[int, int]],\n        location: Tuple[int, int] = (0, 0),\n        level: int = 0,\n        dtype: DtypeLike = np.uint8,\n    ):\n        if self.backend == \"tifffile\":\n            # Read the entire image\n            if size is not None:\n                raise ValueError(\n                    f\"TiffFile backend reads the entire image only, so size '{size}'' should not be provided!\",\n                    \"For more flexibility or extracting regions, please use cuCIM or OpenSlide backend.\",\n                )\n            if location != (0, 0):\n                raise ValueError(\n                    f\"TiffFile backend reads the entire image only, so location '{location}' should not be provided!\",\n                    \"For more flexibility and extracting regions, please use cuCIM or OpenSlide backend.\",\n                )\n            region = img_obj.asarray(level=level)\n        else:\n            # Get region size to be extracted\n            region_size = self._get_image_size(img_obj, size, level, location)\n            # reverse the order of location's dimensions to become WxH (for cuCIM and OpenSlide)\n            region_location = location[::-1]\n            # Extract a region (or the entire image)\n            region = img_obj.read_region(location=region_location, size=region_size, level=level)\n\n        region = self.convert_to_rgb_array(region, dtype)\n        return region", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_WSIReader.convert_to_rgb_array_WSIReader.convert_to_rgb_array.return.raw_region": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_WSIReader.convert_to_rgb_array_WSIReader.convert_to_rgb_array.return.raw_region", "embedding": null, "metadata": {"file_path": "monai/data/image_reader.py", "file_name": "image_reader.py", "file_type": "text/x-python", "category": "implementation", "start_line": 874, "end_line": 887, "span_ids": ["WSIReader.convert_to_rgb_array"], "tokens": 123}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class WSIReader(ImageReader):\n\n    def convert_to_rgb_array(self, raw_region, dtype: DtypeLike = np.uint8):\n        \"\"\"Convert to RGB mode and numpy array\"\"\"\n        if self.backend == \"openslide\":\n            # convert to RGB\n            raw_region = raw_region.convert(\"RGB\")\n\n        # convert to numpy (if not already in numpy)\n        raw_region = np.asarray(raw_region, dtype=dtype)\n\n        # remove alpha channel if exist (RGBA)\n        if raw_region.shape[-1] > 3:\n            raw_region = raw_region[..., :3]\n\n        return raw_region", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_WSIReader._extract_patches_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_WSIReader._extract_patches_", "embedding": null, "metadata": {"file_path": "monai/data/image_reader.py", "file_name": "image_reader.py", "file_type": "text/x-python", "category": "implementation", "start_line": 783, "end_line": 814, "span_ids": ["WSIReader._extract_patches"], "tokens": 319}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class WSIReader(ImageReader):\n\n    def _extract_patches(\n        self,\n        region: np.ndarray,\n        grid_shape: Tuple[int, int] = (1, 1),\n        patch_size: Optional[Tuple[int, int]] = None,\n        dtype: DtypeLike = np.uint8,\n    ):\n        if patch_size is None and grid_shape == (1, 1):\n            return region\n\n        n_patches = grid_shape[0] * grid_shape[1]\n        region_size = region.shape[1:]\n\n        if patch_size is None:\n            patch_size = (region_size[0] // grid_shape[0], region_size[1] // grid_shape[1])\n\n        # split the region into patches on the grid and center crop them to patch size\n        flat_patch_grid = np.zeros((n_patches, 3, patch_size[0], patch_size[1]), dtype=dtype)\n        start_points = [\n            np.round(region_size[i] * (0.5 + np.arange(grid_shape[i])) / grid_shape[i] - patch_size[i] / 2).astype(int)\n            for i in range(2)\n        ]\n        idx = 0\n        for y_start in start_points[1]:\n            for x_start in start_points[0]:\n                x_end = x_start + patch_size[0]\n                y_end = y_start + patch_size[1]\n                flat_patch_grid[idx] = region[:, x_start:x_end, y_start:y_end]\n                idx += 1\n\n        return flat_patch_grid", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/samplers.py_from_typing_import_Option_DistributedSampler.__init__.if_not_even_divisible_.self.total_size.data_len": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/samplers.py_from_typing_import_Option_DistributedSampler.__init__.if_not_even_divisible_.self.total_size.data_len", "embedding": null, "metadata": {"file_path": "monai/data/samplers.py", "file_name": "samplers.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 57, "span_ids": ["DistributedSampler", "DistributedSampler.__init__", "docstring"], "tokens": 423}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Optional, Sequence\n\nimport torch\nfrom torch.utils.data import Dataset\nfrom torch.utils.data import DistributedSampler as _TorchDistributedSampler\n\n__all__ = [\"DistributedSampler\", \"DistributedWeightedRandomSampler\"]\n\n\nclass DistributedSampler(_TorchDistributedSampler):\n    \"\"\"\n    Enhance PyTorch DistributedSampler to support non-evenly divisible sampling.\n\n    Args:\n        dataset: Dataset used for sampling.\n        even_divisible: if False, different ranks can have different data length.\n            for example, input data: [1, 2, 3, 4, 5], rank 0: [1, 3, 5], rank 1: [2, 4].\n        num_replicas: number of processes participating in distributed training.\n            by default, `world_size` is retrieved from the current distributed group.\n        rank: rank of the current process within `num_replicas`. by default,\n            `rank` is retrieved from the current distributed group.\n        shuffle: if `True`, sampler will shuffle the indices, default to True.\n        kwargs: additional arguments for `DistributedSampler` super class, can be `seed` and `drop_last`.\n\n    More information about DistributedSampler, please check:\n    https://pytorch.org/docs/stable/data.html#torch.utils.data.distributed.DistributedSampler.\n\n    \"\"\"\n\n    def __init__(\n        self,\n        dataset: Dataset,\n        even_divisible: bool = True,\n        num_replicas: Optional[int] = None,\n        rank: Optional[int] = None,\n        shuffle: bool = True,\n        **kwargs,\n    ):\n        super().__init__(dataset=dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle, **kwargs)\n\n        if not even_divisible:\n            data_len = len(dataset)  # type: ignore\n            extra_size = self.total_size - data_len\n            if self.rank + extra_size >= self.num_replicas:\n                self.num_samples -= 1\n            self.total_size = data_len", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/samplers.py_DistributedWeightedRandomSampler_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/samplers.py_DistributedWeightedRandomSampler_", "embedding": null, "metadata": {"file_path": "monai/data/samplers.py", "file_name": "samplers.py", "file_type": "text/x-python", "category": "implementation", "start_line": 60, "end_line": 120, "span_ids": ["DistributedWeightedRandomSampler", "DistributedWeightedRandomSampler.__len__", "DistributedWeightedRandomSampler.__iter__", "DistributedWeightedRandomSampler.__init__"], "tokens": 571}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DistributedWeightedRandomSampler(DistributedSampler):\n    \"\"\"\n    Extend the `DistributedSampler` to support weighted sampling.\n    Refer to `torch.utils.data.WeightedRandomSampler`, for more details please check:\n    https://pytorch.org/docs/stable/data.html#torch.utils.data.WeightedRandomSampler.\n\n    Args:\n        dataset: Dataset used for sampling.\n        weights: a sequence of weights, not necessary summing up to one, length should exactly\n            match the full dataset.\n        num_samples_per_rank: number of samples to draw for every rank, sample from\n            the distributed subset of dataset.\n            if None, default to the length of dataset split by DistributedSampler.\n        generator: PyTorch Generator used in sampling.\n        even_divisible: if False, different ranks can have different data length.\n            for example, input data: [1, 2, 3, 4, 5], rank 0: [1, 3, 5], rank 1: [2, 4].'\n        num_replicas: number of processes participating in distributed training.\n            by default, `world_size` is retrieved from the current distributed group.\n        rank: rank of the current process within `num_replicas`. by default,\n            `rank` is retrieved from the current distributed group.\n        shuffle: if `True`, sampler will shuffle the indices, default to True.\n        kwargs: additional arguments for `DistributedSampler` super class, can be `seed` and `drop_last`.\n\n    \"\"\"\n\n    def __init__(\n        self,\n        dataset: Dataset,\n        weights: Sequence[float],\n        num_samples_per_rank: Optional[int] = None,\n        generator: Optional[torch.Generator] = None,\n        even_divisible: bool = True,\n        num_replicas: Optional[int] = None,\n        rank: Optional[int] = None,\n        shuffle: bool = True,\n        **kwargs,\n    ):\n        super().__init__(\n            dataset=dataset,\n            even_divisible=even_divisible,\n            num_replicas=num_replicas,\n            rank=rank,\n            shuffle=shuffle,\n            **kwargs,\n        )\n        self.weights = weights\n        self.num_samples_per_rank = num_samples_per_rank if num_samples_per_rank is not None else self.num_samples\n        self.generator = generator\n\n    def __iter__(self):\n        indices = list(super().__iter__())\n        weights = torch.as_tensor([self.weights[i] for i in indices], dtype=torch.double)\n        # sample based on the provided weights\n        rand_tensor = torch.multinomial(weights, self.num_samples_per_rank, True, generator=self.generator)\n\n        for i in rand_tensor:\n            yield indices[i]\n\n    def __len__(self):\n        return self.num_samples_per_rank", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/test_time_augmentation.py_TestTimeAugmentation_TestTimeAugmentation._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/test_time_augmentation.py_TestTimeAugmentation_TestTimeAugmentation._", "embedding": null, "metadata": {"file_path": "monai/data/test_time_augmentation.py", "file_name": "test_time_augmentation.py", "file_type": "text/x-python", "category": "test", "start_line": 45, "end_line": 96, "span_ids": ["TestTimeAugmentation"], "tokens": 681}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestTimeAugmentation:\n    \"\"\"\n    Class for performing test time augmentations. This will pass the same image through the network multiple times.\n\n    The user passes transform(s) to be applied to each realisation, and provided that at least one of those transforms\n    is random, the network's output will vary. Provided that inverse transformations exist for all supplied spatial\n    transforms, the inverse can be applied to each realisation of the network's output. Once in the same spatial\n    reference, the results can then be combined and metrics computed.\n\n    Test time augmentations are a useful feature for computing network uncertainty, as well as observing the network's\n    dependency on the applied random transforms.\n\n    Reference:\n        Wang et al.,\n        Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional\n        neural networks,\n        https://doi.org/10.1016/j.neucom.2019.01.103\n\n    Args:\n        transform: transform (or composed) to be applied to each realisation. At least one transform must be of type\n            `Randomizable`. All random transforms must be of type `InvertibleTransform`.\n        batch_size: number of realisations to infer at once.\n        num_workers: how many subprocesses to use for data.\n        inferrer_fn: function to use to perform inference.\n        device: device on which to perform inference.\n        image_key: key used to extract image from input dictionary.\n        orig_key: the key of the original input data in the dict. will get the applied transform information\n            for this input data, then invert them for the expected data with `image_key`.\n        orig_meta_keys: the key of the meta data of original input data, will get the `affine`, `data_shape`, etc.\n            the meta data is a dictionary object which contains: filename, original_shape, etc.\n            if None, will try to construct meta_keys by `{orig_key}_{meta_key_postfix}`.\n        meta_key_postfix: use `key_{postfix}` to to fetch the meta data according to the key data,\n            default is `meta_dict`, the meta data is a dictionary object.\n            For example, to handle key `image`,  read/write affine matrices from the\n            metadata `image_meta_dict` dictionary's `affine` field.\n            this arg only works when `meta_keys=None`.\n        return_full_data: normally, metrics are returned (mode, mean, std, vvc). Setting this flag to `True` will return the\n            full data. Dimensions will be same size as when passing a single image through `inferrer_fn`, with a dimension appended\n            equal in size to `num_examples` (N), i.e., `[N,C,H,W,[D]]`.\n        progress: whether to display a progress bar.\n\n    Example:\n        .. code-block:: python\n\n            transform = RandAffined(keys, ...)\n            post_trans = Compose([Activations(sigmoid=True), AsDiscrete(threshold=0.5)])\n\n            tt_aug = TestTimeAugmentation(\n                transform, batch_size=5, num_workers=0, inferrer_fn=lambda x: post_trans(model(x)), device=device\n            )\n            mode, mean, std, vvc = tt_aug(test_data)\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/test_time_augmentation.py_TestTimeAugmentation.__init___TestTimeAugmentation.__init__.self__check_transforms_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/test_time_augmentation.py_TestTimeAugmentation.__init___TestTimeAugmentation.__init__.self__check_transforms_", "embedding": null, "metadata": {"file_path": "monai/data/test_time_augmentation.py", "file_name": "test_time_augmentation.py", "file_type": "text/x-python", "category": "test", "start_line": 97, "end_line": 126, "span_ids": ["TestTimeAugmentation.__init__"], "tokens": 256}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestTimeAugmentation:\n\n    def __init__(\n        self,\n        transform: InvertibleTransform,\n        batch_size: int,\n        num_workers: int = 0,\n        inferrer_fn: Callable = _identity,\n        device: Union[str, torch.device] = \"cpu\",\n        image_key=CommonKeys.IMAGE,\n        orig_key=CommonKeys.LABEL,\n        nearest_interp: bool = True,\n        orig_meta_keys: Optional[str] = None,\n        meta_key_postfix=\"meta_dict\",\n        return_full_data: bool = False,\n        progress: bool = True,\n    ) -> None:\n        self.transform = transform\n        self.batch_size = batch_size\n        self.num_workers = num_workers\n        self.inferrer_fn = inferrer_fn\n        self.device = device\n        self.image_key = image_key\n        self.orig_key = orig_key\n        self.nearest_interp = nearest_interp\n        self.orig_meta_keys = orig_meta_keys\n        self.meta_key_postfix = meta_key_postfix\n        self.return_full_data = return_full_data\n        self.progress = progress\n\n        # check that the transform has at least one random component, and that all random transforms are invertible\n        self._check_transforms()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/test_time_augmentation.py_TestTimeAugmentation.__call___": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/test_time_augmentation.py_TestTimeAugmentation.__call___", "embedding": null, "metadata": {"file_path": "monai/data/test_time_augmentation.py", "file_name": "test_time_augmentation.py", "file_type": "text/x-python", "category": "test", "start_line": 146, "end_line": 225, "span_ids": ["TestTimeAugmentation.__call__"], "tokens": 836}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestTimeAugmentation:\n\n    def __call__(\n        self, data: Dict[str, Any], num_examples: int = 10\n    ) -> Union[Tuple[np.ndarray, np.ndarray, np.ndarray, float], np.ndarray]:\n        \"\"\"\n        Args:\n            data: dictionary data to be processed.\n            num_examples: number of realisations to be processed and results combined.\n\n        Returns:\n            - if `return_full_data==False`: mode, mean, std, vvc. The mode, mean and standard deviation are calculated across\n                `num_examples` outputs at each voxel. The volume variation coefficient (VVC) is `std/mean` across the whole output,\n                including `num_examples`. See original paper for clarification.\n            - if `return_full_data==False`: data is returned as-is after applying the `inferrer_fn` and then concatenating across\n                the first dimension containing `num_examples`. This allows the user to perform their own analysis if desired.\n        \"\"\"\n        d = dict(data)\n\n        # check num examples is multiple of batch size\n        if num_examples % self.batch_size != 0:\n            raise ValueError(\"num_examples should be multiple of batch size.\")\n\n        # generate batch of data of size == batch_size, dataset and dataloader\n        data_in = [deepcopy(d) for _ in range(num_examples)]\n        ds = Dataset(data_in, self.transform)\n        dl = DataLoader(ds, num_workers=self.num_workers, batch_size=self.batch_size, collate_fn=pad_list_data_collate)\n\n        transform_key = InvertibleTransform.trace_key(self.orig_key)\n\n        # create inverter\n        inverter = BatchInverseTransform(self.transform, dl, collate_fn=list_data_collate)\n\n        outputs: List[np.ndarray] = []\n\n        for batch_data in tqdm(dl) if has_tqdm and self.progress else dl:\n\n            batch_images = batch_data[self.image_key].to(self.device)\n\n            # do model forward pass\n            batch_output = self.inferrer_fn(batch_images)\n            if isinstance(batch_output, torch.Tensor):\n                batch_output = batch_output.detach().cpu()\n            if isinstance(batch_output, np.ndarray):\n                batch_output = torch.Tensor(batch_output)\n            transform_info = batch_data.get(transform_key, None)\n            if transform_info is None:\n                # no invertible transforms, adding dummy info for identity invertible\n                transform_info = [[TraceKeys.NONE] for _ in range(self.batch_size)]\n            if self.nearest_interp:\n                transform_info = convert_inverse_interp_mode(\n                    trans_info=deepcopy(transform_info), mode=\"nearest\", align_corners=None\n                )\n\n            # create a dictionary containing the inferred batch and their transforms\n            inferred_dict = {self.orig_key: batch_output, transform_key: transform_info}\n            # if meta dict is present, add that too (required for some inverse transforms)\n            meta_dict_key = self.orig_meta_keys or f\"{self.orig_key}_{self.meta_key_postfix}\"\n            if meta_dict_key in batch_data:\n                inferred_dict[meta_dict_key] = batch_data[meta_dict_key]\n\n            # do inverse transformation (allow missing keys as only inverting the orig_key)\n            with allow_missing_keys_mode(self.transform):  # type: ignore\n                inv_batch = inverter(inferred_dict)\n\n            # append\n            outputs.append(inv_batch[self.orig_key])\n\n        # output\n        output: np.ndarray = np.concatenate(outputs)\n\n        if self.return_full_data:\n            return output\n\n        # calculate metrics\n        output_t, *_ = convert_data_type(output, output_type=torch.Tensor, dtype=np.int64)\n        mode: np.ndarray = np.asarray(torch.mode(output_t, dim=0).values)  # type: ignore\n        mean: np.ndarray = np.mean(output, axis=0)  # type: ignore\n        std: np.ndarray = np.std(output, axis=0)  # type: ignore\n        vvc: float = (np.std(output) / np.mean(output)).item()\n        return mode, mean, std, vvc", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/thread_buffer.py_from_queue_import_Empty__ThreadBuffer.__iter__.try_.finally_._ensure_thread_completio": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/thread_buffer.py_from_queue_import_Empty__ThreadBuffer.__iter__.try_.finally_._ensure_thread_completio", "embedding": null, "metadata": {"file_path": "monai/data/thread_buffer.py", "file_name": "thread_buffer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 13, "end_line": 77, "span_ids": ["ThreadBuffer.enqueue_values", "ThreadBuffer.__iter__", "ThreadBuffer.stop", "ThreadBuffer.__init__", "docstring", "ThreadBuffer"], "tokens": 506}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from queue import Empty, Full, Queue\nfrom threading import Thread\n\nfrom monai.data import DataLoader, Dataset\n\n\nclass ThreadBuffer:\n    \"\"\"\n    Iterates over values from self.src in a separate thread but yielding them in the current thread. This allows values\n    to be queued up asynchronously. The internal thread will continue running so long as the source has values or until\n    the stop() method is called.\n\n    One issue raised by using a thread in this way is that during the lifetime of the thread the source object is being\n    iterated over, so if the thread hasn't finished another attempt to iterate over it will raise an exception or yield\n    unexpected results. To ensure the thread releases the iteration and proper cleanup is done the stop() method must\n    be called which will join with the thread.\n\n    Args:\n        src: Source data iterable\n        buffer_size: Number of items to buffer from the source\n        timeout: Time to wait for an item from the buffer, or to wait while the buffer is full when adding items\n    \"\"\"\n\n    def __init__(self, src, buffer_size: int = 1, timeout: float = 0.01):\n        self.src = src\n        self.buffer_size = buffer_size\n        self.timeout = timeout\n        self.buffer: Queue = Queue(self.buffer_size)\n        self.gen_thread = None\n        self.is_running = False\n\n    def enqueue_values(self):\n        for src_val in self.src:\n            while self.is_running:\n                try:\n                    self.buffer.put(src_val, timeout=self.timeout)\n                except Full:\n                    pass  # try to add the item again\n                else:\n                    break  # successfully added the item, quit trying\n            else:  # quit the thread cleanly when requested to stop\n                break\n\n    def stop(self):\n        self.is_running = False  # signal the thread to exit\n\n        if self.gen_thread is not None:\n            self.gen_thread.join()\n\n        self.gen_thread = None\n\n    def __iter__(self):\n\n        self.is_running = True\n        self.gen_thread = Thread(target=self.enqueue_values, daemon=True)\n        self.gen_thread.start()\n\n        try:\n            while self.is_running and (self.gen_thread.is_alive() or not self.buffer.empty()):\n                try:\n                    yield self.buffer.get(timeout=self.timeout)\n                except Empty:\n                    pass  # queue was empty this time, try again\n        finally:\n            self.stop()  # ensure thread completion", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/thread_buffer.py_ThreadDataLoader_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/thread_buffer.py_ThreadDataLoader_", "embedding": null, "metadata": {"file_path": "monai/data/thread_buffer.py", "file_name": "thread_buffer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 80, "end_line": 126, "span_ids": ["ThreadDataLoader.__iter__", "ThreadDataLoader", "ThreadDataLoader.__init__"], "tokens": 552}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ThreadDataLoader(DataLoader):\n    \"\"\"\n    Subclass of `DataLoader` using a `ThreadBuffer` object to implement `__iter__` method asynchronously. This will\n    iterate over data from the loader as expected however the data is generated on a separate thread. Use this class\n    where a `DataLoader` instance is required and not just an iterable object.\n\n    The default behaviour with `repeats` set to 1 is to yield each batch as it is generated, however with a higher\n    value the generated batch is yielded that many times while underlying dataset asynchronously generates the next.\n    Typically not all relevant information is learned from a batch in a single iteration so training multiple times\n    on the same batch will still produce good training with minimal short-term overfitting while allowing a slow batch\n    generation process more time to produce a result.\n\n    See:\n        * Fischetti et al. \"Faster SGD training by minibatch persistency.\" ArXiv (2018) https://arxiv.org/abs/1806.07353\n        * Dami et al., \"Faster Neural Network Training with Data Echoing\" ArXiv (2020) https://arxiv.org/abs/1907.05550\n        * Ramezani et al. \"GCN meets GPU: Decoupling \"When to Sample\" from \"How to Sample\".\" NeurIPS (2020).\n          https://proceedings.neurips.cc/paper/2020/file/d714d2c5a796d5814c565d78dd16188d-Paper.pdf\n\n    Args:\n        dataset: input dataset.\n        buffer_size: number of items to buffer from the data source.\n        buffer_timeout: time to wait for an item from the buffer, or to wait while the buffer is full when adding items.\n        num_workers: number of the multi-processing workers in PyTorch DataLoader.\n        repeats: number of times to yield the same batch\n    \"\"\"\n\n    def __init__(\n        self,\n        dataset: Dataset,\n        buffer_size: int = 1,\n        buffer_timeout: float = 0.01,\n        num_workers: int = 0,\n        repeats: int = 1,\n        **kwargs,\n    ):\n        super().__init__(dataset, num_workers, **kwargs)\n        self.buffer_size = buffer_size\n        self.buffer_timeout = buffer_timeout\n        self.repeats = repeats\n\n    def __iter__(self):\n        buffer = ThreadBuffer(src=super().__iter__(), buffer_size=self.buffer_size, timeout=self.buffer_timeout)\n\n        for batch in buffer:\n            for _ in range(self.repeats):\n                yield batch", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_dense_patch_slices_dense_patch_slices.return._tuple_slice_s_s_patch": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_dense_patch_slices_dense_patch_slices.return._tuple_slice_s_s_patch", "embedding": null, "metadata": {"file_path": "monai/data/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 137, "end_line": 174, "span_ids": ["dense_patch_slices"], "tokens": 368}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def dense_patch_slices(\n    image_size: Sequence[int], patch_size: Sequence[int], scan_interval: Sequence[int]\n) -> List[Tuple[slice, ...]]:\n    \"\"\"\n    Enumerate all slices defining ND patches of size `patch_size` from an `image_size` input image.\n\n    Args:\n        image_size: dimensions of image to iterate over\n        patch_size: size of patches to generate slices\n        scan_interval: dense patch sampling interval\n\n    Returns:\n        a list of slice objects defining each patch\n\n    \"\"\"\n    num_spatial_dims = len(image_size)\n    patch_size = get_valid_patch_size(image_size, patch_size)\n    scan_interval = ensure_tuple_size(scan_interval, num_spatial_dims)\n\n    scan_num = []\n    for i in range(num_spatial_dims):\n        if scan_interval[i] == 0:\n            scan_num.append(1)\n        else:\n            num = int(math.ceil(float(image_size[i]) / scan_interval[i]))\n            scan_dim = first(d for d in range(num) if d * scan_interval[i] + patch_size[i] >= image_size[i])\n            scan_num.append(scan_dim + 1 if scan_dim is not None else 1)\n\n    starts = []\n    for dim in range(num_spatial_dims):\n        dim_starts = []\n        for idx in range(scan_num[dim]):\n            start_idx = idx * scan_interval[dim]\n            start_idx -= max(start_idx + patch_size[dim] - image_size[dim], 0)\n            dim_starts.append(start_idx)\n        starts.append(dim_starts)\n    out = np.asarray([x.flatten() for x in np.meshgrid(*starts, indexing=\"ij\")]).T\n    return [tuple(slice(s, s + patch_size[d]) for d, s in enumerate(x)) for x in out]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_pad_list_data_collate_pad_list_data_collate.return.PadListDataCollate_method": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_pad_list_data_collate_pad_list_data_collate.return.PadListDataCollate_method", "embedding": null, "metadata": {"file_path": "monai/data/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 419, "end_line": 447, "span_ids": ["pad_list_data_collate"], "tokens": 339}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def pad_list_data_collate(\n    batch: Sequence,\n    method: Union[Method, str] = Method.SYMMETRIC,\n    mode: Union[NumpyPadMode, str] = NumpyPadMode.CONSTANT,\n    **np_kwargs,\n):\n    \"\"\"\n    Function version of :py:class:`monai.transforms.croppad.batch.PadListDataCollate`.\n\n    Same as MONAI's ``list_data_collate``, except any tensors are centrally padded to match the shape of the biggest\n    tensor in each dimension. This transform is useful if some of the applied transforms generate batch data of\n    different sizes.\n\n    This can be used on both list and dictionary data. In the case of the dictionary data, this transform will be added\n    to the list of invertible transforms.\n\n    The inverse can be called using the static method: `monai.transforms.croppad.batch.PadListDataCollate.inverse`.\n\n    Args:\n        batch: batch of data to pad-collate\n        method: padding method (see :py:class:`monai.transforms.SpatialPad`)\n        mode: padding mode (see :py:class:`monai.transforms.SpatialPad`)\n        np_kwargs: other args for `np.pad` API, note that `np.pad` treats channel dimension as the first dimension.\n            more details: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html\n\n    \"\"\"\n    from monai.transforms.croppad.batch import PadListDataCollate  # needs to be here to avoid circular import\n\n    return PadListDataCollate(method=method, mode=mode, **np_kwargs)(batch)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_no_collation_set_rnd.return.seed": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_no_collation_set_rnd.return.seed", "embedding": null, "metadata": {"file_path": "monai/data/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 440, "end_line": 474, "span_ids": ["set_rnd", "no_collation", "worker_init_fn"], "tokens": 248}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def no_collation(x):\n    \"\"\"\n    No any collation operation.\n    \"\"\"\n    return x\n\n\ndef worker_init_fn(worker_id: int) -> None:\n    \"\"\"\n    Callback function for PyTorch DataLoader `worker_init_fn`.\n    It can set different random seed for the transforms in different workers.\n\n    \"\"\"\n    worker_info = torch.utils.data.get_worker_info()\n    set_rnd(worker_info.dataset, seed=worker_info.seed)\n\n\ndef set_rnd(obj, seed: int) -> int:\n    \"\"\"\n    Set seed or random state for all randomizable properties of obj.\n\n    Args:\n        obj: object to set seed or random state for.\n        seed: set the random state with an integer seed.\n    \"\"\"\n    if not hasattr(obj, \"__dict__\"):\n        return seed  # no attribute\n    if hasattr(obj, \"set_random_state\"):\n        obj.set_random_state(seed=seed % MAX_SEED)\n        return seed + 1  # a different seed for the next component\n    for key in obj.__dict__:\n        if key.startswith(\"__\"):  # skip the private methods\n            continue\n        seed = set_rnd(obj.__dict__[key], seed=seed)\n    return seed", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_is_supported_format_is_supported_format.return.True": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_is_supported_format_is_supported_format.return.True", "embedding": null, "metadata": {"file_path": "monai/data/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 788, "end_line": 805, "span_ids": ["is_supported_format"], "tokens": 179}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def is_supported_format(filename: Union[Sequence[PathLike], PathLike], suffixes: Sequence[str]) -> bool:\n    \"\"\"\n    Verify whether the specified file or files format match supported suffixes.\n    If supported suffixes is None, skip the verification and return True.\n\n    Args:\n        filename: file name or a list of file names to read.\n            if a list of files, verify all the suffixes.\n        suffixes: all the supported image suffixes of current reader, must be a list of lower case suffixes.\n\n    \"\"\"\n    filenames: Sequence[PathLike] = ensure_tuple(filename)\n    for name in filenames:\n        tokens: Sequence[str] = PurePath(name).suffixes\n        if len(tokens) == 0 or all(\".\" + s.lower() not in \"\".join(tokens) for s in suffixes):\n            return False\n\n    return True", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_partition_dataset_classes_partition_dataset_classes.return.datasets": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_partition_dataset_classes_partition_dataset_classes.return.datasets", "embedding": null, "metadata": {"file_path": "monai/data/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 927, "end_line": 992, "span_ids": ["partition_dataset_classes"], "tokens": 687}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def partition_dataset_classes(\n    data: Sequence,\n    classes: Sequence[int],\n    ratios: Optional[Sequence[float]] = None,\n    num_partitions: Optional[int] = None,\n    shuffle: bool = False,\n    seed: int = 0,\n    drop_last: bool = False,\n    even_divisible: bool = False,\n):\n    \"\"\"\n    Split the dataset into N partitions based on the given class labels.\n    It can make sure the same ratio of classes in every partition.\n    Others are same as :py:class:`monai.data.partition_dataset`.\n\n    Args:\n        data: input dataset to split, expect a list of data.\n        classes: a list of labels to help split the data, the length must match the length of data.\n        ratios: a list of ratio number to split the dataset, like [8, 1, 1].\n        num_partitions: expected number of the partitions to evenly split, only works when no `ratios`.\n        shuffle: whether to shuffle the original dataset before splitting.\n        seed: random seed to shuffle the dataset, only works when `shuffle` is True.\n        drop_last: only works when `even_divisible` is False and no ratios specified.\n            if True, will drop the tail of the data to make it evenly divisible across partitions.\n            if False, will add extra indices to make the data evenly divisible across partitions.\n        even_divisible: if True, guarantee every partition has same length.\n\n    Examples::\n\n        >>> data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]\n        >>> classes = [2, 0, 2, 1, 3, 2, 2, 0, 2, 0, 3, 3, 1, 3]\n        >>> partition_dataset_classes(data, classes, shuffle=False, ratios=[2, 1])\n        [[2, 8, 4, 1, 3, 6, 5, 11, 12], [10, 13, 7, 9, 14]]\n\n    \"\"\"\n    if not issequenceiterable(classes) or len(classes) != len(data):\n        raise ValueError(f\"length of classes {classes} must match the dataset length {len(data)}.\")\n    datasets = []\n    class_indices = defaultdict(list)\n    for i, c in enumerate(classes):\n        class_indices[c].append(i)\n\n    class_partition_indices: List[Sequence] = []\n    for _, per_class_indices in sorted(class_indices.items()):\n        per_class_partition_indices = partition_dataset(\n            data=per_class_indices,\n            ratios=ratios,\n            num_partitions=num_partitions,\n            shuffle=shuffle,\n            seed=seed,\n            drop_last=drop_last,\n            even_divisible=even_divisible,\n        )\n        if not class_partition_indices:\n            class_partition_indices = per_class_partition_indices\n        else:\n            for part, data_indices in zip(class_partition_indices, per_class_partition_indices):\n                part += data_indices\n\n    rs = np.random.RandomState(seed)\n    for indices in class_partition_indices:\n        if shuffle:\n            rs.shuffle(indices)\n        datasets.append([data[j] for j in indices])\n\n    return datasets", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_select_cross_validation_folds_select_cross_validation_folds.return._data_item_for_fold_id_in": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_select_cross_validation_folds_select_cross_validation_folds.return._data_item_for_fold_id_in", "embedding": null, "metadata": {"file_path": "monai/data/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 898, "end_line": 920, "span_ids": ["select_cross_validation_folds"], "tokens": 237}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def select_cross_validation_folds(partitions: Sequence[Iterable], folds: Union[Sequence[int], int]) -> List:\n    \"\"\"\n    Select cross validation data based on data partitions and specified fold index.\n    if a list of fold indices is provided, concatenate the partitions of these folds.\n\n    Args:\n        partitions: a sequence of datasets, each item is a iterable\n        folds: the indices of the partitions to be combined.\n\n    Returns:\n        A list of combined datasets.\n\n    Example::\n\n        >>> partitions = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]]\n        >>> select_cross_validation_folds(partitions, 2)\n        [5, 6]\n        >>> select_cross_validation_folds(partitions, [1, 2])\n        [3, 4, 5, 6]\n        >>> select_cross_validation_folds(partitions, [-1, 2])\n        [9, 10, 5, 6]\n    \"\"\"\n    return [data_item for fold_id in ensure_tuple(folds) for data_item in partitions[fold_id]]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/__init__.py_EnsembleEvaluator_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/__init__.py_EnsembleEvaluator_", "embedding": null, "metadata": {"file_path": "monai/engines/__init__.py", "file_name": "__init__.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 28, "span_ids": ["docstring"], "tokens": 122}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from .evaluator import EnsembleEvaluator, Evaluator, SupervisedEvaluator\nfrom .multi_gpu_supervised_trainer import create_multigpu_supervised_evaluator, create_multigpu_supervised_trainer\nfrom .trainer import GanTrainer, SupervisedTrainer, Trainer\nfrom .utils import (\n    GanKeys,\n    IterationEvents,\n    PrepareBatch,\n    PrepareBatchDefault,\n    PrepareBatchExtraInput,\n    default_make_latent,\n    default_metric_cmp_fn,\n    default_prepare_batch,\n    engine_apply_transform,\n    get_devices_spec,\n)\nfrom .workflow import BaseWorkflow, Workflow", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/evaluator.py_from_typing_import_TYPE_C___all__._Evaluator_Supervised": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/evaluator.py_from_typing_import_TYPE_C___all__._Evaluator_Supervised", "embedding": null, "metadata": {"file_path": "monai/engines/evaluator.py", "file_name": "evaluator.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 35, "span_ids": ["docstring"], "tokens": 264}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import TYPE_CHECKING, Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union\n\nimport torch\nfrom torch.utils.data import DataLoader\n\nfrom monai.config import IgniteInfo\nfrom monai.engines.utils import IterationEvents, default_metric_cmp_fn, default_prepare_batch\nfrom monai.engines.workflow import Workflow\nfrom monai.inferers import Inferer, SimpleInferer\nfrom monai.networks.utils import eval_mode, train_mode\nfrom monai.transforms import Transform\nfrom monai.utils import ForwardMode, ensure_tuple, min_version, optional_import\nfrom monai.utils.enums import CommonKeys as Keys\nfrom monai.utils.module import look_up_option\n\nif TYPE_CHECKING:\n    from ignite.engine import Engine, EventEnum\n    from ignite.metrics import Metric\nelse:\n    Engine, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Engine\")\n    Metric, _ = optional_import(\"ignite.metrics\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Metric\")\n    EventEnum, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"EventEnum\")\n\n__all__ = [\"Evaluator\", \"SupervisedEvaluator\", \"EnsembleEvaluator\"]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/evaluator.py_Evaluator_Evaluator._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/evaluator.py_Evaluator_Evaluator._", "embedding": null, "metadata": {"file_path": "monai/engines/evaluator.py", "file_name": "evaluator.py", "file_type": "text/x-python", "category": "implementation", "start_line": 38, "end_line": 78, "span_ids": ["Evaluator"], "tokens": 636}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Evaluator(Workflow):\n    \"\"\"\n    Base class for all kinds of evaluators, inherits from Workflow.\n\n    Args:\n        device: an object representing the device on which to run.\n        val_data_loader: Ignite engine use data_loader to run, must be Iterable or torch.DataLoader.\n        epoch_length: number of iterations for one epoch, default to `len(val_data_loader)`.\n        non_blocking: if True and this copy is between CPU and GPU, the copy may occur asynchronously\n            with respect to the host. For other cases, this argument has no effect.\n        prepare_batch: function to parse expected data (usually `image`, `label` and other network args)\n            from `engine.state.batch` for every iteration, for more details please refer to:\n            https://pytorch.org/ignite/generated/ignite.engine.create_supervised_trainer.html.\n        iteration_update: the callable function for every iteration, expect to accept `engine`\n            and `engine.state.batch` as inputs, return data will be stored in `engine.state.output`.\n            if not provided, use `self._iteration()` instead. for more details please refer to:\n            https://pytorch.org/ignite/generated/ignite.engine.engine.Engine.html.\n        postprocessing: execute additional transformation for the model output data.\n            Typically, several Tensor based transforms composed by `Compose`.\n        key_val_metric: compute metric when every iteration completed, and save average value to\n            engine.state.metrics when epoch completed. key_val_metric is the main metric to compare and save the\n            checkpoint into files.\n        additional_metrics: more Ignite metrics that also attach to Ignite Engine.\n        metric_cmp_fn: function to compare current key metric with previous best key metric value,\n            it must accept 2 args (current_metric, previous_best) and return a bool result: if `True`, will update\n            `best_metric` and `best_metric_epoch` with current metric and epoch, default to `greater than`.\n        val_handlers: every handler is a set of Ignite Event-Handlers, must have `attach` function, like:\n            CheckpointHandler, StatsHandler, SegmentationSaver, etc.\n        amp: whether to enable auto-mixed-precision evaluation, default is False.\n        mode: model forward mode during evaluation, should be 'eval' or 'train',\n            which maps to `model.eval()` or `model.train()`, default to 'eval'.\n        event_names: additional custom ignite events that will register to the engine.\n            new events can be a list of str or `ignite.engine.events.EventEnum`.\n        event_to_attr: a dictionary to map an event to a state attribute, then add to `engine.state`.\n            for more details, check: https://pytorch.org/ignite/generated/ignite.engine.engine.Engine.html\n            #ignite.engine.engine.Engine.register_events.\n        decollate: whether to decollate the batch-first data to a list of data after model computation,\n            recommend `decollate=True` when `postprocessing` uses components from `monai.transforms`.\n            default to `True`.\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/evaluator.py_Evaluator.__init___Evaluator.get_validation_stats.return._best_validation_metric_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/evaluator.py_Evaluator.__init___Evaluator.get_validation_stats.return._best_validation_metric_", "embedding": null, "metadata": {"file_path": "monai/engines/evaluator.py", "file_name": "evaluator.py", "file_type": "text/x-python", "category": "implementation", "start_line": 80, "end_line": 140, "span_ids": ["Evaluator.get_validation_stats", "Evaluator.__init__", "Evaluator.run"], "tokens": 521}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Evaluator(Workflow):\n\n    def __init__(\n        self,\n        device: torch.device,\n        val_data_loader: Union[Iterable, DataLoader],\n        epoch_length: Optional[int] = None,\n        non_blocking: bool = False,\n        prepare_batch: Callable = default_prepare_batch,\n        iteration_update: Optional[Callable[[Engine, Any], Any]] = None,\n        postprocessing: Optional[Transform] = None,\n        key_val_metric: Optional[Dict[str, Metric]] = None,\n        additional_metrics: Optional[Dict[str, Metric]] = None,\n        metric_cmp_fn: Callable = default_metric_cmp_fn,\n        val_handlers: Optional[Sequence] = None,\n        amp: bool = False,\n        mode: Union[ForwardMode, str] = ForwardMode.EVAL,\n        event_names: Optional[List[Union[str, EventEnum]]] = None,\n        event_to_attr: Optional[dict] = None,\n        decollate: bool = True,\n    ) -> None:\n        super().__init__(\n            device=device,\n            max_epochs=1,\n            data_loader=val_data_loader,\n            epoch_length=epoch_length,\n            non_blocking=non_blocking,\n            prepare_batch=prepare_batch,\n            iteration_update=iteration_update,\n            postprocessing=postprocessing,\n            key_metric=key_val_metric,\n            additional_metrics=additional_metrics,\n            metric_cmp_fn=metric_cmp_fn,\n            handlers=val_handlers,\n            amp=amp,\n            event_names=event_names,\n            event_to_attr=event_to_attr,\n            decollate=decollate,\n        )\n        self.mode = look_up_option(mode, ForwardMode)\n        if mode == ForwardMode.EVAL:\n            self.mode = eval_mode\n        elif mode == ForwardMode.TRAIN:\n            self.mode = train_mode\n        else:\n            raise ValueError(f\"unsupported mode: {mode}, should be 'eval' or 'train'.\")\n\n    def run(self, global_epoch: int = 1) -> None:\n        \"\"\"\n        Execute validation/evaluation based on Ignite Engine.\n\n        Args:\n            global_epoch: the overall epoch if during a training. evaluator engine can get it from trainer.\n\n        \"\"\"\n        # init env value for current validation process\n        self.state.max_epochs = global_epoch\n        self.state.epoch = global_epoch - 1\n        self.state.iteration = 0\n        super().run()\n\n    def get_validation_stats(self) -> Dict[str, float]:\n        return {\"best_validation_metric\": self.state.best_metric, \"best_validation_epoch\": self.state.best_metric_epoch}", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/evaluator.py_SupervisedEvaluator_SupervisedEvaluator._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/evaluator.py_SupervisedEvaluator_SupervisedEvaluator._", "embedding": null, "metadata": {"file_path": "monai/engines/evaluator.py", "file_name": "evaluator.py", "file_type": "text/x-python", "category": "implementation", "start_line": 143, "end_line": 185, "span_ids": ["SupervisedEvaluator"], "tokens": 685}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SupervisedEvaluator(Evaluator):\n    \"\"\"\n    Standard supervised evaluation method with image and label(optional), inherits from evaluator and Workflow.\n\n    Args:\n        device: an object representing the device on which to run.\n        val_data_loader: Ignite engine use data_loader to run, must be Iterable, typically be torch.DataLoader.\n        network: network to evaluate in the evaluator, should be regular PyTorch `torch.nn.Module`.\n        epoch_length: number of iterations for one epoch, default to `len(val_data_loader)`.\n        non_blocking: if True and this copy is between CPU and GPU, the copy may occur asynchronously\n            with respect to the host. For other cases, this argument has no effect.\n        prepare_batch: function to parse expected data (usually `image`, `label` and other network args)\n            from `engine.state.batch` for every iteration, for more details please refer to:\n            https://pytorch.org/ignite/generated/ignite.engine.create_supervised_trainer.html.\n        iteration_update: the callable function for every iteration, expect to accept `engine`\n            and `engine.state.batch` as inputs, return data will be stored in `engine.state.output`.\n            if not provided, use `self._iteration()` instead. for more details please refer to:\n            https://pytorch.org/ignite/generated/ignite.engine.engine.Engine.html.\n        inferer: inference method that execute model forward on input data, like: SlidingWindow, etc.\n        postprocessing: execute additional transformation for the model output data.\n            Typically, several Tensor based transforms composed by `Compose`.\n        key_val_metric: compute metric when every iteration completed, and save average value to\n            engine.state.metrics when epoch completed. key_val_metric is the main metric to compare and save the\n            checkpoint into files.\n        additional_metrics: more Ignite metrics that also attach to Ignite Engine.\n        metric_cmp_fn: function to compare current key metric with previous best key metric value,\n            it must accept 2 args (current_metric, previous_best) and return a bool result: if `True`, will update\n            `best_metric` and `best_metric_epoch` with current metric and epoch, default to `greater than`.\n        val_handlers: every handler is a set of Ignite Event-Handlers, must have `attach` function, like:\n            CheckpointHandler, StatsHandler, SegmentationSaver, etc.\n        amp: whether to enable auto-mixed-precision evaluation, default is False.\n        mode: model forward mode during evaluation, should be 'eval' or 'train',\n            which maps to `model.eval()` or `model.train()`, default to 'eval'.\n        event_names: additional custom ignite events that will register to the engine.\n            new events can be a list of str or `ignite.engine.events.EventEnum`.\n        event_to_attr: a dictionary to map an event to a state attribute, then add to `engine.state`.\n            for more details, check: https://pytorch.org/ignite/generated/ignite.engine.engine.Engine.html\n            #ignite.engine.engine.Engine.register_events.\n        decollate: whether to decollate the batch-first data to a list of data after model computation,\n            recommend `decollate=True` when `postprocessing` uses components from `monai.transforms`.\n            default to `True`.\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/evaluator.py_SupervisedEvaluator.__init___SupervisedEvaluator.__init__.self.inferer.SimpleInferer_if_infere": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/evaluator.py_SupervisedEvaluator.__init___SupervisedEvaluator.__init__.self.inferer.SimpleInferer_if_infere", "embedding": null, "metadata": {"file_path": "monai/engines/evaluator.py", "file_name": "evaluator.py", "file_type": "text/x-python", "category": "implementation", "start_line": 187, "end_line": 228, "span_ids": ["SupervisedEvaluator.__init__"], "tokens": 367}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SupervisedEvaluator(Evaluator):\n\n    def __init__(\n        self,\n        device: torch.device,\n        val_data_loader: Union[Iterable, DataLoader],\n        network: torch.nn.Module,\n        epoch_length: Optional[int] = None,\n        non_blocking: bool = False,\n        prepare_batch: Callable = default_prepare_batch,\n        iteration_update: Optional[Callable[[Engine, Any], Any]] = None,\n        inferer: Optional[Inferer] = None,\n        postprocessing: Optional[Transform] = None,\n        key_val_metric: Optional[Dict[str, Metric]] = None,\n        additional_metrics: Optional[Dict[str, Metric]] = None,\n        metric_cmp_fn: Callable = default_metric_cmp_fn,\n        val_handlers: Optional[Sequence] = None,\n        amp: bool = False,\n        mode: Union[ForwardMode, str] = ForwardMode.EVAL,\n        event_names: Optional[List[Union[str, EventEnum]]] = None,\n        event_to_attr: Optional[dict] = None,\n        decollate: bool = True,\n    ) -> None:\n        super().__init__(\n            device=device,\n            val_data_loader=val_data_loader,\n            epoch_length=epoch_length,\n            non_blocking=non_blocking,\n            prepare_batch=prepare_batch,\n            iteration_update=iteration_update,\n            postprocessing=postprocessing,\n            key_val_metric=key_val_metric,\n            additional_metrics=additional_metrics,\n            metric_cmp_fn=metric_cmp_fn,\n            val_handlers=val_handlers,\n            amp=amp,\n            mode=mode,\n            event_names=event_names,\n            event_to_attr=event_to_attr,\n            decollate=decollate,\n        )\n\n        self.network = network\n        self.inferer = SimpleInferer() if inferer is None else inferer", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/evaluator.py_SupervisedEvaluator._iteration_SupervisedEvaluator._iteration.return.engine_state_output": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/evaluator.py_SupervisedEvaluator._iteration_SupervisedEvaluator._iteration.return.engine_state_output", "embedding": null, "metadata": {"file_path": "monai/engines/evaluator.py", "file_name": "evaluator.py", "file_type": "text/x-python", "category": "implementation", "start_line": 222, "end_line": 261, "span_ids": ["SupervisedEvaluator._iteration"], "tokens": 391}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SupervisedEvaluator(Evaluator):\n\n    def _iteration(self, engine: Engine, batchdata: Dict[str, torch.Tensor]):\n        \"\"\"\n        callback function for the Supervised Evaluation processing logic of 1 iteration in Ignite Engine.\n        Return below items in a dictionary:\n            - IMAGE: image Tensor data for model input, already moved to device.\n            - LABEL: label Tensor data corresponding to the image, already moved to device.\n            - PRED: prediction result of model.\n\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n            batchdata: input data for this iteration, usually can be dictionary or tuple of Tensor data.\n\n        Raises:\n            ValueError: When ``batchdata`` is None.\n\n        \"\"\"\n        if batchdata is None:\n            raise ValueError(\"Must provide batch data for current iteration.\")\n        batch = self.prepare_batch(batchdata, engine.state.device, engine.non_blocking)  # type: ignore\n        if len(batch) == 2:\n            inputs, targets = batch\n            args: Tuple = ()\n            kwargs: Dict = {}\n        else:\n            inputs, targets, args, kwargs = batch\n\n        # put iteration outputs into engine.state\n        engine.state.output = {Keys.IMAGE: inputs, Keys.LABEL: targets}  # type: ignore\n\n        # execute forward computation\n        with self.mode(self.network):\n            if self.amp:\n                with torch.cuda.amp.autocast():\n                    engine.state.output[Keys.PRED] = self.inferer(inputs, self.network, *args, **kwargs)  # type: ignore\n            else:\n                engine.state.output[Keys.PRED] = self.inferer(inputs, self.network, *args, **kwargs)  # type: ignore\n        engine.fire_event(IterationEvents.FORWARD_COMPLETED)\n        engine.fire_event(IterationEvents.MODEL_COMPLETED)\n\n        return engine.state.output", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/evaluator.py_EnsembleEvaluator_EnsembleEvaluator._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/evaluator.py_EnsembleEvaluator_EnsembleEvaluator._", "embedding": null, "metadata": {"file_path": "monai/engines/evaluator.py", "file_name": "evaluator.py", "file_type": "text/x-python", "category": "implementation", "start_line": 272, "end_line": 317, "span_ids": ["EnsembleEvaluator"], "tokens": 724}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class EnsembleEvaluator(Evaluator):\n    \"\"\"\n    Ensemble evaluation for multiple models, inherits from evaluator and Workflow.\n    It accepts a list of models for inference and outputs a list of predictions for further operations.\n\n    Args:\n        device: an object representing the device on which to run.\n        val_data_loader: Ignite engine use data_loader to run, must be Iterable, typically be torch.DataLoader.\n        epoch_length: number of iterations for one epoch, default to `len(val_data_loader)`.\n        networks: networks to evaluate in order in the evaluator, should be regular PyTorch `torch.nn.Module`.\n        pred_keys: the keys to store every prediction data.\n            the length must exactly match the number of networks.\n        non_blocking: if True and this copy is between CPU and GPU, the copy may occur asynchronously\n            with respect to the host. For other cases, this argument has no effect.\n        prepare_batch: function to parse expected data (usually `image`, `label` and other network args)\n            from `engine.state.batch` for every iteration, for more details please refer to:\n            https://pytorch.org/ignite/generated/ignite.engine.create_supervised_trainer.html.\n        iteration_update: the callable function for every iteration, expect to accept `engine`\n            and `engine.state.batch` as inputs, return data will be stored in `engine.state.output`.\n            if not provided, use `self._iteration()` instead. for more details please refer to:\n            https://pytorch.org/ignite/generated/ignite.engine.engine.Engine.html.\n        inferer: inference method that execute model forward on input data, like: SlidingWindow, etc.\n        postprocessing: execute additional transformation for the model output data.\n            Typically, several Tensor based transforms composed by `Compose`.\n        key_val_metric: compute metric when every iteration completed, and save average value to\n            engine.state.metrics when epoch completed. key_val_metric is the main metric to compare and save the\n            checkpoint into files.\n        additional_metrics: more Ignite metrics that also attach to Ignite Engine.\n        metric_cmp_fn: function to compare current key metric with previous best key metric value,\n            it must accept 2 args (current_metric, previous_best) and return a bool result: if `True`, will update\n            `best_metric` and `best_metric_epoch` with current metric and epoch, default to `greater than`.\n        val_handlers: every handler is a set of Ignite Event-Handlers, must have `attach` function, like:\n            CheckpointHandler, StatsHandler, SegmentationSaver, etc.\n        amp: whether to enable auto-mixed-precision evaluation, default is False.\n        mode: model forward mode during evaluation, should be 'eval' or 'train',\n            which maps to `model.eval()` or `model.train()`, default to 'eval'.\n        event_names: additional custom ignite events that will register to the engine.\n            new events can be a list of str or `ignite.engine.events.EventEnum`.\n        event_to_attr: a dictionary to map an event to a state attribute, then add to `engine.state`.\n            for more details, check: https://pytorch.org/ignite/generated/ignite.engine.engine.Engine.html\n            #ignite.engine.engine.Engine.register_events.\n        decollate: whether to decollate the batch-first data to a list of data after model computation,\n            recommend `decollate=True` when `postprocessing` uses components from `monai.transforms`.\n            default to `True`.\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/evaluator.py_EnsembleEvaluator.__init___EnsembleEvaluator.__init__.self.inferer.SimpleInferer_if_infere": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/evaluator.py_EnsembleEvaluator.__init___EnsembleEvaluator.__init__.self.inferer.SimpleInferer_if_infere", "embedding": null, "metadata": {"file_path": "monai/engines/evaluator.py", "file_name": "evaluator.py", "file_type": "text/x-python", "category": "implementation", "start_line": 319, "end_line": 362, "span_ids": ["EnsembleEvaluator.__init__"], "tokens": 389}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class EnsembleEvaluator(Evaluator):\n\n    def __init__(\n        self,\n        device: torch.device,\n        val_data_loader: Union[Iterable, DataLoader],\n        networks: Sequence[torch.nn.Module],\n        pred_keys: Sequence[str],\n        epoch_length: Optional[int] = None,\n        non_blocking: bool = False,\n        prepare_batch: Callable = default_prepare_batch,\n        iteration_update: Optional[Callable[[Engine, Any], Any]] = None,\n        inferer: Optional[Inferer] = None,\n        postprocessing: Optional[Transform] = None,\n        key_val_metric: Optional[Dict[str, Metric]] = None,\n        additional_metrics: Optional[Dict[str, Metric]] = None,\n        metric_cmp_fn: Callable = default_metric_cmp_fn,\n        val_handlers: Optional[Sequence] = None,\n        amp: bool = False,\n        mode: Union[ForwardMode, str] = ForwardMode.EVAL,\n        event_names: Optional[List[Union[str, EventEnum]]] = None,\n        event_to_attr: Optional[dict] = None,\n        decollate: bool = True,\n    ) -> None:\n        super().__init__(\n            device=device,\n            val_data_loader=val_data_loader,\n            epoch_length=epoch_length,\n            non_blocking=non_blocking,\n            prepare_batch=prepare_batch,\n            iteration_update=iteration_update,\n            postprocessing=postprocessing,\n            key_val_metric=key_val_metric,\n            additional_metrics=additional_metrics,\n            metric_cmp_fn=metric_cmp_fn,\n            val_handlers=val_handlers,\n            amp=amp,\n            mode=mode,\n            event_names=event_names,\n            event_to_attr=event_to_attr,\n            decollate=decollate,\n        )\n\n        self.networks = ensure_tuple(networks)\n        self.pred_keys = ensure_tuple(pred_keys)\n        self.inferer = SimpleInferer() if inferer is None else inferer", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/trainer.py_from_typing_import_TYPE_C___all__._Trainer_SupervisedTr": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/trainer.py_from_typing_import_TYPE_C___all__._Trainer_SupervisedTr", "embedding": null, "metadata": {"file_path": "monai/engines/trainer.py", "file_name": "trainer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 40, "span_ids": ["docstring"], "tokens": 265}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import TYPE_CHECKING, Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union\n\nimport torch\nfrom torch.optim.optimizer import Optimizer\nfrom torch.utils.data import DataLoader\n\nfrom monai.config import IgniteInfo\nfrom monai.engines.utils import (\n    GanKeys,\n    IterationEvents,\n    default_make_latent,\n    default_metric_cmp_fn,\n    default_prepare_batch,\n)\nfrom monai.engines.workflow import Workflow\nfrom monai.inferers import Inferer, SimpleInferer\nfrom monai.transforms import Transform\nfrom monai.utils import min_version, optional_import, pytorch_after\nfrom monai.utils.enums import CommonKeys as Keys\n\nif TYPE_CHECKING:\n    from ignite.engine import Engine, EventEnum\n    from ignite.metrics import Metric\nelse:\n    Engine, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Engine\")\n    Metric, _ = optional_import(\"ignite.metrics\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Metric\")\n    EventEnum, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"EventEnum\")\n\n__all__ = [\"Trainer\", \"SupervisedTrainer\", \"GanTrainer\"]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/trainer.py_SupervisedTrainer_SupervisedTrainer._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/trainer.py_SupervisedTrainer_SupervisedTrainer._", "embedding": null, "metadata": {"file_path": "monai/engines/trainer.py", "file_name": "trainer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 62, "end_line": 109, "span_ids": ["SupervisedTrainer"], "tokens": 771}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SupervisedTrainer(Trainer):\n    \"\"\"\n    Standard supervised training method with image and label, inherits from ``Trainer`` and ``Workflow``.\n\n    Args:\n        device: an object representing the device on which to run.\n        max_epochs: the total epoch number for trainer to run.\n        train_data_loader: Ignite engine use data_loader to run, must be Iterable or torch.DataLoader.\n        network: network to train in the trainer, should be regular PyTorch `torch.nn.Module`.\n        optimizer: the optimizer associated to the network, should be regular PyTorch optimizer from `torch.optim`\n            or its subclass.\n        loss_function: the loss function associated to the optimizer, should be regular PyTorch loss,\n            which inherit from `torch.nn.modules.loss`.\n        epoch_length: number of iterations for one epoch, default to `len(train_data_loader)`.\n        non_blocking: if True and this copy is between CPU and GPU, the copy may occur asynchronously\n            with respect to the host. For other cases, this argument has no effect.\n        prepare_batch: function to parse expected data (usually `image`, `label` and other network args)\n            from `engine.state.batch` for every iteration, for more details please refer to:\n            https://pytorch.org/ignite/generated/ignite.engine.create_supervised_trainer.html.\n        iteration_update: the callable function for every iteration, expect to accept `engine`\n            and `engine.state.batch` as inputs, return data will be stored in `engine.state.output`.\n            if not provided, use `self._iteration()` instead. for more details please refer to:\n            https://pytorch.org/ignite/generated/ignite.engine.engine.Engine.html.\n        inferer: inference method that execute model forward on input data, like: SlidingWindow, etc.\n        postprocessing: execute additional transformation for the model output data.\n            Typically, several Tensor based transforms composed by `Compose`.\n        key_train_metric: compute metric when every iteration completed, and save average value to\n            engine.state.metrics when epoch completed. key_train_metric is the main metric to compare and save the\n            checkpoint into files.\n        additional_metrics: more Ignite metrics that also attach to Ignite Engine.\n        metric_cmp_fn: function to compare current key metric with previous best key metric value,\n            it must accept 2 args (current_metric, previous_best) and return a bool result: if `True`, will update\n            `best_metric` and `best_metric_epoch` with current metric and epoch, default to `greater than`.\n        train_handlers: every handler is a set of Ignite Event-Handlers, must have `attach` function, like:\n            CheckpointHandler, StatsHandler, SegmentationSaver, etc.\n        amp: whether to enable auto-mixed-precision training, default is False.\n        event_names: additional custom ignite events that will register to the engine.\n            new events can be a list of str or `ignite.engine.events.EventEnum`.\n        event_to_attr: a dictionary to map an event to a state attribute, then add to `engine.state`.\n            for more details, check: https://pytorch.org/ignite/generated/ignite.engine.engine.Engine.html\n            #ignite.engine.engine.Engine.register_events.\n        decollate: whether to decollate the batch-first data to a list of data after model computation,\n            recommend `decollate=True` when `postprocessing` uses components from `monai.transforms`.\n            default to `True`.\n        optim_set_to_none: when calling `optimizer.zero_grad()`, instead of setting to zero, set the grads to None.\n            more details: https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html.\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/trainer.py_SupervisedTrainer._iteration_SupervisedTrainer._iteration.return.engine_state_output": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/trainer.py_SupervisedTrainer._iteration_SupervisedTrainer._iteration.return.engine_state_output", "embedding": null, "metadata": {"file_path": "monai/engines/trainer.py", "file_name": "trainer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 156, "end_line": 212, "span_ids": ["SupervisedTrainer._iteration"], "tokens": 559}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SupervisedTrainer(Trainer):\n\n    def _iteration(self, engine: Engine, batchdata: Dict[str, torch.Tensor]):\n        \"\"\"\n        Callback function for the Supervised Training processing logic of 1 iteration in Ignite Engine.\n        Return below items in a dictionary:\n            - IMAGE: image Tensor data for model input, already moved to device.\n            - LABEL: label Tensor data corresponding to the image, already moved to device.\n            - PRED: prediction result of model.\n            - LOSS: loss value computed by loss function.\n\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n            batchdata: input data for this iteration, usually can be dictionary or tuple of Tensor data.\n\n        Raises:\n            ValueError: When ``batchdata`` is None.\n\n        \"\"\"\n        if batchdata is None:\n            raise ValueError(\"Must provide batch data for current iteration.\")\n        batch = self.prepare_batch(batchdata, engine.state.device, engine.non_blocking)  # type: ignore\n        if len(batch) == 2:\n            inputs, targets = batch\n            args: Tuple = ()\n            kwargs: Dict = {}\n        else:\n            inputs, targets, args, kwargs = batch\n        # put iteration outputs into engine.state\n        engine.state.output = {Keys.IMAGE: inputs, Keys.LABEL: targets}  # type: ignore\n\n        def _compute_pred_loss():\n            engine.state.output[Keys.PRED] = self.inferer(inputs, self.network, *args, **kwargs)\n            engine.fire_event(IterationEvents.FORWARD_COMPLETED)\n            engine.state.output[Keys.LOSS] = self.loss_function(engine.state.output[Keys.PRED], targets).mean()\n            engine.fire_event(IterationEvents.LOSS_COMPLETED)\n\n        self.network.train()\n        # `set_to_none` only work from PyTorch 1.7.0\n        if not pytorch_after(1, 7):\n            self.optimizer.zero_grad()\n        else:\n            self.optimizer.zero_grad(set_to_none=self.optim_set_to_none)\n\n        if self.amp and self.scaler is not None:\n            with torch.cuda.amp.autocast():\n                _compute_pred_loss()\n            self.scaler.scale(engine.state.output[Keys.LOSS]).backward()  # type: ignore\n            engine.fire_event(IterationEvents.BACKWARD_COMPLETED)\n            self.scaler.step(self.optimizer)\n            self.scaler.update()\n        else:\n            _compute_pred_loss()\n            engine.state.output[Keys.LOSS].backward()  # type: ignore\n            engine.fire_event(IterationEvents.BACKWARD_COMPLETED)\n            self.optimizer.step()\n        engine.fire_event(IterationEvents.MODEL_COMPLETED)\n\n        return engine.state.output", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/checkpoint_saver.py_logging_if_TYPE_CHECKING_.else_.DiskSaver___optional_i": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/checkpoint_saver.py_logging_if_TYPE_CHECKING_.else_.DiskSaver___optional_i", "embedding": null, "metadata": {"file_path": "monai/handlers/checkpoint_saver.py", "file_name": "checkpoint_saver.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 27, "span_ids": ["docstring"], "tokens": 167}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import logging\nimport warnings\nfrom typing import TYPE_CHECKING, Dict, Mapping, Optional\n\nfrom monai.config import IgniteInfo\nfrom monai.utils import is_scalar, min_version, optional_import\n\nEvents, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Events\")\nCheckpoint, _ = optional_import(\"ignite.handlers\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Checkpoint\")\n\nif TYPE_CHECKING:\n    from ignite.engine import Engine\n    from ignite.handlers import DiskSaver\nelse:\n    Engine, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Engine\")\n    DiskSaver, _ = optional_import(\"ignite.handlers\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"DiskSaver\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/checkpoint_saver.py_CheckpointSaver.__init___CheckpointSaver.__init__.if_save_interval_0_.self._interval_checkpoint.Checkpoint_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/checkpoint_saver.py_CheckpointSaver.__init___CheckpointSaver.__init__.if_save_interval_0_.self._interval_checkpoint.Checkpoint_", "embedding": null, "metadata": {"file_path": "monai/handlers/checkpoint_saver.py", "file_name": "checkpoint_saver.py", "file_type": "text/x-python", "category": "implementation", "start_line": 86, "end_line": 198, "span_ids": ["CheckpointSaver.__init__"], "tokens": 940}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CheckpointSaver:\n\n    def __init__(\n        self,\n        save_dir: str,\n        save_dict: Dict,\n        name: Optional[str] = None,\n        file_prefix: str = \"\",\n        save_final: bool = False,\n        final_filename: Optional[str] = None,\n        save_key_metric: bool = False,\n        key_metric_name: Optional[str] = None,\n        key_metric_n_saved: int = 1,\n        key_metric_filename: Optional[str] = None,\n        key_metric_save_state: bool = False,\n        key_metric_greater_or_equal: bool = False,\n        key_metric_negative_sign: bool = False,\n        epoch_level: bool = True,\n        save_interval: int = 0,\n        n_saved: Optional[int] = None,\n    ) -> None:\n        if save_dir is None:\n            raise AssertionError(\"must provide directory to save the checkpoints.\")\n        self.save_dir = save_dir\n        if not (save_dict is not None and len(save_dict) > 0):\n            raise AssertionError(\"must provide source objects to save.\")\n        self.save_dict = save_dict\n        self.logger = logging.getLogger(name)\n        self.epoch_level = epoch_level\n        self.save_interval = save_interval\n        self._final_checkpoint = self._key_metric_checkpoint = self._interval_checkpoint = None\n        self._name = name\n\n        class _DiskSaver(DiskSaver):\n            \"\"\"\n            Enhance the DiskSaver to support fixed filename.\n\n            \"\"\"\n\n            def __init__(self, dirname: str, filename: Optional[str] = None):\n                # set `atomic=False` as `atomic=True` only gives read/write permission to the user who saved the file,\n                # without group/others read permission\n                super().__init__(dirname=dirname, require_empty=False, atomic=False)\n                self.filename = filename\n\n            def __call__(self, checkpoint: Mapping, filename: str, metadata: Optional[Mapping] = None) -> None:\n                if self.filename is not None:\n                    filename = self.filename\n                super().__call__(checkpoint=checkpoint, filename=filename, metadata=metadata)\n\n            def remove(self, filename: str) -> None:\n                if self.filename is not None:\n                    filename = self.filename\n                super().remove(filename=filename)\n\n        if save_final:\n\n            def _final_func(engine: Engine):\n                return engine.state.iteration\n\n            self._final_checkpoint = Checkpoint(\n                to_save=self.save_dict,\n                save_handler=_DiskSaver(dirname=self.save_dir, filename=final_filename),\n                filename_prefix=file_prefix,\n                score_function=_final_func,\n                score_name=\"final_iteration\",\n            )\n\n        if save_key_metric:\n\n            def _score_func(engine: Engine):\n                if isinstance(key_metric_name, str):\n                    metric_name = key_metric_name\n                elif hasattr(engine.state, \"key_metric_name\"):\n                    metric_name = engine.state.key_metric_name  # type: ignore\n                else:\n                    raise ValueError(\n                        f\"Incompatible values: save_key_metric=True and key_metric_name={key_metric_name}.\"\n                    )\n                metric = engine.state.metrics[metric_name]\n                if not is_scalar(metric):\n                    warnings.warn(\n                        \"key metric is not a scalar value, skip metric comparison and don't save a model.\"\n                        \"please use other metrics as key metric, or change the `reduction` mode to 'mean'.\"\n                    )\n                    return -1\n                return (-1 if key_metric_negative_sign else 1) * metric\n\n            if key_metric_filename is not None and key_metric_n_saved > 1:\n                raise ValueError(\"if using fixed filename to save the best metric model, we should only save 1 model.\")\n\n            self._key_metric_checkpoint = Checkpoint(\n                to_save=self.save_dict,\n                save_handler=_DiskSaver(dirname=self.save_dir, filename=key_metric_filename),\n                filename_prefix=file_prefix,\n                score_function=_score_func,\n                score_name=\"key_metric\",\n                n_saved=key_metric_n_saved,\n                include_self=key_metric_save_state,\n                greater_or_equal=key_metric_greater_or_equal,\n            )\n\n        if save_interval > 0:\n\n            def _interval_func(engine: Engine):\n                return engine.state.epoch if self.epoch_level else engine.state.iteration\n\n            self._interval_checkpoint = Checkpoint(\n                to_save=self.save_dict,\n                save_handler=_DiskSaver(dirname=self.save_dir),\n                filename_prefix=file_prefix,\n                score_function=_interval_func,\n                score_name=\"epoch\" if self.epoch_level else \"iteration\",\n                n_saved=n_saved,\n            )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/checkpoint_saver.py_CheckpointSaver.load_state_dict_CheckpointSaver.load_state_dict.if_self__key_metric_check.else_.warnings_warn_no_key_met": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/checkpoint_saver.py_CheckpointSaver.load_state_dict_CheckpointSaver.load_state_dict.if_self__key_metric_check.else_.warnings_warn_no_key_met", "embedding": null, "metadata": {"file_path": "monai/handlers/checkpoint_saver.py", "file_name": "checkpoint_saver.py", "file_type": "text/x-python", "category": "implementation", "start_line": 187, "end_line": 211, "span_ids": ["CheckpointSaver.load_state_dict"], "tokens": 212}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CheckpointSaver:\n\n    def load_state_dict(self, state_dict: Dict) -> None:\n        \"\"\"\n        Utility to resume the internal state of key metric tracking list if configured to save\n        checkpoints based on the key metric value.\n        Note to set `key_metric_save_state=True` when saving the previous checkpoint.\n\n        Example::\n\n            CheckpointSaver(\n                ...\n                save_key_metric=True,\n                key_metric_save_state=True,  # config to also save the state of this saver\n            ).attach(engine)\n            engine.run(...)\n\n            # resumed training with a new CheckpointSaver\n            saver = CheckpointSaver(save_key_metric=True, ...)\n            # load the previous key metric tracking list into saver\n            CheckpointLoader(\"/test/model.pt\"), {\"checkpointer\": saver}).attach(engine)\n\n        \"\"\"\n        if self._key_metric_checkpoint is not None:\n            self._key_metric_checkpoint.load_state_dict(state_dict)\n        else:\n            warnings.warn(\"no key metric checkpoint saver to resume the key metric tracking list.\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/checkpoint_saver.py_CheckpointSaver._delete_previous_final_ckpt_CheckpointSaver.completed.self_logger_info_f_Train_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/checkpoint_saver.py_CheckpointSaver._delete_previous_final_ckpt_CheckpointSaver.completed.self_logger_info_f_Train_", "embedding": null, "metadata": {"file_path": "monai/handlers/checkpoint_saver.py", "file_name": "checkpoint_saver.py", "file_type": "text/x-python", "category": "implementation", "start_line": 231, "end_line": 254, "span_ids": ["CheckpointSaver._delete_previous_final_ckpt", "CheckpointSaver.completed"], "tokens": 230}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CheckpointSaver:\n\n    def _delete_previous_final_ckpt(self):\n        saved = self._final_checkpoint._saved\n        if len(saved) > 0:\n            item = saved.pop(0)\n            self._final_checkpoint.save_handler.remove(item.filename)\n            self.logger.info(f\"Deleted previous saved final checkpoint: {item.filename}\")\n\n    def completed(self, engine: Engine) -> None:\n        \"\"\"Callback for train or validation/evaluation completed Event.\n        Save final checkpoint if configure save_final is True.\n\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n        \"\"\"\n        if not callable(self._final_checkpoint):\n            raise AssertionError(\"Error: _final_checkpoint function not specified.\")\n        # delete previous saved final checkpoint if existing\n        self._delete_previous_final_ckpt()\n        self._final_checkpoint(engine)\n        if self.logger is None:\n            raise AssertionError\n        if not hasattr(self.logger, \"info\"):\n            raise AssertionError(\"Error, provided logger has not info attribute.\")\n        self.logger.info(f\"Train completed, saved final checkpoint: {self._final_checkpoint.last_checkpoint}\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/classification_saver.py_logging_if_TYPE_CHECKING_.else_.Engine___optional_impo": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/classification_saver.py_logging_if_TYPE_CHECKING_.else_.Engine___optional_impo", "embedding": null, "metadata": {"file_path": "monai/handlers/classification_saver.py", "file_name": "classification_saver.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 28, "span_ids": ["docstring"], "tokens": 167}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import logging\nimport warnings\nfrom typing import TYPE_CHECKING, Callable, List, Optional\n\nimport torch\n\nfrom monai.config import IgniteInfo\nfrom monai.data import CSVSaver, decollate_batch\nfrom monai.utils import ImageMetaKey as Key\nfrom monai.utils import evenly_divisible_all_gather, min_version, optional_import, string_list_all_gather\n\nidist, _ = optional_import(\"ignite\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"distributed\")\nEvents, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Events\")\nif TYPE_CHECKING:\n    from ignite.engine import Engine\nelse:\n    Engine, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Engine\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/earlystop_handler.py_from_typing_import_TYPE_C_if_TYPE_CHECKING_.else_.Engine___optional_impo": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/earlystop_handler.py_from_typing_import_TYPE_C_if_TYPE_CHECKING_.else_.Engine___optional_impo", "embedding": null, "metadata": {"file_path": "monai/handlers/earlystop_handler.py", "file_name": "earlystop_handler.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 23, "span_ids": ["docstring"], "tokens": 120}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import TYPE_CHECKING, Callable, Optional\n\nfrom monai.config import IgniteInfo\nfrom monai.utils import min_version, optional_import\n\nEvents, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Events\")\nEarlyStopping, _ = optional_import(\"ignite.handlers\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"EarlyStopping\")\n\nif TYPE_CHECKING:\n    from ignite.engine import Engine\nelse:\n    Engine, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Engine\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/earlystop_handler.py_EarlyStopHandler_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/earlystop_handler.py_EarlyStopHandler_", "embedding": null, "metadata": {"file_path": "monai/handlers/earlystop_handler.py", "file_name": "earlystop_handler.py", "file_type": "text/x-python", "category": "implementation", "start_line": 25, "end_line": 96, "span_ids": ["EarlyStopHandler.set_trainer", "EarlyStopHandler.attach", "EarlyStopHandler", "EarlyStopHandler.__init__", "EarlyStopHandler.__call__"], "tokens": 654}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class EarlyStopHandler:\n    \"\"\"\n    EarlyStopHandler acts as an Ignite handler to stop training if no improvement after a given number of events.\n    It\u2018s based on the `EarlyStopping` handler in ignite.\n\n    Args:\n        patience: number of events to wait if no improvement and then stop the training.\n        score_function: It should be a function taking a single argument, an :class:`~ignite.engine.engine.Engine`\n            object that the handler attached, can be a trainer or validator, and return a score `float`.\n            an improvement is considered if the score is higher.\n        trainer: trainer engine to stop the run if no improvement, if None, must call `set_trainer()` before training.\n        min_delta: a minimum increase in the score to qualify as an improvement,\n            i.e. an increase of less than or equal to `min_delta`, will count as no improvement.\n        cumulative_delta: if True, `min_delta` defines an increase since the last `patience` reset, otherwise,\n            it defines an increase after the last event, default to False.\n        epoch_level: check early stopping for every epoch or every iteration of the attached engine,\n            `True` is epoch level, `False` is iteration level, default to epoch level.\n\n    Note:\n        If in distributed training and uses loss value of every iteration to detect early stopping,\n        the values may be different in different ranks.\n        User may attach this handler to validator engine to detect validation metrics and stop the training,\n        in this case, the `score_function` is executed on validator engine and `trainer` is the trainer engine.\n\n    \"\"\"\n\n    def __init__(\n        self,\n        patience: int,\n        score_function: Callable,\n        trainer: Optional[Engine] = None,\n        min_delta: float = 0.0,\n        cumulative_delta: bool = False,\n        epoch_level: bool = True,\n    ) -> None:\n        self.patience = patience\n        self.score_function = score_function\n        self.min_delta = min_delta\n        self.cumulative_delta = cumulative_delta\n        self.epoch_level = epoch_level\n        self._handler = None\n\n        if trainer is not None:\n            self.set_trainer(trainer=trainer)\n\n    def attach(self, engine: Engine) -> None:\n        \"\"\"\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n        \"\"\"\n        if self.epoch_level:\n            engine.add_event_handler(Events.EPOCH_COMPLETED, self)\n        else:\n            engine.add_event_handler(Events.ITERATION_COMPLETED, self)\n\n    def set_trainer(self, trainer: Engine):\n        \"\"\"\n        Set trainer to execute early stop if not setting properly in `__init__()`.\n        \"\"\"\n        self._handler = EarlyStopping(\n            patience=self.patience,\n            score_function=self.score_function,\n            trainer=trainer,\n            min_delta=self.min_delta,\n            cumulative_delta=self.cumulative_delta,\n        )\n\n    def __call__(self, engine: Engine) -> None:\n        if self._handler is None:\n            raise RuntimeError(\"please set trainer in __init__() or call set_trainer() before training.\")\n        self._handler(engine)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/garbage_collector.py_gc_GarbageCollector.attach.if_not_engine_has_event_h.engine_add_event_handler_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/garbage_collector.py_gc_GarbageCollector.attach.if_not_engine_has_event_h.engine_add_event_handler_", "embedding": null, "metadata": {"file_path": "monai/handlers/garbage_collector.py", "file_name": "garbage_collector.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 62, "span_ids": ["GarbageCollector.__init__", "GarbageCollector", "GarbageCollector.attach", "docstring"], "tokens": 434}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import gc\nfrom typing import TYPE_CHECKING\n\nfrom monai.config import IgniteInfo\nfrom monai.utils import min_version, optional_import\n\nif TYPE_CHECKING:\n    from ignite.engine import Engine, Events\nelse:\n    Engine, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Engine\")\n    Events, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Events\")\n\n\nclass GarbageCollector:\n    \"\"\"\n    Run garbage collector after each epoch\n\n    Args:\n        trigger_event: the event that trigger a call to this handler.\n            - \"epoch\", after completion of each epoch (equivalent of ignite.engine.Events.EPOCH_COMPLETED)\n            - \"iteration\", after completion of each iteration (equivalent of ignite.engine.Events.ITERATION_COMPLETED)\n            - any ignite built-in event from ignite.engine.Events.\n            Defaults to \"epoch\".\n        log_level: log level (integer) for some garbage collection information as below. Defaults to 10 (DEBUG).\n            - 50 (CRITICAL)\n            - 40 (ERROR)\n            - 30 (WARNING)\n            - 20 (INFO)\n            - 10 (DEBUG)\n            - 0 (NOTSET)\n    \"\"\"\n\n    def __init__(self, trigger_event: str = \"epoch\", log_level: int = 10):\n        self.trigger_event: Events\n        if isinstance(trigger_event, Events):\n            self.trigger_event = trigger_event\n        elif trigger_event.lower() == \"epoch\":\n            self.trigger_event = Events.EPOCH_COMPLETED\n        elif trigger_event.lower() == \"iteration\":\n            self.trigger_event = Events.ITERATION_COMPLETED\n        else:\n            raise ValueError(\n                f\"'trigger_event' should be either epoch, iteration, or an ignite built-in event from\"\n                f\" ignite.engine.Events, '{trigger_event}' was given.\"\n            )\n\n        self.log_level = log_level\n\n    def attach(self, engine: Engine) -> None:\n        if not engine.has_event_handler(self, self.trigger_event):\n            engine.add_event_handler(self.trigger_event, self)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/garbage_collector.py_GarbageCollector.__call___": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/garbage_collector.py_GarbageCollector.__call___", "embedding": null, "metadata": {"file_path": "monai/handlers/garbage_collector.py", "file_name": "garbage_collector.py", "file_type": "text/x-python", "category": "implementation", "start_line": 64, "end_line": 83, "span_ids": ["GarbageCollector.__call__"], "tokens": 151}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GarbageCollector:\n\n    def __call__(self, engine: Engine) -> None:\n        \"\"\"\n        This method calls python garbage collector.\n\n        Args:\n            engine: Ignite Engine, it should be either a trainer or validator.\n        \"\"\"\n        # get count before garbage collection\n        pre_count = gc.get_count()\n        # first call to garbage collector\n        gc.collect()\n        # second call to garbage collector\n        unreachable = gc.collect()\n        # get count after garbage collection\n        after_count = gc.get_count()\n        engine.logger.log(\n            self.log_level,\n            f\"Garbage Count: [before: {pre_count}] -> [after: {after_count}] (unreachable : {unreachable})\",\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/hausdorff_distance.py_from_typing_import_Callab_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/hausdorff_distance.py_from_typing_import_Callab_", "embedding": null, "metadata": {"file_path": "monai/handlers/hausdorff_distance.py", "file_name": "hausdorff_distance.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 66, "span_ids": ["HausdorffDistance", "HausdorffDistance.__init__", "docstring"], "tokens": 648}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Callable, Optional, Union\n\nfrom monai.handlers.ignite_metric import IgniteMetric\nfrom monai.metrics import HausdorffDistanceMetric\nfrom monai.utils import MetricReduction\n\n\nclass HausdorffDistance(IgniteMetric):\n    \"\"\"\n    Computes Hausdorff distance from full size Tensor and collects average over batch, class-channels, iterations.\n    \"\"\"\n\n    def __init__(\n        self,\n        include_background: bool = False,\n        distance_metric: str = \"euclidean\",\n        percentile: Optional[float] = None,\n        directed: bool = False,\n        reduction: Union[MetricReduction, str] = MetricReduction.MEAN,\n        output_transform: Callable = lambda x: x,\n        save_details: bool = True,\n    ) -> None:\n        \"\"\"\n\n        Args:\n            include_background: whether to include distance computation on the first channel of the predicted output.\n                Defaults to ``False``.\n            distance_metric: : [``\"euclidean\"``, ``\"chessboard\"``, ``\"taxicab\"``]\n                the metric used to compute surface distance. Defaults to ``\"euclidean\"``.\n            percentile: an optional float number between 0 and 100. If specified, the corresponding\n                percentile of the Hausdorff Distance rather than the maximum result will be achieved.\n                Defaults to ``None``.\n            directed: whether to calculate directed Hausdorff distance. Defaults to ``False``.\n            reduction: define the mode to reduce metrics, will only execute reduction on `not-nan` values,\n                available reduction modes: {``\"none\"``, ``\"mean\"``, ``\"sum\"``, ``\"mean_batch\"``, ``\"sum_batch\"``,\n                ``\"mean_channel\"``, ``\"sum_channel\"``}, default to ``\"mean\"``. if \"none\", will not do reduction.\n            output_transform: callable to extract `y_pred` and `y` from `ignite.engine.state.output` then\n                construct `(y_pred, y)` pair, where `y_pred` and `y` can be `batch-first` Tensors or\n                lists of `channel-first` Tensors. the form of `(y_pred, y)` is required by the `update()`.\n                `engine.state` and `output_transform` inherit from the ignite concept:\n                https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial:\n                https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb.\n            save_details: whether to save metric computation details per image, for example: hausdorff distance\n                of every image. default to True, will save to `engine.state.metric_details` dict with the metric name as key.\n\n        \"\"\"\n        metric_fn = HausdorffDistanceMetric(\n            include_background=include_background,\n            distance_metric=distance_metric,\n            percentile=percentile,\n            directed=directed,\n            reduction=reduction,\n        )\n        super().__init__(metric_fn=metric_fn, output_transform=output_transform, save_details=save_details)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/mean_dice.py_from_typing_import_Callab_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/mean_dice.py_from_typing_import_Callab_", "embedding": null, "metadata": {"file_path": "monai/handlers/mean_dice.py", "file_name": "mean_dice.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 53, "span_ids": ["MeanDice", "MeanDice.__init__", "docstring"], "tokens": 489}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Callable, Union\n\nfrom monai.handlers.ignite_metric import IgniteMetric\nfrom monai.metrics import DiceMetric\nfrom monai.utils import MetricReduction\n\n\nclass MeanDice(IgniteMetric):\n    \"\"\"\n    Computes Dice score metric from full size Tensor and collects average over batch, class-channels, iterations.\n    \"\"\"\n\n    def __init__(\n        self,\n        include_background: bool = True,\n        reduction: Union[MetricReduction, str] = MetricReduction.MEAN,\n        output_transform: Callable = lambda x: x,\n        save_details: bool = True,\n    ) -> None:\n        \"\"\"\n\n        Args:\n            include_background: whether to include dice computation on the first channel of the predicted output.\n                Defaults to True.\n            reduction: define the mode to reduce metrics, will only execute reduction on `not-nan` values,\n                available reduction modes: {``\"none\"``, ``\"mean\"``, ``\"sum\"``, ``\"mean_batch\"``, ``\"sum_batch\"``,\n                ``\"mean_channel\"``, ``\"sum_channel\"``}, default to ``\"mean\"``. if \"none\", will not do reduction.\n            output_transform: callable to extract `y_pred` and `y` from `ignite.engine.state.output` then\n                construct `(y_pred, y)` pair, where `y_pred` and `y` can be `batch-first` Tensors or\n                lists of `channel-first` Tensors. the form of `(y_pred, y)` is required by the `update()`.\n                `engine.state` and `output_transform` inherit from the ignite concept:\n                https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial:\n                https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb.\n            save_details: whether to save metric computation details per image, for example: mean dice of every image.\n                default to True, will save to `engine.state.metric_details` dict with the metric name as key.\n\n        See also:\n            :py:meth:`monai.metrics.meandice.compute_meandice`\n        \"\"\"\n        metric_fn = DiceMetric(include_background=include_background, reduction=reduction)\n        super().__init__(metric_fn=metric_fn, output_transform=output_transform, save_details=save_details)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/metric_logger.py_from_collections_import_d_MetricLoggerKeys.LOSS._Loss_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/metric_logger.py_from_collections_import_d_MetricLoggerKeys.LOSS._Loss_", "embedding": null, "metadata": {"file_path": "monai/handlers/metric_logger.py", "file_name": "metric_logger.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 34, "span_ids": ["_get_loss_from_output", "MetricLoggerKeys", "docstring"], "tokens": 171}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from collections import defaultdict\nfrom enum import Enum\nfrom threading import RLock\nfrom typing import TYPE_CHECKING, Callable, DefaultDict, List, Optional\n\nfrom monai.config import IgniteInfo\nfrom monai.utils import min_version, optional_import\nfrom monai.utils.enums import CommonKeys\n\nEvents, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Events\")\nif TYPE_CHECKING:\n    from ignite.engine import Engine\nelse:\n    Engine, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Engine\")\n\n\ndef _get_loss_from_output(output, loss_key: str = CommonKeys.LOSS):\n    return output[0][loss_key]\n\n\nclass MetricLoggerKeys(Enum):\n    METRICS = \"Metrics\"\n    LOSS = \"Loss\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/metric_logger.py_MetricLogger_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/metric_logger.py_MetricLogger_", "embedding": null, "metadata": {"file_path": "monai/handlers/metric_logger.py", "file_name": "metric_logger.py", "file_type": "text/x-python", "category": "implementation", "start_line": 37, "end_line": 133, "span_ids": ["MetricLogger.log_metrics", "MetricLogger.attach", "MetricLogger.state_dict", "MetricLogger", "MetricLogger.attach_evaluator", "impl:5", "MetricLogger.__init__", "MetricLogger.__call__", "MetricLogger.load_state_dict"], "tokens": 839}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class MetricLogger:\n    \"\"\"\n    Collect per-iteration metrics and loss value from the attached trainer. This will also collect metric values from\n    a given evaluator object which is expected to perform evaluation at the end of training epochs. This class is\n    useful for collecting loss and metric values in one place for storage with checkpoint savers (`state_dict` and\n    `load_state_dict` methods provided as expected by Pytorch and Ignite) and for graphing during training.\n\n    Example::\n        # construct an evaluator saving mean dice metric values in the key \"val_mean_dice\"\n        evaluator = SupervisedEvaluator(..., key_val_metric={\"val_mean_dice\": MeanDice(...)})\n\n        # construct the logger and associate with evaluator to extract metric values from\n        logger = MetricLogger(evaluator=evaluator)\n\n        # construct the trainer with the logger passed in as a handler so that it logs loss values\n        trainer = SupervisedTrainer(..., train_handlers=[logger, ValidationHandler(1, evaluator)])\n\n        # run training, logger.loss will be a list of (iteration, loss) values, logger.metrics a dict with key\n        # \"val_mean_dice\" storing a list of (iteration, metric) values\n        trainer.run()\n\n    Args:\n        loss_transform: Converts the `output` value from the trainer's state into a loss value\n            `engine.state` and `loss_transform` inherit from the ignite concept:\n            https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial:\n            https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb.\n        metric_transform: Converts the metric value coming from the trainer/evaluator's state into a storable value\n        evaluator: Optional evaluator to consume metric results from at the end of its evaluation run\n    \"\"\"\n\n    def __init__(\n        self,\n        loss_transform: Callable = _get_loss_from_output,\n        metric_transform: Callable = lambda x: x,\n        evaluator: Optional[Engine] = None,\n    ) -> None:\n        self.loss_transform = loss_transform\n        self.metric_transform = metric_transform\n        self.loss: List = []\n        self.metrics: DefaultDict = defaultdict(list)\n        self.iteration = 0\n        self.lock = RLock()\n\n        if evaluator is not None:\n            self.attach_evaluator(evaluator)\n\n    def attach(self, engine: Engine) -> None:\n        \"\"\"\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n        \"\"\"\n        engine.add_event_handler(Events.ITERATION_COMPLETED, self)\n\n    def attach_evaluator(self, evaluator: Engine) -> None:\n        \"\"\"\n        Attach event  handlers to the given evaluator to log metric values from it.\n\n        Args:\n            evaluator: Ignite Engine implementing network evaluation\n        \"\"\"\n        evaluator.add_event_handler(Events.COMPLETED, self.log_metrics)\n\n    def __call__(self, engine: Engine) -> None:\n        \"\"\"\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n        \"\"\"\n        with self.lock:\n            self.iteration = engine.state.iteration\n            lossval = self.loss_transform(engine.state.output)\n\n            self.loss.append((self.iteration, lossval))\n            self.log_metrics(engine)\n\n    def log_metrics(self, engine: Engine) -> None:\n        \"\"\"\n        Log metrics from the given Engine's state member.\n\n        Args:\n            engine: Ignite Engine to log from\n        \"\"\"\n        with self.lock:\n            for m, v in engine.state.metrics.items():\n                v = self.metric_transform(v)\n                self.metrics[m].append((self.iteration, v))\n\n    def state_dict(self):\n        return {MetricLoggerKeys.LOSS: self.loss, MetricLoggerKeys.METRICS: self.metrics}\n\n    def load_state_dict(self, state_dict):\n        self.loss[:] = state_dict[MetricLoggerKeys.LOSS]\n        self.metrics.clear()\n        self.metrics.update(state_dict[MetricLoggerKeys.METRICS])\n\n\nmetriclogger = MetricLogger", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/metrics_saver.py_from_typing_import_TYPE_C_if_TYPE_CHECKING_.else_.Engine___optional_impo": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/metrics_saver.py_from_typing_import_TYPE_C_if_TYPE_CHECKING_.else_.Engine___optional_impo", "embedding": null, "metadata": {"file_path": "monai/handlers/metrics_saver.py", "file_name": "metrics_saver.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 25, "span_ids": ["docstring"], "tokens": 164}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import TYPE_CHECKING, Callable, List, Optional, Sequence, Union\n\nfrom monai.config import IgniteInfo\nfrom monai.data import decollate_batch\nfrom monai.handlers.utils import write_metrics_reports\nfrom monai.utils import ImageMetaKey as Key\nfrom monai.utils import ensure_tuple, min_version, optional_import, string_list_all_gather\n\nEvents, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Events\")\nidist, _ = optional_import(\"ignite\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"distributed\")\nif TYPE_CHECKING:\n    from ignite.engine import Engine\nelse:\n    Engine, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Engine\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/metrics_saver.py_MetricsSaver.__call___": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/metrics_saver.py_MetricsSaver.__call___", "embedding": null, "metadata": {"file_path": "monai/handlers/metrics_saver.py", "file_name": "metrics_saver.py", "file_type": "text/x-python", "category": "implementation", "start_line": 120, "end_line": 154, "span_ids": ["MetricsSaver.__call__"], "tokens": 323}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class MetricsSaver:\n\n    def __call__(self, engine: Engine) -> None:\n        \"\"\"\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n        \"\"\"\n        ws = idist.get_world_size()\n        if self.save_rank >= ws:\n            raise ValueError(\"target save rank is greater than the distributed group size.\")\n\n        # all gather file names across ranks\n        _images = string_list_all_gather(strings=self._filenames) if ws > 1 else self._filenames\n\n        # only save metrics to file in specified rank\n        if idist.get_rank() == self.save_rank:\n            _metrics = {}\n            if self.metrics is not None and len(engine.state.metrics) > 0:\n                _metrics = {k: v for k, v in engine.state.metrics.items() if k in self.metrics or \"*\" in self.metrics}\n            _metric_details = {}\n            if hasattr(engine.state, \"metric_details\"):\n                details = engine.state.metric_details  # type: ignore\n                if self.metric_details is not None and len(details) > 0:\n                    for k, v in details.items():\n                        if k in self.metric_details or \"*\" in self.metric_details:\n                            _metric_details[k] = v\n\n            write_metrics_reports(\n                save_dir=self.save_dir,\n                images=None if len(_images) == 0 else _images,\n                metrics=_metrics,\n                metric_details=_metric_details,\n                summary_ops=self.summary_ops,\n                deli=self.deli,\n                output_type=self.output_type,\n            )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/parameter_scheduler.py_ParamSchedulerHandler._linear_ParamSchedulerHandler._linear.return.initial_value_delta": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/parameter_scheduler.py_ParamSchedulerHandler._linear_ParamSchedulerHandler._linear.return.initial_value_delta", "embedding": null, "metadata": {"file_path": "monai/handlers/parameter_scheduler.py", "file_name": "parameter_scheduler.py", "file_type": "text/x-python", "category": "implementation", "start_line": 93, "end_line": 118, "span_ids": ["ParamSchedulerHandler._linear"], "tokens": 240}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ParamSchedulerHandler:\n\n    @staticmethod\n    def _linear(\n        initial_value: float, step_constant: int, step_max_value: int, max_value: float, current_step: int\n    ) -> float:\n        \"\"\"\n        Keeps the parameter value to zero until step_zero steps passed and then linearly increases it to 1 until an\n        additional step_one steps passed. Continues the trend until it reaches max_value.\n\n        Args:\n            initial_value (float): Starting value of the parameter.\n            step_constant (int): Step index until parameter's value is kept constant.\n            step_max_value (int): Step index at which parameter's value becomes max_value.\n            max_value (float): Max parameter value.\n            current_step (int): Current step index.\n\n        Returns:\n            float: new parameter value\n        \"\"\"\n        if current_step <= step_constant:\n            delta = 0.0\n        elif current_step > step_max_value:\n            delta = max_value - initial_value\n        else:\n            delta = (max_value - initial_value) / (step_max_value - step_constant) * (current_step - step_constant)\n\n        return initial_value + delta", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/parameter_scheduler.py_ParamSchedulerHandler._exponential_ParamSchedulerHandler._exponential.return.initial_value_gamma_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/parameter_scheduler.py_ParamSchedulerHandler._exponential_ParamSchedulerHandler._exponential.return.initial_value_gamma_", "embedding": null, "metadata": {"file_path": "monai/handlers/parameter_scheduler.py", "file_name": "parameter_scheduler.py", "file_type": "text/x-python", "category": "implementation", "start_line": 121, "end_line": 137, "span_ids": ["ParamSchedulerHandler._exponential"], "tokens": 137}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ParamSchedulerHandler:\n\n    @staticmethod\n    def _exponential(initial_value: float, gamma: float, current_step: int) -> float:\n        \"\"\"\n        Decays the parameter value by gamma every step.\n\n        Based on the closed form of ExponentialLR from Pytorch:\n        https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.ExponentialLR.html.\n\n        Args:\n            initial_value (float): Starting value of the parameter.\n            gamma (float): Multiplicative factor of parameter value decay.\n            current_step (int): Current step index.\n\n        Returns:\n            float: new parameter value\n        \"\"\"\n        return initial_value * gamma ** current_step", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/parameter_scheduler.py_ParamSchedulerHandler._step_ParamSchedulerHandler._step.return.initial_value_gamma_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/parameter_scheduler.py_ParamSchedulerHandler._step_ParamSchedulerHandler._step.return.initial_value_gamma_", "embedding": null, "metadata": {"file_path": "monai/handlers/parameter_scheduler.py", "file_name": "parameter_scheduler.py", "file_type": "text/x-python", "category": "implementation", "start_line": 139, "end_line": 156, "span_ids": ["ParamSchedulerHandler._step"], "tokens": 153}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ParamSchedulerHandler:\n\n    @staticmethod\n    def _step(initial_value: float, gamma: float, step_size: int, current_step: int) -> float:\n        \"\"\"\n        Decays the parameter value by gamma every step_size.\n\n        Based on StepLR from Pytorch:\n        https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.StepLR.html.\n\n        Args:\n            initial_value (float): Starting value of the parameter.\n            gamma (float): Multiplicative factor of parameter value decay.\n            step_size (int): Period of parameter value decay.\n            current_step (int): Current step index.\n\n        Returns\n            float: new parameter value\n        \"\"\"\n        return initial_value * gamma ** (current_step // step_size)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/parameter_scheduler.py_ParamSchedulerHandler._multistep_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/parameter_scheduler.py_ParamSchedulerHandler._multistep_", "embedding": null, "metadata": {"file_path": "monai/handlers/parameter_scheduler.py", "file_name": "parameter_scheduler.py", "file_type": "text/x-python", "category": "implementation", "start_line": 158, "end_line": 176, "span_ids": ["ParamSchedulerHandler._multistep"], "tokens": 170}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ParamSchedulerHandler:\n\n    @staticmethod\n    def _multistep(initial_value: float, gamma: float, milestones: List[int], current_step: int) -> float:\n        \"\"\"\n        Decays the parameter value by gamma once the number of steps reaches one of the milestones.\n\n        Based on MultiStepLR from Pytorch.\n        https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.MultiStepLR.html.\n\n        Args:\n            initial_value (float): Starting value of the parameter.\n            gamma (float): Multiplicative factor of parameter value decay.\n            milestones (List[int]): List of step indices. Must be increasing.\n            current_step (int): Current step index.\n\n        Returns:\n            float: new parameter value\n        \"\"\"\n        return initial_value * gamma ** bisect_right(milestones, current_step)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/surface_distance.py_from_typing_import_Callab_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/surface_distance.py_from_typing_import_Callab_", "embedding": null, "metadata": {"file_path": "monai/handlers/surface_distance.py", "file_name": "surface_distance.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 62, "span_ids": ["SurfaceDistance.__init__", "SurfaceDistance", "docstring"], "tokens": 580}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Callable, Union\n\nfrom monai.handlers.ignite_metric import IgniteMetric\nfrom monai.metrics import SurfaceDistanceMetric\nfrom monai.utils import MetricReduction\n\n\nclass SurfaceDistance(IgniteMetric):\n    \"\"\"\n    Computes surface distance from full size Tensor and collects average over batch, class-channels, iterations.\n    \"\"\"\n\n    def __init__(\n        self,\n        include_background: bool = False,\n        symmetric: bool = False,\n        distance_metric: str = \"euclidean\",\n        reduction: Union[MetricReduction, str] = MetricReduction.MEAN,\n        output_transform: Callable = lambda x: x,\n        save_details: bool = True,\n    ) -> None:\n        \"\"\"\n\n        Args:\n            include_background: whether to include distance computation on the first channel of the predicted output.\n                Defaults to ``False``.\n            symmetric: whether to calculate the symmetric average surface distance between\n                `seg_pred` and `seg_gt`. Defaults to ``False``.\n            distance_metric: : [``\"euclidean\"``, ``\"chessboard\"``, ``\"taxicab\"``]\n                the metric used to compute surface distance. Defaults to ``\"euclidean\"``.\n            reduction: define the mode to reduce metrics, will only execute reduction on `not-nan` values,\n                available reduction modes: {``\"none\"``, ``\"mean\"``, ``\"sum\"``, ``\"mean_batch\"``, ``\"sum_batch\"``,\n                ``\"mean_channel\"``, ``\"sum_channel\"``}, default to ``\"mean\"``. if \"none\", will not do reduction.\n            output_transform: callable to extract `y_pred` and `y` from `ignite.engine.state.output` then\n                construct `(y_pred, y)` pair, where `y_pred` and `y` can be `batch-first` Tensors or\n                lists of `channel-first` Tensors. the form of `(y_pred, y)` is required by the `update()`.\n                `engine.state` and `output_transform` inherit from the ignite concept:\n                https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial:\n                https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb.\n            save_details: whether to save metric computation details per image, for example: surface dice\n                of every image. default to True, will save to `engine.state.metric_details` dict with the metric name as key.\n\n        \"\"\"\n        metric_fn = SurfaceDistanceMetric(\n            include_background=include_background,\n            symmetric=symmetric,\n            distance_metric=distance_metric,\n            reduction=reduction,\n        )\n        super().__init__(metric_fn=metric_fn, output_transform=output_transform, save_details=save_details)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/tensorboard_handlers.py_TensorBoardHandler_TensorBoardHandler.close.if_self_internal_writer_.self__writer_close_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/tensorboard_handlers.py_TensorBoardHandler_TensorBoardHandler.close.if_self_internal_writer_.self__writer_close_", "embedding": null, "metadata": {"file_path": "monai/handlers/tensorboard_handlers.py", "file_name": "tensorboard_handlers.py", "file_type": "text/x-python", "category": "implementation", "start_line": 34, "end_line": 62, "span_ids": ["TensorBoardHandler.__init__", "TensorBoardHandler", "TensorBoardHandler.attach", "TensorBoardHandler.close"], "tokens": 209}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TensorBoardHandler:\n    \"\"\"\n    Base class for the handlers to write data into TensorBoard.\n\n    Args:\n        summary_writer: user can specify TensorBoard or TensorBoardX SummaryWriter,\n            default to create a new TensorBoard writer.\n        log_dir: if using default SummaryWriter, write logs to this directory, default is `./runs`.\n\n    \"\"\"\n\n    def __init__(self, summary_writer: Optional[SummaryWriter] = None, log_dir: str = \"./runs\"):\n        if summary_writer is None:\n            self._writer = SummaryWriter(log_dir=log_dir)\n            self.internal_writer = True\n        else:\n            self._writer = summary_writer\n            self.internal_writer = False\n\n    def attach(self, engine: Engine) -> None:\n        raise NotImplementedError(f\"Subclass {self.__class__.__name__} must implement this method.\")\n\n    def close(self):\n        \"\"\"\n        Close the summary writer if created in this TensorBoard handler.\n\n        \"\"\"\n        if self.internal_writer:\n            self._writer.close()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/utils.py_os_stopping_fn_from_loss.return.stopping_fn": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/utils.py_os_stopping_fn_from_loss.return.stopping_fn", "embedding": null, "metadata": {"file_path": "monai/handlers/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 50, "span_ids": ["stopping_fn_from_metric", "stopping_fn_from_loss", "docstring"], "tokens": 260}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nfrom collections import OrderedDict\nfrom typing import TYPE_CHECKING, Dict, Optional, Sequence, Union\n\nimport numpy as np\nimport torch\n\nfrom monai.config import IgniteInfo, KeysCollection, PathLike\nfrom monai.utils import ensure_tuple, look_up_option, min_version, optional_import\n\nidist, _ = optional_import(\"ignite\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"distributed\")\nif TYPE_CHECKING:\n    from ignite.engine import Engine\nelse:\n    Engine, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Engine\")\n\n__all__ = [\"stopping_fn_from_metric\", \"stopping_fn_from_loss\", \"write_metrics_reports\", \"from_engine\"]\n\n\ndef stopping_fn_from_metric(metric_name: str):\n    \"\"\"\n    Returns a stopping function for ignite.handlers.EarlyStopping using the given metric name.\n    \"\"\"\n\n    def stopping_fn(engine: Engine):\n        return engine.state.metrics[metric_name]\n\n    return stopping_fn\n\n\ndef stopping_fn_from_loss():\n    \"\"\"\n    Returns a stopping function for ignite.handlers.EarlyStopping using the loss value.\n    \"\"\"\n\n    def stopping_fn(engine: Engine):\n        return -engine.state.output  # type:ignore\n\n    return stopping_fn", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/inferers/__init__.py_Inferer_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/inferers/__init__.py_Inferer_", "embedding": null, "metadata": {"file_path": "monai/inferers/__init__.py", "file_name": "__init__.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 14, "span_ids": ["docstring"], "tokens": 35}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from .inferer import Inferer, SaliencyInferer, SimpleInferer, SlidingWindowInferer\nfrom .utils import sliding_window_inference", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/inferers/inferer.py_from_abc_import_ABC_abst_Inferer._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/inferers/inferer.py_from_abc_import_ABC_abst_Inferer._", "embedding": null, "metadata": {"file_path": "monai/inferers/inferer.py", "file_name": "inferer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 42, "span_ids": ["Inferer", "docstring"], "tokens": 214}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from abc import ABC, abstractmethod\nfrom typing import Any, Callable, Optional, Sequence, Union\n\nimport torch\nimport torch.nn as nn\n\nfrom monai.inferers.utils import sliding_window_inference\nfrom monai.utils import BlendMode, PytorchPadMode\nfrom monai.visualize import CAM, GradCAM, GradCAMpp\n\n__all__ = [\"Inferer\", \"SimpleInferer\", \"SlidingWindowInferer\", \"SaliencyInferer\"]\n\n\nclass Inferer(ABC):\n    \"\"\"\n    A base class for model inference.\n    Extend this class to support operations during inference, e.g. a sliding window method.\n\n    Example code::\n\n        device = torch.device(\"cuda:0\")\n        data = ToTensor()(LoadImage()(filename=img_path)).to(device)\n        model = UNet(...).to(device)\n        inferer = SlidingWindowInferer(...)\n\n        model.eval()\n        with torch.no_grad():\n            pred = inferer(inputs=data, network=model)\n        ...\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/inferers/inferer.py_Inferer.__call___Inferer.__call__.raise_NotImplementedError": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/inferers/inferer.py_Inferer.__call___Inferer.__call__.raise_NotImplementedError", "embedding": null, "metadata": {"file_path": "monai/inferers/inferer.py", "file_name": "inferer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 44, "end_line": 59, "span_ids": ["Inferer.__call__"], "tokens": 138}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Inferer(ABC):\n\n    @abstractmethod\n    def __call__(self, inputs: torch.Tensor, network: Callable[..., torch.Tensor], *args: Any, **kwargs: Any):\n        \"\"\"\n        Run inference on `inputs` with the `network` model.\n\n        Args:\n            inputs: input of the model inference.\n            network: model for inference.\n            args: optional args to be passed to ``network``.\n            kwargs: optional keyword args to be passed to ``network``.\n\n        Raises:\n            NotImplementedError: When the subclass does not override this method.\n\n        \"\"\"\n        raise NotImplementedError(f\"Subclass {self.__class__.__name__} must implement this method.\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/inferers/inferer.py_SimpleInferer_SimpleInferer.__call__.return.network_inputs_args_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/inferers/inferer.py_SimpleInferer_SimpleInferer.__call__.return.network_inputs_args_", "embedding": null, "metadata": {"file_path": "monai/inferers/inferer.py", "file_name": "inferer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 62, "end_line": 83, "span_ids": ["SimpleInferer", "SimpleInferer.__init__", "SimpleInferer.__call__"], "tokens": 192}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SimpleInferer(Inferer):\n    \"\"\"\n    SimpleInferer is the normal inference method that run model forward() directly.\n    Usage example can be found in the :py:class:`monai.inferers.Inferer` base class.\n\n    \"\"\"\n\n    def __init__(self) -> None:\n        Inferer.__init__(self)\n\n    def __call__(self, inputs: torch.Tensor, network: Callable[..., torch.Tensor], *args: Any, **kwargs: Any):\n        \"\"\"Unified callable function API of Inferers.\n\n        Args:\n            inputs: model input data for inference.\n            network: target model to execute inference.\n                supports callables such as ``lambda x: my_torch_model(x, additional_config)``\n            args: optional args to be passed to ``network``.\n            kwargs: optional keyword args to be passed to ``network``.\n\n        \"\"\"\n        return network(inputs, *args, **kwargs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/inferers/inferer.py_SlidingWindowInferer_SlidingWindowInferer._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/inferers/inferer.py_SlidingWindowInferer_SlidingWindowInferer._", "embedding": null, "metadata": {"file_path": "monai/inferers/inferer.py", "file_name": "inferer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 98, "end_line": 138, "span_ids": ["SlidingWindowInferer"], "tokens": 547}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SlidingWindowInferer(Inferer):\n    \"\"\"\n    Sliding window method for model inference,\n    with `sw_batch_size` windows for every model.forward().\n    Usage example can be found in the :py:class:`monai.inferers.Inferer` base class.\n\n    Args:\n        roi_size: the window size to execute SlidingWindow evaluation.\n            If it has non-positive components, the corresponding `inputs` size will be used.\n            if the components of the `roi_size` are non-positive values, the transform will use the\n            corresponding components of img size. For example, `roi_size=(32, -1)` will be adapted\n            to `(32, 64)` if the second spatial dimension size of img is `64`.\n        sw_batch_size: the batch size to run window slices.\n        overlap: Amount of overlap between scans.\n        mode: {``\"constant\"``, ``\"gaussian\"``}\n            How to blend output of overlapping windows. Defaults to ``\"constant\"``.\n\n            - ``\"constant``\": gives equal weight to all predictions.\n            - ``\"gaussian``\": gives less weight to predictions on edges of windows.\n\n        sigma_scale: the standard deviation coefficient of the Gaussian window when `mode` is ``\"gaussian\"``.\n            Default: 0.125. Actual window sigma is ``sigma_scale`` * ``dim_size``.\n            When sigma_scale is a sequence of floats, the values denote sigma_scale at the corresponding\n            spatial dimensions.\n        padding_mode: {``\"constant\"``, ``\"reflect\"``, ``\"replicate\"``, ``\"circular\"``}\n            Padding mode when ``roi_size`` is larger than inputs. Defaults to ``\"constant\"``\n            See also: https://pytorch.org/docs/stable/nn.functional.html#pad\n        cval: fill value for 'constant' padding mode. Default: 0\n        sw_device: device for the window data.\n            By default the device (and accordingly the memory) of the `inputs` is used.\n            Normally `sw_device` should be consistent with the device where `predictor` is defined.\n        device: device for the stitched output prediction.\n            By default the device (and accordingly the memory) of the `inputs` is used. If for example\n            set to device=torch.device('cpu') the gpu memory consumption is less and independent of the\n            `inputs` and `roi_size`. Output is on the `device`.\n\n    Note:\n        ``sw_batch_size`` denotes the max number of windows per network inference iteration,\n        not the batch size of inputs.\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/inferers/inferer.py_SlidingWindowInferer.__init___SlidingWindowInferer.__init__.self.device.device": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/inferers/inferer.py_SlidingWindowInferer.__init___SlidingWindowInferer.__init__.self.device.device", "embedding": null, "metadata": {"file_path": "monai/inferers/inferer.py", "file_name": "inferer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 140, "end_line": 161, "span_ids": ["SlidingWindowInferer.__init__"], "tokens": 235}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SlidingWindowInferer(Inferer):\n\n    def __init__(\n        self,\n        roi_size: Union[Sequence[int], int],\n        sw_batch_size: int = 1,\n        overlap: float = 0.25,\n        mode: Union[BlendMode, str] = BlendMode.CONSTANT,\n        sigma_scale: Union[Sequence[float], float] = 0.125,\n        padding_mode: Union[PytorchPadMode, str] = PytorchPadMode.CONSTANT,\n        cval: float = 0.0,\n        sw_device: Union[torch.device, str, None] = None,\n        device: Union[torch.device, str, None] = None,\n    ) -> None:\n        Inferer.__init__(self)\n        self.roi_size = roi_size\n        self.sw_batch_size = sw_batch_size\n        self.overlap = overlap\n        self.mode: BlendMode = BlendMode(mode)\n        self.sigma_scale = sigma_scale\n        self.padding_mode = padding_mode\n        self.cval = cval\n        self.sw_device = sw_device\n        self.device = device", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/inferers/inferer.py_SlidingWindowInferer.__call___SlidingWindowInferer.__call__.return.sliding_window_inference_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/inferers/inferer.py_SlidingWindowInferer.__call___SlidingWindowInferer.__call__.return.sliding_window_inference_", "embedding": null, "metadata": {"file_path": "monai/inferers/inferer.py", "file_name": "inferer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 151, "end_line": 178, "span_ids": ["SlidingWindowInferer.__call__"], "tokens": 187}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SlidingWindowInferer(Inferer):\n\n    def __call__(\n        self, inputs: torch.Tensor, network: Callable[..., torch.Tensor], *args: Any, **kwargs: Any\n    ) -> torch.Tensor:\n        \"\"\"\n\n        Args:\n            inputs: model input data for inference.\n            network: target model to execute inference.\n                supports callables such as ``lambda x: my_torch_model(x, additional_config)``\n            args: optional args to be passed to ``network``.\n            kwargs: optional keyword args to be passed to ``network``.\n\n        \"\"\"\n        return sliding_window_inference(\n            inputs,\n            self.roi_size,\n            self.sw_batch_size,\n            network,\n            self.overlap,\n            self.mode,\n            self.sigma_scale,\n            self.padding_mode,\n            self.cval,\n            self.sw_device,\n            self.device,\n            *args,\n            **kwargs,\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/inferers/inferer.py_SaliencyInferer_SaliencyInferer.__init__.self.kwargs.kwargs": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/inferers/inferer.py_SaliencyInferer_SaliencyInferer.__init__.self.kwargs.kwargs", "embedding": null, "metadata": {"file_path": "monai/inferers/inferer.py", "file_name": "inferer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 197, "end_line": 218, "span_ids": ["SaliencyInferer", "SaliencyInferer.__init__"], "tokens": 253}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SaliencyInferer(Inferer):\n    \"\"\"\n    SaliencyInferer is inference with activation maps.\n\n    Args:\n        cam_name: expected CAM method name, should be: \"CAM\", \"GradCAM\" or \"GradCAMpp\".\n        target_layers: name of the model layer to generate the feature map.\n        class_idx: index of the class to be visualized. if None, default to argmax(logits).\n        args: other optional args to be passed to the `__init__` of cam.\n        kwargs: other optional keyword args to be passed to `__init__` of cam.\n\n    \"\"\"\n\n    def __init__(self, cam_name: str, target_layers: str, class_idx: Optional[int] = None, *args, **kwargs) -> None:\n        Inferer.__init__(self)\n        if cam_name.lower() not in (\"cam\", \"gradcam\", \"gradcampp\"):\n            raise ValueError(\"cam_name should be: 'CAM', 'GradCAM' or 'GradCAMpp'.\")\n        self.cam_name = cam_name.lower()\n        self.target_layers = target_layers\n        self.class_idx = class_idx\n        self.args = args\n        self.kwargs = kwargs", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/inferers/inferer.py_SaliencyInferer.__call___": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/inferers/inferer.py_SaliencyInferer.__call___", "embedding": null, "metadata": {"file_path": "monai/inferers/inferer.py", "file_name": "inferer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 204, "end_line": 224, "span_ids": ["SaliencyInferer.__call__"], "tokens": 243}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SaliencyInferer(Inferer):\n\n    def __call__(self, inputs: torch.Tensor, network: nn.Module, *args: Any, **kwargs: Any):  # type: ignore\n        \"\"\"Unified callable function API of Inferers.\n\n        Args:\n            inputs: model input data for inference.\n            network: target model to execute inference.\n                supports callables such as ``lambda x: my_torch_model(x, additional_config)``\n            args: other optional args to be passed to the `__call__` of cam.\n            kwargs: other optional keyword args to be passed to `__call__` of cam.\n\n        \"\"\"\n        cam: Union[CAM, GradCAM, GradCAMpp]\n        if self.cam_name == \"cam\":\n            cam = CAM(network, self.target_layers, *self.args, **self.kwargs)\n        elif self.cam_name == \"gradcam\":\n            cam = GradCAM(network, self.target_layers, *self.args, **self.kwargs)\n        else:\n            cam = GradCAMpp(network, self.target_layers, *self.args, **self.kwargs)\n\n        return cam(inputs, self.class_idx, *args, **kwargs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/inferers/utils.py_from_typing_import_Any_C_sliding_window_inference._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/inferers/utils.py_from_typing_import_Any_C_sliding_window_inference._", "embedding": null, "metadata": {"file_path": "monai/inferers/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 83, "span_ids": ["sliding_window_inference", "docstring"], "tokens": 903}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Any, Callable, List, Sequence, Tuple, Union\n\nimport torch\nimport torch.nn.functional as F\n\nfrom monai.data.utils import compute_importance_map, dense_patch_slices, get_valid_patch_size\nfrom monai.utils import BlendMode, PytorchPadMode, fall_back_tuple, look_up_option\n\n__all__ = [\"sliding_window_inference\"]\n\n\ndef sliding_window_inference(\n    inputs: torch.Tensor,\n    roi_size: Union[Sequence[int], int],\n    sw_batch_size: int,\n    predictor: Callable[..., torch.Tensor],\n    overlap: float = 0.25,\n    mode: Union[BlendMode, str] = BlendMode.CONSTANT,\n    sigma_scale: Union[Sequence[float], float] = 0.125,\n    padding_mode: Union[PytorchPadMode, str] = PytorchPadMode.CONSTANT,\n    cval: float = 0.0,\n    sw_device: Union[torch.device, str, None] = None,\n    device: Union[torch.device, str, None] = None,\n    *args: Any,\n    **kwargs: Any,\n) -> torch.Tensor:\n    \"\"\"\n    Sliding window inference on `inputs` with `predictor`.\n\n    When roi_size is larger than the inputs' spatial size, the input image are padded during inference.\n    To maintain the same spatial sizes, the output image will be cropped to the original input size.\n\n    Args:\n        inputs: input image to be processed (assuming NCHW[D])\n        roi_size: the spatial window size for inferences.\n            When its components have None or non-positives, the corresponding inputs dimension will be used.\n            if the components of the `roi_size` are non-positive values, the transform will use the\n            corresponding components of img size. For example, `roi_size=(32, -1)` will be adapted\n            to `(32, 64)` if the second spatial dimension size of img is `64`.\n        sw_batch_size: the batch size to run window slices.\n        predictor: given input tensor `patch_data` in shape NCHW[D], `predictor(patch_data)`\n            should return a prediction with the same spatial shape and batch_size, i.e. NMHW[D];\n            where HW[D] represents the patch spatial size, M is the number of output channels, N is `sw_batch_size`.\n        overlap: Amount of overlap between scans.\n        mode: {``\"constant\"``, ``\"gaussian\"``}\n            How to blend output of overlapping windows. Defaults to ``\"constant\"``.\n\n            - ``\"constant``\": gives equal weight to all predictions.\n            - ``\"gaussian``\": gives less weight to predictions on edges of windows.\n\n        sigma_scale: the standard deviation coefficient of the Gaussian window when `mode` is ``\"gaussian\"``.\n            Default: 0.125. Actual window sigma is ``sigma_scale`` * ``dim_size``.\n            When sigma_scale is a sequence of floats, the values denote sigma_scale at the corresponding\n            spatial dimensions.\n        padding_mode: {``\"constant\"``, ``\"reflect\"``, ``\"replicate\"``, ``\"circular\"``}\n            Padding mode for ``inputs``, when ``roi_size`` is larger than inputs. Defaults to ``\"constant\"``\n            See also: https://pytorch.org/docs/stable/nn.functional.html#pad\n        cval: fill value for 'constant' padding mode. Default: 0\n        sw_device: device for the window data.\n            By default the device (and accordingly the memory) of the `inputs` is used.\n            Normally `sw_device` should be consistent with the device where `predictor` is defined.\n        device: device for the stitched output prediction.\n            By default the device (and accordingly the memory) of the `inputs` is used. If for example\n            set to device=torch.device('cpu') the gpu memory consumption is less and independent of the\n            `inputs` and `roi_size`. Output is on the `device`.\n        args: optional args to be passed to ``predictor``.\n        kwargs: optional keyword args to be passed to ``predictor``.\n\n    Note:\n        - input must be channel-first and have a batch dim, supports N-D sliding window.\n\n    \"\"\"\n    # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/inferers/utils.py_sliding_window_inference.num_spatial_dims_sliding_window_inference._initialized.False": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/inferers/utils.py_sliding_window_inference.num_spatial_dims_sliding_window_inference._initialized.False", "embedding": null, "metadata": {"file_path": "monai/inferers/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 84, "end_line": 122, "span_ids": ["sliding_window_inference"], "tokens": 584}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def sliding_window_inference(\n    inputs: torch.Tensor,\n    roi_size: Union[Sequence[int], int],\n    sw_batch_size: int,\n    predictor: Callable[..., torch.Tensor],\n    overlap: float = 0.25,\n    mode: Union[BlendMode, str] = BlendMode.CONSTANT,\n    sigma_scale: Union[Sequence[float], float] = 0.125,\n    padding_mode: Union[PytorchPadMode, str] = PytorchPadMode.CONSTANT,\n    cval: float = 0.0,\n    sw_device: Union[torch.device, str, None] = None,\n    device: Union[torch.device, str, None] = None,\n    *args: Any,\n    **kwargs: Any,\n) -> torch.Tensor:\n    num_spatial_dims = len(inputs.shape) - 2\n    if overlap < 0 or overlap >= 1:\n        raise AssertionError(\"overlap must be >= 0 and < 1.\")\n\n    # determine image spatial size and batch size\n    # Note: all input images must have the same image size and batch size\n    image_size_ = list(inputs.shape[2:])\n    batch_size = inputs.shape[0]\n\n    if device is None:\n        device = inputs.device\n    if sw_device is None:\n        sw_device = inputs.device\n\n    roi_size = fall_back_tuple(roi_size, image_size_)\n    # in case that image size is smaller than roi size\n    image_size = tuple(max(image_size_[i], roi_size[i]) for i in range(num_spatial_dims))\n    pad_size = []\n    for k in range(len(inputs.shape) - 1, 1, -1):\n        diff = max(roi_size[k - 2] - inputs.shape[k], 0)\n        half = diff // 2\n        pad_size.extend([half, diff - half])\n    inputs = F.pad(inputs, pad=pad_size, mode=look_up_option(padding_mode, PytorchPadMode).value, value=cval)\n\n    scan_interval = _get_scan_interval(image_size, roi_size, num_spatial_dims, overlap)\n\n    # Store all slices in list\n    slices = dense_patch_slices(image_size, roi_size, scan_interval)\n    num_win = len(slices)  # number of windows per image\n    total_slices = num_win * batch_size  # total number of windows\n\n    # Create window-level importance map\n    importance_map = compute_importance_map(\n        get_valid_patch_size(image_size, roi_size), mode=mode, sigma_scale=sigma_scale, device=device\n    )\n\n    # Perform predictions\n    output_image, count_map = torch.tensor(0.0, device=device), torch.tensor(0.0, device=device)\n    _initialized = False\n    # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/inferers/utils.py_sliding_window_inference.for_slice_g_in_range_0_t_sliding_window_inference.return.output_image_final_slicin": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/inferers/utils.py_sliding_window_inference.for_slice_g_in_range_0_t_sliding_window_inference.return.output_image_final_slicin", "embedding": null, "metadata": {"file_path": "monai/inferers/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 123, "end_line": 154, "span_ids": ["sliding_window_inference"], "tokens": 575}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def sliding_window_inference(\n    inputs: torch.Tensor,\n    roi_size: Union[Sequence[int], int],\n    sw_batch_size: int,\n    predictor: Callable[..., torch.Tensor],\n    overlap: float = 0.25,\n    mode: Union[BlendMode, str] = BlendMode.CONSTANT,\n    sigma_scale: Union[Sequence[float], float] = 0.125,\n    padding_mode: Union[PytorchPadMode, str] = PytorchPadMode.CONSTANT,\n    cval: float = 0.0,\n    sw_device: Union[torch.device, str, None] = None,\n    device: Union[torch.device, str, None] = None,\n    *args: Any,\n    **kwargs: Any,\n) -> torch.Tensor:\n    # ... other code\n    for slice_g in range(0, total_slices, sw_batch_size):\n        slice_range = range(slice_g, min(slice_g + sw_batch_size, total_slices))\n        unravel_slice = [\n            [slice(int(idx / num_win), int(idx / num_win) + 1), slice(None)] + list(slices[idx % num_win])\n            for idx in slice_range\n        ]\n        window_data = torch.cat([inputs[win_slice] for win_slice in unravel_slice]).to(sw_device)\n        seg_prob = predictor(window_data, *args, **kwargs).to(device)  # batched patch segmentation\n\n        if not _initialized:  # init. buffer at the first iteration\n            output_classes = seg_prob.shape[1]\n            output_shape = [batch_size, output_classes] + list(image_size)\n            # allocate memory to store the full output and the count for overlapping parts\n            output_image = torch.zeros(output_shape, dtype=torch.float32, device=device)\n            count_map = torch.zeros(output_shape, dtype=torch.float32, device=device)\n            _initialized = True\n\n        # store the result in the proper location of the full output. Apply weights from importance map.\n        for idx, original_idx in zip(slice_range, unravel_slice):\n            output_image[original_idx] += importance_map * seg_prob[idx - slice_g]\n            count_map[original_idx] += importance_map\n\n    # account for any overlapping sections\n    output_image = output_image / count_map\n\n    final_slicing: List[slice] = []\n    for sp in range(num_spatial_dims):\n        slice_dim = slice(pad_size[sp * 2], image_size_[num_spatial_dims - sp - 1] + pad_size[sp * 2])\n        final_slicing.insert(0, slice_dim)\n    while len(final_slicing) < len(output_image.shape):\n        final_slicing.insert(0, slice(None))\n    return output_image[final_slicing]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/deform.py_from_typing_import_Union_spatial_gradient.return._x_slicing_s_x_slicing": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/deform.py_from_typing_import_Union_spatial_gradient.return._x_slicing_s_x_slicing", "embedding": null, "metadata": {"file_path": "monai/losses/deform.py", "file_name": "deform.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 44, "span_ids": ["spatial_gradient", "docstring"], "tokens": 294}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Union\n\nimport torch\nfrom torch.nn.modules.loss import _Loss\n\nfrom monai.utils import LossReduction\n\n\ndef spatial_gradient(x: torch.Tensor, dim: int) -> torch.Tensor:\n    \"\"\"\n    Calculate gradients on single dimension of a tensor using central finite difference.\n    It moves the tensor along the dimension to calculate the approximate gradient\n    dx[i] = (x[i+1] - x[i-1]) / 2.\n    Adapted from:\n        DeepReg (https://github.com/DeepRegNet/DeepReg)\n\n    Args:\n        x: the shape should be BCH(WD).\n        dim: dimension to calculate gradient along.\n    Returns:\n        gradient_dx: the shape should be BCH(WD)\n    \"\"\"\n    slice_1 = slice(1, -1)\n    slice_2_s = slice(2, None)\n    slice_2_e = slice(None, -2)\n    slice_all = slice(None)\n    slicing_s, slicing_e = [slice_all, slice_all], [slice_all, slice_all]\n    while len(slicing_s) < x.ndim:\n        slicing_s = slicing_s + [slice_1]\n        slicing_e = slicing_e + [slice_1]\n    slicing_s[dim] = slice_2_s\n    slicing_e[dim] = slice_2_e\n    return (x[slicing_s] - x[slicing_e]) / 2.0", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/deform.py_BendingEnergyLoss.forward_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/deform.py_BendingEnergyLoss.forward_", "embedding": null, "metadata": {"file_path": "monai/losses/deform.py", "file_name": "deform.py", "file_type": "text/x-python", "category": "implementation", "start_line": 71, "end_line": 119, "span_ids": ["BendingEnergyLoss.forward"], "tokens": 558}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class BendingEnergyLoss(_Loss):\n\n    def forward(self, pred: torch.Tensor) -> torch.Tensor:\n        \"\"\"\n        Args:\n            pred: the shape should be BCH(WD)\n\n        Raises:\n            ValueError: When ``self.reduction`` is not one of [\"mean\", \"sum\", \"none\"].\n\n        \"\"\"\n        if pred.ndim not in [3, 4, 5]:\n            raise ValueError(f\"Expecting 3-d, 4-d or 5-d pred, instead got pred of shape {pred.shape}\")\n        for i in range(pred.ndim - 2):\n            if pred.shape[-i - 1] <= 4:\n                raise ValueError(f\"All spatial dimensions must be > 4, got spatial dimensions {pred.shape[2:]}\")\n        if pred.shape[1] != pred.ndim - 2:\n            raise ValueError(\n                f\"Number of vector components, {pred.shape[1]}, does not match number of spatial dimensions, {pred.ndim-2}\"\n            )\n\n        # first order gradient\n        first_order_gradient = [spatial_gradient(pred, dim) for dim in range(2, pred.ndim)]\n\n        # spatial dimensions in a shape suited for broadcasting below\n        if self.normalize:\n            spatial_dims = torch.tensor(pred.shape, device=pred.device)[2:].reshape((1, -1) + (pred.ndim - 2) * (1,))\n\n        energy = torch.tensor(0)\n        for dim_1, g in enumerate(first_order_gradient):\n            dim_1 += 2\n            if self.normalize:\n                g *= pred.shape[dim_1] / spatial_dims\n                energy = energy + (spatial_gradient(g, dim_1) * pred.shape[dim_1]) ** 2\n            else:\n                energy = energy + spatial_gradient(g, dim_1) ** 2\n            for dim_2 in range(dim_1 + 1, pred.ndim):\n                if self.normalize:\n                    energy = energy + 2 * (spatial_gradient(g, dim_2) * pred.shape[dim_2]) ** 2\n                else:\n                    energy = energy + 2 * spatial_gradient(g, dim_2) ** 2\n\n        if self.reduction == LossReduction.MEAN.value:\n            energy = torch.mean(energy)  # the batch and channel average\n        elif self.reduction == LossReduction.SUM.value:\n            energy = torch.sum(energy)  # sum over the batch and channel dims\n        elif self.reduction != LossReduction.NONE.value:\n            raise ValueError(f'Unsupported reduction: {self.reduction}, available options are [\"mean\", \"sum\", \"none\"].')\n\n        return energy", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/dice.py_DiceLoss.__init___DiceLoss.__init__.self.batch.batch": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/dice.py_DiceLoss.__init___DiceLoss.__init__.self.batch.batch", "embedding": null, "metadata": {"file_path": "monai/losses/dice.py", "file_name": "dice.py", "file_type": "text/x-python", "category": "implementation", "start_line": 44, "end_line": 104, "span_ids": ["DiceLoss.__init__"], "tokens": 734}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DiceLoss(_Loss):\n\n    def __init__(\n        self,\n        include_background: bool = True,\n        to_onehot_y: bool = False,\n        sigmoid: bool = False,\n        softmax: bool = False,\n        other_act: Optional[Callable] = None,\n        squared_pred: bool = False,\n        jaccard: bool = False,\n        reduction: Union[LossReduction, str] = LossReduction.MEAN,\n        smooth_nr: float = 1e-5,\n        smooth_dr: float = 1e-5,\n        batch: bool = False,\n    ) -> None:\n        \"\"\"\n        Args:\n            include_background: if False, channel index 0 (background category) is excluded from the calculation.\n                if the non-background segmentations are small compared to the total image size they can get overwhelmed\n                by the signal from the background so excluding it in such cases helps convergence.\n            to_onehot_y: whether to convert `y` into the one-hot format. Defaults to False.\n            sigmoid: if True, apply a sigmoid function to the prediction.\n            softmax: if True, apply a softmax function to the prediction.\n            other_act: if don't want to use `sigmoid` or `softmax`, use other callable function to execute\n                other activation layers, Defaults to ``None``. for example:\n                `other_act = torch.tanh`.\n            squared_pred: use squared versions of targets and predictions in the denominator or not.\n            jaccard: compute Jaccard Index (soft IoU) instead of dice or not.\n            reduction: {``\"none\"``, ``\"mean\"``, ``\"sum\"``}\n                Specifies the reduction to apply to the output. Defaults to ``\"mean\"``.\n\n                - ``\"none\"``: no reduction will be applied.\n                - ``\"mean\"``: the sum of the output will be divided by the number of elements in the output.\n                - ``\"sum\"``: the output will be summed.\n\n            smooth_nr: a small constant added to the numerator to avoid zero.\n            smooth_dr: a small constant added to the denominator to avoid nan.\n            batch: whether to sum the intersection and union areas over the batch dimension before the dividing.\n                Defaults to False, a Dice loss value is computed independently from each item in the batch\n                before any `reduction`.\n\n        Raises:\n            TypeError: When ``other_act`` is not an ``Optional[Callable]``.\n            ValueError: When more than 1 of [``sigmoid=True``, ``softmax=True``, ``other_act is not None``].\n                Incompatible values.\n\n        \"\"\"\n        super().__init__(reduction=LossReduction(reduction).value)\n        if other_act is not None and not callable(other_act):\n            raise TypeError(f\"other_act must be None or callable but is {type(other_act).__name__}.\")\n        if int(sigmoid) + int(softmax) + int(other_act is not None) > 1:\n            raise ValueError(\"Incompatible values: more than 1 of [sigmoid=True, softmax=True, other_act is not None].\")\n        self.include_background = include_background\n        self.to_onehot_y = to_onehot_y\n        self.sigmoid = sigmoid\n        self.softmax = softmax\n        self.other_act = other_act\n        self.squared_pred = squared_pred\n        self.jaccard = jaccard\n        self.smooth_nr = float(smooth_nr)\n        self.smooth_dr = float(smooth_dr)\n        self.batch = batch", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/dice.py_GeneralizedDiceLoss_GeneralizedDiceLoss.w_func.return.torch_ones_like_grnd_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/dice.py_GeneralizedDiceLoss_GeneralizedDiceLoss.w_func.return.torch_ones_like_grnd_", "embedding": null, "metadata": {"file_path": "monai/losses/dice.py", "file_name": "dice.py", "file_type": "text/x-python", "category": "implementation", "start_line": 223, "end_line": 298, "span_ids": ["GeneralizedDiceLoss.w_func", "GeneralizedDiceLoss.__init__", "GeneralizedDiceLoss"], "tokens": 838}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GeneralizedDiceLoss(_Loss):\n    \"\"\"\n    Compute the generalised Dice loss defined in:\n\n        Sudre, C. et. al. (2017) Generalised Dice overlap as a deep learning\n        loss function for highly unbalanced segmentations. DLMIA 2017.\n\n    Adapted from:\n        https://github.com/NifTK/NiftyNet/blob/v0.6.0/niftynet/layer/loss_segmentation.py#L279\n    \"\"\"\n\n    def __init__(\n        self,\n        include_background: bool = True,\n        to_onehot_y: bool = False,\n        sigmoid: bool = False,\n        softmax: bool = False,\n        other_act: Optional[Callable] = None,\n        w_type: Union[Weight, str] = Weight.SQUARE,\n        reduction: Union[LossReduction, str] = LossReduction.MEAN,\n        smooth_nr: float = 1e-5,\n        smooth_dr: float = 1e-5,\n        batch: bool = False,\n    ) -> None:\n        \"\"\"\n        Args:\n            include_background: If False channel index 0 (background category) is excluded from the calculation.\n            to_onehot_y: whether to convert `y` into the one-hot format. Defaults to False.\n            sigmoid: If True, apply a sigmoid function to the prediction.\n            softmax: If True, apply a softmax function to the prediction.\n            other_act: if don't want to use `sigmoid` or `softmax`, use other callable function to execute\n                other activation layers, Defaults to ``None``. for example:\n                `other_act = torch.tanh`.\n            w_type: {``\"square\"``, ``\"simple\"``, ``\"uniform\"``}\n                Type of function to transform ground truth volume to a weight factor. Defaults to ``\"square\"``.\n            reduction: {``\"none\"``, ``\"mean\"``, ``\"sum\"``}\n                Specifies the reduction to apply to the output. Defaults to ``\"mean\"``.\n\n                - ``\"none\"``: no reduction will be applied.\n                - ``\"mean\"``: the sum of the output will be divided by the number of elements in the output.\n                - ``\"sum\"``: the output will be summed.\n            smooth_nr: a small constant added to the numerator to avoid zero.\n            smooth_dr: a small constant added to the denominator to avoid nan.\n            batch: whether to sum the intersection and union areas over the batch dimension before the dividing.\n                Defaults to False, intersection over union is computed from each item in the batch.\n\n        Raises:\n            TypeError: When ``other_act`` is not an ``Optional[Callable]``.\n            ValueError: When more than 1 of [``sigmoid=True``, ``softmax=True``, ``other_act is not None``].\n                Incompatible values.\n\n        \"\"\"\n        super().__init__(reduction=LossReduction(reduction).value)\n        if other_act is not None and not callable(other_act):\n            raise TypeError(f\"other_act must be None or callable but is {type(other_act).__name__}.\")\n        if int(sigmoid) + int(softmax) + int(other_act is not None) > 1:\n            raise ValueError(\"Incompatible values: more than 1 of [sigmoid=True, softmax=True, other_act is not None].\")\n\n        self.include_background = include_background\n        self.to_onehot_y = to_onehot_y\n        self.sigmoid = sigmoid\n        self.softmax = softmax\n        self.other_act = other_act\n\n        self.w_type = look_up_option(w_type, Weight)\n\n        self.smooth_nr = float(smooth_nr)\n        self.smooth_dr = float(smooth_dr)\n        self.batch = batch\n\n    def w_func(self, grnd):\n        if self.w_type == Weight.SIMPLE:\n            return torch.reciprocal(grnd)\n        if self.w_type == Weight.SQUARE:\n            return torch.reciprocal(grnd * grnd)\n        return torch.ones_like(grnd)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/dice.py_GeneralizedWassersteinDiceLoss_GeneralizedWassersteinDiceLoss._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/dice.py_GeneralizedWassersteinDiceLoss_GeneralizedWassersteinDiceLoss._", "embedding": null, "metadata": {"file_path": "monai/losses/dice.py", "file_name": "dice.py", "file_type": "text/x-python", "category": "implementation", "start_line": 366, "end_line": 381, "span_ids": ["GeneralizedWassersteinDiceLoss"], "tokens": 169}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GeneralizedWassersteinDiceLoss(_Loss):\n    \"\"\"\n    Compute the generalized Wasserstein Dice Loss defined in:\n\n        Fidon L. et al. (2017) Generalised Wasserstein Dice Score for Imbalanced Multi-class\n        Segmentation using Holistic Convolutional Networks. BrainLes 2017.\n\n    Or its variant (use the option weighting_mode=\"GDL\") defined in the Appendix of:\n\n        Tilborghs, S. et al. (2020) Comparative study of deep learning methods for the automatic\n        segmentation of lung, lesion and lesion type in CT scans of COVID-19 patients.\n        arXiv preprint arXiv:2007.15546\n\n    Adapted from:\n        https://github.com/LucasFidon/GeneralizedWassersteinDiceLoss\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/dice.py_GeneralizedWassersteinDiceLoss.__init___GeneralizedWassersteinDiceLoss.__init__.self.smooth_dr.float_smooth_dr_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/dice.py_GeneralizedWassersteinDiceLoss.__init___GeneralizedWassersteinDiceLoss.__init__.self.smooth_dr.float_smooth_dr_", "embedding": null, "metadata": {"file_path": "monai/losses/dice.py", "file_name": "dice.py", "file_type": "text/x-python", "category": "implementation", "start_line": 369, "end_line": 438, "span_ids": ["GeneralizedWassersteinDiceLoss.__init__"], "tokens": 884}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GeneralizedWassersteinDiceLoss(_Loss):\n\n    def __init__(\n        self,\n        dist_matrix: Union[np.ndarray, torch.Tensor],\n        weighting_mode: str = \"default\",\n        reduction: Union[LossReduction, str] = LossReduction.MEAN,\n        smooth_nr: float = 1e-5,\n        smooth_dr: float = 1e-5,\n    ) -> None:\n        \"\"\"\n        Args:\n            dist_matrix: 2d tensor or 2d numpy array; matrix of distances between the classes.\n            It must have dimension C x C where C is the number of classes.\n            weighting_mode: {``\"default\"``, ``\"GDL\"``}\n                Specifies how to weight the class-specific sum of errors.\n                Default to ``\"default\"``.\n\n                - ``\"default\"``: (recommended) use the original weighting method as in:\n                    Fidon L. et al. (2017) Generalised Wasserstein Dice Score for Imbalanced Multi-class\n                    Segmentation using Holistic Convolutional Networks. BrainLes 2017.\n                - ``\"GDL\"``: use a GDL-like weighting method as in the Appendix of:\n                    Tilborghs, S. et al. (2020) Comparative study of deep learning methods for the automatic\n                    segmentation of lung, lesion and lesion type in CT scans of COVID-19 patients.\n                    arXiv preprint arXiv:2007.15546\n            reduction: {``\"none\"``, ``\"mean\"``, ``\"sum\"``}\n                Specifies the reduction to apply to the output. Defaults to ``\"mean\"``.\n\n                - ``\"none\"``: no reduction will be applied.\n                - ``\"mean\"``: the sum of the output will be divided by the number of elements in the output.\n                - ``\"sum\"``: the output will be summed.\n            smooth_nr: a small constant added to the numerator to avoid zero.\n            smooth_dr: a small constant added to the denominator to avoid nan.\n\n        Raises:\n            ValueError: When ``dist_matrix`` is not a square matrix.\n\n        Example:\n            .. code-block:: python\n\n                import torch\n                import numpy as np\n                from monai.losses import GeneralizedWassersteinDiceLoss\n\n                # Example with 3 classes (including the background: label 0).\n                # The distance between the background class (label 0) and the other classes is the maximum, equal to 1.\n                # The distance between class 1 and class 2 is 0.5.\n                dist_mat = np.array([[0.0, 1.0, 1.0], [1.0, 0.0, 0.5], [1.0, 0.5, 0.0]], dtype=np.float32)\n                wass_loss = GeneralizedWassersteinDiceLoss(dist_matrix=dist_mat)\n\n                pred_score = torch.tensor([[1000, 0, 0], [0, 1000, 0], [0, 0, 1000]], dtype=torch.float32)\n                grnd = torch.tensor([0, 1, 2], dtype=torch.int64)\n                wass_loss(pred_score, grnd)  # 0\n\n        \"\"\"\n        super().__init__(reduction=LossReduction(reduction).value)\n\n        if dist_matrix.shape[0] != dist_matrix.shape[1]:\n            raise ValueError(f\"dist_matrix must be C x C, got {dist_matrix.shape[0]} x {dist_matrix.shape[1]}.\")\n\n        if weighting_mode not in [\"default\", \"GDL\"]:\n            raise ValueError(\"weighting_mode must be either 'default' or 'GDL, got %s.\" % weighting_mode)\n\n        self.m = dist_matrix\n        if isinstance(self.m, np.ndarray):\n            self.m = torch.from_numpy(self.m)\n        if torch.max(self.m) != 1:\n            self.m = self.m / torch.max(self.m)\n        self.alpha_mode = weighting_mode\n        self.num_classes = self.m.size(0)\n        self.smooth_nr = float(smooth_nr)\n        self.smooth_dr = float(smooth_dr)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/dice.py_GeneralizedWassersteinDiceLoss.forward_GeneralizedWassersteinDiceLoss.forward.return.wass_dice_loss": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/dice.py_GeneralizedWassersteinDiceLoss.forward_GeneralizedWassersteinDiceLoss.forward.return.wass_dice_loss", "embedding": null, "metadata": {"file_path": "monai/losses/dice.py", "file_name": "dice.py", "file_type": "text/x-python", "category": "implementation", "start_line": 465, "end_line": 514, "span_ids": ["GeneralizedWassersteinDiceLoss.forward"], "tokens": 615}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GeneralizedWassersteinDiceLoss(_Loss):\n\n    def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:\n        \"\"\"\n        Args:\n            input: the shape should be BNH[WD].\n            target: the shape should be BNH[WD].\n\n        \"\"\"\n        # Aggregate spatial dimensions\n        flat_input = input.reshape(input.size(0), input.size(1), -1)\n        flat_target = target.reshape(target.size(0), -1).long()\n\n        # Apply the softmax to the input scores map\n        probs = F.softmax(flat_input, dim=1)\n\n        # Compute the Wasserstein distance map\n        wass_dist_map = self.wasserstein_distance_map(probs, flat_target)\n\n        # Compute the values of alpha to use\n        alpha = self._compute_alpha_generalized_true_positives(flat_target)\n\n        # Compute the numerator and denominator of the generalized Wasserstein Dice loss\n        if self.alpha_mode == \"GDL\":\n            # use GDL-style alpha weights (i.e. normalize by the volume of each class)\n            # contrary to the original definition we also use alpha in the \"generalized all error\".\n            true_pos = self._compute_generalized_true_positive(alpha, flat_target, wass_dist_map)\n            denom = self._compute_denominator(alpha, flat_target, wass_dist_map)\n        else:  # default: as in the original paper\n            # (i.e. alpha=1 for all foreground classes and 0 for the background).\n            # Compute the generalised number of true positives\n            true_pos = self._compute_generalized_true_positive(alpha, flat_target, wass_dist_map)\n            all_error = torch.sum(wass_dist_map, dim=1)\n            denom = 2 * true_pos + all_error\n\n        # Compute the final loss\n        wass_dice: torch.Tensor = (2.0 * true_pos + self.smooth_nr) / (denom + self.smooth_dr)\n        wass_dice_loss: torch.Tensor = 1.0 - wass_dice\n\n        if self.reduction == LossReduction.MEAN.value:\n            wass_dice_loss = torch.mean(wass_dice_loss)  # the batch and channel average\n        elif self.reduction == LossReduction.SUM.value:\n            wass_dice_loss = torch.sum(wass_dice_loss)  # sum over the batch and channel dims\n        elif self.reduction == LossReduction.NONE.value:\n            # If we are not computing voxelwise loss components at least\n            # make sure a none reduction maintains a broadcastable shape\n            broadcast_shape = input.shape[0:2] + (1,) * (len(input.shape) - 2)\n            wass_dice_loss = wass_dice_loss.view(broadcast_shape)\n        else:\n            raise ValueError(f'Unsupported reduction: {self.reduction}, available options are [\"mean\", \"sum\", \"none\"].')\n\n        return wass_dice_loss", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/dice.py_GeneralizedWassersteinDiceLoss._compute_alpha_generalized_true_positives_GeneralizedWassersteinDiceLoss._compute_alpha_generalized_true_positives.return.alpha": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/dice.py_GeneralizedWassersteinDiceLoss._compute_alpha_generalized_true_positives_GeneralizedWassersteinDiceLoss._compute_alpha_generalized_true_positives.return.alpha", "embedding": null, "metadata": {"file_path": "monai/losses/dice.py", "file_name": "dice.py", "file_type": "text/x-python", "category": "implementation", "start_line": 574, "end_line": 589, "span_ids": ["GeneralizedWassersteinDiceLoss._compute_alpha_generalized_true_positives"], "tokens": 225}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GeneralizedWassersteinDiceLoss(_Loss):\n\n    def _compute_alpha_generalized_true_positives(self, flat_target: torch.Tensor) -> torch.Tensor:\n        \"\"\"\n        Args:\n            flat_target: the target tensor.\n        \"\"\"\n        alpha: torch.Tensor = torch.ones((flat_target.size(0), self.num_classes)).float().to(flat_target.device)\n        if self.alpha_mode == \"GDL\":  # GDL style\n            # Define alpha like in the generalized dice loss\n            # i.e. the inverse of the volume of each class.\n            one_hot_f = F.one_hot(flat_target, num_classes=self.num_classes).permute(0, 2, 1).float()\n            volumes = torch.sum(one_hot_f, dim=2)\n            alpha = 1.0 / (volumes + 1.0)\n        else:  # default, i.e. like in the original paper\n            # alpha weights are 0 for the background and 1 the other classes\n            alpha[:, 0] = 0.0\n        return alpha", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/dice.py_DiceCELoss_DiceCELoss.__init__.self.lambda_ce.lambda_ce": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/dice.py_DiceCELoss_DiceCELoss.__init__.self.lambda_ce.lambda_ce", "embedding": null, "metadata": {"file_path": "monai/losses/dice.py", "file_name": "dice.py", "file_type": "text/x-python", "category": "implementation", "start_line": 576, "end_line": 660, "span_ids": ["DiceCELoss", "DiceCELoss.__init__"], "tokens": 962}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DiceCELoss(_Loss):\n    \"\"\"\n    Compute both Dice loss and Cross Entropy Loss, and return the weighted sum of these two losses.\n    The details of Dice loss is shown in ``monai.losses.DiceLoss``.\n    The details of Cross Entropy Loss is shown in ``torch.nn.CrossEntropyLoss``. In this implementation,\n    two deprecated parameters ``size_average`` and ``reduce``, and the parameter ``ignore_index`` are\n    not supported.\n\n    \"\"\"\n\n    def __init__(\n        self,\n        include_background: bool = True,\n        to_onehot_y: bool = False,\n        sigmoid: bool = False,\n        softmax: bool = False,\n        other_act: Optional[Callable] = None,\n        squared_pred: bool = False,\n        jaccard: bool = False,\n        reduction: str = \"mean\",\n        smooth_nr: float = 1e-5,\n        smooth_dr: float = 1e-5,\n        batch: bool = False,\n        ce_weight: Optional[torch.Tensor] = None,\n        lambda_dice: float = 1.0,\n        lambda_ce: float = 1.0,\n    ) -> None:\n        \"\"\"\n        Args:\n            ``ce_weight`` and ``lambda_ce`` are only used for cross entropy loss.\n            ``reduction`` is used for both losses and other parameters are only used for dice loss.\n\n            include_background: if False channel index 0 (background category) is excluded from the calculation.\n            to_onehot_y: whether to convert `y` into the one-hot format. Defaults to False.\n            sigmoid: if True, apply a sigmoid function to the prediction, only used by the `DiceLoss`,\n                don't need to specify activation function for `CrossEntropyLoss`.\n            softmax: if True, apply a softmax function to the prediction, only used by the `DiceLoss`,\n                don't need to specify activation function for `CrossEntropyLoss`.\n            other_act: if don't want to use `sigmoid` or `softmax`, use other callable function to execute\n                other activation layers, Defaults to ``None``. for example: `other_act = torch.tanh`.\n                only used by the `DiceLoss`, don't need to specify activation function for `CrossEntropyLoss`.\n            squared_pred: use squared versions of targets and predictions in the denominator or not.\n            jaccard: compute Jaccard Index (soft IoU) instead of dice or not.\n            reduction: {``\"mean\"``, ``\"sum\"``}\n                Specifies the reduction to apply to the output. Defaults to ``\"mean\"``. The dice loss should\n                as least reduce the spatial dimensions, which is different from cross entropy loss, thus here\n                the ``none`` option cannot be used.\n\n                - ``\"mean\"``: the sum of the output will be divided by the number of elements in the output.\n                - ``\"sum\"``: the output will be summed.\n\n            smooth_nr: a small constant added to the numerator to avoid zero.\n            smooth_dr: a small constant added to the denominator to avoid nan.\n            batch: whether to sum the intersection and union areas over the batch dimension before the dividing.\n                Defaults to False, a Dice loss value is computed independently from each item in the batch\n                before any `reduction`.\n            ce_weight: a rescaling weight given to each class for cross entropy loss.\n                See ``torch.nn.CrossEntropyLoss()`` for more information.\n            lambda_dice: the trade-off weight value for dice loss. The value should be no less than 0.0.\n                Defaults to 1.0.\n            lambda_ce: the trade-off weight value for cross entropy loss. The value should be no less than 0.0.\n                Defaults to 1.0.\n\n        \"\"\"\n        super().__init__()\n        self.dice = DiceLoss(\n            include_background=include_background,\n            to_onehot_y=to_onehot_y,\n            sigmoid=sigmoid,\n            softmax=softmax,\n            other_act=other_act,\n            squared_pred=squared_pred,\n            jaccard=jaccard,\n            reduction=reduction,\n            smooth_nr=smooth_nr,\n            smooth_dr=smooth_dr,\n            batch=batch,\n        )\n        self.cross_entropy = nn.CrossEntropyLoss(weight=ce_weight, reduction=reduction)\n        if lambda_dice < 0.0:\n            raise ValueError(\"lambda_dice should be no less than 0.0.\")\n        if lambda_ce < 0.0:\n            raise ValueError(\"lambda_ce should be no less than 0.0.\")\n        self.lambda_dice = lambda_dice\n        self.lambda_ce = lambda_ce", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/dice.py_DiceCELoss.forward_DiceCELoss.forward.return.total_loss": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/dice.py_DiceCELoss.forward_DiceCELoss.forward.return.total_loss", "embedding": null, "metadata": {"file_path": "monai/losses/dice.py", "file_name": "dice.py", "file_type": "text/x-python", "category": "implementation", "start_line": 678, "end_line": 696, "span_ids": ["DiceCELoss.forward"], "tokens": 174}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DiceCELoss(_Loss):\n\n    def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:\n        \"\"\"\n        Args:\n            input: the shape should be BNH[WD].\n            target: the shape should be BNH[WD] or B1H[WD].\n\n        Raises:\n            ValueError: When number of dimensions for input and target are different.\n            ValueError: When number of channels for target is neither 1 nor the same as input.\n\n        \"\"\"\n        if len(input.shape) != len(target.shape):\n            raise ValueError(\"the number of dimensions for input and target should be the same.\")\n\n        dice_loss = self.dice(input, target)\n        ce_loss = self.ce(input, target)\n        total_loss: torch.Tensor = self.lambda_dice * dice_loss + self.lambda_ce * ce_loss\n\n        return total_loss", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/dice.py_DiceFocalLoss_DiceFocalLoss.__init__.self.lambda_focal.lambda_focal": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/dice.py_DiceFocalLoss_DiceFocalLoss.__init__.self.lambda_focal.lambda_focal", "embedding": null, "metadata": {"file_path": "monai/losses/dice.py", "file_name": "dice.py", "file_type": "text/x-python", "category": "implementation", "start_line": 699, "end_line": 789, "span_ids": ["DiceFocalLoss.__init__", "DiceFocalLoss"], "tokens": 1032}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DiceFocalLoss(_Loss):\n    \"\"\"\n    Compute both Dice loss and Focal Loss, and return the weighted sum of these two losses.\n    The details of Dice loss is shown in ``monai.losses.DiceLoss``.\n    The details of Focal Loss is shown in ``monai.losses.FocalLoss``.\n\n    \"\"\"\n\n    def __init__(\n        self,\n        include_background: bool = True,\n        to_onehot_y: bool = False,\n        sigmoid: bool = False,\n        softmax: bool = False,\n        other_act: Optional[Callable] = None,\n        squared_pred: bool = False,\n        jaccard: bool = False,\n        reduction: str = \"mean\",\n        smooth_nr: float = 1e-5,\n        smooth_dr: float = 1e-5,\n        batch: bool = False,\n        gamma: float = 2.0,\n        focal_weight: Optional[Union[Sequence[float], float, int, torch.Tensor]] = None,\n        lambda_dice: float = 1.0,\n        lambda_focal: float = 1.0,\n    ) -> None:\n        \"\"\"\n        Args:\n            ``gamma``, ``focal_weight`` and ``lambda_focal`` are only used for focal loss.\n            ``include_background``, ``to_onehot_y``and ``reduction`` are used for both losses\n            and other parameters are only used for dice loss.\n            include_background: if False channel index 0 (background category) is excluded from the calculation.\n            to_onehot_y: whether to convert `y` into the one-hot format. Defaults to False.\n            sigmoid: if True, apply a sigmoid function to the prediction, only used by the `DiceLoss`,\n                don't need to specify activation function for `FocalLoss`.\n            softmax: if True, apply a softmax function to the prediction, only used by the `DiceLoss`,\n                don't need to specify activation function for `FocalLoss`.\n            other_act: if don't want to use `sigmoid` or `softmax`, use other callable function to execute\n                other activation layers, Defaults to ``None``. for example: `other_act = torch.tanh`.\n                only used by the `DiceLoss`, don't need to specify activation function for `FocalLoss`.\n            squared_pred: use squared versions of targets and predictions in the denominator or not.\n            jaccard: compute Jaccard Index (soft IoU) instead of dice or not.\n            reduction: {``\"none\"``, ``\"mean\"``, ``\"sum\"``}\n                Specifies the reduction to apply to the output. Defaults to ``\"mean\"``.\n\n                - ``\"none\"``: no reduction will be applied.\n                - ``\"mean\"``: the sum of the output will be divided by the number of elements in the output.\n                - ``\"sum\"``: the output will be summed.\n\n            smooth_nr: a small constant added to the numerator to avoid zero.\n            smooth_dr: a small constant added to the denominator to avoid nan.\n            batch: whether to sum the intersection and union areas over the batch dimension before the dividing.\n                Defaults to False, a Dice loss value is computed independently from each item in the batch\n                before any `reduction`.\n            gamma: value of the exponent gamma in the definition of the Focal loss.\n            focal_weight: weights to apply to the voxels of each class. If None no weights are applied.\n                The input can be a single value (same weight for all classes), a sequence of values (the length\n                of the sequence should be the same as the number of classes).\n            lambda_dice: the trade-off weight value for dice loss. The value should be no less than 0.0.\n                Defaults to 1.0.\n            lambda_focal: the trade-off weight value for focal loss. The value should be no less than 0.0.\n                Defaults to 1.0.\n\n        \"\"\"\n        super().__init__()\n        self.dice = DiceLoss(\n            include_background=include_background,\n            to_onehot_y=to_onehot_y,\n            sigmoid=sigmoid,\n            softmax=softmax,\n            other_act=other_act,\n            squared_pred=squared_pred,\n            jaccard=jaccard,\n            reduction=reduction,\n            smooth_nr=smooth_nr,\n            smooth_dr=smooth_dr,\n            batch=batch,\n        )\n        self.focal = FocalLoss(\n            include_background=include_background,\n            to_onehot_y=to_onehot_y,\n            gamma=gamma,\n            weight=focal_weight,\n            reduction=reduction,\n        )\n        if lambda_dice < 0.0:\n            raise ValueError(\"lambda_dice should be no less than 0.0.\")\n        if lambda_focal < 0.0:\n            raise ValueError(\"lambda_focal should be no less than 0.0.\")\n        self.lambda_dice = lambda_dice\n        self.lambda_focal = lambda_focal", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/dice.py_DiceFocalLoss.forward_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/dice.py_DiceFocalLoss.forward_", "embedding": null, "metadata": {"file_path": "monai/losses/dice.py", "file_name": "dice.py", "file_type": "text/x-python", "category": "implementation", "start_line": 821, "end_line": 847, "span_ids": ["DiceFocalLoss.forward", "impl"], "tokens": 246}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DiceFocalLoss(_Loss):\n\n    def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:\n        \"\"\"\n        Args:\n            input: the shape should be BNH[WD]. The input should be the original logits\n                due to the restriction of ``monai.losses.FocalLoss``.\n            target: the shape should be BNH[WD] or B1H[WD].\n\n        Raises:\n            ValueError: When number of dimensions for input and target are different.\n            ValueError: When number of channels for target is neither 1 nor the same as input.\n\n        \"\"\"\n        if len(input.shape) != len(target.shape):\n            raise ValueError(\"the number of dimensions for input and target should be the same.\")\n\n        dice_loss = self.dice(input, target)\n        focal_loss = self.focal(input, target)\n        total_loss: torch.Tensor = self.lambda_dice * dice_loss + self.lambda_focal * focal_loss\n        return total_loss\n\n\nDice = DiceLoss\ndice_ce = DiceCELoss\ndice_focal = DiceFocalLoss\ngeneralized_dice = GeneralizedDiceLoss\ngeneralized_wasserstein_dice = GeneralizedWassersteinDiceLoss", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/image_dissimilarity.py_LocalNormalizedCrossCorrelationLoss_LocalNormalizedCrossCorrelationLoss.get_kernel_vol.return.torch_sum_vol_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/image_dissimilarity.py_LocalNormalizedCrossCorrelationLoss_LocalNormalizedCrossCorrelationLoss.get_kernel_vol.return.torch_sum_vol_", "embedding": null, "metadata": {"file_path": "monai/losses/image_dissimilarity.py", "file_name": "image_dissimilarity.py", "file_type": "text/x-python", "category": "implementation", "start_line": 50, "end_line": 114, "span_ids": ["LocalNormalizedCrossCorrelationLoss", "LocalNormalizedCrossCorrelationLoss.get_kernel_vol", "LocalNormalizedCrossCorrelationLoss.__init__"], "tokens": 690}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LocalNormalizedCrossCorrelationLoss(_Loss):\n    \"\"\"\n    Local squared zero-normalized cross-correlation.\n    The loss is based on a moving kernel/window over the y_true/y_pred,\n    within the window the square of zncc is calculated.\n    The kernel can be a rectangular / triangular / gaussian window.\n    The final loss is the averaged loss over all windows.\n\n    Adapted from:\n        https://github.com/voxelmorph/voxelmorph/blob/legacy/src/losses.py\n        DeepReg (https://github.com/DeepRegNet/DeepReg)\n    \"\"\"\n\n    @deprecated_arg(name=\"ndim\", since=\"0.6\", msg_suffix=\"Please use `spatial_dims` instead.\")\n    def __init__(\n        self,\n        spatial_dims: int = 3,\n        kernel_size: int = 3,\n        kernel_type: str = \"rectangular\",\n        reduction: Union[LossReduction, str] = LossReduction.MEAN,\n        smooth_nr: float = 1e-5,\n        smooth_dr: float = 1e-5,\n        ndim: Optional[int] = None,\n    ) -> None:\n        \"\"\"\n        Args:\n            spatial_dims: number of spatial dimensions, {``1``, ``2``, ``3``}. Defaults to 3.\n            kernel_size: kernel spatial size, must be odd.\n            kernel_type: {``\"rectangular\"``, ``\"triangular\"``, ``\"gaussian\"``}. Defaults to ``\"rectangular\"``.\n            reduction: {``\"none\"``, ``\"mean\"``, ``\"sum\"``}\n                Specifies the reduction to apply to the output. Defaults to ``\"mean\"``.\n\n                - ``\"none\"``: no reduction will be applied.\n                - ``\"mean\"``: the sum of the output will be divided by the number of elements in the output.\n                - ``\"sum\"``: the output will be summed.\n            smooth_nr: a small constant added to the numerator to avoid nan.\n            smooth_dr: a small constant added to the denominator to avoid nan.\n\n        .. deprecated:: 0.6.0\n            ``ndim`` is deprecated, use ``spatial_dims``.\n        \"\"\"\n        super().__init__(reduction=LossReduction(reduction).value)\n\n        if ndim is not None:\n            spatial_dims = ndim\n        self.ndim = spatial_dims\n        if self.ndim not in {1, 2, 3}:\n            raise ValueError(f\"Unsupported ndim: {self.ndim}-d, only 1-d, 2-d, and 3-d inputs are supported\")\n\n        self.kernel_size = kernel_size\n        if self.kernel_size % 2 == 0:\n            raise ValueError(f\"kernel_size must be odd, got {self.kernel_size}\")\n\n        _kernel = look_up_option(kernel_type, kernel_dict)\n        self.kernel = _kernel(self.kernel_size)\n        self.kernel_vol = self.get_kernel_vol()\n\n        self.smooth_nr = float(smooth_nr)\n        self.smooth_dr = float(smooth_dr)\n\n    def get_kernel_vol(self):\n        vol = self.kernel\n        for _ in range(self.ndim - 1):\n            vol = torch.matmul(vol.unsqueeze(-1), self.kernel.unsqueeze(0))\n        return torch.sum(vol)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/image_dissimilarity.py_LocalNormalizedCrossCorrelationLoss.forward_LocalNormalizedCrossCorrelationLoss.forward.raise_ValueError_f_Unsupp": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/image_dissimilarity.py_LocalNormalizedCrossCorrelationLoss.forward_LocalNormalizedCrossCorrelationLoss.forward.raise_ValueError_f_Unsupp", "embedding": null, "metadata": {"file_path": "monai/losses/image_dissimilarity.py", "file_name": "image_dissimilarity.py", "file_type": "text/x-python", "category": "implementation", "start_line": 111, "end_line": 160, "span_ids": ["LocalNormalizedCrossCorrelationLoss.forward"], "tokens": 733}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LocalNormalizedCrossCorrelationLoss(_Loss):\n\n    def forward(self, pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor:\n        \"\"\"\n        Args:\n            pred: the shape should be BNH[WD].\n            target: the shape should be BNH[WD].\n        Raises:\n            ValueError: When ``self.reduction`` is not one of [\"mean\", \"sum\", \"none\"].\n        \"\"\"\n        if pred.ndim - 2 != self.ndim:\n            raise ValueError(f\"expecting pred with {self.ndim} spatial dimensions, got pred of shape {pred.shape}\")\n        if target.shape != pred.shape:\n            raise ValueError(f\"ground truth has differing shape ({target.shape}) from pred ({pred.shape})\")\n\n        t2, p2, tp = target ** 2, pred ** 2, target * pred\n        kernel, kernel_vol = self.kernel.to(pred), self.kernel_vol.to(pred)\n        # sum over kernel\n        t_sum = separable_filtering(target, kernels=[kernel.to(pred)] * self.ndim)\n        p_sum = separable_filtering(pred, kernels=[kernel.to(pred)] * self.ndim)\n        t2_sum = separable_filtering(t2, kernels=[kernel.to(pred)] * self.ndim)\n        p2_sum = separable_filtering(p2, kernels=[kernel.to(pred)] * self.ndim)\n        tp_sum = separable_filtering(tp, kernels=[kernel.to(pred)] * self.ndim)\n\n        # average over kernel\n        t_avg = t_sum / kernel_vol\n        p_avg = p_sum / kernel_vol\n\n        # normalized cross correlation between t and p\n        # sum[(t - mean[t]) * (p - mean[p])] / std[t] / std[p]\n        # denoted by num / denom\n        # assume we sum over N values\n        # num = sum[t * p - mean[t] * p - t * mean[p] + mean[t] * mean[p]]\n        #     = sum[t*p] - sum[t] * sum[p] / N * 2 + sum[t] * sum[p] / N\n        #     = sum[t*p] - sum[t] * sum[p] / N\n        #     = sum[t*p] - sum[t] * mean[p] = cross\n        # the following is actually squared ncc\n        cross = tp_sum - p_avg * t_sum\n        t_var = t2_sum - t_avg * t_sum  # std[t] ** 2\n        p_var = p2_sum - p_avg * p_sum  # std[p] ** 2\n        t_var = torch.max(t_var, torch.zeros_like(t_var))\n        p_var = torch.max(p_var, torch.zeros_like(p_var))\n        ncc: torch.Tensor = (cross * cross + self.smooth_nr) / (t_var * p_var + self.smooth_dr)\n        # shape = (batch, 1, D, H, W)\n\n        if self.reduction == LossReduction.SUM.value:\n            return torch.sum(ncc).neg()  # sum over the batch, channel and spatial ndims\n        if self.reduction == LossReduction.NONE.value:\n            return ncc.neg()\n        if self.reduction == LossReduction.MEAN.value:\n            return torch.mean(ncc).neg()  # average over the batch, channel and spatial ndims\n        raise ValueError(f'Unsupported reduction: {self.reduction}, available options are [\"mean\", \"sum\", \"none\"].')", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/image_dissimilarity.py_GlobalMutualInformationLoss_GlobalMutualInformationLoss.__init__.self.smooth_dr.float_smooth_dr_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/image_dissimilarity.py_GlobalMutualInformationLoss_GlobalMutualInformationLoss.__init__.self.smooth_dr.float_smooth_dr_", "embedding": null, "metadata": {"file_path": "monai/losses/image_dissimilarity.py", "file_name": "image_dissimilarity.py", "file_type": "text/x-python", "category": "implementation", "start_line": 168, "end_line": 223, "span_ids": ["GlobalMutualInformationLoss.__init__", "GlobalMutualInformationLoss"], "tokens": 734}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GlobalMutualInformationLoss(_Loss):\n    \"\"\"\n    Differentiable global mutual information loss via Parzen windowing method.\n\n    Reference:\n        https://dspace.mit.edu/handle/1721.1/123142, Section 3.1, equation 3.1-3.5, Algorithm 1\n    \"\"\"\n\n    def __init__(\n        self,\n        kernel_type: str = \"gaussian\",\n        num_bins: int = 23,\n        sigma_ratio: float = 0.5,\n        reduction: Union[LossReduction, str] = LossReduction.MEAN,\n        smooth_nr: float = 1e-7,\n        smooth_dr: float = 1e-7,\n    ) -> None:\n        \"\"\"\n        Args:\n            kernel_type: {``\"gaussian\"``, ``\"b-spline\"``}\n                ``\"gaussian\"``: adapted from DeepReg\n                Reference: https://dspace.mit.edu/handle/1721.1/123142, Section 3.1, equation 3.1-3.5, Algorithm 1.\n                ``\"b-spline\"``: based on the method of Mattes et al [1,2] and adapted from ITK\n                References:\n                  [1] \"Nonrigid multimodality image registration\"\n                      D. Mattes, D. R. Haynor, H. Vesselle, T. Lewellen and W. Eubank\n                      Medical Imaging 2001: Image Processing, 2001, pp. 1609-1620.\n                  [2] \"PET-CT Image Registration in the Chest Using Free-form Deformations\"\n                      D. Mattes, D. R. Haynor, H. Vesselle, T. Lewellen and W. Eubank\n                      IEEE Transactions in Medical Imaging. Vol.22, No.1,\n                      January 2003. pp.120-128.\n\n            num_bins: number of bins for intensity\n            sigma_ratio: a hyper param for gaussian function\n            reduction: {``\"none\"``, ``\"mean\"``, ``\"sum\"``}\n                Specifies the reduction to apply to the output. Defaults to ``\"mean\"``.\n\n                - ``\"none\"``: no reduction will be applied.\n                - ``\"mean\"``: the sum of the output will be divided by the number of elements in the output.\n                - ``\"sum\"``: the output will be summed.\n            smooth_nr: a small constant added to the numerator to avoid nan.\n            smooth_dr: a small constant added to the denominator to avoid nan.\n        \"\"\"\n        super().__init__(reduction=LossReduction(reduction).value)\n        if num_bins <= 0:\n            raise ValueError(\"num_bins must > 0, got {num_bins}\")\n        bin_centers = torch.linspace(0.0, 1.0, num_bins)  # (num_bins,)\n        sigma = torch.mean(bin_centers[1:] - bin_centers[:-1]) * sigma_ratio\n        self.kernel_type = look_up_option(kernel_type, [\"gaussian\", \"b-spline\"])\n        self.num_bins = num_bins\n        self.kernel_type = kernel_type\n        if self.kernel_type == \"gaussian\":\n            self.preterm = 1 / (2 * sigma ** 2)\n            self.bin_centers = bin_centers[None, None, ...]\n        self.smooth_nr = float(smooth_nr)\n        self.smooth_dr = float(smooth_dr)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/image_dissimilarity.py_GlobalMutualInformationLoss.forward_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/image_dissimilarity.py_GlobalMutualInformationLoss.forward_", "embedding": null, "metadata": {"file_path": "monai/losses/image_dissimilarity.py", "file_name": "image_dissimilarity.py", "file_type": "text/x-python", "category": "implementation", "start_line": 309, "end_line": 334, "span_ids": ["GlobalMutualInformationLoss.forward"], "tokens": 390}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GlobalMutualInformationLoss(_Loss):\n\n    def forward(self, pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor:\n        \"\"\"\n        Args:\n            pred: the shape should be B[NDHW].\n            target: the shape should be same as the pred shape.\n        Raises:\n            ValueError: When ``self.reduction`` is not one of [\"mean\", \"sum\", \"none\"].\n        \"\"\"\n        if target.shape != pred.shape:\n            raise ValueError(f\"ground truth has differing shape ({target.shape}) from pred ({pred.shape})\")\n        wa, pa, wb, pb = self.parzen_windowing(pred, target)  # (batch, num_sample, num_bin), (batch, 1, num_bin)\n\n        pab = torch.bmm(wa.permute(0, 2, 1), wb.to(wa)).div(wa.shape[1])  # (batch, num_bins, num_bins)\n        papb = torch.bmm(pa.permute(0, 2, 1), pb.to(pa))  # (batch, num_bins, num_bins)\n        mi = torch.sum(\n            pab * torch.log((pab + self.smooth_nr) / (papb + self.smooth_dr) + self.smooth_dr), dim=(1, 2)\n        )  # (batch)\n\n        if self.reduction == LossReduction.SUM.value:\n            return torch.sum(mi).neg()  # sum over the batch and channel ndims\n        if self.reduction == LossReduction.NONE.value:\n            return mi.neg()\n        if self.reduction == LossReduction.MEAN.value:\n            return torch.mean(mi).neg()  # average over the batch and channel ndims\n        raise ValueError(f'Unsupported reduction: {self.reduction}, available options are [\"mean\", \"sum\", \"none\"].')", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/multi_scale.py_MultiScaleLoss_MultiScaleLoss.__init__.self.scales.scales": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/multi_scale.py_MultiScaleLoss_MultiScaleLoss.__init__.self.scales.scales", "embedding": null, "metadata": {"file_path": "monai/losses/multi_scale.py", "file_name": "multi_scale.py", "file_type": "text/x-python", "category": "implementation", "start_line": 40, "end_line": 67, "span_ids": ["MultiScaleLoss", "MultiScaleLoss.__init__"], "tokens": 230}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class MultiScaleLoss(_Loss):\n    \"\"\"\n    This is a wrapper class.\n    It smooths the input and target at different scales before passing them into the wrapped loss function.\n\n    Adapted from:\n        DeepReg (https://github.com/DeepRegNet/DeepReg)\n    \"\"\"\n\n    def __init__(\n        self,\n        loss: _Loss,\n        scales: Optional[List] = None,\n        kernel: str = \"gaussian\",\n        reduction: Union[LossReduction, str] = LossReduction.MEAN,\n    ) -> None:\n        \"\"\"\n        Args:\n            loss: loss function to be wrapped\n            scales: list of scalars or None, if None, do not apply any scaling.\n            kernel: gaussian or cauchy.\n        \"\"\"\n        super().__init__(reduction=LossReduction(reduction).value)\n        if kernel not in kernel_fn_dict.keys():\n            raise ValueError(f\"got unsupported kernel type: {kernel}\", \"only support gaussian and cauchy\")\n        self.kernel_fn = kernel_fn_dict[kernel]\n        self.loss = loss\n        self.scales = scales", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/multi_scale.py_MultiScaleLoss.forward_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/multi_scale.py_MultiScaleLoss.forward_", "embedding": null, "metadata": {"file_path": "monai/losses/multi_scale.py", "file_name": "multi_scale.py", "file_type": "text/x-python", "category": "implementation", "start_line": 69, "end_line": 95, "span_ids": ["MultiScaleLoss.forward"], "tokens": 272}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class MultiScaleLoss(_Loss):\n\n    def forward(self, y_true: torch.Tensor, y_pred: torch.Tensor) -> torch.Tensor:\n        if self.scales is None:\n            loss: torch.Tensor = self.loss(y_pred, y_true)\n        else:\n            loss_list = []\n            for s in self.scales:\n                if s == 0:\n                    # no smoothing\n                    loss_list.append(self.loss(y_pred, y_true))\n                else:\n                    loss_list.append(\n                        self.loss(\n                            separable_filtering(y_pred, [self.kernel_fn(s).to(y_pred)] * (y_true.ndim - 2)),\n                            separable_filtering(y_true, [self.kernel_fn(s).to(y_pred)] * (y_true.ndim - 2)),\n                        )\n                    )\n            loss = torch.stack(loss_list, dim=0)\n\n        if self.reduction == LossReduction.MEAN.value:\n            loss = torch.mean(loss)  # the batch and channel average\n        elif self.reduction == LossReduction.SUM.value:\n            loss = torch.sum(loss)  # sum over the batch and channel dims\n        elif self.reduction != LossReduction.NONE.value:\n            raise ValueError(f'Unsupported reduction: {self.reduction}, available options are [\"mean\", \"sum\", \"none\"].')\n\n        return loss", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/tversky.py_warnings_TverskyLoss.__init__.self.batch.batch": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/tversky.py_warnings_TverskyLoss.__init__.self.batch.batch", "embedding": null, "metadata": {"file_path": "monai/losses/tversky.py", "file_name": "tversky.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 94, "span_ids": ["TverskyLoss.__init__", "TverskyLoss", "docstring"], "tokens": 814}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import warnings\nfrom typing import Callable, List, Optional, Union\n\nimport torch\nfrom torch.nn.modules.loss import _Loss\n\nfrom monai.networks import one_hot\nfrom monai.utils import LossReduction\n\n\nclass TverskyLoss(_Loss):\n\n    \"\"\"\n    Compute the Tversky loss defined in:\n\n        Sadegh et al. (2017) Tversky loss function for image segmentation\n        using 3D fully convolutional deep networks. (https://arxiv.org/abs/1706.05721)\n\n    Adapted from:\n        https://github.com/NifTK/NiftyNet/blob/v0.6.0/niftynet/layer/loss_segmentation.py#L631\n\n    \"\"\"\n\n    def __init__(\n        self,\n        include_background: bool = True,\n        to_onehot_y: bool = False,\n        sigmoid: bool = False,\n        softmax: bool = False,\n        other_act: Optional[Callable] = None,\n        alpha: float = 0.5,\n        beta: float = 0.5,\n        reduction: Union[LossReduction, str] = LossReduction.MEAN,\n        smooth_nr: float = 1e-5,\n        smooth_dr: float = 1e-5,\n        batch: bool = False,\n    ) -> None:\n        \"\"\"\n        Args:\n            include_background: If False channel index 0 (background category) is excluded from the calculation.\n            to_onehot_y: whether to convert `y` into the one-hot format. Defaults to False.\n            sigmoid: If True, apply a sigmoid function to the prediction.\n            softmax: If True, apply a softmax function to the prediction.\n            other_act: if don't want to use `sigmoid` or `softmax`, use other callable function to execute\n                other activation layers, Defaults to ``None``. for example:\n                `other_act = torch.tanh`.\n            alpha: weight of false positives\n            beta: weight of false negatives\n            reduction: {``\"none\"``, ``\"mean\"``, ``\"sum\"``}\n                Specifies the reduction to apply to the output. Defaults to ``\"mean\"``.\n\n                - ``\"none\"``: no reduction will be applied.\n                - ``\"mean\"``: the sum of the output will be divided by the number of elements in the output.\n                - ``\"sum\"``: the output will be summed.\n\n            smooth_nr: a small constant added to the numerator to avoid zero.\n            smooth_dr: a small constant added to the denominator to avoid nan.\n            batch: whether to sum the intersection and union areas over the batch dimension before the dividing.\n                Defaults to False, a Dice loss value is computed independently from each item in the batch\n                before any `reduction`.\n\n        Raises:\n            TypeError: When ``other_act`` is not an ``Optional[Callable]``.\n            ValueError: When more than 1 of [``sigmoid=True``, ``softmax=True``, ``other_act is not None``].\n                Incompatible values.\n\n        \"\"\"\n\n        super().__init__(reduction=LossReduction(reduction).value)\n        if other_act is not None and not callable(other_act):\n            raise TypeError(f\"other_act must be None or callable but is {type(other_act).__name__}.\")\n        if int(sigmoid) + int(softmax) + int(other_act is not None) > 1:\n            raise ValueError(\"Incompatible values: more than 1 of [sigmoid=True, softmax=True, other_act is not None].\")\n        self.include_background = include_background\n        self.to_onehot_y = to_onehot_y\n        self.sigmoid = sigmoid\n        self.softmax = softmax\n        self.other_act = other_act\n        self.alpha = alpha\n        self.beta = beta\n        self.smooth_nr = float(smooth_nr)\n        self.smooth_dr = float(smooth_dr)\n        self.batch = batch", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/__init__.py_ConfusionMatrixMetric_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/__init__.py_ConfusionMatrixMetric_", "embedding": null, "metadata": {"file_path": "monai/metrics/__init__.py", "file_name": "__init__.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 22, "span_ids": ["docstring"], "tokens": 186}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from .confusion_matrix import ConfusionMatrixMetric, compute_confusion_matrix_metric, get_confusion_matrix\nfrom .cumulative_average import CumulativeAverage\nfrom .froc import compute_fp_tp_probs, compute_froc_curve_data, compute_froc_score\nfrom .hausdorff_distance import HausdorffDistanceMetric, compute_hausdorff_distance, compute_percent_hausdorff_distance\nfrom .meandice import DiceMetric, compute_meandice\nfrom .metric import Cumulative, CumulativeIterationMetric, IterationMetric, Metric\nfrom .regression import MAEMetric, MSEMetric, PSNRMetric, RMSEMetric\nfrom .rocauc import ROCAUCMetric, compute_roc_auc\nfrom .surface_distance import SurfaceDistanceMetric, compute_average_surface_distance\nfrom .utils import do_metric_reduction, get_mask_edges, get_surface_distance, ignore_background", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/confusion_matrix.py_get_confusion_matrix_get_confusion_matrix.return.torch_stack_tp_fp_tn_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/confusion_matrix.py_get_confusion_matrix_get_confusion_matrix.return.torch_stack_tp_fp_tn_", "embedding": null, "metadata": {"file_path": "monai/metrics/confusion_matrix.py", "file_name": "confusion_matrix.py", "file_type": "text/x-python", "category": "implementation", "start_line": 128, "end_line": 173, "span_ids": ["get_confusion_matrix"], "tokens": 452}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_confusion_matrix(y_pred: torch.Tensor, y: torch.Tensor, include_background: bool = True):\n    \"\"\"\n    Compute confusion matrix. A tensor with the shape [BC4] will be returned. Where, the third dimension\n    represents the number of true positive, false positive, true negative and false negative values for\n    each channel of each sample within the input batch. Where, B equals to the batch size and C equals to\n    the number of classes that need to be computed.\n\n    Args:\n        y_pred: input data to compute. It must be one-hot format and first dim is batch.\n            The values should be binarized.\n        y: ground truth to compute the metric. It must be one-hot format and first dim is batch.\n            The values should be binarized.\n        include_background: whether to skip metric computation on the first channel of\n            the predicted output. Defaults to True.\n\n    Raises:\n        ValueError: when `y_pred` and `y` have different shapes.\n    \"\"\"\n\n    if not include_background:\n        y_pred, y = ignore_background(y_pred=y_pred, y=y)\n\n    y = y.float()\n    y_pred = y_pred.float()\n\n    if y.shape != y_pred.shape:\n        raise ValueError(\"y_pred and y should have same shapes.\")\n\n    # get confusion matrix related metric\n    batch_size, n_class = y_pred.shape[:2]\n    # convert to [BNS], where S is the number of pixels for one sample.\n    # As for classification tasks, S equals to 1.\n    y_pred = y_pred.view(batch_size, n_class, -1)\n    y = y.view(batch_size, n_class, -1)\n    tp = ((y_pred + y) == 2).float()\n    tn = ((y_pred + y) == 0).float()\n\n    tp = tp.sum(dim=[2])\n    tn = tn.sum(dim=[2])\n    p = y.sum(dim=[2])\n    n = y.shape[-1] - p\n\n    fn = p - tp\n    fp = n - tn\n\n    return torch.stack([tp, fp, tn, fn], dim=-1)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/confusion_matrix.py_compute_confusion_matrix_metric_compute_confusion_matrix_metric.nan_tensor.torch_tensor_float_nan_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/confusion_matrix.py_compute_confusion_matrix_metric_compute_confusion_matrix_metric.nan_tensor.torch_tensor_float_nan_", "embedding": null, "metadata": {"file_path": "monai/metrics/confusion_matrix.py", "file_name": "confusion_matrix.py", "file_type": "text/x-python", "category": "implementation", "start_line": 173, "end_line": 210, "span_ids": ["compute_confusion_matrix_metric"], "tokens": 404}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def compute_confusion_matrix_metric(metric_name: str, confusion_matrix: torch.Tensor):\n    \"\"\"\n    This function is used to compute confusion matrix related metric.\n\n    Args:\n        metric_name: [``\"sensitivity\"``, ``\"specificity\"``, ``\"precision\"``, ``\"negative predictive value\"``,\n            ``\"miss rate\"``, ``\"fall out\"``, ``\"false discovery rate\"``, ``\"false omission rate\"``,\n            ``\"prevalence threshold\"``, ``\"threat score\"``, ``\"accuracy\"``, ``\"balanced accuracy\"``,\n            ``\"f1 score\"``, ``\"matthews correlation coefficient\"``, ``\"fowlkes mallows index\"``,\n            ``\"informedness\"``, ``\"markedness\"``]\n            Some of the metrics have multiple aliases (as shown in the wikipedia page aforementioned),\n            and you can also input those names instead.\n        confusion_matrix: Please see the doc string of the function ``get_confusion_matrix`` for more details.\n\n    Raises:\n        ValueError: when the size of the last dimension of confusion_matrix is not 4.\n        NotImplementedError: when specify a not implemented metric_name.\n\n    \"\"\"\n\n    metric = check_confusion_matrix_metric_name(metric_name)\n\n    input_dim = confusion_matrix.ndimension()\n    if input_dim == 1:\n        confusion_matrix = confusion_matrix.unsqueeze(dim=0)\n    if confusion_matrix.shape[-1] != 4:\n        raise ValueError(\"the size of the last dimension of confusion_matrix should be 4.\")\n\n    tp = confusion_matrix[..., 0]\n    fp = confusion_matrix[..., 1]\n    tn = confusion_matrix[..., 2]\n    fn = confusion_matrix[..., 3]\n    p = tp + fn\n    n = fp + tn\n    # calculate metric\n    numerator: torch.Tensor\n    denominator: Union[torch.Tensor, float]\n    nan_tensor = torch.tensor(float(\"nan\"), device=confusion_matrix.device)\n    # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/confusion_matrix.py_compute_confusion_matrix_metric.if_metric_tpr__compute_confusion_matrix_metric.return.numerator_denominator": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/confusion_matrix.py_compute_confusion_matrix_metric.if_metric_tpr__compute_confusion_matrix_metric.return.numerator_denominator", "embedding": null, "metadata": {"file_path": "monai/metrics/confusion_matrix.py", "file_name": "confusion_matrix.py", "file_type": "text/x-python", "category": "implementation", "start_line": 211, "end_line": 265, "span_ids": ["compute_confusion_matrix_metric"], "tokens": 692}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def compute_confusion_matrix_metric(metric_name: str, confusion_matrix: torch.Tensor):\n    # ... other code\n    if metric == \"tpr\":\n        numerator, denominator = tp, p\n    elif metric == \"tnr\":\n        numerator, denominator = tn, n\n    elif metric == \"ppv\":\n        numerator, denominator = tp, (tp + fp)\n    elif metric == \"npv\":\n        numerator, denominator = tn, (tn + fn)\n    elif metric == \"fnr\":\n        numerator, denominator = fn, p\n    elif metric == \"fpr\":\n        numerator, denominator = fp, n\n    elif metric == \"fdr\":\n        numerator, denominator = fp, (fp + tp)\n    elif metric == \"for\":\n        numerator, denominator = fn, (fn + tn)\n    elif metric == \"pt\":\n        tpr = torch.where(p > 0, tp / p, nan_tensor)\n        tnr = torch.where(n > 0, tn / n, nan_tensor)\n        numerator = torch.sqrt(tpr * (1.0 - tnr)) + tnr - 1.0\n        denominator = tpr + tnr - 1.0\n    elif metric == \"ts\":\n        numerator, denominator = tp, (tp + fn + fp)\n    elif metric == \"acc\":\n        numerator, denominator = (tp + tn), (p + n)\n    elif metric == \"ba\":\n        tpr = torch.where(p > 0, tp / p, nan_tensor)\n        tnr = torch.where(n > 0, tn / n, nan_tensor)\n        numerator, denominator = (tpr + tnr), 2.0\n    elif metric == \"f1\":\n        numerator, denominator = tp * 2.0, (tp * 2.0 + fn + fp)\n    elif metric == \"mcc\":\n        numerator = tp * tn - fp * fn\n        denominator = torch.sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn))\n    elif metric == \"fm\":\n        tpr = torch.where(p > 0, tp / p, nan_tensor)\n        ppv = torch.where((tp + fp) > 0, tp / (tp + fp), nan_tensor)\n        numerator = torch.sqrt(ppv * tpr)\n        denominator = 1.0\n    elif metric == \"bm\":\n        tpr = torch.where(p > 0, tp / p, nan_tensor)\n        tnr = torch.where(n > 0, tn / n, nan_tensor)\n        numerator = tpr + tnr - 1.0\n        denominator = 1.0\n    elif metric == \"mk\":\n        ppv = torch.where((tp + fp) > 0, tp / (tp + fp), nan_tensor)\n        npv = torch.where((tn + fn) > 0, tn / (tn + fn), nan_tensor)\n        numerator = ppv + npv - 1.0\n        denominator = 1.0\n    else:\n        raise NotImplementedError(\"the metric is not implemented.\")\n\n    if isinstance(denominator, torch.Tensor):\n        return torch.where(denominator != 0, numerator / denominator, nan_tensor)\n    return numerator / denominator", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/confusion_matrix.py_check_confusion_matrix_metric_name_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/confusion_matrix.py_check_confusion_matrix_metric_name_", "embedding": null, "metadata": {"file_path": "monai/metrics/confusion_matrix.py", "file_name": "confusion_matrix.py", "file_type": "text/x-python", "category": "implementation", "start_line": 268, "end_line": 317, "span_ids": ["check_confusion_matrix_metric_name"], "tokens": 495}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def check_confusion_matrix_metric_name(metric_name: str):\n    \"\"\"\n    There are many metrics related to confusion matrix, and some of the metrics have\n    more than one names. In addition, some of the names are very long.\n    Therefore, this function is used to check and simplify the name.\n\n    Returns:\n        Simplified metric name.\n\n    Raises:\n        NotImplementedError: when the metric is not implemented.\n    \"\"\"\n    metric_name = metric_name.replace(\" \", \"_\")\n    metric_name = metric_name.lower()\n    if metric_name in [\"sensitivity\", \"recall\", \"hit_rate\", \"true_positive_rate\", \"tpr\"]:\n        return \"tpr\"\n    if metric_name in [\"specificity\", \"selectivity\", \"true_negative_rate\", \"tnr\"]:\n        return \"tnr\"\n    if metric_name in [\"precision\", \"positive_predictive_value\", \"ppv\"]:\n        return \"ppv\"\n    if metric_name in [\"negative_predictive_value\", \"npv\"]:\n        return \"npv\"\n    if metric_name in [\"miss_rate\", \"false_negative_rate\", \"fnr\"]:\n        return \"fnr\"\n    if metric_name in [\"fall_out\", \"false_positive_rate\", \"fpr\"]:\n        return \"fpr\"\n    if metric_name in [\"false_discovery_rate\", \"fdr\"]:\n        return \"fdr\"\n    if metric_name in [\"false_omission_rate\", \"for\"]:\n        return \"for\"\n    if metric_name in [\"prevalence_threshold\", \"pt\"]:\n        return \"pt\"\n    if metric_name in [\"threat_score\", \"critical_success_index\", \"ts\", \"csi\"]:\n        return \"ts\"\n    if metric_name in [\"accuracy\", \"acc\"]:\n        return \"acc\"\n    if metric_name in [\"balanced_accuracy\", \"ba\"]:\n        return \"ba\"\n    if metric_name in [\"f1_score\", \"f1\"]:\n        return \"f1\"\n    if metric_name in [\"matthews_correlation_coefficient\", \"mcc\"]:\n        return \"mcc\"\n    if metric_name in [\"fowlkes_mallows_index\", \"fm\"]:\n        return \"fm\"\n    if metric_name in [\"informedness\", \"bookmaker_informedness\", \"bm\"]:\n        return \"bm\"\n    if metric_name in [\"markedness\", \"deltap\", \"mk\"]:\n        return \"mk\"\n    raise NotImplementedError(\"the metric is not implemented.\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/froc.py_from_typing_import_List__compute_fp_tp_probs.return.fp_probs_tp_probs_num_t": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/froc.py_from_typing_import_List__compute_fp_tp_probs.return.fp_probs_tp_probs_num_t", "embedding": null, "metadata": {"file_path": "monai/metrics/froc.py", "file_name": "froc.py", "file_type": "text/x-python", "category": "implementation", "start_line": 13, "end_line": 76, "span_ids": ["compute_fp_tp_probs", "docstring"], "tokens": 622}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import List, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\n\n\ndef compute_fp_tp_probs(\n    probs: Union[np.ndarray, torch.Tensor],\n    y_coord: Union[np.ndarray, torch.Tensor],\n    x_coord: Union[np.ndarray, torch.Tensor],\n    evaluation_mask: Union[np.ndarray, torch.Tensor],\n    labels_to_exclude: Optional[List] = None,\n    resolution_level: int = 0,\n):\n    \"\"\"\n    This function is modified from the official evaluation code of\n    `CAMELYON 16 Challenge <https://camelyon16.grand-challenge.org/>`_, and used to distinguish\n    true positive and false positive predictions. A true positive prediction is defined when\n    the detection point is within the annotated ground truth region.\n\n    Args:\n        probs: an array with shape (n,) that represents the probabilities of the detections.\n            Where, n is the number of predicted detections.\n        y_coord: an array with shape (n,) that represents the Y-coordinates of the detections.\n        x_coord: an array with shape (n,) that represents the X-coordinates of the detections.\n        evaluation_mask: the ground truth mask for evaluation.\n        labels_to_exclude: labels in this list will not be counted for metric calculation.\n        resolution_level: the level at which the evaluation mask is made.\n\n    Returns:\n        fp_probs: an array that contains the probabilities of the false positive detections.\n        tp_probs: an array that contains the probabilities of the True positive detections.\n        num_targets: the total number of targets (excluding `labels_to_exclude`) for all images under evaluation.\n\n    \"\"\"\n    if not (probs.shape == y_coord.shape == x_coord.shape):\n        raise AssertionError(\"the shapes for coordinates and probabilities should be the same.\")\n\n    if isinstance(probs, torch.Tensor):\n        probs = probs.detach().cpu().numpy()\n    if isinstance(y_coord, torch.Tensor):\n        y_coord = y_coord.detach().cpu().numpy()\n    if isinstance(x_coord, torch.Tensor):\n        x_coord = x_coord.detach().cpu().numpy()\n    if isinstance(evaluation_mask, torch.Tensor):\n        evaluation_mask = evaluation_mask.detach().cpu().numpy()\n\n    if labels_to_exclude is None:\n        labels_to_exclude = []\n\n    max_label = np.max(evaluation_mask)\n    tp_probs = np.zeros((max_label,), dtype=np.float32)\n\n    y_coord = (y_coord / pow(2, resolution_level)).astype(int)\n    x_coord = (x_coord / pow(2, resolution_level)).astype(int)\n\n    hittedlabel = evaluation_mask[y_coord, x_coord]\n    fp_probs = probs[np.where(hittedlabel == 0)]\n    for i in range(1, max_label + 1):\n        if i not in labels_to_exclude and i in hittedlabel:\n            tp_probs[i - 1] = probs[np.where(hittedlabel == i)].max()\n\n    num_targets = max_label - len(labels_to_exclude)\n    return fp_probs, tp_probs, num_targets", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/froc.py_compute_froc_curve_data_compute_froc_curve_data.return.fps_per_image_total_sens": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/froc.py_compute_froc_curve_data_compute_froc_curve_data.return.fps_per_image_total_sens", "embedding": null, "metadata": {"file_path": "monai/metrics/froc.py", "file_name": "froc.py", "file_type": "text/x-python", "category": "implementation", "start_line": 79, "end_line": 115, "span_ids": ["compute_froc_curve_data"], "tokens": 367}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def compute_froc_curve_data(\n    fp_probs: Union[np.ndarray, torch.Tensor],\n    tp_probs: Union[np.ndarray, torch.Tensor],\n    num_targets: int,\n    num_images: int,\n):\n    \"\"\"\n    This function is modified from the official evaluation code of\n    `CAMELYON 16 Challenge <https://camelyon16.grand-challenge.org/>`_, and used to compute\n    the required data for plotting the Free Response Operating Characteristic (FROC) curve.\n\n    Args:\n        fp_probs: an array that contains the probabilities of the false positive detections for all\n            images under evaluation.\n        tp_probs: an array that contains the probabilities of the True positive detections for all\n            images under evaluation.\n        num_targets: the total number of targets (excluding `labels_to_exclude`) for all images under evaluation.\n        num_images: the number of images under evaluation.\n\n    \"\"\"\n    if not isinstance(fp_probs, type(tp_probs)):\n        raise AssertionError(\"fp and tp probs should have same type.\")\n    if isinstance(fp_probs, torch.Tensor):\n        fp_probs = fp_probs.detach().cpu().numpy()\n    if isinstance(tp_probs, torch.Tensor):\n        tp_probs = tp_probs.detach().cpu().numpy()\n\n    total_fps, total_tps = [], []\n    all_probs = sorted(set(list(fp_probs) + list(tp_probs)))\n    for thresh in all_probs[1:]:\n        total_fps.append((fp_probs >= thresh).sum())\n        total_tps.append((tp_probs >= thresh).sum())\n    total_fps.append(0)\n    total_tps.append(0)\n    fps_per_image = np.asarray(total_fps) / float(num_images)\n    total_sensitivity = np.asarray(total_tps) / float(num_targets)\n    return fps_per_image, total_sensitivity", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/froc.py_compute_froc_score_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/froc.py_compute_froc_score_", "embedding": null, "metadata": {"file_path": "monai/metrics/froc.py", "file_name": "froc.py", "file_type": "text/x-python", "category": "implementation", "start_line": 118, "end_line": 136, "span_ids": ["compute_froc_score"], "tokens": 246}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def compute_froc_score(\n    fps_per_image: np.ndarray, total_sensitivity: np.ndarray, eval_thresholds: Tuple = (0.25, 0.5, 1, 2, 4, 8)\n):\n    \"\"\"\n    This function is modified from the official evaluation code of\n    `CAMELYON 16 Challenge <https://camelyon16.grand-challenge.org/>`_, and used to compute\n    the challenge's second evaluation metric, which is defined as the average sensitivity at\n    the predefined false positive rates per whole slide image.\n\n    Args:\n        fps_per_image: the average number of false positives per image for different thresholds.\n        total_sensitivity: sensitivities (true positive rates) for different thresholds.\n        eval_thresholds: the false positive rates for calculating the average sensitivity. Defaults\n            to (0.25, 0.5, 1, 2, 4, 8) which is the same as the CAMELYON 16 Challenge.\n\n    \"\"\"\n    interp_sens = np.interp(eval_thresholds, fps_per_image[::-1], total_sensitivity[::-1])\n    return np.mean(interp_sens)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/hausdorff_distance.py_compute_hausdorff_distance_compute_hausdorff_distance.return.torch_from_numpy_hd_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/hausdorff_distance.py_compute_hausdorff_distance_compute_hausdorff_distance.return.torch_from_numpy_hd_", "embedding": null, "metadata": {"file_path": "monai/metrics/hausdorff_distance.py", "file_name": "hausdorff_distance.py", "file_type": "text/x-python", "category": "implementation", "start_line": 116, "end_line": 168, "span_ids": ["compute_hausdorff_distance"], "tokens": 597}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def compute_hausdorff_distance(\n    y_pred: Union[np.ndarray, torch.Tensor],\n    y: Union[np.ndarray, torch.Tensor],\n    include_background: bool = False,\n    distance_metric: str = \"euclidean\",\n    percentile: Optional[float] = None,\n    directed: bool = False,\n):\n    \"\"\"\n    Compute the Hausdorff distance.\n\n    Args:\n        y_pred: input data to compute, typical segmentation model output.\n            It must be one-hot format and first dim is batch, example shape: [16, 3, 32, 32]. The values\n            should be binarized.\n        y: ground truth to compute mean the distance. It must be one-hot format and first dim is batch.\n            The values should be binarized.\n        include_background: whether to skip distance computation on the first channel of\n            the predicted output. Defaults to ``False``.\n        distance_metric: : [``\"euclidean\"``, ``\"chessboard\"``, ``\"taxicab\"``]\n            the metric used to compute surface distance. Defaults to ``\"euclidean\"``.\n        percentile: an optional float number between 0 and 100. If specified, the corresponding\n            percentile of the Hausdorff Distance rather than the maximum result will be achieved.\n            Defaults to ``None``.\n        directed: whether to calculate directed Hausdorff distance. Defaults to ``False``.\n    \"\"\"\n\n    if not include_background:\n        y_pred, y = ignore_background(y_pred=y_pred, y=y)\n    if isinstance(y, torch.Tensor):\n        y = y.float()\n    if isinstance(y_pred, torch.Tensor):\n        y_pred = y_pred.float()\n\n    if y.shape != y_pred.shape:\n        raise ValueError(\"y_pred and y should have same shapes.\")\n\n    batch_size, n_class = y_pred.shape[:2]\n    hd = np.empty((batch_size, n_class))\n    for b, c in np.ndindex(batch_size, n_class):\n        (edges_pred, edges_gt) = get_mask_edges(y_pred[b, c], y[b, c])\n        if not np.any(edges_gt):\n            warnings.warn(f\"the ground truth of class {c} is all 0, this may result in nan/inf distance.\")\n        if not np.any(edges_pred):\n            warnings.warn(f\"the prediction of class {c} is all 0, this may result in nan/inf distance.\")\n\n        distance_1 = compute_percent_hausdorff_distance(edges_pred, edges_gt, distance_metric, percentile)\n        if directed:\n            hd[b, c] = distance_1\n        else:\n            distance_2 = compute_percent_hausdorff_distance(edges_gt, edges_pred, distance_metric, percentile)\n            hd[b, c] = max(distance_1, distance_2)\n    return torch.from_numpy(hd)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/meandice.py_compute_meandice_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/meandice.py_compute_meandice_", "embedding": null, "metadata": {"file_path": "monai/metrics/meandice.py", "file_name": "meandice.py", "file_type": "text/x-python", "category": "implementation", "start_line": 96, "end_line": 135, "span_ids": ["compute_meandice"], "tokens": 369}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def compute_meandice(y_pred: torch.Tensor, y: torch.Tensor, include_background: bool = True) -> torch.Tensor:\n    \"\"\"Computes Dice score metric from full size Tensor and collects average.\n\n    Args:\n        y_pred: input data to compute, typical segmentation model output.\n            It must be one-hot format and first dim is batch, example shape: [16, 3, 32, 32]. The values\n            should be binarized.\n        y: ground truth to compute mean dice metric. It must be one-hot format and first dim is batch.\n            The values should be binarized.\n        include_background: whether to skip Dice computation on the first channel of\n            the predicted output. Defaults to True.\n\n    Returns:\n        Dice scores per batch and per class, (shape [batch_size, num_classes]).\n\n    Raises:\n        ValueError: when `y_pred` and `y` have different shapes.\n\n    \"\"\"\n\n    if not include_background:\n        y_pred, y = ignore_background(y_pred=y_pred, y=y)\n\n    y = y.float()\n    y_pred = y_pred.float()\n\n    if y.shape != y_pred.shape:\n        raise ValueError(\"y_pred and y should have same shapes.\")\n\n    # reducing only spatial dimensions (not batch nor channels)\n    n_len = len(y_pred.shape)\n    reduce_axis = list(range(2, n_len))\n    intersection = torch.sum(y * y_pred, dim=reduce_axis)\n\n    y_o = torch.sum(y, reduce_axis)\n    y_pred_o = torch.sum(y_pred, dim=reduce_axis)\n    denominator = y_o + y_pred_o\n\n    return torch.where(y_o > 0, (2.0 * intersection) / denominator, torch.tensor(float(\"nan\"), device=y_o.device))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/rocauc.py_compute_roc_auc_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/rocauc.py_compute_roc_auc_", "embedding": null, "metadata": {"file_path": "monai/metrics/rocauc.py", "file_name": "rocauc.py", "file_type": "text/x-python", "category": "implementation", "start_line": 96, "end_line": 160, "span_ids": ["compute_roc_auc"], "tokens": 861}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def compute_roc_auc(y_pred: torch.Tensor, y: torch.Tensor, average: Union[Average, str] = Average.MACRO):\n    \"\"\"Computes Area Under the Receiver Operating Characteristic Curve (ROC AUC). Referring to:\n    `sklearn.metrics.roc_auc_score <https://scikit-learn.org/stable/modules/generated/\n    sklearn.metrics.roc_auc_score.html#sklearn.metrics.roc_auc_score>`_.\n\n    Args:\n        y_pred: input data to compute, typical classification model output.\n            the first dim must be batch, if multi-classes, it must be in One-Hot format.\n            for example: shape `[16]` or `[16, 1]` for a binary data, shape `[16, 2]` for 2 classes data.\n        y: ground truth to compute ROC AUC metric, the first dim must be batch.\n            if multi-classes, it must be in One-Hot format.\n            for example: shape `[16]` or `[16, 1]` for a binary data, shape `[16, 2]` for 2 classes data.\n        average: {``\"macro\"``, ``\"weighted\"``, ``\"micro\"``, ``\"none\"``}\n            Type of averaging performed if not binary classification.\n            Defaults to ``\"macro\"``.\n\n            - ``\"macro\"``: calculate metrics for each label, and find their unweighted mean.\n                This does not take label imbalance into account.\n            - ``\"weighted\"``: calculate metrics for each label, and find their average,\n                weighted by support (the number of true instances for each label).\n            - ``\"micro\"``: calculate metrics globally by considering each element of the label\n                indicator matrix as a label.\n            - ``\"none\"``: the scores for each class are returned.\n\n    Raises:\n        ValueError: When ``y_pred`` dimension is not one of [1, 2].\n        ValueError: When ``y`` dimension is not one of [1, 2].\n        ValueError: When ``average`` is not one of [\"macro\", \"weighted\", \"micro\", \"none\"].\n\n    Note:\n        ROCAUC expects y to be comprised of 0's and 1's. `y_pred` must be either prob. estimates or confidence values.\n\n    \"\"\"\n    y_pred_ndim = y_pred.ndimension()\n    y_ndim = y.ndimension()\n    if y_pred_ndim not in (1, 2):\n        raise ValueError(\"Predictions should be of shape (batch_size, num_classes) or (batch_size, ).\")\n    if y_ndim not in (1, 2):\n        raise ValueError(\"Targets should be of shape (batch_size, num_classes) or (batch_size, ).\")\n    if y_pred_ndim == 2 and y_pred.shape[1] == 1:\n        y_pred = y_pred.squeeze(dim=-1)\n        y_pred_ndim = 1\n    if y_ndim == 2 and y.shape[1] == 1:\n        y = y.squeeze(dim=-1)\n\n    if y_pred_ndim == 1:\n        return _calculate(y_pred, y)\n\n    if y.shape != y_pred.shape:\n        raise AssertionError(\"data shapes of y_pred and y do not match.\")\n\n    average = look_up_option(average, Average)\n    if average == Average.MICRO:\n        return _calculate(y_pred.flatten(), y.flatten())\n    y, y_pred = y.transpose(0, 1), y_pred.transpose(0, 1)\n    auc_values = [_calculate(y_pred_, y_) for y_pred_, y_ in zip(y_pred, y)]\n    if average == Average.NONE:\n        return auc_values\n    if average == Average.MACRO:\n        return np.mean(auc_values)\n    if average == Average.WEIGHTED:\n        weights = [sum(y_) for y_ in y]\n        return np.average(auc_values, weights=weights)\n    raise ValueError(f'Unsupported average: {average}, available options are [\"macro\", \"weighted\", \"micro\", \"none\"].')", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/surface_distance.py_compute_average_surface_distance_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/surface_distance.py_compute_average_surface_distance_", "embedding": null, "metadata": {"file_path": "monai/metrics/surface_distance.py", "file_name": "surface_distance.py", "file_type": "text/x-python", "category": "implementation", "start_line": 108, "end_line": 173, "span_ids": ["compute_average_surface_distance"], "tokens": 698}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def compute_average_surface_distance(\n    y_pred: Union[np.ndarray, torch.Tensor],\n    y: Union[np.ndarray, torch.Tensor],\n    include_background: bool = False,\n    symmetric: bool = False,\n    distance_metric: str = \"euclidean\",\n):\n    \"\"\"\n    This function is used to compute the Average Surface Distance from `y_pred` to `y`\n    under the default setting.\n    In addition, if sets ``symmetric = True``, the average symmetric surface distance between\n    these two inputs will be returned.\n    The implementation refers to `DeepMind's implementation <https://github.com/deepmind/surface-distance>`_.\n\n    Args:\n        y_pred: input data to compute, typical segmentation model output.\n            It must be one-hot format and first dim is batch, example shape: [16, 3, 32, 32]. The values\n            should be binarized.\n        y: ground truth to compute mean the distance. It must be one-hot format and first dim is batch.\n            The values should be binarized.\n        include_background: whether to skip distance computation on the first channel of\n            the predicted output. Defaults to ``False``.\n        symmetric: whether to calculate the symmetric average surface distance between\n            `seg_pred` and `seg_gt`. Defaults to ``False``.\n        distance_metric: : [``\"euclidean\"``, ``\"chessboard\"``, ``\"taxicab\"``]\n            the metric used to compute surface distance. Defaults to ``\"euclidean\"``.\n    \"\"\"\n\n    if not include_background:\n        y_pred, y = ignore_background(y_pred=y_pred, y=y)\n\n    if isinstance(y, torch.Tensor):\n        y = y.float()\n    if isinstance(y_pred, torch.Tensor):\n        y_pred = y_pred.float()\n\n    if y.shape != y_pred.shape:\n        raise ValueError(\"y_pred and y should have same shapes.\")\n\n    batch_size, n_class = y_pred.shape[:2]\n    asd = np.empty((batch_size, n_class))\n\n    for b, c in np.ndindex(batch_size, n_class):\n        (edges_pred, edges_gt) = get_mask_edges(y_pred[b, c], y[b, c])\n        if not np.any(edges_gt):\n            warnings.warn(f\"the ground truth of class {c} is all 0, this may result in nan/inf distance.\")\n        if not np.any(edges_pred):\n            warnings.warn(f\"the prediction of class {c} is all 0, this may result in nan/inf distance.\")\n\n        surface_distance = get_surface_distance(edges_pred, edges_gt, distance_metric=distance_metric)\n        if surface_distance.shape == (0,):\n            avg_surface_distance = np.nan\n        else:\n            avg_surface_distance = surface_distance.mean()  # type: ignore\n        if not symmetric:\n            asd[b, c] = avg_surface_distance\n        else:\n            surface_distance_2 = get_surface_distance(edges_gt, edges_pred, distance_metric=distance_metric)\n            if surface_distance_2.shape == (0,):\n                avg_surface_distance_2 = np.nan\n            else:\n                avg_surface_distance_2 = surface_distance_2.mean()  # type: ignore\n            asd[b, c] = np.mean((avg_surface_distance, avg_surface_distance_2))\n\n    return torch.from_numpy(asd)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/utils.py_ignore_background_ignore_background.return.y_pred_y": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/utils.py_ignore_background_ignore_background.return.y_pred_y", "embedding": null, "metadata": {"file_path": "monai/metrics/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 28, "end_line": 42, "span_ids": ["ignore_background"], "tokens": 164}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def ignore_background(y_pred: Union[np.ndarray, torch.Tensor], y: Union[np.ndarray, torch.Tensor]):\n    \"\"\"\n    This function is used to remove background (the first channel) for `y_pred` and `y`.\n\n    Args:\n        y_pred: predictions. As for classification tasks,\n            `y_pred` should has the shape [BN] where N is larger than 1. As for segmentation tasks,\n            the shape should be [BNHW] or [BNHWD].\n        y: ground truth, the first dim is batch.\n\n    \"\"\"\n\n    y = y[:, 1:] if y.shape[1] > 1 else y\n    y_pred = y_pred[:, 1:] if y_pred.shape[1] > 1 else y_pred\n    return y_pred, y", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/utils.py_do_metric_reduction_do_metric_reduction.return.f_not_nans": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/utils.py_do_metric_reduction_do_metric_reduction.return.f_not_nans", "embedding": null, "metadata": {"file_path": "monai/metrics/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 45, "end_line": 103, "span_ids": ["do_metric_reduction"], "tokens": 799}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def do_metric_reduction(f: torch.Tensor, reduction: Union[MetricReduction, str] = MetricReduction.MEAN):\n    \"\"\"\n    This function is to do the metric reduction for calculated `not-nan` metrics of each sample's each class.\n    The function also returns `not_nans`, which counts the number of not nans for the metric.\n\n    Args:\n        f: a tensor that contains the calculated metric scores per batch and\n            per class. The first two dims should be batch and class.\n        reduction: define the mode to reduce metrics, will only execute reduction on `not-nan` values,\n            available reduction modes: {``\"none\"``, ``\"mean\"``, ``\"sum\"``, ``\"mean_batch\"``, ``\"sum_batch\"``,\n            ``\"mean_channel\"``, ``\"sum_channel\"``}, default to ``\"mean\"``.\n            if \"none\", return the input f tensor and not_nans.\n        Define the mode to reduce computation result of 1 batch data. Defaults to ``\"mean\"``.\n\n    Raises:\n        ValueError: When ``reduction`` is not one of\n            [\"mean\", \"sum\", \"mean_batch\", \"sum_batch\", \"mean_channel\", \"sum_channel\" \"none\"].\n    \"\"\"\n\n    # some elements might be Nan (if ground truth y was missing (zeros))\n    # we need to account for it\n    nans = torch.isnan(f)\n    not_nans = (~nans).float()\n\n    t_zero = torch.zeros(1, device=f.device, dtype=f.dtype)\n    reduction = look_up_option(reduction, MetricReduction)\n    if reduction == MetricReduction.NONE:\n        return f, not_nans\n\n    f[nans] = 0\n    if reduction == MetricReduction.MEAN:\n        # 2 steps, first, mean by channel (accounting for nans), then by batch\n        not_nans = not_nans.sum(dim=1)\n        f = torch.where(not_nans > 0, f.sum(dim=1) / not_nans, t_zero)  # channel average\n\n        not_nans = (not_nans > 0).float().sum(dim=0)\n        f = torch.where(not_nans > 0, f.sum(dim=0) / not_nans, t_zero)  # batch average\n\n    elif reduction == MetricReduction.SUM:\n        not_nans = not_nans.sum(dim=[0, 1])\n        f = torch.sum(f, dim=[0, 1])  # sum over the batch and channel dims\n    elif reduction == MetricReduction.MEAN_BATCH:\n        not_nans = not_nans.sum(dim=0)\n        f = torch.where(not_nans > 0, f.sum(dim=0) / not_nans, t_zero)  # batch average\n    elif reduction == MetricReduction.SUM_BATCH:\n        not_nans = not_nans.sum(dim=0)\n        f = f.sum(dim=0)  # the batch sum\n    elif reduction == MetricReduction.MEAN_CHANNEL:\n        not_nans = not_nans.sum(dim=1)\n        f = torch.where(not_nans > 0, f.sum(dim=1) / not_nans, t_zero)  # channel average\n    elif reduction == MetricReduction.SUM_CHANNEL:\n        not_nans = not_nans.sum(dim=1)\n        f = f.sum(dim=1)  # the channel sum\n    elif reduction != MetricReduction.NONE:\n        raise ValueError(\n            f\"Unsupported reduction: {reduction}, available options are \"\n            '[\"mean\", \"sum\", \"mean_batch\", \"sum_batch\", \"mean_channel\", \"sum_channel\" \"none\"].'\n        )\n    return f, not_nans", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/__init__.py_ADN_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/__init__.py_ADN_", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/__init__.py", "file_name": "__init__.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 39, "span_ids": ["docstring"], "tokens": 345}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from .acti_norm import ADN\nfrom .activation import MemoryEfficientSwish, Mish, Swish\nfrom .aspp import SimpleASPP\nfrom .convolutions import Convolution, ResidualUnit\nfrom .crf import CRF\nfrom .dints_block import ActiConvNormBlock, FactorizedIncreaseBlock, FactorizedReduceBlock, P3DActiConvNormBlock\nfrom .downsample import MaxAvgPool\nfrom .dynunet_block import UnetBasicBlock, UnetOutBlock, UnetResBlock, UnetUpBlock, get_output_padding, get_padding\nfrom .fcn import FCN, GCN, MCFCN, Refine\nfrom .localnet_block import LocalNetDownSampleBlock, LocalNetFeatureExtractorBlock, LocalNetUpSampleBlock\nfrom .mlp import MLPBlock\nfrom .patchembedding import PatchEmbeddingBlock\nfrom .regunet_block import RegistrationDownSampleBlock, RegistrationExtractionBlock, RegistrationResidualConvBlock\nfrom .segresnet_block import ResBlock\nfrom .selfattention import SABlock\nfrom .squeeze_and_excitation import (\n    ChannelSELayer,\n    ResidualSELayer,\n    SEBlock,\n    SEBottleneck,\n    SEResNetBottleneck,\n    SEResNeXtBottleneck,\n)\nfrom .transformerblock import TransformerBlock\nfrom .unetr_block import UnetrBasicBlock, UnetrPrUpBlock, UnetrUpBlock\nfrom .upsample import SubpixelUpsample, Subpixelupsample, SubpixelUpSample, Upsample, UpSample\nfrom .warp import DVF2DDF, Warp", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/acti_norm.py_from_typing_import_Option_ADN._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/acti_norm.py_from_typing_import_Option_ADN._", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/acti_norm.py", "file_name": "acti_norm.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 67, "span_ids": ["ADN", "docstring"], "tokens": 551}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Optional, Tuple, Union\n\nimport torch.nn as nn\n\nfrom monai.networks.layers.utils import get_act_layer, get_dropout_layer, get_norm_layer\n\n\nclass ADN(nn.Sequential):\n    \"\"\"\n    Constructs a sequential module of optional activation, dropout, and normalization layers\n    (with an arbitrary order)::\n\n        -- (Norm) -- (Dropout) -- (Acti) --\n\n    Args:\n        ordering: a string representing the ordering of activation, dropout, and normalization. Defaults to \"NDA\".\n        in_channels: `C` from an expected input of size (N, C, H[, W, D]).\n        act: activation type and arguments. Defaults to PReLU.\n        norm: feature normalization type and arguments. Defaults to instance norm.\n        norm_dim: determine the spatial dimensions of the normalization layer.\n            defaults to `dropout_dim` if unspecified.\n        dropout: dropout ratio. Defaults to no dropout.\n        dropout_dim: determine the spatial dimensions of dropout.\n            defaults to `norm_dim` if unspecified.\n\n            - When dropout_dim = 1, randomly zeroes some of the elements for each channel.\n            - When dropout_dim = 2, Randomly zeroes out entire channels (a channel is a 2D feature map).\n            - When dropout_dim = 3, Randomly zeroes out entire channels (a channel is a 3D feature map).\n\n    Examples::\n\n        # activation, group norm, dropout\n        >>> norm_params = (\"GROUP\", {\"num_groups\": 1, \"affine\": False})\n        >>> ADN(norm=norm_params, in_channels=1, dropout_dim=1, dropout=0.8, ordering=\"AND\")\n        ADN(\n            (A): ReLU()\n            (N): GroupNorm(1, 1, eps=1e-05, affine=False)\n            (D): Dropout(p=0.8, inplace=False)\n        )\n\n        # LeakyReLU, dropout\n        >>> act_params = (\"leakyrelu\", {\"negative_slope\": 0.1, \"inplace\": True})\n        >>> ADN(act=act_params, in_channels=1, dropout_dim=1, dropout=0.8, ordering=\"AD\")\n        ADN(\n            (A): LeakyReLU(negative_slope=0.1, inplace=True)\n            (D): Dropout(p=0.8, inplace=False)\n        )\n\n    See also:\n\n        :py:class:`monai.networks.layers.Dropout`\n        :py:class:`monai.networks.layers.Act`\n        :py:class:`monai.networks.layers.Norm`\n        :py:class:`monai.networks.layers.split_args`\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/acti_norm.py_ADN.__init___": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/acti_norm.py_ADN.__init___", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/acti_norm.py", "file_name": "acti_norm.py", "file_type": "text/x-python", "category": "implementation", "start_line": 69, "end_line": 102, "span_ids": ["ADN.__init__"], "tokens": 351}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ADN(nn.Sequential):\n\n    def __init__(\n        self,\n        ordering: str = \"NDA\",\n        in_channels: Optional[int] = None,\n        act: Optional[Union[Tuple, str]] = \"RELU\",\n        norm: Optional[Union[Tuple, str]] = None,\n        norm_dim: Optional[int] = None,\n        dropout: Optional[Union[Tuple, str, float]] = None,\n        dropout_dim: Optional[int] = None,\n    ) -> None:\n        super().__init__()\n\n        op_dict = {\"A\": None, \"D\": None, \"N\": None}\n        # define the normalization type and the arguments to the constructor\n        if norm is not None:\n            if norm_dim is None and dropout_dim is None:\n                raise ValueError(\"norm_dim or dropout_dim needs to be specified.\")\n            op_dict[\"N\"] = get_norm_layer(name=norm, spatial_dims=norm_dim or dropout_dim, channels=in_channels)\n\n        # define the activation type and the arguments to the constructor\n        if act is not None:\n            op_dict[\"A\"] = get_act_layer(act)\n\n        if dropout is not None:\n            if norm_dim is None and dropout_dim is None:\n                raise ValueError(\"norm_dim or dropout_dim needs to be specified.\")\n            op_dict[\"D\"] = get_dropout_layer(name=dropout, dropout_dim=dropout_dim or norm_dim)\n\n        for item in ordering.upper():\n            if item not in op_dict:\n                raise ValueError(f\"ordering must be a string of {op_dict}, got {item} in it.\")\n            if op_dict[item] is not None:\n                self.add_module(item, op_dict[item])  # type: ignore", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/activation.py_SwishImplementation_SwishImplementation.backward.return.grad_output_sigmoid_in": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/activation.py_SwishImplementation_SwishImplementation.backward.return.grad_output_sigmoid_in", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/activation.py", "file_name": "activation.py", "file_type": "text/x-python", "category": "implementation", "start_line": 46, "end_line": 64, "span_ids": ["SwishImplementation.forward", "SwishImplementation", "SwishImplementation.backward"], "tokens": 151}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SwishImplementation(torch.autograd.Function):\n    r\"\"\"Memory efficient implementation for training\n    Follows recommendation from:\n    https://github.com/lukemelas/EfficientNet-PyTorch/issues/18#issuecomment-511677853\n\n    Results in ~ 30% memory saving during training as compared to Swish()\n    \"\"\"\n\n    @staticmethod\n    def forward(ctx, input):\n        result = input * torch.sigmoid(input)\n        ctx.save_for_backward(input)\n        return result\n\n    @staticmethod\n    def backward(ctx, grad_output):\n        input = ctx.saved_tensors[0]\n        sigmoid_input = torch.sigmoid(input)\n        return grad_output * (sigmoid_input * (1 + input * (1 - sigmoid_input)))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/activation.py_Mish_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/activation.py_Mish_", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/activation.py", "file_name": "activation.py", "file_type": "text/x-python", "category": "implementation", "start_line": 133, "end_line": 165, "span_ids": ["Mish.forward", "Mish.__init__", "Mish"], "tokens": 279}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Mish(nn.Module):\n    r\"\"\"Applies the element-wise function:\n\n    .. math::\n        \\text{Mish}(x) = x * tanh(\\text{softplus}(x)).\n\n    Citation: Mish: A Self Regularized Non-Monotonic Activation Function, Diganta Misra, 2019, https://arxiv.org/abs/1908.08681.\n\n    From Pytorch 1.9.0+, the optimized version of `Mish` is implemented,\n    this class will utilize `torch.nn.functional.mish` to do the calculation if meets the version.\n\n    Shape:\n        - Input: :math:`(N, *)` where `*` means, any number of additional dimensions\n        - Output: :math:`(N, *)`, same shape as the input\n\n\n    Examples::\n\n        >>> import torch\n        >>> from monai.networks.layers.factories import Act\n        >>> m = Act['mish']()\n        >>> input = torch.randn(2)\n        >>> output = m(input)\n    \"\"\"\n\n    def __init__(self, inplace: bool = False):\n        super().__init__()\n        # inplace only works when using torch.nn.functional.mish\n        self.inplace = inplace\n\n    def forward(self, input: torch.Tensor):\n        return monai_mish(input, self.inplace)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/convolutions.py_Convolution.__init___Convolution.__init__.if_not_conv_only_.self_add_module_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/convolutions.py_Convolution.__init___Convolution.__init__.if_not_conv_only_.self_add_module_", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/convolutions.py", "file_name": "convolutions.py", "file_type": "text/x-python", "category": "implementation", "start_line": 100, "end_line": 174, "span_ids": ["Convolution.__init__"], "tokens": 553}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Convolution(nn.Sequential):\n\n    @deprecated_arg(\n        name=\"dimensions\", new_name=\"spatial_dims\", since=\"0.6\", msg_suffix=\"Please use `spatial_dims` instead.\"\n    )\n    def __init__(\n        self,\n        spatial_dims: int,\n        in_channels: int,\n        out_channels: int,\n        strides: Union[Sequence[int], int] = 1,\n        kernel_size: Union[Sequence[int], int] = 3,\n        adn_ordering: str = \"NDA\",\n        act: Optional[Union[Tuple, str]] = \"PRELU\",\n        norm: Optional[Union[Tuple, str]] = \"INSTANCE\",\n        dropout: Optional[Union[Tuple, str, float]] = None,\n        dropout_dim: Optional[int] = 1,\n        dilation: Union[Sequence[int], int] = 1,\n        groups: int = 1,\n        bias: bool = True,\n        conv_only: bool = False,\n        is_transposed: bool = False,\n        padding: Optional[Union[Sequence[int], int]] = None,\n        output_padding: Optional[Union[Sequence[int], int]] = None,\n        dimensions: Optional[int] = None,\n    ) -> None:\n        super().__init__()\n        self.dimensions = spatial_dims if dimensions is None else dimensions\n        self.in_channels = in_channels\n        self.out_channels = out_channels\n        self.is_transposed = is_transposed\n        if padding is None:\n            padding = same_padding(kernel_size, dilation)\n        conv_type = Conv[Conv.CONVTRANS if is_transposed else Conv.CONV, self.dimensions]\n\n        conv: nn.Module\n        if is_transposed:\n            if output_padding is None:\n                output_padding = stride_minus_kernel_padding(1, strides)\n            conv = conv_type(\n                in_channels,\n                out_channels,\n                kernel_size=kernel_size,\n                stride=strides,\n                padding=padding,\n                output_padding=output_padding,\n                groups=groups,\n                bias=bias,\n                dilation=dilation,\n            )\n        else:\n            conv = conv_type(\n                in_channels,\n                out_channels,\n                kernel_size=kernel_size,\n                stride=strides,\n                padding=padding,\n                dilation=dilation,\n                groups=groups,\n                bias=bias,\n            )\n\n        self.add_module(\"conv\", conv)\n\n        if not conv_only:\n            self.add_module(\n                \"adn\",\n                ADN(\n                    ordering=adn_ordering,\n                    in_channels=out_channels,\n                    act=act,\n                    norm=norm,\n                    norm_dim=self.dimensions,\n                    dropout=dropout,\n                    dropout_dim=dropout_dim,\n                ),\n            )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/localnet_block.py_ResidualBlock_ResidualBlock.forward.return.out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/localnet_block.py_ResidualBlock_ResidualBlock.forward.return.out", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/localnet_block.py", "file_name": "localnet_block.py", "file_type": "text/x-python", "category": "implementation", "start_line": 72, "end_line": 92, "span_ids": ["ResidualBlock.forward", "ResidualBlock.__init__", "ResidualBlock"], "tokens": 217}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ResidualBlock(nn.Module):\n    def __init__(\n        self, spatial_dims: int, in_channels: int, out_channels: int, kernel_size: Union[Sequence[int], int]\n    ) -> None:\n        super().__init__()\n        if in_channels != out_channels:\n            raise ValueError(\n                f\"expecting in_channels == out_channels, \" f\"got in_channels={in_channels}, out_channels={out_channels}\"\n            )\n        self.conv_block = get_conv_block(\n            spatial_dims=spatial_dims, in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size\n        )\n        self.conv = get_conv_layer(\n            spatial_dims=spatial_dims, in_channels=out_channels, out_channels=out_channels, kernel_size=kernel_size\n        )\n        self.norm = Norm[Norm.BATCH, spatial_dims](out_channels)\n        self.relu = nn.ReLU()\n\n    def forward(self, x) -> torch.Tensor:\n        out: torch.Tensor = self.relu(self.norm(self.conv(self.conv_block(x))) + x)\n        return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/localnet_block.py_LocalNetResidualBlock_LocalNetResidualBlock.forward.return.out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/localnet_block.py_LocalNetResidualBlock_LocalNetResidualBlock.forward.return.out", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/localnet_block.py", "file_name": "localnet_block.py", "file_type": "text/x-python", "category": "implementation", "start_line": 95, "end_line": 108, "span_ids": ["LocalNetResidualBlock.__init__", "LocalNetResidualBlock", "LocalNetResidualBlock.forward"], "tokens": 163}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LocalNetResidualBlock(nn.Module):\n    def __init__(self, spatial_dims: int, in_channels: int, out_channels: int) -> None:\n        super().__init__()\n        if in_channels != out_channels:\n            raise ValueError(\n                f\"expecting in_channels == out_channels, \" f\"got in_channels={in_channels}, out_channels={out_channels}\"\n            )\n        self.conv_layer = get_conv_layer(spatial_dims=spatial_dims, in_channels=in_channels, out_channels=out_channels)\n        self.norm = Norm[Norm.BATCH, spatial_dims](out_channels)\n        self.relu = nn.ReLU()\n\n    def forward(self, x, mid) -> torch.Tensor:\n        out: torch.Tensor = self.relu(self.norm(self.conv_layer(x)) + mid)\n        return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/localnet_block.py_LocalNetDownSampleBlock_LocalNetDownSampleBlock.__init__.self.max_pool.Pool_Pool_MAX_spatial_di": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/localnet_block.py_LocalNetDownSampleBlock_LocalNetDownSampleBlock.__init__.self.max_pool.Pool_Pool_MAX_spatial_di", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/localnet_block.py", "file_name": "localnet_block.py", "file_type": "text/x-python", "category": "implementation", "start_line": 111, "end_line": 142, "span_ids": ["LocalNetDownSampleBlock", "LocalNetDownSampleBlock.__init__"], "tokens": 312}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LocalNetDownSampleBlock(nn.Module):\n    \"\"\"\n    A down-sample module that can be used for LocalNet, based on:\n    `Weakly-supervised convolutional neural networks for multimodal image registration\n    <https://doi.org/10.1016/j.media.2018.07.002>`_.\n    `Label-driven weakly-supervised learning for multimodal deformable image registration\n    <https://arxiv.org/abs/1711.01666>`_.\n\n    Adapted from:\n        DeepReg (https://github.com/DeepRegNet/DeepReg)\n    \"\"\"\n\n    def __init__(\n        self, spatial_dims: int, in_channels: int, out_channels: int, kernel_size: Union[Sequence[int], int]\n    ) -> None:\n        \"\"\"\n        Args:\n            spatial_dims: number of spatial dimensions.\n            in_channels: number of input channels.\n            out_channels: number of output channels.\n            kernel_size: convolution kernel size.\n        Raises:\n            NotImplementedError: when ``kernel_size`` is even\n        \"\"\"\n        super().__init__()\n        self.conv_block = get_conv_block(\n            spatial_dims=spatial_dims, in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size\n        )\n        self.residual_block = ResidualBlock(\n            spatial_dims=spatial_dims, in_channels=out_channels, out_channels=out_channels, kernel_size=kernel_size\n        )\n        self.max_pool = Pool[Pool.MAX, spatial_dims](kernel_size=2)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/localnet_block.py_LocalNetDownSampleBlock.forward_LocalNetDownSampleBlock.forward.return.x_mid": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/localnet_block.py_LocalNetDownSampleBlock.forward_LocalNetDownSampleBlock.forward.return.x_mid", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/localnet_block.py", "file_name": "localnet_block.py", "file_type": "text/x-python", "category": "implementation", "start_line": 176, "end_line": 196, "span_ids": ["LocalNetDownSampleBlock.forward"], "tokens": 245}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LocalNetDownSampleBlock(nn.Module):\n\n    def forward(self, x) -> Tuple[torch.Tensor, torch.Tensor]:\n        \"\"\"\n        Halves the spatial dimensions.\n        A tuple of (x, mid) is returned:\n\n            -  x is the downsample result, in shape (batch, ``out_channels``, insize_1 / 2, insize_2 / 2, [insize_3 / 2]),\n            -  mid is the mid-level feature, in shape (batch, ``out_channels``, insize_1, insize_2, [insize_3])\n\n        Args:\n            x: Tensor in shape (batch, ``in_channels``, insize_1, insize_2, [insize_3])\n\n        Raises:\n            ValueError: when input spatial dimensions are not even.\n        \"\"\"\n        for i in x.shape[2:]:\n            if i % 2 != 0:\n                raise ValueError(\"expecting x spatial dimensions be even, \" f\"got x of shape {x.shape}\")\n        x = self.conv_block(x)\n        mid = self.residual_block(x)\n        x = self.max_pool(mid)\n        return x, mid", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/localnet_block.py_LocalNetUpSampleBlock_LocalNetUpSampleBlock.addictive_upsampling.return.out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/localnet_block.py_LocalNetUpSampleBlock_LocalNetUpSampleBlock.addictive_upsampling.return.out", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/localnet_block.py", "file_name": "localnet_block.py", "file_type": "text/x-python", "category": "implementation", "start_line": 167, "end_line": 209, "span_ids": ["LocalNetUpSampleBlock", "LocalNetUpSampleBlock.__init__", "LocalNetUpSampleBlock.addictive_upsampling"], "tokens": 443}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LocalNetUpSampleBlock(nn.Module):\n    \"\"\"\n    A up-sample module that can be used for LocalNet, based on:\n    `Weakly-supervised convolutional neural networks for multimodal image registration\n    <https://doi.org/10.1016/j.media.2018.07.002>`_.\n    `Label-driven weakly-supervised learning for multimodal deformable image registration\n    <https://arxiv.org/abs/1711.01666>`_.\n\n    Adapted from:\n        DeepReg (https://github.com/DeepRegNet/DeepReg)\n    \"\"\"\n\n    def __init__(self, spatial_dims: int, in_channels: int, out_channels: int) -> None:\n        \"\"\"\n        Args:\n            spatial_dims: number of spatial dimensions.\n            in_channels: number of input channels.\n            out_channels: number of output channels.\n        Raises:\n            ValueError: when ``in_channels != 2 * out_channels``\n        \"\"\"\n        super().__init__()\n        self.deconv_block = get_deconv_block(\n            spatial_dims=spatial_dims, in_channels=in_channels, out_channels=out_channels\n        )\n        self.conv_block = get_conv_block(spatial_dims=spatial_dims, in_channels=out_channels, out_channels=out_channels)\n        self.residual_block = LocalNetResidualBlock(\n            spatial_dims=spatial_dims, in_channels=out_channels, out_channels=out_channels\n        )\n        if in_channels / out_channels != 2:\n            raise ValueError(\n                f\"expecting in_channels == 2 * out_channels, \"\n                f\"got in_channels={in_channels}, out_channels={out_channels}\"\n            )\n        self.out_channels = out_channels\n\n    def addictive_upsampling(self, x, mid) -> torch.Tensor:\n        x = F.interpolate(x, mid.shape[2:])\n        # [(batch, out_channels, ...), (batch, out_channels, ...)]\n        x = x.split(split_size=int(self.out_channels), dim=1)\n        # (batch, out_channels, ...)\n        out: torch.Tensor = torch.sum(torch.stack(x, dim=-1), dim=-1)\n        return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/localnet_block.py_LocalNetUpSampleBlock.forward_LocalNetUpSampleBlock.forward.return.out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/localnet_block.py_LocalNetUpSampleBlock.forward_LocalNetUpSampleBlock.forward.return.out", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/localnet_block.py", "file_name": "localnet_block.py", "file_type": "text/x-python", "category": "implementation", "start_line": 256, "end_line": 278, "span_ids": ["LocalNetUpSampleBlock.forward"], "tokens": 257}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LocalNetUpSampleBlock(nn.Module):\n\n    def forward(self, x, mid) -> torch.Tensor:\n        \"\"\"\n        Halves the channel and doubles the spatial dimensions.\n\n        Args:\n            x: feature to be up-sampled, in shape (batch, ``in_channels``, insize_1, insize_2, [insize_3])\n            mid: mid-level feature saved during down-sampling,\n                in shape (batch, ``out_channels``, midsize_1, midsize_2, [midsize_3])\n\n        Raises:\n            ValueError: when ``midsize != insize * 2``\n        \"\"\"\n        for i, j in zip(x.shape[2:], mid.shape[2:]):\n            if j != 2 * i:\n                raise ValueError(\n                    \"expecting mid spatial dimensions be exactly the double of x spatial dimensions, \"\n                    f\"got x of shape {x.shape}, mid of shape {mid.shape}\"\n                )\n        h0 = self.deconv_block(x) + self.addictive_upsampling(x, mid)\n        r1 = h0 + mid\n        r2 = self.conv_block(h0)\n        out: torch.Tensor = self.residual_block(r2, r1)\n        return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/localnet_block.py_LocalNetFeatureExtractorBlock_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/localnet_block.py_LocalNetFeatureExtractorBlock_", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/localnet_block.py", "file_name": "localnet_block.py", "file_type": "text/x-python", "category": "implementation", "start_line": 236, "end_line": 287, "span_ids": ["LocalNetFeatureExtractorBlock.forward", "LocalNetFeatureExtractorBlock.__init__", "LocalNetFeatureExtractorBlock"], "tokens": 466}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LocalNetFeatureExtractorBlock(nn.Module):\n    \"\"\"\n    A feature-extraction module that can be used for LocalNet, based on:\n    `Weakly-supervised convolutional neural networks for multimodal image registration\n    <https://doi.org/10.1016/j.media.2018.07.002>`_.\n    `Label-driven weakly-supervised learning for multimodal deformable image registration\n    <https://arxiv.org/abs/1711.01666>`_.\n\n    Adapted from:\n        DeepReg (https://github.com/DeepRegNet/DeepReg)\n    \"\"\"\n\n    def __init__(\n        self,\n        spatial_dims: int,\n        in_channels: int,\n        out_channels: int,\n        act: Optional[Union[Tuple, str]] = \"RELU\",\n        initializer: str = \"kaiming_uniform\",\n    ) -> None:\n        \"\"\"\n        Args:\n        spatial_dims: number of spatial dimensions.\n        in_channels: number of input channels.\n        out_channels: number of output channels.\n        act: activation type and arguments. Defaults to ReLU.\n        kernel_initializer: kernel initializer. Defaults to None.\n        \"\"\"\n        super().__init__()\n        self.conv_block = get_conv_block(\n            spatial_dims=spatial_dims, in_channels=in_channels, out_channels=out_channels, act=act, norm=None\n        )\n        conv_type: Type[Union[nn.Conv1d, nn.Conv2d, nn.Conv3d]] = Conv[Conv.CONV, spatial_dims]\n        for m in self.conv_block.modules():\n            if isinstance(m, conv_type):\n                if initializer == \"kaiming_uniform\":\n                    nn.init.kaiming_normal_(torch.as_tensor(m.weight))\n                elif initializer == \"zeros\":\n                    nn.init.zeros_(torch.as_tensor(m.weight))\n                else:\n                    raise ValueError(\n                        f\"initializer {initializer} is not supported, \" \"currently supporting kaiming_uniform and zeros\"\n                    )\n\n    def forward(self, x) -> torch.Tensor:\n        \"\"\"\n        Args:\n            x: Tensor in shape (batch, ``in_channels``, insize_1, insize_2, [insize_3])\n        \"\"\"\n        out: torch.Tensor = self.conv_block(x)\n        return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/regunet_block.py_from_typing_import_List__get_conv_block.return.conv_block": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/regunet_block.py_from_typing_import_List__get_conv_block.return.conv_block", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/regunet_block.py", "file_name": "regunet_block.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 58, "span_ids": ["get_conv_block", "docstring"], "tokens": 370}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import List, Optional, Sequence, Tuple, Type, Union\n\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nfrom monai.networks.blocks import Convolution\nfrom monai.networks.layers import Conv, Norm, Pool, same_padding\n\n\ndef get_conv_block(\n    spatial_dims: int,\n    in_channels: int,\n    out_channels: int,\n    kernel_size: Union[Sequence[int], int] = 3,\n    strides: int = 1,\n    padding: Optional[Union[Tuple[int, ...], int]] = None,\n    act: Optional[Union[Tuple, str]] = \"RELU\",\n    norm: Optional[Union[Tuple, str]] = \"BATCH\",\n    initializer: Optional[str] = \"kaiming_uniform\",\n) -> nn.Module:\n    if padding is None:\n        padding = same_padding(kernel_size)\n    conv_block: nn.Module = Convolution(\n        spatial_dims,\n        in_channels,\n        out_channels,\n        kernel_size=kernel_size,\n        strides=strides,\n        act=act,\n        norm=norm,\n        bias=False,\n        conv_only=False,\n        padding=padding,\n    )\n    conv_type: Type[Union[nn.Conv1d, nn.Conv2d, nn.Conv3d]] = Conv[Conv.CONV, spatial_dims]\n    for m in conv_block.modules():\n        if isinstance(m, conv_type):\n            if initializer == \"kaiming_uniform\":\n                nn.init.kaiming_normal_(torch.as_tensor(m.weight))\n            elif initializer == \"zeros\":\n                nn.init.zeros_(torch.as_tensor(m.weight))\n            else:\n                raise ValueError(\n                    f\"initializer {initializer} is not supported, \" \"currently supporting kaiming_uniform and zeros\"\n                )\n    return conv_block", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/regunet_block.py_get_conv_layer_RegistrationResidualConvBlock.__init__.self.acts.nn_ModuleList_nn_ReLU_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/regunet_block.py_get_conv_layer_RegistrationResidualConvBlock.__init__.self.acts.nn_ModuleList_nn_ReLU_", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/regunet_block.py", "file_name": "regunet_block.py", "file_type": "text/x-python", "category": "implementation", "start_line": 61, "end_line": 103, "span_ids": ["RegistrationResidualConvBlock", "RegistrationResidualConvBlock.__init__", "get_conv_layer"], "tokens": 348}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_conv_layer(\n    spatial_dims: int, in_channels: int, out_channels: int, kernel_size: Union[Sequence[int], int] = 3\n) -> nn.Module:\n    padding = same_padding(kernel_size)\n    mod: nn.Module = Convolution(\n        spatial_dims, in_channels, out_channels, kernel_size=kernel_size, bias=False, conv_only=True, padding=padding\n    )\n    return mod\n\n\nclass RegistrationResidualConvBlock(nn.Module):\n    \"\"\"\n    A block with skip links and layer - norm - activation.\n    Only changes the number of channels, the spatial size is kept same.\n    \"\"\"\n\n    def __init__(\n        self, spatial_dims: int, in_channels: int, out_channels: int, num_layers: int = 2, kernel_size: int = 3\n    ):\n        \"\"\"\n\n        Args:\n            spatial_dims: number of spatial dimensions\n            in_channels: number of input channels\n            out_channels: number of output channels\n            num_layers: number of layers inside the block\n            kernel_size: kernel_size\n        \"\"\"\n        super().__init__()\n        self.num_layers = num_layers\n        self.layers = nn.ModuleList(\n            [\n                get_conv_layer(\n                    spatial_dims=spatial_dims,\n                    in_channels=in_channels if i == 0 else out_channels,\n                    out_channels=out_channels,\n                    kernel_size=kernel_size,\n                )\n                for i in range(num_layers)\n            ]\n        )\n        self.norms = nn.ModuleList([Norm[Norm.BATCH, spatial_dims](out_channels) for _ in range(num_layers)])\n        self.acts = nn.ModuleList([nn.ReLU() for _ in range(num_layers)])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/regunet_block.py_RegistrationResidualConvBlock.forward_RegistrationResidualConvBlock.forward.return.x": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/regunet_block.py_RegistrationResidualConvBlock.forward_RegistrationResidualConvBlock.forward.return.x", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/regunet_block.py", "file_name": "regunet_block.py", "file_type": "text/x-python", "category": "implementation", "start_line": 118, "end_line": 136, "span_ids": ["RegistrationResidualConvBlock.forward"], "tokens": 173}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RegistrationResidualConvBlock(nn.Module):\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        \"\"\"\n\n        Args:\n            x: Tensor in shape (batch, ``in_channels``, insize_1, insize_2, [insize_3])\n\n        Returns:\n            Tensor in shape (batch, ``out_channels``, insize_1, insize_2, [insize_3]),\n            with the same spatial size as ``x``\n        \"\"\"\n        skip = x\n        for i, (conv, norm, act) in enumerate(zip(self.layers, self.norms, self.acts)):\n            x = conv(x)\n            x = norm(x)\n            if i == self.num_layers - 1:\n                # last block\n                x = x + skip\n            x = act(x)\n        return x", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/regunet_block.py_RegistrationDownSampleBlock_RegistrationDownSampleBlock.__init__.if_pooling_.else_.self.layer.get_conv_block_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/regunet_block.py_RegistrationDownSampleBlock_RegistrationDownSampleBlock.__init__.if_pooling_.else_.self.layer.get_conv_block_", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/regunet_block.py", "file_name": "regunet_block.py", "file_type": "text/x-python", "category": "implementation", "start_line": 126, "end_line": 153, "span_ids": ["RegistrationDownSampleBlock.__init__", "RegistrationDownSampleBlock"], "tokens": 192}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RegistrationDownSampleBlock(nn.Module):\n    \"\"\"\n    A down-sample module used in RegUNet to half the spatial size.\n    The number of channels is kept same.\n\n    Adapted from:\n        DeepReg (https://github.com/DeepRegNet/DeepReg)\n    \"\"\"\n\n    def __init__(self, spatial_dims: int, channels: int, pooling: bool) -> None:\n        \"\"\"\n        Args:\n            spatial_dims: number of spatial dimensions.\n            channels: channels\n            pooling: use MaxPool if True, strided conv if False\n        \"\"\"\n        super().__init__()\n        if pooling:\n            self.layer = Pool[Pool.MAX, spatial_dims](kernel_size=2)\n        else:\n            self.layer = get_conv_block(\n                spatial_dims=spatial_dims,\n                in_channels=channels,\n                out_channels=channels,\n                kernel_size=2,\n                strides=2,\n                padding=0,\n            )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/regunet_block.py_RegistrationExtractionBlock_RegistrationExtractionBlock.__init__.self.layers.nn_ModuleList_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/regunet_block.py_RegistrationExtractionBlock_RegistrationExtractionBlock.__init__.self.layers.nn_ModuleList_", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/regunet_block.py", "file_name": "regunet_block.py", "file_type": "text/x-python", "category": "implementation", "start_line": 189, "end_line": 230, "span_ids": ["RegistrationExtractionBlock", "RegistrationExtractionBlock.__init__"], "tokens": 274}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RegistrationExtractionBlock(nn.Module):\n    \"\"\"\n    The Extraction Block used in RegUNet.\n    Extracts feature from each ``extract_levels`` and takes the average.\n    \"\"\"\n\n    def __init__(\n        self,\n        spatial_dims: int,\n        extract_levels: Tuple[int],\n        num_channels: Union[Tuple[int], List[int]],\n        out_channels: int,\n        kernel_initializer: Optional[str] = \"kaiming_uniform\",\n        activation: Optional[str] = None,\n    ):\n        \"\"\"\n\n        Args:\n            spatial_dims: number of spatial dimensions\n            extract_levels: spatial levels to extract feature from, 0 refers to the input scale\n            num_channels: number of channels at each scale level,\n                List or Tuple of length equals to `depth` of the RegNet\n            out_channels: number of output channels\n            kernel_initializer: kernel initializer\n            activation: kernel activation function\n        \"\"\"\n        super().__init__()\n        self.extract_levels = extract_levels\n        self.max_level = max(extract_levels)\n        self.layers = nn.ModuleList(\n            [\n                get_conv_block(\n                    spatial_dims=spatial_dims,\n                    in_channels=num_channels[d],\n                    out_channels=out_channels,\n                    norm=None,\n                    act=activation,\n                    initializer=kernel_initializer,\n                )\n                for d in extract_levels\n            ]\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/regunet_block.py_RegistrationExtractionBlock.forward_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/regunet_block.py_RegistrationExtractionBlock.forward_", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/regunet_block.py", "file_name": "regunet_block.py", "file_type": "text/x-python", "category": "implementation", "start_line": 232, "end_line": 248, "span_ids": ["RegistrationExtractionBlock.forward"], "tokens": 161}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RegistrationExtractionBlock(nn.Module):\n\n    def forward(self, x: List[torch.Tensor], image_size: List[int]) -> torch.Tensor:\n        \"\"\"\n\n        Args:\n            x: Decoded feature at different spatial levels, sorted from deep to shallow\n            image_size: output image size\n\n        Returns:\n            Tensor of shape (batch, `out_channels`, size1, size2, size3), where (size1, size2, size3) = ``image_size``\n        \"\"\"\n        feature_list = [\n            F.interpolate(layer(x[self.max_level - level]), size=image_size)\n            for layer, level in zip(self.layers, self.extract_levels)\n        ]\n        out: torch.Tensor = torch.mean(torch.stack(feature_list, dim=0), dim=0)\n        return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/squeeze_and_excitation.py_math_ChannelSELayer.__init__.self.fc.nn_Sequential_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/squeeze_and_excitation.py_math_ChannelSELayer.__init__.self.fc.nn_Sequential_", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/squeeze_and_excitation.py", "file_name": "squeeze_and_excitation.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 71, "span_ids": ["ChannelSELayer.__init__", "ChannelSELayer", "docstring"], "tokens": 522}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import math\nfrom typing import Dict, Optional, Tuple, Union\n\nimport torch\nimport torch.nn as nn\n\nfrom monai.networks.blocks import Convolution\nfrom monai.networks.layers.factories import Act, Conv, Norm, Pool, split_args\n\n\nclass ChannelSELayer(nn.Module):\n    \"\"\"\n    Re-implementation of the Squeeze-and-Excitation block based on:\n    \"Hu et al., Squeeze-and-Excitation Networks, https://arxiv.org/abs/1709.01507\".\n    \"\"\"\n\n    def __init__(\n        self,\n        spatial_dims: int,\n        in_channels: int,\n        r: int = 2,\n        acti_type_1: Union[Tuple[str, Dict], str] = (\"relu\", {\"inplace\": True}),\n        acti_type_2: Union[Tuple[str, Dict], str] = \"sigmoid\",\n        add_residual: bool = False,\n    ) -> None:\n        \"\"\"\n        Args:\n            spatial_dims: number of spatial dimensions, could be 1, 2, or 3.\n            in_channels: number of input channels.\n            r: the reduction ratio r in the paper. Defaults to 2.\n            acti_type_1: activation type of the hidden squeeze layer. Defaults to ``(\"relu\", {\"inplace\": True})``.\n            acti_type_2: activation type of the output squeeze layer. Defaults to \"sigmoid\".\n\n        Raises:\n            ValueError: When ``r`` is nonpositive or larger than ``in_channels``.\n\n        See also:\n\n            :py:class:`monai.networks.layers.Act`\n\n        \"\"\"\n        super().__init__()\n\n        self.add_residual = add_residual\n\n        pool_type = Pool[Pool.ADAPTIVEAVG, spatial_dims]\n        self.avg_pool = pool_type(1)  # spatial size (1, 1, ...)\n\n        channels = int(in_channels // r)\n        if channels <= 0:\n            raise ValueError(f\"r must be positive and smaller than in_channels, got r={r} in_channels={in_channels}.\")\n\n        act_1, act_1_args = split_args(acti_type_1)\n        act_2, act_2_args = split_args(acti_type_2)\n        self.fc = nn.Sequential(\n            nn.Linear(in_channels, channels, bias=True),\n            Act[act_1](**act_1_args),\n            nn.Linear(channels, in_channels, bias=True),\n            Act[act_2](**act_2_args),\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/squeeze_and_excitation.py_ChannelSELayer.forward_ChannelSELayer.forward.return.result": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/squeeze_and_excitation.py_ChannelSELayer.forward_ChannelSELayer.forward.return.result", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/squeeze_and_excitation.py", "file_name": "squeeze_and_excitation.py", "file_type": "text/x-python", "category": "implementation", "start_line": 73, "end_line": 88, "span_ids": ["ChannelSELayer.forward"], "tokens": 154}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ChannelSELayer(nn.Module):\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        \"\"\"\n        Args:\n            x: in shape (batch, in_channels, spatial_1[, spatial_2, ...]).\n        \"\"\"\n        b, c = x.shape[:2]\n        y: torch.Tensor = self.avg_pool(x).view(b, c)\n        y = self.fc(y).view([b, c] + [1] * (x.ndim - 2))\n        result = x * y\n\n        # Residual connection is moved here instead of providing an override of forward in ResidualSELayer since\n        # Torchscript has an issue with using super().\n        if self.add_residual:\n            result += x\n\n        return result", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/squeeze_and_excitation.py_ResidualSELayer_ResidualSELayer.__init__.super___init___": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/squeeze_and_excitation.py_ResidualSELayer_ResidualSELayer.__init__.super___init___", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/squeeze_and_excitation.py", "file_name": "squeeze_and_excitation.py", "file_type": "text/x-python", "category": "implementation", "start_line": 91, "end_line": 126, "span_ids": ["ResidualSELayer.__init__", "ResidualSELayer"], "tokens": 272}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ResidualSELayer(ChannelSELayer):\n    \"\"\"\n    A \"squeeze-and-excitation\"-like layer with a residual connection::\n\n        --+-- SE --o--\n          |        |\n          +--------+\n    \"\"\"\n\n    def __init__(\n        self,\n        spatial_dims: int,\n        in_channels: int,\n        r: int = 2,\n        acti_type_1: Union[Tuple[str, Dict], str] = \"leakyrelu\",\n        acti_type_2: Union[Tuple[str, Dict], str] = \"relu\",\n    ) -> None:\n        \"\"\"\n        Args:\n            spatial_dims: number of spatial dimensions, could be 1, 2, or 3.\n            in_channels: number of input channels.\n            r: the reduction ratio r in the paper. Defaults to 2.\n            acti_type_1: defaults to \"leakyrelu\".\n            acti_type_2: defaults to \"relu\".\n\n        See also:\n            :py:class:`monai.networks.blocks.ChannelSELayer`\n        \"\"\"\n        super().__init__(\n            spatial_dims=spatial_dims,\n            in_channels=in_channels,\n            r=r,\n            acti_type_1=acti_type_1,\n            acti_type_2=acti_type_2,\n            add_residual=True,\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/squeeze_and_excitation.py_SEBlock_SEBlock.forward.return.x": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/squeeze_and_excitation.py_SEBlock_SEBlock.forward.return.x", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/squeeze_and_excitation.py", "file_name": "squeeze_and_excitation.py", "file_type": "text/x-python", "category": "implementation", "start_line": 129, "end_line": 229, "span_ids": ["SEBlock", "SEBlock.__init__", "SEBlock.forward"], "tokens": 1179}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SEBlock(nn.Module):\n    \"\"\"\n    Residual module enhanced with Squeeze-and-Excitation::\n\n        ----+- conv1 --  conv2 -- conv3 -- SE -o---\n            |                                  |\n            +---(channel project if needed)----+\n\n    Re-implementation of the SE-Resnet block based on:\n    \"Hu et al., Squeeze-and-Excitation Networks, https://arxiv.org/abs/1709.01507\".\n    \"\"\"\n\n    def __init__(\n        self,\n        spatial_dims: int,\n        in_channels: int,\n        n_chns_1: int,\n        n_chns_2: int,\n        n_chns_3: int,\n        conv_param_1: Optional[Dict] = None,\n        conv_param_2: Optional[Dict] = None,\n        conv_param_3: Optional[Dict] = None,\n        project: Optional[Convolution] = None,\n        r: int = 2,\n        acti_type_1: Union[Tuple[str, Dict], str] = (\"relu\", {\"inplace\": True}),\n        acti_type_2: Union[Tuple[str, Dict], str] = \"sigmoid\",\n        acti_type_final: Optional[Union[Tuple[str, Dict], str]] = (\"relu\", {\"inplace\": True}),\n    ):\n        \"\"\"\n        Args:\n            spatial_dims: number of spatial dimensions, could be 1, 2, or 3.\n            in_channels: number of input channels.\n            n_chns_1: number of output channels in the 1st convolution.\n            n_chns_2: number of output channels in the 2nd convolution.\n            n_chns_3: number of output channels in the 3rd convolution.\n            conv_param_1: additional parameters to the 1st convolution.\n                Defaults to ``{\"kernel_size\": 1, \"norm\": Norm.BATCH, \"act\": (\"relu\", {\"inplace\": True})}``\n            conv_param_2: additional parameters to the 2nd convolution.\n                Defaults to ``{\"kernel_size\": 3, \"norm\": Norm.BATCH, \"act\": (\"relu\", {\"inplace\": True})}``\n            conv_param_3: additional parameters to the 3rd convolution.\n                Defaults to ``{\"kernel_size\": 1, \"norm\": Norm.BATCH, \"act\": None}``\n            project: in the case of residual chns and output chns doesn't match, a project\n                (Conv) layer/block is used to adjust the number of chns. In SENET, it is\n                consisted with a Conv layer as well as a Norm layer.\n                Defaults to None (chns are matchable) or a Conv layer with kernel size 1.\n            r: the reduction ratio r in the paper. Defaults to 2.\n            acti_type_1: activation type of the hidden squeeze layer. Defaults to \"relu\".\n            acti_type_2: activation type of the output squeeze layer. Defaults to \"sigmoid\".\n            acti_type_final: activation type of the end of the block. Defaults to \"relu\".\n\n        See also:\n\n            :py:class:`monai.networks.blocks.ChannelSELayer`\n\n        \"\"\"\n        super().__init__()\n\n        if not conv_param_1:\n            conv_param_1 = {\"kernel_size\": 1, \"norm\": Norm.BATCH, \"act\": (\"relu\", {\"inplace\": True})}\n        self.conv1 = Convolution(\n            spatial_dims=spatial_dims, in_channels=in_channels, out_channels=n_chns_1, **conv_param_1\n        )\n\n        if not conv_param_2:\n            conv_param_2 = {\"kernel_size\": 3, \"norm\": Norm.BATCH, \"act\": (\"relu\", {\"inplace\": True})}\n        self.conv2 = Convolution(spatial_dims=spatial_dims, in_channels=n_chns_1, out_channels=n_chns_2, **conv_param_2)\n\n        if not conv_param_3:\n            conv_param_3 = {\"kernel_size\": 1, \"norm\": Norm.BATCH, \"act\": None}\n        self.conv3 = Convolution(spatial_dims=spatial_dims, in_channels=n_chns_2, out_channels=n_chns_3, **conv_param_3)\n\n        self.se_layer = ChannelSELayer(\n            spatial_dims=spatial_dims, in_channels=n_chns_3, r=r, acti_type_1=acti_type_1, acti_type_2=acti_type_2\n        )\n\n        if project is None and in_channels != n_chns_3:\n            self.project = Conv[Conv.CONV, spatial_dims](in_channels, n_chns_3, kernel_size=1)\n        elif project is None:\n            self.project = nn.Identity()\n        else:\n            self.project = project\n\n        if acti_type_final is not None:\n            act_final, act_final_args = split_args(acti_type_final)\n            self.act = Act[act_final](**act_final_args)\n        else:\n            self.act = nn.Identity()\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        \"\"\"\n        Args:\n            x: in shape (batch, in_channels, spatial_1[, spatial_2, ...]).\n        \"\"\"\n        residual = self.project(x)\n        x = self.conv1(x)\n        x = self.conv2(x)\n        x = self.conv3(x)\n        x = self.se_layer(x)\n        x += residual\n        x = self.act(x)\n        return x", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/warp.py_warnings_Warp.get_reference_grid.return.grid": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/warp.py_warnings_Warp.get_reference_grid.return.grid", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/warp.py", "file_name": "warp.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 90, "span_ids": ["Warp.__init__", "Warp", "Warp.get_reference_grid", "docstring"], "tokens": 712}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import warnings\nfrom typing import List\n\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nfrom monai.config.deviceconfig import USE_COMPILED\nfrom monai.networks.layers.spatial_transforms import grid_pull\nfrom monai.utils import GridSampleMode, GridSamplePadMode, optional_import\n\n_C, _ = optional_import(\"monai._C\")\n\n__all__ = [\"Warp\", \"DVF2DDF\"]\n\n\nclass Warp(nn.Module):\n    \"\"\"\n    Warp an image with given dense displacement field (DDF).\n    \"\"\"\n\n    def __init__(self, mode=GridSampleMode.BILINEAR.value, padding_mode=GridSamplePadMode.BORDER.value):\n        \"\"\"\n        For pytorch native APIs, the possible values are:\n\n            - mode: ``\"nearest\"``, ``\"bilinear\"``, ``\"bicubic\"``.\n            - padding_mode: ``\"zeros\"``, ``\"border\"``, ``\"reflection\"``\n\n        See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n\n        For MONAI C++/CUDA extensions, the possible values are:\n\n            - mode: ``\"nearest\"``, ``\"bilinear\"``, ``\"bicubic\"``, 0, 1, ...\n            - padding_mode: ``\"zeros\"``, ``\"border\"``, ``\"reflection\"``, 0, 1, ...\n\n        See also: :py:class:`monai.networks.layers.grid_pull`\n        \"\"\"\n        super().__init__()\n        # resolves _interp_mode for different methods\n\n        if USE_COMPILED:\n            if mode in (inter.value for inter in GridSampleMode):\n                mode = GridSampleMode(mode)\n                if mode == GridSampleMode.BILINEAR:\n                    mode = 1\n                elif mode == GridSampleMode.NEAREST:\n                    mode = 0\n                elif mode == GridSampleMode.BICUBIC:\n                    mode = 3\n                else:\n                    mode = 1  # default to linear\n            self._interp_mode = mode\n        else:\n            warnings.warn(\"monai.networks.blocks.Warp: Using PyTorch native grid_sample.\")\n            self._interp_mode = GridSampleMode(mode).value\n\n        # resolves _padding_mode for different methods\n        if USE_COMPILED:\n            if padding_mode in (pad.value for pad in GridSamplePadMode):\n                padding_mode = GridSamplePadMode(padding_mode)\n                if padding_mode == GridSamplePadMode.ZEROS:\n                    padding_mode = 7\n                elif padding_mode == GridSamplePadMode.BORDER:\n                    padding_mode = 0\n                elif padding_mode == GridSamplePadMode.REFLECTION:\n                    padding_mode = 1\n                else:\n                    padding_mode = 0  # default to nearest\n            self._padding_mode = padding_mode\n        else:\n            self._padding_mode = GridSamplePadMode(padding_mode).value\n\n    @staticmethod\n    def get_reference_grid(ddf: torch.Tensor) -> torch.Tensor:\n        mesh_points = [torch.arange(0, dim) for dim in ddf.shape[2:]]\n        grid = torch.stack(torch.meshgrid(*mesh_points), dim=0)  # (spatial_dims, ...)\n        grid = torch.stack([grid] * ddf.shape[0], dim=0)  # (batch, spatial_dims, ...)\n        grid = grid.to(ddf)\n        return grid", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/warp.py_DVF2DDF_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/warp.py_DVF2DDF_", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/warp.py", "file_name": "warp.py", "file_type": "text/x-python", "category": "implementation", "start_line": 125, "end_line": 156, "span_ids": ["DVF2DDF.__init__", "DVF2DDF.forward", "DVF2DDF"], "tokens": 255}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DVF2DDF(nn.Module):\n    \"\"\"\n    Layer calculates a dense displacement field (DDF) from a dense velocity field (DVF)\n    with scaling and squaring.\n\n    Adapted from:\n        DeepReg (https://github.com/DeepRegNet/DeepReg)\n\n    \"\"\"\n\n    def __init__(\n        self, num_steps: int = 7, mode=GridSampleMode.BILINEAR.value, padding_mode=GridSamplePadMode.ZEROS.value\n    ):\n        super().__init__()\n        if num_steps <= 0:\n            raise ValueError(f\"expecting positive num_steps, got {num_steps}\")\n        self.num_steps = num_steps\n        self.warp_layer = Warp(mode=mode, padding_mode=padding_mode)\n\n    def forward(self, dvf):\n        \"\"\"\n        Args:\n            dvf: dvf to be transformed, in shape (batch, ``spatial_dims``, H, W[,D])\n\n        Returns:\n            a dense displacement field\n        \"\"\"\n        ddf: torch.Tensor = dvf / (2 ** self.num_steps)\n        for _ in range(self.num_steps):\n            ddf = ddf + self.warp_layer(image=ddf, ddf=ddf)\n        return ddf", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/__init__.py_calculate_out_shape_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/__init__.py_calculate_out_shape_", "embedding": null, "metadata": {"file_path": "monai/networks/layers/__init__.py", "file_name": "__init__.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 30, "span_ids": ["docstring"], "tokens": 166}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from .convutils import calculate_out_shape, gaussian_1d, polyval, same_padding, stride_minus_kernel_padding\nfrom .factories import Act, Conv, Dropout, LayerFactory, Norm, Pad, Pool, split_args\nfrom .filtering import BilateralFilter, PHLFilter\nfrom .gmm import GaussianMixtureModel\nfrom .simplelayers import (\n    LLTM,\n    ChannelPad,\n    Flatten,\n    GaussianFilter,\n    HilbertTransform,\n    Reshape,\n    SavitzkyGolayFilter,\n    SkipConnection,\n    apply_filter,\n    separable_filtering,\n)\nfrom .spatial_transforms import AffineTransform, grid_count, grid_grad, grid_pull, grid_push\nfrom .utils import get_act_layer, get_dropout_layer, get_norm_layer, get_pool_layer", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/factories.py_split_args_split_args.return.name_obj_name_args": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/factories.py_split_args_split_args.return.name_obj_name_args", "embedding": null, "metadata": {"file_path": "monai/networks/layers/factories.py", "file_name": "factories.py", "file_type": "text/x-python", "category": "implementation", "start_line": 156, "end_line": 187, "span_ids": ["split_args"], "tokens": 262}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def split_args(args):\n    \"\"\"\n    Split arguments in a way to be suitable for using with the factory types. If `args` is a string it's interpreted as\n    the type name.\n\n    Args:\n        args (str or a tuple of object name and kwarg dict): input arguments to be parsed.\n\n    Raises:\n        TypeError: When ``args`` type is not in ``Union[str, Tuple[Union[str, Callable], dict]]``.\n\n    Examples::\n\n        >>> act_type, args = split_args(\"PRELU\")\n        >>> monai.networks.layers.Act[act_type]\n        <class 'torch.nn.modules.activation.PReLU'>\n\n        >>> act_type, args = split_args((\"PRELU\", {\"num_parameters\": 1, \"init\": 0.25}))\n        >>> monai.networks.layers.Act[act_type](**args)\n        PReLU(num_parameters=1)\n\n    \"\"\"\n\n    if isinstance(args, str):\n        return args, {}\n    name_obj, name_args = args\n\n    if not isinstance(name_obj, (str, Callable)) or not isinstance(name_args, dict):\n        msg = \"Layer specifiers must be single strings or pairs of the form (name/object-types, argument dict)\"\n        raise TypeError(msg)\n\n    return name_obj, name_args", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/factories.py__Define_factories_for_th_convtrans_factory.return.types_dim_1_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/factories.py__Define_factories_for_th_convtrans_factory.return.types_dim_1_", "embedding": null, "metadata": {"file_path": "monai/networks/layers/factories.py", "file_name": "factories.py", "file_type": "text/x-python", "category": "implementation", "start_line": 190, "end_line": 287, "span_ids": ["sync_batch_factory", "impl:3", "conv_factory", "split_args", "swish_factory", "local_response_factory", "mish_factory", "memswish_factory", "layer_factory", "group_factory", "convtrans_factory", "dropout_factory", "alpha_dropout_factory", "batch_factory", "instance_factory", "impl:15"], "tokens": 770}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "# Define factories for these layer types\n\nDropout = LayerFactory()\nNorm = LayerFactory()\nAct = LayerFactory()\nConv = LayerFactory()\nPool = LayerFactory()\nPad = LayerFactory()\n\n\n@Dropout.factory_function(\"dropout\")\ndef dropout_factory(dim: int) -> Type[Union[nn.Dropout, nn.Dropout2d, nn.Dropout3d]]:\n    types = (nn.Dropout, nn.Dropout2d, nn.Dropout3d)\n    return types[dim - 1]\n\n\n@Dropout.factory_function(\"alphadropout\")\ndef alpha_dropout_factory(_dim):\n    return nn.AlphaDropout\n\n\n@Norm.factory_function(\"instance\")\ndef instance_factory(dim: int) -> Type[Union[nn.InstanceNorm1d, nn.InstanceNorm2d, nn.InstanceNorm3d]]:\n    types = (nn.InstanceNorm1d, nn.InstanceNorm2d, nn.InstanceNorm3d)\n    return types[dim - 1]\n\n\n@Norm.factory_function(\"batch\")\ndef batch_factory(dim: int) -> Type[Union[nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d]]:\n    types = (nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d)\n    return types[dim - 1]\n\n\n@Norm.factory_function(\"group\")\ndef group_factory(_dim) -> Type[nn.GroupNorm]:\n    return nn.GroupNorm\n\n\n@Norm.factory_function(\"layer\")\ndef layer_factory(_dim) -> Type[nn.LayerNorm]:\n    return nn.LayerNorm\n\n\n@Norm.factory_function(\"localresponse\")\ndef local_response_factory(_dim) -> Type[nn.LocalResponseNorm]:\n    return nn.LocalResponseNorm\n\n\n@Norm.factory_function(\"syncbatch\")\ndef sync_batch_factory(_dim) -> Type[nn.SyncBatchNorm]:\n    return nn.SyncBatchNorm\n\n\nAct.add_factory_callable(\"elu\", lambda: nn.modules.ELU)\nAct.add_factory_callable(\"relu\", lambda: nn.modules.ReLU)\nAct.add_factory_callable(\"leakyrelu\", lambda: nn.modules.LeakyReLU)\nAct.add_factory_callable(\"prelu\", lambda: nn.modules.PReLU)\nAct.add_factory_callable(\"relu6\", lambda: nn.modules.ReLU6)\nAct.add_factory_callable(\"selu\", lambda: nn.modules.SELU)\nAct.add_factory_callable(\"celu\", lambda: nn.modules.CELU)\nAct.add_factory_callable(\"gelu\", lambda: nn.modules.GELU)\nAct.add_factory_callable(\"sigmoid\", lambda: nn.modules.Sigmoid)\nAct.add_factory_callable(\"tanh\", lambda: nn.modules.Tanh)\nAct.add_factory_callable(\"softmax\", lambda: nn.modules.Softmax)\nAct.add_factory_callable(\"logsoftmax\", lambda: nn.modules.LogSoftmax)\n\n\n@Act.factory_function(\"swish\")\ndef swish_factory():\n    from monai.networks.blocks.activation import Swish\n\n    return Swish\n\n\n@Act.factory_function(\"memswish\")\ndef memswish_factory():\n    from monai.networks.blocks.activation import MemoryEfficientSwish\n\n    return MemoryEfficientSwish\n\n\n@Act.factory_function(\"mish\")\ndef mish_factory():\n    from monai.networks.blocks.activation import Mish\n\n    return Mish\n\n\n@Conv.factory_function(\"conv\")\ndef conv_factory(dim: int) -> Type[Union[nn.Conv1d, nn.Conv2d, nn.Conv3d]]:\n    types = (nn.Conv1d, nn.Conv2d, nn.Conv3d)\n    return types[dim - 1]\n\n\n@Conv.factory_function(\"convtrans\")\ndef convtrans_factory(dim: int) -> Type[Union[nn.ConvTranspose1d, nn.ConvTranspose2d, nn.ConvTranspose3d]]:\n    types = (nn.ConvTranspose1d, nn.ConvTranspose2d, nn.ConvTranspose3d)\n    return types[dim - 1]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/factories.py_maxpooling_factory_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/factories.py_maxpooling_factory_", "embedding": null, "metadata": {"file_path": "monai/networks/layers/factories.py", "file_name": "factories.py", "file_type": "text/x-python", "category": "implementation", "start_line": 285, "end_line": 323, "span_ids": ["avgpooling_factory", "adaptive_maxpooling_factory", "maxpooling_factory", "replication_pad_factory", "adaptive_avgpooling_factory", "constant_pad_factory"], "tokens": 468}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@Pool.factory_function(\"max\")\ndef maxpooling_factory(dim: int) -> Type[Union[nn.MaxPool1d, nn.MaxPool2d, nn.MaxPool3d]]:\n    types = (nn.MaxPool1d, nn.MaxPool2d, nn.MaxPool3d)\n    return types[dim - 1]\n\n\n@Pool.factory_function(\"adaptivemax\")\ndef adaptive_maxpooling_factory(\n    dim: int,\n) -> Type[Union[nn.AdaptiveMaxPool1d, nn.AdaptiveMaxPool2d, nn.AdaptiveMaxPool3d]]:\n    types = (nn.AdaptiveMaxPool1d, nn.AdaptiveMaxPool2d, nn.AdaptiveMaxPool3d)\n    return types[dim - 1]\n\n\n@Pool.factory_function(\"avg\")\ndef avgpooling_factory(dim: int) -> Type[Union[nn.AvgPool1d, nn.AvgPool2d, nn.AvgPool3d]]:\n    types = (nn.AvgPool1d, nn.AvgPool2d, nn.AvgPool3d)\n    return types[dim - 1]\n\n\n@Pool.factory_function(\"adaptiveavg\")\ndef adaptive_avgpooling_factory(\n    dim: int,\n) -> Type[Union[nn.AdaptiveAvgPool1d, nn.AdaptiveAvgPool2d, nn.AdaptiveAvgPool3d]]:\n    types = (nn.AdaptiveAvgPool1d, nn.AdaptiveAvgPool2d, nn.AdaptiveAvgPool3d)\n    return types[dim - 1]\n\n\n@Pad.factory_function(\"replicationpad\")\ndef replication_pad_factory(dim: int) -> Type[Union[nn.ReplicationPad1d, nn.ReplicationPad2d, nn.ReplicationPad3d]]:\n    types = (nn.ReplicationPad1d, nn.ReplicationPad2d, nn.ReplicationPad3d)\n    return types[dim - 1]\n\n\n@Pad.factory_function(\"constantpad\")\ndef constant_pad_factory(dim: int) -> Type[Union[nn.ConstantPad1d, nn.ConstantPad2d, nn.ConstantPad3d]]:\n    types = (nn.ConstantPad1d, nn.ConstantPad2d, nn.ConstantPad3d)\n    return types[dim - 1]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/filtering.py_torch_BilateralFilter.backward.return.grad_input_None_None_N": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/filtering.py_torch_BilateralFilter.backward.return.grad_input_None_None_N", "embedding": null, "metadata": {"file_path": "monai/networks/layers/filtering.py", "file_name": "filtering.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 60, "span_ids": ["BilateralFilter", "BilateralFilter.forward", "BilateralFilter.backward", "docstring"], "tokens": 398}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import torch\n\nfrom monai.utils.module import optional_import\n\n_C, _ = optional_import(\"monai._C\")\n\n__all__ = [\"BilateralFilter\", \"PHLFilter\"]\n\n\nclass BilateralFilter(torch.autograd.Function):\n    \"\"\"\n    Blurs the input tensor spatially whilst preserving edges. Can run on 1D, 2D, or 3D,\n    tensors (on top of Batch and Channel dimensions). Two implementations are provided,\n    an exact solution and a much faster approximation which uses a permutohedral lattice.\n\n    See:\n        https://en.wikipedia.org/wiki/Bilateral_filter\n        https://graphics.stanford.edu/papers/permutohedral/\n\n    Args:\n        input: input tensor.\n\n        spatial sigma: the standard deviation of the spatial blur. Higher values can\n            hurt performance when not using the approximate method (see fast approx).\n\n        color sigma: the standard deviation of the color blur. Lower values preserve\n            edges better whilst higher values tend to a simple gaussian spatial blur.\n\n        fast approx: This flag chooses between two implementations. The approximate method may\n            produce artifacts in some scenarios whereas the exact solution may be intolerably\n            slow for high spatial standard deviations.\n\n    Returns:\n        output (torch.Tensor): output tensor.\n    \"\"\"\n\n    @staticmethod\n    def forward(ctx, input, spatial_sigma=5, color_sigma=0.5, fast_approx=True):\n        ctx.ss = spatial_sigma\n        ctx.cs = color_sigma\n        ctx.fa = fast_approx\n        output_data = _C.bilateral_filter(input, spatial_sigma, color_sigma, fast_approx)\n        return output_data\n\n    @staticmethod\n    def backward(ctx, grad_output):\n        spatial_sigma, color_sigma, fast_approx = ctx.ss, ctx.cs, ctx.fa\n        grad_input = _C.bilateral_filter(grad_output, spatial_sigma, color_sigma, fast_approx)\n        return grad_input, None, None, None", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/filtering.py_PHLFilter_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/filtering.py_PHLFilter_", "embedding": null, "metadata": {"file_path": "monai/networks/layers/filtering.py", "file_name": "filtering.py", "file_type": "text/x-python", "category": "implementation", "start_line": 63, "end_line": 102, "span_ids": ["PHLFilter.backward", "PHLFilter.forward", "PHLFilter"], "tokens": 271}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class PHLFilter(torch.autograd.Function):\n    \"\"\"\n    Filters input based on arbitrary feature vectors. Uses a permutohedral\n    lattice data structure to efficiently approximate n-dimensional gaussian\n    filtering. Complexity is broadly independent of kernel size. Most applicable\n    to higher filter dimensions and larger kernel sizes.\n\n    See:\n        https://graphics.stanford.edu/papers/permutohedral/\n\n    Args:\n        input: input tensor to be filtered.\n\n        features: feature tensor used to filter the input.\n\n        sigmas: the standard deviations of each feature in the filter.\n\n    Returns:\n        output (torch.Tensor): output tensor.\n    \"\"\"\n\n    @staticmethod\n    def forward(ctx, input, features, sigmas=None):\n\n        scaled_features = features\n        if sigmas is not None:\n            for i in range(features.size(1)):\n                scaled_features[:, i, ...] /= sigmas[i]\n\n        ctx.save_for_backward(scaled_features)\n        output_data = _C.phl_filter(input, scaled_features)\n        return output_data\n\n    @staticmethod\n    def backward(ctx, grad_output):\n        raise NotImplementedError(\"PHLFilter does not currently support Backpropagation\")\n        # scaled_features, = ctx.saved_variables\n        # grad_input = _C.phl_filter(grad_output, scaled_features)\n        # return grad_input", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/simplelayers.py_math___all__._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/simplelayers.py_math___all__._", "embedding": null, "metadata": {"file_path": "monai/networks/layers/simplelayers.py", "file_name": "simplelayers.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 48, "span_ids": ["docstring"], "tokens": 212}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import math\nfrom copy import deepcopy\nfrom typing import List, Sequence, Union\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn\nfrom torch.autograd import Function\n\nfrom monai.networks.layers.convutils import gaussian_1d\nfrom monai.networks.layers.factories import Conv\nfrom monai.utils import (\n    ChannelMatching,\n    InvalidPyTorchVersionError,\n    SkipMode,\n    look_up_option,\n    optional_import,\n    pytorch_after,\n)\nfrom monai.utils.misc import issequenceiterable\n\n_C, _ = optional_import(\"monai._C\")\nif pytorch_after(1, 7):\n    fft, _ = optional_import(\"torch.fft\")\n\n__all__ = [\n    \"ChannelPad\",\n    \"Flatten\",\n    \"GaussianFilter\",\n    \"HilbertTransform\",\n    \"LLTM\",\n    \"Reshape\",\n    \"SavitzkyGolayFilter\",\n    \"SkipConnection\",\n    \"apply_filter\",\n    \"separable_filtering\",\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/simplelayers.py_ChannelPad_ChannelPad.forward.return.x": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/simplelayers.py_ChannelPad_ChannelPad.forward.return.x", "embedding": null, "metadata": {"file_path": "monai/networks/layers/simplelayers.py", "file_name": "simplelayers.py", "file_type": "text/x-python", "category": "implementation", "start_line": 50, "end_line": 99, "span_ids": ["ChannelPad.__init__", "ChannelPad.forward", "ChannelPad"], "tokens": 434}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ChannelPad(nn.Module):\n    \"\"\"\n    Expand the input tensor's channel dimension from length `in_channels` to `out_channels`,\n    by padding or a projection.\n    \"\"\"\n\n    def __init__(\n        self,\n        spatial_dims: int,\n        in_channels: int,\n        out_channels: int,\n        mode: Union[ChannelMatching, str] = ChannelMatching.PAD,\n    ):\n        \"\"\"\n\n        Args:\n            spatial_dims: number of spatial dimensions of the input image.\n            in_channels: number of input channels.\n            out_channels: number of output channels.\n            mode: {``\"pad\"``, ``\"project\"``}\n                Specifies handling residual branch and conv branch channel mismatches. Defaults to ``\"pad\"``.\n\n                - ``\"pad\"``: with zero padding.\n                - ``\"project\"``: with a trainable conv with kernel size one.\n        \"\"\"\n        super().__init__()\n        self.project = None\n        self.pad = None\n        if in_channels == out_channels:\n            return\n        mode = look_up_option(mode, ChannelMatching)\n        if mode == ChannelMatching.PROJECT:\n            conv_type = Conv[Conv.CONV, spatial_dims]\n            self.project = conv_type(in_channels, out_channels, kernel_size=1)\n            return\n        if mode == ChannelMatching.PAD:\n            if in_channels > out_channels:\n                raise ValueError('Incompatible values: channel_matching=\"pad\" and in_channels > out_channels.')\n            pad_1 = (out_channels - in_channels) // 2\n            pad_2 = out_channels - in_channels - pad_1\n            pad = [0, 0] * spatial_dims + [pad_1, pad_2] + [0, 0]\n            self.pad = tuple(pad)\n            return\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        if self.project is not None:\n            return torch.as_tensor(self.project(x))  # as_tensor used to get around mypy typing bug\n        if self.pad is not None:\n            return F.pad(x, self.pad)\n        return x", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/simplelayers.py_SkipConnection_SkipConnection.forward.raise_NotImplementedError": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/simplelayers.py_SkipConnection_SkipConnection.forward.raise_NotImplementedError", "embedding": null, "metadata": {"file_path": "monai/networks/layers/simplelayers.py", "file_name": "simplelayers.py", "file_type": "text/x-python", "category": "implementation", "start_line": 102, "end_line": 135, "span_ids": ["SkipConnection.forward", "SkipConnection.__init__", "SkipConnection"], "tokens": 276}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SkipConnection(nn.Module):\n    \"\"\"\n    Combine the forward pass input with the result from the given submodule::\n\n        --+--submodule--o--\n          |_____________|\n\n    The available modes are ``\"cat\"``, ``\"add\"``, ``\"mul\"``.\n    \"\"\"\n\n    def __init__(self, submodule, dim: int = 1, mode: Union[str, SkipMode] = \"cat\") -> None:\n        \"\"\"\n\n        Args:\n            submodule: the module defines the trainable branch.\n            dim: the dimension over which the tensors are concatenated.\n                Used when mode is ``\"cat\"``.\n            mode: ``\"cat\"``, ``\"add\"``, ``\"mul\"``. defaults to ``\"cat\"``.\n        \"\"\"\n        super().__init__()\n        self.submodule = submodule\n        self.dim = dim\n        self.mode = look_up_option(mode, SkipMode).value\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        y = self.submodule(x)\n\n        if self.mode == \"cat\":\n            return torch.cat([x, y], dim=self.dim)\n        if self.mode == \"add\":\n            return torch.add(x, y)\n        if self.mode == \"mul\":\n            return torch.mul(x, y)\n        raise NotImplementedError(f\"Unsupported mode {self.mode}.\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/simplelayers.py_Flatten_Reshape.forward.return.x_reshape_shape_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/simplelayers.py_Flatten_Reshape.forward.return.x_reshape_shape_", "embedding": null, "metadata": {"file_path": "monai/networks/layers/simplelayers.py", "file_name": "simplelayers.py", "file_type": "text/x-python", "category": "implementation", "start_line": 136, "end_line": 164, "span_ids": ["Reshape", "Reshape.forward", "Flatten", "Flatten.forward", "Reshape.__init__"], "tokens": 243}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Flatten(nn.Module):\n    \"\"\"\n    Flattens the given input in the forward pass to be [B,-1] in shape.\n    \"\"\"\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        return x.view(x.size(0), -1)\n\n\nclass Reshape(nn.Module):\n    \"\"\"\n    Reshapes input tensors to the given shape (minus batch dimension), retaining original batch size.\n    \"\"\"\n\n    def __init__(self, *shape: int) -> None:\n        \"\"\"\n        Given a shape list/tuple `shape` of integers (s0, s1, ... , sn), this layer will reshape input tensors of\n        shape (batch, s0 * s1 * ... * sn) to shape (batch, s0, s1, ... , sn).\n\n        Args:\n            shape: list/tuple of integer shape dimensions\n        \"\"\"\n        super().__init__()\n        self.shape = (1,) + tuple(shape)\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        shape = list(self.shape)\n        shape[0] = x.shape[0]  # done this way for Torchscript\n        return x.reshape(shape)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/simplelayers.py__separable_filtering_conv__separable_filtering_conv.return.conv_type_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/simplelayers.py__separable_filtering_conv__separable_filtering_conv.return.conv_type_", "embedding": null, "metadata": {"file_path": "monai/networks/layers/simplelayers.py", "file_name": "simplelayers.py", "file_type": "text/x-python", "category": "implementation", "start_line": 167, "end_line": 202, "span_ids": ["_separable_filtering_conv"], "tokens": 369}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _separable_filtering_conv(\n    input_: torch.Tensor,\n    kernels: List[torch.Tensor],\n    pad_mode: str,\n    d: int,\n    spatial_dims: int,\n    paddings: List[int],\n    num_channels: int,\n) -> torch.Tensor:\n\n    if d < 0:\n        return input_\n\n    s = [1] * len(input_.shape)\n    s[d + 2] = -1\n    _kernel = kernels[d].reshape(s)\n\n    # if filter kernel is unity, don't convolve\n    if _kernel.numel() == 1 and _kernel[0] == 1:\n        return _separable_filtering_conv(input_, kernels, pad_mode, d - 1, spatial_dims, paddings, num_channels)\n\n    _kernel = _kernel.repeat([num_channels, 1] + [1] * spatial_dims)\n    _padding = [0] * spatial_dims\n    _padding[d] = paddings[d]\n    conv_type = [F.conv1d, F.conv2d, F.conv3d][spatial_dims - 1]\n\n    # translate padding for input to torch.nn.functional.pad\n    _reversed_padding_repeated_twice: List[List[int]] = [[p, p] for p in reversed(_padding)]\n    _sum_reversed_padding_repeated_twice: List[int] = sum(_reversed_padding_repeated_twice, [])\n    padded_input = F.pad(input_, _sum_reversed_padding_repeated_twice, mode=pad_mode)\n\n    return conv_type(\n        input=_separable_filtering_conv(padded_input, kernels, pad_mode, d - 1, spatial_dims, paddings, num_channels),\n        weight=_kernel,\n        groups=num_channels,\n    )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/simplelayers.py_separable_filtering_separable_filtering.return._separable_filtering_conv": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/simplelayers.py_separable_filtering_separable_filtering.return._separable_filtering_conv", "embedding": null, "metadata": {"file_path": "monai/networks/layers/simplelayers.py", "file_name": "simplelayers.py", "file_type": "text/x-python", "category": "implementation", "start_line": 208, "end_line": 250, "span_ids": ["separable_filtering"], "tokens": 553}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def separable_filtering(x: torch.Tensor, kernels: List[torch.Tensor], mode: str = \"zeros\") -> torch.Tensor:\n    \"\"\"\n    Apply 1-D convolutions along each spatial dimension of `x`.\n\n    Args:\n        x: the input image. must have shape (batch, channels, H[, W, ...]).\n        kernels: kernel along each spatial dimension.\n            could be a single kernel (duplicated for all spatial dimensions), or\n            a list of `spatial_dims` number of kernels.\n        mode (string, optional): padding mode passed to convolution class. ``'zeros'``, ``'reflect'``, ``'replicate'``\n            or ``'circular'``. Default: ``'zeros'``. See ``torch.nn.Conv1d()`` for more information.\n\n    Raises:\n        TypeError: When ``x`` is not a ``torch.Tensor``.\n\n    Examples:\n\n    .. code-block:: python\n\n        >>> import torch\n        >>> from monai.networks.layers import separable_filtering\n        >>> img = torch.randn(2, 4, 32, 32)  # batch_size 2, channels 4, 32x32 2D images\n        # applying a [-1, 0, 1] filter along each of the spatial dimensions.\n        # the output shape is the same as the input shape.\n        >>> out = separable_filtering(img, torch.tensor((-1., 0., 1.)))\n        # applying `[-1, 0, 1]`, `[1, 0, -1]` filters along two spatial dimensions respectively.\n        # the output shape is the same as the input shape.\n        >>> out = separable_filtering(img, [torch.tensor((-1., 0., 1.)), torch.tensor((1., 0., -1.))])\n\n    \"\"\"\n\n    if not isinstance(x, torch.Tensor):\n        raise TypeError(f\"x must be a torch.Tensor but is {type(x).__name__}.\")\n\n    spatial_dims = len(x.shape) - 2\n    if isinstance(kernels, torch.Tensor):\n        kernels = [kernels] * spatial_dims\n    _kernels = [s.to(x) for s in kernels]\n    _paddings = [(k.shape[0] - 1) // 2 for k in _kernels]\n    n_chs = x.shape[1]\n    pad_mode = \"constant\" if mode == \"zeros\" else mode\n\n    return _separable_filtering_conv(x, _kernels, pad_mode, spatial_dims - 1, spatial_dims, _paddings, n_chs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/simplelayers.py_SavitzkyGolayFilter_SavitzkyGolayFilter.__init__.self.coeffs.self__make_coeffs_window_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/simplelayers.py_SavitzkyGolayFilter_SavitzkyGolayFilter.__init__.self.coeffs.self__make_coeffs_window_", "embedding": null, "metadata": {"file_path": "monai/networks/layers/simplelayers.py", "file_name": "simplelayers.py", "file_type": "text/x-python", "category": "implementation", "start_line": 233, "end_line": 253, "span_ids": ["SavitzkyGolayFilter.__init__", "SavitzkyGolayFilter"], "tokens": 238}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SavitzkyGolayFilter(nn.Module):\n    \"\"\"\n    Convolve a Tensor along a particular axis with a Savitzky-Golay kernel.\n\n    Args:\n        window_length: Length of the filter window, must be a positive odd integer.\n        order: Order of the polynomial to fit to each window, must be less than ``window_length``.\n        axis (optional): Axis along which to apply the filter kernel. Default 2 (first spatial dimension).\n        mode (string, optional): padding mode passed to convolution class. ``'zeros'``, ``'reflect'``, ``'replicate'`` or\n        ``'circular'``. Default: ``'zeros'``. See torch.nn.Conv1d() for more information.\n    \"\"\"\n\n    def __init__(self, window_length: int, order: int, axis: int = 2, mode: str = \"zeros\"):\n\n        super().__init__()\n        if order >= window_length:\n            raise ValueError(\"order must be less than window_length.\")\n\n        self.axis = axis\n        self.mode = mode\n        self.coeffs = self._make_coeffs(window_length, order)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/simplelayers.py_SavitzkyGolayFilter.forward_SavitzkyGolayFilter.forward.return.separable_filtering_x_ke": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/simplelayers.py_SavitzkyGolayFilter.forward_SavitzkyGolayFilter.forward.return.separable_filtering_x_ke", "embedding": null, "metadata": {"file_path": "monai/networks/layers/simplelayers.py", "file_name": "simplelayers.py", "file_type": "text/x-python", "category": "implementation", "start_line": 255, "end_line": 286, "span_ids": ["SavitzkyGolayFilter.forward"], "tokens": 392}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SavitzkyGolayFilter(nn.Module):\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        \"\"\"\n        Args:\n            x: Tensor or array-like to filter. Must be real, in shape ``[Batch, chns, spatial1, spatial2, ...]`` and\n                have a device type of ``'cpu'``.\n        Returns:\n            torch.Tensor: ``x`` filtered by Savitzky-Golay kernel with window length ``self.window_length`` using\n            polynomials of order ``self.order``, along axis specified in ``self.axis``.\n        \"\"\"\n\n        # Make input a real tensor on the CPU\n        x = torch.as_tensor(x, device=x.device if isinstance(x, torch.Tensor) else None)\n        if torch.is_complex(x):\n            raise ValueError(\"x must be real.\")\n        x = x.to(dtype=torch.float)\n\n        if (self.axis < 0) or (self.axis > len(x.shape) - 1):\n            raise ValueError(\"Invalid axis for shape of x.\")\n\n        # Create list of filter kernels (1 per spatial dimension). The kernel for self.axis will be the savgol coeffs,\n        # while the other kernels will be set to [1].\n        n_spatial_dims = len(x.shape) - 2\n        spatial_processing_axis = self.axis - 2\n        new_dims_before = spatial_processing_axis\n        new_dims_after = n_spatial_dims - spatial_processing_axis - 1\n        kernel_list = [self.coeffs.to(device=x.device, dtype=x.dtype)]\n        for _ in range(new_dims_before):\n            kernel_list.insert(0, torch.ones(1, device=x.device, dtype=x.dtype))\n        for _ in range(new_dims_after):\n            kernel_list.append(torch.ones(1, device=x.device, dtype=x.dtype))\n\n        return separable_filtering(x, kernel_list, mode=self.mode)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/simplelayers.py_SavitzkyGolayFilter._make_coeffs_SavitzkyGolayFilter._make_coeffs.return.torch_lstsq_y_a_solutio": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/simplelayers.py_SavitzkyGolayFilter._make_coeffs_SavitzkyGolayFilter._make_coeffs.return.torch_lstsq_y_a_solutio", "embedding": null, "metadata": {"file_path": "monai/networks/layers/simplelayers.py", "file_name": "simplelayers.py", "file_type": "text/x-python", "category": "implementation", "start_line": 288, "end_line": 299, "span_ids": ["SavitzkyGolayFilter._make_coeffs"], "tokens": 156}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SavitzkyGolayFilter(nn.Module):\n\n    @staticmethod\n    def _make_coeffs(window_length, order):\n\n        half_length, rem = divmod(window_length, 2)\n        if rem == 0:\n            raise ValueError(\"window_length must be odd.\")\n\n        idx = torch.arange(window_length - half_length - 1, -half_length - 1, -1, dtype=torch.float, device=\"cpu\")\n        a = idx ** torch.arange(order + 1, dtype=torch.float, device=\"cpu\").reshape(-1, 1)\n        y = torch.zeros(order + 1, dtype=torch.float, device=\"cpu\")\n        y[0] = 1.0\n        return torch.lstsq(y, a).solution.squeeze()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/simplelayers.py_HilbertTransform_HilbertTransform.__init__.self.n.n": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/simplelayers.py_HilbertTransform_HilbertTransform.__init__.self.n.n", "embedding": null, "metadata": {"file_path": "monai/networks/layers/simplelayers.py", "file_name": "simplelayers.py", "file_type": "text/x-python", "category": "implementation", "start_line": 377, "end_line": 394, "span_ids": ["HilbertTransform.__init__", "HilbertTransform"], "tokens": 181}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class HilbertTransform(nn.Module):\n    \"\"\"\n    Determine the analytical signal of a Tensor along a particular axis.\n    Requires PyTorch 1.7.0+ and the PyTorch FFT module (which is not included in NVIDIA PyTorch Release 20.10).\n\n    Args:\n        axis: Axis along which to apply Hilbert transform. Default 2 (first spatial dimension).\n        n: Number of Fourier components (i.e. FFT size). Default: ``x.shape[axis]``.\n    \"\"\"\n\n    def __init__(self, axis: int = 2, n: Union[int, None] = None) -> None:\n\n        if not pytorch_after(1, 7):\n            raise InvalidPyTorchVersionError(\"1.7.0\", self.__class__.__name__)\n\n        super().__init__()\n        self.axis = axis\n        self.n = n", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/simplelayers.py_HilbertTransform.forward_HilbertTransform.forward.return.torch_as_tensor_ht_devic": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/simplelayers.py_HilbertTransform.forward_HilbertTransform.forward.return.torch_as_tensor_ht_devic", "embedding": null, "metadata": {"file_path": "monai/networks/layers/simplelayers.py", "file_name": "simplelayers.py", "file_type": "text/x-python", "category": "implementation", "start_line": 321, "end_line": 364, "span_ids": ["HilbertTransform.forward"], "tokens": 475}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class HilbertTransform(nn.Module):\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        \"\"\"\n        Args:\n            x: Tensor or array-like to transform. Must be real and in shape ``[Batch, chns, spatial1, spatial2, ...]``.\n        Returns:\n            torch.Tensor: Analytical signal of ``x``, transformed along axis specified in ``self.axis`` using\n            FFT of size ``self.N``. The absolute value of ``x_ht`` relates to the envelope of ``x`` along axis ``self.axis``.\n        \"\"\"\n\n        # Make input a real tensor\n        x = torch.as_tensor(x, device=x.device if isinstance(x, torch.Tensor) else None)\n        if torch.is_complex(x):\n            raise ValueError(\"x must be real.\")\n        x = x.to(dtype=torch.float)\n\n        if (self.axis < 0) or (self.axis > len(x.shape) - 1):\n            raise ValueError(\"Invalid axis for shape of x.\")\n\n        n = x.shape[self.axis] if self.n is None else self.n\n        if n <= 0:\n            raise ValueError(\"N must be positive.\")\n        x = torch.as_tensor(x, dtype=torch.complex64)\n        # Create frequency axis\n        f = torch.cat(\n            [\n                torch.true_divide(torch.arange(0, (n - 1) // 2 + 1, device=x.device), float(n)),\n                torch.true_divide(torch.arange(-(n // 2), 0, device=x.device), float(n)),\n            ]\n        )\n        xf = fft.fft(x, n=n, dim=self.axis)\n        # Create step function\n        u = torch.heaviside(f, torch.tensor([0.5], device=f.device))\n        u = torch.as_tensor(u, dtype=x.dtype, device=u.device)\n        new_dims_before = self.axis\n        new_dims_after = len(xf.shape) - self.axis - 1\n        for _ in range(new_dims_before):\n            u.unsqueeze_(0)\n        for _ in range(new_dims_after):\n            u.unsqueeze_(-1)\n\n        ht = fft.ifft(xf * 2 * u, dim=self.axis)\n\n        # Apply transform\n        return torch.as_tensor(ht, device=ht.device, dtype=ht.dtype)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/spatial_transforms.py_from_typing_import_Option__GridPull.backward.if_ctx_needs_input_grad_1.return.None_grads_0_None_Non": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/spatial_transforms.py_from_typing_import_Option__GridPull.backward.if_ctx_needs_input_grad_1.return.None_grads_0_None_Non", "embedding": null, "metadata": {"file_path": "monai/networks/layers/spatial_transforms.py", "file_name": "spatial_transforms.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 46, "span_ids": ["_GridPull.forward", "_GridPull", "_GridPull.backward", "docstring"], "tokens": 306}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Optional, Sequence, Union\n\nimport torch\nimport torch.nn as nn\n\nfrom monai.networks import to_norm_affine\nfrom monai.utils import GridSampleMode, GridSamplePadMode, ensure_tuple, look_up_option, optional_import\n\n_C, _ = optional_import(\"monai._C\")\n\n__all__ = [\"AffineTransform\", \"grid_pull\", \"grid_push\", \"grid_count\", \"grid_grad\"]\n\n\nclass _GridPull(torch.autograd.Function):\n    @staticmethod\n    def forward(ctx, input, grid, interpolation, bound, extrapolate):\n        opt = (bound, interpolation, extrapolate)\n        output = _C.grid_pull(input, grid, *opt)\n        if input.requires_grad or grid.requires_grad:\n            ctx.opt = opt\n            ctx.save_for_backward(input, grid)\n\n        return output\n\n    @staticmethod\n    def backward(ctx, grad):\n        if not (ctx.needs_input_grad[0] or ctx.needs_input_grad[1]):\n            return None, None, None, None, None\n        var = ctx.saved_tensors\n        opt = ctx.opt\n        grads = _C.grid_pull_backward(grad, *var, *opt)\n        if ctx.needs_input_grad[0]:\n            return grads[0], grads[1] if ctx.needs_input_grad[1] else None, None, None, None\n        if ctx.needs_input_grad[1]:\n            return None, grads[0], None, None, None", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/spatial_transforms.py__GridPush__GridPush.backward.if_ctx_needs_input_grad_1.return.None_grads_0_None_Non": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/spatial_transforms.py__GridPush__GridPush.backward.if_ctx_needs_input_grad_1.return.None_grads_0_None_Non", "embedding": null, "metadata": {"file_path": "monai/networks/layers/spatial_transforms.py", "file_name": "spatial_transforms.py", "file_type": "text/x-python", "category": "implementation", "start_line": 119, "end_line": 140, "span_ids": ["_GridPush", "_GridPush.forward", "_GridPush.backward"], "tokens": 227}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class _GridPush(torch.autograd.Function):\n    @staticmethod\n    def forward(ctx, input, grid, shape, interpolation, bound, extrapolate):\n        opt = (bound, interpolation, extrapolate)\n        output = _C.grid_push(input, grid, shape, *opt)\n        if input.requires_grad or grid.requires_grad:\n            ctx.opt = opt\n            ctx.save_for_backward(input, grid)\n\n        return output\n\n    @staticmethod\n    def backward(ctx, grad):\n        if not (ctx.needs_input_grad[0] or ctx.needs_input_grad[1]):\n            return None, None, None, None, None, None\n        var = ctx.saved_tensors\n        opt = ctx.opt\n        grads = _C.grid_push_backward(grad, *var, *opt)\n        if ctx.needs_input_grad[0]:\n            return grads[0], grads[1] if ctx.needs_input_grad[1] else None, None, None, None, None\n        if ctx.needs_input_grad[1]:\n            return None, grads[0], None, None, None, None", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/spatial_transforms.py_grid_push_grid_push._Convert_parameters": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/spatial_transforms.py_grid_push_grid_push._Convert_parameters", "embedding": null, "metadata": {"file_path": "monai/networks/layers/spatial_transforms.py", "file_name": "spatial_transforms.py", "file_type": "text/x-python", "category": "implementation", "start_line": 143, "end_line": 207, "span_ids": ["grid_push"], "tokens": 728}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def grid_push(\n    input: torch.Tensor, grid: torch.Tensor, shape=None, interpolation=\"linear\", bound=\"zero\", extrapolate: bool = True\n):\n    \"\"\"\n    Splat an image with respect to a deformation field (pull adjoint).\n\n    `interpolation` can be an int, a string or an InterpolationType.\n    Possible values are::\n\n        - 0 or 'nearest'    or InterpolationType.nearest\n        - 1 or 'linear'     or InterpolationType.linear\n        - 2 or 'quadratic'  or InterpolationType.quadratic\n        - 3 or 'cubic'      or InterpolationType.cubic\n        - 4 or 'fourth'     or InterpolationType.fourth\n        - 5 or 'fifth'      or InterpolationType.fifth\n        - 6 or 'sixth'      or InterpolationType.sixth\n        - 7 or 'seventh'    or InterpolationType.seventh\n\n    A list of values can be provided, in the order `[W, H, D]`,\n    to specify dimension-specific interpolation orders.\n\n    `bound` can be an int, a string or a BoundType.\n    Possible values are::\n\n        - 0 or 'replicate' or 'nearest'      or BoundType.replicate\n        - 1 or 'dct1'      or 'mirror'       or BoundType.dct1\n        - 2 or 'dct2'      or 'reflect'      or BoundType.dct2\n        - 3 or 'dst1'      or 'antimirror'   or BoundType.dst1\n        - 4 or 'dst2'      or 'antireflect'  or BoundType.dst2\n        - 5 or 'dft'       or 'wrap'         or BoundType.dft\n        - 7 or 'zero'                        or BoundType.zero\n\n    A list of values can be provided, in the order `[W, H, D]`,\n    to specify dimension-specific boundary conditions.\n    `sliding` is a specific condition than only applies to flow fields\n    (with as many channels as dimensions). It cannot be dimension-specific.\n    Note that:\n\n        - `dft` corresponds to circular padding\n        - `dct2` corresponds to Neumann boundary conditions (symmetric)\n        - `dst2` corresponds to Dirichlet boundary conditions (antisymmetric)\n\n    See Also:\n\n        - https://en.wikipedia.org/wiki/Discrete_cosine_transform\n        - https://en.wikipedia.org/wiki/Discrete_sine_transform\n        - ``help(monai._C.BoundType)``\n        - ``help(monai._C.InterpolationType)``\n\n    Args:\n        input: Input image `(B, C, Wi, Hi, Di)`.\n        grid: Deformation field `(B, Wi, Hi, Di, 1|2|3)`.\n        shape: Shape of the source image.\n        interpolation (int or list[int] , optional): Interpolation order.\n            Defaults to `'linear'`.\n        bound (BoundType, or list[BoundType], optional): Boundary conditions.\n            Defaults to `'zero'`.\n        extrapolate: Extrapolate out-of-bound data.\n            Defaults to `True`.\n\n    Returns:\n        output (torch.Tensor): Splatted image `(B, C, Wo, Ho, Do)`.\n\n    \"\"\"\n    # Convert parameters\n    # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/spatial_transforms.py_grid_push.bound_grid_push.return._GridPush_apply_input_gr": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/spatial_transforms.py_grid_push.bound_grid_push.return._GridPush_apply_input_gr", "embedding": null, "metadata": {"file_path": "monai/networks/layers/spatial_transforms.py", "file_name": "spatial_transforms.py", "file_type": "text/x-python", "category": "implementation", "start_line": 208, "end_line": 217, "span_ids": ["grid_push"], "tokens": 144}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def grid_push(\n    input: torch.Tensor, grid: torch.Tensor, shape=None, interpolation=\"linear\", bound=\"zero\", extrapolate: bool = True\n):\n    bound = [_C.BoundType.__members__[b] if isinstance(b, str) else _C.BoundType(b) for b in ensure_tuple(bound)]\n    interpolation = [\n        _C.InterpolationType.__members__[i] if isinstance(i, str) else _C.InterpolationType(i)\n        for i in ensure_tuple(interpolation)\n    ]\n\n    if shape is None:\n        shape = tuple(input.shape[2:])\n\n    return _GridPush.apply(input, grid, shape, interpolation, bound, extrapolate)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/spatial_transforms.py__GridCount__GridCount.backward.return.None_None_None_None_N": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/spatial_transforms.py__GridCount__GridCount.backward.return.None_None_None_None_N", "embedding": null, "metadata": {"file_path": "monai/networks/layers/spatial_transforms.py", "file_name": "spatial_transforms.py", "file_type": "text/x-python", "category": "implementation", "start_line": 220, "end_line": 237, "span_ids": ["_GridCount.backward", "_GridCount", "_GridCount.forward"], "tokens": 145}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class _GridCount(torch.autograd.Function):\n    @staticmethod\n    def forward(ctx, grid, shape, interpolation, bound, extrapolate):\n        opt = (bound, interpolation, extrapolate)\n        output = _C.grid_count(grid, shape, *opt)\n        if grid.requires_grad:\n            ctx.opt = opt\n            ctx.save_for_backward(grid)\n\n        return output\n\n    @staticmethod\n    def backward(ctx, grad):\n        if ctx.needs_input_grad[0]:\n            var = ctx.saved_tensors\n            opt = ctx.opt\n            return _C.grid_count_backward(grad, *var, *opt), None, None, None, None\n        return None, None, None, None, None", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/spatial_transforms.py__GridGrad__GridGrad.backward.if_ctx_needs_input_grad_1.return.None_grads_0_None_Non": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/spatial_transforms.py__GridGrad__GridGrad.backward.if_ctx_needs_input_grad_1.return.None_grads_0_None_Non", "embedding": null, "metadata": {"file_path": "monai/networks/layers/spatial_transforms.py", "file_name": "spatial_transforms.py", "file_type": "text/x-python", "category": "implementation", "start_line": 316, "end_line": 337, "span_ids": ["_GridGrad.forward", "_GridGrad", "_GridGrad.backward"], "tokens": 217}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class _GridGrad(torch.autograd.Function):\n    @staticmethod\n    def forward(ctx, input, grid, interpolation, bound, extrapolate):\n        opt = (bound, interpolation, extrapolate)\n        output = _C.grid_grad(input, grid, *opt)\n        if input.requires_grad or grid.requires_grad:\n            ctx.opt = opt\n            ctx.save_for_backward(input, grid)\n\n        return output\n\n    @staticmethod\n    def backward(ctx, grad):\n        if not (ctx.needs_input_grad[0] or ctx.needs_input_grad[1]):\n            return None, None, None, None, None\n        var = ctx.saved_tensors\n        opt = ctx.opt\n        grads = _C.grid_grad_backward(grad, *var, *opt)\n        if ctx.needs_input_grad[0]:\n            return grads[0], grads[1] if ctx.needs_input_grad[1] else None, None, None, None\n        if ctx.needs_input_grad[1]:\n            return None, grads[0], None, None, None", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/spatial_transforms.py_AffineTransform.forward_AffineTransform.forward.src_size.tuple_src_shape_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/spatial_transforms.py_AffineTransform.forward_AffineTransform.forward.src_size.tuple_src_shape_", "embedding": null, "metadata": {"file_path": "monai/networks/layers/spatial_transforms.py", "file_name": "spatial_transforms.py", "file_type": "text/x-python", "category": "implementation", "start_line": 463, "end_line": 515, "span_ids": ["AffineTransform.forward"], "tokens": 785}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AffineTransform(nn.Module):\n\n    def forward(\n        self, src: torch.Tensor, theta: torch.Tensor, spatial_size: Optional[Union[Sequence[int], int]] = None\n    ) -> torch.Tensor:\n        \"\"\"\n        ``theta`` must be an affine transformation matrix with shape\n        3x3 or Nx3x3 or Nx2x3 or 2x3 for spatial 2D transforms,\n        4x4 or Nx4x4 or Nx3x4 or 3x4 for spatial 3D transforms,\n        where `N` is the batch size. `theta` will be converted into float Tensor for the computation.\n\n        Args:\n            src (array_like): image in spatial 2D or 3D (N, C, spatial_dims),\n                where N is the batch dim, C is the number of channels.\n            theta (array_like): Nx3x3, Nx2x3, 3x3, 2x3 for spatial 2D inputs,\n                Nx4x4, Nx3x4, 3x4, 4x4 for spatial 3D inputs. When the batch dimension is omitted,\n                `theta` will be repeated N times, N is the batch dim of `src`.\n            spatial_size: output spatial shape, the full output shape will be\n                `[N, C, *spatial_size]` where N and C are inferred from the `src`.\n\n        Raises:\n            TypeError: When ``theta`` is not a ``torch.Tensor``.\n            ValueError: When ``theta`` is not one of [Nxdxd, dxd].\n            ValueError: When ``theta`` is not one of [Nx3x3, Nx4x4].\n            TypeError: When ``src`` is not a ``torch.Tensor``.\n            ValueError: When ``src`` spatially is not one of [2D, 3D].\n            ValueError: When affine and image batch dimension differ.\n\n        \"\"\"\n        # validate `theta`\n        if not isinstance(theta, torch.Tensor):\n            raise TypeError(f\"theta must be torch.Tensor but is {type(theta).__name__}.\")\n        if theta.dim() not in (2, 3):\n            raise ValueError(f\"theta must be Nxdxd or dxd, got {theta.shape}.\")\n        if theta.dim() == 2:\n            theta = theta[None]  # adds a batch dim.\n        theta = theta.clone()  # no in-place change of theta\n        theta_shape = tuple(theta.shape[1:])\n        if theta_shape in ((2, 3), (3, 4)):  # needs padding to dxd\n            pad_affine = torch.tensor([0, 0, 1] if theta_shape[0] == 2 else [0, 0, 0, 1])\n            pad_affine = pad_affine.repeat(theta.shape[0], 1, 1).to(theta)\n            pad_affine.requires_grad = False\n            theta = torch.cat([theta, pad_affine], dim=1)\n        if tuple(theta.shape[1:]) not in ((3, 3), (4, 4)):\n            raise ValueError(f\"theta must be Nx3x3 or Nx4x4, got {theta.shape}.\")\n\n        # validate `src`\n        if not isinstance(src, torch.Tensor):\n            raise TypeError(f\"src must be torch.Tensor but is {type(src).__name__}.\")\n        sr = src.dim() - 2  # input spatial rank\n        if sr not in (2, 3):\n            raise ValueError(f\"Unsupported src dimension: {sr}, available options are [2, 3].\")\n\n        # set output shape\n        src_size = tuple(src.shape)\n        # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/spatial_transforms.py_AffineTransform.forward.dst_size_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/spatial_transforms.py_AffineTransform.forward.dst_size_", "embedding": null, "metadata": {"file_path": "monai/networks/layers/spatial_transforms.py", "file_name": "spatial_transforms.py", "file_type": "text/x-python", "category": "implementation", "start_line": 516, "end_line": 548, "span_ids": ["AffineTransform.forward"], "tokens": 396}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AffineTransform(nn.Module):\n\n    def forward(\n        self, src: torch.Tensor, theta: torch.Tensor, spatial_size: Optional[Union[Sequence[int], int]] = None\n    ) -> torch.Tensor:\n        # ... other code\n        dst_size = src_size  # default to the src shape\n        if self.spatial_size is not None:\n            dst_size = src_size[:2] + self.spatial_size\n        if spatial_size is not None:\n            dst_size = src_size[:2] + ensure_tuple(spatial_size)\n\n        # reverse and normalize theta if needed\n        if not self.normalized:\n            theta = to_norm_affine(\n                affine=theta, src_size=src_size[2:], dst_size=dst_size[2:], align_corners=self.align_corners\n            )\n        if self.reverse_indexing:\n            rev_idx = torch.as_tensor(range(sr - 1, -1, -1), device=src.device)\n            theta[:, :sr] = theta[:, rev_idx]\n            theta[:, :, :sr] = theta[:, :, rev_idx]\n        if (theta.shape[0] == 1) and src_size[0] > 1:\n            # adds a batch dim to `theta` in order to match `src`\n            theta = theta.repeat(src_size[0], 1, 1)\n        if theta.shape[0] != src_size[0]:\n            raise ValueError(\n                f\"affine and image batch dimension must match, got affine={theta.shape[0]} image={src_size[0]}.\"\n            )\n\n        grid = nn.functional.affine_grid(theta=theta[:, :sr], size=list(dst_size), align_corners=self.align_corners)\n        dst = nn.functional.grid_sample(\n            input=src.contiguous(),\n            grid=grid,\n            mode=self.mode.value,\n            padding_mode=self.padding_mode.value,\n            align_corners=self.align_corners,\n        )\n        return dst", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/__init__.py_AHnet_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/__init__.py_AHnet_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/__init__.py", "file_name": "__init__.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 90, "span_ids": ["imports:18", "docstring"], "tokens": 628}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from .ahnet import AHnet, Ahnet, AHNet\nfrom .autoencoder import AutoEncoder\nfrom .basic_unet import BasicUNet, BasicUnet, Basicunet, basicunet\nfrom .classifier import Classifier, Critic, Discriminator\nfrom .densenet import (\n    DenseNet,\n    Densenet,\n    DenseNet121,\n    Densenet121,\n    DenseNet169,\n    Densenet169,\n    DenseNet201,\n    Densenet201,\n    DenseNet264,\n    Densenet264,\n    densenet121,\n    densenet169,\n    densenet201,\n    densenet264,\n)\nfrom .dints import DiNTS, TopologyConstruction, TopologyInstance, TopologySearch\nfrom .dynunet import DynUNet, DynUnet, Dynunet\nfrom .efficientnet import (\n    BlockArgs,\n    EfficientNet,\n    EfficientNetBN,\n    EfficientNetBNFeatures,\n    drop_connect,\n    get_efficientnet_image_size,\n)\nfrom .fullyconnectednet import FullyConnectedNet, VarFullyConnectedNet\nfrom .generator import Generator\nfrom .highresnet import HighResBlock, HighResNet\nfrom .milmodel import MILModel\nfrom .netadapter import NetAdapter\nfrom .regressor import Regressor\nfrom .regunet import GlobalNet, LocalNet, RegUNet\nfrom .resnet import ResNet, resnet10, resnet18, resnet34, resnet50, resnet101, resnet152, resnet200\nfrom .segresnet import SegResNet, SegResNetVAE\nfrom .senet import (\n    SENet,\n    SEnet,\n    Senet,\n    SENet154,\n    SEnet154,\n    Senet154,\n    SEResNet50,\n    SEresnet50,\n    Seresnet50,\n    SEResNet101,\n    SEresnet101,\n    Seresnet101,\n    SEResNet152,\n    SEresnet152,\n    Seresnet152,\n    SEResNext50,\n    SEResNeXt50,\n    SEresnext50,\n    Seresnext50,\n    SEResNext101,\n    SEResNeXt101,\n    SEresnext101,\n    Seresnext101,\n    senet154,\n    seresnet50,\n    seresnet101,\n    seresnet152,\n    seresnext50,\n    seresnext101,\n)\nfrom .torchvision_fc import TorchVisionFCModel, TorchVisionFullyConvModel\nfrom .transchex import BertAttention, BertMixedLayer, BertOutput, BertPreTrainedModel, MultiModal, Pooler, Transchex\nfrom .unet import UNet, Unet\nfrom .unetr import UNETR\nfrom .varautoencoder import VarAutoEncoder\nfrom .vit import ViT\nfrom .vitautoenc import ViTAutoEnc\nfrom .vnet import VNet", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/ahnet.py_AHNet.copy_from_AHNet.copy_from.for_i_in_range_1_5_.for_m1_m2_in_zip_layer_2.if_isinstance_m1_self_no.copy_bn_param_m1_m2_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/ahnet.py_AHNet.copy_from_AHNet.copy_from.for_i_in_range_1_5_.for_m1_m2_in_zip_layer_2.if_isinstance_m1_self_no.copy_bn_param_m1_m2_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/ahnet.py", "file_name": "ahnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 520, "end_line": 548, "span_ids": ["AHNet.copy_from"], "tokens": 361}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AHNet(nn.Module):\n\n    def copy_from(self, net):\n        # This method only supports for 3D AHNet, the input channel should be 1.\n        p2d, p3d = next(net.conv1.parameters()), next(self.conv1.parameters())\n\n        # From 64x3x7x7 -> 64x3x7x7x1 -> 64x1x7x7x3\n        weights = p2d.data.unsqueeze(dim=4).permute(0, 4, 2, 3, 1).clone()\n        p3d.data = weights.repeat([1, p3d.shape[1], 1, 1, 1])\n\n        # Copy the initial module BN0\n        copy_bn_param(net.bn0, self.bn0)\n\n        # Copy layer1 to layer4\n        for i in range(1, 5):\n            layer_num = \"layer\" + str(i)\n\n            layer_2d = []\n            layer_3d = []\n            for m1 in vars(net)[\"_modules\"][layer_num].modules():\n                if isinstance(m1, (self.norm2d_type, self.conv2d_type)):\n                    layer_2d.append(m1)\n            for m2 in vars(self)[\"_modules\"][layer_num].modules():\n                if isinstance(m2, (self.norm_type, self.conv_type)):\n                    layer_3d.append(m2)\n\n            for m1, m2 in zip(layer_2d, layer_3d):\n                if isinstance(m1, self.conv2d_type):\n                    copy_conv_param(m1, m2)\n                if isinstance(m1, self.norm2d_type):\n                    copy_bn_param(m1, m2)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/ahnet.py_copy_conv_param_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/ahnet.py_copy_conv_param_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/ahnet.py", "file_name": "ahnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 532, "end_line": 543, "span_ids": ["impl:3", "copy_conv_param", "copy_bn_param"], "tokens": 114}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def copy_conv_param(module2d, module3d):\n    for p2d, p3d in zip(module2d.parameters(), module3d.parameters()):\n        p3d.data[:] = p2d.data.unsqueeze(dim=4).clone()[:]\n\n\ndef copy_bn_param(module2d, module3d):\n    for p2d, p3d in zip(module2d.parameters(), module3d.parameters()):\n        p3d.data[:] = p2d.data[:]  # Two parameter gamma and beta\n\n\nAHnet = Ahnet = AHNet", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/autoencoder.py_AutoEncoder._get_intermediate_module_AutoEncoder._get_intermediate_module.return.intermediate_layer_chann": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/autoencoder.py_AutoEncoder._get_intermediate_module_AutoEncoder._get_intermediate_module.return.intermediate_layer_chann", "embedding": null, "metadata": {"file_path": "monai/networks/nets/autoencoder.py", "file_name": "autoencoder.py", "file_type": "text/x-python", "category": "implementation", "start_line": 158, "end_line": 205, "span_ids": ["AutoEncoder._get_intermediate_module"], "tokens": 303}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AutoEncoder(nn.Module):\n\n    def _get_intermediate_module(self, in_channels: int, num_inter_units: int) -> Tuple[nn.Module, int]:\n        \"\"\"\n        Returns the intermediate block of the network which accepts input from the encoder and whose output goes\n        to the decoder.\n        \"\"\"\n        # Define some types\n        intermediate: nn.Module\n        unit: nn.Module\n\n        intermediate = nn.Identity()\n        layer_channels = in_channels\n\n        if self.inter_channels:\n            intermediate = nn.Sequential()\n\n            for i, (dc, di) in enumerate(zip(self.inter_channels, self.inter_dilations)):\n                if self.num_inter_units > 0:\n                    unit = ResidualUnit(\n                        spatial_dims=self.dimensions,\n                        in_channels=layer_channels,\n                        out_channels=dc,\n                        strides=1,\n                        kernel_size=self.kernel_size,\n                        subunits=self.num_inter_units,\n                        act=self.act,\n                        norm=self.norm,\n                        dropout=self.dropout,\n                        dilation=di,\n                        bias=self.bias,\n                    )\n                else:\n                    unit = Convolution(\n                        spatial_dims=self.dimensions,\n                        in_channels=layer_channels,\n                        out_channels=dc,\n                        strides=1,\n                        kernel_size=self.kernel_size,\n                        act=self.act,\n                        norm=self.norm,\n                        dropout=self.dropout,\n                        dilation=di,\n                        bias=self.bias,\n                    )\n\n                intermediate.add_module(\"inter_%i\" % i, unit)\n                layer_channels = dc\n\n        return intermediate, layer_channels", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/autoencoder.py_AutoEncoder._get_decode_module_AutoEncoder._get_decode_module.return.decode_layer_channels": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/autoencoder.py_AutoEncoder._get_decode_module_AutoEncoder._get_decode_module.return.decode_layer_channels", "embedding": null, "metadata": {"file_path": "monai/networks/nets/autoencoder.py", "file_name": "autoencoder.py", "file_type": "text/x-python", "category": "implementation", "start_line": 207, "end_line": 221, "span_ids": ["AutoEncoder._get_decode_module"], "tokens": 149}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AutoEncoder(nn.Module):\n\n    def _get_decode_module(\n        self, in_channels: int, channels: Sequence[int], strides: Sequence[int]\n    ) -> Tuple[nn.Sequential, int]:\n        \"\"\"\n        Returns the decode part of the network by building up a sequence of layers returned by `_get_decode_layer`.\n        \"\"\"\n        decode = nn.Sequential()\n        layer_channels = in_channels\n\n        for i, (c, s) in enumerate(zip(channels, strides)):\n            layer = self._get_decode_layer(layer_channels, c, s, i == (len(strides) - 1))\n            decode.add_module(\"decode_%i\" % i, layer)\n            layer_channels = c\n\n        return decode, layer_channels", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/autoencoder.py_AutoEncoder._get_decode_layer_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/autoencoder.py_AutoEncoder._get_decode_layer_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/autoencoder.py", "file_name": "autoencoder.py", "file_type": "text/x-python", "category": "implementation", "start_line": 256, "end_line": 302, "span_ids": ["AutoEncoder._get_decode_layer", "AutoEncoder.forward"], "tokens": 282}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AutoEncoder(nn.Module):\n\n    def _get_decode_layer(self, in_channels: int, out_channels: int, strides: int, is_last: bool) -> nn.Sequential:\n        \"\"\"\n        Returns a single layer of the decoder part of the network.\n        \"\"\"\n        decode = nn.Sequential()\n\n        conv = Convolution(\n            spatial_dims=self.dimensions,\n            in_channels=in_channels,\n            out_channels=out_channels,\n            strides=strides,\n            kernel_size=self.up_kernel_size,\n            act=self.act,\n            norm=self.norm,\n            dropout=self.dropout,\n            bias=self.bias,\n            conv_only=is_last and self.num_res_units == 0,\n            is_transposed=True,\n        )\n\n        decode.add_module(\"conv\", conv)\n\n        if self.num_res_units > 0:\n            ru = ResidualUnit(\n                spatial_dims=self.dimensions,\n                in_channels=out_channels,\n                out_channels=out_channels,\n                strides=1,\n                kernel_size=self.kernel_size,\n                subunits=1,\n                act=self.act,\n                norm=self.norm,\n                dropout=self.dropout,\n                bias=self.bias,\n                last_conv_only=is_last,\n            )\n\n            decode.add_module(\"resunit\", ru)\n\n        return decode\n\n    def forward(self, x: torch.Tensor) -> Any:\n        x = self.encode(x)\n        x = self.intermediate(x)\n        x = self.decode(x)\n        return x", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/basic_unet.py_Down_Down.__init__.self_add_module_convs_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/basic_unet.py_Down_Down.__init__.self_add_module_convs_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/basic_unet.py", "file_name": "basic_unet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 64, "end_line": 98, "span_ids": ["Down.__init__", "Down"], "tokens": 318}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Down(nn.Sequential):\n    \"\"\"maxpooling downsampling and two convolutions.\"\"\"\n\n    @deprecated_arg(name=\"dim\", new_name=\"spatial_dims\", since=\"0.6\", msg_suffix=\"Please use `spatial_dims` instead.\")\n    def __init__(\n        self,\n        spatial_dims: int,\n        in_chns: int,\n        out_chns: int,\n        act: Union[str, tuple],\n        norm: Union[str, tuple],\n        bias: bool,\n        dropout: Union[float, tuple] = 0.0,\n        dim: Optional[int] = None,\n    ):\n        \"\"\"\n        Args:\n            spatial_dims: number of spatial dimensions.\n            in_chns: number of input channels.\n            out_chns: number of output channels.\n            act: activation type and arguments.\n            norm: feature normalization type and arguments.\n            bias: whether to have a bias term in convolution blocks.\n            dropout: dropout ratio. Defaults to no dropout.\n\n        .. deprecated:: 0.6.0\n            ``dim`` is deprecated, use ``spatial_dims`` instead.\n        \"\"\"\n        super().__init__()\n        if dim is not None:\n            spatial_dims = dim\n        max_pooling = Pool[\"MAX\", spatial_dims](kernel_size=2)\n        convs = TwoConv(spatial_dims, in_chns, out_chns, act, norm, bias, dropout)\n        self.add_module(\"max_pooling\", max_pooling)\n        self.add_module(\"convs\", convs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/basic_unet.py_UpCat.forward_UpCat.forward.return.x": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/basic_unet.py_UpCat.forward_UpCat.forward.return.x", "embedding": null, "metadata": {"file_path": "monai/networks/nets/basic_unet.py", "file_name": "basic_unet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 165, "end_line": 186, "span_ids": ["UpCat.forward"], "tokens": 227}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class UpCat(nn.Module):\n\n    def forward(self, x: torch.Tensor, x_e: Optional[torch.Tensor]):\n        \"\"\"\n\n        Args:\n            x: features to be upsampled.\n            x_e: features from the encoder.\n        \"\"\"\n        x_0 = self.upsample(x)\n\n        if x_e is not None:\n            # handling spatial shapes due to the 2x maxpooling with odd edge lengths.\n            dimensions = len(x.shape) - 2\n            sp = [0] * (dimensions * 2)\n            for i in range(dimensions):\n                if x_e.shape[-i - 1] != x_0.shape[-i - 1]:\n                    sp[i * 2 + 1] = 1\n            x_0 = torch.nn.functional.pad(x_0, sp, \"replicate\")\n            x = self.convs(torch.cat([x_e, x_0], dim=1))  # input channels: (cat_chns + up_chns)\n        else:\n            x = self.convs(x_0)\n\n        return x", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/basic_unet.py_BasicUNet.forward_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/basic_unet.py_BasicUNet.forward_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/basic_unet.py", "file_name": "basic_unet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 209, "end_line": 238, "span_ids": ["impl:3", "BasicUNet.forward"], "tokens": 270}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class BasicUNet(nn.Module):\n\n    def forward(self, x: torch.Tensor):\n        \"\"\"\n        Args:\n            x: input should have spatially N dimensions\n                ``(Batch, in_channels, dim_0[, dim_1, ..., dim_N])``, N is defined by `dimensions`.\n                It is recommended to have ``dim_n % 16 == 0`` to ensure all maxpooling inputs have\n                even edge lengths.\n\n        Returns:\n            A torch Tensor of \"raw\" predictions in shape\n            ``(Batch, out_channels, dim_0[, dim_1, ..., dim_N])``.\n        \"\"\"\n        x0 = self.conv_0(x)\n\n        x1 = self.down_1(x0)\n        x2 = self.down_2(x1)\n        x3 = self.down_3(x2)\n        x4 = self.down_4(x3)\n\n        u4 = self.upcat_4(x4, x3)\n        u3 = self.upcat_3(u4, x2)\n        u2 = self.upcat_2(u3, x1)\n        u1 = self.upcat_1(u2, x0)\n\n        logits = self.final_conv(u1)\n        return logits\n\n\nBasicUnet = Basicunet = basicunet = BasicUNet", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/densenet.py__load_state_dict__load_state_dict.model_load_state_dict_mod": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/densenet.py__load_state_dict__load_state_dict.model_load_state_dict_mod", "embedding": null, "metadata": {"file_path": "monai/networks/nets/densenet.py", "file_name": "densenet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 252, "end_line": 286, "span_ids": ["_load_state_dict"], "tokens": 392}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _load_state_dict(model: nn.Module, arch: str, progress: bool):\n    \"\"\"\n    This function is used to load pretrained models.\n    Adapted from PyTorch Hub 2D version: https://pytorch.org/vision/stable/models.html#id16.\n\n    \"\"\"\n    model_urls = {\n        \"densenet121\": \"https://download.pytorch.org/models/densenet121-a639ec97.pth\",\n        \"densenet169\": \"https://download.pytorch.org/models/densenet169-b2777c0a.pth\",\n        \"densenet201\": \"https://download.pytorch.org/models/densenet201-c1103571.pth\",\n    }\n    model_url = look_up_option(arch, model_urls, None)\n    if model_url is None:\n        raise ValueError(\n            \"only 'densenet121', 'densenet169' and 'densenet201' are supported to load pretrained weights.\"\n        )\n\n    pattern = re.compile(\n        r\"^(.*denselayer\\d+)(\\.(?:norm|relu|conv))\\.((?:[12])\\.(?:weight|bias|running_mean|running_var))$\"\n    )\n\n    state_dict = load_state_dict_from_url(model_url, progress=progress)\n    for key in list(state_dict.keys()):\n        res = pattern.match(key)\n        if res:\n            new_key = res.group(1) + \".layers\" + res.group(2) + res.group(3)\n            state_dict[new_key] = state_dict[key]\n            del state_dict[key]\n\n    model_dict = model.state_dict()\n    state_dict = {\n        k: v for k, v in state_dict.items() if (k in model_dict) and (model_dict[k].shape == state_dict[k].shape)\n    }\n    model_dict.update(state_dict)\n    model.load_state_dict(model_dict)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/densenet.py_DenseNet121_DenseNet121.__init__.if_pretrained_._load_state_dict_self_d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/densenet.py_DenseNet121_DenseNet121.__init__.if_pretrained_._load_state_dict_self_d", "embedding": null, "metadata": {"file_path": "monai/networks/nets/densenet.py", "file_name": "densenet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 289, "end_line": 308, "span_ids": ["DenseNet121.__init__", "DenseNet121"], "tokens": 192}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DenseNet121(DenseNet):\n    \"\"\"DenseNet121 with optional pretrained support when `spatial_dims` is 2.\"\"\"\n\n    def __init__(\n        self,\n        init_features: int = 64,\n        growth_rate: int = 32,\n        block_config: Sequence[int] = (6, 12, 24, 16),\n        pretrained: bool = False,\n        progress: bool = True,\n        **kwargs,\n    ) -> None:\n        super().__init__(init_features=init_features, growth_rate=growth_rate, block_config=block_config, **kwargs)\n        if pretrained:\n            if kwargs[\"spatial_dims\"] > 2:\n                raise NotImplementedError(\n                    \"Parameter `spatial_dims` is > 2 ; currently PyTorch Hub does not\"\n                    \"provide pretrained models for more than two spatial dimensions.\"\n                )\n            _load_state_dict(self, \"densenet121\", progress)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/densenet.py_DenseNet169_DenseNet169.__init__.if_pretrained_._load_state_dict_self_d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/densenet.py_DenseNet169_DenseNet169.__init__.if_pretrained_._load_state_dict_self_d", "embedding": null, "metadata": {"file_path": "monai/networks/nets/densenet.py", "file_name": "densenet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 311, "end_line": 330, "span_ids": ["DenseNet169.__init__", "DenseNet169"], "tokens": 192}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DenseNet169(DenseNet):\n    \"\"\"DenseNet169 with optional pretrained support when `spatial_dims` is 2.\"\"\"\n\n    def __init__(\n        self,\n        init_features: int = 64,\n        growth_rate: int = 32,\n        block_config: Sequence[int] = (6, 12, 32, 32),\n        pretrained: bool = False,\n        progress: bool = True,\n        **kwargs,\n    ) -> None:\n        super().__init__(init_features=init_features, growth_rate=growth_rate, block_config=block_config, **kwargs)\n        if pretrained:\n            if kwargs[\"spatial_dims\"] > 2:\n                raise NotImplementedError(\n                    \"Parameter `spatial_dims` is > 2 ; currently PyTorch Hub does not\"\n                    \"provide pretrained models for more than two spatial dimensions.\"\n                )\n            _load_state_dict(self, \"densenet169\", progress)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/densenet.py_DenseNet201_DenseNet201.__init__.if_pretrained_._load_state_dict_self_d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/densenet.py_DenseNet201_DenseNet201.__init__.if_pretrained_._load_state_dict_self_d", "embedding": null, "metadata": {"file_path": "monai/networks/nets/densenet.py", "file_name": "densenet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 333, "end_line": 352, "span_ids": ["DenseNet201", "DenseNet201.__init__"], "tokens": 192}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DenseNet201(DenseNet):\n    \"\"\"DenseNet201 with optional pretrained support when `spatial_dims` is 2.\"\"\"\n\n    def __init__(\n        self,\n        init_features: int = 64,\n        growth_rate: int = 32,\n        block_config: Sequence[int] = (6, 12, 48, 32),\n        pretrained: bool = False,\n        progress: bool = True,\n        **kwargs,\n    ) -> None:\n        super().__init__(init_features=init_features, growth_rate=growth_rate, block_config=block_config, **kwargs)\n        if pretrained:\n            if kwargs[\"spatial_dims\"] > 2:\n                raise NotImplementedError(\n                    \"Parameter `spatial_dims` is > 2 ; currently PyTorch Hub does not\"\n                    \"provide pretrained models for more than two spatial dimensions.\"\n                )\n            _load_state_dict(self, \"densenet201\", progress)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/densenet.py_DenseNet264_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/densenet.py_DenseNet264_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/densenet.py", "file_name": "densenet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 355, "end_line": 377, "span_ids": ["impl:3", "DenseNet264", "DenseNet264.__init__"], "tokens": 195}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DenseNet264(DenseNet):\n    \"\"\"DenseNet264\"\"\"\n\n    def __init__(\n        self,\n        init_features: int = 64,\n        growth_rate: int = 32,\n        block_config: Sequence[int] = (6, 12, 64, 48),\n        pretrained: bool = False,\n        progress: bool = True,\n        **kwargs,\n    ) -> None:\n        super().__init__(init_features=init_features, growth_rate=growth_rate, block_config=block_config, **kwargs)\n        if pretrained:\n            raise NotImplementedError(\"Currently PyTorch Hub does not provide densenet264 pretrained models.\")\n\n\nDensenet = DenseNet\nDensenet121 = densenet121 = DenseNet121\nDensenet169 = densenet169 = DenseNet169\nDensenet201 = densenet201 = DenseNet201\nDensenet264 = densenet264 = DenseNet264", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dynunet.py_from_typing_import_List__DynUNetSkipLayer.forward.return.upout": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dynunet.py_from_typing_import_List__DynUNetSkipLayer.forward.return.upout", "embedding": null, "metadata": {"file_path": "monai/networks/nets/dynunet.py", "file_name": "dynunet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 13, "end_line": 52, "span_ids": ["DynUNetSkipLayer.__init__", "DynUNetSkipLayer.forward", "DynUNetSkipLayer", "docstring"], "tokens": 386}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import List, Optional, Sequence, Tuple, Union\n\nimport torch\nimport torch.nn as nn\nfrom torch.nn.functional import interpolate\n\nfrom monai.networks.blocks.dynunet_block import UnetBasicBlock, UnetOutBlock, UnetResBlock, UnetUpBlock\n\n__all__ = [\"DynUNet\", \"DynUnet\", \"Dynunet\"]\n\n\nclass DynUNetSkipLayer(nn.Module):\n    \"\"\"\n    Defines a layer in the UNet topology which combines the downsample and upsample pathways with the skip connection.\n    The member `next_layer` may refer to instances of this class or the final bottleneck layer at the bottom the UNet\n    structure. The purpose of using a recursive class like this is to get around the Torchscript restrictions on\n    looping over lists of layers and accumulating lists of output tensors which must be indexed. The `heads` list is\n    shared amongst all the instances of this class and is used to store the output from the supervision heads during\n    forward passes of the network.\n    \"\"\"\n\n    heads: Optional[List[torch.Tensor]]\n\n    def __init__(self, index, downsample, upsample, next_layer, heads=None, super_head=None):\n        super().__init__()\n        self.downsample = downsample\n        self.next_layer = next_layer\n        self.upsample = upsample\n        self.super_head = super_head\n        self.heads = heads\n        self.index = index\n\n    def forward(self, x):\n        downout = self.downsample(x)\n        nextout = self.next_layer(downout)\n        upout = self.upsample(nextout, downout)\n        if self.super_head is not None and self.heads is not None and self.index > 0:\n            self.heads[self.index - 1] = self.super_head(upout)\n\n        return upout", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dynunet.py_DynUNet_DynUNet._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dynunet.py_DynUNet_DynUNet._", "embedding": null, "metadata": {"file_path": "monai/networks/nets/dynunet.py", "file_name": "dynunet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 55, "end_line": 121, "span_ids": ["DynUNet"], "tokens": 1112}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DynUNet(nn.Module):\n    \"\"\"\n    This reimplementation of a dynamic UNet (DynUNet) is based on:\n    `Automated Design of Deep Learning Methods for Biomedical Image Segmentation <https://arxiv.org/abs/1904.08128>`_.\n    `nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation <https://arxiv.org/abs/1809.10486>`_.\n    `Optimized U-Net for Brain Tumor Segmentation <https://arxiv.org/pdf/2110.03352.pdf>`_.\n\n    This model is more flexible compared with ``monai.networks.nets.UNet`` in three\n    places:\n\n        - Residual connection is supported in conv blocks.\n        - Anisotropic kernel sizes and strides can be used in each layers.\n        - Deep supervision heads can be added.\n\n    The model supports 2D or 3D inputs and is consisted with four kinds of blocks:\n    one input block, `n` downsample blocks, one bottleneck and `n+1` upsample blocks. Where, `n>0`.\n    The first and last kernel and stride values of the input sequences are used for input block and\n    bottleneck respectively, and the rest value(s) are used for downsample and upsample blocks.\n    Therefore, pleasure ensure that the length of input sequences (``kernel_size`` and ``strides``)\n    is no less than 3 in order to have at least one downsample and upsample blocks.\n\n    To meet the requirements of the structure, the input size for each spatial dimension should be divisible\n    by `2 * the product of all strides in the corresponding dimension`. The output size for each spatial dimension\n    equals to the input size of the corresponding dimension divided by the stride in strides[0].\n    For example, if `strides=((1, 2, 4), 2, 1, 1)`, the minimal spatial size of the input is `(8, 16, 32)`, and\n    the spatial size of the output is `(8, 8, 8)`.\n\n    For backwards compatibility with old weights, please set `strict=False` when calling `load_state_dict`.\n\n    Usage example with medical segmentation decathlon dataset is available at:\n    https://github.com/Project-MONAI/tutorials/tree/master/modules/dynunet_pipeline.\n\n    Args:\n        spatial_dims: number of spatial dimensions.\n        in_channels: number of input channels.\n        out_channels: number of output channels.\n        kernel_size: convolution kernel size.\n        strides: convolution strides for each blocks.\n        upsample_kernel_size: convolution kernel size for transposed convolution layers. The values should\n            equal to strides[1:].\n        filters: number of output channels for each blocks. Different from nnU-Net, in this implementation we add\n            this argument to make the network more flexible. As shown in the third reference, one way to determine\n            this argument is like:\n            ``[64, 96, 128, 192, 256, 384, 512, 768, 1024][: len(strides)]``.\n            The above way is used in the network that wins task 1 in the BraTS21 Challenge.\n            If not specified, the way which nnUNet used will be employed. Defaults to ``None``.\n        dropout: dropout ratio. Defaults to no dropout.\n        norm_name: feature normalization type and arguments. Defaults to ``INSTANCE``.\n        act_name: activation layer type and arguments. Defaults to ``leakyrelu``.\n        deep_supervision: whether to add deep supervision head before output. Defaults to ``False``.\n            If ``True``, in training mode, the forward function will output not only the final feature map\n            (from `output_block`), but also the feature maps that come from the intermediate up sample layers.\n            In order to unify the return type (the restriction of TorchScript), all intermediate\n            feature maps are interpolated into the same size as the final feature map and stacked together\n            (with a new dimension in the first axis)into one single tensor.\n            For instance, if there are two intermediate feature maps with shapes: (1, 2, 16, 12) and\n            (1, 2, 8, 6), and the final feature map has the shape (1, 2, 32, 24), then all intermediate feature maps\n            will be interpolated into (1, 2, 32, 24), and the stacked tensor will has the shape (1, 3, 2, 32, 24).\n            When calculating the loss, you can use torch.unbind to get all feature maps can compute the loss\n            one by one with the ground truth, then do a weighted average for all losses to achieve the final loss.\n        deep_supr_num: number of feature maps that will output during deep supervision head. The\n            value should be larger than 0 and less than the number of up sample layers.\n            Defaults to 1.\n        res_block: whether to use residual connection based convolution blocks during the network.\n            Defaults to ``False``.\n        trans_bias: whether to set the bias parameter in transposed convolution layers. Defaults to ``False``.\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/efficientnet.py_math_efficientnet_params._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/efficientnet.py_math_efficientnet_params._", "embedding": null, "metadata": {"file_path": "monai/networks/nets/efficientnet.py", "file_name": "efficientnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 47, "span_ids": ["docstring"], "tokens": 459}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import math\nimport operator\nimport re\nfrom functools import reduce\nfrom typing import List, NamedTuple, Optional, Tuple, Type, Union\n\nimport torch\nfrom torch import nn\nfrom torch.utils import model_zoo\n\nfrom monai.networks.layers.factories import Act, Conv, Pad, Pool\nfrom monai.networks.layers.utils import get_norm_layer\nfrom monai.utils.module import look_up_option\n\n__all__ = [\n    \"EfficientNet\",\n    \"EfficientNetBN\",\n    \"get_efficientnet_image_size\",\n    \"drop_connect\",\n    \"EfficientNetBNFeatures\",\n    \"BlockArgs\",\n]\n\nefficientnet_params = {\n    # model_name: (width_mult, depth_mult, image_size, dropout_rate, dropconnect_rate)\n    \"efficientnet-b0\": (1.0, 1.0, 224, 0.2, 0.2),\n    \"efficientnet-b1\": (1.0, 1.1, 240, 0.2, 0.2),\n    \"efficientnet-b2\": (1.1, 1.2, 260, 0.3, 0.2),\n    \"efficientnet-b3\": (1.2, 1.4, 300, 0.3, 0.2),\n    \"efficientnet-b4\": (1.4, 1.8, 380, 0.4, 0.2),\n    \"efficientnet-b5\": (1.6, 2.2, 456, 0.4, 0.2),\n    \"efficientnet-b6\": (1.8, 2.6, 528, 0.5, 0.2),\n    \"efficientnet-b7\": (2.0, 3.1, 600, 0.5, 0.2),\n    \"efficientnet-b8\": (2.2, 3.6, 672, 0.5, 0.2),\n    \"efficientnet-l2\": (4.3, 5.3, 800, 0.5, 0.2),\n}", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/efficientnet.py_EfficientNet.forward_EfficientNet.forward.return.x": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/efficientnet.py_EfficientNet.forward_EfficientNet.forward.return.x", "embedding": null, "metadata": {"file_path": "monai/networks/nets/efficientnet.py", "file_name": "efficientnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 410, "end_line": 434, "span_ids": ["EfficientNet.forward"], "tokens": 205}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class EfficientNet(nn.Module):\n\n    def forward(self, inputs: torch.Tensor):\n        \"\"\"\n        Args:\n            inputs: input should have spatially N dimensions\n            ``(Batch, in_channels, dim_0[, dim_1, ..., dim_N])``, N is defined by `dimensions`.\n\n        Returns:\n            a torch Tensor of classification prediction in shape ``(Batch, num_classes)``.\n        \"\"\"\n        # Stem\n        x = self._conv_stem(self._conv_stem_padding(inputs))\n        x = self._swish(self._bn0(x))\n        # Blocks\n        x = self._blocks(x)\n        # Head\n        x = self._conv_head(self._conv_head_padding(x))\n        x = self._swish(self._bn1(x))\n\n        # Pooling and final linear layer\n        x = self._avg_pooling(x)\n\n        x = x.flatten(start_dim=1)\n        x = self._dropout(x)\n        x = self._fc(x)\n        return x", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/efficientnet.py_EfficientNet._initialize_weights_EfficientNet._initialize_weights.for___m_in_self_named_mo.if_isinstance_m_nn_Conv.elif_isinstance_m_nn_Lin.m_bias_data_zero__": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/efficientnet.py_EfficientNet._initialize_weights_EfficientNet._initialize_weights.for___m_in_self_named_mo.if_isinstance_m_nn_Conv.elif_isinstance_m_nn_Lin.m_bias_data_zero__", "embedding": null, "metadata": {"file_path": "monai/networks/nets/efficientnet.py", "file_name": "efficientnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 435, "end_line": 459, "span_ids": ["EfficientNet._initialize_weights"], "tokens": 311}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class EfficientNet(nn.Module):\n\n    def _initialize_weights(self) -> None:\n        \"\"\"\n        Args:\n            None, initializes weights for conv/linear/batchnorm layers\n            following weight init methods from\n            `official Tensorflow EfficientNet implementation\n            <https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/efficientnet_model.py#L61>`_.\n            Adapted from `EfficientNet-PyTorch's init method\n            <https://github.com/rwightman/gen-efficientnet-pytorch/blob/master/geffnet/efficientnet_builder.py>`_.\n        \"\"\"\n        for _, m in self.named_modules():\n            if isinstance(m, (nn.Conv1d, nn.Conv2d, nn.Conv3d)):\n                fan_out = reduce(operator.mul, m.kernel_size, 1) * m.out_channels\n                m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))\n                if m.bias is not None:\n                    m.bias.data.zero_()\n            elif isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d)):\n                m.weight.data.fill_(1.0)\n                m.bias.data.zero_()\n            elif isinstance(m, nn.Linear):\n                fan_out = m.weight.size(0)\n                fan_in = 0\n                init_range = 1.0 / math.sqrt(fan_in + fan_out)\n                m.weight.data.uniform_(-init_range, init_range)\n                m.bias.data.zero_()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/efficientnet.py_get_efficientnet_image_size_get_efficientnet_image_size.return.res": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/efficientnet.py_get_efficientnet_image_size_get_efficientnet_image_size.return.res", "embedding": null, "metadata": {"file_path": "monai/networks/nets/efficientnet.py", "file_name": "efficientnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 539, "end_line": 558, "span_ids": ["get_efficientnet_image_size"], "tokens": 164}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_efficientnet_image_size(model_name: str) -> int:\n    \"\"\"\n    Get the input image size for a given efficientnet model.\n\n    Args:\n        model_name: name of model to initialize, can be from [efficientnet-b0, ..., efficientnet-b7].\n\n    Returns:\n        Image size for single spatial dimension as integer.\n\n    \"\"\"\n    # check if model_name is valid model\n    if model_name not in efficientnet_params.keys():\n        raise ValueError(\n            \"invalid model_name {} found, must be one of {} \".format(model_name, \", \".join(efficientnet_params.keys()))\n        )\n\n    # return input image size (all dims equal so only need to return for one dim)\n    _, _, res, _, _ = efficientnet_params[model_name]\n    return res", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/efficientnet.py_drop_connect_drop_connect.return.output": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/efficientnet.py_drop_connect_drop_connect.return.output", "embedding": null, "metadata": {"file_path": "monai/networks/nets/efficientnet.py", "file_name": "efficientnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 663, "end_line": 707, "span_ids": ["drop_connect"], "tokens": 488}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def drop_connect(inputs: torch.Tensor, p: float, training: bool) -> torch.Tensor:\n    \"\"\"\n    Drop connect layer that drops individual connections.\n    Differs from dropout as dropconnect drops connections instead of whole neurons as in dropout.\n\n    Based on `Deep Networks with Stochastic Depth <https://arxiv.org/pdf/1603.09382.pdf>`_.\n    Adapted from `Official Tensorflow EfficientNet utils\n    <https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/utils.py>`_.\n\n    This function is generalized for MONAI's N-Dimensional spatial activations\n    e.g. 1D activations [B, C, H], 2D activations [B, C, H, W] and 3D activations [B, C, H, W, D]\n\n    Args:\n        inputs: input tensor with [B, C, dim_1, dim_2, ..., dim_N] where N=spatial_dims.\n        p: probability to use for dropping connections.\n        training: whether in training or evaluation mode.\n\n    Returns:\n        output: output tensor after applying drop connection.\n    \"\"\"\n    if p < 0.0 or p > 1.0:\n        raise ValueError(f\"p must be in range of [0, 1], found {p}\")\n\n    # eval mode: drop_connect is switched off - so return input without modifying\n    if not training:\n        return inputs\n\n    # train mode: calculate and apply drop_connect\n    batch_size: int = inputs.shape[0]\n    keep_prob: float = 1 - p\n    num_dims: int = len(inputs.shape) - 2\n\n    # build dimensions for random tensor, use num_dims to populate appropriate spatial dims\n    random_tensor_shape: List[int] = [batch_size, 1] + [1] * num_dims\n\n    # generate binary_tensor mask according to probability (p for 0, 1-p for 1)\n    random_tensor: torch.Tensor = torch.rand(random_tensor_shape, dtype=inputs.dtype, device=inputs.device)\n    random_tensor += keep_prob\n\n    # round to form binary tensor\n    binary_tensor: torch.Tensor = torch.floor(random_tensor)\n\n    # drop connect using binary tensor\n    output: torch.Tensor = inputs / keep_prob * binary_tensor\n    return output", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/efficientnet.py__get_same_padding_conv_nd__get_same_padding_conv_nd.return._paddings_ret": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/efficientnet.py__get_same_padding_conv_nd__get_same_padding_conv_nd.return._paddings_ret", "embedding": null, "metadata": {"file_path": "monai/networks/nets/efficientnet.py", "file_name": "efficientnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 642, "end_line": 681, "span_ids": ["_get_same_padding_conv_nd"], "tokens": 466}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _get_same_padding_conv_nd(\n    image_size: List[int], kernel_size: Tuple[int, ...], dilation: Tuple[int, ...], stride: Tuple[int, ...]\n) -> List[int]:\n    \"\"\"\n    Helper for getting padding (nn.ConstantPadNd) to be used to get SAME padding\n    conv operations similar to Tensorflow's SAME padding.\n\n    This function is generalized for MONAI's N-Dimensional spatial operations (e.g. Conv1D, Conv2D, Conv3D)\n\n    Args:\n        image_size: input image/feature spatial size.\n        kernel_size: conv kernel's spatial size.\n        dilation: conv dilation rate for Atrous conv.\n        stride: stride for conv operation.\n\n    Returns:\n        paddings for ConstantPadNd padder to be used on input tensor to conv op.\n    \"\"\"\n    # get number of spatial dimensions, corresponds to kernel size length\n    num_dims = len(kernel_size)\n\n    # additional checks to populate dilation and stride (in case they are single entry tuples)\n    if len(dilation) == 1:\n        dilation = dilation * num_dims\n\n    if len(stride) == 1:\n        stride = stride * num_dims\n\n    # equation to calculate (pad^+ + pad^-) size\n    _pad_size: List[int] = [\n        max((math.ceil(_i_s / _s) - 1) * _s + (_k_s - 1) * _d + 1 - _i_s, 0)\n        for _i_s, _k_s, _d, _s in zip(image_size, kernel_size, dilation, stride)\n    ]\n    # distribute paddings into pad^+ and pad^- following Tensorflow's same padding strategy\n    _paddings: List[Tuple[int, int]] = [(_p // 2, _p - _p // 2) for _p in _pad_size]\n\n    # unroll list of tuples to tuples, and then to list\n    # reversed as nn.ConstantPadNd expects paddings starting with last dimension\n    _paddings_ret: List[int] = [outer for inner in reversed(_paddings) for outer in inner]\n    return _paddings_ret", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/efficientnet.py__round_filters__round_filters.return.int_new_filters_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/efficientnet.py__round_filters__round_filters.return.int_new_filters_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/efficientnet.py", "file_name": "efficientnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 709, "end_line": 733, "span_ids": ["_round_filters"], "tokens": 198}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _round_filters(filters: int, width_coefficient: Optional[float], depth_divisor: float) -> int:\n    \"\"\"\n    Calculate and round number of filters based on width coefficient multiplier and depth divisor.\n\n    Args:\n        filters: number of input filters.\n        width_coefficient: width coefficient for model.\n        depth_divisor: depth divisor to use.\n\n    Returns:\n        new_filters: new number of filters after calculation.\n    \"\"\"\n\n    if not width_coefficient:\n        return filters\n\n    multiplier: float = width_coefficient\n    divisor: float = depth_divisor\n    filters_float: float = filters * multiplier\n\n    # follow the formula transferred from official TensorFlow implementation\n    new_filters: float = max(divisor, int(filters_float + divisor / 2) // divisor * divisor)\n    if new_filters < 0.9 * filters_float:  # prevent rounding by more than 10%\n        new_filters += divisor\n    return int(new_filters)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/efficientnet.py__round_repeats__round_repeats.return.int_math_ceil_depth_coeff": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/efficientnet.py__round_repeats__round_repeats.return.int_math_ceil_depth_coeff", "embedding": null, "metadata": {"file_path": "monai/networks/nets/efficientnet.py", "file_name": "efficientnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 736, "end_line": 751, "span_ids": ["_round_repeats"], "tokens": 109}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _round_repeats(repeats: int, depth_coefficient: Optional[float]) -> int:\n    \"\"\"\n    Re-calculate module's repeat number of a block based on depth coefficient multiplier.\n\n    Args:\n        repeats: number of original repeats.\n        depth_coefficient: depth coefficient for model.\n\n    Returns:\n        new repeat: new number of repeat after calculating.\n    \"\"\"\n    if not depth_coefficient:\n        return repeats\n\n    # follow the formula transferred from official TensorFlow impl.\n    return int(math.ceil(depth_coefficient * repeats))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/efficientnet.py__calculate_output_image_size__calculate_output_image_size.return._int_math_ceil_im_sz_st": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/efficientnet.py__calculate_output_image_size__calculate_output_image_size.return._int_math_ceil_im_sz_st", "embedding": null, "metadata": {"file_path": "monai/networks/nets/efficientnet.py", "file_name": "efficientnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 835, "end_line": 857, "span_ids": ["_calculate_output_image_size"], "tokens": 186}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _calculate_output_image_size(input_image_size: List[int], stride: Union[int, Tuple[int]]):\n    \"\"\"\n    Calculates the output image size when using _make_same_padder with a stride.\n    Required for static padding.\n\n    Args:\n        input_image_size: input image/feature spatial size.\n        stride: Conv2d operation\"s stride.\n\n    Returns:\n        output_image_size: output image/feature spatial size.\n    \"\"\"\n\n    # checks to extract integer stride in case tuple was received\n    if isinstance(stride, tuple):\n        all_strides_equal = all(stride[0] == s for s in stride)\n        if not all_strides_equal:\n            raise ValueError(f\"unequal strides are not possible, got {stride}\")\n\n        stride = stride[0]\n\n    # return output image size\n    return [int(math.ceil(im_sz / stride)) for im_sz in input_image_size]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/fullyconnectednet.py_from_typing_import_Option__get_adn_layer.return.ADN_act_act_dropout_drop": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/fullyconnectednet.py_from_typing_import_Option__get_adn_layer.return.ADN_act_act_dropout_drop", "embedding": null, "metadata": {"file_path": "monai/networks/nets/fullyconnectednet.py", "file_name": "fullyconnectednet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 28, "span_ids": ["_get_adn_layer", "docstring"], "tokens": 139}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Optional, Sequence, Tuple, Union\n\nimport torch\nimport torch.nn as nn\n\nfrom monai.networks.blocks import ADN\nfrom monai.networks.layers.factories import Act\n\n__all__ = [\"FullyConnectedNet\", \"VarFullyConnectedNet\"]\n\n\ndef _get_adn_layer(\n    act: Optional[Union[Tuple, str]], dropout: Optional[Union[Tuple, str, float]], ordering: Optional[str]\n) -> ADN:\n    if ordering:\n        return ADN(act=act, dropout=dropout, dropout_dim=1, ordering=ordering)\n    return ADN(act=act, dropout=dropout, dropout_dim=1)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/fullyconnectednet.py_FullyConnectedNet_FullyConnectedNet._get_layer.return.seq": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/fullyconnectednet.py_FullyConnectedNet_FullyConnectedNet._get_layer.return.seq", "embedding": null, "metadata": {"file_path": "monai/networks/nets/fullyconnectednet.py", "file_name": "fullyconnectednet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 31, "end_line": 88, "span_ids": ["FullyConnectedNet.__init__", "FullyConnectedNet._get_layer", "FullyConnectedNet"], "tokens": 571}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class FullyConnectedNet(nn.Sequential):\n    \"\"\"\n    Simple full-connected layer neural network composed of a sequence of linear layers with PReLU activation and\n    dropout.  The network accepts input with `in_channels` channels, has output with `out_channels` channels, and\n    hidden layer output channels given in `hidden_channels`. If `bias` is True then linear units have a bias term.\n\n    Args:\n        in_channels: number of input channels.\n        out_channels: number of output channels.\n        hidden_channels: number of output channels for each hidden layer.\n        dropout: dropout ratio. Defaults to no dropout.\n        act: activation type and arguments. Defaults to PReLU.\n        bias: whether to have a bias term in linear units. Defaults to True.\n        adn_ordering: order of operations in :py:class:`monai.networks.blocks.ADN`.\n\n    Examples::\n\n        # accepts 4 values and infers 3 values as output, has 3 hidden layers with 10, 20, 10 values as output\n        net = FullyConnectedNet(4, 3, [10, 20, 10], dropout=0.2)\n\n    \"\"\"\n\n    def __init__(\n        self,\n        in_channels: int,\n        out_channels: int,\n        hidden_channels: Sequence[int],\n        dropout: Optional[Union[Tuple, str, float]] = None,\n        act: Optional[Union[Tuple, str]] = Act.PRELU,\n        bias: bool = True,\n        adn_ordering: Optional[str] = None,\n    ) -> None:\n        \"\"\"\n        Defines a network accept input with `in_channels` channels, output of `out_channels` channels, and hidden layers\n        with channels given in `hidden_channels`. If `bias` is True then linear units have a bias term.\n        \"\"\"\n        super().__init__()\n        self.in_channels = in_channels\n        self.out_channels = out_channels\n        self.hidden_channels = list(hidden_channels)\n        self.act = act\n        self.dropout = dropout\n        self.adn_ordering = adn_ordering\n\n        self.add_module(\"flatten\", nn.Flatten())\n\n        prev_channels = self.in_channels\n        for i, c in enumerate(hidden_channels):\n            self.add_module(\"hidden_%i\" % i, self._get_layer(prev_channels, c, bias))\n            prev_channels = c\n\n        self.add_module(\"output\", nn.Linear(prev_channels, out_channels, bias))\n\n    def _get_layer(self, in_channels: int, out_channels: int, bias: bool) -> nn.Sequential:\n        seq = nn.Sequential(\n            nn.Linear(in_channels, out_channels, bias), _get_adn_layer(self.act, self.dropout, self.adn_ordering)\n        )\n        return seq", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/fullyconnectednet.py_VarFullyConnectedNet_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/fullyconnectednet.py_VarFullyConnectedNet_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/fullyconnectednet.py", "file_name": "fullyconnectednet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 91, "end_line": 186, "span_ids": ["VarFullyConnectedNet.forward", "VarFullyConnectedNet", "VarFullyConnectedNet.__init__", "VarFullyConnectedNet._get_layer", "VarFullyConnectedNet.encode_forward", "VarFullyConnectedNet.reparameterize", "VarFullyConnectedNet.decode_forward"], "tokens": 865}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class VarFullyConnectedNet(nn.Module):\n    \"\"\"\n    Variational fully-connected network. This is composed of an encode layer, reparameterization layer, and then a\n    decode layer.\n\n    Args:\n        in_channels: number of input channels.\n        out_channels: number of output channels.\n        latent_size: number of latent variables to use.\n        encode_channels: number of output channels for each hidden layer of the encode half.\n        decode_channels: number of output channels for each hidden layer of the decode half.\n        dropout: dropout ratio. Defaults to no dropout.\n        act: activation type and arguments. Defaults to PReLU.\n        bias: whether to have a bias term in linear units. Defaults to True.\n        adn_ordering: order of operations in :py:class:`monai.networks.blocks.ADN`.\n\n    Examples::\n\n        # accepts inputs with 4 values, uses a latent space of 2 variables, and produces outputs of 3 values\n        net = VarFullyConnectedNet(4, 3, 2, [5, 10], [10, 5])\n\n    \"\"\"\n\n    def __init__(\n        self,\n        in_channels: int,\n        out_channels: int,\n        latent_size: int,\n        encode_channels: Sequence[int],\n        decode_channels: Sequence[int],\n        dropout: Optional[Union[Tuple, str, float]] = None,\n        act: Optional[Union[Tuple, str]] = Act.PRELU,\n        bias: bool = True,\n        adn_ordering: Optional[str] = None,\n    ) -> None:\n        super().__init__()\n        self.in_channels = in_channels\n        self.out_channels = out_channels\n        self.latent_size = latent_size\n\n        self.encode = nn.Sequential()\n        self.decode = nn.Sequential()\n        self.flatten = nn.Flatten()\n\n        self.adn_layer = _get_adn_layer(act, dropout, adn_ordering)\n\n        prev_channels = self.in_channels\n        for i, c in enumerate(encode_channels):\n            self.encode.add_module(\"encode_%i\" % i, self._get_layer(prev_channels, c, bias))\n            prev_channels = c\n\n        self.mu = nn.Linear(prev_channels, self.latent_size)\n        self.logvar = nn.Linear(prev_channels, self.latent_size)\n        self.decodeL = nn.Linear(self.latent_size, prev_channels)\n\n        for i, c in enumerate(decode_channels):\n            self.decode.add_module(\"decode%i\" % i, self._get_layer(prev_channels, c, bias))\n            prev_channels = c\n\n        self.decode.add_module(\"final\", nn.Linear(prev_channels, out_channels, bias))\n\n    def _get_layer(self, in_channels: int, out_channels: int, bias: bool) -> nn.Sequential:\n        seq = nn.Sequential(nn.Linear(in_channels, out_channels, bias))\n        seq.add_module(\"ADN\", self.adn_layer)\n        return seq\n\n    def encode_forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:\n        x = self.encode(x)\n        x = self.flatten(x)\n        mu = self.mu(x)\n        logvar = self.logvar(x)\n        return mu, logvar\n\n    def decode_forward(self, z: torch.Tensor, use_sigmoid: bool = True) -> torch.Tensor:\n        x: torch.Tensor\n        x = self.decodeL(z)\n        x = torch.relu(x)\n        x = self.flatten(x)\n        x = self.decode(x)\n        if use_sigmoid:\n            x = torch.sigmoid(x)\n        return x\n\n    def reparameterize(self, mu: torch.Tensor, logvar: torch.Tensor) -> torch.Tensor:\n        std = torch.exp(0.5 * logvar)\n\n        if self.training:  # multiply random noise with std only during training\n            std = torch.randn_like(std).mul(std)\n\n        return std.add_(mu)\n\n    def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:\n        mu, logvar = self.encode_forward(x)\n        z = self.reparameterize(mu, logvar)\n        return self.decode_forward(z), mu, logvar, z", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/highresnet.py_HighResBlock_HighResBlock.forward.return.x_conv_torch_as_tensor_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/highresnet.py_HighResBlock_HighResBlock.forward.return.x_conv_torch_as_tensor_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/highresnet.py", "file_name": "highresnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 36, "end_line": 102, "span_ids": ["HighResBlock.__init__", "HighResBlock", "HighResBlock.forward"], "tokens": 651}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class HighResBlock(nn.Module):\n    def __init__(\n        self,\n        spatial_dims: int,\n        in_channels: int,\n        out_channels: int,\n        kernels: Sequence[int] = (3, 3),\n        dilation: Union[Sequence[int], int] = 1,\n        norm_type: Union[Tuple, str] = (\"batch\", {\"affine\": True}),\n        acti_type: Union[Tuple, str] = (\"relu\", {\"inplace\": True}),\n        bias: bool = False,\n        channel_matching: Union[ChannelMatching, str] = ChannelMatching.PAD,\n    ) -> None:\n        \"\"\"\n        Args:\n            spatial_dims: number of spatial dimensions of the input image.\n            in_channels: number of input channels.\n            out_channels: number of output channels.\n            kernels: each integer k in `kernels` corresponds to a convolution layer with kernel size k.\n            dilation: spacing between kernel elements.\n            norm_type: feature normalization type and arguments.\n                Defaults to ``(\"batch\", {\"affine\": True})``.\n            acti_type: {``\"relu\"``, ``\"prelu\"``, ``\"relu6\"``}\n                Non-linear activation using ReLU or PReLU. Defaults to ``\"relu\"``.\n            bias: whether to have a bias term in convolution blocks. Defaults to False.\n                According to `Performance Tuning Guide <https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html>`_,\n                if a conv layer is directly followed by a batch norm layer, bias should be False.\n            channel_matching: {``\"pad\"``, ``\"project\"``}\n                Specifies handling residual branch and conv branch channel mismatches. Defaults to ``\"pad\"``.\n\n                - ``\"pad\"``: with zero padding.\n                - ``\"project\"``: with a trainable conv with kernel size one.\n\n        Raises:\n            ValueError: When ``channel_matching=pad`` and ``in_channels > out_channels``. Incompatible values.\n\n        \"\"\"\n        super().__init__()\n        self.chn_pad = ChannelPad(\n            spatial_dims=spatial_dims, in_channels=in_channels, out_channels=out_channels, mode=channel_matching\n        )\n\n        layers = nn.ModuleList()\n        _in_chns, _out_chns = in_channels, out_channels\n\n        for kernel_size in kernels:\n            layers.append(\n                ADN(ordering=\"NA\", in_channels=_in_chns, act=acti_type, norm=norm_type, norm_dim=spatial_dims)\n            )\n            layers.append(\n                Convolution(\n                    spatial_dims=spatial_dims,\n                    in_channels=_in_chns,\n                    out_channels=_out_chns,\n                    kernel_size=kernel_size,\n                    dilation=dilation,\n                    bias=bias,\n                    conv_only=True,\n                )\n            )\n            _in_chns = _out_chns\n\n        self.layers = nn.Sequential(*layers)\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        x_conv: torch.Tensor = self.layers(x)\n        return x_conv + torch.as_tensor(self.chn_pad(x))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/regunet.py_from_typing_import_List__RegUNet.build_layers.self_build_decode_layers_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/regunet.py_from_typing_import_List__RegUNet.build_layers.self_build_decode_layers_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/regunet.py", "file_name": "regunet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 11, "end_line": 111, "span_ids": ["RegUNet.build_layers", "RegUNet.__init__", "RegUNet", "docstring"], "tokens": 799}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import List, Optional, Tuple, Union\n\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nfrom monai.networks.blocks.regunet_block import (\n    RegistrationDownSampleBlock,\n    RegistrationExtractionBlock,\n    RegistrationResidualConvBlock,\n    get_conv_block,\n    get_deconv_block,\n)\n\n__all__ = [\"RegUNet\", \"AffineHead\", \"GlobalNet\", \"LocalNet\"]\n\n\nclass RegUNet(nn.Module):\n    \"\"\"\n    Class that implements an adapted UNet. This class also serve as the parent class of LocalNet and GlobalNet\n\n    Reference:\n        O. Ronneberger, P. Fischer, and T. Brox,\n        \u201cU-net: Convolutional networks for biomedical image segmentation,\u201d,\n        Lecture Notes in Computer Science, 2015, vol. 9351, pp. 234\u2013241.\n        https://arxiv.org/abs/1505.04597\n\n    Adapted from:\n        DeepReg (https://github.com/DeepRegNet/DeepReg)\n    \"\"\"\n\n    def __init__(\n        self,\n        spatial_dims: int,\n        in_channels: int,\n        num_channel_initial: int,\n        depth: int,\n        out_kernel_initializer: Optional[str] = \"kaiming_uniform\",\n        out_activation: Optional[str] = None,\n        out_channels: int = 3,\n        extract_levels: Optional[Tuple[int]] = None,\n        pooling: bool = True,\n        concat_skip: bool = False,\n        encode_kernel_sizes: Union[int, List[int]] = 3,\n    ):\n        \"\"\"\n        Args:\n            spatial_dims: number of spatial dims\n            in_channels: number of input channels\n            num_channel_initial: number of initial channels\n            depth: input is at level 0, bottom is at level depth.\n            out_kernel_initializer: kernel initializer for the last layer\n            out_activation: activation at the last layer\n            out_channels: number of channels for the output\n            extract_levels: list, which levels from net to extract. The maximum level must equal to ``depth``\n            pooling: for down-sampling, use non-parameterized pooling if true, otherwise use conv3d\n            concat_skip: when up-sampling, concatenate skipped tensor if true, otherwise use addition\n            encode_kernel_sizes: kernel size for down-sampling\n        \"\"\"\n        super().__init__()\n        if not extract_levels:\n            extract_levels = (depth,)\n        if max(extract_levels) != depth:\n            raise AssertionError\n\n        # save parameters\n        self.spatial_dims = spatial_dims\n        self.in_channels = in_channels\n        self.num_channel_initial = num_channel_initial\n        self.depth = depth\n        self.out_kernel_initializer = out_kernel_initializer\n        self.out_activation = out_activation\n        self.out_channels = out_channels\n        self.extract_levels = extract_levels\n        self.pooling = pooling\n        self.concat_skip = concat_skip\n\n        if isinstance(encode_kernel_sizes, int):\n            encode_kernel_sizes = [encode_kernel_sizes] * (self.depth + 1)\n        if len(encode_kernel_sizes) != self.depth + 1:\n            raise AssertionError\n        self.encode_kernel_sizes: List[int] = encode_kernel_sizes\n\n        self.num_channels = [self.num_channel_initial * (2 ** d) for d in range(self.depth + 1)]\n        self.min_extract_level = min(self.extract_levels)\n\n        # init layers\n        # all lists start with d = 0\n        self.encode_convs = None\n        self.encode_pools = None\n        self.bottom_block = None\n        self.decode_deconvs = None\n        self.decode_convs = None\n        self.output_block = None\n\n        # build layers\n        self.build_layers()\n\n    def build_layers(self):\n        self.build_encode_layers()\n        self.build_decode_layers()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/regunet.py_RegUNet.build_encode_layers_RegUNet.build_encode_layers.self.bottom_block.self_build_bottom_block_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/regunet.py_RegUNet.build_encode_layers_RegUNet.build_encode_layers.self.bottom_block.self_build_bottom_block_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/regunet.py", "file_name": "regunet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 113, "end_line": 130, "span_ids": ["RegUNet.build_encode_layers"], "tokens": 153}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RegUNet(nn.Module):\n\n    def build_encode_layers(self):\n        # encoding / down-sampling\n        self.encode_convs = nn.ModuleList(\n            [\n                self.build_conv_block(\n                    in_channels=self.in_channels if d == 0 else self.num_channels[d - 1],\n                    out_channels=self.num_channels[d],\n                    kernel_size=self.encode_kernel_sizes[d],\n                )\n                for d in range(self.depth)\n            ]\n        )\n        self.encode_pools = nn.ModuleList(\n            [self.build_down_sampling_block(channels=self.num_channels[d]) for d in range(self.depth)]\n        )\n        self.bottom_block = self.build_bottom_block(\n            in_channels=self.num_channels[-2], out_channels=self.num_channels[-1]\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/regunet.py_RegUNet.build_conv_block_RegUNet.build_bottom_block.return.nn_Sequential_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/regunet.py_RegUNet.build_conv_block_RegUNet.build_bottom_block.return.nn_Sequential_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/regunet.py", "file_name": "regunet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 132, "end_line": 166, "span_ids": ["RegUNet.build_bottom_block", "RegUNet.build_conv_block", "RegUNet.build_down_sampling_block"], "tokens": 235}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RegUNet(nn.Module):\n\n    def build_conv_block(self, in_channels, out_channels, kernel_size):\n        return nn.Sequential(\n            get_conv_block(\n                spatial_dims=self.spatial_dims,\n                in_channels=in_channels,\n                out_channels=out_channels,\n                kernel_size=kernel_size,\n            ),\n            RegistrationResidualConvBlock(\n                spatial_dims=self.spatial_dims,\n                in_channels=out_channels,\n                out_channels=out_channels,\n                kernel_size=kernel_size,\n            ),\n        )\n\n    def build_down_sampling_block(self, channels: int):\n        return RegistrationDownSampleBlock(spatial_dims=self.spatial_dims, channels=channels, pooling=self.pooling)\n\n    def build_bottom_block(self, in_channels: int, out_channels: int):\n        kernel_size = self.encode_kernel_sizes[self.depth]\n        return nn.Sequential(\n            get_conv_block(\n                spatial_dims=self.spatial_dims,\n                in_channels=in_channels,\n                out_channels=out_channels,\n                kernel_size=kernel_size,\n            ),\n            RegistrationResidualConvBlock(\n                spatial_dims=self.spatial_dims,\n                in_channels=out_channels,\n                out_channels=out_channels,\n                kernel_size=kernel_size,\n            ),\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/regunet.py_RegUNet.build_decode_layers_RegUNet.build_decode_layers.self.output_block.self_build_output_block_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/regunet.py_RegUNet.build_decode_layers_RegUNet.build_decode_layers.self.output_block.self_build_output_block_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/regunet.py", "file_name": "regunet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 181, "end_line": 202, "span_ids": ["RegUNet.build_decode_layers"], "tokens": 197}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RegUNet(nn.Module):\n\n    def build_decode_layers(self):\n        # decoding / up-sampling\n        # [depth - 1, depth - 2, ..., min_extract_level]\n        self.decode_deconvs = nn.ModuleList(\n            [\n                self.build_up_sampling_block(in_channels=self.num_channels[d + 1], out_channels=self.num_channels[d])\n                for d in range(self.depth - 1, self.min_extract_level - 1, -1)\n            ]\n        )\n        self.decode_convs = nn.ModuleList(\n            [\n                self.build_conv_block(\n                    in_channels=(2 * self.num_channels[d] if self.concat_skip else self.num_channels[d]),\n                    out_channels=self.num_channels[d],\n                    kernel_size=3,\n                )\n                for d in range(self.depth - 1, self.min_extract_level - 1, -1)\n            ]\n        )\n\n        # extraction\n        self.output_block = self.build_output_block()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/regunet.py_RegUNet.build_up_sampling_block_RegUNet.build_output_block.return.RegistrationExtractionBlo": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/regunet.py_RegUNet.build_up_sampling_block_RegUNet.build_output_block.return.RegistrationExtractionBlo", "embedding": null, "metadata": {"file_path": "monai/networks/nets/regunet.py", "file_name": "regunet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 191, "end_line": 202, "span_ids": ["RegUNet.build_up_sampling_block", "RegUNet.build_output_block"], "tokens": 114}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RegUNet(nn.Module):\n\n    def build_up_sampling_block(self, in_channels: int, out_channels: int) -> nn.Module:\n        return get_deconv_block(spatial_dims=self.spatial_dims, in_channels=in_channels, out_channels=out_channels)\n\n    def build_output_block(self) -> nn.Module:\n        return RegistrationExtractionBlock(\n            spatial_dims=self.spatial_dims,\n            extract_levels=self.extract_levels,\n            num_channels=self.num_channels,\n            out_channels=self.out_channels,\n            kernel_initializer=self.out_kernel_initializer,\n            activation=self.out_activation,\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/regunet.py_RegUNet.forward_RegUNet.forward.return.out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/regunet.py_RegUNet.forward_RegUNet.forward.return.out", "embedding": null, "metadata": {"file_path": "monai/networks/nets/regunet.py", "file_name": "regunet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 221, "end_line": 252, "span_ids": ["RegUNet.forward"], "tokens": 304}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RegUNet(nn.Module):\n\n    def forward(self, x):\n        \"\"\"\n        Args:\n            x: Tensor in shape (batch, ``in_channels``, insize_1, insize_2, [insize_3])\n\n        Returns:\n            Tensor in shape (batch, ``out_channels``, insize_1, insize_2, [insize_3]), with the same spatial size as ``x``\n        \"\"\"\n        image_size = x.shape[2:]\n        skips = []  # [0, ..., depth - 1]\n        encoded = x\n        for encode_conv, encode_pool in zip(self.encode_convs, self.encode_pools):\n            skip = encode_conv(encoded)\n            encoded = encode_pool(skip)\n            skips.append(skip)\n        decoded = self.bottom_block(encoded)\n\n        outs = [decoded]\n\n        # [depth - 1, ..., min_extract_level]\n        for i, (decode_deconv, decode_conv) in enumerate(zip(self.decode_deconvs, self.decode_convs)):\n            # [depth - 1, depth - 2, ..., min_extract_level]\n            decoded = decode_deconv(decoded)\n            if self.concat_skip:\n                decoded = torch.cat([decoded, skips[-i - 1]], dim=1)\n            else:\n                decoded = decoded + skips[-i - 1]\n            decoded = decode_conv(decoded)\n            outs.append(decoded)\n\n        out = self.output_block(outs, image_size=image_size)\n        return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/regunet.py_AffineHead_AffineHead.get_reference_grid.return.grid_to_dtype_torch_float": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/regunet.py_AffineHead_AffineHead.get_reference_grid.return.grid_to_dtype_torch_float", "embedding": null, "metadata": {"file_path": "monai/networks/nets/regunet.py", "file_name": "regunet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 238, "end_line": 264, "span_ids": ["AffineHead", "AffineHead.get_reference_grid", "AffineHead.__init__"], "tokens": 355}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AffineHead(nn.Module):\n    def __init__(self, spatial_dims: int, image_size: List[int], decode_size: List[int], in_channels: int):\n        super().__init__()\n        self.spatial_dims = spatial_dims\n        if spatial_dims == 2:\n            in_features = in_channels * decode_size[0] * decode_size[1]\n            out_features = 6\n            out_init = torch.tensor([1, 0, 0, 0, 1, 0], dtype=torch.float)\n        elif spatial_dims == 3:\n            in_features = in_channels * decode_size[0] * decode_size[1] * decode_size[2]\n            out_features = 12\n            out_init = torch.tensor([1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0], dtype=torch.float)\n        else:\n            raise ValueError(f\"only support 2D/3D operation, got spatial_dims={spatial_dims}\")\n\n        self.fc = nn.Linear(in_features=in_features, out_features=out_features)\n        self.grid = self.get_reference_grid(image_size)  # (spatial_dims, ...)\n\n        # init weight/bias\n        self.fc.weight.data.zero_()\n        self.fc.bias.data.copy_(out_init)\n\n    @staticmethod\n    def get_reference_grid(image_size: Union[Tuple[int], List[int]]) -> torch.Tensor:\n        mesh_points = [torch.arange(0, dim) for dim in image_size]\n        grid = torch.stack(torch.meshgrid(*mesh_points), dim=0)  # (spatial_dims, ...)\n        return grid.to(dtype=torch.float)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/regunet.py_AffineHead.affine_transform_AffineHead.forward.return.out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/regunet.py_AffineHead.affine_transform_AffineHead.forward.return.out", "embedding": null, "metadata": {"file_path": "monai/networks/nets/regunet.py", "file_name": "regunet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 289, "end_line": 307, "span_ids": ["AffineHead.forward", "AffineHead.affine_transform"], "tokens": 266}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AffineHead(nn.Module):\n\n    def affine_transform(self, theta: torch.Tensor):\n        # (spatial_dims, ...) -> (spatial_dims + 1, ...)\n        grid_padded = torch.cat([self.grid, torch.ones_like(self.grid[:1])])\n\n        # grid_warped[b,p,...] = sum_over_q(grid_padded[q,...] * theta[b,p,q]\n        if self.spatial_dims == 2:\n            grid_warped = torch.einsum(\"qij,bpq->bpij\", grid_padded, theta.reshape(-1, 2, 3))\n        elif self.spatial_dims == 3:\n            grid_warped = torch.einsum(\"qijk,bpq->bpijk\", grid_padded, theta.reshape(-1, 3, 4))\n        else:\n            raise ValueError(f\"do not support spatial_dims={self.spatial_dims}\")\n        return grid_warped\n\n    def forward(self, x: List[torch.Tensor], image_size: List[int]) -> torch.Tensor:\n        f = x[0]\n        self.grid = self.grid.to(device=f.device)\n        theta = self.fc(f.reshape(f.shape[0], -1))\n        out: torch.Tensor = self.affine_transform(theta) - self.grid\n        return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/regunet.py_GlobalNet_GlobalNet.build_output_block.return.AffineHead_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/regunet.py_GlobalNet_GlobalNet.build_output_block.return.AffineHead_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/regunet.py", "file_name": "regunet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 310, "end_line": 362, "span_ids": ["GlobalNet.build_output_block", "GlobalNet", "GlobalNet.__init__"], "tokens": 376}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GlobalNet(RegUNet):\n    \"\"\"\n    Build GlobalNet for image registration.\n\n    Reference:\n        Hu, Yipeng, et al.\n        \"Label-driven weakly-supervised learning\n        for multimodal deformable image registration,\"\n        https://arxiv.org/abs/1711.01666\n    \"\"\"\n\n    def __init__(\n        self,\n        image_size: List[int],\n        spatial_dims: int,\n        in_channels: int,\n        num_channel_initial: int,\n        depth: int,\n        out_kernel_initializer: Optional[str] = \"kaiming_uniform\",\n        out_activation: Optional[str] = None,\n        pooling: bool = True,\n        concat_skip: bool = False,\n        encode_kernel_sizes: Union[int, List[int]] = 3,\n    ):\n        for size in image_size:\n            if size % (2 ** depth) != 0:\n                raise ValueError(\n                    f\"given depth {depth}, \"\n                    f\"all input spatial dimension must be divisible by {2 ** depth}, \"\n                    f\"got input of size {image_size}\"\n                )\n        self.image_size = image_size\n        self.decode_size = [size // (2 ** depth) for size in image_size]\n        super().__init__(\n            spatial_dims=spatial_dims,\n            in_channels=in_channels,\n            num_channel_initial=num_channel_initial,\n            depth=depth,\n            out_kernel_initializer=out_kernel_initializer,\n            out_activation=out_activation,\n            out_channels=spatial_dims,\n            pooling=pooling,\n            concat_skip=concat_skip,\n            encode_kernel_sizes=encode_kernel_sizes,\n        )\n\n    def build_output_block(self):\n        return AffineHead(\n            spatial_dims=self.spatial_dims,\n            image_size=self.image_size,\n            decode_size=self.decode_size,\n            in_channels=self.num_channels[-1],\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/regunet.py_AdditiveUpSampleBlock_AdditiveUpSampleBlock.forward.return.out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/regunet.py_AdditiveUpSampleBlock_AdditiveUpSampleBlock.forward.return.out", "embedding": null, "metadata": {"file_path": "monai/networks/nets/regunet.py", "file_name": "regunet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 342, "end_line": 353, "span_ids": ["AdditiveUpSampleBlock.__init__", "AdditiveUpSampleBlock.forward", "AdditiveUpSampleBlock"], "tokens": 163}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AdditiveUpSampleBlock(nn.Module):\n    def __init__(self, spatial_dims: int, in_channels: int, out_channels: int):\n        super().__init__()\n        self.deconv = get_deconv_block(spatial_dims=spatial_dims, in_channels=in_channels, out_channels=out_channels)\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        output_size = (size * 2 for size in x.shape[2:])\n        deconved = self.deconv(x)\n        resized = F.interpolate(x, output_size)\n        resized = torch.sum(torch.stack(resized.split(split_size=resized.shape[1] // 2, dim=1), dim=-1), dim=-1)\n        out: torch.Tensor = deconved + resized\n        return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/regunet.py_LocalNet_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/regunet.py_LocalNet_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/regunet.py", "file_name": "regunet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 356, "end_line": 418, "span_ids": ["LocalNet.build_bottom_block", "LocalNet.__init__", "LocalNet.build_up_sampling_block", "LocalNet"], "tokens": 560}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LocalNet(RegUNet):\n    \"\"\"\n    Reimplementation of LocalNet, based on:\n    `Weakly-supervised convolutional neural networks for multimodal image registration\n    <https://doi.org/10.1016/j.media.2018.07.002>`_.\n    `Label-driven weakly-supervised learning for multimodal deformable image registration\n    <https://arxiv.org/abs/1711.01666>`_.\n\n    Adapted from:\n        DeepReg (https://github.com/DeepRegNet/DeepReg)\n    \"\"\"\n\n    def __init__(\n        self,\n        spatial_dims: int,\n        in_channels: int,\n        num_channel_initial: int,\n        extract_levels: Tuple[int],\n        out_kernel_initializer: Optional[str] = \"kaiming_uniform\",\n        out_activation: Optional[str] = None,\n        out_channels: int = 3,\n        pooling: bool = True,\n        concat_skip: bool = False,\n    ):\n        \"\"\"\n        Args:\n            spatial_dims: number of spatial dims\n            in_channels: number of input channels\n            num_channel_initial: number of initial channels\n            out_kernel_initializer: kernel initializer for the last layer\n            out_activation: activation at the last layer\n            out_channels: number of channels for the output\n            extract_levels: list, which levels from net to extract. The maximum level must equal to ``depth``\n            pooling: for down-sampling, use non-parameterized pooling if true, otherwise use conv3d\n            concat_skip: when up-sampling, concatenate skipped tensor if true, otherwise use addition\n        \"\"\"\n        super().__init__(\n            spatial_dims=spatial_dims,\n            in_channels=in_channels,\n            num_channel_initial=num_channel_initial,\n            depth=max(extract_levels),\n            out_kernel_initializer=out_kernel_initializer,\n            out_activation=out_activation,\n            out_channels=out_channels,\n            pooling=pooling,\n            concat_skip=concat_skip,\n            encode_kernel_sizes=[7] + [3] * max(extract_levels),\n        )\n\n    def build_bottom_block(self, in_channels: int, out_channels: int):\n        kernel_size = self.encode_kernel_sizes[self.depth]\n        return get_conv_block(\n            spatial_dims=self.spatial_dims, in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size\n        )\n\n    def build_up_sampling_block(self, in_channels: int, out_channels: int) -> nn.Module:\n        if self._use_additive_upsampling:\n            return AdditiveUpSampleBlock(\n                spatial_dims=self.spatial_dims, in_channels=in_channels, out_channels=out_channels\n            )\n\n        return get_deconv_block(spatial_dims=self.spatial_dims, in_channels=in_channels, out_channels=out_channels)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/segresnet.py_SegResNet._make_down_layers_SegResNet._make_down_layers.return.down_layers": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/segresnet.py_SegResNet._make_down_layers_SegResNet._make_down_layers.return.down_layers", "embedding": null, "metadata": {"file_path": "monai/networks/nets/segresnet.py", "file_name": "segresnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 101, "end_line": 116, "span_ids": ["SegResNet._make_down_layers"], "tokens": 166}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SegResNet(nn.Module):\n\n    def _make_down_layers(self):\n        down_layers = nn.ModuleList()\n        blocks_down, spatial_dims, filters, norm = (self.blocks_down, self.spatial_dims, self.init_filters, self.norm)\n        for i in range(len(blocks_down)):\n            layer_in_channels = filters * 2 ** i\n            pre_conv = (\n                get_conv_layer(spatial_dims, layer_in_channels // 2, layer_in_channels, stride=2)\n                if i > 0\n                else nn.Identity()\n            )\n            down_layer = nn.Sequential(\n                pre_conv,\n                *[ResBlock(spatial_dims, layer_in_channels, norm=norm, act=self.act) for _ in range(blocks_down[i])],\n            )\n            down_layers.append(down_layer)\n        return down_layers", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/segresnet.py_SegResNet._make_up_layers_SegResNet._make_up_layers.return.up_layers_up_samples": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/segresnet.py_SegResNet._make_up_layers_SegResNet._make_up_layers.return.up_layers_up_samples", "embedding": null, "metadata": {"file_path": "monai/networks/nets/segresnet.py", "file_name": "segresnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 118, "end_line": 146, "span_ids": ["SegResNet._make_up_layers"], "tokens": 228}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SegResNet(nn.Module):\n\n    def _make_up_layers(self):\n        up_layers, up_samples = nn.ModuleList(), nn.ModuleList()\n        upsample_mode, blocks_up, spatial_dims, filters, norm = (\n            self.upsample_mode,\n            self.blocks_up,\n            self.spatial_dims,\n            self.init_filters,\n            self.norm,\n        )\n        n_up = len(blocks_up)\n        for i in range(n_up):\n            sample_in_channels = filters * 2 ** (n_up - i)\n            up_layers.append(\n                nn.Sequential(\n                    *[\n                        ResBlock(spatial_dims, sample_in_channels // 2, norm=norm, act=self.act)\n                        for _ in range(blocks_up[i])\n                    ]\n                )\n            )\n            up_samples.append(\n                nn.Sequential(\n                    *[\n                        get_conv_layer(spatial_dims, sample_in_channels, sample_in_channels // 2, kernel_size=1),\n                        get_upsample_layer(spatial_dims, sample_in_channels // 2, upsample_mode=upsample_mode),\n                    ]\n                )\n            )\n        return up_layers, up_samples", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/segresnet.py_SegResNet._make_final_conv_SegResNet.forward.return.x": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/segresnet.py_SegResNet._make_final_conv_SegResNet.forward.return.x", "embedding": null, "metadata": {"file_path": "monai/networks/nets/segresnet.py", "file_name": "segresnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 148, "end_line": 183, "span_ids": ["SegResNet.encode", "SegResNet._make_final_conv", "SegResNet.forward", "SegResNet.decode"], "tokens": 276}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SegResNet(nn.Module):\n\n    def _make_final_conv(self, out_channels: int):\n        return nn.Sequential(\n            get_norm_layer(name=self.norm, spatial_dims=self.spatial_dims, channels=self.init_filters),\n            self.act_mod,\n            get_conv_layer(self.spatial_dims, self.init_filters, out_channels, kernel_size=1, bias=True),\n        )\n\n    def encode(self, x: torch.Tensor) -> Tuple[torch.Tensor, List[torch.Tensor]]:\n        x = self.convInit(x)\n        if self.dropout_prob is not None:\n            x = self.dropout(x)\n\n        down_x = []\n\n        for down in self.down_layers:\n            x = down(x)\n            down_x.append(x)\n\n        return x, down_x\n\n    def decode(self, x: torch.Tensor, down_x: List[torch.Tensor]) -> torch.Tensor:\n        for i, (up, upl) in enumerate(zip(self.up_samples, self.up_layers)):\n            x = up(x) + down_x[i + 1]\n            x = upl(x)\n\n        if self.use_conv_final:\n            x = self.conv_final(x)\n\n        return x\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        x, down_x = self.encode(x)\n        down_x.reverse()\n\n        x = self.decode(x, down_x)\n        return x", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/segresnet.py_SegResNetVAE_SegResNetVAE._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/segresnet.py_SegResNetVAE_SegResNetVAE._", "embedding": null, "metadata": {"file_path": "monai/networks/nets/segresnet.py", "file_name": "segresnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 178, "end_line": 209, "span_ids": ["SegResNetVAE"], "tokens": 497}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SegResNetVAE(SegResNet):\n    \"\"\"\n    SegResNetVAE based on `3D MRI brain tumor segmentation using autoencoder regularization\n    <https://arxiv.org/pdf/1810.11654.pdf>`_.\n    The module contains the variational autoencoder (VAE).\n    The model supports 2D or 3D inputs.\n\n    Args:\n        input_image_size: the size of images to input into the network. It is used to\n            determine the in_features of the fc layer in VAE.\n        vae_estimate_std: whether to estimate the standard deviations in VAE. Defaults to ``False``.\n        vae_default_std: if not to estimate the std, use the default value. Defaults to 0.3.\n        vae_nz: number of latent variables in VAE. Defaults to 256.\n            Where, 128 to represent mean, and 128 to represent std.\n        spatial_dims: spatial dimension of the input data. Defaults to 3.\n        init_filters: number of output channels for initial convolution layer. Defaults to 8.\n        in_channels: number of input channels for the network. Defaults to 1.\n        out_channels: number of output channels for the network. Defaults to 2.\n        dropout_prob: probability of an element to be zero-ed. Defaults to ``None``.\n        act: activation type and arguments. Defaults to ``RELU``.\n        norm: feature normalization type and arguments. Defaults to ``GROUP``.\n        use_conv_final: if add a final convolution block to output. Defaults to ``True``.\n        blocks_down: number of down sample blocks in each layer. Defaults to ``[1,2,2,4]``.\n        blocks_up: number of up sample blocks in each layer. Defaults to ``[1,1,1]``.\n        upsample_mode: [``\"deconv\"``, ``\"nontrainable\"``, ``\"pixelshuffle\"``]\n            The mode of upsampling manipulations.\n            Using the ``nontrainable`` modes cannot guarantee the model's reproducibility. Defaults to``nontrainable``.\n\n            - ``deconv``, uses transposed convolution layers.\n            - ``nontrainable``, uses non-trainable `linear` interpolation.\n            - ``pixelshuffle``, uses :py:class:`monai.networks.blocks.SubpixelUpsample`.\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/segresnet.py_SegResNetVAE.__init___SegResNetVAE.__init__.self.vae_conv_final.self__make_final_conv_in_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/segresnet.py_SegResNetVAE.__init___SegResNetVAE.__init__.self.vae_conv_final.self__make_final_conv_in_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/segresnet.py", "file_name": "segresnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 214, "end_line": 256, "span_ids": ["SegResNetVAE.__init__"], "tokens": 411}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SegResNetVAE(SegResNet):\n\n    def __init__(\n        self,\n        input_image_size: Sequence[int],\n        vae_estimate_std: bool = False,\n        vae_default_std: float = 0.3,\n        vae_nz: int = 256,\n        spatial_dims: int = 3,\n        init_filters: int = 8,\n        in_channels: int = 1,\n        out_channels: int = 2,\n        dropout_prob: Optional[float] = None,\n        act: Union[str, tuple] = (\"RELU\", {\"inplace\": True}),\n        norm: Union[Tuple, str] = (\"GROUP\", {\"num_groups\": 8}),\n        use_conv_final: bool = True,\n        blocks_down: tuple = (1, 2, 2, 4),\n        blocks_up: tuple = (1, 1, 1),\n        upsample_mode: Union[UpsampleMode, str] = UpsampleMode.NONTRAINABLE,\n    ):\n        super().__init__(\n            spatial_dims=spatial_dims,\n            init_filters=init_filters,\n            in_channels=in_channels,\n            out_channels=out_channels,\n            dropout_prob=dropout_prob,\n            act=act,\n            norm=norm,\n            use_conv_final=use_conv_final,\n            blocks_down=blocks_down,\n            blocks_up=blocks_up,\n            upsample_mode=upsample_mode,\n        )\n\n        self.input_image_size = input_image_size\n        self.smallest_filters = 16\n\n        zoom = 2 ** (len(self.blocks_down) - 1)\n        self.fc_insize = [s // (2 * zoom) for s in self.input_image_size]\n\n        self.vae_estimate_std = vae_estimate_std\n        self.vae_default_std = vae_default_std\n        self.vae_nz = vae_nz\n        self._prepare_vae_modules()\n        self.vae_conv_final = self._make_final_conv(in_channels)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/segresnet.py_SegResNetVAE._prepare_vae_modules_SegResNetVAE._prepare_vae_modules.self.vae_fc_up_sample.nn_Sequential_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/segresnet.py_SegResNetVAE._prepare_vae_modules_SegResNetVAE._prepare_vae_modules.self.vae_fc_up_sample.nn_Sequential_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/segresnet.py", "file_name": "segresnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 263, "end_line": 284, "span_ids": ["SegResNetVAE._prepare_vae_modules"], "tokens": 277}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SegResNetVAE(SegResNet):\n\n    def _prepare_vae_modules(self):\n        zoom = 2 ** (len(self.blocks_down) - 1)\n        v_filters = self.init_filters * zoom\n        total_elements = int(self.smallest_filters * np.prod(self.fc_insize))\n\n        self.vae_down = nn.Sequential(\n            get_norm_layer(name=self.norm, spatial_dims=self.spatial_dims, channels=v_filters),\n            self.act_mod,\n            get_conv_layer(self.spatial_dims, v_filters, self.smallest_filters, stride=2, bias=True),\n            get_norm_layer(name=self.norm, spatial_dims=self.spatial_dims, channels=self.smallest_filters),\n            self.act_mod,\n        )\n        self.vae_fc1 = nn.Linear(total_elements, self.vae_nz)\n        self.vae_fc2 = nn.Linear(total_elements, self.vae_nz)\n        self.vae_fc3 = nn.Linear(self.vae_nz, total_elements)\n\n        self.vae_fc_up_sample = nn.Sequential(\n            get_conv_layer(self.spatial_dims, self.smallest_filters, v_filters, kernel_size=1),\n            get_upsample_layer(self.spatial_dims, v_filters, upsample_mode=self.upsample_mode),\n            get_norm_layer(name=self.norm, spatial_dims=self.spatial_dims, channels=v_filters),\n            self.act_mod,\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/senet.py_SENet_SENet._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/senet.py_SENet_SENet._", "embedding": null, "metadata": {"file_path": "monai/networks/nets/senet.py", "file_name": "senet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 25, "end_line": 69, "span_ids": ["SENet"], "tokens": 584}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SENet(nn.Module):\n    \"\"\"\n    SENet based on `Squeeze-and-Excitation Networks <https://arxiv.org/pdf/1709.01507.pdf>`_.\n    Adapted from `Cadene Hub 2D version\n    <https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/senet.py>`_.\n\n    Args:\n        spatial_dims: spatial dimension of the input data.\n        in_channels: channel number of the input data.\n        block: SEBlock class.\n            for SENet154: SEBottleneck\n            for SE-ResNet models: SEResNetBottleneck\n            for SE-ResNeXt models:  SEResNeXtBottleneck\n        layers: number of residual blocks for 4 layers of the network (layer1...layer4).\n        groups: number of groups for the 3x3 convolution in each bottleneck block.\n            for SENet154: 64\n            for SE-ResNet models: 1\n            for SE-ResNeXt models:  32\n        reduction: reduction ratio for Squeeze-and-Excitation modules.\n            for all models: 16\n        dropout_prob: drop probability for the Dropout layer.\n            if `None` the Dropout layer is not used.\n            for SENet154: 0.2\n            for SE-ResNet models: None\n            for SE-ResNeXt models: None\n        dropout_dim: determine the dimensions of dropout. Defaults to 1.\n            When dropout_dim = 1, randomly zeroes some of the elements for each channel.\n            When dropout_dim = 2, Randomly zeroes out entire channels (a channel is a 2D feature map).\n            When dropout_dim = 3, Randomly zeroes out entire channels (a channel is a 3D feature map).\n        inplanes:  number of input channels for layer1.\n            for SENet154: 128\n            for SE-ResNet models: 64\n            for SE-ResNeXt models: 64\n        downsample_kernel_size: kernel size for downsampling convolutions in layer2, layer3 and layer4.\n            for SENet154: 3\n            for SE-ResNet models: 1\n            for SE-ResNeXt models: 1\n        input_3x3: If `True`, use three 3x3 convolutions instead of\n            a single 7x7 convolution in layer0.\n            - For SENet154: True\n            - For SE-ResNet models: False\n            - For SE-ResNeXt models: False\n        num_classes: number of outputs in `last_linear` layer.\n            for all models: 1000\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/senet.py_SENet.__init___SENet.__init__.for_m_in_self_modules_.if_isinstance_m_conv_typ.elif_isinstance_m_nn_Lin.nn_init_constant__torch_a": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/senet.py_SENet.__init___SENet.__init__.for_m_in_self_modules_.if_isinstance_m_conv_typ.elif_isinstance_m_nn_Lin.nn_init_constant__torch_a", "embedding": null, "metadata": {"file_path": "monai/networks/nets/senet.py", "file_name": "senet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 94, "end_line": 199, "span_ids": ["SENet.__init__"], "tokens": 1009}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SENet(nn.Module):\n\n    def __init__(\n        self,\n        spatial_dims: int,\n        in_channels: int,\n        block: Type[Union[SEBottleneck, SEResNetBottleneck, SEResNeXtBottleneck]],\n        layers: Sequence[int],\n        groups: int,\n        reduction: int,\n        dropout_prob: Optional[float] = 0.2,\n        dropout_dim: int = 1,\n        inplanes: int = 128,\n        downsample_kernel_size: int = 3,\n        input_3x3: bool = True,\n        num_classes: int = 1000,\n    ) -> None:\n\n        super().__init__()\n\n        relu_type: Type[nn.ReLU] = Act[Act.RELU]\n        conv_type: Type[Union[nn.Conv1d, nn.Conv2d, nn.Conv3d]] = Conv[Conv.CONV, spatial_dims]\n        pool_type: Type[Union[nn.MaxPool1d, nn.MaxPool2d, nn.MaxPool3d]] = Pool[Pool.MAX, spatial_dims]\n        norm_type: Type[Union[nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d]] = Norm[Norm.BATCH, spatial_dims]\n        dropout_type: Type[Union[nn.Dropout, nn.Dropout2d, nn.Dropout3d]] = Dropout[Dropout.DROPOUT, dropout_dim]\n        avg_pool_type: Type[Union[nn.AdaptiveAvgPool1d, nn.AdaptiveAvgPool2d, nn.AdaptiveAvgPool3d]] = Pool[\n            Pool.ADAPTIVEAVG, spatial_dims\n        ]\n\n        self.inplanes = inplanes\n        self.spatial_dims = spatial_dims\n\n        layer0_modules: List[Tuple[str, Any]]\n\n        if input_3x3:\n            layer0_modules = [\n                (\n                    \"conv1\",\n                    conv_type(in_channels=in_channels, out_channels=64, kernel_size=3, stride=2, padding=1, bias=False),\n                ),\n                (\"bn1\", norm_type(num_features=64)),\n                (\"relu1\", relu_type(inplace=True)),\n                (\"conv2\", conv_type(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1, bias=False)),\n                (\"bn2\", norm_type(num_features=64)),\n                (\"relu2\", relu_type(inplace=True)),\n                (\n                    \"conv3\",\n                    conv_type(in_channels=64, out_channels=inplanes, kernel_size=3, stride=1, padding=1, bias=False),\n                ),\n                (\"bn3\", norm_type(num_features=inplanes)),\n                (\"relu3\", relu_type(inplace=True)),\n            ]\n        else:\n            layer0_modules = [\n                (\n                    \"conv1\",\n                    conv_type(\n                        in_channels=in_channels, out_channels=inplanes, kernel_size=7, stride=2, padding=3, bias=False\n                    ),\n                ),\n                (\"bn1\", norm_type(num_features=inplanes)),\n                (\"relu1\", relu_type(inplace=True)),\n            ]\n\n        layer0_modules.append((\"pool\", pool_type(kernel_size=3, stride=2, ceil_mode=True)))\n        self.layer0 = nn.Sequential(OrderedDict(layer0_modules))\n        self.layer1 = self._make_layer(\n            block, planes=64, blocks=layers[0], groups=groups, reduction=reduction, downsample_kernel_size=1\n        )\n        self.layer2 = self._make_layer(\n            block,\n            planes=128,\n            blocks=layers[1],\n            stride=2,\n            groups=groups,\n            reduction=reduction,\n            downsample_kernel_size=downsample_kernel_size,\n        )\n        self.layer3 = self._make_layer(\n            block,\n            planes=256,\n            blocks=layers[2],\n            stride=2,\n            groups=groups,\n            reduction=reduction,\n            downsample_kernel_size=downsample_kernel_size,\n        )\n        self.layer4 = self._make_layer(\n            block,\n            planes=512,\n            blocks=layers[3],\n            stride=2,\n            groups=groups,\n            reduction=reduction,\n            downsample_kernel_size=downsample_kernel_size,\n        )\n        self.adaptive_avg_pool = avg_pool_type(1)\n        self.dropout = dropout_type(dropout_prob) if dropout_prob is not None else None\n        self.last_linear = nn.Linear(512 * block.expansion, num_classes)\n\n        for m in self.modules():\n            if isinstance(m, conv_type):\n                nn.init.kaiming_normal_(torch.as_tensor(m.weight))\n            elif isinstance(m, norm_type):\n                nn.init.constant_(torch.as_tensor(m.weight), 1)\n                nn.init.constant_(torch.as_tensor(m.bias), 0)\n            elif isinstance(m, nn.Linear):\n                nn.init.constant_(torch.as_tensor(m.bias), 0)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/senet.py_SENet._make_layer_SENet._make_layer.return.nn_Sequential_layers_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/senet.py_SENet._make_layer_SENet._make_layer.return.nn_Sequential_layers_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/senet.py", "file_name": "senet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 201, "end_line": 249, "span_ids": ["SENet._make_layer"], "tokens": 298}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SENet(nn.Module):\n\n    def _make_layer(\n        self,\n        block: Type[Union[SEBottleneck, SEResNetBottleneck, SEResNeXtBottleneck]],\n        planes: int,\n        blocks: int,\n        groups: int,\n        reduction: int,\n        stride: int = 1,\n        downsample_kernel_size: int = 1,\n    ) -> nn.Sequential:\n\n        downsample = None\n        if stride != 1 or self.inplanes != planes * block.expansion:\n            downsample = Convolution(\n                spatial_dims=self.spatial_dims,\n                in_channels=self.inplanes,\n                out_channels=planes * block.expansion,\n                strides=stride,\n                kernel_size=downsample_kernel_size,\n                act=None,\n                norm=Norm.BATCH,\n                bias=False,\n            )\n\n        layers = []\n        layers.append(\n            block(\n                spatial_dims=self.spatial_dims,\n                inplanes=self.inplanes,\n                planes=planes,\n                groups=groups,\n                reduction=reduction,\n                stride=stride,\n                downsample=downsample,\n            )\n        )\n        self.inplanes = planes * block.expansion\n        for _num in range(1, blocks):\n            layers.append(\n                block(\n                    spatial_dims=self.spatial_dims,\n                    inplanes=self.inplanes,\n                    planes=planes,\n                    groups=groups,\n                    reduction=reduction,\n                )\n            )\n\n        return nn.Sequential(*layers)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/senet.py_SENet.features_SENet.forward.return.x": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/senet.py_SENet.features_SENet.forward.return.x", "embedding": null, "metadata": {"file_path": "monai/networks/nets/senet.py", "file_name": "senet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 228, "end_line": 247, "span_ids": ["SENet.forward", "SENet.features", "SENet.logits"], "tokens": 149}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SENet(nn.Module):\n\n    def features(self, x: torch.Tensor):\n        x = self.layer0(x)\n        x = self.layer1(x)\n        x = self.layer2(x)\n        x = self.layer3(x)\n        x = self.layer4(x)\n        return x\n\n    def logits(self, x: torch.Tensor):\n        x = self.adaptive_avg_pool(x)\n        if self.dropout is not None:\n            x = self.dropout(x)\n        x = torch.flatten(x, 1)\n        x = self.last_linear(x)\n        return x\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        x = self.features(x)\n        x = self.logits(x)\n        return x", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/senet.py__load_state_dict__load_state_dict.model_load_state_dict_mod": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/senet.py__load_state_dict__load_state_dict.model_load_state_dict_mod", "embedding": null, "metadata": {"file_path": "monai/networks/nets/senet.py", "file_name": "senet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 273, "end_line": 321, "span_ids": ["_load_state_dict"], "tokens": 658}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _load_state_dict(model: nn.Module, arch: str, progress: bool):\n    \"\"\"\n    This function is used to load pretrained models.\n    \"\"\"\n    model_url = look_up_option(arch, SE_NET_MODELS, None)\n    if model_url is None:\n        raise ValueError(\n            \"only 'senet154', 'se_resnet50', 'se_resnet101',  'se_resnet152', 'se_resnext50_32x4d', \"\n            + \"and se_resnext101_32x4d are supported to load pretrained weights.\"\n        )\n\n    pattern_conv = re.compile(r\"^(layer[1-4]\\.\\d\\.(?:conv)\\d\\.)(\\w*)$\")\n    pattern_bn = re.compile(r\"^(layer[1-4]\\.\\d\\.)(?:bn)(\\d\\.)(\\w*)$\")\n    pattern_se = re.compile(r\"^(layer[1-4]\\.\\d\\.)(?:se_module.fc1.)(\\w*)$\")\n    pattern_se2 = re.compile(r\"^(layer[1-4]\\.\\d\\.)(?:se_module.fc2.)(\\w*)$\")\n    pattern_down_conv = re.compile(r\"^(layer[1-4]\\.\\d\\.)(?:downsample.0.)(\\w*)$\")\n    pattern_down_bn = re.compile(r\"^(layer[1-4]\\.\\d\\.)(?:downsample.1.)(\\w*)$\")\n\n    if isinstance(model_url, dict):\n        download_url(model_url[\"url\"], filepath=model_url[\"filename\"])\n        state_dict = torch.load(model_url[\"filename\"], map_location=None)\n    else:\n        state_dict = load_state_dict_from_url(model_url, progress=progress)\n    for key in list(state_dict.keys()):\n        new_key = None\n        if pattern_conv.match(key):\n            new_key = re.sub(pattern_conv, r\"\\1conv.\\2\", key)\n        elif pattern_bn.match(key):\n            new_key = re.sub(pattern_bn, r\"\\1conv\\2adn.N.\\3\", key)\n        elif pattern_se.match(key):\n            state_dict[key] = state_dict[key].squeeze()\n            new_key = re.sub(pattern_se, r\"\\1se_layer.fc.0.\\2\", key)\n        elif pattern_se2.match(key):\n            state_dict[key] = state_dict[key].squeeze()\n            new_key = re.sub(pattern_se2, r\"\\1se_layer.fc.2.\\2\", key)\n        elif pattern_down_conv.match(key):\n            new_key = re.sub(pattern_down_conv, r\"\\1project.conv.\\2\", key)\n        elif pattern_down_bn.match(key):\n            new_key = re.sub(pattern_down_bn, r\"\\1project.adn.N.\\2\", key)\n        if new_key:\n            state_dict[new_key] = state_dict[key]\n            del state_dict[key]\n\n    model_dict = model.state_dict()\n    state_dict = {\n        k: v for k, v in state_dict.items() if (k in model_dict) and (model_dict[k].shape == state_dict[k].shape)\n    }\n    model_dict.update(state_dict)\n    model.load_state_dict(model_dict)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/senet.py_SENet154_SENet154.__init__.if_pretrained_._load_state_dict_self_s": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/senet.py_SENet154_SENet154.__init__.if_pretrained_._load_state_dict_self_s", "embedding": null, "metadata": {"file_path": "monai/networks/nets/senet.py", "file_name": "senet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 324, "end_line": 339, "span_ids": ["SENet154.__init__", "SENet154"], "tokens": 160}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SENet154(SENet):\n    \"\"\"SENet154 based on `Squeeze-and-Excitation Networks` with optional pretrained support when spatial_dims is 2.\"\"\"\n\n    def __init__(\n        self,\n        layers: Sequence[int] = (3, 8, 36, 3),\n        groups: int = 64,\n        reduction: int = 16,\n        pretrained: bool = False,\n        progress: bool = True,\n        **kwargs,\n    ) -> None:\n        super().__init__(block=SEBottleneck, layers=layers, groups=groups, reduction=reduction, **kwargs)\n        if pretrained:\n            # it only worked when `spatial_dims` is 2\n            _load_state_dict(self, \"senet154\", progress)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/senet.py_SEResNet50_SEResNet50.__init__.if_pretrained_._load_state_dict_self_s": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/senet.py_SEResNet50_SEResNet50.__init__.if_pretrained_._load_state_dict_self_s", "embedding": null, "metadata": {"file_path": "monai/networks/nets/senet.py", "file_name": "senet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 342, "end_line": 371, "span_ids": ["SEResNet50", "SEResNet50.__init__"], "tokens": 249}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SEResNet50(SENet):\n    \"\"\"SEResNet50 based on `Squeeze-and-Excitation Networks` with optional pretrained support when spatial_dims is 2.\"\"\"\n\n    def __init__(\n        self,\n        layers: Sequence[int] = (3, 4, 6, 3),\n        groups: int = 1,\n        reduction: int = 16,\n        dropout_prob: Optional[float] = None,\n        inplanes: int = 64,\n        downsample_kernel_size: int = 1,\n        input_3x3: bool = False,\n        pretrained: bool = False,\n        progress: bool = True,\n        **kwargs,\n    ) -> None:\n        super().__init__(\n            block=SEResNetBottleneck,\n            layers=layers,\n            groups=groups,\n            reduction=reduction,\n            dropout_prob=dropout_prob,\n            inplanes=inplanes,\n            downsample_kernel_size=downsample_kernel_size,\n            input_3x3=input_3x3,\n            **kwargs,\n        )\n        if pretrained:\n            # it only worked when `spatial_dims` is 2\n            _load_state_dict(self, \"se_resnet50\", progress)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/senet.py_SEResNet101_SEResNet101.__init__.if_pretrained_._load_state_dict_self_s": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/senet.py_SEResNet101_SEResNet101.__init__.if_pretrained_._load_state_dict_self_s", "embedding": null, "metadata": {"file_path": "monai/networks/nets/senet.py", "file_name": "senet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 374, "end_line": 403, "span_ids": ["SEResNet101", "SEResNet101.__init__"], "tokens": 235}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SEResNet101(SENet):\n    \"\"\"\n    SEResNet101 based on `Squeeze-and-Excitation Networks` with optional pretrained support when spatial_dims is 2.\n    \"\"\"\n\n    def __init__(\n        self,\n        layers: Sequence[int] = (3, 4, 23, 3),\n        groups: int = 1,\n        reduction: int = 16,\n        inplanes: int = 64,\n        downsample_kernel_size: int = 1,\n        input_3x3: bool = False,\n        pretrained: bool = False,\n        progress: bool = True,\n        **kwargs,\n    ) -> None:\n        super().__init__(\n            block=SEResNetBottleneck,\n            layers=layers,\n            groups=groups,\n            reduction=reduction,\n            inplanes=inplanes,\n            downsample_kernel_size=downsample_kernel_size,\n            input_3x3=input_3x3,\n            **kwargs,\n        )\n        if pretrained:\n            # it only worked when `spatial_dims` is 2\n            _load_state_dict(self, \"se_resnet101\", progress)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/senet.py_SEResNet152_SEResNet152.__init__.if_pretrained_._load_state_dict_self_s": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/senet.py_SEResNet152_SEResNet152.__init__.if_pretrained_._load_state_dict_self_s", "embedding": null, "metadata": {"file_path": "monai/networks/nets/senet.py", "file_name": "senet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 406, "end_line": 435, "span_ids": ["SEResNet152.__init__", "SEResNet152"], "tokens": 235}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SEResNet152(SENet):\n    \"\"\"\n    SEResNet152 based on `Squeeze-and-Excitation Networks` with optional pretrained support when spatial_dims is 2.\n    \"\"\"\n\n    def __init__(\n        self,\n        layers: Sequence[int] = (3, 8, 36, 3),\n        groups: int = 1,\n        reduction: int = 16,\n        inplanes: int = 64,\n        downsample_kernel_size: int = 1,\n        input_3x3: bool = False,\n        pretrained: bool = False,\n        progress: bool = True,\n        **kwargs,\n    ) -> None:\n        super().__init__(\n            block=SEResNetBottleneck,\n            layers=layers,\n            groups=groups,\n            reduction=reduction,\n            inplanes=inplanes,\n            downsample_kernel_size=downsample_kernel_size,\n            input_3x3=input_3x3,\n            **kwargs,\n        )\n        if pretrained:\n            # it only worked when `spatial_dims` is 2\n            _load_state_dict(self, \"se_resnet152\", progress)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/senet.py_SEResNext50_SEResNext50.__init__.if_pretrained_._load_state_dict_self_s": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/senet.py_SEResNext50_SEResNext50.__init__.if_pretrained_._load_state_dict_self_s", "embedding": null, "metadata": {"file_path": "monai/networks/nets/senet.py", "file_name": "senet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 438, "end_line": 469, "span_ids": ["SEResNext50", "SEResNext50.__init__"], "tokens": 259}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SEResNext50(SENet):\n    \"\"\"\n    SEResNext50 based on `Squeeze-and-Excitation Networks` with optional pretrained support when spatial_dims is 2.\n    \"\"\"\n\n    def __init__(\n        self,\n        layers: Sequence[int] = (3, 4, 6, 3),\n        groups: int = 32,\n        reduction: int = 16,\n        dropout_prob: Optional[float] = None,\n        inplanes: int = 64,\n        downsample_kernel_size: int = 1,\n        input_3x3: bool = False,\n        pretrained: bool = False,\n        progress: bool = True,\n        **kwargs,\n    ) -> None:\n        super().__init__(\n            block=SEResNeXtBottleneck,\n            layers=layers,\n            groups=groups,\n            dropout_prob=dropout_prob,\n            reduction=reduction,\n            inplanes=inplanes,\n            downsample_kernel_size=downsample_kernel_size,\n            input_3x3=input_3x3,\n            **kwargs,\n        )\n        if pretrained:\n            # it only worked when `spatial_dims` is 2\n            _load_state_dict(self, \"se_resnext50_32x4d\", progress)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/senet.py_SEResNext101_SEResNext101.__init__.if_pretrained_._load_state_dict_self_s": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/senet.py_SEResNext101_SEResNext101.__init__.if_pretrained_._load_state_dict_self_s", "embedding": null, "metadata": {"file_path": "monai/networks/nets/senet.py", "file_name": "senet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 472, "end_line": 503, "span_ids": ["SEResNext101.__init__", "SEResNext101"], "tokens": 259}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SEResNext101(SENet):\n    \"\"\"\n    SEResNext101 based on `Squeeze-and-Excitation Networks` with optional pretrained support when spatial_dims is 2.\n    \"\"\"\n\n    def __init__(\n        self,\n        layers: Sequence[int] = (3, 4, 23, 3),\n        groups: int = 32,\n        reduction: int = 16,\n        dropout_prob: Optional[float] = None,\n        inplanes: int = 64,\n        downsample_kernel_size: int = 1,\n        input_3x3: bool = False,\n        pretrained: bool = False,\n        progress: bool = True,\n        **kwargs,\n    ) -> None:\n        super().__init__(\n            block=SEResNeXtBottleneck,\n            layers=layers,\n            groups=groups,\n            dropout_prob=dropout_prob,\n            reduction=reduction,\n            inplanes=inplanes,\n            downsample_kernel_size=downsample_kernel_size,\n            input_3x3=input_3x3,\n            **kwargs,\n        )\n        if pretrained:\n            # it only worked when `spatial_dims` is 2\n            _load_state_dict(self, \"se_resnext101_32x4d\", progress)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/senet.py_SEnet_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/senet.py_SEnet_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/senet.py", "file_name": "senet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 506, "end_line": 513, "span_ids": ["impl:5"], "tokens": 138}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "SEnet = Senet = SENet\nSEnet154 = Senet154 = senet154 = SENet154\nSEresnet50 = Seresnet50 = seresnet50 = SEResNet50\nSEresnet101 = Seresnet101 = seresnet101 = SEResNet101\nSEresnet152 = Seresnet152 = seresnet152 = SEResNet152\nSEResNeXt50 = SEresnext50 = Seresnext50 = seresnext50 = SEResNext50\nSEResNeXt101 = SEresnext101 = Seresnext101 = seresnext101 = SEResNext101", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/utils.py_slice_channels_predict_segmentation.return.logits_argmax_1_keepdim_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/utils.py_slice_channels_predict_segmentation.return.logits_argmax_1_keepdim_", "embedding": null, "metadata": {"file_path": "monai/networks/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 67, "end_line": 91, "span_ids": ["predict_segmentation", "slice_channels"], "tokens": 272}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def slice_channels(tensor: torch.Tensor, *slicevals: Optional[int]) -> torch.Tensor:\n    slices = [slice(None)] * len(tensor.shape)\n    slices[1] = slice(*slicevals)\n\n    return tensor[slices]\n\n\ndef predict_segmentation(logits: torch.Tensor, mutually_exclusive: bool = False, threshold: float = 0.0) -> Any:\n    \"\"\"\n    Given the logits from a network, computing the segmentation by thresholding all values above 0\n    if multi-labels task, computing the `argmax` along the channel axis if multi-classes task,\n    logits has shape `BCHW[D]`.\n\n    Args:\n        logits: raw data of model output.\n        mutually_exclusive: if True, `logits` will be converted into a binary matrix using\n            a combination of argmax, which is suitable for multi-classes task. Defaults to False.\n        threshold: thresholding the prediction values if multi-labels task.\n    \"\"\"\n    if not mutually_exclusive:\n        return (logits >= threshold).int()\n    if logits.shape[1] == 1:\n        warnings.warn(\"single channel prediction, `mutually_exclusive=True` ignored, use threshold instead.\")\n        return (logits >= threshold).int()\n    return logits.argmax(1, keepdim=True)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/utils.py_to_norm_affine_to_norm_affine.return.src_xform_affine_torc": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/utils.py_to_norm_affine_to_norm_affine.return.src_xform_affine_torc", "embedding": null, "metadata": {"file_path": "monai/networks/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 129, "end_line": 160, "span_ids": ["to_norm_affine"], "tokens": 391}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def to_norm_affine(\n    affine: torch.Tensor, src_size: Sequence[int], dst_size: Sequence[int], align_corners: bool = False\n) -> torch.Tensor:\n    \"\"\"\n    Given ``affine`` defined for coordinates in the pixel space, compute the corresponding affine\n    for the normalized coordinates.\n\n    Args:\n        affine: Nxdxd batched square matrix\n        src_size: source image spatial shape\n        dst_size: target image spatial shape\n        align_corners: if True, consider -1 and 1 to refer to the centers of the\n            corner pixels rather than the image corners.\n            See also: https://pytorch.org/docs/stable/nn.functional.html#torch.nn.functional.grid_sample\n\n    Raises:\n        TypeError: When ``affine`` is not a ``torch.Tensor``.\n        ValueError: When ``affine`` is not Nxdxd.\n        ValueError: When ``src_size`` or ``dst_size`` dimensions differ from ``affine``.\n\n    \"\"\"\n    if not isinstance(affine, torch.Tensor):\n        raise TypeError(f\"affine must be a torch.Tensor but is {type(affine).__name__}.\")\n    if affine.ndimension() != 3 or affine.shape[1] != affine.shape[2]:\n        raise ValueError(f\"affine must be Nxdxd, got {tuple(affine.shape)}.\")\n    sr = affine.shape[1] - 1\n    if sr != len(src_size) or sr != len(dst_size):\n        raise ValueError(f\"affine suggests {sr}D, got src={len(src_size)}D, dst={len(dst_size)}D.\")\n\n    src_xform = normalize_transform(src_size, affine.device, affine.dtype, align_corners)\n    dst_xform = normalize_transform(dst_size, affine.device, affine.dtype, align_corners)\n    return src_xform @ affine @ torch.inverse(dst_xform)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/utils.py_pixelshuffle_pixelshuffle.return.x": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/utils.py_pixelshuffle_pixelshuffle.return.x", "embedding": null, "metadata": {"file_path": "monai/networks/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 233, "end_line": 283, "span_ids": ["pixelshuffle"], "tokens": 505}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@deprecated_arg(\n    name=\"dimensions\", new_name=\"spatial_dims\", since=\"0.6\", msg_suffix=\"Please use `spatial_dims` instead.\"\n)\ndef pixelshuffle(\n    x: torch.Tensor, spatial_dims: int, scale_factor: int, dimensions: Optional[int] = None\n) -> torch.Tensor:\n    \"\"\"\n    Apply pixel shuffle to the tensor `x` with spatial dimensions `spatial_dims` and scaling factor `scale_factor`.\n\n    See: Shi et al., 2016, \"Real-Time Single Image and Video Super-Resolution\n    Using a nEfficient Sub-Pixel Convolutional Neural Network.\"\n\n    See: Aitken et al., 2017, \"Checkerboard artifact free sub-pixel convolution\".\n\n    Args:\n        x: Input tensor\n        spatial_dims: number of spatial dimensions, typically 2 or 3 for 2D or 3D\n        scale_factor: factor to rescale the spatial dimensions by, must be >=1\n\n    .. deprecated:: 0.6.0\n        ``dimensions`` is deprecated, use ``spatial_dims`` instead.\n\n    Returns:\n        Reshuffled version of `x`.\n\n    Raises:\n        ValueError: When input channels of `x` are not divisible by (scale_factor ** spatial_dims)\n    \"\"\"\n    if dimensions is not None:\n        spatial_dims = dimensions\n    dim, factor = spatial_dims, scale_factor\n    input_size = list(x.size())\n    batch_size, channels = input_size[:2]\n    scale_divisor = factor ** dim\n\n    if channels % scale_divisor != 0:\n        raise ValueError(\n            f\"Number of input channels ({channels}) must be evenly \"\n            f\"divisible by scale_factor ** dimensions ({factor}**{dim}={scale_divisor}).\"\n        )\n\n    org_channels = channels // scale_divisor\n    output_size = [batch_size, org_channels] + [d * factor for d in input_size[2:]]\n\n    indices = tuple(range(2, 2 + 2 * dim))\n    indices_factor, indices_dim = indices[:dim], indices[dim:]\n    permute_indices = (0, 1) + sum(zip(indices_dim, indices_factor), ())\n\n    x = x.reshape(batch_size, org_channels, *([factor] * dim + input_size[2:]))\n    x = x.permute(permute_indices).reshape(output_size)\n    return x", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/utils.py_eval_mode_eval_mode.try_.finally_.for_n_in_training_.n_train_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/utils.py_eval_mode_eval_mode.try_.finally_.for_n_in_training_.n_train_", "embedding": null, "metadata": {"file_path": "monai/networks/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 248, "end_line": 278, "span_ids": ["eval_mode"], "tokens": 197}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@contextmanager\ndef eval_mode(*nets: nn.Module):\n    \"\"\"\n    Set network(s) to eval mode and then return to original state at the end.\n\n    Args:\n        nets: Input network(s)\n\n    Examples\n\n    .. code-block:: python\n\n        t=torch.rand(1,1,16,16)\n        p=torch.nn.Conv2d(1,1,3)\n        print(p.training)  # True\n        with eval_mode(p):\n            print(p.training)  # False\n            print(p(t).sum().backward())  # will correctly raise an exception as gradients are calculated\n    \"\"\"\n\n    # Get original state of network(s)\n    training = [n for n in nets if n.training]\n\n    try:\n        # set to eval mode\n        with torch.no_grad():\n            yield [n.eval() for n in nets]\n    finally:\n        # Return required networks to training\n        for n in training:\n            n.train()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/optimizers/__init__.py_LearningRateFinder_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/optimizers/__init__.py_LearningRateFinder_", "embedding": null, "metadata": {"file_path": "monai/optimizers/__init__.py", "file_name": "__init__.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 16, "span_ids": ["docstring"], "tokens": 42}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from .lr_finder import LearningRateFinder\nfrom .lr_scheduler import ExponentialLR, LinearLR, WarmupCosineSchedule\nfrom .novograd import Novograd\nfrom .utils import generate_param_groups", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/optimizers/lr_finder.py_DataLoaderIter_DataLoaderIter.__next__.return.self_inputs_labels_from_b": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/optimizers/lr_finder.py_DataLoaderIter_DataLoaderIter.__next__.return.self_inputs_labels_from_b", "embedding": null, "metadata": {"file_path": "monai/optimizers/lr_finder.py", "file_name": "lr_finder.py", "file_type": "text/x-python", "category": "implementation", "start_line": 40, "end_line": 65, "span_ids": ["DataLoaderIter.__next__", "DataLoaderIter.__init__", "DataLoaderIter.__iter__", "DataLoaderIter.inputs_labels_from_batch", "DataLoaderIter.dataset", "DataLoaderIter"], "tokens": 187}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataLoaderIter:\n    def __init__(self, data_loader: DataLoader, image_extractor: Callable, label_extractor: Callable) -> None:\n        if not isinstance(data_loader, DataLoader):\n            raise ValueError(\n                f\"Loader has unsupported type: {type(data_loader)}. Expected type was `torch.utils.data.DataLoader`\"\n            )\n        self.data_loader = data_loader\n        self._iterator = iter(data_loader)\n        self.image_extractor = image_extractor\n        self.label_extractor = label_extractor\n\n    @property\n    def dataset(self):\n        return self.data_loader.dataset\n\n    def inputs_labels_from_batch(self, batch_data):\n        images = self.image_extractor(batch_data)\n        labels = self.label_extractor(batch_data)\n        return images, labels\n\n    def __iter__(self):\n        return self\n\n    def __next__(self):\n        batch = next(self._iterator)\n        return self.inputs_labels_from_batch(batch)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/optimizers/lr_finder.py_TrainDataLoaderIter_TrainDataLoaderIter.__next__.return.inputs_labels": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/optimizers/lr_finder.py_TrainDataLoaderIter_TrainDataLoaderIter.__next__.return.inputs_labels", "embedding": null, "metadata": {"file_path": "monai/optimizers/lr_finder.py", "file_name": "lr_finder.py", "file_type": "text/x-python", "category": "implementation", "start_line": 68, "end_line": 86, "span_ids": ["TrainDataLoaderIter.__init__", "TrainDataLoaderIter", "TrainDataLoaderIter.__next__"], "tokens": 146}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TrainDataLoaderIter(DataLoaderIter):\n    def __init__(\n        self, data_loader: DataLoader, image_extractor: Callable, label_extractor: Callable, auto_reset: bool = True\n    ) -> None:\n        super().__init__(data_loader, image_extractor, label_extractor)\n        self.auto_reset = auto_reset\n\n    def __next__(self):\n        try:\n            batch = next(self._iterator)\n            inputs, labels = self.inputs_labels_from_batch(batch)\n        except StopIteration:\n            if not self.auto_reset:\n                raise\n            self._iterator = iter(self.data_loader)\n            batch = next(self._iterator)\n            inputs, labels = self.inputs_labels_from_batch(batch)\n\n        return inputs, labels", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/optimizers/lr_finder.py_ValDataLoaderIter_default_label_extractor.return.out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/optimizers/lr_finder.py_ValDataLoaderIter_default_label_extractor.return.out", "embedding": null, "metadata": {"file_path": "monai/optimizers/lr_finder.py", "file_name": "lr_finder.py", "file_type": "text/x-python", "category": "implementation", "start_line": 89, "end_line": 135, "span_ids": ["ValDataLoaderIter.__next__", "ValDataLoaderIter", "ValDataLoaderIter.__iter__", "ValDataLoaderIter.__init__", "default_image_extractor", "default_label_extractor"], "tokens": 390}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ValDataLoaderIter(DataLoaderIter):\n    \"\"\"This iterator will reset itself **only** when it is acquired by\n    the syntax of normal `iterator`. That is, this iterator just works\n    like a `torch.data.DataLoader`. If you want to restart it, you\n    should use it like:\n\n        ```\n        loader_iter = ValDataLoaderIter(data_loader)\n        for batch in loader_iter:\n            ...\n\n        # `loader_iter` should run out of values now, you can restart it by:\n        # 1. the way we use a `torch.data.DataLoader`\n        for batch in loader_iter:        # __iter__ is called implicitly\n            ...\n\n        # 2. passing it into `iter()` manually\n        loader_iter = iter(loader_iter)  # __iter__ is called by `iter()`\n        ```\n    \"\"\"\n\n    def __init__(self, data_loader: DataLoader, image_extractor: Callable, label_extractor: Callable) -> None:\n        super().__init__(data_loader, image_extractor, label_extractor)\n        self.run_limit = len(self.data_loader)\n        self.run_counter = 0\n\n    def __iter__(self):\n        if self.run_counter >= self.run_limit:\n            self._iterator = iter(self.data_loader)\n            self.run_counter = 0\n        return self\n\n    def __next__(self):\n        self.run_counter += 1\n        return super().__next__()\n\n\ndef default_image_extractor(x: Any) -> torch.Tensor:\n    \"\"\"Default callable for getting image from batch data.\"\"\"\n    out: torch.Tensor = x[\"image\"] if isinstance(x, dict) else x[0]\n    return out\n\n\ndef default_label_extractor(x: Any) -> torch.Tensor:\n    \"\"\"Default callable for getting label from batch data.\"\"\"\n    out: torch.Tensor = x[\"label\"] if isinstance(x, dict) else x[1]\n    return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/optimizers/lr_finder.py_LearningRateFinder_LearningRateFinder._Learning_rate_range_te": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/optimizers/lr_finder.py_LearningRateFinder_LearningRateFinder._Learning_rate_range_te", "embedding": null, "metadata": {"file_path": "monai/optimizers/lr_finder.py", "file_name": "lr_finder.py", "file_type": "text/x-python", "category": "implementation", "start_line": 140, "end_line": 177, "span_ids": ["LearningRateFinder"], "tokens": 474}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LearningRateFinder:\n    \"\"\"Learning rate range test.\n\n    The learning rate range test increases the learning rate in a pre-training run\n    between two boundaries in a linear or exponential manner. It provides valuable\n    information on how well the network can be trained over a range of learning rates\n    and what is the optimal learning rate.\n\n    Example (fastai approach):\n    >>> lr_finder = LearningRateFinder(net, optimizer, criterion)\n    >>> lr_finder.range_test(data_loader, end_lr=100, num_iter=100)\n    >>> lr_finder.get_steepest_gradient()\n    >>> lr_finder.plot() # to inspect the loss-learning rate graph\n\n    Example (Leslie Smith's approach):\n    >>> lr_finder = LearningRateFinder(net, optimizer, criterion)\n    >>> lr_finder.range_test(train_loader, val_loader=val_loader, end_lr=1, num_iter=100, step_mode=\"linear\")\n\n    Gradient accumulation is supported; example:\n    >>> train_data = ...    # prepared dataset\n    >>> desired_bs, real_bs = 32, 4         # batch size\n    >>> accumulation_steps = desired_bs // real_bs     # required steps for accumulation\n    >>> data_loader = torch.utils.data.DataLoader(train_data, batch_size=real_bs, shuffle=True)\n    >>> acc_lr_finder = LearningRateFinder(net, optimizer, criterion)\n    >>> acc_lr_finder.range_test(data_loader, end_lr=10, num_iter=100, accumulation_steps=accumulation_steps)\n\n    By default, image will be extracted from data loader with x[\"image\"] and x[0], depending on whether\n    batch data is a dictionary or not (and similar behaviour for extracting the label). If your data loader\n    returns something other than this, pass a callable function to extract it, e.g.:\n    >>> image_extractor = lambda x: x[\"input\"]\n    >>> label_extractor = lambda x: x[100]\n    >>> lr_finder = LearningRateFinder(net, optimizer, criterion)\n    >>> lr_finder.range_test(train_loader, val_loader, image_extractor, label_extractor)\n\n    References:\n    Modified from: https://github.com/davidtvs/pytorch-lr-finder.\n    Cyclical Learning Rates for Training Neural Networks: https://arxiv.org/abs/1506.01186\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/optimizers/lr_finder.py_LearningRateFinder.__init___LearningRateFinder.reset.self_model_to_self_model_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/optimizers/lr_finder.py_LearningRateFinder.__init___LearningRateFinder.reset.self_model_to_self_model_", "embedding": null, "metadata": {"file_path": "monai/optimizers/lr_finder.py", "file_name": "lr_finder.py", "file_type": "text/x-python", "category": "implementation", "start_line": 179, "end_line": 248, "span_ids": ["LearningRateFinder.__init__", "LearningRateFinder.reset"], "tokens": 681}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LearningRateFinder:\n\n    def __init__(\n        self,\n        model: nn.Module,\n        optimizer: Optimizer,\n        criterion: torch.nn.Module,\n        device: Optional[Union[str, torch.device]] = None,\n        memory_cache: bool = True,\n        cache_dir: Optional[str] = None,\n        amp: bool = False,\n        pickle_module=pickle,\n        pickle_protocol: int = DEFAULT_PROTOCOL,\n        verbose: bool = True,\n    ) -> None:\n        \"\"\"Constructor.\n\n        Args:\n            model: wrapped model.\n            optimizer: wrapped optimizer.\n            criterion: wrapped loss function.\n            device: device on which to test. run a string (\"cpu\" or \"cuda\") with an\n                optional ordinal for the device type (e.g. \"cuda:X\", where is the ordinal).\n                Alternatively, can be an object representing the device on which the\n                computation will take place. Default: None, uses the same device as `model`.\n            memory_cache: if this flag is set to True, `state_dict` of\n                model and optimizer will be cached in memory. Otherwise, they will be saved\n                to files under the `cache_dir`.\n            cache_dir: path for storing temporary files. If no path is\n                specified, system-wide temporary directory is used. Notice that this\n                parameter will be ignored if `memory_cache` is True.\n            amp: use Automatic Mixed Precision\n            pickle_module: module used for pickling metadata and objects, default to `pickle`.\n                this arg is used by `torch.save`, for more details, please check:\n                https://pytorch.org/docs/stable/generated/torch.save.html#torch.save.\n            pickle_protocol: can be specified to override the default protocol, default to `2`.\n                this arg is used by `torch.save`, for more details, please check:\n                https://pytorch.org/docs/stable/generated/torch.save.html#torch.save.\n            verbose: verbose output\n        Returns:\n            None\n        \"\"\"\n        # Check if the optimizer is already attached to a scheduler\n        self.optimizer = optimizer\n        self._check_for_scheduler()\n\n        self.model = model\n        self.criterion = criterion\n        self.history: Dict[str, list] = {\"lr\": [], \"loss\": []}\n        self.memory_cache = memory_cache\n        self.cache_dir = cache_dir\n        self.amp = amp\n        self.verbose = verbose\n\n        # Save the original state of the model and optimizer so they can be restored if\n        # needed\n        self.model_device = next(self.model.parameters()).device\n        self.state_cacher = StateCacher(\n            in_memory=memory_cache, cache_dir=cache_dir, pickle_module=pickle_module, pickle_protocol=pickle_protocol\n        )\n        self.state_cacher.store(\"model\", self.model.state_dict())\n        self.state_cacher.store(\"optimizer\", self.optimizer.state_dict())\n\n        # If device is None, use the same as the model\n        self.device = device if device else self.model_device\n\n    def reset(self) -> None:\n        \"\"\"Restores the model and optimizer to their initial states.\"\"\"\n\n        self.model.load_state_dict(self.state_cacher.retrieve(\"model\"))\n        self.optimizer.load_state_dict(self.state_cacher.retrieve(\"optimizer\"))\n        self.model.to(self.model_device)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/optimizers/lr_finder.py_LearningRateFinder.range_test_LearningRateFinder.range_test.trange": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/optimizers/lr_finder.py_LearningRateFinder.range_test_LearningRateFinder.range_test.trange", "embedding": null, "metadata": {"file_path": "monai/optimizers/lr_finder.py", "file_name": "lr_finder.py", "file_type": "text/x-python", "category": "implementation", "start_line": 238, "end_line": 318, "span_ids": ["LearningRateFinder.range_test"], "tokens": 781}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LearningRateFinder:\n\n    def range_test(\n        self,\n        train_loader: DataLoader,\n        val_loader: Optional[DataLoader] = None,\n        image_extractor: Callable = default_image_extractor,\n        label_extractor: Callable = default_label_extractor,\n        start_lr: Optional[float] = None,\n        end_lr: int = 10,\n        num_iter: int = 100,\n        step_mode: str = \"exp\",\n        smooth_f: float = 0.05,\n        diverge_th: int = 5,\n        accumulation_steps: int = 1,\n        non_blocking_transfer: bool = True,\n        auto_reset: bool = True,\n    ) -> None:\n        \"\"\"Performs the learning rate range test.\n\n        Args:\n            train_loader: training set data loader.\n            val_loader: validation data loader (if desired).\n            image_extractor: callable function to get the image from a batch of data.\n                Default: `x[\"image\"] if isinstance(x, dict) else x[0]`.\n            label_extractor: callable function to get the label from a batch of data.\n                Default: `x[\"label\"] if isinstance(x, dict) else x[1]`.\n            start_lr : the starting learning rate for the range test.\n                The default is the optimizer's learning rate.\n            end_lr: the maximum learning rate to test. The test may stop earlier than\n                this if the result starts diverging.\n            num_iter: the max number of iterations for test.\n            step_mode: schedule for increasing learning rate: (`linear` or `exp`).\n            smooth_f: the loss smoothing factor within the `[0, 1[` interval. Disabled\n                if set to `0`, otherwise loss is smoothed using exponential smoothing.\n            diverge_th: test is stopped when loss surpasses threshold:\n                `diverge_th * best_loss`.\n            accumulation_steps: steps for gradient accumulation. If set to `1`,\n                gradients are not accumulated.\n            non_blocking_transfer: when `True`, moves data to device asynchronously if\n                possible, e.g., moving CPU Tensors with pinned memory to CUDA devices.\n            auto_reset: if `True`, returns model and optimizer to original states at end\n                of test.\n        Returns:\n            None\n        \"\"\"\n\n        # Reset test results\n        self.history = {\"lr\": [], \"loss\": []}\n        best_loss = -float(\"inf\")\n\n        # Move the model to the proper device\n        self.model.to(self.device)\n\n        # Check if the optimizer is already attached to a scheduler\n        self._check_for_scheduler()\n\n        # Set the starting learning rate\n        if start_lr:\n            self._set_learning_rate(start_lr)\n\n        # Check number of iterations\n        if num_iter <= 1:\n            raise ValueError(\"`num_iter` must be larger than 1\")\n\n        # Initialize the proper learning rate policy\n        lr_schedule: Union[ExponentialLR, LinearLR]\n        if step_mode.lower() == \"exp\":\n            lr_schedule = ExponentialLR(self.optimizer, end_lr, num_iter)\n        elif step_mode.lower() == \"linear\":\n            lr_schedule = LinearLR(self.optimizer, end_lr, num_iter)\n        else:\n            raise ValueError(f\"expected one of (exp, linear), got {step_mode}\")\n\n        if smooth_f < 0 or smooth_f >= 1:\n            raise ValueError(\"smooth_f is outside the range [0, 1[\")\n\n        # Create an iterator to get data batch by batch\n        train_iter = TrainDataLoaderIter(train_loader, image_extractor, label_extractor)\n        if val_loader:\n            val_iter = ValDataLoaderIter(val_loader, image_extractor, label_extractor)\n\n        trange: Union[partial[tqdm.trange], Type[range]]\n        # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/optimizers/lr_finder.py_LearningRateFinder.range_test.if_self_verbose_and_has_t_LearningRateFinder.range_test.if_auto_reset_.self_reset_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/optimizers/lr_finder.py_LearningRateFinder.range_test.if_self_verbose_and_has_t_LearningRateFinder.range_test.if_auto_reset_.self_reset_", "embedding": null, "metadata": {"file_path": "monai/optimizers/lr_finder.py", "file_name": "lr_finder.py", "file_type": "text/x-python", "category": "implementation", "start_line": 319, "end_line": 358, "span_ids": ["LearningRateFinder.range_test"], "tokens": 475}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LearningRateFinder:\n\n    def range_test(\n        self,\n        train_loader: DataLoader,\n        val_loader: Optional[DataLoader] = None,\n        image_extractor: Callable = default_image_extractor,\n        label_extractor: Callable = default_label_extractor,\n        start_lr: Optional[float] = None,\n        end_lr: int = 10,\n        num_iter: int = 100,\n        step_mode: str = \"exp\",\n        smooth_f: float = 0.05,\n        diverge_th: int = 5,\n        accumulation_steps: int = 1,\n        non_blocking_transfer: bool = True,\n        auto_reset: bool = True,\n    ) -> None:\n        # ... other code\n        if self.verbose and has_tqdm:\n            trange = partial(tqdm.trange, desc=\"Computing optimal learning rate\")\n            tprint = tqdm.tqdm.write\n        else:\n            trange = range\n            tprint = print\n\n        for iteration in trange(num_iter):\n            if self.verbose and not has_tqdm:\n                print(f\"Computing optimal learning rate, iteration {iteration + 1}/{num_iter}\")\n\n            # Train on batch and retrieve loss\n            loss = self._train_batch(train_iter, accumulation_steps, non_blocking_transfer=non_blocking_transfer)\n            if val_loader:\n                loss = self._validate(val_iter, non_blocking_transfer=non_blocking_transfer)\n\n            # Update the learning rate\n            self.history[\"lr\"].append(lr_schedule.get_lr()[0])\n            lr_schedule.step()\n\n            # Track the best loss and smooth it if smooth_f is specified\n            if iteration == 0:\n                best_loss = loss\n            else:\n                if smooth_f > 0:\n                    loss = smooth_f * loss + (1 - smooth_f) * self.history[\"loss\"][-1]\n                if loss < best_loss:\n                    best_loss = loss\n\n            # Check if the loss has diverged; if it has, stop the test\n            self.history[\"loss\"].append(loss)\n            if loss > diverge_th * best_loss:\n                if self.verbose:\n                    tprint(\"Stopping early, the loss has diverged\")\n                break\n\n        if auto_reset:\n            if self.verbose:\n                print(\"Resetting model and optimizer\")\n            self.reset()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/optimizers/lr_finder.py_LearningRateFinder._set_learning_rate_LearningRateFinder._check_for_scheduler.for_param_group_in_self_o.if_initial_lr_in_param_.raise_RuntimeError_Optim": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/optimizers/lr_finder.py_LearningRateFinder._set_learning_rate_LearningRateFinder._check_for_scheduler.for_param_group_in_self_o.if_initial_lr_in_param_.raise_RuntimeError_Optim", "embedding": null, "metadata": {"file_path": "monai/optimizers/lr_finder.py", "file_name": "lr_finder.py", "file_type": "text/x-python", "category": "implementation", "start_line": 364, "end_line": 380, "span_ids": ["LearningRateFinder._check_for_scheduler", "LearningRateFinder._set_learning_rate"], "tokens": 187}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LearningRateFinder:\n\n    def _set_learning_rate(self, new_lrs: Union[float, list]) -> None:\n        \"\"\"Set learning rate(s) for optimizer.\"\"\"\n        if not isinstance(new_lrs, list):\n            new_lrs = [new_lrs] * len(self.optimizer.param_groups)\n        if len(new_lrs) != len(self.optimizer.param_groups):\n            raise ValueError(\n                \"Length of `new_lrs` is not equal to the number of parameter groups \" + \"in the given optimizer\"\n            )\n\n        for param_group, new_lr in zip(self.optimizer.param_groups, new_lrs):\n            param_group[\"lr\"] = new_lr\n\n    def _check_for_scheduler(self):\n        \"\"\"Check optimizer doesn't already have scheduler.\"\"\"\n        for param_group in self.optimizer.param_groups:\n            if \"initial_lr\" in param_group:\n                raise RuntimeError(\"Optimizer already has a scheduler attached to it\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/optimizers/lr_finder.py_LearningRateFinder._train_batch_LearningRateFinder._train_batch.return.total_loss": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/optimizers/lr_finder.py_LearningRateFinder._train_batch_LearningRateFinder._train_batch.return.total_loss", "embedding": null, "metadata": {"file_path": "monai/optimizers/lr_finder.py", "file_name": "lr_finder.py", "file_type": "text/x-python", "category": "implementation", "start_line": 382, "end_line": 413, "span_ids": ["LearningRateFinder._train_batch"], "tokens": 262}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LearningRateFinder:\n\n    def _train_batch(self, train_iter, accumulation_steps: int, non_blocking_transfer: bool = True) -> float:\n        self.model.train()\n        total_loss = 0\n\n        self.optimizer.zero_grad()\n        for i in range(accumulation_steps):\n            inputs, labels = next(train_iter)\n            inputs, labels = copy_to_device([inputs, labels], device=self.device, non_blocking=non_blocking_transfer)\n\n            # Forward pass\n            outputs = self.model(inputs)\n            loss = self.criterion(outputs, labels)\n\n            # Loss should be averaged in each step\n            loss /= accumulation_steps\n\n            # Backward pass\n            if self.amp and hasattr(self.optimizer, \"_amp_stash\"):\n                # For minor performance optimization, see also:\n                # https://nvidia.github.io/apex/advanced.html#gradient-accumulation-across-iterations\n                delay_unscale = ((i + 1) % accumulation_steps) != 0\n\n                with torch.cuda.amp.scale_loss(loss, self.optimizer, delay_unscale=delay_unscale) as scaled_loss:  # type: ignore\n                    scaled_loss.backward()\n            else:\n                loss.backward()\n\n            total_loss += loss.item()\n\n        self.optimizer.step()\n\n        return total_loss", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/optimizers/lr_finder.py_LearningRateFinder._validate_LearningRateFinder._validate.return.running_loss_len_val_it": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/optimizers/lr_finder.py_LearningRateFinder._validate_LearningRateFinder._validate.return.running_loss_len_val_it", "embedding": null, "metadata": {"file_path": "monai/optimizers/lr_finder.py", "file_name": "lr_finder.py", "file_type": "text/x-python", "category": "implementation", "start_line": 415, "end_line": 430, "span_ids": ["LearningRateFinder._validate"], "tokens": 148}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LearningRateFinder:\n\n    def _validate(self, val_iter: ValDataLoaderIter, non_blocking_transfer: bool = True) -> float:\n        # Set model to evaluation mode and disable gradient computation\n        running_loss = 0\n        with eval_mode(self.model):\n            for inputs, labels in val_iter:\n                # Copy data to the correct device\n                inputs, labels = copy_to_device(\n                    [inputs, labels], device=self.device, non_blocking=non_blocking_transfer\n                )\n\n                # Forward pass and loss computation\n                outputs = self.model(inputs)\n                loss = self.criterion(outputs, labels)\n                running_loss += loss.item() * len(labels)\n\n        return running_loss / len(val_iter.dataset)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/optimizers/lr_finder.py_LearningRateFinder.get_lrs_and_losses_LearningRateFinder.get_lrs_and_losses.return.lrs_losses": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/optimizers/lr_finder.py_LearningRateFinder.get_lrs_and_losses_LearningRateFinder.get_lrs_and_losses.return.lrs_losses", "embedding": null, "metadata": {"file_path": "monai/optimizers/lr_finder.py", "file_name": "lr_finder.py", "file_type": "text/x-python", "category": "implementation", "start_line": 428, "end_line": 446, "span_ids": ["LearningRateFinder.get_lrs_and_losses"], "tokens": 173}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LearningRateFinder:\n\n    def get_lrs_and_losses(self, skip_start: int = 0, skip_end: int = 0) -> Tuple[list, list]:\n        \"\"\"Get learning rates and their corresponding losses\n\n        Args:\n            skip_start: number of batches to trim from the start.\n            skip_end: number of batches to trim from the end.\n        \"\"\"\n        if skip_start < 0:\n            raise ValueError(\"skip_start cannot be negative\")\n        if skip_end < 0:\n            raise ValueError(\"skip_end cannot be negative\")\n\n        lrs = self.history[\"lr\"]\n        losses = self.history[\"loss\"]\n        end_idx = len(lrs) - skip_end - 1\n        lrs = lrs[skip_start:end_idx]\n        losses = losses[skip_start:end_idx]\n\n        return lrs, losses", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/optimizers/lr_finder.py_LearningRateFinder.get_steepest_gradient_LearningRateFinder.get_steepest_gradient.try_.except_ValueError_.return.None_None": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/optimizers/lr_finder.py_LearningRateFinder.get_steepest_gradient_LearningRateFinder.get_steepest_gradient.try_.except_ValueError_.return.None_None", "embedding": null, "metadata": {"file_path": "monai/optimizers/lr_finder.py", "file_name": "lr_finder.py", "file_type": "text/x-python", "category": "implementation", "start_line": 448, "end_line": 467, "span_ids": ["LearningRateFinder.get_steepest_gradient"], "tokens": 186}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LearningRateFinder:\n\n    def get_steepest_gradient(\n        self, skip_start: int = 0, skip_end: int = 0\n    ) -> Union[Tuple[float, float], Tuple[None, None]]:\n        \"\"\"Get learning rate which has steepest gradient and its corresponding loss\n\n        Args:\n            skip_start: number of batches to trim from the start.\n            skip_end: number of batches to trim from the end.\n\n        Returns:\n            Learning rate which has steepest gradient and its corresponding loss\n        \"\"\"\n        lrs, losses = self.get_lrs_and_losses(skip_start, skip_end)\n\n        try:\n            min_grad_idx = np.gradient(np.array(losses)).argmin()\n            return lrs[min_grad_idx], losses[min_grad_idx]\n        except ValueError:\n            print(\"Failed to compute the gradients, there might not be enough points.\")\n            return None, None", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/optimizers/lr_finder.py_LearningRateFinder.plot_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/optimizers/lr_finder.py_LearningRateFinder.plot_", "embedding": null, "metadata": {"file_path": "monai/optimizers/lr_finder.py", "file_name": "lr_finder.py", "file_type": "text/x-python", "category": "implementation", "start_line": 469, "end_line": 525, "span_ids": ["LearningRateFinder.plot"], "tokens": 469}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LearningRateFinder:\n\n    def plot(self, skip_start: int = 0, skip_end: int = 0, log_lr: bool = True, ax=None, steepest_lr: bool = True):\n        \"\"\"Plots the learning rate range test.\n\n        Args:\n            skip_start: number of batches to trim from the start.\n            skip_end: number of batches to trim from the start.\n            log_lr: True to plot the learning rate in a logarithmic\n                scale; otherwise, plotted in a linear scale.\n            ax: the plot is created in the specified matplotlib axes object and the\n                figure is not be shown. If `None`, then the figure and axes object are\n                created in this method and the figure is shown.\n            steepest_lr: plot the learning rate which had the steepest gradient.\n\n        Returns:\n            The `matplotlib.axes.Axes` object that contains the plot. Returns `None` if\n            `matplotlib` is not installed.\n        \"\"\"\n        if not has_matplotlib:\n            warnings.warn(\"Matplotlib is missing, can't plot result\")\n            return None\n\n        lrs, losses = self.get_lrs_and_losses(skip_start, skip_end)\n\n        # Create the figure and axes object if axes was not already given\n        fig = None\n        if ax is None:\n            fig, ax = plt.subplots()\n\n        # Plot loss as a function of the learning rate\n        ax.plot(lrs, losses)\n\n        # Plot the LR with steepest gradient\n        if steepest_lr:\n            lr_at_steepest_grad, loss_at_steepest_grad = self.get_steepest_gradient(skip_start, skip_end)\n            if lr_at_steepest_grad is not None:\n                ax.scatter(\n                    lr_at_steepest_grad,\n                    loss_at_steepest_grad,\n                    s=75,\n                    marker=\"o\",\n                    color=\"red\",\n                    zorder=3,\n                    label=\"steepest gradient\",\n                )\n                ax.legend()\n\n        if log_lr:\n            ax.set_xscale(\"log\")\n        ax.set_xlabel(\"Learning rate\")\n        ax.set_ylabel(\"Loss\")\n\n        # Show only if the figure was created internally\n        if fig is not None:\n            plt.show()\n\n        return ax", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/optimizers/utils.py_from_typing_import_Callab_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/optimizers/utils.py_from_typing_import_Callab_", "embedding": null, "metadata": {"file_path": "monai/optimizers/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 100, "span_ids": ["generate_param_groups", "docstring"], "tokens": 812}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Callable, Sequence\n\nimport torch\n\nfrom monai.utils import ensure_tuple, ensure_tuple_rep\n\n__all__ = [\"generate_param_groups\"]\n\n\ndef generate_param_groups(\n    network: torch.nn.Module,\n    layer_matches: Sequence[Callable],\n    match_types: Sequence[str],\n    lr_values: Sequence[float],\n    include_others: bool = True,\n):\n    \"\"\"\n    Utility function to generate parameter groups with different LR values for optimizer.\n    The output parameter groups have the same order as `layer_match` functions.\n\n    Args:\n        network: source network to generate parameter groups from.\n        layer_matches: a list of callable functions to select or filter out network layer groups,\n            for \"select\" type, the input will be the `network`, for \"filter\" type,\n            the input will be every item of `network.named_parameters()`.\n            for \"select\", the parameters will be\n            `select_func(network).parameters()`.\n            for \"filter\", the parameters will be\n            `map(lambda x: x[1], filter(filter_func, network.named_parameters()))`\n        match_types: a list of tags to identify the matching type corresponding to the `layer_matches` functions,\n            can be \"select\" or \"filter\".\n        lr_values: a list of LR values corresponding to the `layer_matches` functions.\n        include_others: whether to include the rest layers as the last group, default to True.\n\n    It's mainly used to set different LR values for different network elements, for example:\n\n    .. code-block:: python\n\n        net = Unet(spatial_dims=3, in_channels=1, out_channels=3, channels=[2, 2, 2], strides=[1, 1, 1])\n        print(net)  # print out network components to select expected items\n        print(net.named_parameters())  # print out all the named parameters to filter out expected items\n        params = generate_param_groups(\n            network=net,\n            layer_matches=[lambda x: x.model[0], lambda x: \"2.0.conv\" in x[0]],\n            match_types=[\"select\", \"filter\"],\n            lr_values=[1e-2, 1e-3],\n        )\n        # the groups will be a list of dictionaries:\n        # [{'params': <generator object Module.parameters at 0x7f9090a70bf8>, 'lr': 0.01},\n        #  {'params': <filter object at 0x7f9088fd0dd8>, 'lr': 0.001},\n        #  {'params': <filter object at 0x7f9088fd0da0>}]\n        optimizer = torch.optim.Adam(params, 1e-4)\n\n    \"\"\"\n    layer_matches = ensure_tuple(layer_matches)\n    match_types = ensure_tuple_rep(match_types, len(layer_matches))\n    lr_values = ensure_tuple_rep(lr_values, len(layer_matches))\n\n    def _get_select(f):\n        def _select():\n            return f(network).parameters()\n\n        return _select\n\n    def _get_filter(f):\n        def _filter():\n            # should eventually generate a list of network parameters\n            return map(lambda x: x[1], filter(f, network.named_parameters()))\n\n        return _filter\n\n    params = []\n    _layers = []\n    for func, ty, lr in zip(layer_matches, match_types, lr_values):\n        if ty.lower() == \"select\":\n            layer_params = _get_select(func)\n        elif ty.lower() == \"filter\":\n            layer_params = _get_filter(func)\n        else:\n            raise ValueError(f\"unsupported layer match type: {ty}.\")\n\n        params.append({\"params\": layer_params(), \"lr\": lr})\n        _layers.extend(list(map(id, layer_params())))\n\n    if include_others:\n        params.append({\"params\": filter(lambda p: id(p) not in _layers, network.parameters())})\n\n    return params", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/__init__.py_FunctionSignature_AdjustContrast": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/__init__.py_FunctionSignature_AdjustContrast", "embedding": null, "metadata": {"file_path": "monai/transforms/__init__.py", "file_name": "__init__.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 113, "span_ids": ["imports:5", "docstring"], "tokens": 609}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from .adaptors import FunctionSignature, adaptor, apply_alias, to_kwargs\nfrom .compose import Compose, OneOf\nfrom .croppad.array import (\n    BorderPad,\n    BoundingRect,\n    CenterScaleCrop,\n    CenterSpatialCrop,\n    CropForeground,\n    DivisiblePad,\n    Pad,\n    RandCropByLabelClasses,\n    RandCropByPosNegLabel,\n    RandScaleCrop,\n    RandSpatialCrop,\n    RandSpatialCropSamples,\n    RandWeightedCrop,\n    ResizeWithPadOrCrop,\n    SpatialCrop,\n    SpatialPad,\n)\nfrom .croppad.batch import PadListDataCollate\nfrom .croppad.dictionary import (\n    BorderPadd,\n    BorderPadD,\n    BorderPadDict,\n    BoundingRectd,\n    BoundingRectD,\n    BoundingRectDict,\n    CenterScaleCropd,\n    CenterScaleCropD,\n    CenterScaleCropDict,\n    CenterSpatialCropd,\n    CenterSpatialCropD,\n    CenterSpatialCropDict,\n    CropForegroundd,\n    CropForegroundD,\n    CropForegroundDict,\n    DivisiblePadd,\n    DivisiblePadD,\n    DivisiblePadDict,\n    PadModeSequence,\n    RandCropByLabelClassesd,\n    RandCropByLabelClassesD,\n    RandCropByLabelClassesDict,\n    RandCropByPosNegLabeld,\n    RandCropByPosNegLabelD,\n    RandCropByPosNegLabelDict,\n    RandScaleCropd,\n    RandScaleCropD,\n    RandScaleCropDict,\n    RandSpatialCropd,\n    RandSpatialCropD,\n    RandSpatialCropDict,\n    RandSpatialCropSamplesd,\n    RandSpatialCropSamplesD,\n    RandSpatialCropSamplesDict,\n    RandWeightedCropd,\n    RandWeightedCropD,\n    RandWeightedCropDict,\n    ResizeWithPadOrCropd,\n    ResizeWithPadOrCropD,\n    ResizeWithPadOrCropDict,\n    SpatialCropd,\n    SpatialCropD,\n    SpatialCropDict,\n    SpatialPadd,\n    SpatialPadD,\n    SpatialPadDict,\n)\nfrom .intensity.array import (\n    AdjustContrast,\n    DetectEnvelope,\n    GaussianSharpen,\n    GaussianSmooth,\n    GibbsNoise,\n    HistogramNormalize,\n    KSpaceSpikeNoise,\n    MaskIntensity,\n    NormalizeIntensity,\n    RandAdjustContrast,\n    RandBiasField,\n    RandCoarseDropout,\n    RandCoarseShuffle,\n    RandCoarseTransform,\n    RandGaussianNoise,\n    RandGaussianSharpen,\n    RandGaussianSmooth,\n    RandGibbsNoise,\n    RandHistogramShift,\n    RandKSpaceSpikeNoise,\n    RandRicianNoise,\n    RandScaleIntensity,\n    RandShiftIntensity,\n    RandStdShiftIntensity,\n    SavitzkyGolaySmooth,\n    ScaleIntensity,\n    ScaleIntensityRange,\n    ScaleIntensityRangePercentiles,\n    ShiftIntensity,\n    StdShiftIntensity,\n    ThresholdIntensity,\n)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/compose.py_Compose.flatten_Compose.flatten.return.Compose_new_transforms_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/compose.py_Compose.flatten_Compose.flatten.return.Compose_new_transforms_", "embedding": null, "metadata": {"file_path": "monai/transforms/compose.py", "file_name": "compose.py", "file_type": "text/x-python", "category": "implementation", "start_line": 138, "end_line": 152, "span_ids": ["Compose.flatten"], "tokens": 162}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Compose(Randomizable, InvertibleTransform):\n\n    def flatten(self):\n        \"\"\"Return a Composition with a simple list of transforms, as opposed to any nested Compositions.\n\n        e.g., `t1 = Compose([x, x, x, x, Compose([Compose([x, x]), x, x])]).flatten()`\n        will result in the equivalent of `t1 = Compose([x, x, x, x, x, x, x, x])`.\n\n        \"\"\"\n        new_transforms = []\n        for t in self.transforms:\n            if isinstance(t, Compose) and not isinstance(t, OneOf):\n                new_transforms += t.flatten().transforms\n            else:\n                new_transforms.append(t)\n\n        return Compose(new_transforms)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_RandWeightedCrop_RandWeightedCrop.randomize._using_only_the_first_ch": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_RandWeightedCrop_RandWeightedCrop.randomize._using_only_the_first_ch", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 777, "end_line": 806, "span_ids": ["RandWeightedCrop", "RandWeightedCrop.__init__", "RandWeightedCrop.randomize"], "tokens": 316}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandWeightedCrop(Randomizable, Transform):\n    \"\"\"\n    Samples a list of `num_samples` image patches according to the provided `weight_map`.\n\n    Args:\n        spatial_size: the spatial size of the image patch e.g. [224, 224, 128].\n            If its components have non-positive values, the corresponding size of `img` will be used.\n        num_samples: number of samples (image patches) to take in the returned list.\n        weight_map: weight map used to generate patch samples. The weights must be non-negative.\n            Each element denotes a sampling weight of the spatial location. 0 indicates no sampling.\n            It should be a single-channel array in shape, for example, `(1, spatial_dim_0, spatial_dim_1, ...)`.\n    \"\"\"\n\n    backend = SpatialCrop.backend\n\n    def __init__(\n        self,\n        spatial_size: Union[Sequence[int], int],\n        num_samples: int = 1,\n        weight_map: Optional[NdarrayOrTensor] = None,\n    ):\n        self.spatial_size = ensure_tuple(spatial_size)\n        self.num_samples = int(num_samples)\n        self.weight_map = weight_map\n        self.centers: List[np.ndarray] = []\n\n    def randomize(self, weight_map: NdarrayOrTensor) -> None:\n        self.centers = weighted_patch_samples(\n            spatial_size=self.spatial_size, w=weight_map[0], n_samples=self.num_samples, r_state=self.R\n        )  # using only the first channel as weight map", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_RandWeightedCrop.__call___RandWeightedCrop.__call__.return.results": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_RandWeightedCrop.__call___RandWeightedCrop.__call__.return.results", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 808, "end_line": 832, "span_ids": ["RandWeightedCrop.__call__"], "tokens": 303}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandWeightedCrop(Randomizable, Transform):\n\n    def __call__(self, img: NdarrayOrTensor, weight_map: Optional[NdarrayOrTensor] = None) -> List[NdarrayOrTensor]:\n        \"\"\"\n        Args:\n            img: input image to sample patches from. assuming `img` is a channel-first array.\n            weight_map: weight map used to generate patch samples. The weights must be non-negative.\n                Each element denotes a sampling weight of the spatial location. 0 indicates no sampling.\n                It should be a single-channel array in shape, for example, `(1, spatial_dim_0, spatial_dim_1, ...)`\n\n        Returns:\n            A list of image patches\n        \"\"\"\n        if weight_map is None:\n            weight_map = self.weight_map\n        if weight_map is None:\n            raise ValueError(\"weight map must be provided for weighted patch sampling.\")\n        if img.shape[1:] != weight_map.shape[1:]:\n            raise ValueError(f\"image and weight map spatial shape mismatch: {img.shape[1:]} vs {weight_map.shape[1:]}.\")\n\n        self.randomize(weight_map)\n        _spatial_size = fall_back_tuple(self.spatial_size, weight_map.shape[1:])\n        results: List[NdarrayOrTensor] = []\n        for center in self.centers:\n            cropper = SpatialCrop(roi_center=center, roi_size=_spatial_size)\n            results.append(cropper(img))\n        return results", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/batch.py_from_copy_import_deepcopy_replace_element.return.batch": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/batch.py_from_copy_import_deepcopy_replace_element.return.batch", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/batch.py", "file_name": "batch.py", "file_type": "text/x-python", "category": "implementation", "start_line": 16, "end_line": 39, "span_ids": ["replace_element", "docstring"], "tokens": 180}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from copy import deepcopy\nfrom typing import Any, Dict, Hashable, Union\n\nimport numpy as np\nimport torch\n\nfrom monai.data.utils import list_data_collate\nfrom monai.transforms.croppad.array import CenterSpatialCrop, SpatialPad\nfrom monai.transforms.inverse import InvertibleTransform\nfrom monai.utils.enums import Method, NumpyPadMode, TraceKeys\n\n__all__ = [\"PadListDataCollate\"]\n\n\ndef replace_element(to_replace, batch, idx, key_or_idx):\n    # since tuple is immutable we'll have to recreate\n    if isinstance(batch[idx], tuple):\n        batch_idx_list = list(batch[idx])\n        batch_idx_list[key_or_idx] = to_replace\n        batch[idx] = tuple(batch_idx_list)\n    # else, replace\n    else:\n        batch[idx][key_or_idx] = to_replace\n    return batch", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/batch.py_PadListDataCollate.__call___PadListDataCollate.__call__.return.list_data_collate_batch_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/batch.py_PadListDataCollate.__call___PadListDataCollate.__call__.return.list_data_collate_batch_", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/batch.py", "file_name": "batch.py", "file_type": "text/x-python", "category": "implementation", "start_line": 74, "end_line": 109, "span_ids": ["PadListDataCollate.__call__"], "tokens": 386}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class PadListDataCollate(InvertibleTransform):\n\n    def __call__(self, batch: Any):\n        \"\"\"\n        Args:\n            batch: batch of data to pad-collate\n        \"\"\"\n        # data is either list of dicts or list of lists\n        is_list_of_dicts = isinstance(batch[0], dict)\n        # loop over items inside of each element in a batch\n        for key_or_idx in batch[0].keys() if is_list_of_dicts else range(len(batch[0])):\n            # calculate max size of each dimension\n            max_shapes = []\n            for elem in batch:\n                if not isinstance(elem[key_or_idx], (torch.Tensor, np.ndarray)):\n                    break\n                max_shapes.append(elem[key_or_idx].shape[1:])\n            # len > 0 if objects were arrays, else skip as no padding to be done\n            if not max_shapes:\n                continue\n            max_shape = np.array(max_shapes).max(axis=0)\n            # If all same size, skip\n            if np.all(np.array(max_shapes).min(axis=0) == max_shape):\n                continue\n\n            # Use `SpatialPad` to match sizes, Default params are central padding, padding with 0's\n            padder = SpatialPad(spatial_size=max_shape, method=self.method, mode=self.mode, **self.np_kwargs)\n            for idx, batch_i in enumerate(batch):\n                orig_size = batch_i[key_or_idx].shape[1:]\n                padded = padder(batch_i[key_or_idx])\n                batch = replace_element(padded, batch, idx, key_or_idx)\n\n                # If we have a dictionary of data, append to list\n                if is_list_of_dicts:\n                    self.push_transform(batch[idx], key_or_idx, orig_size=orig_size)\n\n        # After padding, use default list collator\n        return list_data_collate(batch)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/batch.py_PadListDataCollate.inverse_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/batch.py_PadListDataCollate.inverse_", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/batch.py", "file_name": "batch.py", "file_type": "text/x-python", "category": "implementation", "start_line": 111, "end_line": 128, "span_ids": ["PadListDataCollate.inverse"], "tokens": 167}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class PadListDataCollate(InvertibleTransform):\n\n    @staticmethod\n    def inverse(data: dict) -> Dict[Hashable, np.ndarray]:\n        if not isinstance(data, dict):\n            raise RuntimeError(\"Inverse can only currently be applied on dictionaries.\")\n\n        d = deepcopy(data)\n        for key in d:\n            transform_key = InvertibleTransform.trace_key(key)\n            if transform_key in d:\n                transform = d[transform_key][-1]\n                if not isinstance(transform, Dict):\n                    continue\n                if transform.get(TraceKeys.CLASS_NAME) == PadListDataCollate.__name__:\n                    d[key] = CenterSpatialCrop(transform.get(\"orig_size\", -1))(d[key])  # fallback to image size\n                    # remove transform\n                    d[transform_key].pop()\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_SpatialPadd.inverse_SpatialPadd.inverse.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_SpatialPadd.inverse_SpatialPadd.inverse.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 161, "end_line": 179, "span_ids": ["SpatialPadd.inverse"], "tokens": 237}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SpatialPadd(MapTransform, InvertibleTransform):\n\n    def inverse(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = deepcopy(dict(data))\n        for key in self.key_iterator(d):\n            transform = self.get_most_recent_transform(d, key)\n            # Create inverse transform\n            orig_size = transform[TraceKeys.ORIG_SIZE]\n            if self.padder.method == Method.SYMMETRIC:\n                current_size = d[key].shape[1:]\n                roi_center = [floor(i / 2) if r % 2 == 0 else (i - 1) // 2 for r, i in zip(orig_size, current_size)]\n            else:\n                roi_center = [floor(r / 2) if r % 2 == 0 else (r - 1) // 2 for r in orig_size]\n\n            inverse_transform = SpatialCrop(roi_center, orig_size)\n            # Apply inverse transform\n            d[key] = inverse_transform(d[key])\n            # Remove the applied transform\n            self.pop_transform(d, key)\n\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_BorderPadd.inverse_BorderPadd.inverse.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_BorderPadd.inverse_BorderPadd.inverse.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 236, "end_line": 258, "span_ids": ["BorderPadd.inverse"], "tokens": 254}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class BorderPadd(MapTransform, InvertibleTransform):\n\n    def inverse(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = deepcopy(dict(data))\n\n        for key in self.key_iterator(d):\n            transform = self.get_most_recent_transform(d, key)\n            # Create inverse transform\n            orig_size = np.array(transform[TraceKeys.ORIG_SIZE])\n            roi_start = np.array(self.padder.spatial_border)\n            # Need to convert single value to [min1,min2,...]\n            if roi_start.size == 1:\n                roi_start = np.full((len(orig_size)), roi_start)\n            # need to convert [min1,max1,min2,...] to [min1,min2,...]\n            elif roi_start.size == 2 * orig_size.size:\n                roi_start = roi_start[::2]\n            roi_end = np.array(transform[TraceKeys.ORIG_SIZE]) + roi_start\n\n            inverse_transform = SpatialCrop(roi_start=roi_start, roi_end=roi_end)\n            # Apply inverse transform\n            d[key] = inverse_transform(d[key])\n            # Remove the applied transform\n            self.pop_transform(d, key)\n\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_DivisiblePadd.inverse_DivisiblePadd.inverse.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_DivisiblePadd.inverse_DivisiblePadd.inverse.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 312, "end_line": 328, "span_ids": ["DivisiblePadd.inverse"], "tokens": 182}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DivisiblePadd(MapTransform, InvertibleTransform):\n\n    def inverse(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = deepcopy(dict(data))\n\n        for key in self.key_iterator(d):\n            transform = self.get_most_recent_transform(d, key)\n            # Create inverse transform\n            orig_size = np.array(transform[TraceKeys.ORIG_SIZE])\n            current_size = np.array(d[key].shape[1:])\n            roi_start = np.floor((current_size - orig_size) / 2)\n            roi_end = orig_size + roi_start\n            inverse_transform = SpatialCrop(roi_start=roi_start, roi_end=roi_end)\n            # Apply inverse transform\n            d[key] = inverse_transform(d[key])\n            # Remove the applied transform\n            self.pop_transform(d, key)\n\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_SpatialCropd.inverse_SpatialCropd.inverse.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_SpatialCropd.inverse_SpatialCropd.inverse.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 381, "end_line": 400, "span_ids": ["SpatialCropd.inverse"], "tokens": 224}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SpatialCropd(MapTransform, InvertibleTransform):\n\n    def inverse(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = deepcopy(dict(data))\n\n        for key in self.key_iterator(d):\n            transform = self.get_most_recent_transform(d, key)\n            # Create inverse transform\n            orig_size = np.array(transform[TraceKeys.ORIG_SIZE])\n            current_size = np.array(d[key].shape[1:])\n            # get required pad to start and end\n            pad_to_start = np.array([s.indices(o)[0] for s, o in zip(self.cropper.slices, orig_size)])\n            pad_to_end = orig_size - current_size - pad_to_start\n            # interleave mins and maxes\n            pad = list(chain(*zip(pad_to_start.tolist(), pad_to_end.tolist())))\n            inverse_transform = BorderPad(pad)\n            # Apply inverse transform\n            d[key] = inverse_transform(d[key])\n            # Remove the applied transform\n            self.pop_transform(d, key)\n\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_CenterSpatialCropd.inverse_CenterSpatialCropd.inverse.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_CenterSpatialCropd.inverse_CenterSpatialCropd.inverse.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 437, "end_line": 456, "span_ids": ["CenterSpatialCropd.inverse"], "tokens": 247}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CenterSpatialCropd(MapTransform, InvertibleTransform):\n\n    def inverse(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = deepcopy(dict(data))\n\n        for key in self.key_iterator(d):\n            transform = self.get_most_recent_transform(d, key)\n            # Create inverse transform\n            orig_size = np.array(transform[TraceKeys.ORIG_SIZE])\n            current_size = np.array(d[key].shape[1:])\n            pad_to_start = np.floor((orig_size - current_size) / 2).astype(int)\n            # in each direction, if original size is even and current size is odd, += 1\n            pad_to_start[np.logical_and(orig_size % 2 == 0, current_size % 2 == 1)] += 1\n            pad_to_end = orig_size - current_size - pad_to_start\n            pad = list(chain(*zip(pad_to_start.tolist(), pad_to_end.tolist())))\n            inverse_transform = BorderPad(pad)\n            # Apply inverse transform\n            d[key] = inverse_transform(d[key])\n            # Remove the applied transform\n            self.pop_transform(d, key)\n\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_RandSpatialCropd.inverse_RandSpatialCropd.inverse.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_RandSpatialCropd.inverse_RandSpatialCropd.inverse.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 591, "end_line": 621, "span_ids": ["RandSpatialCropd.inverse"], "tokens": 371}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandSpatialCropd(Randomizable, MapTransform, InvertibleTransform):\n\n    def inverse(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = deepcopy(dict(data))\n\n        for key in self.key_iterator(d):\n            transform = self.get_most_recent_transform(d, key)\n            # Create inverse transform\n            orig_size = transform[TraceKeys.ORIG_SIZE]\n            random_center = self.random_center\n            pad_to_start = np.empty((len(orig_size)), dtype=np.int32)\n            pad_to_end = np.empty((len(orig_size)), dtype=np.int32)\n            if random_center:\n                for i, _slice in enumerate(transform[TraceKeys.EXTRA_INFO][\"slices\"]):\n                    pad_to_start[i] = _slice[0]\n                    pad_to_end[i] = orig_size[i] - _slice[1]\n            else:\n                current_size = d[key].shape[1:]\n                for i, (o_s, c_s) in enumerate(zip(orig_size, current_size)):\n                    pad_to_start[i] = pad_to_end[i] = (o_s - c_s) / 2\n                    if o_s % 2 == 0 and c_s % 2 == 1:\n                        pad_to_start[i] += 1\n                    elif o_s % 2 == 1 and c_s % 2 == 0:\n                        pad_to_end[i] += 1\n            # interleave mins and maxes\n            pad = list(chain(*zip(pad_to_start.tolist(), pad_to_end.tolist())))\n            inverse_transform = BorderPad(pad)\n            # Apply inverse transform\n            d[key] = inverse_transform(d[key])\n            # Remove the applied transform\n            self.pop_transform(d, key)\n\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_RandSpatialCropSamplesd.__call___RandSpatialCropSamplesd.__call__.return.ret": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_RandSpatialCropSamplesd.__call___RandSpatialCropSamplesd.__call__.return.ret", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 769, "end_line": 788, "span_ids": ["RandSpatialCropSamplesd.__call__"], "tokens": 301}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandSpatialCropSamplesd(Randomizable, MapTransform, InvertibleTransform):\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> List[Dict[Hashable, NdarrayOrTensor]]:\n        ret = []\n        for i in range(self.num_samples):\n            d = dict(data)\n            # deep copy all the unmodified data\n            for key in set(data.keys()).difference(set(self.keys)):\n                d[key] = deepcopy(data[key])\n            cropped = self.cropper(d)\n            # self.cropper will have added RandSpatialCropd to the list. Change to RandSpatialCropSamplesd\n            for key in self.key_iterator(cropped):\n                cropped[self.trace_key(key)][-1][TraceKeys.CLASS_NAME] = self.__class__.__name__  # type: ignore\n                cropped[self.trace_key(key)][-1][TraceKeys.ID] = id(self)  # type: ignore\n            # add `patch_index` to the meta data\n            for key, meta_key, meta_key_postfix in self.key_iterator(d, self.meta_keys, self.meta_key_postfix):\n                meta_key = meta_key or f\"{key}_{meta_key_postfix}\"\n                if meta_key not in cropped:\n                    cropped[meta_key] = {}  # type: ignore\n                cropped[meta_key][Key.PATCH_INDEX] = i  # type: ignore\n            ret.append(cropped)\n        return ret", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_CropForegroundd.__call___CropForegroundd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_CropForegroundd.__call___CropForegroundd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 862, "end_line": 870, "span_ids": ["CropForegroundd.__call__"], "tokens": 162}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CropForegroundd(MapTransform, InvertibleTransform):\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        box_start, box_end = self.cropper.compute_bounding_box(img=d[self.source_key])\n        d[self.start_coord_key] = box_start\n        d[self.end_coord_key] = box_end\n        for key, m in self.key_iterator(d, self.mode):\n            self.push_transform(d, key, extra_info={\"box_start\": box_start, \"box_end\": box_end})\n            d[key] = self.cropper.crop_pad(img=d[key], box_start=box_start, box_end=box_end, mode=m)\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_CropForegroundd.inverse_CropForegroundd.inverse.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_CropForegroundd.inverse_CropForegroundd.inverse.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 875, "end_line": 901, "span_ids": ["CropForegroundd.inverse"], "tokens": 300}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CropForegroundd(MapTransform, InvertibleTransform):\n\n    def inverse(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = deepcopy(dict(data))\n        for key in self.key_iterator(d):\n            transform = self.get_most_recent_transform(d, key)\n            # Create inverse transform\n            orig_size = np.asarray(transform[TraceKeys.ORIG_SIZE])\n            cur_size = np.asarray(d[key].shape[1:])\n            extra_info = transform[TraceKeys.EXTRA_INFO]\n            box_start = np.asarray(extra_info[\"box_start\"])\n            box_end = np.asarray(extra_info[\"box_end\"])\n            # first crop the padding part\n            roi_start = np.maximum(-box_start, 0)\n            roi_end = cur_size - np.maximum(box_end - orig_size, 0)\n\n            d[key] = SpatialCrop(roi_start=roi_start, roi_end=roi_end)(d[key])\n\n            # update bounding box to pad\n            pad_to_start = np.maximum(box_start, 0)\n            pad_to_end = orig_size - np.minimum(box_end, orig_size)\n            # interleave mins and maxes\n            pad = list(chain(*zip(pad_to_start.tolist(), pad_to_end.tolist())))\n            # second pad back the original size\n            d[key] = BorderPad(pad)(d[key])\n            # Remove the applied transform\n            self.pop_transform(d, key)\n\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_RandWeightedCropd_RandWeightedCropd.randomize.self.centers.weighted_patch_samples_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_RandWeightedCropd_RandWeightedCropd.randomize.self.centers.weighted_patch_samples_", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 901, "end_line": 956, "span_ids": ["RandWeightedCropd.randomize", "RandWeightedCropd", "RandWeightedCropd.__init__"], "tokens": 675}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandWeightedCropd(Randomizable, MapTransform, InvertibleTransform):\n    \"\"\"\n    Samples a list of `num_samples` image patches according to the provided `weight_map`.\n\n    Args:\n        keys: keys of the corresponding items to be transformed.\n            See also: :py:class:`monai.transforms.compose.MapTransform`\n        w_key: key for the weight map. The corresponding value will be used as the sampling weights,\n            it should be a single-channel array in size, for example, `(1, spatial_dim_0, spatial_dim_1, ...)`\n        spatial_size: the spatial size of the image patch e.g. [224, 224, 128].\n            If its components have non-positive values, the corresponding size of `img` will be used.\n        num_samples: number of samples (image patches) to take in the returned list.\n        center_coord_key: if specified, the actual sampling location will be stored with the corresponding key.\n        meta_keys: explicitly indicate the key of the corresponding meta data dictionary.\n            used to add `patch_index` to the meta dict.\n            for example, for data with key `image`, the metadata by default is in `image_meta_dict`.\n            the meta data is a dictionary object which contains: filename, original_shape, etc.\n            it can be a sequence of string, map to the `keys`.\n            if None, will try to construct meta_keys by `key_{meta_key_postfix}`.\n        meta_key_postfix: if meta_keys is None, use `key_{postfix}` to to fetch the meta data according\n            to the key data, default is `meta_dict`, the meta data is a dictionary object.\n            used to add `patch_index` to the meta dict.\n        allow_missing_keys: don't raise exception if key is missing.\n\n    See Also:\n        :py:class:`monai.transforms.RandWeightedCrop`\n    \"\"\"\n\n    backend = SpatialCrop.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        w_key: str,\n        spatial_size: Union[Sequence[int], int],\n        num_samples: int = 1,\n        center_coord_key: Optional[str] = None,\n        meta_keys: Optional[KeysCollection] = None,\n        meta_key_postfix: str = \"meta_dict\",\n        allow_missing_keys: bool = False,\n    ):\n        MapTransform.__init__(self, keys, allow_missing_keys)\n        self.spatial_size = ensure_tuple(spatial_size)\n        self.w_key = w_key\n        self.num_samples = int(num_samples)\n        self.center_coord_key = center_coord_key\n        self.meta_keys = ensure_tuple_rep(None, len(self.keys)) if meta_keys is None else ensure_tuple(meta_keys)\n        if len(self.keys) != len(self.meta_keys):\n            raise ValueError(\"meta_keys should have the same length as keys.\")\n        self.meta_key_postfix = ensure_tuple_rep(meta_key_postfix, len(self.keys))\n        self.centers: List[np.ndarray] = []\n\n    def randomize(self, weight_map: NdarrayOrTensor) -> None:\n        self.centers = weighted_patch_samples(\n            spatial_size=self.spatial_size, w=weight_map[0], n_samples=self.num_samples, r_state=self.R\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_RandWeightedCropd.__call___RandWeightedCropd.__call__.return.results": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_RandWeightedCropd.__call___RandWeightedCropd.__call__.return.results", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 958, "end_line": 992, "span_ids": ["RandWeightedCropd.__call__"], "tokens": 465}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandWeightedCropd(Randomizable, MapTransform, InvertibleTransform):\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> List[Dict[Hashable, NdarrayOrTensor]]:\n        d = dict(data)\n        self.randomize(d[self.w_key])\n        _spatial_size = fall_back_tuple(self.spatial_size, d[self.w_key].shape[1:])\n\n        # initialize returned list with shallow copy to preserve key ordering\n        results: List[Dict[Hashable, NdarrayOrTensor]] = [dict(data) for _ in range(self.num_samples)]\n        # fill in the extra keys with unmodified data\n        for i in range(self.num_samples):\n            for key in set(data.keys()).difference(set(self.keys)):\n                results[i][key] = deepcopy(data[key])\n        for key in self.key_iterator(d):\n            img = d[key]\n            if img.shape[1:] != d[self.w_key].shape[1:]:\n                raise ValueError(\n                    f\"data {key} and weight map {self.w_key} spatial shape mismatch: \"\n                    f\"{img.shape[1:]} vs {d[self.w_key].shape[1:]}.\"\n                )\n            for i, center in enumerate(self.centers):\n                cropper = SpatialCrop(roi_center=center, roi_size=_spatial_size)\n                orig_size = img.shape[1:]\n                results[i][key] = cropper(img)\n                self.push_transform(results[i], key, extra_info={\"center\": center}, orig_size=orig_size)\n                if self.center_coord_key:\n                    results[i][self.center_coord_key] = center\n        # fill in the extra keys with unmodified data\n        for i in range(self.num_samples):\n            # add `patch_index` to the meta data\n            for key, meta_key, meta_key_postfix in self.key_iterator(d, self.meta_keys, self.meta_key_postfix):\n                meta_key = meta_key or f\"{key}_{meta_key_postfix}\"\n                if meta_key not in results[i]:\n                    results[i][meta_key] = {}  # type: ignore\n                results[i][meta_key][Key.PATCH_INDEX] = i  # type: ignore\n\n        return results", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_ResizeWithPadOrCropd_ResizeWithPadOrCropd.__init__.self.padcropper.ResizeWithPadOrCrop_spati": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_ResizeWithPadOrCropd_ResizeWithPadOrCropd.__init__.self.padcropper.ResizeWithPadOrCrop_spati", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1382, "end_line": 1417, "span_ids": ["ResizeWithPadOrCropd.__init__", "ResizeWithPadOrCropd"], "tokens": 467}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ResizeWithPadOrCropd(MapTransform, InvertibleTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.ResizeWithPadOrCrop`.\n\n    Args:\n        keys: keys of the corresponding items to be transformed.\n            See also: monai.transforms.MapTransform\n        spatial_size: the spatial size of output data after padding or crop.\n            If has non-positive values, the corresponding size of input image will be used (no padding).\n        mode: {``\"constant\"``, ``\"edge\"``, ``\"linear_ramp\"``, ``\"maximum\"``, ``\"mean\"``,\n            ``\"median\"``, ``\"minimum\"``, ``\"reflect\"``, ``\"symmetric\"``, ``\"wrap\"``, ``\"empty\"``}\n            One of the listed string values or a user supplied function for padding. Defaults to ``\"constant\"``.\n            See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html\n            It also can be a sequence of string, each element corresponds to a key in ``keys``.\n        allow_missing_keys: don't raise exception if key is missing.\n        method: {``\"symmetric\"``, ``\"end\"``}\n            Pad image symmetrically on every side or only pad at the end sides. Defaults to ``\"symmetric\"``.\n        np_kwargs: other args for `np.pad` API, note that `np.pad` treats channel dimension as the first dimension.\n            more details: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html\n\n    \"\"\"\n\n    backend = ResizeWithPadOrCrop.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        spatial_size: Union[Sequence[int], int],\n        mode: NumpyPadModeSequence = NumpyPadMode.CONSTANT,\n        allow_missing_keys: bool = False,\n        method: Union[Method, str] = Method.SYMMETRIC,\n        **np_kwargs,\n    ) -> None:\n        super().__init__(keys, allow_missing_keys)\n        self.mode = ensure_tuple_rep(mode, len(self.keys))\n        self.padcropper = ResizeWithPadOrCrop(spatial_size=spatial_size, method=method, **np_kwargs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_ResizeWithPadOrCropd.__call___ResizeWithPadOrCropd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_ResizeWithPadOrCropd.__call___ResizeWithPadOrCropd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1419, "end_line": 1425, "span_ids": ["ResizeWithPadOrCropd.__call__"], "tokens": 125}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ResizeWithPadOrCropd(MapTransform, InvertibleTransform):\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key, m in self.key_iterator(d, self.mode):\n            orig_size = d[key].shape[1:]\n            d[key] = self.padcropper(d[key], mode=m)\n            self.push_transform(d, key, orig_size=orig_size, extra_info={\"mode\": m.value if isinstance(m, Enum) else m})\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_ResizeWithPadOrCropd.inverse_ResizeWithPadOrCropd.inverse.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_ResizeWithPadOrCropd.inverse_ResizeWithPadOrCropd.inverse.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1430, "end_line": 1463, "span_ids": ["ResizeWithPadOrCropd.inverse"], "tokens": 468}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ResizeWithPadOrCropd(MapTransform, InvertibleTransform):\n\n    def inverse(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = deepcopy(dict(data))\n        for key in self.key_iterator(d):\n            transform = self.get_most_recent_transform(d, key)\n            # Create inverse transform\n            orig_size = np.array(transform[TraceKeys.ORIG_SIZE])\n            current_size = np.array(d[key].shape[1:])\n            # Unfortunately, we can't just use ResizeWithPadOrCrop with original size because of odd/even rounding.\n            # Instead, we first pad any smaller dimensions, and then we crop any larger dimensions.\n\n            # First, do pad\n            if np.any((orig_size - current_size) > 0):\n                pad_to_start = np.floor((orig_size - current_size) / 2).astype(int)\n                # in each direction, if original size is even and current size is odd, += 1\n                pad_to_start[np.logical_and(orig_size % 2 == 0, current_size % 2 == 1)] += 1\n                pad_to_start[pad_to_start < 0] = 0\n                pad_to_end = orig_size - current_size - pad_to_start\n                pad_to_end[pad_to_end < 0] = 0\n                pad = list(chain(*zip(pad_to_start.tolist(), pad_to_end.tolist())))\n                d[key] = BorderPad(pad)(d[key])\n\n            # Next crop\n            if np.any((orig_size - current_size) < 0):\n                if self.padcropper.padder.method == Method.SYMMETRIC:\n                    roi_center = [floor(i / 2) if r % 2 == 0 else (i - 1) // 2 for r, i in zip(orig_size, current_size)]\n                else:\n                    roi_center = [floor(r / 2) if r % 2 == 0 else (r - 1) // 2 for r in orig_size]\n\n                d[key] = SpatialCrop(roi_center, orig_size)(d[key])\n\n            # Remove the applied transform\n            self.pop_transform(d, key)\n\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_BoundingRectd_BoundingRectd.__init__.self.bbox_key_postfix.bbox_key_postfix": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_BoundingRectd_BoundingRectd.__init__.self.bbox_key_postfix.bbox_key_postfix", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1463, "end_line": 1487, "span_ids": ["BoundingRectd.__init__", "BoundingRectd"], "tokens": 205}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class BoundingRectd(MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.BoundingRect`.\n\n    Args:\n        keys: keys of the corresponding items to be transformed.\n            See also: monai.transforms.MapTransform\n        bbox_key_postfix: the output bounding box coordinates will be\n            written to the value of `{key}_{bbox_key_postfix}`.\n        select_fn: function to select expected foreground, default is to select values > 0.\n        allow_missing_keys: don't raise exception if key is missing.\n    \"\"\"\n\n    backend = BoundingRect.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        bbox_key_postfix: str = \"bbox\",\n        select_fn: Callable = is_positive,\n        allow_missing_keys: bool = False,\n    ):\n        super().__init__(keys, allow_missing_keys)\n        self.bbox = BoundingRect(select_fn=select_fn)\n        self.bbox_key_postfix = bbox_key_postfix", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_BoundingRectd.__call___BoundingRectd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_BoundingRectd.__call___BoundingRectd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1489, "end_line": 1500, "span_ids": ["BoundingRectd.__call__"], "tokens": 139}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class BoundingRectd(MapTransform):\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        \"\"\"\n        See also: :py:class:`monai.transforms.utils.generate_spatial_bounding_box`.\n        \"\"\"\n        d = dict(data)\n        for key in self.key_iterator(d):\n            bbox = self.bbox(d[key])\n            key_to_add = f\"{key}_{self.bbox_key_postfix}\"\n            if key_to_add in d:\n                raise KeyError(f\"Bounding box data with key {key_to_add} already exists.\")\n            d[key_to_add] = bbox\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_DetectEnvelope_DetectEnvelope.__init__.self.n.n": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_DetectEnvelope_DetectEnvelope.__init__.self.n.n", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1082, "end_line": 1105, "span_ids": ["DetectEnvelope", "DetectEnvelope.__init__"], "tokens": 218}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DetectEnvelope(Transform):\n    \"\"\"\n    Find the envelope of the input data along the requested axis using a Hilbert transform.\n    Requires PyTorch 1.7.0+ and the PyTorch FFT module (which is not included in NVIDIA PyTorch Release 20.10).\n\n    Args:\n        axis: Axis along which to detect the envelope. Default 1, i.e. the first spatial dimension.\n        n: FFT size. Default img.shape[axis]. Input will be zero-padded or truncated to this size along dimension\n        ``axis``.\n\n    \"\"\"\n\n    backend = [TransformBackends.TORCH]\n\n    def __init__(self, axis: int = 1, n: Union[int, None] = None) -> None:\n\n        if not pytorch_after(1, 7):\n            raise InvalidPyTorchVersionError(\"1.7.0\", self.__class__.__name__)\n\n        if axis < 0:\n            raise ValueError(\"axis must be zero or positive.\")\n\n        self.axis = axis\n        self.n = n", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_StdShiftIntensityd_StdShiftIntensityd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_StdShiftIntensityd_StdShiftIntensityd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 391, "end_line": 425, "span_ids": ["StdShiftIntensityd", "StdShiftIntensityd.__init__", "StdShiftIntensityd.__call__"], "tokens": 310}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class StdShiftIntensityd(MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.StdShiftIntensity`.\n    \"\"\"\n\n    backend = StdShiftIntensity.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        factor: float,\n        nonzero: bool = False,\n        channel_wise: bool = False,\n        dtype: DtypeLike = np.float32,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to be transformed.\n                See also: :py:class:`monai.transforms.compose.MapTransform`\n            factor: factor shift by ``v = v + factor * std(v)``.\n            nonzero: whether only count non-zero values.\n            channel_wise: if True, calculate on each channel separately. Please ensure\n                that the first dimension represents the channel of the image if True.\n            dtype: output data type, if None, same as input image. defaults to float32.\n            allow_missing_keys: don't raise exception if key is missing.\n        \"\"\"\n        super().__init__(keys, allow_missing_keys)\n        self.shifter = StdShiftIntensity(factor, nonzero, channel_wise, dtype)\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key in self.key_iterator(d):\n            d[key] = self.shifter(d[key])\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_RandStdShiftIntensityd_RandStdShiftIntensityd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_RandStdShiftIntensityd_RandStdShiftIntensityd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 428, "end_line": 480, "span_ids": ["RandStdShiftIntensityd.__call__", "RandStdShiftIntensityd", "RandStdShiftIntensityd.__init__", "RandStdShiftIntensityd.set_random_state"], "tokens": 482}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandStdShiftIntensityd(RandomizableTransform, MapTransform):\n    \"\"\"\n    Dictionary-based version :py:class:`monai.transforms.RandStdShiftIntensity`.\n    \"\"\"\n\n    backend = RandStdShiftIntensity.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        factors: Union[Tuple[float, float], float],\n        prob: float = 0.1,\n        nonzero: bool = False,\n        channel_wise: bool = False,\n        dtype: DtypeLike = np.float32,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to be transformed.\n                See also: :py:class:`monai.transforms.compose.MapTransform`\n            factors: if tuple, the randomly picked range is (min(factors), max(factors)).\n                If single number, the range is (-factors, factors).\n            prob: probability of std shift.\n            nonzero: whether only count non-zero values.\n            channel_wise: if True, calculate on each channel separately.\n            dtype: output data type, if None, same as input image. defaults to float32.\n            allow_missing_keys: don't raise exception if key is missing.\n        \"\"\"\n        MapTransform.__init__(self, keys, allow_missing_keys)\n        RandomizableTransform.__init__(self, prob)\n        self.shifter = RandStdShiftIntensity(\n            factors=factors, nonzero=nonzero, channel_wise=channel_wise, dtype=dtype, prob=1.0\n        )\n\n    def set_random_state(\n        self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None\n    ) -> \"RandStdShiftIntensityd\":\n        super().set_random_state(seed, state)\n        self.shifter.set_random_state(seed, state)\n        return self\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        self.randomize(None)\n        if not self._do_transform:\n            return d\n\n        # all the keys share the same random shift factor\n        self.shifter.randomize(None)\n        for key in self.key_iterator(d):\n            d[key] = self.shifter(d[key], randomize=False)\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/inverse.py_InvertibleTransform.get_most_recent_transform_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/inverse.py_InvertibleTransform.get_most_recent_transform_", "embedding": null, "metadata": {"file_path": "monai/transforms/inverse.py", "file_name": "inverse.py", "file_type": "text/x-python", "category": "implementation", "start_line": 132, "end_line": 149, "span_ids": ["InvertibleTransform.inverse", "InvertibleTransform.get_most_recent_transform"], "tokens": 145}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class InvertibleTransform(TraceableTransform):\n\n    def get_most_recent_transform(self, data: Mapping, key: Hashable = None):\n        \"\"\"Get most recent transform.\"\"\"\n        if not self.tracing:\n            raise RuntimeError(\"Transform Tracing must be enabled to get the most recent transform.\")\n        transform = data[self.trace_key(key)][-1]\n        self.check_transforms_match(transform)\n        return transform\n\n    def inverse(self, data: dict) -> dict:\n        \"\"\"\n        Inverse of ``__call__``.\n\n        Raises:\n            NotImplementedError: When the subclass does not override this method.\n\n        \"\"\"\n        raise NotImplementedError(f\"Subclass {self.__class__.__name__} must implement this method.\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/io/array.py_switch_endianness_switch_endianness.return.data": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/io/array.py_switch_endianness_switch_endianness.return.data", "embedding": null, "metadata": {"file_path": "monai/transforms/io/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 49, "end_line": 73, "span_ids": ["switch_endianness"], "tokens": 265}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def switch_endianness(data, new=\"<\"):\n    \"\"\"\n    Convert the input `data` endianness to `new`.\n\n    Args:\n        data: input to be converted.\n        new: the target endianness, currently support \"<\" or \">\".\n    \"\"\"\n    if isinstance(data, np.ndarray):\n        # default to system endian\n        sys_native = \"<\" if (sys.byteorder == \"little\") else \">\"\n        current_ = sys_native if data.dtype.byteorder not in (\"<\", \">\") else data.dtype.byteorder\n        if new not in (\"<\", \">\"):\n            raise NotImplementedError(f\"Not implemented option new={new}.\")\n        if current_ != new:\n            data = data.byteswap().newbyteorder(new)\n    elif isinstance(data, tuple):\n        data = tuple(switch_endianness(x, new) for x in data)\n    elif isinstance(data, list):\n        data = [switch_endianness(x, new) for x in data]\n    elif isinstance(data, dict):\n        data = {k: switch_endianness(v, new) for k, v in data.items()}\n    elif not isinstance(data, (bool, str, float, int, type(None))):\n        raise RuntimeError(f\"Unknown type: {type(data).__name__}\")\n    return data", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/io/array.py_SaveImage_SaveImage._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/io/array.py_SaveImage_SaveImage._", "embedding": null, "metadata": {"file_path": "monai/transforms/io/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 219, "end_line": 284, "span_ids": ["SaveImage"], "tokens": 961}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SaveImage(Transform):\n    \"\"\"\n    Save transformed data into files, support NIfTI and PNG formats.\n    It can work for both numpy array and PyTorch Tensor in both preprocessing transform\n    chain and postprocessing transform chain.\n    The name of saved file will be `{input_image_name}_{output_postfix}{output_ext}`,\n    where the input image name is extracted from the provided meta data dictionary.\n    If no meta data provided, use index from 0 as the filename prefix.\n    It can also save a list of PyTorch Tensor or numpy array without `batch dim`.\n\n    Note: image should be channel-first shape: [C,H,W,[D]].\n\n    Args:\n        output_dir: output image directory.\n        output_postfix: a string appended to all output file names, default to `trans`.\n        output_ext: output file extension name, available extensions: `.nii.gz`, `.nii`, `.png`.\n        resample: whether to resample before saving the data array.\n            if saving PNG format image, based on the `spatial_shape` from metadata.\n            if saving NIfTI format image, based on the `original_affine` from metadata.\n        mode: This option is used when ``resample = True``. Defaults to ``\"nearest\"``.\n\n            - NIfTI files {``\"bilinear\"``, ``\"nearest\"``}\n                Interpolation mode to calculate output values.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            - PNG files {``\"nearest\"``, ``\"linear\"``, ``\"bilinear\"``, ``\"bicubic\"``, ``\"trilinear\"``, ``\"area\"``}\n                The interpolation mode.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#interpolate\n\n        padding_mode: This option is used when ``resample = True``. Defaults to ``\"border\"``.\n\n            - NIfTI files {``\"zeros\"``, ``\"border\"``, ``\"reflection\"``}\n                Padding mode for outside grid values.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            - PNG files\n                This option is ignored.\n\n        scale: {``255``, ``65535``} postprocess data by clipping to [0, 1] and scaling\n            [0, 255] (uint8) or [0, 65535] (uint16). Default is None to disable scaling.\n            it's used for PNG format only.\n        dtype: data type during resampling computation. Defaults to ``np.float64`` for best precision.\n            if None, use the data type of input data. To be compatible with other modules,\n            the output data type is always ``np.float32``.\n            it's used for NIfTI format only.\n        output_dtype: data type for saving data. Defaults to ``np.float32``.\n            it's used for NIfTI format only.\n        squeeze_end_dims: if True, any trailing singleton dimensions will be removed (after the channel\n            has been moved to the end). So if input is (C,H,W,D), this will be altered to (H,W,D,C), and\n            then if C==1, it will be saved as (H,W,D). If D also ==1, it will be saved as (H,W). If false,\n            image will always be saved as (H,W,D,C).\n            it's used for NIfTI format only.\n        data_root_dir: if not empty, it specifies the beginning parts of the input file's\n            absolute path. it's used to compute `input_file_rel_path`, the relative path to the file from\n            `data_root_dir` to preserve folder structure when saving in case there are files in different\n            folders with the same file names. for example:\n            input_file_name: /foo/bar/test1/image.nii,\n            output_postfix: seg\n            output_ext: nii.gz\n            output_dir: /output,\n            data_root_dir: /foo/bar,\n            output will be: /output/test1/image/image_seg.nii.gz\n        separate_folder: whether to save every file in a separate folder, for example: if input filename is\n            `image.nii`, postfix is `seg` and folder_path is `output`, if `True`, save as:\n            `output/image/image_seg.nii`, if `False`, save as `output/image_seg.nii`. default to `True`.\n        print_log: whether to print log about the saved file path, etc. default to `True`.\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/io/dictionary.py_SaveImaged_SaveImaged._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/io/dictionary.py_SaveImaged_SaveImaged._", "embedding": null, "metadata": {"file_path": "monai/transforms/io/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 139, "end_line": 213, "span_ids": ["SaveImaged"], "tokens": 1116}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SaveImaged(MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.SaveImage`.\n\n    Note:\n        Image should be channel-first shape: [C,H,W,[D]].\n        If the data is a patch of big image, will append the patch index to filename.\n\n    Args:\n        keys: keys of the corresponding items to be transformed.\n            See also: :py:class:`monai.transforms.compose.MapTransform`\n        meta_keys: explicitly indicate the key of the corresponding meta data dictionary.\n            for example, for data with key `image`, the metadata by default is in `image_meta_dict`.\n            the meta data is a dictionary object which contains: filename, original_shape, etc.\n            it can be a sequence of string, map to the `keys`.\n            if None, will try to construct meta_keys by `key_{meta_key_postfix}`.\n        meta_key_postfix: if meta_keys is None and `key_{postfix}` was used to store the metadata in `LoadImaged`.\n            need the key to extract metadata to save images, default is `meta_dict`.\n            for example, for data with key `image`, the metadata by default is in `image_meta_dict`.\n            the meta data is a dictionary object which contains: filename, affine, original_shape, etc.\n            if no corresponding metadata, set to `None`.\n        output_dir: output image directory.\n        output_postfix: a string appended to all output file names, default to `trans`.\n        output_ext: output file extension name, available extensions: `.nii.gz`, `.nii`, `.png`.\n        resample: whether to resample before saving the data array.\n            if saving PNG format image, based on the `spatial_shape` from metadata.\n            if saving NIfTI format image, based on the `original_affine` from metadata.\n        mode: This option is used when ``resample = True``. Defaults to ``\"nearest\"``.\n\n            - NIfTI files {``\"bilinear\"``, ``\"nearest\"``}\n                Interpolation mode to calculate output values.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            - PNG files {``\"nearest\"``, ``\"linear\"``, ``\"bilinear\"``, ``\"bicubic\"``, ``\"trilinear\"``, ``\"area\"``}\n                The interpolation mode.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#interpolate\n\n        padding_mode: This option is used when ``resample = True``. Defaults to ``\"border\"``.\n\n            - NIfTI files {``\"zeros\"``, ``\"border\"``, ``\"reflection\"``}\n                Padding mode for outside grid values.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            - PNG files\n                This option is ignored.\n\n        scale: {``255``, ``65535``} postprocess data by clipping to [0, 1] and scaling\n            [0, 255] (uint8) or [0, 65535] (uint16). Default is None to disable scaling.\n            it's used for PNG format only.\n        dtype: data type during resampling computation. Defaults to ``np.float64`` for best precision.\n            if None, use the data type of input data. To be compatible with other modules,\n            the output data type is always ``np.float32``.\n            it's used for NIfTI format only.\n        output_dtype: data type for saving data. Defaults to ``np.float32``.\n            it's used for NIfTI format only.\n        allow_missing_keys: don't raise exception if key is missing.\n        squeeze_end_dims: if True, any trailing singleton dimensions will be removed (after the channel\n            has been moved to the end). So if input is (C,H,W,D), this will be altered to (H,W,D,C), and\n            then if C==1, it will be saved as (H,W,D). If D also ==1, it will be saved as (H,W). If false,\n            image will always be saved as (H,W,D,C).\n            it's used for NIfTI format only.\n        data_root_dir: if not empty, it specifies the beginning parts of the input file's\n            absolute path. it's used to compute `input_file_rel_path`, the relative path to the file from\n            `data_root_dir` to preserve folder structure when saving in case there are files in different\n            folders with the same file names. for example:\n            input_file_name: /foo/bar/test1/image.nii,\n            output_postfix: seg\n            output_ext: nii.gz\n            output_dir: /output,\n            data_root_dir: /foo/bar,\n            output will be: /output/test1/image/image_seg.nii.gz\n        separate_folder: whether to save every file in a separate folder, for example: if input filename is\n            `image.nii`, postfix is `seg` and folder_path is `output`, if `True`, save as:\n            `output/image/image_seg.nii`, if `False`, save as `output/image_seg.nii`. default to `True`.\n        print_log: whether to print log about the saved file path, etc. default to `True`.\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/io/dictionary.py_SaveImaged.__init___": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/io/dictionary.py_SaveImaged.__init___", "embedding": null, "metadata": {"file_path": "monai/transforms/io/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 215, "end_line": 266, "span_ids": ["impl:3", "SaveImaged.__init__", "SaveImaged.__call__"], "tokens": 476}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SaveImaged(MapTransform):\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        meta_keys: Optional[KeysCollection] = None,\n        meta_key_postfix: str = \"meta_dict\",\n        output_dir: Union[Path, str] = \"./\",\n        output_postfix: str = \"trans\",\n        output_ext: str = \".nii.gz\",\n        resample: bool = True,\n        mode: Union[GridSampleMode, InterpolateMode, str] = \"nearest\",\n        padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER,\n        scale: Optional[int] = None,\n        dtype: DtypeLike = np.float64,\n        output_dtype: DtypeLike = np.float32,\n        allow_missing_keys: bool = False,\n        squeeze_end_dims: bool = True,\n        data_root_dir: str = \"\",\n        separate_folder: bool = True,\n        print_log: bool = True,\n    ) -> None:\n        super().__init__(keys, allow_missing_keys)\n        self.meta_keys = ensure_tuple_rep(meta_keys, len(self.keys))\n        self.meta_key_postfix = ensure_tuple_rep(meta_key_postfix, len(self.keys))\n        self._saver = SaveImage(\n            output_dir=output_dir,\n            output_postfix=output_postfix,\n            output_ext=output_ext,\n            resample=resample,\n            mode=mode,\n            padding_mode=padding_mode,\n            scale=scale,\n            dtype=dtype,\n            output_dtype=output_dtype,\n            squeeze_end_dims=squeeze_end_dims,\n            data_root_dir=data_root_dir,\n            separate_folder=separate_folder,\n            print_log=print_log,\n        )\n\n    def __call__(self, data):\n        d = dict(data)\n        for key, meta_key, meta_key_postfix in self.key_iterator(d, self.meta_keys, self.meta_key_postfix):\n            if meta_key is None and meta_key_postfix is not None:\n                meta_key = f\"{key}_{meta_key_postfix}\"\n            meta_data = d[meta_key] if meta_key is not None else None\n            self._saver(img=d[key], meta_data=meta_data)\n        return d\n\n\nLoadImageD = LoadImageDict = LoadImaged\nSaveImageD = SaveImageDict = SaveImaged", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/array.py_warnings___all__._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/array.py_warnings___all__._", "embedding": null, "metadata": {"file_path": "monai/transforms/post/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 16, "end_line": 41, "span_ids": ["docstring"], "tokens": 193}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import warnings\nfrom typing import Callable, Iterable, Optional, Sequence, Union\n\nimport numpy as np\nimport torch\n\nfrom monai.config.type_definitions import NdarrayOrTensor\nfrom monai.networks import one_hot\nfrom monai.networks.layers import GaussianFilter, apply_filter\nfrom monai.transforms.transform import Transform\nfrom monai.transforms.utils import fill_holes, get_largest_connected_component_mask\nfrom monai.transforms.utils_pytorch_numpy_unification import unravel_index\nfrom monai.utils import TransformBackends, convert_data_type, deprecated_arg, ensure_tuple, look_up_option\nfrom monai.utils.type_conversion import convert_to_dst_type\n\n__all__ = [\n    \"Activations\",\n    \"AsDiscrete\",\n    \"FillHoles\",\n    \"KeepLargestConnectedComponent\",\n    \"LabelFilter\",\n    \"LabelToContour\",\n    \"MeanEnsemble\",\n    \"ProbNMS\",\n    \"VoteEnsemble\",\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/array.py_ProbNMS_ProbNMS.__init__.self.box_upper_bd.self_box_size_self_box_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/array.py_ProbNMS_ProbNMS.__init__.self.box_upper_bd.self_box_size_self_box_", "embedding": null, "metadata": {"file_path": "monai/transforms/post/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 626, "end_line": 683, "span_ids": ["ProbNMS.__init__", "ProbNMS"], "tokens": 624}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ProbNMS(Transform):\n    \"\"\"\n    Performs probability based non-maximum suppression (NMS) on the probabilities map via\n    iteratively selecting the coordinate with highest probability and then move it as well\n    as its surrounding values. The remove range is determined by the parameter `box_size`.\n    If multiple coordinates have the same highest probability, only one of them will be\n    selected.\n\n    Args:\n        spatial_dims: number of spatial dimensions of the input probabilities map.\n            Defaults to 2.\n        sigma: the standard deviation for gaussian filter.\n            It could be a single value, or `spatial_dims` number of values. Defaults to 0.0.\n        prob_threshold: the probability threshold, the function will stop searching if\n            the highest probability is no larger than the threshold. The value should be\n            no less than 0.0. Defaults to 0.5.\n        box_size: the box size (in pixel) to be removed around the the pixel with the maximum probability.\n            It can be an integer that defines the size of a square or cube,\n            or a list containing different values for each dimensions. Defaults to 48.\n\n    Return:\n        a list of selected lists, where inner lists contain probability and coordinates.\n        For example, for 3D input, the inner lists are in the form of [probability, x, y, z].\n\n    Raises:\n        ValueError: When ``prob_threshold`` is less than 0.0.\n        ValueError: When ``box_size`` is a list or tuple, and its length is not equal to `spatial_dims`.\n        ValueError: When ``box_size`` has a less than 1 value.\n\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __init__(\n        self,\n        spatial_dims: int = 2,\n        sigma: Union[Sequence[float], float, Sequence[torch.Tensor], torch.Tensor] = 0.0,\n        prob_threshold: float = 0.5,\n        box_size: Union[int, Sequence[int]] = 48,\n    ) -> None:\n        self.sigma = sigma\n        self.spatial_dims = spatial_dims\n        if self.sigma != 0:\n            self.filter = GaussianFilter(spatial_dims=spatial_dims, sigma=sigma)\n        if prob_threshold < 0:\n            raise ValueError(\"prob_threshold should be no less than 0.0.\")\n        self.prob_threshold = prob_threshold\n        if isinstance(box_size, int):\n            self.box_size = np.asarray([box_size] * spatial_dims)\n        elif len(box_size) != spatial_dims:\n            raise ValueError(\"the sequence length of box_size should be the same as spatial_dims.\")\n        else:\n            self.box_size = np.asarray(box_size)\n        if self.box_size.min() <= 0:\n            raise ValueError(\"box_size should be larger than 0.\")\n\n        self.box_lower_bd = self.box_size // 2\n        self.box_upper_bd = self.box_size - self.box_lower_bd", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/array.py_ProbNMS.__call___": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/array.py_ProbNMS.__call___", "embedding": null, "metadata": {"file_path": "monai/transforms/post/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 685, "end_line": 712, "span_ids": ["ProbNMS.__call__"], "tokens": 288}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ProbNMS(Transform):\n\n    def __call__(self, prob_map: NdarrayOrTensor):\n        \"\"\"\n        prob_map: the input probabilities map, it must have shape (H[, W, ...]).\n        \"\"\"\n        if self.sigma != 0:\n            if not isinstance(prob_map, torch.Tensor):\n                prob_map = torch.as_tensor(prob_map, dtype=torch.float)\n            self.filter.to(prob_map)\n            prob_map = self.filter(prob_map)\n\n        prob_map_shape = prob_map.shape\n\n        outputs = []\n        while prob_map.max() > self.prob_threshold:\n            max_idx = unravel_index(prob_map.argmax(), prob_map_shape)\n            prob_max = prob_map[tuple(max_idx)]\n            max_idx = max_idx.cpu().numpy() if isinstance(max_idx, torch.Tensor) else max_idx\n            prob_max = prob_max.item() if isinstance(prob_max, torch.Tensor) else prob_max\n            outputs.append([prob_max] + list(max_idx))\n\n            idx_min_range = (max_idx - self.box_lower_bd).clip(0, None)\n            idx_max_range = (max_idx + self.box_upper_bd).clip(None, prob_map_shape)\n            # for each dimension, set values during index ranges to 0\n            slices = tuple(slice(idx_min_range[i], idx_max_range[i]) for i in range(self.spatial_dims))\n            prob_map[slices] = 0\n\n        return outputs", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/dictionary.py_VoteEnsembled_VoteEnsembled.__init__.super___init___keys_en": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/dictionary.py_VoteEnsembled_VoteEnsembled.__init__.super___init___keys_en", "embedding": null, "metadata": {"file_path": "monai/transforms/post/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 397, "end_line": 418, "span_ids": ["VoteEnsembled.__init__", "VoteEnsembled"], "tokens": 221}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class VoteEnsembled(Ensembled):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.VoteEnsemble`.\n    \"\"\"\n\n    backend = VoteEnsemble.backend\n\n    def __init__(\n        self, keys: KeysCollection, output_key: Optional[str] = None, num_classes: Optional[int] = None\n    ) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to be stack and execute ensemble.\n                if only 1 key provided, suppose it's a PyTorch Tensor with data stacked on dimension `E`.\n            output_key: the key to store ensemble result in the dictionary.\n                if only 1 key provided in `keys`, `output_key` can be None and use `keys` as default.\n            num_classes: if the input is single channel data instead of One-Hot, we can't get class number\n                from channel, need to explicitly specify the number of classes to vote.\n\n        \"\"\"\n        ensemble = VoteEnsemble(num_classes=num_classes)\n        super().__init__(keys, ensemble, output_key)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/dictionary.py_ProbNMSd_ProbNMSd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/dictionary.py_ProbNMSd_ProbNMSd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/post/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 431, "end_line": 482, "span_ids": ["ProbNMSd", "ProbNMSd.__call__", "ProbNMSd.__init__"], "tokens": 540}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ProbNMSd(MapTransform):\n    \"\"\"\n    Performs probability based non-maximum suppression (NMS) on the probabilities map via\n    iteratively selecting the coordinate with highest probability and then move it as well\n    as its surrounding values. The remove range is determined by the parameter `box_size`.\n    If multiple coordinates have the same highest probability, only one of them will be\n    selected.\n\n    Args:\n        spatial_dims: number of spatial dimensions of the input probabilities map.\n            Defaults to 2.\n        sigma: the standard deviation for gaussian filter.\n            It could be a single value, or `spatial_dims` number of values. Defaults to 0.0.\n        prob_threshold: the probability threshold, the function will stop searching if\n            the highest probability is no larger than the threshold. The value should be\n            no less than 0.0. Defaults to 0.5.\n        box_size: the box size (in pixel) to be removed around the the pixel with the maximum probability.\n            It can be an integer that defines the size of a square or cube,\n            or a list containing different values for each dimensions. Defaults to 48.\n\n    Return:\n        a list of selected lists, where inner lists contain probability and coordinates.\n        For example, for 3D input, the inner lists are in the form of [probability, x, y, z].\n\n    Raises:\n        ValueError: When ``prob_threshold`` is less than 0.0.\n        ValueError: When ``box_size`` is a list or tuple, and its length is not equal to `spatial_dims`.\n        ValueError: When ``box_size`` has a less than 1 value.\n\n    \"\"\"\n\n    backend = ProbNMS.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        spatial_dims: int = 2,\n        sigma: Union[Sequence[float], float, Sequence[torch.Tensor], torch.Tensor] = 0.0,\n        prob_threshold: float = 0.5,\n        box_size: Union[int, Sequence[int]] = 48,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        super().__init__(keys, allow_missing_keys)\n        self.prob_nms = ProbNMS(\n            spatial_dims=spatial_dims, sigma=sigma, prob_threshold=prob_threshold, box_size=box_size\n        )\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]):\n        d = dict(data)\n        for key in self.key_iterator(d):\n            d[key] = self.prob_nms(d[key])\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/dictionary.py_ActivationsD_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/dictionary.py_ActivationsD_", "embedding": null, "metadata": {"file_path": "monai/transforms/post/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 697, "end_line": 708, "span_ids": ["impl:3"], "tokens": 158}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "ActivationsD = ActivationsDict = Activationsd\nAsDiscreteD = AsDiscreteDict = AsDiscreted\nFillHolesD = FillHolesDict = FillHolesd\nInvertD = InvertDict = Invertd\nKeepLargestConnectedComponentD = KeepLargestConnectedComponentDict = KeepLargestConnectedComponentd\nLabelFilterD = LabelFilterDict = LabelFilterd\nLabelToContourD = LabelToContourDict = LabelToContourd\nMeanEnsembleD = MeanEnsembleDict = MeanEnsembled\nProbNMSD = ProbNMSDict = ProbNMSd\nSaveClassificationD = SaveClassificationDict = SaveClassificationd\nVoteEnsembleD = VoteEnsembleDict = VoteEnsembled", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_warnings_RandRange.Optional_Union_Sequence_U": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_warnings_RandRange.Optional_Union_Sequence_U", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 15, "end_line": 81, "span_ids": ["impl:4", "docstring"], "tokens": 430}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import warnings\nfrom typing import Any, Callable, List, Optional, Sequence, Tuple, Union\n\nimport numpy as np\nimport torch\n\nfrom monai.config import USE_COMPILED, DtypeLike\nfrom monai.config.type_definitions import NdarrayOrTensor\nfrom monai.data.utils import compute_shape_offset, to_affine_nd, zoom_affine\nfrom monai.networks.layers import AffineTransform, GaussianFilter, grid_pull\nfrom monai.transforms.croppad.array import CenterSpatialCrop, Pad\nfrom monai.transforms.transform import Randomizable, RandomizableTransform, ThreadUnsafe, Transform\nfrom monai.transforms.utils import (\n    create_control_grid,\n    create_grid,\n    create_rotate,\n    create_scale,\n    create_shear,\n    create_translate,\n    map_spatial_axes,\n)\nfrom monai.utils import (\n    GridSampleMode,\n    GridSamplePadMode,\n    InterpolateMode,\n    NumpyPadMode,\n    PytorchPadMode,\n    ensure_tuple,\n    ensure_tuple_rep,\n    ensure_tuple_size,\n    fall_back_tuple,\n    issequenceiterable,\n    optional_import,\n)\nfrom monai.utils.deprecate_utils import deprecated_arg\nfrom monai.utils.enums import TransformBackends\nfrom monai.utils.module import look_up_option\nfrom monai.utils.type_conversion import convert_data_type, convert_to_dst_type\n\nnib, _ = optional_import(\"nibabel\")\n\n__all__ = [\n    \"Spacing\",\n    \"Orientation\",\n    \"Flip\",\n    \"GridDistortion\",\n    \"Resize\",\n    \"Rotate\",\n    \"Zoom\",\n    \"Rotate90\",\n    \"RandRotate90\",\n    \"RandRotate\",\n    \"RandFlip\",\n    \"RandGridDistortion\",\n    \"RandAxisFlip\",\n    \"RandZoom\",\n    \"AffineGrid\",\n    \"RandAffineGrid\",\n    \"RandDeformGrid\",\n    \"Resample\",\n    \"Affine\",\n    \"RandAffine\",\n    \"Rand2DElastic\",\n    \"Rand3DElastic\",\n]\n\nRandRange = Optional[Union[Sequence[Union[Tuple[float, float], float]], float]]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_AffineGrid_AffineGrid.__init__.self.affine.affine": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_AffineGrid_AffineGrid.__init__.self.affine.affine", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1058, "end_line": 1106, "span_ids": ["AffineGrid", "AffineGrid.__init__"], "tokens": 530}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AffineGrid(Transform):\n    \"\"\"\n    Affine transforms on the coordinates.\n\n    Args:\n        rotate_params: a rotation angle in radians, a scalar for 2D image, a tuple of 3 floats for 3D.\n            Defaults to no rotation.\n        shear_params: shearing factors for affine matrix, take a 3D affine as example::\n\n            [\n                [1.0, params[0], params[1], 0.0],\n                [params[2], 1.0, params[3], 0.0],\n                [params[4], params[5], 1.0, 0.0],\n                [0.0, 0.0, 0.0, 1.0],\n            ]\n\n            a tuple of 2 floats for 2D, a tuple of 6 floats for 3D. Defaults to no shearing.\n        translate_params: a tuple of 2 floats for 2D, a tuple of 3 floats for 3D. Translation is in\n            pixel/voxel relative to the center of the input image. Defaults to no translation.\n        scale_params: scale factor for every spatial dims. a tuple of 2 floats for 2D,\n            a tuple of 3 floats for 3D. Defaults to `1.0`.\n        affine: If applied, ignore the params (`rotate_params`, etc.) and use the\n            supplied matrix. Should be square with each side = num of image spatial\n            dimensions + 1.\n\n    .. deprecated:: 0.6.0\n        ``as_tensor_output`` is deprecated.\n\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    @deprecated_arg(name=\"as_tensor_output\", since=\"0.6\")\n    def __init__(\n        self,\n        rotate_params: Optional[Union[Sequence[float], float]] = None,\n        shear_params: Optional[Union[Sequence[float], float]] = None,\n        translate_params: Optional[Union[Sequence[float], float]] = None,\n        scale_params: Optional[Union[Sequence[float], float]] = None,\n        as_tensor_output: bool = True,\n        device: Optional[torch.device] = None,\n        affine: Optional[NdarrayOrTensor] = None,\n    ) -> None:\n        self.rotate_params = rotate_params\n        self.shear_params = shear_params\n        self.translate_params = translate_params\n        self.scale_params = scale_params\n        self.device = device\n        self.affine = affine", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_RandAffineGrid_RandAffineGrid.__init__.self.affine.None": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_RandAffineGrid_RandAffineGrid.__init__.self.affine.None", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1158, "end_line": 1223, "span_ids": ["RandAffineGrid", "RandAffineGrid.__init__"], "tokens": 736}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandAffineGrid(Randomizable, Transform):\n    \"\"\"\n    Generate randomised affine grid.\n\n    \"\"\"\n\n    backend = AffineGrid.backend\n\n    @deprecated_arg(name=\"as_tensor_output\", since=\"0.6\")\n    def __init__(\n        self,\n        rotate_range: RandRange = None,\n        shear_range: RandRange = None,\n        translate_range: RandRange = None,\n        scale_range: RandRange = None,\n        as_tensor_output: bool = True,\n        device: Optional[torch.device] = None,\n    ) -> None:\n        \"\"\"\n        Args:\n            rotate_range: angle range in radians. If element `i` is a pair of (min, max) values, then\n                `uniform[-rotate_range[i][0], rotate_range[i][1])` will be used to generate the rotation parameter\n                for the `i`th spatial dimension. If not, `uniform[-rotate_range[i], rotate_range[i])` will be used.\n                This can be altered on a per-dimension basis. E.g., `((0,3), 1, ...)`: for dim0, rotation will be\n                in range `[0, 3]`, and for dim1 `[-1, 1]` will be used. Setting a single value will use `[-x, x]`\n                for dim0 and nothing for the remaining dimensions.\n            shear_range: shear range with format matching `rotate_range`, it defines the range to randomly select\n                shearing factors(a tuple of 2 floats for 2D, a tuple of 6 floats for 3D) for affine matrix,\n                take a 3D affine as example::\n\n                    [\n                        [1.0, params[0], params[1], 0.0],\n                        [params[2], 1.0, params[3], 0.0],\n                        [params[4], params[5], 1.0, 0.0],\n                        [0.0, 0.0, 0.0, 1.0],\n                    ]\n\n            translate_range: translate range with format matching `rotate_range`, it defines the range to randomly\n                select voxels to translate for every spatial dims.\n            scale_range: scaling range with format matching `rotate_range`. it defines the range to randomly select\n                the scale factor to translate for every spatial dims. A value of 1.0 is added to the result.\n                This allows 0 to correspond to no change (i.e., a scaling of 1.0).\n            device: device to store the output grid data.\n\n        See also:\n            - :py:meth:`monai.transforms.utils.create_rotate`\n            - :py:meth:`monai.transforms.utils.create_shear`\n            - :py:meth:`monai.transforms.utils.create_translate`\n            - :py:meth:`monai.transforms.utils.create_scale`\n\n        .. deprecated:: 0.6.0\n            ``as_tensor_output`` is deprecated.\n\n        \"\"\"\n        self.rotate_range = ensure_tuple(rotate_range)\n        self.shear_range = ensure_tuple(shear_range)\n        self.translate_range = ensure_tuple(translate_range)\n        self.scale_range = ensure_tuple(scale_range)\n\n        self.rotate_params: Optional[List[float]] = None\n        self.shear_params: Optional[List[float]] = None\n        self.translate_params: Optional[List[float]] = None\n        self.scale_params: Optional[List[float]] = None\n\n        self.device = device\n        self.affine: Optional[NdarrayOrTensor] = None", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_RandAffineGrid._get_rand_param_RandAffineGrid.randomize.self.scale_params.self__get_rand_param_self": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_RandAffineGrid._get_rand_param_RandAffineGrid.randomize.self.scale_params.self__get_rand_param_self", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1075, "end_line": 1090, "span_ids": ["RandAffineGrid.randomize", "RandAffineGrid._get_rand_param"], "tokens": 207}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandAffineGrid(Randomizable, Transform):\n\n    def _get_rand_param(self, param_range, add_scalar: float = 0.0):\n        out_param = []\n        for f in param_range:\n            if issequenceiterable(f):\n                if len(f) != 2:\n                    raise ValueError(\"If giving range as [min,max], should only have two elements per dim.\")\n                out_param.append(self.R.uniform(f[0], f[1]) + add_scalar)\n            elif f is not None:\n                out_param.append(self.R.uniform(-f, f) + add_scalar)\n        return out_param\n\n    def randomize(self, data: Optional[Any] = None) -> None:\n        self.rotate_params = self._get_rand_param(self.rotate_range)\n        self.shear_params = self._get_rand_param(self.shear_range)\n        self.translate_params = self._get_rand_param(self.translate_range)\n        self.scale_params = self._get_rand_param(self.scale_range, 1.0)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_RandAffineGrid.__call___RandAffineGrid.get_transformation_matrix.return.self_affine": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_RandAffineGrid.__call___RandAffineGrid.get_transformation_matrix.return.self_affine", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1242, "end_line": 1267, "span_ids": ["RandAffineGrid.get_transformation_matrix", "RandAffineGrid.__call__"], "tokens": 242}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandAffineGrid(Randomizable, Transform):\n\n    def __call__(\n        self, spatial_size: Optional[Sequence[int]] = None, grid: Optional[NdarrayOrTensor] = None\n    ) -> NdarrayOrTensor:\n        \"\"\"\n        Args:\n            spatial_size: output grid size.\n            grid: grid to be transformed. Shape must be (3, H, W) for 2D or (4, H, W, D) for 3D.\n\n        Returns:\n            a 2D (3xHxW) or 3D (4xHxWxD) grid.\n        \"\"\"\n        self.randomize()\n        affine_grid = AffineGrid(\n            rotate_params=self.rotate_params,\n            shear_params=self.shear_params,\n            translate_params=self.translate_params,\n            scale_params=self.scale_params,\n            device=self.device,\n        )\n        _grid: NdarrayOrTensor\n        _grid, self.affine = affine_grid(spatial_size, grid)\n        return _grid\n\n    def get_transformation_matrix(self) -> Optional[NdarrayOrTensor]:\n        \"\"\"Get the most recently applied transformation matrix\"\"\"\n        return self.affine", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_RandAffine_RandAffine.__init__.self.padding_mode.GridSamplePadMode_padding": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_RandAffine_RandAffine.__init__.self.padding_mode.GridSamplePadMode_padding", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1526, "end_line": 1616, "span_ids": ["RandAffine", "RandAffine.__init__"], "tokens": 1117}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandAffine(RandomizableTransform):\n    \"\"\"\n    Random affine transform.\n    A tutorial is available: https://github.com/Project-MONAI/tutorials/blob/0.6.0/modules/transforms_demo_2d.ipynb.\n\n    \"\"\"\n\n    backend = Affine.backend\n\n    @deprecated_arg(name=\"as_tensor_output\", since=\"0.6\")\n    def __init__(\n        self,\n        prob: float = 0.1,\n        rotate_range: RandRange = None,\n        shear_range: RandRange = None,\n        translate_range: RandRange = None,\n        scale_range: RandRange = None,\n        spatial_size: Optional[Union[Sequence[int], int]] = None,\n        mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,\n        padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.REFLECTION,\n        cache_grid: bool = False,\n        as_tensor_output: bool = True,\n        device: Optional[torch.device] = None,\n    ) -> None:\n        \"\"\"\n        Args:\n            prob: probability of returning a randomized affine grid.\n                defaults to 0.1, with 10% chance returns a randomized grid.\n            rotate_range: angle range in radians. If element `i` is a pair of (min, max) values, then\n                `uniform[-rotate_range[i][0], rotate_range[i][1])` will be used to generate the rotation parameter\n                for the `i`th spatial dimension. If not, `uniform[-rotate_range[i], rotate_range[i])` will be used.\n                This can be altered on a per-dimension basis. E.g., `((0,3), 1, ...)`: for dim0, rotation will be\n                in range `[0, 3]`, and for dim1 `[-1, 1]` will be used. Setting a single value will use `[-x, x]`\n                for dim0 and nothing for the remaining dimensions.\n            shear_range: shear range with format matching `rotate_range`, it defines the range to randomly select\n                shearing factors(a tuple of 2 floats for 2D, a tuple of 6 floats for 3D) for affine matrix,\n                take a 3D affine as example::\n\n                    [\n                        [1.0, params[0], params[1], 0.0],\n                        [params[2], 1.0, params[3], 0.0],\n                        [params[4], params[5], 1.0, 0.0],\n                        [0.0, 0.0, 0.0, 1.0],\n                    ]\n\n            translate_range: translate range with format matching `rotate_range`, it defines the range to randomly\n                select pixel/voxel to translate for every spatial dims.\n            scale_range: scaling range with format matching `rotate_range`. it defines the range to randomly select\n                the scale factor to translate for every spatial dims. A value of 1.0 is added to the result.\n                This allows 0 to correspond to no change (i.e., a scaling of 1.0).\n            spatial_size: output image spatial size.\n                if `spatial_size` and `self.spatial_size` are not defined, or smaller than 1,\n                the transform will use the spatial size of `img`.\n                if some components of the `spatial_size` are non-positive values, the transform will use the\n                corresponding components of img size. For example, `spatial_size=(32, -1)` will be adapted\n                to `(32, 64)` if the second spatial dimension size of img is `64`.\n            mode: {``\"bilinear\"``, ``\"nearest\"``}\n                Interpolation mode to calculate output values. Defaults to ``\"bilinear\"``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            padding_mode: {``\"zeros\"``, ``\"border\"``, ``\"reflection\"``}\n                Padding mode for outside grid values. Defaults to ``\"reflection\"``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            cache_grid: whether to cache the identity sampling grid.\n                If the spatial size is not dynamically defined by input image, enabling this option could\n                accelerate the transform.\n            device: device on which the tensor will be allocated.\n\n        See also:\n            - :py:class:`RandAffineGrid` for the random affine parameters configurations.\n            - :py:class:`Affine` for the affine transformation parameters configurations.\n\n        .. deprecated:: 0.6.0\n            ``as_tensor_output`` is deprecated.\n\n        \"\"\"\n        RandomizableTransform.__init__(self, prob)\n\n        self.rand_affine_grid = RandAffineGrid(\n            rotate_range=rotate_range,\n            shear_range=shear_range,\n            translate_range=translate_range,\n            scale_range=scale_range,\n            device=device,\n        )\n        self.resampler = Resample(device=device)\n\n        self.spatial_size = spatial_size\n        self.cache_grid = cache_grid\n        self._cached_grid = self._init_identity_cache()\n        self.mode: GridSampleMode = GridSampleMode(mode)\n        self.padding_mode: GridSamplePadMode = GridSamplePadMode(padding_mode)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Spacingd.inverse_Spacingd.inverse.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Spacingd.inverse_Spacingd.inverse.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 268, "end_line": 300, "span_ids": ["Spacingd.inverse"], "tokens": 361}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Spacingd(MapTransform, InvertibleTransform):\n\n    def inverse(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = deepcopy(dict(data))\n        for key, dtype in self.key_iterator(d, self.dtype):\n            transform = self.get_most_recent_transform(d, key)\n            if self.spacing_transform.diagonal:\n                raise RuntimeError(\n                    \"Spacingd:inverse not yet implemented for diagonal=True. \"\n                    + \"Please raise a github issue if you need this feature\"\n                )\n            # Create inverse transform\n            meta_data = d[transform[TraceKeys.EXTRA_INFO][\"meta_key\"]]\n            old_affine = np.array(transform[TraceKeys.EXTRA_INFO][\"old_affine\"])\n            mode = transform[TraceKeys.EXTRA_INFO][\"mode\"]\n            padding_mode = transform[TraceKeys.EXTRA_INFO][\"padding_mode\"]\n            align_corners = transform[TraceKeys.EXTRA_INFO][\"align_corners\"]\n            orig_size = transform[TraceKeys.ORIG_SIZE]\n            orig_pixdim = np.sqrt(np.sum(np.square(old_affine), 0))[:-1]\n            inverse_transform = Spacing(orig_pixdim, diagonal=self.spacing_transform.diagonal)\n            # Apply inverse\n            d[key], _, new_affine = inverse_transform(\n                data_array=d[key],\n                affine=meta_data[\"affine\"],  # type: ignore\n                mode=mode,\n                padding_mode=padding_mode,\n                align_corners=False if align_corners == TraceKeys.NONE else align_corners,\n                dtype=dtype,\n                output_spatial_shape=orig_size,\n            )\n            meta_data[\"affine\"] = new_affine  # type: ignore\n            # Remove the applied transform\n            self.pop_transform(d, key)\n\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Orientationd.inverse_Orientationd.inverse.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Orientationd.inverse_Orientationd.inverse.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 379, "end_line": 396, "span_ids": ["Orientationd.inverse"], "tokens": 218}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Orientationd(MapTransform, InvertibleTransform):\n\n    def inverse(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = deepcopy(dict(data))\n        for key in self.key_iterator(d):\n            transform = self.get_most_recent_transform(d, key)\n            # Create inverse transform\n            meta_data: Dict = d[transform[TraceKeys.EXTRA_INFO][\"meta_key\"]]  # type: ignore\n            orig_affine = transform[TraceKeys.EXTRA_INFO][\"old_affine\"]\n            orig_axcodes = nib.orientations.aff2axcodes(orig_affine)\n            inverse_transform = Orientation(\n                axcodes=orig_axcodes, as_closest_canonical=False, labels=self.ornt_transform.labels\n            )\n            # Apply inverse\n            d[key], _, new_affine = inverse_transform(d[key], affine=meta_data[\"affine\"])\n            meta_data[\"affine\"] = new_affine\n            # Remove the applied transform\n            self.pop_transform(d, key)\n\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Rotate90d.inverse_Rotate90d.inverse.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Rotate90d.inverse_Rotate90d.inverse.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 424, "end_line": 438, "span_ids": ["Rotate90d.inverse"], "tokens": 156}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Rotate90d(MapTransform, InvertibleTransform):\n\n    def inverse(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = deepcopy(dict(data))\n        for key in self.key_iterator(d):\n            _ = self.get_most_recent_transform(d, key)\n            # Create inverse transform\n            spatial_axes = self.rotator.spatial_axes\n            num_times_rotated = self.rotator.k\n            num_times_to_rotate = 4 - num_times_rotated\n            inverse_transform = Rotate90(num_times_to_rotate, spatial_axes)\n            # Apply inverse\n            d[key] = inverse_transform(d[key])\n            # Remove the applied transform\n            self.pop_transform(d, key)\n\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_RandRotate90d.inverse_RandRotate90d.inverse.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_RandRotate90d.inverse_RandRotate90d.inverse.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 497, "end_line": 512, "span_ids": ["RandRotate90d.inverse"], "tokens": 181}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandRotate90d(RandomizableTransform, MapTransform, InvertibleTransform):\n\n    def inverse(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = deepcopy(dict(data))\n        for key in self.key_iterator(d):\n            transform = self.get_most_recent_transform(d, key)\n            # Check if random transform was actually performed (based on `prob`)\n            if transform[TraceKeys.DO_TRANSFORM]:\n                # Create inverse transform\n                num_times_rotated = transform[TraceKeys.EXTRA_INFO][\"rand_k\"]\n                num_times_to_rotate = 4 - num_times_rotated\n                inverse_transform = Rotate90(num_times_to_rotate, self.spatial_axes)\n                # Apply inverse\n                d[key] = inverse_transform(d[key])\n            # Remove the applied transform\n            self.pop_transform(d, key)\n\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Resized_Resized.__init__.self.resizer.Resize_spatial_size_spati": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Resized_Resized.__init__.self.resizer.Resize_spatial_size_spati", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 513, "end_line": 554, "span_ids": ["Resized.__init__", "Resized"], "tokens": 584}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Resized(MapTransform, InvertibleTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.Resize`.\n\n    Args:\n        keys: keys of the corresponding items to be transformed.\n            See also: :py:class:`monai.transforms.compose.MapTransform`\n        spatial_size: expected shape of spatial dimensions after resize operation.\n            if some components of the `spatial_size` are non-positive values, the transform will use the\n            corresponding components of img size. For example, `spatial_size=(32, -1)` will be adapted\n            to `(32, 64)` if the second spatial dimension size of img is `64`.\n        size_mode: should be \"all\" or \"longest\", if \"all\", will use `spatial_size` for all the spatial dims,\n            if \"longest\", rescale the image so that only the longest side is equal to specified `spatial_size`,\n            which must be an int number in this case, keeping the aspect ratio of the initial image, refer to:\n            https://albumentations.ai/docs/api_reference/augmentations/geometric/resize/\n            #albumentations.augmentations.geometric.resize.LongestMaxSize.\n        mode: {``\"nearest\"``, ``\"linear\"``, ``\"bilinear\"``, ``\"bicubic\"``, ``\"trilinear\"``, ``\"area\"``}\n            The interpolation mode. Defaults to ``\"area\"``.\n            See also: https://pytorch.org/docs/stable/nn.functional.html#interpolate\n            It also can be a sequence of string, each element corresponds to a key in ``keys``.\n        align_corners: This only has an effect when mode is\n            'linear', 'bilinear', 'bicubic' or 'trilinear'. Default: None.\n            See also: https://pytorch.org/docs/stable/nn.functional.html#interpolate\n            It also can be a sequence of bool or None, each element corresponds to a key in ``keys``.\n        allow_missing_keys: don't raise exception if key is missing.\n    \"\"\"\n\n    backend = Resize.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        spatial_size: Union[Sequence[int], int],\n        size_mode: str = \"all\",\n        mode: InterpolateModeSequence = InterpolateMode.AREA,\n        align_corners: Union[Sequence[Optional[bool]], Optional[bool]] = None,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        super().__init__(keys, allow_missing_keys)\n        self.mode = ensure_tuple_rep(mode, len(self.keys))\n        self.align_corners = ensure_tuple_rep(align_corners, len(self.keys))\n        self.resizer = Resize(spatial_size=spatial_size, size_mode=size_mode)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Resized.__call___Resized.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Resized.__call___Resized.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 558, "end_line": 570, "span_ids": ["Resized.__call__"], "tokens": 144}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Resized(MapTransform, InvertibleTransform):\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key, mode, align_corners in self.key_iterator(d, self.mode, self.align_corners):\n            self.push_transform(\n                d,\n                key,\n                extra_info={\n                    \"mode\": mode.value if isinstance(mode, Enum) else mode,\n                    \"align_corners\": align_corners if align_corners is not None else TraceKeys.NONE,\n                },\n            )\n            d[key] = self.resizer(d[key], mode=mode, align_corners=align_corners)\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Resized.inverse_Resized.inverse.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Resized.inverse_Resized.inverse.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 572, "end_line": 590, "span_ids": ["Resized.inverse"], "tokens": 180}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Resized(MapTransform, InvertibleTransform):\n\n    def inverse(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = deepcopy(dict(data))\n        for key in self.key_iterator(d):\n            transform = self.get_most_recent_transform(d, key)\n            orig_size = transform[TraceKeys.ORIG_SIZE]\n            mode = transform[TraceKeys.EXTRA_INFO][\"mode\"]\n            align_corners = transform[TraceKeys.EXTRA_INFO][\"align_corners\"]\n            # Create inverse transform\n            inverse_transform = Resize(\n                spatial_size=orig_size,\n                mode=mode,\n                align_corners=None if align_corners == TraceKeys.NONE else align_corners,\n            )\n            # Apply inverse transform\n            d[key] = inverse_transform(d[key])\n            # Remove the applied transform\n            self.pop_transform(d, key)\n\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Affined_Affined.__init__.self.padding_mode.ensure_tuple_rep_padding_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Affined_Affined.__init__.self.padding_mode.ensure_tuple_rep_padding_", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 592, "end_line": 673, "span_ids": ["Affined.__init__", "Affined"], "tokens": 989}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Affined(MapTransform, InvertibleTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.Affine`.\n    \"\"\"\n\n    backend = Affine.backend\n\n    @deprecated_arg(name=\"as_tensor_output\", since=\"0.6\")\n    def __init__(\n        self,\n        keys: KeysCollection,\n        rotate_params: Optional[Union[Sequence[float], float]] = None,\n        shear_params: Optional[Union[Sequence[float], float]] = None,\n        translate_params: Optional[Union[Sequence[float], float]] = None,\n        scale_params: Optional[Union[Sequence[float], float]] = None,\n        affine: Optional[NdarrayOrTensor] = None,\n        spatial_size: Optional[Union[Sequence[int], int]] = None,\n        mode: GridSampleModeSequence = GridSampleMode.BILINEAR,\n        padding_mode: GridSamplePadModeSequence = GridSamplePadMode.REFLECTION,\n        as_tensor_output: bool = True,\n        device: Optional[torch.device] = None,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to be transformed.\n            rotate_params: a rotation angle in radians, a scalar for 2D image, a tuple of 3 floats for 3D.\n                Defaults to no rotation.\n            shear_params: shearing factors for affine matrix, take a 3D affine as example::\n\n                [\n                    [1.0, params[0], params[1], 0.0],\n                    [params[2], 1.0, params[3], 0.0],\n                    [params[4], params[5], 1.0, 0.0],\n                    [0.0, 0.0, 0.0, 1.0],\n                ]\n\n                a tuple of 2 floats for 2D, a tuple of 6 floats for 3D. Defaults to no shearing.\n            translate_params: a tuple of 2 floats for 2D, a tuple of 3 floats for 3D. Translation is in\n                pixel/voxel relative to the center of the input image. Defaults to no translation.\n            scale_params: scale factor for every spatial dims. a tuple of 2 floats for 2D,\n                a tuple of 3 floats for 3D. Defaults to `1.0`.\n            affine: if applied, ignore the params (`rotate_params`, etc.) and use the\n                supplied matrix. Should be square with each side = num of image spatial\n                dimensions + 1.\n            spatial_size: output image spatial size.\n                if `spatial_size` and `self.spatial_size` are not defined, or smaller than 1,\n                the transform will use the spatial size of `img`.\n                if some components of the `spatial_size` are non-positive values, the transform will use the\n                corresponding components of img size. For example, `spatial_size=(32, -1)` will be adapted\n                to `(32, 64)` if the second spatial dimension size of img is `64`.\n            mode: {``\"bilinear\"``, ``\"nearest\"``}\n                Interpolation mode to calculate output values. Defaults to ``\"bilinear\"``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n                It also can be a sequence of string, each element corresponds to a key in ``keys``.\n            padding_mode: {``\"zeros\"``, ``\"border\"``, ``\"reflection\"``}\n                Padding mode for outside grid values. Defaults to ``\"reflection\"``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n                It also can be a sequence of string, each element corresponds to a key in ``keys``.\n            device: device on which the tensor will be allocated.\n            allow_missing_keys: don't raise exception if key is missing.\n\n        See also:\n            - :py:class:`monai.transforms.compose.MapTransform`\n            - :py:class:`RandAffineGrid` for the random affine parameters configurations.\n\n        .. deprecated:: 0.6.0\n            ``as_tensor_output`` is deprecated.\n\n        \"\"\"\n        MapTransform.__init__(self, keys, allow_missing_keys)\n        self.affine = Affine(\n            rotate_params=rotate_params,\n            shear_params=shear_params,\n            translate_params=translate_params,\n            scale_params=scale_params,\n            affine=affine,\n            spatial_size=spatial_size,\n            device=device,\n        )\n        self.mode = ensure_tuple_rep(mode, len(self.keys))\n        self.padding_mode = ensure_tuple_rep(padding_mode, len(self.keys))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Affined.__call___Affined.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Affined.__call___Affined.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 667, "end_line": 682, "span_ids": ["Affined.__call__"], "tokens": 172}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Affined(MapTransform, InvertibleTransform):\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key, mode, padding_mode in self.key_iterator(d, self.mode, self.padding_mode):\n            orig_size = d[key].shape[1:]\n            d[key], affine = self.affine(d[key], mode=mode, padding_mode=padding_mode)\n            self.push_transform(\n                d,\n                key,\n                orig_size=orig_size,\n                extra_info={\n                    \"affine\": affine,\n                    \"mode\": mode.value if isinstance(mode, Enum) else mode,\n                    \"padding_mode\": padding_mode.value if isinstance(padding_mode, Enum) else padding_mode,\n                },\n            )\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Affined.inverse_Affined.inverse.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Affined.inverse_Affined.inverse.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 688, "end_line": 709, "span_ids": ["Affined.inverse"], "tokens": 215}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Affined(MapTransform, InvertibleTransform):\n\n    def inverse(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = deepcopy(dict(data))\n\n        for key in self.key_iterator(d):\n            transform = self.get_most_recent_transform(d, key)\n            orig_size = transform[TraceKeys.ORIG_SIZE]\n            # Create inverse transform\n            fwd_affine = transform[TraceKeys.EXTRA_INFO][\"affine\"]\n            mode = transform[TraceKeys.EXTRA_INFO][\"mode\"]\n            padding_mode = transform[TraceKeys.EXTRA_INFO][\"padding_mode\"]\n            inv_affine = np.linalg.inv(fwd_affine)\n\n            affine_grid = AffineGrid(affine=inv_affine)\n            grid, _ = affine_grid(orig_size)  # type: ignore\n\n            # Apply inverse transform\n            d[key] = self.affine.resampler(d[key], grid, mode, padding_mode)\n\n            # Remove the applied transform\n            self.pop_transform(d, key)\n\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_RandAffined.inverse_RandAffined.inverse.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_RandAffined.inverse_RandAffined.inverse.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 850, "end_line": 873, "span_ids": ["RandAffined.inverse"], "tokens": 257}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandAffined(RandomizableTransform, MapTransform, InvertibleTransform):\n\n    def inverse(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = deepcopy(dict(data))\n\n        for key in self.key_iterator(d):\n            transform = self.get_most_recent_transform(d, key)\n            # if transform was not performed and spatial size is None, nothing to do.\n            if transform[TraceKeys.DO_TRANSFORM] or self.rand_affine.spatial_size is not None:\n                orig_size = transform[TraceKeys.ORIG_SIZE]\n                # Create inverse transform\n                fwd_affine = transform[TraceKeys.EXTRA_INFO][\"affine\"]\n                mode = transform[TraceKeys.EXTRA_INFO][\"mode\"]\n                padding_mode = transform[TraceKeys.EXTRA_INFO][\"padding_mode\"]\n                inv_affine = np.linalg.inv(fwd_affine)\n\n                affine_grid = AffineGrid(affine=inv_affine)\n                grid, _ = affine_grid(orig_size)  # type: ignore\n\n                # Apply inverse transform\n                d[key] = self.rand_affine.resampler(d[key], grid, mode, padding_mode)\n\n            # Remove the applied transform\n            self.pop_transform(d, key)\n\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_RandFlipd.inverse_RandFlipd.inverse.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_RandFlipd.inverse_RandFlipd.inverse.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1219, "end_line": 1229, "span_ids": ["RandFlipd.inverse"], "tokens": 138}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandFlipd(RandomizableTransform, MapTransform, InvertibleTransform):\n\n    def inverse(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = deepcopy(dict(data))\n        for key in self.key_iterator(d):\n            transform = self.get_most_recent_transform(d, key)\n            # Check if random transform was actually performed (based on `prob`)\n            if transform[TraceKeys.DO_TRANSFORM]:\n                # Inverse is same as forward\n                d[key] = self.flipper(d[key], randomize=False)\n            # Remove the applied transform\n            self.pop_transform(d, key)\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_RandAxisFlipd.inverse_RandAxisFlipd.inverse.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_RandAxisFlipd.inverse_RandAxisFlipd.inverse.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1272, "end_line": 1283, "span_ids": ["RandAxisFlipd.inverse"], "tokens": 152}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandAxisFlipd(RandomizableTransform, MapTransform, InvertibleTransform):\n\n    def inverse(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = deepcopy(dict(data))\n        for key in self.key_iterator(d):\n            transform = self.get_most_recent_transform(d, key)\n            # Check if random transform was actually performed (based on `prob`)\n            if transform[TraceKeys.DO_TRANSFORM]:\n                flipper = Flip(spatial_axis=transform[TraceKeys.EXTRA_INFO][\"axis\"])\n                # Inverse is same as forward\n                d[key] = flipper(d[key])\n            # Remove the applied transform\n            self.pop_transform(d, key)\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Rotated_Rotated.__init__.self.dtype.ensure_tuple_rep_dtype_l": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Rotated_Rotated.__init__.self.dtype.ensure_tuple_rep_dtype_l", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1281, "end_line": 1328, "span_ids": ["Rotated.__init__", "Rotated"], "tokens": 603}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Rotated(MapTransform, InvertibleTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.Rotate`.\n\n    Args:\n        keys: Keys to pick data for transformation.\n        angle: Rotation angle(s) in radians.\n        keep_size: If it is False, the output shape is adapted so that the\n            input array is contained completely in the output.\n            If it is True, the output shape is the same as the input. Default is True.\n        mode: {``\"bilinear\"``, ``\"nearest\"``}\n            Interpolation mode to calculate output values. Defaults to ``\"bilinear\"``.\n            See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            It also can be a sequence of string, each element corresponds to a key in ``keys``.\n        padding_mode: {``\"zeros\"``, ``\"border\"``, ``\"reflection\"``}\n            Padding mode for outside grid values. Defaults to ``\"border\"``.\n            See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            It also can be a sequence of string, each element corresponds to a key in ``keys``.\n        align_corners: Defaults to False.\n            See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            It also can be a sequence of bool, each element corresponds to a key in ``keys``.\n        dtype: data type for resampling computation. Defaults to ``np.float64`` for best precision.\n            If None, use the data type of input data. To be compatible with other modules,\n            the output data type is always ``np.float32``.\n            It also can be a sequence of dtype or None, each element corresponds to a key in ``keys``.\n        allow_missing_keys: don't raise exception if key is missing.\n    \"\"\"\n\n    backend = Rotate.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        angle: Union[Sequence[float], float],\n        keep_size: bool = True,\n        mode: GridSampleModeSequence = GridSampleMode.BILINEAR,\n        padding_mode: GridSamplePadModeSequence = GridSamplePadMode.BORDER,\n        align_corners: Union[Sequence[bool], bool] = False,\n        dtype: Union[Sequence[Union[DtypeLike, torch.dtype]], Union[DtypeLike, torch.dtype]] = np.float64,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        super().__init__(keys, allow_missing_keys)\n        self.rotator = Rotate(angle=angle, keep_size=keep_size)\n\n        self.mode = ensure_tuple_rep(mode, len(self.keys))\n        self.padding_mode = ensure_tuple_rep(padding_mode, len(self.keys))\n        self.align_corners = ensure_tuple_rep(align_corners, len(self.keys))\n        self.dtype = ensure_tuple_rep(dtype, len(self.keys))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Rotated.__call___Rotated.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Rotated.__call___Rotated.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1335, "end_line": 1356, "span_ids": ["Rotated.__call__"], "tokens": 230}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Rotated(MapTransform, InvertibleTransform):\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key, mode, padding_mode, align_corners, dtype in self.key_iterator(\n            d, self.mode, self.padding_mode, self.align_corners, self.dtype\n        ):\n            orig_size = d[key].shape[1:]\n            d[key] = self.rotator(\n                d[key], mode=mode, padding_mode=padding_mode, align_corners=align_corners, dtype=dtype\n            )\n            rot_mat = self.rotator.get_rotation_matrix()\n            self.push_transform(\n                d,\n                key,\n                orig_size=orig_size,\n                extra_info={\n                    \"rot_mat\": rot_mat,\n                    \"mode\": mode.value if isinstance(mode, Enum) else mode,\n                    \"padding_mode\": padding_mode.value if isinstance(padding_mode, Enum) else padding_mode,\n                    \"align_corners\": align_corners if align_corners is not None else TraceKeys.NONE,\n                },\n            )\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Rotated.inverse_Rotated.inverse.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Rotated.inverse_Rotated.inverse.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1358, "end_line": 1387, "span_ids": ["Rotated.inverse"], "tokens": 327}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Rotated(MapTransform, InvertibleTransform):\n\n    def inverse(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = deepcopy(dict(data))\n        for key, dtype in self.key_iterator(d, self.dtype):\n            transform = self.get_most_recent_transform(d, key)\n            # Create inverse transform\n            fwd_rot_mat = transform[TraceKeys.EXTRA_INFO][\"rot_mat\"]\n            mode = transform[TraceKeys.EXTRA_INFO][\"mode\"]\n            padding_mode = transform[TraceKeys.EXTRA_INFO][\"padding_mode\"]\n            align_corners = transform[TraceKeys.EXTRA_INFO][\"align_corners\"]\n            inv_rot_mat = np.linalg.inv(fwd_rot_mat)\n\n            xform = AffineTransform(\n                normalized=False,\n                mode=mode,\n                padding_mode=padding_mode,\n                align_corners=False if align_corners == TraceKeys.NONE else align_corners,\n                reverse_indexing=True,\n            )\n            img_t: torch.Tensor\n            img_t, *_ = convert_data_type(d[key], torch.Tensor, dtype=dtype)  # type: ignore\n            transform_t: torch.Tensor\n            transform_t, *_ = convert_to_dst_type(inv_rot_mat, img_t)  # type: ignore\n\n            out = xform(img_t.unsqueeze(0), transform_t, spatial_size=transform[TraceKeys.ORIG_SIZE]).squeeze(0)\n            out, *_ = convert_to_dst_type(out, dst=d[key], dtype=out.dtype)\n            d[key] = out\n            # Remove the applied transform\n            self.pop_transform(d, key)\n\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_RandRotated.inverse_RandRotated.inverse.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_RandRotated.inverse_RandRotated.inverse.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1490, "end_line": 1521, "span_ids": ["RandRotated.inverse"], "tokens": 362}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandRotated(RandomizableTransform, MapTransform, InvertibleTransform):\n\n    def inverse(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = deepcopy(dict(data))\n        for key, dtype in self.key_iterator(d, self.dtype):\n            transform = self.get_most_recent_transform(d, key)\n            # Check if random transform was actually performed (based on `prob`)\n            if transform[TraceKeys.DO_TRANSFORM]:\n                # Create inverse transform\n                fwd_rot_mat = transform[TraceKeys.EXTRA_INFO][\"rot_mat\"]\n                mode = transform[TraceKeys.EXTRA_INFO][\"mode\"]\n                padding_mode = transform[TraceKeys.EXTRA_INFO][\"padding_mode\"]\n                align_corners = transform[TraceKeys.EXTRA_INFO][\"align_corners\"]\n                inv_rot_mat = np.linalg.inv(fwd_rot_mat)\n\n                xform = AffineTransform(\n                    normalized=False,\n                    mode=mode,\n                    padding_mode=padding_mode,\n                    align_corners=False if align_corners == TraceKeys.NONE else align_corners,\n                    reverse_indexing=True,\n                )\n                img_t: torch.Tensor\n                img_t, *_ = convert_data_type(d[key], torch.Tensor, dtype=dtype)  # type: ignore\n                transform_t: torch.Tensor\n                transform_t, *_ = convert_to_dst_type(inv_rot_mat, img_t)  # type: ignore\n                output: torch.Tensor\n                out = xform(img_t.unsqueeze(0), transform_t, spatial_size=transform[TraceKeys.ORIG_SIZE]).squeeze(0)\n                out, *_ = convert_to_dst_type(out, dst=d[key], dtype=out.dtype)\n                d[key] = out\n            # Remove the applied transform\n            self.pop_transform(d, key)\n\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Zoomd_Zoomd.__init__.self.zoomer.Zoom_zoom_zoom_keep_size": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Zoomd_Zoomd.__init__.self.zoomer.Zoom_zoom_zoom_keep_size", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1519, "end_line": 1567, "span_ids": ["Zoomd.__init__", "Zoomd"], "tokens": 665}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Zoomd(MapTransform, InvertibleTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.Zoom`.\n\n    Args:\n        keys: Keys to pick data for transformation.\n        zoom: The zoom factor along the spatial axes.\n            If a float, zoom is the same for each spatial axis.\n            If a sequence, zoom should contain one value for each spatial axis.\n        mode: {``\"nearest\"``, ``\"linear\"``, ``\"bilinear\"``, ``\"bicubic\"``, ``\"trilinear\"``, ``\"area\"``}\n            The interpolation mode. Defaults to ``\"area\"``.\n            See also: https://pytorch.org/docs/stable/nn.functional.html#interpolate\n            It also can be a sequence of string, each element corresponds to a key in ``keys``.\n        padding_mode: available modes for numpy array:{``\"constant\"``, ``\"edge\"``, ``\"linear_ramp\"``, ``\"maximum\"``,\n            ``\"mean\"``, ``\"median\"``, ``\"minimum\"``, ``\"reflect\"``, ``\"symmetric\"``, ``\"wrap\"``, ``\"empty\"``}\n            available modes for PyTorch Tensor: {``\"constant\"``, ``\"reflect\"``, ``\"replicate\"``, ``\"circular\"``}.\n            One of the listed string values or a user supplied function. Defaults to ``\"constant\"``.\n            The mode to pad data after zooming.\n            See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html\n            https://pytorch.org/docs/stable/generated/torch.nn.functional.pad.html\n        align_corners: This only has an effect when mode is\n            'linear', 'bilinear', 'bicubic' or 'trilinear'. Default: None.\n            See also: https://pytorch.org/docs/stable/nn.functional.html#interpolate\n            It also can be a sequence of bool or None, each element corresponds to a key in ``keys``.\n        keep_size: Should keep original size (pad if needed), default is True.\n        allow_missing_keys: don't raise exception if key is missing.\n        kwargs: other arguments for the `np.pad` or `torch.pad` function.\n            note that `np.pad` treats channel dimension as the first dimension.\n\n    \"\"\"\n\n    backend = Zoom.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        zoom: Union[Sequence[float], float],\n        mode: InterpolateModeSequence = InterpolateMode.AREA,\n        padding_mode: PadModeSequence = NumpyPadMode.EDGE,\n        align_corners: Union[Sequence[Optional[bool]], Optional[bool]] = None,\n        keep_size: bool = True,\n        allow_missing_keys: bool = False,\n        **kwargs,\n    ) -> None:\n        super().__init__(keys, allow_missing_keys)\n        self.mode = ensure_tuple_rep(mode, len(self.keys))\n        self.padding_mode = ensure_tuple_rep(padding_mode, len(self.keys))\n        self.align_corners = ensure_tuple_rep(align_corners, len(self.keys))\n        self.zoomer = Zoom(zoom=zoom, keep_size=keep_size, **kwargs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Zoomd.__call___Zoomd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Zoomd.__call___Zoomd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1574, "end_line": 1589, "span_ids": ["Zoomd.__call__"], "tokens": 180}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Zoomd(MapTransform, InvertibleTransform):\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key, mode, padding_mode, align_corners in self.key_iterator(\n            d, self.mode, self.padding_mode, self.align_corners\n        ):\n            self.push_transform(\n                d,\n                key,\n                extra_info={\n                    \"mode\": mode.value if isinstance(mode, Enum) else mode,\n                    \"padding_mode\": padding_mode.value if isinstance(padding_mode, Enum) else padding_mode,\n                    \"align_corners\": align_corners if align_corners is not None else TraceKeys.NONE,\n                },\n            )\n            d[key] = self.zoomer(d[key], mode=mode, padding_mode=padding_mode, align_corners=align_corners)\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Zoomd.inverse_Zoomd.inverse.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Zoomd.inverse_Zoomd.inverse.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1591, "end_line": 1613, "span_ids": ["Zoomd.inverse"], "tokens": 250}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Zoomd(MapTransform, InvertibleTransform):\n\n    def inverse(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = deepcopy(dict(data))\n        for key in self.key_iterator(d):\n            transform = self.get_most_recent_transform(d, key)\n            # Create inverse transform\n            zoom = np.array(self.zoomer.zoom)\n            inverse_transform = Zoom(zoom=(1 / zoom).tolist(), keep_size=self.zoomer.keep_size)\n            mode = transform[TraceKeys.EXTRA_INFO][\"mode\"]\n            padding_mode = transform[TraceKeys.EXTRA_INFO][\"padding_mode\"]\n            align_corners = transform[TraceKeys.EXTRA_INFO][\"align_corners\"]\n            # Apply inverse\n            d[key] = inverse_transform(\n                d[key],\n                mode=mode,\n                padding_mode=padding_mode,\n                align_corners=None if align_corners == TraceKeys.NONE else align_corners,\n            )\n            # Size might be out by 1 voxel so pad\n            d[key] = SpatialPad(transform[TraceKeys.ORIG_SIZE], mode=\"edge\")(d[key])  # type: ignore\n            # Remove the applied transform\n            self.pop_transform(d, key)\n\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_RandZoomd.__call___RandZoomd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_RandZoomd.__call___RandZoomd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1703, "end_line": 1730, "span_ids": ["RandZoomd.__call__"], "tokens": 271}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandZoomd(RandomizableTransform, MapTransform, InvertibleTransform):\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        first_key: Union[Hashable, List] = self.first_key(d)\n        if first_key == []:\n            return d\n\n        self.randomize(None)\n\n        # all the keys share the same random zoom factor\n        self.rand_zoom.randomize(d[first_key])  # type: ignore\n        for key, mode, padding_mode, align_corners in self.key_iterator(\n            d, self.mode, self.padding_mode, self.align_corners\n        ):\n            if self._do_transform:\n                d[key] = self.rand_zoom(\n                    d[key], mode=mode, padding_mode=padding_mode, align_corners=align_corners, randomize=False\n                )\n            self.push_transform(\n                d,\n                key,\n                extra_info={\n                    \"zoom\": self.rand_zoom._zoom,\n                    \"mode\": mode.value if isinstance(mode, Enum) else mode,\n                    \"padding_mode\": padding_mode.value if isinstance(padding_mode, Enum) else padding_mode,\n                    \"align_corners\": align_corners if align_corners is not None else TraceKeys.NONE,\n                },\n            )\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_RandZoomd.inverse_RandZoomd.inverse.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_RandZoomd.inverse_RandZoomd.inverse.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1709, "end_line": 1733, "span_ids": ["RandZoomd.inverse"], "tokens": 283}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandZoomd(RandomizableTransform, MapTransform, InvertibleTransform):\n\n    def inverse(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = deepcopy(dict(data))\n        for key in self.key_iterator(d):\n            transform = self.get_most_recent_transform(d, key)\n            # Check if random transform was actually performed (based on `prob`)\n            if transform[TraceKeys.DO_TRANSFORM]:\n                # Create inverse transform\n                zoom = np.array(transform[TraceKeys.EXTRA_INFO][\"zoom\"])\n                mode = transform[TraceKeys.EXTRA_INFO][\"mode\"]\n                padding_mode = transform[TraceKeys.EXTRA_INFO][\"padding_mode\"]\n                align_corners = transform[TraceKeys.EXTRA_INFO][\"align_corners\"]\n                inverse_transform = Zoom(zoom=(1 / zoom).tolist(), keep_size=self.rand_zoom.keep_size)\n                # Apply inverse\n                d[key] = inverse_transform(\n                    d[key],\n                    mode=mode,\n                    padding_mode=padding_mode,\n                    align_corners=None if align_corners == TraceKeys.NONE else align_corners,\n                )\n                # Size might be out by 1 voxel so pad\n                d[key] = SpatialPad(transform[TraceKeys.ORIG_SIZE], mode=\"edge\")(d[key])  # type: ignore\n            # Remove the applied transform\n            self.pop_transform(d, key)\n\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/transform.py_Randomizable.randomize_Randomizable.randomize.raise_NotImplementedError": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/transform.py_Randomizable.randomize_Randomizable.randomize.raise_NotImplementedError", "embedding": null, "metadata": {"file_path": "monai/transforms/transform.py", "file_name": "transform.py", "file_type": "text/x-python", "category": "implementation", "start_line": 172, "end_line": 184, "span_ids": ["Randomizable.randomize"], "tokens": 136}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Randomizable(ABC, ThreadUnsafe):\n\n    def randomize(self, data: Any) -> None:\n        \"\"\"\n        Within this method, :py:attr:`self.R` should be used, instead of `np.random`, to introduce random factors.\n\n        all :py:attr:`self.R` calls happen here so that we have a better chance to\n        identify errors of sync the random state.\n\n        This method can generate the random factors based on properties of the input data.\n\n        Raises:\n            NotImplementedError: When the subclass does not override this method.\n        \"\"\"\n        raise NotImplementedError(f\"Subclass {self.__class__.__name__} must implement this method.\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/transform.py_Transform.__call___Transform.__call__.raise_NotImplementedError": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/transform.py_Transform.__call___Transform.__call__.raise_NotImplementedError", "embedding": null, "metadata": {"file_path": "monai/transforms/transform.py", "file_name": "transform.py", "file_type": "text/x-python", "category": "implementation", "start_line": 211, "end_line": 235, "span_ids": ["Transform.__call__"], "tokens": 272}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Transform(ABC):\n\n    @abstractmethod\n    def __call__(self, data: Any):\n        \"\"\"\n        ``data`` is an element which often comes from an iteration over an\n        iterable, such as :py:class:`torch.utils.data.Dataset`. This method should\n        return an updated version of ``data``.\n        To simplify the input validations, most of the transforms assume that\n\n        - ``data`` is a Numpy ndarray, PyTorch Tensor or string,\n        - the data shape can be:\n\n          #. string data without shape, `LoadImage` transform expects file paths,\n          #. most of the pre-/post-processing transforms expect: ``(num_channels, spatial_dim_1[, spatial_dim_2, ...])``,\n             except for example: `AddChannel` expects (spatial_dim_1[, spatial_dim_2, ...]) and\n             `AsChannelFirst` expects (spatial_dim_1[, spatial_dim_2, ...], num_channels),\n\n        - the channel dimension is often not omitted even if number of channels is one.\n\n        This method can optionally take additional arguments to help execute transformation operation.\n\n        Raises:\n            NotImplementedError: When the subclass does not override this method.\n\n        \"\"\"\n        raise NotImplementedError(f\"Subclass {self.__class__.__name__} must implement this method.\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/transform.py_RandomizableTransform_RandomizableTransform.__init__.self.prob.min_max_prob_0_0_1_0_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/transform.py_RandomizableTransform_RandomizableTransform.__init__.self.prob.min_max_prob_0_0_1_0_", "embedding": null, "metadata": {"file_path": "monai/transforms/transform.py", "file_name": "transform.py", "file_type": "text/x-python", "category": "implementation", "start_line": 179, "end_line": 209, "span_ids": ["RandomizableTransform.__init__", "RandomizableTransform"], "tokens": 228}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandomizableTransform(Randomizable, Transform):\n    \"\"\"\n    An interface for handling random state locally, currently based on a class variable `R`,\n    which is an instance of `np.random.RandomState`.\n    This class introduces a randomized flag `_do_transform`, is mainly for randomized data augmentation transforms.\n    For example:\n\n    .. code-block:: python\n\n        from monai.transforms import RandomizableTransform\n\n        class RandShiftIntensity100(RandomizableTransform):\n            def randomize(self):\n                super().randomize(None)\n                self._offset = self.R.uniform(low=0, high=100)\n\n            def __call__(self, img):\n                self.randomize()\n                if not self._do_transform:\n                    return img\n                return img + self._offset\n\n        transform = RandShiftIntensity()\n        transform.set_random_state(seed=0)\n        print(transform(10))\n\n    \"\"\"\n\n    def __init__(self, prob: float = 1.0, do_transform: bool = True):\n        self._do_transform = do_transform\n        self.prob = min(max(prob, 0.0), 1.0)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/transform.py_RandomizableTransform.randomize_RandomizableTransform.randomize.self._do_transform.self_R_rand_self_prob": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/transform.py_RandomizableTransform.randomize_RandomizableTransform.randomize.self._do_transform.self_R_rand_self_prob", "embedding": null, "metadata": {"file_path": "monai/transforms/transform.py", "file_name": "transform.py", "file_type": "text/x-python", "category": "implementation", "start_line": 211, "end_line": 220, "span_ids": ["RandomizableTransform.randomize"], "tokens": 114}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandomizableTransform(Randomizable, Transform):\n\n    def randomize(self, data: Any) -> None:\n        \"\"\"\n        Within this method, :py:attr:`self.R` should be used, instead of `np.random`, to introduce random factors.\n\n        all :py:attr:`self.R` calls happen here so that we have a better chance to\n        identify errors of sync the random state.\n\n        This method can generate the random factors based on properties of the input data.\n        \"\"\"\n        self._do_transform = self.R.rand() < self.prob", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/transform.py_MapTransform_MapTransform.__init__.for_key_in_self_keys_.if_not_isinstance_key_Ha.raise_TypeError_f_keys_mu": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/transform.py_MapTransform_MapTransform.__init__.for_key_in_self_keys_.if_not_isinstance_key_Ha.raise_TypeError_f_keys_mu", "embedding": null, "metadata": {"file_path": "monai/transforms/transform.py", "file_name": "transform.py", "file_type": "text/x-python", "category": "implementation", "start_line": 223, "end_line": 255, "span_ids": ["MapTransform", "MapTransform.__init__"], "tokens": 296}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class MapTransform(Transform):\n    \"\"\"\n    A subclass of :py:class:`monai.transforms.Transform` with an assumption\n    that the ``data`` input of ``self.__call__`` is a MutableMapping such as ``dict``.\n\n    The ``keys`` parameter will be used to get and set the actual data\n    item to transform.  That is, the callable of this transform should\n    follow the pattern:\n\n        .. code-block:: python\n\n            def __call__(self, data):\n                for key in self.keys:\n                    if key in data:\n                        # update output data with some_transform_function(data[key]).\n                    else:\n                        # raise exception unless allow_missing_keys==True.\n                return data\n\n    Raises:\n        ValueError: When ``keys`` is an empty iterable.\n        TypeError: When ``keys`` type is not in ``Union[Hashable, Iterable[Hashable]]``.\n\n    \"\"\"\n\n    def __init__(self, keys: KeysCollection, allow_missing_keys: bool = False) -> None:\n        self.keys: Tuple[Hashable, ...] = ensure_tuple(keys)\n        self.allow_missing_keys = allow_missing_keys\n        if not self.keys:\n            raise ValueError(\"keys must be non empty.\")\n        for key in self.keys:\n            if not isinstance(key, Hashable):\n                raise TypeError(f\"keys must be one of (Hashable, Iterable[Hashable]) but is {type(keys).__name__}.\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/transform.py_MapTransform.__call___MapTransform.__call__.raise_NotImplementedError": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/transform.py_MapTransform.__call___MapTransform.__call__.raise_NotImplementedError", "embedding": null, "metadata": {"file_path": "monai/transforms/transform.py", "file_name": "transform.py", "file_type": "text/x-python", "category": "implementation", "start_line": 316, "end_line": 342, "span_ids": ["MapTransform.__call__"], "tokens": 279}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class MapTransform(Transform):\n\n    @abstractmethod\n    def __call__(self, data):\n        \"\"\"\n        ``data`` often comes from an iteration over an iterable,\n        such as :py:class:`torch.utils.data.Dataset`.\n\n        To simplify the input validations, this method assumes:\n\n        - ``data`` is a Python dictionary,\n        - ``data[key]`` is a Numpy ndarray, PyTorch Tensor or string, where ``key`` is an element\n          of ``self.keys``, the data shape can be:\n\n          #. string data without shape, `LoadImaged` transform expects file paths,\n          #. most of the pre-/post-processing transforms expect: ``(num_channels, spatial_dim_1[, spatial_dim_2, ...])``,\n             except for example: `AddChanneld` expects (spatial_dim_1[, spatial_dim_2, ...]) and\n             `AsChannelFirstd` expects (spatial_dim_1[, spatial_dim_2, ...], num_channels)\n\n        - the channel dimension is often not omitted even if number of channels is one.\n\n        Raises:\n            NotImplementedError: When the subclass does not override this method.\n\n        returns:\n            An updated dictionary version of ``data`` by applying the transform.\n\n        \"\"\"\n        raise NotImplementedError(f\"Subclass {self.__class__.__name__} must implement this method.\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/transform.py_MapTransform.key_iterator_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/transform.py_MapTransform.key_iterator_", "embedding": null, "metadata": {"file_path": "monai/transforms/transform.py", "file_name": "transform.py", "file_type": "text/x-python", "category": "implementation", "start_line": 344, "end_line": 375, "span_ids": ["MapTransform.first_key", "MapTransform.key_iterator"], "tokens": 319}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class MapTransform(Transform):\n\n    def key_iterator(self, data: Dict[Hashable, Any], *extra_iterables: Optional[Iterable]) -> Generator:\n        \"\"\"\n        Iterate across keys and optionally extra iterables. If key is missing, exception is raised if\n        `allow_missing_keys==False` (default). If `allow_missing_keys==True`, key is skipped.\n\n        Args:\n            data: data that the transform will be applied to\n            extra_iterables: anything else to be iterated through\n        \"\"\"\n        # if no extra iterables given, create a dummy list of Nones\n        ex_iters = extra_iterables or [[None] * len(self.keys)]\n\n        # loop over keys and any extra iterables\n        _ex_iters: List[Any]\n        for key, *_ex_iters in zip(self.keys, *ex_iters):\n            # all normal, yield (what we yield depends on whether extra iterables were given)\n            if key in data:\n                yield (key,) + tuple(_ex_iters) if extra_iterables else key\n            elif not self.allow_missing_keys:\n                raise KeyError(f\"Key was missing ({key}) and allow_missing_keys==False\")\n\n    def first_key(self, data: Dict[Hashable, Any]):\n        \"\"\"\n        Get the first available key of `self.keys` in the input `data` dictionary.\n        If no available key, return an empty list `[]`.\n\n        Args:\n            data: data that the transform will be applied to.\n\n        \"\"\"\n        return first(self.key_iterator(data), [])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_Lambda.__call___Lambda.__call__.raise_ValueError_Incompa": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_Lambda.__call___Lambda.__call__.raise_ValueError_Incompa", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 662, "end_line": 680, "span_ids": ["Lambda.__call__"], "tokens": 166}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Lambda(Transform):\n\n    def __call__(self, img: NdarrayOrTensor, func: Optional[Callable] = None):\n        \"\"\"\n        Apply `self.func` to `img`.\n\n        Args:\n            func: Lambda/function to be applied. Defaults to `self.func`.\n\n        Raises:\n            TypeError: When ``func`` is not an ``Optional[Callable]``.\n            ValueError: When ``func=None`` and ``self.func=None``. Incompatible values.\n\n        \"\"\"\n        if func is not None:\n            if not callable(func):\n                raise TypeError(f\"func must be None or callable but is {type(func).__name__}.\")\n            return func(img)\n        if self.func is not None:\n            return self.func(img)\n        raise ValueError(\"Incompatible values: func=None and self.func=None.\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_FgBgToIndices.__call___FgBgToIndices.__call__.return.fg_indices_bg_indices": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_FgBgToIndices.__call___FgBgToIndices.__call__.return.fg_indices_bg_indices", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 794, "end_line": 814, "span_ids": ["FgBgToIndices.__call__"], "tokens": 220}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class FgBgToIndices(Transform):\n\n    def __call__(\n        self,\n        label: NdarrayOrTensor,\n        image: Optional[NdarrayOrTensor] = None,\n        output_shape: Optional[Sequence[int]] = None,\n    ) -> Tuple[NdarrayOrTensor, NdarrayOrTensor]:\n        \"\"\"\n        Args:\n            label: input data to compute foreground and background indices.\n            image: if image is not None, use ``label = 0 & image > image_threshold``\n                to define background. so the output items will not map to all the voxels in the label.\n            output_shape: expected shape of output indices. if None, use `self.output_shape` instead.\n\n        \"\"\"\n        if output_shape is None:\n            output_shape = self.output_shape\n        fg_indices, bg_indices = map_binary_to_indices(label, image, self.image_threshold)\n        if output_shape is not None:\n            fg_indices = unravel_indices(fg_indices, output_shape)\n            bg_indices = unravel_indices(bg_indices, output_shape)\n        return fg_indices, bg_indices", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_AddExtremePointsChannel_AddExtremePointsChannel.randomize.self._points.get_extreme_points_label_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_AddExtremePointsChannel_AddExtremePointsChannel.randomize.self._points.get_extreme_points_label_", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 895, "end_line": 923, "span_ids": ["AddExtremePointsChannel.__init__", "AddExtremePointsChannel.randomize", "AddExtremePointsChannel"], "tokens": 327}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AddExtremePointsChannel(Randomizable, Transform):\n    \"\"\"\n    Add extreme points of label to the image as a new channel. This transform generates extreme\n    point from label and applies a gaussian filter. The pixel values in points image are rescaled\n    to range [rescale_min, rescale_max] and added as a new channel to input image. The algorithm is\n    described in Roth et al., Going to Extremes: Weakly Supervised Medical Image Segmentation\n    https://arxiv.org/abs/2009.11988.\n\n    This transform only supports single channel labels (1, spatial_dim1, [spatial_dim2, ...]). The\n    background ``index`` is ignored when calculating extreme points.\n\n    Args:\n        background: Class index of background label, defaults to 0.\n        pert: Random perturbation amount to add to the points, defaults to 0.0.\n\n    Raises:\n        ValueError: When no label image provided.\n        ValueError: When label image is not single channel.\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __init__(self, background: int = 0, pert: float = 0.0) -> None:\n        self._background = background\n        self._pert = pert\n        self._points: List[Tuple[int, ...]] = []\n\n    def randomize(self, label: NdarrayOrTensor) -> None:\n        self._points = get_extreme_points(label, rand_state=self.R, background=self._background, pert=self._pert)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py___all_____all__._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py___all_____all__._", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 69, "end_line": 181, "span_ids": ["docstring"], "tokens": 762}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "__all__ = [\n    \"AddChannelD\",\n    \"AddChannelDict\",\n    \"AddChanneld\",\n    \"AddCoordinateChannelsD\",\n    \"AddCoordinateChannelsDict\",\n    \"AddCoordinateChannelsd\",\n    \"AddExtremePointsChannelD\",\n    \"AddExtremePointsChannelDict\",\n    \"AddExtremePointsChanneld\",\n    \"AsChannelFirstD\",\n    \"AsChannelFirstDict\",\n    \"AsChannelFirstd\",\n    \"AsChannelLastD\",\n    \"AsChannelLastDict\",\n    \"AsChannelLastd\",\n    \"CastToTypeD\",\n    \"CastToTypeDict\",\n    \"CastToTyped\",\n    \"ConcatItemsD\",\n    \"ConcatItemsDict\",\n    \"ConcatItemsd\",\n    \"ConvertToMultiChannelBasedOnBratsClassesD\",\n    \"ConvertToMultiChannelBasedOnBratsClassesDict\",\n    \"ConvertToMultiChannelBasedOnBratsClassesd\",\n    \"CopyItemsD\",\n    \"CopyItemsDict\",\n    \"CopyItemsd\",\n    \"CuCIMd\",\n    \"CuCIMD\",\n    \"CuCIMDict\",\n    \"DataStatsD\",\n    \"DataStatsDict\",\n    \"DataStatsd\",\n    \"DeleteItemsD\",\n    \"DeleteItemsDict\",\n    \"DeleteItemsd\",\n    \"EnsureChannelFirstD\",\n    \"EnsureChannelFirstDict\",\n    \"EnsureChannelFirstd\",\n    \"EnsureTypeD\",\n    \"EnsureTypeDict\",\n    \"EnsureTyped\",\n    \"FgBgToIndicesD\",\n    \"FgBgToIndicesDict\",\n    \"FgBgToIndicesd\",\n    \"IdentityD\",\n    \"IdentityDict\",\n    \"Identityd\",\n    \"IntensityStatsd\",\n    \"IntensityStatsD\",\n    \"IntensityStatsDict\",\n    \"LabelToMaskD\",\n    \"LabelToMaskDict\",\n    \"LabelToMaskd\",\n    \"LambdaD\",\n    \"LambdaDict\",\n    \"Lambdad\",\n    \"MapLabelValueD\",\n    \"MapLabelValueDict\",\n    \"MapLabelValued\",\n    \"RandCuCIMd\",\n    \"RandCuCIMD\",\n    \"RandCuCIMDict\",\n    \"RandLambdaD\",\n    \"RandLambdaDict\",\n    \"RandLambdad\",\n    \"RandTorchVisionD\",\n    \"RandTorchVisionDict\",\n    \"RandTorchVisiond\",\n    \"RemoveRepeatedChannelD\",\n    \"RemoveRepeatedChannelDict\",\n    \"RemoveRepeatedChanneld\",\n    \"RepeatChannelD\",\n    \"RepeatChannelDict\",\n    \"RepeatChanneld\",\n    \"SelectItemsD\",\n    \"SelectItemsDict\",\n    \"SelectItemsd\",\n    \"SimulateDelayD\",\n    \"SimulateDelayDict\",\n    \"SimulateDelayd\",\n    \"SplitChannelD\",\n    \"SplitChannelDict\",\n    \"SplitChanneld\",\n    \"SqueezeDimD\",\n    \"SqueezeDimDict\",\n    \"SqueezeDimd\",\n    \"ToCupyD\",\n    \"ToCupyDict\",\n    \"ToCupyd\",\n    \"ToDeviced\",\n    \"ToDeviceD\",\n    \"ToDeviceDict\",\n    \"ToNumpyD\",\n    \"ToNumpyDict\",\n    \"ToNumpyd\",\n    \"ToPILD\",\n    \"ToPILDict\",\n    \"ToPILd\",\n    \"ToTensorD\",\n    \"ToTensorDict\",\n    \"ToTensord\",\n    \"TorchVisionD\",\n    \"TorchVisionDict\",\n    \"TorchVisiond\",\n    \"Transposed\",\n    \"TransposeDict\",\n    \"TransposeD\",\n    \"ClassesToIndicesd\",\n    \"ClassesToIndicesD\",\n    \"ClassesToIndicesDict\",\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_EnsureChannelFirstd_EnsureChannelFirstd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_EnsureChannelFirstd_EnsureChannelFirstd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 276, "end_line": 314, "span_ids": ["EnsureChannelFirstd.__call__", "EnsureChannelFirstd.__init__", "EnsureChannelFirstd"], "tokens": 434}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class EnsureChannelFirstd(MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.EnsureChannelFirst`.\n    \"\"\"\n\n    backend = EnsureChannelFirst.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        meta_keys: Optional[KeysCollection] = None,\n        meta_key_postfix: str = \"meta_dict\",\n        strict_check: bool = True,\n    ) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to be transformed.\n                See also: :py:class:`monai.transforms.compose.MapTransform`\n            meta_keys: explicitly indicate the key of the corresponding meta data dictionary.\n                for example, for data with key `image`, the metadata by default is in `image_meta_dict`.\n                the meta data is a dictionary object which contains: filename, original_shape, etc.\n                it can be a sequence of string, map to the `keys`.\n                if None, will try to construct meta_keys by `key_{meta_key_postfix}`.\n            meta_key_postfix: if meta_keys is None and `key_{postfix}` was used to store the metadata in `LoadImaged`.\n                So need the key to extract metadata for channel dim information, default is `meta_dict`.\n                For example, for data with key `image`, metadata by default is in `image_meta_dict`.\n            strict_check: whether to raise an error when the meta information is insufficient.\n\n        \"\"\"\n        super().__init__(keys)\n        self.adjuster = EnsureChannelFirst(strict_check=strict_check)\n        self.meta_keys = ensure_tuple_rep(meta_keys, len(self.keys))\n        self.meta_key_postfix = ensure_tuple_rep(meta_key_postfix, len(self.keys))\n\n    def __call__(self, data) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key, meta_key, meta_key_postfix in zip(self.keys, self.meta_keys, self.meta_key_postfix):\n            d[key] = self.adjuster(d[key], d[meta_key or f\"{key}_{meta_key_postfix}\"])\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_RemoveRepeatedChanneld_RemoveRepeatedChanneld.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_RemoveRepeatedChanneld_RemoveRepeatedChanneld.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 342, "end_line": 364, "span_ids": ["RemoveRepeatedChanneld.__init__", "RemoveRepeatedChanneld", "RemoveRepeatedChanneld.__call__"], "tokens": 204}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RemoveRepeatedChanneld(MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.RemoveRepeatedChannel`.\n    \"\"\"\n\n    backend = RemoveRepeatedChannel.backend\n\n    def __init__(self, keys: KeysCollection, repeats: int, allow_missing_keys: bool = False) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to be transformed.\n                See also: :py:class:`monai.transforms.compose.MapTransform`\n            repeats: the number of repetitions for each element.\n            allow_missing_keys: don't raise exception if key is missing.\n        \"\"\"\n        super().__init__(keys, allow_missing_keys)\n        self.repeater = RemoveRepeatedChannel(repeats)\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key in self.key_iterator(d):\n            d[key] = self.repeater(d[key])\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_SplitChanneld_SplitChanneld.__init__.self.splitter.SplitChannel_channel_dim_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_SplitChanneld_SplitChanneld.__init__.self.splitter.SplitChannel_channel_dim_", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 367, "end_line": 396, "span_ids": ["SplitChanneld.__init__", "SplitChanneld"], "tokens": 293}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SplitChanneld(MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.SplitChannel`.\n    All the input specified by `keys` should be split into same count of data.\n    \"\"\"\n\n    backend = SplitChannel.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        output_postfixes: Optional[Sequence[str]] = None,\n        channel_dim: int = 0,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to be transformed.\n                See also: :py:class:`monai.transforms.compose.MapTransform`\n            output_postfixes: the postfixes to construct keys to store split data.\n                for example: if the key of input data is `pred` and split 2 classes, the output\n                data keys will be: pred_(output_postfixes[0]), pred_(output_postfixes[1])\n                if None, using the index number: `pred_0`, `pred_1`, ... `pred_N`.\n            channel_dim: which dimension of input image is the channel, default to 0.\n            allow_missing_keys: don't raise exception if key is missing.\n\n        \"\"\"\n        super().__init__(keys, allow_missing_keys)\n        self.output_postfixes = output_postfixes\n        self.splitter = SplitChannel(channel_dim=channel_dim)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_SplitChanneld.__call___SplitChanneld.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_SplitChanneld.__call___SplitChanneld.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 398, "end_line": 410, "span_ids": ["SplitChanneld.__call__"], "tokens": 174}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SplitChanneld(MapTransform):\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key in self.key_iterator(d):\n            rets = self.splitter(d[key])\n            postfixes: Sequence = list(range(len(rets))) if self.output_postfixes is None else self.output_postfixes\n            if len(postfixes) != len(rets):\n                raise AssertionError(\"count of split results must match output_postfixes.\")\n            for i, r in enumerate(rets):\n                split_key = f\"{key}_{postfixes[i]}\"\n                if split_key in d:\n                    raise RuntimeError(f\"input data already contains key {split_key}.\")\n                d[split_key] = r\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_ToTensord_ToTensord.inverse.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_ToTensord_ToTensord.inverse.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 451, "end_line": 495, "span_ids": ["ToTensord", "ToTensord.__call__", "ToTensord.inverse", "ToTensord.__init__"], "tokens": 433}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ToTensord(MapTransform, InvertibleTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.ToTensor`.\n    \"\"\"\n\n    backend = ToTensor.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        dtype: Optional[torch.dtype] = None,\n        device: Optional[torch.device] = None,\n        wrap_sequence: bool = True,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to be transformed.\n                See also: :py:class:`monai.transforms.compose.MapTransform`\n            dtype: target data content type to convert, for example: torch.float, etc.\n            device: specify the target device to put the Tensor data.\n            wrap_sequence: if `False`, then lists will recursively call this function, default to `True`.\n                E.g., if `False`, `[1, 2]` -> `[tensor(1), tensor(2)]`, if `True`, then `[1, 2]` -> `tensor([1, 2])`.\n            allow_missing_keys: don't raise exception if key is missing.\n        \"\"\"\n        super().__init__(keys, allow_missing_keys)\n        self.converter = ToTensor(dtype=dtype, device=device, wrap_sequence=wrap_sequence)\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key in self.key_iterator(d):\n            self.push_transform(d, key)\n            d[key] = self.converter(d[key])\n        return d\n\n    def inverse(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = deepcopy(dict(data))\n        for key in self.key_iterator(d):\n            # Create inverse transform\n            inverse_transform = ToNumpy()\n            # Apply inverse\n            d[key] = inverse_transform(d[key])\n            # Remove the applied transform\n            self.pop_transform(d, key)\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_ToPILd_ToPILd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_ToPILd_ToPILd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 596, "end_line": 617, "span_ids": ["ToPILd.__init__", "ToPILd", "ToPILd.__call__"], "tokens": 178}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ToPILd(MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.ToNumpy`.\n    \"\"\"\n\n    backend = ToPIL.backend\n\n    def __init__(self, keys: KeysCollection, allow_missing_keys: bool = False) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to be transformed.\n                See also: :py:class:`monai.transforms.compose.MapTransform`\n            allow_missing_keys: don't raise exception if key is missing.\n        \"\"\"\n        super().__init__(keys, allow_missing_keys)\n        self.converter = ToPIL()\n\n    def __call__(self, data: Mapping[Hashable, Any]) -> Dict[Hashable, Any]:\n        d = dict(data)\n        for key in self.key_iterator(d):\n            d[key] = self.converter(d[key])\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_DataStatsd_DataStatsd.__init__.self.printer.DataStats_logger_handler_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_DataStatsd_DataStatsd.__init__.self.printer.DataStats_logger_handler_", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 731, "end_line": 782, "span_ids": ["DataStatsd", "DataStatsd.__init__"], "tokens": 619}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataStatsd(MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.DataStats`.\n    \"\"\"\n\n    backend = DataStats.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        prefix: Union[Sequence[str], str] = \"Data\",\n        data_type: Union[Sequence[bool], bool] = True,\n        data_shape: Union[Sequence[bool], bool] = True,\n        value_range: Union[Sequence[bool], bool] = True,\n        data_value: Union[Sequence[bool], bool] = False,\n        additional_info: Optional[Union[Sequence[Callable], Callable]] = None,\n        logger_handler: Optional[logging.Handler] = None,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to be transformed.\n                See also: :py:class:`monai.transforms.compose.MapTransform`\n            prefix: will be printed in format: \"{prefix} statistics\".\n                it also can be a sequence of string, each element corresponds to a key in ``keys``.\n            data_type: whether to show the type of input data.\n                it also can be a sequence of bool, each element corresponds to a key in ``keys``.\n            data_shape: whether to show the shape of input data.\n                it also can be a sequence of bool, each element corresponds to a key in ``keys``.\n            value_range: whether to show the value range of input data.\n                it also can be a sequence of bool, each element corresponds to a key in ``keys``.\n            data_value: whether to show the raw value of input data.\n                it also can be a sequence of bool, each element corresponds to a key in ``keys``.\n                a typical example is to print some properties of Nifti image: affine, pixdim, etc.\n            additional_info: user can define callable function to extract\n                additional info from input data. it also can be a sequence of string, each element\n                corresponds to a key in ``keys``.\n            logger_handler: add additional handler to output data: save to file, etc.\n                add existing python logging handlers: https://docs.python.org/3/library/logging.handlers.html\n                the handler should have a logging level of at least `INFO`.\n            allow_missing_keys: don't raise exception if key is missing.\n\n        \"\"\"\n        super().__init__(keys, allow_missing_keys)\n        self.prefix = ensure_tuple_rep(prefix, len(self.keys))\n        self.data_type = ensure_tuple_rep(data_type, len(self.keys))\n        self.data_shape = ensure_tuple_rep(data_shape, len(self.keys))\n        self.value_range = ensure_tuple_rep(value_range, len(self.keys))\n        self.data_value = ensure_tuple_rep(data_value, len(self.keys))\n        self.additional_info = ensure_tuple_rep(additional_info, len(self.keys))\n        self.logger_handler = logger_handler\n        self.printer = DataStats(logger_handler=logger_handler)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_DataStatsd.__call___DataStatsd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_DataStatsd.__call___DataStatsd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 784, "end_line": 790, "span_ids": ["DataStatsd.__call__"], "tokens": 127}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataStatsd(MapTransform):\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key, prefix, data_type, data_shape, value_range, data_value, additional_info in self.key_iterator(\n            d, self.prefix, self.data_type, self.data_shape, self.value_range, self.data_value, self.additional_info\n        ):\n            d[key] = self.printer(d[key], prefix, data_type, data_shape, value_range, data_value, additional_info)\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_CopyItemsd_CopyItemsd.__init__.self.names.names": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_CopyItemsd_CopyItemsd.__init__.self.names.names", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 823, "end_line": 860, "span_ids": ["CopyItemsd", "CopyItemsd.__init__"], "tokens": 390}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CopyItemsd(MapTransform):\n    \"\"\"\n    Copy specified items from data dictionary and save with different key names.\n    It can copy several items together and copy several times.\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __init__(\n        self, keys: KeysCollection, times: int, names: KeysCollection, allow_missing_keys: bool = False\n    ) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to be transformed.\n                See also: :py:class:`monai.transforms.compose.MapTransform`\n            times: expected copy times, for example, if keys is \"img\", times is 3,\n                it will add 3 copies of \"img\" data to the dictionary.\n            names: the names corresponding to the newly copied data,\n                the length should match `len(keys) x times`. for example, if keys is [\"img\", \"seg\"]\n                and times is 2, names can be: [\"img_1\", \"seg_1\", \"img_2\", \"seg_2\"].\n            allow_missing_keys: don't raise exception if key is missing.\n\n        Raises:\n            ValueError: When ``times`` is nonpositive.\n            ValueError: When ``len(names)`` is not ``len(keys) * times``. Incompatible values.\n\n        \"\"\"\n        super().__init__(keys, allow_missing_keys)\n        if times < 1:\n            raise ValueError(f\"times must be positive, got {times}.\")\n        self.times = times\n        names = ensure_tuple(names)\n        if len(names) != (len(self.keys) * times):\n            raise ValueError(\n                \"len(names) must match len(keys) * times, \"\n                f\"got len(names)={len(names)} len(keys) * times={len(self.keys) * times}.\"\n            )\n        self.names = names", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_CopyItemsd.__call___CopyItemsd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_CopyItemsd.__call___CopyItemsd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 862, "end_line": 879, "span_ids": ["CopyItemsd.__call__"], "tokens": 175}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CopyItemsd(MapTransform):\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        \"\"\"\n        Raises:\n            KeyError: When a key in ``self.names`` already exists in ``data``.\n\n        \"\"\"\n        d = dict(data)\n        key_len = len(self.keys)\n        for i in range(self.times):\n            for key, new_key in self.key_iterator(d, self.names[i * key_len : (i + 1) * key_len]):\n                if new_key in d:\n                    raise KeyError(f\"Key {new_key} already exists in data.\")\n                val = d[key]\n                if isinstance(val, torch.Tensor):\n                    d[new_key] = val.detach().clone()\n                else:\n                    d[new_key] = deepcopy(val)\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_ConvertToMultiChannelBasedOnBratsClassesd_ConvertToMultiChannelBasedOnBratsClassesd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_ConvertToMultiChannelBasedOnBratsClassesd_ConvertToMultiChannelBasedOnBratsClassesd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1184, "end_line": 1205, "span_ids": ["ConvertToMultiChannelBasedOnBratsClassesd.__call__", "ConvertToMultiChannelBasedOnBratsClassesd.__init__", "ConvertToMultiChannelBasedOnBratsClassesd"], "tokens": 235}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ConvertToMultiChannelBasedOnBratsClassesd(MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.ConvertToMultiChannelBasedOnBratsClasses`.\n    Convert labels to multi channels based on brats18 classes:\n    label 1 is the necrotic and non-enhancing tumor core\n    label 2 is the the peritumoral edema\n    label 4 is the GD-enhancing tumor\n    The possible classes are TC (Tumor core), WT (Whole tumor)\n    and ET (Enhancing tumor).\n    \"\"\"\n\n    backend = ConvertToMultiChannelBasedOnBratsClasses.backend\n\n    def __init__(self, keys: KeysCollection, allow_missing_keys: bool = False):\n        super().__init__(keys, allow_missing_keys)\n        self.converter = ConvertToMultiChannelBasedOnBratsClasses()\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key in self.key_iterator(d):\n            d[key] = self.converter(d[key])\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_AddExtremePointsChanneld_AddExtremePointsChanneld.randomize.self.points.get_extreme_points_label_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_AddExtremePointsChanneld_AddExtremePointsChanneld.randomize.self.points.get_extreme_points_label_", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1208, "end_line": 1250, "span_ids": ["AddExtremePointsChanneld", "AddExtremePointsChanneld.randomize", "AddExtremePointsChanneld.__init__"], "tokens": 417}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AddExtremePointsChanneld(Randomizable, MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.AddExtremePointsChannel`.\n\n    Args:\n        keys: keys of the corresponding items to be transformed.\n            See also: :py:class:`monai.transforms.compose.MapTransform`\n        label_key: key to label source to get the extreme points.\n        background: Class index of background label, defaults to 0.\n        pert: Random perturbation amount to add to the points, defaults to 0.0.\n        sigma: if a list of values, must match the count of spatial dimensions of input data,\n            and apply every value in the list to 1 spatial dimension. if only 1 value provided,\n            use it for all spatial dimensions.\n        rescale_min: minimum value of output data.\n        rescale_max: maximum value of output data.\n        allow_missing_keys: don't raise exception if key is missing.\n\n    \"\"\"\n\n    backend = AddExtremePointsChannel.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        label_key: str,\n        background: int = 0,\n        pert: float = 0.0,\n        sigma: Union[Sequence[float], float, Sequence[torch.Tensor], torch.Tensor] = 3.0,\n        rescale_min: float = -1.0,\n        rescale_max: float = 1.0,\n        allow_missing_keys: bool = False,\n    ):\n        MapTransform.__init__(self, keys, allow_missing_keys)\n        self.background = background\n        self.pert = pert\n        self.points: List[Tuple[int, ...]] = []\n        self.label_key = label_key\n        self.sigma = sigma\n        self.rescale_min = rescale_min\n        self.rescale_max = rescale_max\n\n    def randomize(self, label: NdarrayOrTensor) -> None:\n        self.points = get_extreme_points(label, rand_state=self.R, background=self.background, pert=self.pert)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_AddExtremePointsChanneld.__call___AddExtremePointsChanneld.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_AddExtremePointsChanneld.__call___AddExtremePointsChanneld.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1252, "end_line": 1272, "span_ids": ["AddExtremePointsChanneld.__call__"], "tokens": 192}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AddExtremePointsChanneld(Randomizable, MapTransform):\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        label = d[self.label_key]\n        if label.shape[0] != 1:\n            raise ValueError(\"Only supports single channel labels!\")\n\n        # Generate extreme points\n        self.randomize(label[0, :])\n\n        for key in self.key_iterator(d):\n            img = d[key]\n            points_image = extreme_points_to_image(\n                points=self.points,\n                label=label,\n                sigma=self.sigma,\n                rescale_min=self.rescale_min,\n                rescale_max=self.rescale_max,\n            )\n            points_image, *_ = convert_to_dst_type(points_image, img)  # type: ignore\n            d[key] = concatenate([img, points_image], axis=0)\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_TorchVisiond_TorchVisiond.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_TorchVisiond_TorchVisiond.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1308, "end_line": 1339, "span_ids": ["TorchVisiond.__init__", "TorchVisiond", "TorchVisiond.__call__"], "tokens": 320}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TorchVisiond(MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.TorchVision` for non-randomized transforms.\n    For randomized transforms of TorchVision use :py:class:`monai.transforms.RandTorchVisiond`.\n\n    Note:\n        As most of the TorchVision transforms only work for PIL image and PyTorch Tensor, this transform expects input\n        data to be dict of PyTorch Tensors, users can easily call `ToTensord` transform to convert Numpy to Tensor.\n    \"\"\"\n\n    backend = TorchVision.backend\n\n    def __init__(self, keys: KeysCollection, name: str, allow_missing_keys: bool = False, *args, **kwargs) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to be transformed.\n                See also: :py:class:`monai.transforms.compose.MapTransform`\n            name: The transform name in TorchVision package.\n            allow_missing_keys: don't raise exception if key is missing.\n            args: parameters for the TorchVision transform.\n            kwargs: parameters for the TorchVision transform.\n\n        \"\"\"\n        super().__init__(keys, allow_missing_keys)\n        self.name = name\n        self.trans = TorchVision(name, *args, **kwargs)\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key in self.key_iterator(d):\n            d[key] = self.trans(d[key])\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_RandTorchVisiond_RandTorchVisiond.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_RandTorchVisiond_RandTorchVisiond.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1342, "end_line": 1379, "span_ids": ["RandTorchVisiond", "RandTorchVisiond.__call__", "RandTorchVisiond.__init__"], "tokens": 391}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandTorchVisiond(Randomizable, MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.TorchVision` for randomized transforms.\n    For deterministic non-randomized transforms of TorchVision use :py:class:`monai.transforms.TorchVisiond`.\n\n    Note:\n\n        - As most of the TorchVision transforms only work for PIL image and PyTorch Tensor, this transform expects input\n          data to be dict of PyTorch Tensors, users can easily call `ToTensord` transform to convert Numpy to Tensor.\n        - This class inherits the ``Randomizable`` purely to prevent any dataset caching to skip the transform\n          computation. If the random factor of the underlying torchvision transform is not derived from `self.R`,\n          the results may not be deterministic.\n          See Also: :py:class:`monai.transforms.Randomizable`.\n\n    \"\"\"\n\n    backend = TorchVision.backend\n\n    def __init__(self, keys: KeysCollection, name: str, allow_missing_keys: bool = False, *args, **kwargs) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to be transformed.\n                See also: :py:class:`monai.transforms.compose.MapTransform`\n            name: The transform name in TorchVision package.\n            allow_missing_keys: don't raise exception if key is missing.\n            args: parameters for the TorchVision transform.\n            kwargs: parameters for the TorchVision transform.\n\n        \"\"\"\n        MapTransform.__init__(self, keys, allow_missing_keys)\n        self.name = name\n        self.trans = TorchVision(name, *args, **kwargs)\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key in self.key_iterator(d):\n            d[key] = self.trans(d[key])\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_MapLabelValued_MapLabelValued.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_MapLabelValued_MapLabelValued.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1347, "end_line": 1379, "span_ids": ["MapLabelValued", "MapLabelValued.__init__", "MapLabelValued.__call__"], "tokens": 271}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class MapLabelValued(MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.MapLabelValue`.\n    \"\"\"\n\n    backend = MapLabelValue.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        orig_labels: Sequence,\n        target_labels: Sequence,\n        dtype: DtypeLike = np.float32,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to be transformed.\n                See also: :py:class:`monai.transforms.compose.MapTransform`\n            orig_labels: original labels that map to others.\n            target_labels: expected label values, 1: 1 map to the `orig_labels`.\n            dtype: convert the output data to dtype, default to float32.\n            allow_missing_keys: don't raise exception if key is missing.\n\n        \"\"\"\n        super().__init__(keys, allow_missing_keys)\n        self.mapper = MapLabelValue(orig_labels=orig_labels, target_labels=target_labels, dtype=dtype)\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key in self.key_iterator(d):\n            d[key] = self.mapper(d[key])\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_rescale_instance_array_rescale_array_int_max.return.np_asarray_rescale_array_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_rescale_instance_array_rescale_array_int_max.return.np_asarray_rescale_array_", "embedding": null, "metadata": {"file_path": "monai/transforms/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 182, "end_line": 200, "span_ids": ["rescale_instance_array", "rescale_array_int_max"], "tokens": 206}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def rescale_instance_array(\n    arr: np.ndarray, minv: Optional[float] = 0.0, maxv: Optional[float] = 1.0, dtype: DtypeLike = np.float32\n) -> np.ndarray:\n    \"\"\"\n    Rescale each array slice along the first dimension of `arr` independently.\n    \"\"\"\n    out: np.ndarray = np.zeros(arr.shape, dtype or arr.dtype)\n    for i in range(arr.shape[0]):\n        out[i] = rescale_array(arr[i], minv, maxv, dtype)\n\n    return out\n\n\ndef rescale_array_int_max(arr: np.ndarray, dtype: DtypeLike = np.uint16) -> np.ndarray:\n    \"\"\"\n    Rescale the array `arr` to be between the minimum and maximum values of the type `dtype`.\n    \"\"\"\n    info: np.iinfo = np.iinfo(dtype or arr.dtype)\n    return np.asarray(rescale_array(arr, info.min, info.max), dtype=dtype or arr.dtype)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_resize_center_resize_center.return.img_srcslices_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_resize_center_resize_center.return.img_srcslices_", "embedding": null, "metadata": {"file_path": "monai/transforms/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 217, "end_line": 235, "span_ids": ["resize_center"], "tokens": 265}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def resize_center(img: np.ndarray, *resize_dims: Optional[int], fill_value: float = 0.0, inplace: bool = True):\n    \"\"\"\n    Resize `img` by cropping or expanding the image from the center. The `resize_dims` values are the output dimensions\n    (or None to use original dimension of `img`). If a dimension is smaller than that of `img` then the result will be\n    cropped and if larger padded with zeros, in both cases this is done relative to the center of `img`. The result is\n    a new image with the specified dimensions and values from `img` copied into its center.\n    \"\"\"\n\n    resize_dims = fall_back_tuple(resize_dims, img.shape)\n\n    half_img_shape = (np.asarray(img.shape) // 2).tolist()\n    half_dest_shape = (np.asarray(resize_dims) // 2).tolist()\n    srcslices, destslices = copypaste_arrays(img.shape, resize_dims, half_img_shape, half_dest_shape, resize_dims)\n\n    if not inplace:\n        dest = np.full(resize_dims, fill_value, img.dtype)  # type: ignore\n        dest[destslices] = img[srcslices]\n        return dest\n    return img[srcslices]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_get_extreme_points_get_extreme_points.if_np_size_indices_0_.raise_ValueError_get_ext": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_get_extreme_points_get_extreme_points.if_np_size_indices_0_.raise_ValueError_get_ext", "embedding": null, "metadata": {"file_path": "monai/transforms/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 994, "end_line": 1021, "span_ids": ["get_extreme_points"], "tokens": 318}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_extreme_points(\n    img: NdarrayOrTensor, rand_state: Optional[np.random.RandomState] = None, background: int = 0, pert: float = 0.0\n) -> List[Tuple[int, ...]]:\n    \"\"\"\n    Generate extreme points from an image. These are used to generate initial segmentation\n    for annotation models. An optional perturbation can be passed to simulate user clicks.\n\n    Args:\n        img:\n            Image to generate extreme points from. Expected Shape is ``(spatial_dim1, [, spatial_dim2, ...])``.\n        rand_state: `np.random.RandomState` object used to select random indices.\n        background: Value to be consider as background, defaults to 0.\n        pert: Random perturbation amount to add to the points, defaults to 0.0.\n\n    Returns:\n        A list of extreme points, its length is equal to 2 * spatial dimension of input image.\n        The output format of the coordinates is:\n\n        [1st_spatial_dim_min, 1st_spatial_dim_max, 2nd_spatial_dim_min, ..., Nth_spatial_dim_max]\n\n    Raises:\n        ValueError: When the input image does not have any foreground pixel.\n    \"\"\"\n    if rand_state is None:\n        rand_state = np.random.random.__self__  # type: ignore\n    indices = where(img != background)\n    if np.size(indices[0]) == 0:\n        raise ValueError(\"get_extreme_points: no foreground object in mask!\")\n    # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_get_extreme_points._get_point_get_extreme_points.return.points": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_get_extreme_points._get_point_get_extreme_points.return.points", "embedding": null, "metadata": {"file_path": "monai/transforms/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1023, "end_line": 1048, "span_ids": ["get_extreme_points"], "tokens": 262}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_extreme_points(\n    img: NdarrayOrTensor, rand_state: Optional[np.random.RandomState] = None, background: int = 0, pert: float = 0.0\n) -> List[Tuple[int, ...]]:\n    # ... other code\n\n    def _get_point(val, dim):\n        \"\"\"\n        Select one of the indices within slice containing val.\n\n        Args:\n            val : value for comparison\n            dim : dimension in which to look for value\n        \"\"\"\n        idx = where(indices[dim] == val)[0]\n        idx = idx.cpu() if isinstance(idx, torch.Tensor) else idx\n        idx = rand_state.choice(idx)\n        pt = []\n        for j in range(img.ndim):\n            # add +- pert to each dimension\n            val = int(indices[j][idx] + 2.0 * pert * (rand_state.rand() - 0.5))\n            val = max(val, 0)\n            val = min(val, img.shape[j] - 1)\n            pt.append(val)\n        return pt\n\n    points = []\n    for i in range(img.ndim):\n        points.append(tuple(_get_point(indices[i].min(), i)))\n        points.append(tuple(_get_point(indices[i].max(), i)))\n\n    return points", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_map_spatial_axes_map_spatial_axes.return.spatial_axes_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_map_spatial_axes_map_spatial_axes.return.spatial_axes_", "embedding": null, "metadata": {"file_path": "monai/transforms/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1097, "end_line": 1132, "span_ids": ["map_spatial_axes"], "tokens": 389}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def map_spatial_axes(\n    img_ndim: int, spatial_axes: Optional[Union[Sequence[int], int]] = None, channel_first: bool = True\n) -> List[int]:\n    \"\"\"\n    Utility to map the spatial axes to real axes in channel first/last shape.\n    For example:\n    If `channel_first` is True, and `img` has 3 spatial dims, map spatial axes to real axes as below:\n    None -> [1, 2, 3]\n    [0, 1] -> [1, 2]\n    [0, -1] -> [1, -1]\n    If `channel_first` is False, and `img` has 3 spatial dims, map spatial axes to real axes as below:\n    None -> [0, 1, 2]\n    [0, 1] -> [0, 1]\n    [0, -1] -> [0, -2]\n\n    Args:\n        img_ndim: dimension number of the target image.\n        spatial_axes: spatial axes to be converted, default is None.\n            The default `None` will convert to all the spatial axes of the image.\n            If axis is negative it counts from the last to the first axis.\n            If axis is a tuple of ints.\n        channel_first: the image data is channel first or channel last, default to channel first.\n\n    \"\"\"\n    if spatial_axes is None:\n        spatial_axes_ = list(range(1, img_ndim) if channel_first else range(img_ndim - 1))\n\n    else:\n        spatial_axes_ = []\n        for a in ensure_tuple(spatial_axes):\n            if channel_first:\n                spatial_axes_.append(a if a < 0 else a + 1)\n            else:\n                spatial_axes_.append(a - 1 if a < 0 else a)\n\n    return spatial_axes_", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_allow_missing_keys_mode_allow_missing_keys_mode.try_.finally_.for_t_o_s_in_zip_transfo.t.allow_missing_keys.o_s": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_allow_missing_keys_mode_allow_missing_keys_mode.try_.finally_.for_t_o_s_in_zip_transfo.t.allow_missing_keys.o_s", "embedding": null, "metadata": {"file_path": "monai/transforms/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 725, "end_line": 769, "span_ids": ["allow_missing_keys_mode"], "tokens": 372}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@contextmanager\ndef allow_missing_keys_mode(transform: Union[MapTransform, Compose, Tuple[MapTransform], Tuple[Compose]]):\n    \"\"\"Temporarily set all MapTransforms to not throw an error if keys are missing. After, revert to original states.\n\n    Args:\n        transform: either MapTransform or a Compose\n\n    Example:\n\n    .. code-block:: python\n\n        data = {\"image\": np.arange(16, dtype=float).reshape(1, 4, 4)}\n        t = SpatialPadd([\"image\", \"label\"], 10, allow_missing_keys=False)\n        _ = t(data)  # would raise exception\n        with allow_missing_keys_mode(t):\n            _ = t(data)  # OK!\n    \"\"\"\n    # If given a sequence of transforms, Compose them to get a single list\n    if issequenceiterable(transform):\n        transform = Compose(transform)\n\n    # Get list of MapTransforms\n    transforms = []\n    if isinstance(transform, MapTransform):\n        transforms = [transform]\n    elif isinstance(transform, Compose):\n        # Only keep contained MapTransforms\n        transforms = [t for t in transform.flatten().transforms if isinstance(t, MapTransform)]\n    if len(transforms) == 0:\n        raise TypeError(\n            \"allow_missing_keys_mode expects either MapTransform(s) or Compose(s) containing MapTransform(s)\"\n        )\n\n    # Get the state of each `allow_missing_keys`\n    orig_states = [t.allow_missing_keys for t in transforms]\n\n    try:\n        # Set all to True\n        for t in transforms:\n            t.allow_missing_keys = True\n        yield\n    finally:\n        # Revert\n        for t, o_s in zip(transforms, orig_states):\n            t.allow_missing_keys = o_s", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/__init__.py_alias_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/__init__.py_alias_", "embedding": null, "metadata": {"file_path": "monai/utils/__init__.py", "file_name": "__init__.py", "file_type": "text/x-python", "category": "implementation", "start_line": 13, "end_line": 92, "span_ids": ["imports:10", "docstring"], "tokens": 452}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from .aliases import alias, resolve_name\nfrom .decorators import MethodReplacer, RestartGenerator\nfrom .deprecate_utils import DeprecatedError, deprecated, deprecated_arg\nfrom .dist import evenly_divisible_all_gather, get_dist_device, string_list_all_gather\nfrom .enums import (\n    Average,\n    BlendMode,\n    ChannelMatching,\n    CommonKeys,\n    ForwardMode,\n    GridSampleMode,\n    GridSamplePadMode,\n    InterpolateMode,\n    InverseKeys,\n    JITMetadataKeys,\n    LossReduction,\n    Method,\n    MetricReduction,\n    NumpyPadMode,\n    PytorchPadMode,\n    SkipMode,\n    TraceKeys,\n    TransformBackends,\n    UpsampleMode,\n    Weight,\n)\nfrom .jupyter_utils import StatusMembers, ThreadContainer\nfrom .misc import (\n    MAX_SEED,\n    ImageMetaKey,\n    copy_to_device,\n    ensure_tuple,\n    ensure_tuple_rep,\n    ensure_tuple_size,\n    fall_back_tuple,\n    first,\n    get_seed,\n    has_option,\n    is_module_ver_at_least,\n    is_scalar,\n    is_scalar_tensor,\n    issequenceiterable,\n    list_to_dict,\n    progress_bar,\n    set_determinism,\n    star_zip_with,\n    zip_with,\n)\nfrom .module import (\n    InvalidPyTorchVersionError,\n    OptionalImportError,\n    damerau_levenshtein_distance,\n    exact_version,\n    export,\n    get_full_type_name,\n    get_package_version,\n    get_torch_version_tuple,\n    load_submodules,\n    look_up_option,\n    min_version,\n    optional_import,\n    pytorch_after,\n    require_pkg,\n    version_leq,\n)\nfrom .nvtx import Range\nfrom .profiling import PerfContext, torch_profiler_full, torch_profiler_time_cpu_gpu, torch_profiler_time_end_to_end\nfrom .state_cacher import StateCacher\nfrom .type_conversion import (\n    convert_data_type,\n    convert_to_cupy,\n    convert_to_dst_type,\n    convert_to_numpy,\n    convert_to_tensor,\n    dtype_numpy_to_torch,\n    dtype_torch_to_numpy,\n    get_dtype,\n    get_equivalent_dtype,\n)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/decorators.py_from_functools_import_wra_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/decorators.py_from_functools_import_wra_", "embedding": null, "metadata": {"file_path": "monai/utils/decorators.py", "file_name": "decorators.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 79, "span_ids": ["RestartGenerator.__iter__", "RestartGenerator.__init__", "MethodReplacer.__init__", "MethodReplacer", "docstring", "MethodReplacer.replace_method", "RestartGenerator", "MethodReplacer.__set_name__"], "tokens": 517}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from functools import wraps\n\n__all__ = [\"RestartGenerator\", \"MethodReplacer\"]\n\n\nclass RestartGenerator:\n    \"\"\"\n    Wraps a generator callable which will be called whenever this class is iterated and its result returned. This is\n    used to create an iterator which can start iteration over the given generator multiple times.\n    \"\"\"\n\n    def __init__(self, create_gen) -> None:\n        self.create_gen = create_gen\n\n    def __iter__(self):\n        return self.create_gen()\n\n\nclass MethodReplacer:\n    \"\"\"\n    Base class for method decorators which can be used to replace methods pass to replace_method() with wrapped versions.\n    \"\"\"\n\n    replace_list_name = \"__replacemethods__\"\n\n    def __init__(self, meth) -> None:\n        self.meth = meth\n\n    def replace_method(self, meth):\n        \"\"\"\n        Return a new method to replace `meth` in the instantiated object, or `meth` to do nothing.\n        \"\"\"\n        return meth\n\n    def __set_name__(self, owner, name):\n        \"\"\"\n        Add the (name,self.replace_method) pair to the list named by replace_list_name in `owner`, creating the list and\n        replacing the constructor of `owner` if necessary. The replaced constructor will call the old one then do the\n        replacing operation of substituting, for each (name,self.replace_method) pair, the named method with the returned\n        value from self.replace_method.\n        \"\"\"\n        entry = (name, owner, self.replace_method)\n\n        if not hasattr(owner, self.replace_list_name):\n            oldinit = owner.__init__\n\n            # replace the constructor with a new one which calls the old then replaces methods\n            @wraps(oldinit)\n            def newinit(_self, *args, **kwargs):\n                oldinit(_self, *args, **kwargs)\n\n                # replace each listed method of this newly constructed object\n                for m, owner, replacer in getattr(_self, self.replace_list_name):\n                    if isinstance(_self, owner):\n                        meth = getattr(_self, m)\n                        newmeth = replacer(meth)\n                        setattr(_self, m, newmeth)\n\n            owner.__init__ = newinit\n            setattr(owner, self.replace_list_name, [entry])\n        else:\n            namelist = getattr(owner, self.replace_list_name)\n\n            if not any(nl[0] == name for nl in namelist):\n                namelist.append(entry)\n\n        setattr(owner, name, self.meth)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/enums.py_from_enum_import_Enum___all__._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/enums.py_from_enum_import_Enum___all__._", "embedding": null, "metadata": {"file_path": "monai/utils/enums.py", "file_name": "enums.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 36, "span_ids": ["docstring"], "tokens": 126}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from enum import Enum\n\nfrom monai.utils.deprecate_utils import deprecated\n\n__all__ = [\n    \"NumpyPadMode\",\n    \"GridSampleMode\",\n    \"InterpolateMode\",\n    \"UpsampleMode\",\n    \"BlendMode\",\n    \"PytorchPadMode\",\n    \"GridSamplePadMode\",\n    \"Average\",\n    \"MetricReduction\",\n    \"LossReduction\",\n    \"Weight\",\n    \"ChannelMatching\",\n    \"SkipMode\",\n    \"Method\",\n    \"TraceKeys\",\n    \"InverseKeys\",\n    \"CommonKeys\",\n    \"ForwardMode\",\n    \"TransformBackends\",\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/enums.py_NumpyPadMode_NumpyPadMode.EMPTY._empty_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/enums.py_NumpyPadMode_NumpyPadMode.EMPTY._empty_", "embedding": null, "metadata": {"file_path": "monai/utils/enums.py", "file_name": "enums.py", "file_type": "text/x-python", "category": "implementation", "start_line": 36, "end_line": 51, "span_ids": ["NumpyPadMode"], "tokens": 112}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class NumpyPadMode(Enum):\n    \"\"\"\n    See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html\n    \"\"\"\n\n    CONSTANT = \"constant\"\n    EDGE = \"edge\"\n    LINEAR_RAMP = \"linear_ramp\"\n    MAXIMUM = \"maximum\"\n    MEAN = \"mean\"\n    MEDIAN = \"median\"\n    MINIMUM = \"minimum\"\n    REFLECT = \"reflect\"\n    SYMMETRIC = \"symmetric\"\n    WRAP = \"wrap\"\n    EMPTY = \"empty\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/enums.py_GridSampleMode_GridSampleMode.BICUBIC._bicubic_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/enums.py_GridSampleMode_GridSampleMode.BICUBIC._bicubic_", "embedding": null, "metadata": {"file_path": "monai/utils/enums.py", "file_name": "enums.py", "file_type": "text/x-python", "category": "implementation", "start_line": 54, "end_line": 69, "span_ids": ["GridSampleMode"], "tokens": 144}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GridSampleMode(Enum):\n    \"\"\"\n    See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n\n    interpolation mode of `torch.nn.functional.grid_sample`\n\n    Note:\n        (documentation from `torch.nn.functional.grid_sample`)\n        `mode='bicubic'` supports only 4-D input.\n        When `mode='bilinear'` and the input is 5-D, the interpolation mode used internally will actually be trilinear.\n        However, when the input is 4-D, the interpolation mode will legitimately be bilinear.\n    \"\"\"\n\n    NEAREST = \"nearest\"\n    BILINEAR = \"bilinear\"\n    BICUBIC = \"bicubic\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/enums.py_CommonKeys_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/enums.py_CommonKeys_", "embedding": null, "metadata": {"file_path": "monai/utils/enums.py", "file_name": "enums.py", "file_type": "text/x-python", "category": "implementation", "start_line": 245, "end_line": 281, "span_ids": ["CommonKeys", "TransformBackends", "JITMetadataKeys"], "tokens": 210}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CommonKeys:\n    \"\"\"\n    A set of common keys for dictionary based supervised training process.\n    `IMAGE` is the input image data.\n    `LABEL` is the training or evaluation label of segmentation or classification task.\n    `PRED` is the prediction data of model output.\n    `LOSS` is the loss value of current iteration.\n    `INFO` is some useful information during training or evaluation, like loss value, etc.\n\n    \"\"\"\n\n    IMAGE = \"image\"\n    LABEL = \"label\"\n    PRED = \"pred\"\n    LOSS = \"loss\"\n\n\nclass TransformBackends(Enum):\n    \"\"\"\n    Transform backends.\n    \"\"\"\n\n    TORCH = \"torch\"\n    NUMPY = \"numpy\"\n\n\nclass JITMetadataKeys(Enum):\n    \"\"\"\n    Keys stored in the metadata file for saved Torchscript models. Some of these are generated by the routines\n    and others are optionally provided by users.\n    \"\"\"\n\n    NAME = \"name\"\n    TIMESTAMP = \"timestamp\"\n    VERSION = \"version\"\n    DESCRIPTION = \"description\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/jupyter_utils.py_plot_metric_images_plot_metric_images.return.axes": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/jupyter_utils.py_plot_metric_images_plot_metric_images.return.axes", "embedding": null, "metadata": {"file_path": "monai/utils/jupyter_utils.py", "file_name": "jupyter_utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 90, "end_line": 135, "span_ids": ["plot_metric_images"], "tokens": 482}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def plot_metric_images(\n    fig,\n    title: str,\n    graphmap: Dict[str, Union[List[float], Tuple[List[float], List[float]]]],\n    imagemap: Dict[str, np.ndarray],\n    yscale: str = \"log\",\n    avg_keys: Tuple[str] = (LOSS_NAME,),\n    window_fraction: int = 20,\n) -> List:\n    \"\"\"\n    Plot metric graph data with images below into figure `fig`. The intended use is for the graph data to be\n    metrics from a training run and the images to be the batch and output from the last iteration. This uses\n    `plot_metric_graph` to plot the metric graph.\n\n    Args:\n        fig: Figure object to plot into, reuse from previous plotting for flicker-free refreshing\n        title: graph title\n        graphmap: dictionary of named graph values, which are lists of values or (index, value) pairs\n        imagemap: dictionary of named images to show with metric plot\n        yscale: for metric plot, scale for y-axis compatible with `Axes.set_yscale`\n        avg_keys: for metric plot, tuple of keys in `graphmap` to provide running average plots for\n        window_fraction: for metric plot, what fraction of the graph value length to use as the running average window\n\n    Returns:\n        list of Axes objects for graph followed by images\n    \"\"\"\n    gridshape = (4, max(1, len(imagemap)))\n\n    graph = plt.subplot2grid(gridshape, (0, 0), colspan=gridshape[1], fig=fig)\n\n    plot_metric_graph(graph, title, graphmap, yscale, avg_keys, window_fraction)\n\n    axes = [graph]\n    for i, n in enumerate(imagemap):\n        im = plt.subplot2grid(gridshape, (1, i), rowspan=2, fig=fig)\n\n        if imagemap[n].shape[0] == 3:\n            im.imshow(imagemap[n].transpose([1, 2, 0]))\n        else:\n            im.imshow(np.squeeze(imagemap[n]), cmap=\"gray\")\n\n        im.set_title(f\"{n}\\n{imagemap[n].min():.3g} -> {imagemap[n].max():.3g}\")\n        im.axis(\"off\")\n        axes.append(im)\n\n    return axes", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/jupyter_utils.py_plot_engine_status_plot_engine_status.return.fig_axes": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/jupyter_utils.py_plot_engine_status_plot_engine_status.return.fig_axes", "embedding": null, "metadata": {"file_path": "monai/utils/jupyter_utils.py", "file_name": "jupyter_utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 155, "end_line": 230, "span_ids": ["plot_engine_status"], "tokens": 832}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def plot_engine_status(\n    engine: Engine,\n    logger,\n    title: str = \"Training Log\",\n    yscale: str = \"log\",\n    avg_keys: Tuple[str] = (LOSS_NAME,),\n    window_fraction: int = 20,\n    image_fn: Optional[Callable] = tensor_to_images,\n    fig=None,\n    selected_inst: int = 0,\n) -> Tuple:\n    \"\"\"\n    Plot the status of the given Engine with its logger. The plot will consist of a graph of loss values and metrics\n    taken from the logger, and images taken from the `output` and `batch` members of `engine.state`. The images are\n    converted to Numpy arrays suitable for input to `Axes.imshow` using `image_fn`, if this is None then no image\n    plotting is done.\n\n    Args:\n        engine: Engine to extract images from\n        logger: MetricLogger to extract loss and metric data from\n        title: graph title\n        yscale: for metric plot, scale for y-axis compatible with `Axes.set_yscale`\n        avg_keys: for metric plot, tuple of keys in `graphmap` to provide running average plots for\n        window_fraction: for metric plot, what fraction of the graph value length to use as the running average window\n        image_fn: callable converting tensors keyed to a name in the Engine to a tuple of images to plot\n        fig: Figure object to plot into, reuse from previous plotting for flicker-free refreshing\n        selected_inst: index of the instance to show in the image plot\n\n    Returns:\n        Figure object (or `fig` if given), list of Axes objects for graph and images\n    \"\"\"\n    if fig is not None:\n        fig.clf()\n    else:\n        fig = plt.Figure(figsize=(20, 10), tight_layout=True, facecolor=\"white\")\n\n    graphmap = {LOSS_NAME: logger.loss}\n    graphmap.update(logger.metrics)\n\n    imagemap: Dict = {}\n    if image_fn is not None and engine.state is not None and engine.state.batch is not None:\n        for src in (engine.state.batch, engine.state.output):\n            label = \"Batch\" if src is engine.state.batch else \"Output\"\n            batch_selected_inst = selected_inst  # selected batch index, set to 0 when src is decollated\n\n            # if the src object is a list of elements, ie. a decollated batch, select an element and keep it as\n            # a dictionary of tensors with a batch dimension added\n            if isinstance(src, list):\n                selected_dict = src[selected_inst]  # select this element\n                batch_selected_inst = 0  # set the selection to be the single index in the batch dimension\n                # store each tensor that is interpretable as an image with an added batch dimension\n                src = {k: v[None] for k, v in selected_dict.items() if isinstance(v, torch.Tensor) and v.ndim >= 3}\n\n            # images will be generated from the batch item selected above only, or from the single item given as `src`\n\n            if isinstance(src, dict):\n                for k, v in src.items():\n                    if isinstance(v, torch.Tensor) and v.ndim >= 4:\n                        image = image_fn(k, v[batch_selected_inst])\n\n                        # if we have images add each one separately to the map\n                        if image is not None:\n                            for i, im in enumerate(image):\n                                imagemap[f\"{k}_{i}\"] = im\n\n            elif isinstance(src, torch.Tensor):\n                image = image_fn(label, src)\n                if image is not None:\n                    imagemap[f\"{label}_{i}\"] = image\n\n    axes = plot_metric_images(fig, title, graphmap, imagemap, yscale, avg_keys, window_fraction)\n\n    if logger.loss:\n        axes[0].axhline(logger.loss[-1][1], c=\"k\", ls=\":\")  # draw dotted horizontal line at last loss value\n\n    return fig, axes", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/jupyter_utils.py__get_loss_from_output_StatusMembers.LOSS._Loss_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/jupyter_utils.py__get_loss_from_output_StatusMembers.LOSS._Loss_", "embedding": null, "metadata": {"file_path": "monai/utils/jupyter_utils.py", "file_name": "jupyter_utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 232, "end_line": 253, "span_ids": ["_get_loss_from_output", "StatusMembers"], "tokens": 139}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _get_loss_from_output(output: Union[Dict[str, torch.Tensor], torch.Tensor]):\n    \"\"\"Returns a single value from the network output, which is a dict or tensor.\"\"\"\n\n    def _get_loss(data):\n        if isinstance(data, dict):\n            return data[\"loss\"]\n        return data\n\n    if isinstance(output, list):\n        return _get_loss(output[0])\n    return _get_loss(output)\n\n\nclass StatusMembers(Enum):\n    \"\"\"\n    Named members of the status dictionary, others may be present for named metric values.\n    \"\"\"\n\n    STATUS = \"Status\"\n    EPOCHS = \"Epochs\"\n    ITERS = \"Iters\"\n    LOSS = \"Loss\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/jupyter_utils.py_ThreadContainer_ThreadContainer.stop.self_join_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/jupyter_utils.py_ThreadContainer_ThreadContainer.stop.self_join_", "embedding": null, "metadata": {"file_path": "monai/utils/jupyter_utils.py", "file_name": "jupyter_utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 235, "end_line": 276, "span_ids": ["ThreadContainer.__init__", "ThreadContainer", "ThreadContainer.stop", "ThreadContainer.run"], "tokens": 371}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ThreadContainer(Thread):\n    \"\"\"\n    Contains a running `Engine` object within a separate thread from main thread in a Jupyter notebook. This\n    allows an engine to begin a run in the background and allow the starting notebook cell to complete. A\n    user can thus start a run and then navigate away from the notebook without concern for loosing connection\n    with the running cell. All output is acquired through methods which synchronize with the running engine\n    using an internal `lock` member, acquiring this lock allows the engine to be inspected while it's prevented\n    from starting the next iteration.\n\n    Args:\n        engine: wrapped `Engine` object, when the container is started its `run` method is called\n        loss_transform: callable to convert an output dict into a single numeric value\n        metric_transform: callable to convert a named metric value into a single numeric value\n        status_format: format string for status key-value pairs.\n    \"\"\"\n\n    def __init__(\n        self,\n        engine: Engine,\n        loss_transform: Callable = _get_loss_from_output,\n        metric_transform: Callable = lambda name, value: value,\n        status_format: str = \"{}: {:.4}\",\n    ):\n        super().__init__()\n        self.lock = RLock()\n        self.engine = engine\n        self._status_dict: Dict[str, Any] = {}\n        self.loss_transform = loss_transform\n        self.metric_transform = metric_transform\n        self.fig = None\n        self.status_format = status_format\n\n        self.engine.add_event_handler(Events.ITERATION_COMPLETED, self._update_status)\n\n    def run(self):\n        \"\"\"Calls the `run` method of the wrapped engine.\"\"\"\n        self.engine.run()\n\n    def stop(self):\n        \"\"\"Stop the engine and join the thread.\"\"\"\n        self.engine.terminate()\n        self.join()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/jupyter_utils.py_ThreadContainer._update_status_ThreadContainer._update_status.with_self_lock_.self__status_dict_update_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/jupyter_utils.py_ThreadContainer._update_status_ThreadContainer._update_status.with_self_lock_.self__status_dict_update_", "embedding": null, "metadata": {"file_path": "monai/utils/jupyter_utils.py", "file_name": "jupyter_utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 278, "end_line": 309, "span_ids": ["ThreadContainer._update_status"], "tokens": 249}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ThreadContainer(Thread):\n\n    def _update_status(self):\n        \"\"\"Called as an event, updates the internal status dict at the end of iterations.\"\"\"\n        with self.lock:\n            state = self.engine.state\n            stats = {\n                StatusMembers.EPOCHS.value: 0,\n                StatusMembers.ITERS.value: 0,\n                StatusMembers.LOSS.value: float(\"nan\"),\n            }\n\n            if state is not None:\n                if state.max_epochs >= 1:\n                    epoch = f\"{state.epoch}/{state.max_epochs}\"\n                else:\n                    epoch = str(state.epoch)\n\n                if state.epoch_length is not None:\n                    iters = f\"{state.iteration % state.epoch_length}/{state.epoch_length}\"\n                else:\n                    iters = str(state.iteration)\n\n                stats[StatusMembers.EPOCHS.value] = epoch\n                stats[StatusMembers.ITERS.value] = iters\n                stats[StatusMembers.LOSS.value] = self.loss_transform(state.output)\n\n                metrics = state.metrics or {}\n                for m, v in metrics.items():\n                    v = self.metric_transform(m, v)\n                    if v is not None:\n                        stats[m].append(v)\n\n            self._status_dict.update(stats)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/jupyter_utils.py_ThreadContainer.status_dict_ThreadContainer.status.return._join_msgs_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/jupyter_utils.py_ThreadContainer.status_dict_ThreadContainer.status.return._join_msgs_", "embedding": null, "metadata": {"file_path": "monai/utils/jupyter_utils.py", "file_name": "jupyter_utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 311, "end_line": 333, "span_ids": ["ThreadContainer.status_dict", "ThreadContainer.status"], "tokens": 187}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ThreadContainer(Thread):\n\n    @property\n    def status_dict(self) -> Dict[str, str]:\n        \"\"\"A dictionary containing status information, current loss, and current metric values.\"\"\"\n        with self.lock:\n            stats = {StatusMembers.STATUS.value: \"Running\" if self.is_alive() else \"Stopped\"}\n            stats.update(self._status_dict)\n            return stats\n\n    def status(self) -> str:\n        \"\"\"Returns a status string for the current state of the engine.\"\"\"\n        stats = self.status_dict\n\n        msgs = [stats.pop(StatusMembers.STATUS.value), \"Iters: \" + str(stats.pop(StatusMembers.ITERS.value, 0))]\n\n        for key, val in stats.items():\n            if isinstance(val, float):\n                msg = self.status_format.format(key, val)\n            else:\n                msg = f\"{key}: {val}\"\n\n            msgs.append(msg)\n\n        return \", \".join(msgs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/jupyter_utils.py_ThreadContainer.plot_status_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/jupyter_utils.py_ThreadContainer.plot_status_", "embedding": null, "metadata": {"file_path": "monai/utils/jupyter_utils.py", "file_name": "jupyter_utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 336, "end_line": 347, "span_ids": ["ThreadContainer.plot_status"], "tokens": 175}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ThreadContainer(Thread):\n\n    def plot_status(self, logger, plot_func: Callable = plot_engine_status):\n        \"\"\"\n        Generate a plot of the current status of the contained engine whose loss and metrics were tracked by `logger`.\n        The function `plot_func` must accept arguments `title`, `engine`, `logger`, and `fig` which are the plot title,\n        `self.engine`, `logger`, and `self.fig` respectively. The return value must be a figure object (stored in\n        `self.fig`) and a list of Axes objects for the plots in the figure. Only the figure is returned by this method,\n        which holds the internal lock during the plot generation.\n        \"\"\"\n        with self.lock:\n            self.fig, _ = plot_func(title=self.status(), engine=self.engine, logger=logger, fig=self.fig)\n            return self.fig", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/misc.py_list_to_dict_list_to_dict.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/misc.py_list_to_dict_list_to_dict.return.d", "embedding": null, "metadata": {"file_path": "monai/utils/misc.py", "file_name": "misc.py", "file_type": "text/x-python", "category": "implementation", "start_line": 268, "end_line": 297, "span_ids": ["list_to_dict"], "tokens": 262}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def list_to_dict(items):\n    \"\"\"\n    To convert a list of \"key=value\" pairs into a dictionary.\n    For examples: items: `[\"a=1\", \"b=2\", \"c=3\"]`, return: {\"a\": \"1\", \"b\": \"2\", \"c\": \"3\"}.\n    If no \"=\" in the pair, use None as the value, for example: [\"a\"], return: {\"a\": None}.\n    Note that it will remove the blanks around keys and values.\n\n    \"\"\"\n\n    def _parse_var(s):\n        items = s.split(\"=\", maxsplit=1)\n        key = items[0].strip(\" \\n\\r\\t'\")\n        value = items[1].strip(\" \\n\\r\\t'\") if len(items) > 1 else None\n        return key, value\n\n    d = {}\n    if items:\n        for item in items:\n            key, value = _parse_var(item)\n\n            try:\n                if key in d:\n                    raise KeyError(f\"encounter duplicated key {key}.\")\n                d[key] = literal_eval(value)\n            except ValueError:\n                try:\n                    d[key] = bool(strtobool(str(value)))\n                except ValueError:\n                    d[key] = value\n    return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/module.py_export_export.return._inner": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/module.py_export_export.return._inner", "embedding": null, "metadata": {"file_path": "monai/utils/module.py", "file_name": "module.py", "file_type": "text/x-python", "category": "implementation", "start_line": 42, "end_line": 61, "span_ids": ["export"], "tokens": 162}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def export(modname):\n    \"\"\"\n    Make the decorated object a member of the named module. This will also add the object under its aliases if it has\n    a `__aliases__` member, thus this decorator should be before the `alias` decorator to pick up those names. Alias\n    names which conflict with package names or existing members will be ignored.\n    \"\"\"\n\n    def _inner(obj):\n        mod = import_module(modname)\n        if not hasattr(mod, obj.__name__):\n            setattr(mod, obj.__name__, obj)\n\n            # add the aliases for `obj` to the target module\n            for alias in getattr(obj, \"__aliases__\", ()):\n                if not hasattr(mod, alias):\n                    setattr(mod, alias, obj)\n\n        return obj\n\n    return _inner", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/module.py_get_full_type_name_min_version.return.mod_version_required": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/module.py_get_full_type_name_min_version.return.mod_version_required", "embedding": null, "metadata": {"file_path": "monai/utils/module.py", "file_name": "module.py", "file_type": "text/x-python", "category": "implementation", "start_line": 85, "end_line": 104, "span_ids": ["min_version", "get_full_type_name"], "tokens": 191}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_full_type_name(typeobj):\n    module = typeobj.__module__\n    if module is None or module == str.__class__.__module__:\n        return typeobj.__name__  # Avoid reporting __builtin__\n    return module + \".\" + typeobj.__name__\n\n\ndef min_version(the_module, min_version_str: str = \"\") -> bool:\n    \"\"\"\n    Convert version strings into tuples of int and compare them.\n\n    Returns True if the module's version is greater or equal to the 'min_version'.\n    When min_version_str is not provided, it always returns True.\n    \"\"\"\n    if not min_version_str or not hasattr(the_module, \"__version__\"):\n        return True  # always valid version\n\n    mod_version = tuple(int(x) for x in the_module.__version__.split(\".\")[:2])\n    required = tuple(int(x) for x in min_version_str.split(\".\")[:2])\n    return mod_version >= required", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/module.py_exact_version_OptionalImportError._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/module.py_exact_version_OptionalImportError._", "embedding": null, "metadata": {"file_path": "monai/utils/module.py", "file_name": "module.py", "file_type": "text/x-python", "category": "implementation", "start_line": 107, "end_line": 131, "span_ids": ["exact_version", "InvalidPyTorchVersionError", "OptionalImportError", "InvalidPyTorchVersionError.__init__"], "tokens": 173}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def exact_version(the_module, version_str: str = \"\") -> bool:\n    \"\"\"\n    Returns True if the module's __version__ matches version_str\n    \"\"\"\n    if not hasattr(the_module, \"__version__\"):\n        warnings.warn(f\"{the_module} has no attribute __version__ in exact_version check.\")\n        return False\n    return bool(the_module.__version__ == version_str)\n\n\nclass InvalidPyTorchVersionError(Exception):\n    \"\"\"\n    Raised when called function or method requires a more recent\n    PyTorch version than that installed.\n    \"\"\"\n\n    def __init__(self, required_version, name):\n        message = f\"{name} requires PyTorch version {required_version} or later\"\n        super().__init__(message)\n\n\nclass OptionalImportError(ImportError):\n    \"\"\"\n    Could not import APIs from an optional dependency.\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/module.py_optional_import.if_version_and_tb_is_None_optional_import.return._LazyRaise_False": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/module.py_optional_import.if_version_and_tb_is_None_optional_import.return._LazyRaise_False", "embedding": null, "metadata": {"file_path": "monai/utils/module.py", "file_name": "module.py", "file_type": "text/x-python", "category": "implementation", "start_line": 211, "end_line": 242, "span_ids": ["optional_import"], "tokens": 313}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def optional_import(\n    module: str,\n    version: str = \"\",\n    version_checker: Callable[..., bool] = min_version,\n    name: str = \"\",\n    descriptor: str = OPTIONAL_IMPORT_MSG_FMT,\n    version_args=None,\n    allow_namespace_pkg: bool = False,\n) -> Tuple[Any, bool]:\n    # ... other code\n    if version and tb is None:  # a pure version issue\n        msg += f\" (requires '{module} {version}' by '{version_checker.__name__}')\"\n    if exception_str:\n        msg += f\" ({exception_str})\"\n\n    class _LazyRaise:\n        def __init__(self, *_args, **_kwargs):\n            _default_msg = (\n                f\"{msg}.\"\n                + \"\\n\\nFor details about installing the optional dependencies, please visit:\"\n                + \"\\n    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\"\n            )\n            if tb is None:\n                self._exception = OptionalImportError(_default_msg)\n            else:\n                self._exception = OptionalImportError(_default_msg).with_traceback(tb)\n\n        def __getattr__(self, name):\n            \"\"\"\n            Raises:\n                OptionalImportError: When you call this method.\n            \"\"\"\n            raise self._exception\n\n        def __call__(self, *_args, **_kwargs):\n            \"\"\"\n            Raises:\n                OptionalImportError: When you call this method.\n            \"\"\"\n            raise self._exception\n\n    return _LazyRaise(), False", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/profiling.py_time_torch_profiler_full.return.wrapper": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/profiling.py_time_torch_profiler_full.return.wrapper", "embedding": null, "metadata": {"file_path": "monai/utils/profiling.py", "file_name": "profiling.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 37, "span_ids": ["torch_profiler_full", "docstring"], "tokens": 146}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import time\nfrom functools import wraps\n\nimport torch\n\n__all__ = [\"torch_profiler_full\", \"torch_profiler_time_cpu_gpu\", \"torch_profiler_time_end_to_end\", \"PerfContext\"]\n\n\ndef torch_profiler_full(func):\n    \"\"\"\n    A decorator which will run the torch profiler for the decorated function,\n    printing the results in full.\n    Note: Enforces a gpu sync point which could slow down pipelines.\n    \"\"\"\n\n    @wraps(func)\n    def wrapper(*args, **kwargs):\n\n        with torch.autograd.profiler.profile(use_cuda=True) as prof:\n            result = func(*args, **kwargs)\n\n        print(prof, flush=True)\n\n        return result\n\n    return wrapper", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/profiling.py_torch_profiler_time_cpu_gpu_torch_profiler_time_cpu_gpu.return.wrapper": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/profiling.py_torch_profiler_time_cpu_gpu_torch_profiler_time_cpu_gpu.return.wrapper", "embedding": null, "metadata": {"file_path": "monai/utils/profiling.py", "file_name": "profiling.py", "file_type": "text/x-python", "category": "implementation", "start_line": 40, "end_line": 63, "span_ids": ["torch_profiler_time_cpu_gpu"], "tokens": 178}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def torch_profiler_time_cpu_gpu(func):\n    \"\"\"\n    A decorator which measures the execution time of both the CPU and GPU components\n    of the decorated function, printing both results.\n    Note: Enforces a gpu sync point which could slow down pipelines.\n    \"\"\"\n\n    @wraps(func)\n    def wrapper(*args, **kwargs):\n\n        with torch.autograd.profiler.profile(use_cuda=True) as prof:\n            result = func(*args, **kwargs)\n\n        cpu_time = prof.self_cpu_time_total\n        gpu_time = sum(evt.self_cuda_time_total for evt in prof.function_events)\n\n        cpu_time = torch.autograd.profiler.format_time(cpu_time)\n        gpu_time = torch.autograd.profiler.format_time(gpu_time)\n\n        print(f\"cpu time: {cpu_time}, gpu time: {gpu_time}\", flush=True)\n\n        return result\n\n    return wrapper", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/profiling.py_torch_profiler_time_end_to_end_torch_profiler_time_end_to_end.return.wrapper": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/profiling.py_torch_profiler_time_end_to_end_torch_profiler_time_end_to_end.return.wrapper", "embedding": null, "metadata": {"file_path": "monai/utils/profiling.py", "file_name": "profiling.py", "file_type": "text/x-python", "category": "implementation", "start_line": 66, "end_line": 90, "span_ids": ["torch_profiler_time_end_to_end"], "tokens": 166}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def torch_profiler_time_end_to_end(func):\n    \"\"\"\n    A decorator which measures the total execution time from when the decorated\n    function is called to when the last cuda operation finishes, printing the result.\n    Note: Enforces a gpu sync point which could slow down pipelines.\n    \"\"\"\n\n    @wraps(func)\n    def wrapper(*args, **kwargs):\n\n        torch.cuda.synchronize()\n        start = time.perf_counter()\n\n        result = func(*args, **kwargs)\n\n        torch.cuda.synchronize()\n        end = time.perf_counter()\n\n        total_time = (end - start) * 1e6\n        total_time_str = torch.autograd.profiler.format_time(total_time)\n        print(f\"end to end time: {total_time_str}\", flush=True)\n\n        return result\n\n    return wrapper", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/profiling.py_PerfContext_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/profiling.py_PerfContext_", "embedding": null, "metadata": {"file_path": "monai/utils/profiling.py", "file_name": "profiling.py", "file_type": "text/x-python", "category": "implementation", "start_line": 93, "end_line": 111, "span_ids": ["PerfContext", "PerfContext.__exit__", "PerfContext.__enter__", "PerfContext.__init__"], "tokens": 142}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class PerfContext:\n    \"\"\"\n    Context manager for tracking how much time is spent within context blocks. This uses `time.perf_counter` to\n    accumulate the total amount of time in seconds in the attribute `total_time` over however many context blocks\n    the object is used in.\n    \"\"\"\n\n    def __init__(self):\n        self.total_time = 0\n        self.start_time = None\n\n    def __enter__(self):\n        self.start_time = time.perf_counter()\n        return self\n\n    def __exit__(self, exc_type, exc_value, exc_traceback):\n        self.total_time += time.perf_counter() - self.start_time\n        self.start_time = None", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/state_cacher.py_copy_StateCacher.__init__.self.cached._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/state_cacher.py_copy_StateCacher.__init__.self.cached._", "embedding": null, "metadata": {"file_path": "monai/utils/state_cacher.py", "file_name": "state_cacher.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 76, "span_ids": ["StateCacher", "StateCacher.__init__", "docstring"], "tokens": 530}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import copy\nimport os\nimport pickle\nimport tempfile\nfrom typing import Dict, Optional\n\nimport torch\nfrom torch.serialization import DEFAULT_PROTOCOL\n\nfrom monai.config.type_definitions import PathLike\n\n__all__ = [\"StateCacher\"]\n\n\nclass StateCacher:\n    \"\"\"Class to cache and retrieve the state of an object.\n\n    Objects can either be stored in memory or on disk. If stored on disk, they can be\n    stored in a given directory, or alternatively a temporary location will be used.\n\n    If necessary/possible, restored objects will be returned to their original device.\n\n    Example:\n\n    >>> state_cacher = StateCacher(memory_cache, cache_dir=cache_dir)\n    >>> state_cacher.store(\"model\", model.state_dict())\n    >>> model.load_state_dict(state_cacher.retrieve(\"model\"))\n    \"\"\"\n\n    def __init__(\n        self,\n        in_memory: bool,\n        cache_dir: Optional[PathLike] = None,\n        allow_overwrite: bool = True,\n        pickle_module=pickle,\n        pickle_protocol: int = DEFAULT_PROTOCOL,\n    ) -> None:\n        \"\"\"Constructor.\n\n        Args:\n            in_memory: boolean to determine if the object will be cached in memory or on\n                disk.\n            cache_dir: directory for data to be cached if `in_memory==False`. Defaults\n                to using a temporary directory. Any created files will be deleted during\n                the `StateCacher`'s destructor.\n            allow_overwrite: allow the cache to be overwritten. If set to `False`, an\n                error will be thrown if a matching already exists in the list of cached\n                objects.\n            pickle_module: module used for pickling metadata and objects, default to `pickle`.\n                this arg is used by `torch.save`, for more details, please check:\n                https://pytorch.org/docs/stable/generated/torch.save.html#torch.save.\n            pickle_protocol: can be specified to override the default protocol, default to `2`.\n                this arg is used by `torch.save`, for more details, please check:\n                https://pytorch.org/docs/stable/generated/torch.save.html#torch.save.\n\n        \"\"\"\n        self.in_memory = in_memory\n        self.cache_dir = tempfile.gettempdir() if cache_dir is None else cache_dir\n        if not os.path.isdir(self.cache_dir):\n            raise ValueError(\"Given `cache_dir` is not a valid directory.\")\n\n        self.allow_overwrite = allow_overwrite\n        self.pickle_module = pickle_module\n        self.pickle_protocol = pickle_protocol\n        self.cached: Dict = {}", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/state_cacher.py_StateCacher.store_StateCacher.store.if_self_in_memory_.else_.if_hasattr_data_obj_dev.self_cached_key_device_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/state_cacher.py_StateCacher.store_StateCacher.store.if_self_in_memory_.else_.if_hasattr_data_obj_dev.self_cached_key_device_", "embedding": null, "metadata": {"file_path": "monai/utils/state_cacher.py", "file_name": "state_cacher.py", "file_type": "text/x-python", "category": "implementation", "start_line": 78, "end_line": 108, "span_ids": ["StateCacher.store"], "tokens": 340}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class StateCacher:\n\n    def store(self, key, data_obj, pickle_module=None, pickle_protocol: Optional[int] = None):\n        \"\"\"\n        Store a given object with the given key name.\n\n        Args:\n            key: key of the data object to store.\n            data_obj: data object to store.\n            pickle_module: module used for pickling metadata and objects, default to `self.pickle_module`.\n                this arg is used by `torch.save`, for more details, please check:\n                https://pytorch.org/docs/stable/generated/torch.save.html#torch.save.\n            pickle_protocol: can be specified to override the default protocol, default to `self.pickle_protocol`.\n                this arg is used by `torch.save`, for more details, please check:\n                https://pytorch.org/docs/stable/generated/torch.save.html#torch.save.\n\n        \"\"\"\n        if key in self.cached and not self.allow_overwrite:\n            raise RuntimeError(\"Cached key already exists and overwriting is disabled.\")\n        if self.in_memory:\n            self.cached.update({key: {\"obj\": copy.deepcopy(data_obj)}})\n        else:\n            fn = os.path.join(self.cache_dir, f\"state_{key}_{id(self)}.pt\")\n            self.cached.update({key: {\"obj\": fn}})\n            torch.save(\n                obj=data_obj,\n                f=fn,\n                pickle_module=self.pickle_module if pickle_module is None else pickle_module,\n                pickle_protocol=self.pickle_protocol if pickle_protocol is None else pickle_protocol,\n            )\n            # store object's device if relevant\n            if hasattr(data_obj, \"device\"):\n                self.cached[key][\"device\"] = data_obj.device", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/state_cacher.py_StateCacher.retrieve_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/state_cacher.py_StateCacher.retrieve_", "embedding": null, "metadata": {"file_path": "monai/utils/state_cacher.py", "file_name": "state_cacher.py", "file_type": "text/x-python", "category": "implementation", "start_line": 81, "end_line": 104, "span_ids": ["StateCacher.retrieve", "StateCacher.__del__"], "tokens": 237}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class StateCacher:\n\n    def retrieve(self, key):\n        \"\"\"Retrieve the object stored under a given key name.\"\"\"\n        if key not in self.cached:\n            raise KeyError(f\"Target {key} was not cached.\")\n\n        if self.in_memory:\n            return self.cached[key][\"obj\"]\n\n        fn = self.cached[key][\"obj\"]  # pytype: disable=attribute-error\n        if not os.path.exists(fn):  # pytype: disable=wrong-arg-types\n            raise RuntimeError(f\"Failed to load state in {fn}. File doesn't exist anymore.\")\n        data_obj = torch.load(fn, map_location=lambda storage, location: storage)\n        # copy back to device if necessary\n        if \"device\" in self.cached[key]:\n            data_obj = data_obj.to(self.cached[key][\"device\"])\n        return data_obj\n\n    def __del__(self):\n        \"\"\"If necessary, delete any cached files existing in `cache_dir`.\"\"\"\n        if not self.in_memory:\n            for k in self.cached:\n                if os.path.exists(self.cached[k][\"obj\"]):\n                    os.remove(self.cached[k][\"obj\"])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/__init__.py_CAM_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/__init__.py_CAM_", "embedding": null, "metadata": {"file_path": "monai/visualize/__init__.py", "file_name": "__init__.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 17, "span_ids": ["docstring"], "tokens": 86}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from .class_activation_maps import CAM, GradCAM, GradCAMpp, ModelWithHooks, default_normalizer\nfrom .img2tensorboard import add_animated_gif, make_animated_gif_summary, plot_2d_or_3d_image\nfrom .occlusion_sensitivity import OcclusionSensitivity\nfrom .utils import blend_images, matshow3d\nfrom .visualizer import default_upsampler", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/class_activation_maps.py_ModelWithHooks_ModelWithHooks.forward_hook.return._hook": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/class_activation_maps.py_ModelWithHooks_ModelWithHooks.forward_hook.return._hook", "embedding": null, "metadata": {"file_path": "monai/visualize/class_activation_maps.py", "file_name": "class_activation_maps.py", "file_type": "text/x-python", "category": "implementation", "start_line": 47, "end_line": 105, "span_ids": ["ModelWithHooks", "ModelWithHooks.forward_hook", "ModelWithHooks.backward_hook", "ModelWithHooks.__init__"], "tokens": 448}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ModelWithHooks:\n    \"\"\"\n    A model wrapper to run model forward/backward steps and storing some intermediate feature/gradient information.\n    \"\"\"\n\n    def __init__(\n        self,\n        nn_module,\n        target_layer_names: Union[str, Sequence[str]],\n        register_forward: bool = False,\n        register_backward: bool = False,\n    ):\n        \"\"\"\n\n        Args:\n            nn_module: the model to be wrapped.\n            target_layer_names: the names of the layer to cache.\n            register_forward: whether to cache the forward pass output corresponding to `target_layer_names`.\n            register_backward: whether to cache the backward pass output corresponding to `target_layer_names`.\n        \"\"\"\n        self.model = nn_module\n        self.target_layers = ensure_tuple(target_layer_names)\n\n        self.gradients: Dict[str, torch.Tensor] = {}\n        self.activations: Dict[str, torch.Tensor] = {}\n        self.score = None\n        self.class_idx = None\n        self.register_backward = register_backward\n        self.register_forward = register_forward\n\n        _registered = []\n        for name, mod in nn_module.named_modules():\n            if name not in self.target_layers:\n                continue\n            _registered.append(name)\n            if self.register_backward:\n                if pytorch_after(1, 8):\n                    if \"inplace\" in mod.__dict__ and mod.__dict__[\"inplace\"]:\n                        # inplace=True causes errors for register_full_backward_hook\n                        mod.__dict__[\"inplace\"] = False\n                    mod.register_full_backward_hook(self.backward_hook(name))\n                else:\n                    mod.register_backward_hook(self.backward_hook(name))\n            if self.register_forward:\n                mod.register_forward_hook(self.forward_hook(name))\n        if len(_registered) != len(self.target_layers):\n            warnings.warn(f\"Not all target_layers exist in the network module: targets: {self.target_layers}.\")\n\n    def backward_hook(self, name):\n        def _hook(_module, _grad_input, grad_output):\n            self.gradients[name] = grad_output[0]\n\n        return _hook\n\n    def forward_hook(self, name):\n        def _hook(_module, _input, output):\n            self.activations[name] = output\n\n        return _hook", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/class_activation_maps.py_ModelWithHooks.get_layer_ModelWithHooks.class_score.return.logits_class_idx_sque": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/class_activation_maps.py_ModelWithHooks.get_layer_ModelWithHooks.class_score.return.logits_class_idx_sque", "embedding": null, "metadata": {"file_path": "monai/visualize/class_activation_maps.py", "file_name": "class_activation_maps.py", "file_type": "text/x-python", "category": "implementation", "start_line": 100, "end_line": 119, "span_ids": ["ModelWithHooks.get_layer", "ModelWithHooks.class_score"], "tokens": 150}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ModelWithHooks:\n\n    def get_layer(self, layer_id: Union[str, Callable]):\n        \"\"\"\n\n        Args:\n            layer_id: a layer name string or a callable. If it is a callable such as `lambda m: m.fc`,\n                this method will return the module `self.model.fc`.\n\n        Returns:\n            a submodule from self.model.\n        \"\"\"\n        if callable(layer_id):\n            return layer_id(self.model)\n        if isinstance(layer_id, str):\n            for name, mod in self.model.named_modules():\n                if name == layer_id:\n                    return mod\n        raise NotImplementedError(f\"Could not find {layer_id}.\")\n\n    def class_score(self, logits, class_idx):\n        return logits[:, class_idx].squeeze()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/class_activation_maps.py_ModelWithHooks.__call___ModelWithHooks.get_wrapped_net.return.self_model": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/class_activation_maps.py_ModelWithHooks.__call___ModelWithHooks.get_wrapped_net.return.self_model", "embedding": null, "metadata": {"file_path": "monai/visualize/class_activation_maps.py", "file_name": "class_activation_maps.py", "file_type": "text/x-python", "category": "implementation", "start_line": 128, "end_line": 151, "span_ids": ["ModelWithHooks.get_wrapped_net", "ModelWithHooks.__call__"], "tokens": 230}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ModelWithHooks:\n\n    def __call__(self, x, class_idx=None, retain_graph=False):\n        train = self.model.training\n        self.model.eval()\n        logits = self.model(x)\n        self.class_idx = logits.max(1)[-1] if class_idx is None else class_idx\n        acti, grad = None, None\n        if self.register_forward:\n            acti = tuple(self.activations[layer] for layer in self.target_layers)\n        if self.register_backward:\n            self.score = self.class_score(logits, self.class_idx)\n            self.model.zero_grad()\n            self.score.sum().backward(retain_graph=retain_graph)\n            for layer in self.target_layers:\n                if layer not in self.gradients:\n                    raise RuntimeError(\n                        f\"Backward hook for {layer} is not triggered; `requires_grad` of {layer} should be `True`.\"\n                    )\n            grad = tuple(self.gradients[layer] for layer in self.target_layers)\n        if train:\n            self.model.train()\n        return logits, acti, grad\n\n    def get_wrapped_net(self):\n        return self.model", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/class_activation_maps.py_CAMBase_CAMBase.__init__.self.postprocessing.postprocessing": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/class_activation_maps.py_CAMBase_CAMBase.__init__.self.postprocessing.postprocessing", "embedding": null, "metadata": {"file_path": "monai/visualize/class_activation_maps.py", "file_name": "class_activation_maps.py", "file_type": "text/x-python", "category": "implementation", "start_line": 142, "end_line": 164, "span_ids": ["CAMBase.__init__", "CAMBase"], "tokens": 146}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CAMBase:\n    \"\"\"\n    Base class for CAM methods.\n    \"\"\"\n\n    def __init__(\n        self,\n        nn_module: nn.Module,\n        target_layers: str,\n        upsampler: Callable = default_upsampler,\n        postprocessing: Callable = default_normalizer,\n        register_backward: bool = True,\n    ) -> None:\n        # Convert to model with hooks if necessary\n        if not isinstance(nn_module, ModelWithHooks):\n            self.nn_module = ModelWithHooks(\n                nn_module, target_layers, register_forward=True, register_backward=register_backward\n            )\n        else:\n            self.nn_module = nn_module\n\n        self.upsampler = upsampler\n        self.postprocessing = postprocessing", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/class_activation_maps.py_CAMBase.feature_map_size_CAMBase.feature_map_size.return.self_compute_map_torch_ze": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/class_activation_maps.py_CAMBase.feature_map_size_CAMBase.feature_map_size.return.self_compute_map_torch_ze", "embedding": null, "metadata": {"file_path": "monai/visualize/class_activation_maps.py", "file_name": "class_activation_maps.py", "file_type": "text/x-python", "category": "implementation", "start_line": 166, "end_line": 176, "span_ids": ["CAMBase.feature_map_size"], "tokens": 123}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CAMBase:\n\n    def feature_map_size(self, input_size, device=\"cpu\", layer_idx=-1):\n        \"\"\"\n        Computes the actual feature map size given `nn_module` and the target_layer name.\n        Args:\n            input_size: shape of the input tensor\n            device: the device used to initialise the input tensor\n            layer_idx: index of the target layer if there are multiple target layers. Defaults to -1.\n        Returns:\n            shape of the actual feature map.\n        \"\"\"\n        return self.compute_map(torch.zeros(*input_size, device=device), layer_idx=layer_idx).shape", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/class_activation_maps.py_CAMBase.compute_map_CAMBase.__call__.raise_NotImplementedError": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/class_activation_maps.py_CAMBase.compute_map_CAMBase.__call__.raise_NotImplementedError", "embedding": null, "metadata": {"file_path": "monai/visualize/class_activation_maps.py", "file_name": "class_activation_maps.py", "file_type": "text/x-python", "category": "implementation", "start_line": 178, "end_line": 202, "span_ids": ["CAMBase._upsample_and_post_process", "CAMBase.compute_map", "CAMBase.__call__"], "tokens": 212}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CAMBase:\n\n    def compute_map(self, x, class_idx=None, layer_idx=-1):\n        \"\"\"\n        Compute the actual feature map with input tensor `x`.\n\n        Args:\n            x: input to `nn_module`.\n            class_idx: index of the class to be visualized. Default to `None` (computing `class_idx` from `argmax`)\n            layer_idx: index of the target layer if there are multiple target layers. Defaults to -1.\n\n        Returns:\n            activation maps (raw outputs without upsampling/post-processing.)\n        \"\"\"\n        raise NotImplementedError()\n\n    def _upsample_and_post_process(self, acti_map, x):\n        # upsampling and postprocessing\n        if self.upsampler:\n            img_spatial = x.shape[2:]\n            acti_map = self.upsampler(img_spatial)(acti_map)\n        if self.postprocessing:\n            acti_map = self.postprocessing(acti_map)\n        return acti_map\n\n    def __call__(self):\n        raise NotImplementedError()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/class_activation_maps.py_CAM_CAM.__init__.self.fc_layers.fc_layers": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/class_activation_maps.py_CAM_CAM.__init__.self.fc_layers.fc_layers", "embedding": null, "metadata": {"file_path": "monai/visualize/class_activation_maps.py", "file_name": "class_activation_maps.py", "file_type": "text/x-python", "category": "implementation", "start_line": 217, "end_line": 287, "span_ids": ["CAM.__init__", "CAM"], "tokens": 607}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CAM(CAMBase):\n    \"\"\"\n    Compute class activation map from the last fully-connected layers before the spatial pooling.\n    This implementation is based on:\n\n        Zhou et al., Learning Deep Features for Discriminative Localization. CVPR '16,\n        https://arxiv.org/abs/1512.04150\n\n    Examples\n\n    .. code-block:: python\n\n        import torch\n\n        # densenet 2d\n        from monai.networks.nets import DenseNet121\n        from monai.visualize import CAM\n\n        model_2d = DenseNet121(spatial_dims=2, in_channels=1, out_channels=3)\n        cam = CAM(nn_module=model_2d, target_layers=\"class_layers.relu\", fc_layers=\"class_layers.out\")\n        result = cam(x=torch.rand((1, 1, 48, 64)))\n\n        # resnet 2d\n        from monai.networks.nets import se_resnet50\n        from monai.visualize import CAM\n\n        model_2d = se_resnet50(spatial_dims=2, in_channels=3, num_classes=4)\n        cam = CAM(nn_module=model_2d, target_layers=\"layer4\", fc_layers=\"last_linear\")\n        result = cam(x=torch.rand((2, 3, 48, 64)))\n\n    N.B.: To help select the target layer, it may be useful to list all layers:\n\n    .. code-block:: python\n\n        for name, _ in model.named_modules(): print(name)\n\n    See Also:\n\n        - :py:class:`monai.visualize.class_activation_maps.GradCAM`\n\n    \"\"\"\n\n    def __init__(\n        self,\n        nn_module: nn.Module,\n        target_layers: str,\n        fc_layers: Union[str, Callable] = \"fc\",\n        upsampler: Callable = default_upsampler,\n        postprocessing: Callable = default_normalizer,\n    ) -> None:\n        \"\"\"\n        Args:\n            nn_module: the model to be visualized\n            target_layers: name of the model layer to generate the feature map.\n            fc_layers: a string or a callable used to get fully-connected weights to compute activation map\n                from the target_layers (without pooling).  and evaluate it at every spatial location.\n            upsampler: An upsampling method to upsample the output image. Default is\n                N dimensional linear (bilinear, trilinear, etc.) depending on num spatial\n                dimensions of input.\n            postprocessing: a callable that applies on the upsampled output image.\n                Default is normalizing between min=1 and max=0 (i.e., largest input will become 0 and\n                smallest input will become 1).\n        \"\"\"\n        super().__init__(\n            nn_module=nn_module,\n            target_layers=target_layers,\n            upsampler=upsampler,\n            postprocessing=postprocessing,\n            register_backward=False,\n        )\n        self.fc_layers = fc_layers", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/class_activation_maps.py_CAM.compute_map_CAM.compute_map._resume_the_spatial_dims": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/class_activation_maps.py_CAM.compute_map_CAM.compute_map._resume_the_spatial_dims", "embedding": null, "metadata": {"file_path": "monai/visualize/class_activation_maps.py", "file_name": "class_activation_maps.py", "file_type": "text/x-python", "category": "implementation", "start_line": 275, "end_line": 285, "span_ids": ["CAM.compute_map"], "tokens": 197}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CAM(CAMBase):\n\n    def compute_map(self, x, class_idx=None, layer_idx=-1):\n        logits, acti, _ = self.nn_module(x)\n        acti = acti[layer_idx]\n        if class_idx is None:\n            class_idx = logits.max(1)[-1]\n        b, c, *spatial = acti.shape\n        acti = torch.split(acti.reshape(b, c, -1), 1, dim=2)  # make the spatial dims 1D\n        fc_layers = self.nn_module.get_layer(self.fc_layers)\n        output = torch.stack([fc_layers(a[..., 0]) for a in acti], dim=2)\n        output = torch.stack([output[i, b : b + 1] for i, b in enumerate(class_idx)], dim=0)\n        return output.reshape(b, 1, *spatial)  # resume the spatial dims on the selected class", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/class_activation_maps.py_CAM.__call___CAM.__call__.return.self__upsample_and_post_p": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/class_activation_maps.py_CAM.__call___CAM.__call__.return.self__upsample_and_post_p", "embedding": null, "metadata": {"file_path": "monai/visualize/class_activation_maps.py", "file_name": "class_activation_maps.py", "file_type": "text/x-python", "category": "implementation", "start_line": 287, "end_line": 300, "span_ids": ["CAM.__call__"], "tokens": 136}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CAM(CAMBase):\n\n    def __call__(self, x, class_idx=None, layer_idx=-1):\n        \"\"\"\n        Compute the activation map with upsampling and postprocessing.\n\n        Args:\n            x: input tensor, shape must be compatible with `nn_module`.\n            class_idx: index of the class to be visualized. Default to argmax(logits)\n            layer_idx: index of the target layer if there are multiple target layers. Defaults to -1.\n\n        Returns:\n            activation maps\n        \"\"\"\n        acti_map = self.compute_map(x, class_idx, layer_idx)\n        return self._upsample_and_post_process(acti_map, x)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/class_activation_maps.py_GradCAM_GradCAM._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/class_activation_maps.py_GradCAM_GradCAM._", "embedding": null, "metadata": {"file_path": "monai/visualize/class_activation_maps.py", "file_name": "class_activation_maps.py", "file_type": "text/x-python", "category": "implementation", "start_line": 317, "end_line": 357, "span_ids": ["GradCAM"], "tokens": 333}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GradCAM(CAMBase):\n    \"\"\"\n    Computes Gradient-weighted Class Activation Mapping (Grad-CAM).\n    This implementation is based on:\n\n        Selvaraju et al., Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization,\n        https://arxiv.org/abs/1610.02391\n\n    Examples\n\n    .. code-block:: python\n\n        import torch\n\n        # densenet 2d\n        from monai.networks.nets import DenseNet121\n        from monai.visualize import GradCAM\n\n        model_2d = DenseNet121(spatial_dims=2, in_channels=1, out_channels=3)\n        cam = GradCAM(nn_module=model_2d, target_layers=\"class_layers.relu\")\n        result = cam(x=torch.rand((1, 1, 48, 64)))\n\n        # resnet 2d\n        from monai.networks.nets import se_resnet50\n        from monai.visualize import GradCAM\n\n        model_2d = se_resnet50(spatial_dims=2, in_channels=3, num_classes=4)\n        cam = GradCAM(nn_module=model_2d, target_layers=\"layer4\")\n        result = cam(x=torch.rand((2, 3, 48, 64)))\n\n    N.B.: To help select the target layer, it may be useful to list all layers:\n\n    .. code-block:: python\n\n        for name, _ in model.named_modules(): print(name)\n\n    See Also:\n\n        - :py:class:`monai.visualize.class_activation_maps.CAM`\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/class_activation_maps.py_GradCAM.compute_map_GradCAM.compute_map.return.F_relu_acti_map_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/class_activation_maps.py_GradCAM.compute_map_GradCAM.compute_map.return.F_relu_acti_map_", "embedding": null, "metadata": {"file_path": "monai/visualize/class_activation_maps.py", "file_name": "class_activation_maps.py", "file_type": "text/x-python", "category": "implementation", "start_line": 343, "end_line": 349, "span_ids": ["GradCAM.compute_map"], "tokens": 135}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GradCAM(CAMBase):\n\n    def compute_map(self, x, class_idx=None, retain_graph=False, layer_idx=-1):\n        _, acti, grad = self.nn_module(x, class_idx=class_idx, retain_graph=retain_graph)\n        acti, grad = acti[layer_idx], grad[layer_idx]\n        b, c, *spatial = grad.shape\n        weights = grad.view(b, c, -1).mean(2).view(b, c, *[1] * len(spatial))\n        acti_map = (weights * acti).sum(1, keepdim=True)\n        return F.relu(acti_map)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/class_activation_maps.py_GradCAM.__call___GradCAM.__call__.return.self__upsample_and_post_p": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/class_activation_maps.py_GradCAM.__call___GradCAM.__call__.return.self__upsample_and_post_p", "embedding": null, "metadata": {"file_path": "monai/visualize/class_activation_maps.py", "file_name": "class_activation_maps.py", "file_type": "text/x-python", "category": "implementation", "start_line": 351, "end_line": 365, "span_ids": ["GradCAM.__call__"], "tokens": 167}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GradCAM(CAMBase):\n\n    def __call__(self, x, class_idx=None, layer_idx=-1, retain_graph=False):\n        \"\"\"\n        Compute the activation map with upsampling and postprocessing.\n\n        Args:\n            x: input tensor, shape must be compatible with `nn_module`.\n            class_idx: index of the class to be visualized. Default to argmax(logits)\n            layer_idx: index of the target layer if there are multiple target layers. Defaults to -1.\n            retain_graph: whether to retain_graph for torch module backward call.\n\n        Returns:\n            activation maps\n        \"\"\"\n        acti_map = self.compute_map(x, class_idx=class_idx, retain_graph=retain_graph, layer_idx=layer_idx)\n        return self._upsample_and_post_process(acti_map, x)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/class_activation_maps.py_GradCAMpp_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/class_activation_maps.py_GradCAMpp_", "embedding": null, "metadata": {"file_path": "monai/visualize/class_activation_maps.py", "file_name": "class_activation_maps.py", "file_type": "text/x-python", "category": "implementation", "start_line": 368, "end_line": 394, "span_ids": ["GradCAMpp", "GradCAMpp.compute_map"], "tokens": 334}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GradCAMpp(GradCAM):\n    \"\"\"\n    Computes Gradient-weighted Class Activation Mapping (Grad-CAM++).\n    This implementation is based on:\n\n        Chattopadhyay et al., Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks,\n        https://arxiv.org/abs/1710.11063\n\n    See Also:\n\n        - :py:class:`monai.visualize.class_activation_maps.GradCAM`\n\n    \"\"\"\n\n    def compute_map(self, x, class_idx=None, retain_graph=False, layer_idx=-1):\n        _, acti, grad = self.nn_module(x, class_idx=class_idx, retain_graph=retain_graph)\n        acti, grad = acti[layer_idx], grad[layer_idx]\n        b, c, *spatial = grad.shape\n        alpha_nr = grad.pow(2)\n        alpha_dr = alpha_nr.mul(2) + acti.mul(grad.pow(3)).view(b, c, -1).sum(-1).view(b, c, *[1] * len(spatial))\n        alpha_dr = torch.where(alpha_dr != 0.0, alpha_dr, torch.ones_like(alpha_dr))\n        alpha = alpha_nr.div(alpha_dr + 1e-7)\n        relu_grad = F.relu(self.nn_module.score.exp() * grad)\n        weights = (alpha * relu_grad).view(b, c, -1).sum(-1).view(b, c, *[1] * len(spatial))\n        acti_map = (weights * acti).sum(1, keepdim=True)\n        return F.relu(acti_map)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/img2tensorboard.py_from_typing_import_TYPE_C___all__._make_animated_gif_summa": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/img2tensorboard.py_from_typing_import_TYPE_C___all__._make_animated_gif_summa", "embedding": null, "metadata": {"file_path": "monai/visualize/img2tensorboard.py", "file_name": "img2tensorboard.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 33, "span_ids": ["docstring"], "tokens": 221}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import TYPE_CHECKING, List, Optional, Union\n\nimport numpy as np\nimport torch\n\nfrom monai.config import NdarrayTensor\nfrom monai.transforms import rescale_array\nfrom monai.utils import convert_data_type, optional_import\n\nPIL, _ = optional_import(\"PIL\")\nGifImage, _ = optional_import(\"PIL.GifImagePlugin\", name=\"Image\")\nSummaryX, _ = optional_import(\"tensorboardX.proto.summary_pb2\", name=\"Summary\")\nSummaryWriterX, has_tensorboardx = optional_import(\"tensorboardX\", name=\"SummaryWriter\")\n\nif TYPE_CHECKING:\n    from tensorboard.compat.proto.summary_pb2 import Summary\n    from torch.utils.tensorboard import SummaryWriter\nelse:\n    Summary, _ = optional_import(\"tensorboard.compat.proto.summary_pb2\", name=\"Summary\")\n    SummaryWriter, _ = optional_import(\"torch.utils.tensorboard\", name=\"SummaryWriter\")\n\n__all__ = [\"make_animated_gif_summary\", \"add_animated_gif\", \"plot_2d_or_3d_image\"]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/occlusion_sensitivity.py_from_collections_abc_impo__check_input_image.if_image_shape_0_1_.raise_RuntimeError_Expec": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/occlusion_sensitivity.py_from_collections_abc_impo__check_input_image.if_image_shape_0_1_.raise_RuntimeError_Expec", "embedding": null, "metadata": {"file_path": "monai/visualize/occlusion_sensitivity.py", "file_name": "occlusion_sensitivity.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 41, "span_ids": ["_check_input_image", "docstring"], "tokens": 234}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from collections.abc import Sequence\nfrom functools import partial\nfrom typing import Callable, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\n\nfrom monai.networks.utils import eval_mode\nfrom monai.visualize.visualizer import default_upsampler\n\ntry:\n    from tqdm import trange\n\n    trange = partial(trange, desc=\"Computing occlusion sensitivity\")\nexcept (ImportError, AttributeError):\n    trange = range\n\n# For stride two (for example),\n# if input array is:     |0|1|2|3|4|5|6|7|\n# downsampled output is: | 0 | 1 | 2 | 3 |\n# So the upsampling should do it by the corners of the image, not their centres\ndefault_upsampler = partial(default_upsampler, align_corners=True)\n\n\ndef _check_input_image(image):\n    \"\"\"Check that the input image is as expected.\"\"\"\n    # Only accept batch size of 1\n    if image.shape[0] > 1:\n        raise RuntimeError(\"Expected batch size of 1.\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/occlusion_sensitivity.py__check_input_bounding_box__check_input_bounding_box.return.b_box_min_b_box_max": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/occlusion_sensitivity.py__check_input_bounding_box__check_input_bounding_box.return.b_box_min_b_box_max", "embedding": null, "metadata": {"file_path": "monai/visualize/occlusion_sensitivity.py", "file_name": "occlusion_sensitivity.py", "file_type": "text/x-python", "category": "implementation", "start_line": 44, "end_line": 68, "span_ids": ["_check_input_bounding_box"], "tokens": 304}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _check_input_bounding_box(b_box, im_shape):\n    \"\"\"Check that the bounding box (if supplied) is as expected.\"\"\"\n    # If no bounding box has been supplied, set min and max to None\n    if b_box is None:\n        b_box_min = b_box_max = None\n\n    # Bounding box has been supplied\n    else:\n        # Should be twice as many elements in `b_box` as `im_shape`\n        if len(b_box) != 2 * len(im_shape):\n            raise ValueError(\"Bounding box should contain upper and lower for all dimensions (except batch number)\")\n\n        # If any min's or max's are -ve, set them to 0 and im_shape-1, respectively.\n        b_box_min = np.array(b_box[::2])\n        b_box_max = np.array(b_box[1::2])\n        b_box_min[b_box_min < 0] = 0\n        b_box_max[b_box_max < 0] = im_shape[b_box_max < 0] - 1\n        # Check all max's are < im_shape\n        if np.any(b_box_max >= im_shape):\n            raise ValueError(\"Max bounding box should be < image size for all values\")\n        # Check all min's are <= max's\n        if np.any(b_box_min > b_box_max):\n            raise ValueError(\"Min bounding box should be <= max for all values\")\n\n    return b_box_min, b_box_max", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/occlusion_sensitivity.py__append_to_sensitivity_ims__get_as_np_array.return.out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/occlusion_sensitivity.py__append_to_sensitivity_ims__get_as_np_array.return.out", "embedding": null, "metadata": {"file_path": "monai/visualize/occlusion_sensitivity.py", "file_name": "occlusion_sensitivity.py", "file_type": "text/x-python", "category": "implementation", "start_line": 71, "end_line": 94, "span_ids": ["_append_to_sensitivity_ims", "_get_as_np_array"], "tokens": 257}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _append_to_sensitivity_ims(model, batch_images, sensitivity_ims):\n    \"\"\"Infer given images. Append to previous evaluations. Store each class separately.\"\"\"\n    batch_images = torch.cat(batch_images, dim=0)\n    scores = model(batch_images).detach()\n    for i in range(scores.shape[1]):\n        sensitivity_ims[i] = torch.cat((sensitivity_ims[i], scores[:, i]))\n    return sensitivity_ims\n\n\ndef _get_as_np_array(val, numel):\n    # If not a sequence, then convert scalar to numpy array\n    if not isinstance(val, Sequence):\n        out = np.full(numel, val, dtype=np.int32)\n        out[0] = 1  # mask_size and stride always 1 in channel dimension\n    else:\n        # Convert to numpy array and check dimensions match\n        out = np.array(val, dtype=np.int32)\n        # Add stride of 1 to the channel direction (since user input was only for spatial dimensions)\n        out = np.insert(out, 0, 1)\n        if out.size != numel:\n            raise ValueError(\n                \"If supplying stride/mask_size as sequence, number of elements should match number of spatial dimensions.\"\n            )\n    return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/occlusion_sensitivity.py_OcclusionSensitivity_OcclusionSensitivity._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/occlusion_sensitivity.py_OcclusionSensitivity_OcclusionSensitivity._", "embedding": null, "metadata": {"file_path": "monai/visualize/occlusion_sensitivity.py", "file_name": "occlusion_sensitivity.py", "file_type": "text/x-python", "category": "implementation", "start_line": 97, "end_line": 143, "span_ids": ["OcclusionSensitivity"], "tokens": 654}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class OcclusionSensitivity:\n    \"\"\"\n    This class computes the occlusion sensitivity for a model's prediction of a given image. By occlusion sensitivity,\n    we mean how the probability of a given prediction changes as the occluded section of an image changes. This can be\n    useful to understand why a network is making certain decisions.\n\n    As important parts of the image are occluded, the probability of classifying the image correctly will decrease.\n    Hence, more negative values imply the corresponding occluded volume was more important in the decision process.\n\n    Two ``torch.Tensor`` will be returned by the ``__call__`` method: an occlusion map and an image of the most probable\n    class. Both images will be cropped if a bounding box used, but voxel sizes will always match the input.\n\n    The occlusion map shows the inference probabilities when the corresponding part of the image is occluded. Hence,\n    more -ve values imply that region was important in the decision process. The map will have shape ``BCHW(D)N``,\n    where ``N`` is the number of classes to be inferred by the network. Hence, the occlusion for class ``i`` can\n    be seen with ``map[...,i]``.\n\n    The most probable class is an image of the probable class when the corresponding part of the image is occluded\n    (equivalent to ``occ_map.argmax(dim=-1)``).\n\n    See: R. R. Selvaraju et al. Grad-CAM: Visual Explanations from Deep Networks via\n    Gradient-based Localization. https://doi.org/10.1109/ICCV.2017.74.\n\n    Examples:\n\n    .. code-block:: python\n\n        # densenet 2d\n        from monai.networks.nets import DenseNet121\n        from monai.visualize import OcclusionSensitivity\n\n        model_2d = DenseNet121(spatial_dims=2, in_channels=1, out_channels=3)\n        occ_sens = OcclusionSensitivity(nn_module=model_2d)\n        occ_map, most_probable_class = occ_sens(x=torch.rand((1, 1, 48, 64)), b_box=[-1, -1, 2, 40, 1, 62])\n\n        # densenet 3d\n        from monai.networks.nets import DenseNet\n        from monai.visualize import OcclusionSensitivity\n\n        model_3d = DenseNet(spatial_dims=3, in_channels=1, out_channels=3, init_features=2, growth_rate=2, block_config=(6,))\n        occ_sens = OcclusionSensitivity(nn_module=model_3d, n_batch=10, stride=3)\n        occ_map, most_probable_class = occ_sens(torch.rand(1, 1, 6, 6, 6), b_box=[-1, -1, 1, 3, -1, -1, -1, -1])\n\n    See Also:\n\n        - :py:class:`monai.visualize.occlusion_sensitivity.OcclusionSensitivity.`\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/occlusion_sensitivity.py_OcclusionSensitivity.__init___OcclusionSensitivity.__init__.self.verbose.verbose": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/occlusion_sensitivity.py_OcclusionSensitivity.__init___OcclusionSensitivity.__init__.self.verbose.verbose", "embedding": null, "metadata": {"file_path": "monai/visualize/occlusion_sensitivity.py", "file_name": "occlusion_sensitivity.py", "file_type": "text/x-python", "category": "implementation", "start_line": 145, "end_line": 179, "span_ids": ["OcclusionSensitivity.__init__"], "tokens": 374}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class OcclusionSensitivity:\n\n    def __init__(\n        self,\n        nn_module: nn.Module,\n        pad_val: Optional[float] = None,\n        mask_size: Union[int, Sequence] = 15,\n        n_batch: int = 128,\n        stride: Union[int, Sequence] = 1,\n        upsampler: Optional[Callable] = default_upsampler,\n        verbose: bool = True,\n    ) -> None:\n        \"\"\"Occlusion sensitivity constructor.\n\n        Args:\n            nn_module: Classification model to use for inference\n            pad_val: When occluding part of the image, which values should we put\n                in the image? If ``None`` is used, then the average of the image will be used.\n            mask_size: Size of box to be occluded, centred on the central voxel. To ensure that the occluded area\n                is correctly centred, ``mask_size`` and ``stride`` should both be odd or even.\n            n_batch: Number of images in a batch for inference.\n            stride: Stride in spatial directions for performing occlusions. Can be single\n                value or sequence (for varying stride in the different directions).\n                Should be >= 1. Striding in the channel direction will always be 1.\n            upsampler: An upsampling method to upsample the output image. Default is\n                N-dimensional linear (bilinear, trilinear, etc.) depending on num spatial\n                dimensions of input.\n            verbose: Use ``tqdm.trange`` output (if available).\n        \"\"\"\n\n        self.nn_module = nn_module\n        self.upsampler = upsampler\n        self.pad_val = pad_val\n        self.mask_size = mask_size\n        self.n_batch = n_batch\n        self.stride = stride\n        self.verbose = verbose", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/occlusion_sensitivity.py_OcclusionSensitivity._compute_occlusion_sensitivity_OcclusionSensitivity._compute_occlusion_sensitivity.verbose_range.trange_if_self_verbose_el": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/occlusion_sensitivity.py_OcclusionSensitivity._compute_occlusion_sensitivity_OcclusionSensitivity._compute_occlusion_sensitivity.verbose_range.trange_if_self_verbose_el", "embedding": null, "metadata": {"file_path": "monai/visualize/occlusion_sensitivity.py", "file_name": "occlusion_sensitivity.py", "file_type": "text/x-python", "category": "implementation", "start_line": 181, "end_line": 232, "span_ids": ["OcclusionSensitivity._compute_occlusion_sensitivity"], "tokens": 591}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class OcclusionSensitivity:\n\n    def _compute_occlusion_sensitivity(self, x, b_box):\n\n        # Get bounding box\n        im_shape = np.array(x.shape[1:])\n        b_box_min, b_box_max = _check_input_bounding_box(b_box, im_shape)\n\n        # Get the number of prediction classes\n        num_classes = self.nn_module(x).numel()\n\n        # If pad val not supplied, get the mean of the image\n        pad_val = x.mean() if self.pad_val is None else self.pad_val\n\n        # List containing a batch of images to be inferred\n        batch_images = []\n\n        # List of sensitivity images, one for each inferred class\n        sensitivity_ims = num_classes * [torch.empty(0, dtype=torch.float32, device=x.device)]\n\n        # If no bounding box supplied, output shape is same as input shape.\n        # If bounding box is present, shape is max - min + 1\n        output_im_shape = im_shape if b_box is None else b_box_max - b_box_min + 1\n\n        # Get the stride and mask_size as numpy arrays\n        self.stride = _get_as_np_array(self.stride, len(im_shape))\n        self.mask_size = _get_as_np_array(self.mask_size, len(im_shape))\n\n        # For each dimension, ...\n        for o, s in zip(output_im_shape, self.stride):\n            # if the size is > 1, then check that the stride is a factor of the output image shape\n            if o > 1 and o % s != 0:\n                raise ValueError(\n                    \"Stride should be a factor of the image shape. Im shape \"\n                    + f\"(taking bounding box into account): {output_im_shape}, stride: {self.stride}\"\n                )\n\n        # to ensure the occluded area is nicely centred if stride is even, ensure that so is the mask_size\n        if np.any(self.mask_size % 2 != self.stride % 2):\n            raise ValueError(\n                \"Stride and mask size should both be odd or even (element-wise). \"\n                + f\"``stride={self.stride}``, ``mask_size={self.mask_size}``\"\n            )\n\n        downsampled_im_shape = (output_im_shape / self.stride).astype(np.int32)\n        downsampled_im_shape[downsampled_im_shape == 0] = 1  # make sure dimension sizes are >= 1\n        num_required_predictions = np.prod(downsampled_im_shape)\n\n        # Get bottom left and top right corners of occluded region\n        lower_corner = (self.stride - self.mask_size) // 2\n        upper_corner = (self.stride + self.mask_size) // 2\n\n        # Loop 1D over image\n        verbose_range = trange if self.verbose else range\n        # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/occlusion_sensitivity.py_OcclusionSensitivity._compute_occlusion_sensitivity.for_i_in_verbose_range_nu_OcclusionSensitivity._compute_occlusion_sensitivity.return.sensitivity_ims_output_i": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/occlusion_sensitivity.py_OcclusionSensitivity._compute_occlusion_sensitivity.for_i_in_verbose_range_nu_OcclusionSensitivity._compute_occlusion_sensitivity.return.sensitivity_ims_output_i", "embedding": null, "metadata": {"file_path": "monai/visualize/occlusion_sensitivity.py", "file_name": "occlusion_sensitivity.py", "file_type": "text/x-python", "category": "implementation", "start_line": 233, "end_line": 265, "span_ids": ["OcclusionSensitivity._compute_occlusion_sensitivity"], "tokens": 375}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class OcclusionSensitivity:\n\n    def _compute_occlusion_sensitivity(self, x, b_box):\n        # ... other code\n        for i in verbose_range(num_required_predictions):\n            # Get corresponding ND index\n            idx = np.unravel_index(i, downsampled_im_shape)\n            # Multiply by stride\n            idx *= self.stride\n            # If a bounding box is being used, we need to add on\n            # the min to shift to start of region of interest\n            if b_box_min is not None:\n                idx += b_box_min\n\n            # Get min and max index of box to occlude (and make sure it's in bounds)\n            min_idx = np.maximum(idx + lower_corner, 0)\n            max_idx = np.minimum(idx + upper_corner, im_shape)\n\n            # Clone and replace target area with `pad_val`\n            occlu_im = x.detach().clone()\n            occlu_im[(...,) + tuple(slice(i, j) for i, j in zip(min_idx, max_idx))] = pad_val\n\n            # Add to list\n            batch_images.append(occlu_im)\n\n            # Once the batch is complete (or on last iteration)\n            if len(batch_images) == self.n_batch or i == num_required_predictions - 1:\n                # Do the predictions and append to sensitivity maps\n                sensitivity_ims = _append_to_sensitivity_ims(self.nn_module, batch_images, sensitivity_ims)\n                # Clear lists\n                batch_images = []\n\n        # Reshape to match downsampled image, and unsqueeze to add batch dimension back in\n        for i in range(num_classes):\n            sensitivity_ims[i] = sensitivity_ims[i].reshape(tuple(downsampled_im_shape)).unsqueeze(0)\n\n        return sensitivity_ims, output_im_shape", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/occlusion_sensitivity.py_OcclusionSensitivity.__call___": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/occlusion_sensitivity.py_OcclusionSensitivity.__call___", "embedding": null, "metadata": {"file_path": "monai/visualize/occlusion_sensitivity.py", "file_name": "occlusion_sensitivity.py", "file_type": "text/x-python", "category": "implementation", "start_line": 267, "end_line": 316, "span_ids": ["OcclusionSensitivity.__call__"], "tokens": 576}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class OcclusionSensitivity:\n\n    def __call__(  # type: ignore\n        self, x: torch.Tensor, b_box: Optional[Sequence] = None\n    ) -> Tuple[torch.Tensor, torch.Tensor]:\n        \"\"\"\n        Args:\n            x: Image to use for inference. Should be a tensor consisting of 1 batch.\n            b_box: Bounding box on which to perform the analysis. The output image will be limited to this size.\n                There should be a minimum and maximum for all dimensions except batch: ``[min1, max1, min2, max2,...]``.\n                * By default, the whole image will be used. Decreasing the size will speed the analysis up, which might\n                    be useful for larger images.\n                * Min and max are inclusive, so ``[0, 63, ...]`` will have size ``(64, ...)``.\n                * Use -ve to use ``min=0`` and ``max=im.shape[x]-1`` for xth dimension.\n\n        Returns:\n            * Occlusion map:\n                * Shows the inference probabilities when the corresponding part of the image is occluded.\n                    Hence, more -ve values imply that region was important in the decision process.\n                * The map will have shape ``BCHW(D)N``, where N is the number of classes to be inferred by the\n                    network. Hence, the occlusion for class ``i`` can be seen with ``map[...,i]``.\n            * Most probable class:\n                * The most probable class when the corresponding part of the image is occluded (``argmax(dim=-1)``).\n            Both images will be cropped if a bounding box used, but voxel sizes will always match the input.\n        \"\"\"\n\n        with eval_mode(self.nn_module):\n\n            # Check input arguments\n            _check_input_image(x)\n\n            # Generate sensitivity images\n            sensitivity_ims_list, output_im_shape = self._compute_occlusion_sensitivity(x, b_box)\n\n            # Loop over image for each classification\n            for i, sens_i in enumerate(sensitivity_ims_list):\n                # upsample\n                if self.upsampler is not None:\n                    if len(sens_i.shape) != len(x.shape):\n                        raise AssertionError\n                    if np.any(sens_i.shape != x.shape):\n                        img_spatial = tuple(output_im_shape[1:])\n                        sensitivity_ims_list[i] = self.upsampler(img_spatial)(sens_i)\n\n            # Convert list of tensors to tensor\n            sensitivity_ims = torch.stack(sensitivity_ims_list, dim=-1)\n\n            # The most probable class is the max in the classification dimension (last)\n            most_probable_class = sensitivity_ims.argmax(dim=-1)\n\n            return sensitivity_ims, most_probable_class", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/visualizer.py_from_typing_import_Callab_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/visualizer.py_from_typing_import_Callab_", "embedding": null, "metadata": {"file_path": "monai/visualize/visualizer.py", "file_name": "visualizer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 13, "end_line": 37, "span_ids": ["default_upsampler", "docstring"], "tokens": 171}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Callable\n\nimport torch\nimport torch.nn.functional as F\n\nfrom monai.utils import InterpolateMode\n\n__all__ = [\"default_upsampler\"]\n\n\ndef default_upsampler(spatial_size, align_corners=False) -> Callable[[torch.Tensor], torch.Tensor]:\n    \"\"\"\n    A linear interpolation method for upsampling the feature map.\n    The output of this function is a callable `func`,\n    such that `func(x)` returns an upsampled tensor.\n    \"\"\"\n\n    def up(x):\n\n        linear_mode = [InterpolateMode.LINEAR, InterpolateMode.BILINEAR, InterpolateMode.TRILINEAR]\n        interp_mode = linear_mode[len(spatial_size) - 1]\n        return F.interpolate(x, size=spatial_size, mode=str(interp_mode.value), align_corners=align_corners)\n\n    return up", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/runner.py_argparse_TimeLoggingTestResult.stopTest.super_stopTest_test_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/runner.py_argparse_TimeLoggingTestResult.stopTest.super_stopTest_test_", "embedding": null, "metadata": {"file_path": "tests/runner.py", "file_name": "runner.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 48, "span_ids": ["TimeLoggingTestResult.__init__", "TimeLoggingTestResult.startTest", "TimeLoggingTestResult.stopTest", "docstring", "TimeLoggingTestResult"], "tokens": 258}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import argparse\nimport glob\nimport inspect\nimport os\nimport re\nimport sys\nimport time\nimport unittest\n\nfrom monai.utils import PerfContext\n\nresults: dict = {}\n\n\nclass TimeLoggingTestResult(unittest.TextTestResult):\n    \"\"\"Overload the default results so that we can store the results.\"\"\"\n\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        self.timed_tests = {}\n\n    def startTest(self, test):  # noqa: N802\n        \"\"\"Start timer, print test name, do normal test.\"\"\"\n        self.start_time = time.time()\n        name = self.getDescription(test)\n        self.stream.write(f\"Starting test: {name}...\\n\")\n        super().startTest(test)\n\n    def stopTest(self, test):  # noqa: N802\n        \"\"\"On test end, get time, print, store and do normal behaviour.\"\"\"\n        elapsed = time.time() - self.start_time\n        name = self.getDescription(test)\n        self.stream.write(f\"Finished test: {name} ({elapsed:.03}s)\\n\")\n        if name in results:\n            raise AssertionError(\"expected all keys to be unique\")\n        results[name] = elapsed\n        super().stopTest(test)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/runner.py_print_results_print_results.print_Remember_to_check_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/runner.py_print_results_print_results.print_Remember_to_check_", "embedding": null, "metadata": {"file_path": "tests/runner.py", "file_name": "runner.py", "file_type": "text/x-python", "category": "implementation", "start_line": 51, "end_line": 64, "span_ids": ["print_results"], "tokens": 162}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def print_results(results, discovery_time, thresh, status):\n    # only keep results >= threshold\n    results = dict(filter(lambda x: x[1] > thresh, results.items()))\n    if len(results) == 0:\n        return\n    print(f\"\\n\\n{status}, printing completed times >{thresh}s in ascending order...\\n\")\n    timings = dict(sorted(results.items(), key=lambda item: item[1]))\n\n    for r in timings:\n        if timings[r] >= thresh:\n            print(f\"{r} ({timings[r]:.03}s)\")\n    print(f\"test discovery time: {discovery_time:.03}s\")\n    print(f\"total testing time: {sum(results.values()):.03}s\")\n    print(\"Remember to check above times for any errors!\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/runner.py_parse_args_parse_args.return.args": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/runner.py_parse_args_parse_args.return.args", "embedding": null, "metadata": {"file_path": "tests/runner.py", "file_name": "runner.py", "file_type": "text/x-python", "category": "implementation", "start_line": 67, "end_line": 105, "span_ids": ["parse_args"], "tokens": 301}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def parse_args():\n    parser = argparse.ArgumentParser(description=\"Runner for MONAI unittests with timing.\")\n    parser.add_argument(\n        \"-s\", action=\"store\", dest=\"path\", default=\".\", help=\"Directory to start discovery (default: '%(default)s')\"\n    )\n    parser.add_argument(\n        \"-p\",\n        action=\"store\",\n        dest=\"pattern\",\n        default=\"test_*.py\",\n        help=\"Pattern to match tests (default: '%(default)s')\",\n    )\n    parser.add_argument(\n        \"-t\",\n        \"--thresh\",\n        dest=\"thresh\",\n        default=10.0,\n        type=float,\n        help=\"Display tests longer than given threshold (default: %(default)d)\",\n    )\n    parser.add_argument(\n        \"-v\",\n        \"--verbosity\",\n        action=\"store\",\n        dest=\"verbosity\",\n        type=int,\n        default=1,\n        help=\"Verbosity level (default: %(default)d)\",\n    )\n    parser.add_argument(\"-q\", \"--quick\", action=\"store_true\", dest=\"quick\", default=False, help=\"Only do quick tests\")\n    parser.add_argument(\n        \"-f\", \"--failfast\", action=\"store_true\", dest=\"failfast\", default=False, help=\"Stop testing on first failure\"\n    )\n    args = parser.parse_args()\n    print(f\"Running tests in folder: '{args.path}'\")\n    if args.pattern:\n        print(f\"With file pattern: '{args.pattern}'\")\n\n    return args", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/runner.py_get_default_pattern_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/runner.py_get_default_pattern_", "embedding": null, "metadata": {"file_path": "tests/runner.py", "file_name": "runner.py", "file_type": "text/x-python", "category": "implementation", "start_line": 108, "end_line": 152, "span_ids": ["impl:3", "get_default_pattern"], "tokens": 404}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_default_pattern(loader):\n    signature = inspect.signature(loader.discover)\n    params = {k: v.default for k, v in signature.parameters.items() if v.default is not inspect.Parameter.empty}\n    return params[\"pattern\"]\n\n\nif __name__ == \"__main__\":\n\n    # Parse input arguments\n    args = parse_args()\n\n    # If quick is desired, set environment variable\n    if args.quick:\n        os.environ[\"QUICKTEST\"] = \"True\"\n\n    # Get all test names (optionally from some path with some pattern)\n    with PerfContext() as pc:\n        # the files are searched from `tests/` folder, starting with `test_`\n        files = glob.glob(os.path.join(os.path.dirname(__file__), \"test_*.py\"))\n        cases = []\n        for test_module in {os.path.basename(f)[:-3] for f in files}:\n            if re.match(args.pattern, test_module):\n                cases.append(f\"tests.{test_module}\")\n            else:\n                print(f\"monai test runner: excluding tests.{test_module}\")\n        tests = unittest.TestLoader().loadTestsFromNames(cases)\n    discovery_time = pc.total_time\n    print(f\"time to discover tests: {discovery_time}s, total cases: {tests.countTestCases()}.\")\n\n    test_runner = unittest.runner.TextTestRunner(\n        resultclass=TimeLoggingTestResult, verbosity=args.verbosity, failfast=args.failfast\n    )\n\n    # Use try catches to print the current results if encountering exception or keyboard interruption\n    try:\n        test_result = test_runner.run(tests)\n        print_results(results, discovery_time, args.thresh, \"tests finished\")\n        sys.exit(not test_result.wasSuccessful())\n    except KeyboardInterrupt:\n        print_results(results, discovery_time, args.thresh, \"tests cancelled\")\n        sys.exit(1)\n    except Exception:\n        print_results(results, discovery_time, args.thresh, \"exception reached\")\n        raise", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_activations.py_TestActivations_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_activations.py_TestActivations_", "embedding": null, "metadata": {"file_path": "tests/test_activations.py", "file_name": "test_activations.py", "file_type": "text/x-python", "category": "test", "start_line": 78, "end_line": 103, "span_ids": ["TestActivations.test_monai_activations_value_shape", "impl:13", "TestActivations.test_value_shape", "TestActivations"], "tokens": 229}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestActivations(unittest.TestCase):\n    @parameterized.expand(TEST_CASES[:3])\n    def test_value_shape(self, input_param, img, out, expected_shape):\n        result = Activations(**input_param)(img)\n\n        def _compare(ret, out, shape):\n            assert_allclose(ret, out, rtol=1e-3)\n            self.assertTupleEqual(ret.shape, shape)\n\n        if isinstance(result, (list, tuple)):\n            for r, e in zip(result, out):\n                _compare(r, e, expected_shape)\n        else:\n            _compare(result, out, expected_shape)\n\n    @parameterized.expand([TEST_CASE_4, TEST_CASE_5, TEST_CASE_6])\n    def test_monai_activations_value_shape(self, input_param, img, out, expected_shape):\n        act = Act[input_param]()\n        result = act(img)\n        torch.testing.assert_allclose(result, out, rtol=1e-2, atol=1e-5)\n        self.assertTupleEqual(result.shape, expected_shape)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_add_extreme_points_channel.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_add_extreme_points_channel.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_add_extreme_points_channel.py", "file_name": "test_add_extreme_points_channel.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 79, "span_ids": ["impl:9", "TestAddExtremePointsChannel", "TestAddExtremePointsChannel.test_correct_results", "docstring"], "tokens": 564}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import AddExtremePointsChannel\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nIMG_CHANNEL = 3\nTESTS = []\nfor p in TEST_NDARRAYS:\n    for q in TEST_NDARRAYS:\n        TESTS.append(\n            [\n                {\n                    \"img\": p(np.zeros((IMG_CHANNEL, 4, 3))),\n                    \"label\": q(np.array([[[0, 1, 0], [0, 0, 1], [0, 1, 0], [1, 0, 0]]])),\n                    \"sigma\": 1.0,\n                    \"rescale_min\": 0.0,\n                    \"rescale_max\": 1.0,\n                },\n                p(\n                    np.array(\n                        [\n                            [0.38318458, 0.98615628, 0.85551184],\n                            [0.35422316, 0.94430935, 1.0],\n                            [0.46000731, 0.57319659, 0.46000722],\n                            [0.64577687, 0.38318464, 0.0],\n                        ]\n                    )\n                ),\n            ]\n        )\n\n        TESTS.append(\n            [\n                {\n                    \"img\": p(np.zeros((IMG_CHANNEL, 4, 3))),\n                    \"label\": q(np.array([[[0, 1, 0], [1, 1, 1], [0, 1, 0], [0, 1, 0]]])),\n                    \"sigma\": 1.0,\n                    \"rescale_min\": 0.0,\n                    \"rescale_max\": 1.0,\n                },\n                p(\n                    np.array(\n                        [\n                            [0.44628328, 0.80495411, 0.44628328],\n                            [0.6779086, 1.0, 0.67790854],\n                            [0.33002687, 0.62079221, 0.33002687],\n                            [0.0, 0.31848389, 0.0],\n                        ]\n                    )\n                ),\n            ]\n        )\n\n\nclass TestAddExtremePointsChannel(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_correct_results(self, input_data, expected):\n        add_extreme_points_channel = AddExtremePointsChannel()\n        result = add_extreme_points_channel(**input_data)\n        assert_allclose(result[IMG_CHANNEL], expected, rtol=1e-4)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_add_extreme_points_channeld.py_TestAddExtremePointsChanneld_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_add_extreme_points_channeld.py_TestAddExtremePointsChanneld_", "embedding": null, "metadata": {"file_path": "tests/test_add_extreme_points_channeld.py", "file_name": "test_add_extreme_points_channeld.py", "file_type": "text/x-python", "category": "test", "start_line": 64, "end_line": 76, "span_ids": ["impl:9", "TestAddExtremePointsChanneld", "TestAddExtremePointsChanneld.test_correct_results"], "tokens": 123}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestAddExtremePointsChanneld(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_correct_results(self, input_data, expected):\n        add_extreme_points_channel = AddExtremePointsChanneld(\n            keys=\"img\", label_key=\"label\", sigma=1.0, rescale_min=0.0, rescale_max=1.0\n        )\n        result = add_extreme_points_channel(input_data)\n        assert_allclose(result[\"img\"][IMG_CHANNEL], expected, rtol=1e-4)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_adn.py_unittest_TEST_CASES_3D._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_adn.py_unittest_TEST_CASES_3D._", "embedding": null, "metadata": {"file_path": "tests/test_adn.py", "file_name": "test_adn.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 56, "span_ids": ["docstring"], "tokens": 600}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nfrom parameterized import parameterized\n\nfrom monai.networks.blocks import ADN\nfrom tests.utils import TorchImageTestCase2D, TorchImageTestCase3D\n\nTEST_CASES_2D = [\n    [{\"act\": None}],\n    [{\"norm_dim\": 2}],\n    [{\"norm_dim\": 2, \"act\": \"relu\", \"dropout\": 0.8, \"ordering\": \"DA\"}],\n    [{\"dropout_dim\": 1, \"dropout\": 0.8, \"ordering\": \"DA\"}],\n    [{\"norm\": \"BATCH\", \"norm_dim\": 2, \"in_channels\": 1, \"dropout_dim\": 1, \"dropout\": 0.8, \"ordering\": \"NDA\"}],\n    [{\"norm\": \"BATCH\", \"norm_dim\": 2, \"in_channels\": 1, \"dropout_dim\": 1, \"dropout\": 0.8, \"ordering\": \"AND\"}],\n    [{\"norm\": \"INSTANCE\", \"norm_dim\": 2, \"dropout_dim\": 1, \"dropout\": 0.8, \"ordering\": \"AND\"}],\n    [\n        {\n            \"norm\": (\"GROUP\", {\"num_groups\": 1, \"affine\": False}),\n            \"in_channels\": 1,\n            \"norm_dim\": 2,\n            \"dropout_dim\": 1,\n            \"dropout\": 0.8,\n            \"ordering\": \"AND\",\n        }\n    ],\n    [{\"norm\": (\"localresponse\", {\"size\": 4}), \"norm_dim\": 2, \"dropout_dim\": 1, \"dropout\": 0.8, \"ordering\": \"AND\"}],\n]\n\nTEST_CASES_3D = [\n    [{\"norm_dim\": 3}],\n    [{\"act\": \"prelu\", \"dropout_dim\": 1, \"dropout\": 0.8, \"ordering\": \"DA\"}],\n    [{\"dropout_dim\": 1, \"dropout\": 0.8, \"ordering\": \"DA\"}],\n    [{\"norm\": \"BATCH\", \"norm_dim\": 3, \"in_channels\": 1, \"dropout_dim\": 1, \"dropout\": 0.8, \"ordering\": \"NDA\"}],\n    [{\"norm\": \"BATCH\", \"norm_dim\": 3, \"in_channels\": 1, \"dropout_dim\": 1, \"dropout\": 0.8, \"ordering\": \"AND\"}],\n    [{\"norm\": \"INSTANCE\", \"norm_dim\": 3, \"dropout_dim\": 1, \"dropout\": 0.8, \"ordering\": \"AND\"}],\n    [\n        {\n            \"norm\": (\"layer\", {\"normalized_shape\": (64, 80)}),\n            \"norm_dim\": 3,\n            \"dropout_dim\": 1,\n            \"dropout\": 0.8,\n            \"ordering\": \"AND\",\n        }\n    ],\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_adn.py_TestADN2D_TestADN2D.test_no_input.with_self_assertRaises_Va.ADN_norm_instance_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_adn.py_TestADN2D_TestADN2D.test_no_input.with_self_assertRaises_Va.ADN_norm_instance_", "embedding": null, "metadata": {"file_path": "tests/test_adn.py", "file_name": "test_adn.py", "file_type": "text/x-python", "category": "test", "start_line": 59, "end_line": 70, "span_ids": ["TestADN2D", "TestADN2D.test_adn_2d", "TestADN2D.test_no_input"], "tokens": 112}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestADN2D(TorchImageTestCase2D):\n    @parameterized.expand(TEST_CASES_2D)\n    def test_adn_2d(self, args):\n        adn = ADN(**args)\n        print(adn)\n        out = adn(self.imt)\n        expected_shape = (1, self.input_channels, self.im_shape[0], self.im_shape[1])\n        self.assertEqual(out.shape, expected_shape)\n\n    def test_no_input(self):\n        with self.assertRaises(ValueError):\n            ADN(norm=\"instance\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_adn.py_TestADN3D_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_adn.py_TestADN3D_", "embedding": null, "metadata": {"file_path": "tests/test_adn.py", "file_name": "test_adn.py", "file_type": "text/x-python", "category": "test", "start_line": 73, "end_line": 85, "span_ids": ["impl:5", "TestADN3D.test_adn_3d", "TestADN3D"], "tokens": 110}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestADN3D(TorchImageTestCase3D):\n    @parameterized.expand(TEST_CASES_3D)\n    def test_adn_3d(self, args):\n        adn = ADN(**args)\n        print(adn)\n        out = adn(self.imt)\n        expected_shape = (1, self.input_channels, self.im_shape[1], self.im_shape[0], self.im_shape[2])\n        self.assertEqual(out.shape, expected_shape)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ahnet.py_unittest_TEST_CASE_MCFCN_WITH_PRETRAIN_1._batch_5": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ahnet.py_unittest_TEST_CASE_MCFCN_WITH_PRETRAIN_1._batch_5", "embedding": null, "metadata": {"file_path": "tests/test_ahnet.py", "file_name": "test_ahnet.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 69, "span_ids": ["impl:13", "docstring"], "tokens": 685}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks import eval_mode\nfrom monai.networks.blocks import FCN, MCFCN\nfrom monai.networks.nets import AHNet\nfrom tests.utils import skip_if_quick, test_pretrained_networks, test_script_save\n\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\nTEST_CASE_FCN_1 = [  # batch 2\n    {\"out_channels\": 3, \"upsample_mode\": \"transpose\", \"pretrained\": False},\n    (2, 3, 32, 32),\n    (2, 3, 32, 32),\n]\nTEST_CASE_FCN_2 = [\n    {\"out_channels\": 2, \"upsample_mode\": \"transpose\", \"pretrained\": False, \"progress\": False},\n    (1, 3, 32, 32),\n    (1, 2, 32, 32),\n]\nTEST_CASE_FCN_3 = [\n    {\"out_channels\": 1, \"upsample_mode\": \"bilinear\", \"pretrained\": False},\n    (1, 3, 32, 32),\n    (1, 1, 32, 32),\n]\nTEST_CASE_FCN_WITH_PRETRAIN_1 = [  # batch 2\n    {\"out_channels\": 3, \"upsample_mode\": \"transpose\", \"pretrained\": True},\n    (2, 3, 32, 32),\n    (2, 3, 32, 32),\n]\nTEST_CASE_FCN_WITH_PRETRAIN_2 = [\n    {\"out_channels\": 2, \"upsample_mode\": \"transpose\", \"pretrained\": True, \"progress\": False},\n    (1, 3, 32, 32),\n    (1, 2, 32, 32),\n]\n\nTEST_CASE_MCFCN_1 = [  # batch 5\n    {\"out_channels\": 3, \"in_channels\": 8, \"upsample_mode\": \"transpose\", \"pretrained\": False, \"progress\": False},\n    (5, 8, 32, 32),\n    (5, 3, 32, 32),\n]\nTEST_CASE_MCFCN_2 = [\n    {\"out_channels\": 2, \"in_channels\": 1, \"upsample_mode\": \"transpose\", \"pretrained\": False, \"progress\": True},\n    (1, 1, 32, 32),\n    (1, 2, 32, 32),\n]\nTEST_CASE_MCFCN_3 = [\n    {\"out_channels\": 1, \"in_channels\": 2, \"upsample_mode\": \"bilinear\", \"pretrained\": False},\n    (1, 2, 32, 32),\n    (1, 1, 32, 32),\n]\nTEST_CASE_MCFCN_WITH_PRETRAIN_1 = [  # batch 5\n    {\"out_channels\": 3, \"in_channels\": 8, \"upsample_mode\": \"transpose\", \"pretrained\": True, \"progress\": False},\n    (5, 8, 32, 32),\n    (5, 3, 32, 32),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ahnet.py_TEST_CASE_MCFCN_WITH_PRETRAIN_2_TEST_CASE_AHNET_3D_WITH_PRETRAIN_2._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ahnet.py_TEST_CASE_MCFCN_WITH_PRETRAIN_2_TEST_CASE_AHNET_3D_WITH_PRETRAIN_2._", "embedding": null, "metadata": {"file_path": "tests/test_ahnet.py", "file_name": "test_ahnet.py", "file_type": "text/x-python", "category": "test", "start_line": 70, "end_line": 117, "span_ids": ["impl:13", "impl:27"], "tokens": 701}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "TEST_CASE_MCFCN_WITH_PRETRAIN_2 = [\n    {\"out_channels\": 2, \"in_channels\": 1, \"upsample_mode\": \"transpose\", \"pretrained\": True, \"progress\": True},\n    (1, 1, 32, 32),\n    (1, 2, 32, 32),\n]\n\nTEST_CASE_AHNET_2D_1 = [\n    {\"spatial_dims\": 2, \"upsample_mode\": \"bilinear\", \"psp_block_num\": 0},\n    (1, 1, 32, 64),\n    (1, 1, 32, 64),\n]\nTEST_CASE_AHNET_2D_2 = [\n    {\"spatial_dims\": 2, \"upsample_mode\": \"transpose\", \"out_channels\": 2, \"psp_block_num\": 1},\n    (1, 1, 64, 32),\n    (1, 2, 64, 32),\n]\nTEST_CASE_AHNET_2D_3 = [\n    {\"spatial_dims\": 2, \"upsample_mode\": \"bilinear\", \"out_channels\": 2, \"psp_block_num\": 2},\n    (1, 1, 64, 32),\n    (1, 2, 64, 32),\n]\nTEST_CASE_AHNET_3D_1 = [\n    {\"spatial_dims\": 3, \"upsample_mode\": \"trilinear\", \"psp_block_num\": 0},\n    (2, 1, 32, 32, 64),\n    (2, 1, 32, 32, 64),\n]\nTEST_CASE_AHNET_3D_2 = [\n    {\"spatial_dims\": 3, \"upsample_mode\": \"transpose\", \"out_channels\": 2, \"psp_block_num\": 1},\n    (1, 1, 32, 32, 64),\n    (1, 2, 32, 32, 64),\n]\nTEST_CASE_AHNET_3D_3 = [\n    {\"spatial_dims\": 3, \"upsample_mode\": \"nearest\", \"out_channels\": 2, \"psp_block_num\": 3},\n    (1, 1, 96, 128, 32),\n    (1, 2, 96, 128, 32),\n]\nTEST_CASE_AHNET_3D_WITH_PRETRAIN_1 = [\n    {\"spatial_dims\": 3, \"upsample_mode\": \"trilinear\", \"psp_block_num\": 0},\n    (2, 1, 32, 32, 64),\n    (2, 1, 32, 32, 64),\n    {\"out_channels\": 1, \"upsample_mode\": \"transpose\"},\n]\nTEST_CASE_AHNET_3D_WITH_PRETRAIN_2 = [\n    {\"spatial_dims\": 3, \"upsample_mode\": \"transpose\", \"out_channels\": 2, \"psp_block_num\": 3},\n    (1, 1, 64, 64, 64),\n    (1, 2, 64, 64, 64),\n    {\"out_channels\": 1, \"upsample_mode\": \"bilinear\"},\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ahnet.py_TEST_CASE_AHNET_3D_WITH_PRETRAIN_3_TestMCFCNWithPretrain.test_mcfcn_shape.with_eval_mode_net_.self_assertEqual_result_s": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ahnet.py_TEST_CASE_AHNET_3D_WITH_PRETRAIN_3_TestMCFCNWithPretrain.test_mcfcn_shape.with_eval_mode_net_.self_assertEqual_result_s", "embedding": null, "metadata": {"file_path": "tests/test_ahnet.py", "file_name": "test_ahnet.py", "file_type": "text/x-python", "category": "test", "start_line": 118, "end_line": 161, "span_ids": ["TestMCFCN", "TestMCFCNWithPretrain.test_mcfcn_shape", "TestFCN", "TestFCNWithPretrain.test_fcn_shape", "TestFCNWithPretrain", "TestMCFCN.test_mcfcn_shape", "impl:39", "TestFCN.test_fcn_shape", "TestMCFCNWithPretrain"], "tokens": 495}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "TEST_CASE_AHNET_3D_WITH_PRETRAIN_3 = [\n    {\"spatial_dims\": 3, \"upsample_mode\": \"transpose\", \"in_channels\": 2, \"out_channels\": 3},\n    (1, 2, 128, 128, 32),\n    (1, 3, 128, 128, 32),\n    {\"out_channels\": 1, \"upsample_mode\": \"bilinear\"},\n]\n\n\nclass TestFCN(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_FCN_1, TEST_CASE_FCN_2, TEST_CASE_FCN_3])\n    @skip_if_quick\n    def test_fcn_shape(self, input_param, input_shape, expected_shape):\n        net = FCN(**input_param).to(device)\n        with eval_mode(net):\n            result = net.forward(torch.randn(input_shape).to(device))\n            self.assertEqual(result.shape, expected_shape)\n\n\nclass TestFCNWithPretrain(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_FCN_WITH_PRETRAIN_1, TEST_CASE_FCN_WITH_PRETRAIN_2])\n    @skip_if_quick\n    def test_fcn_shape(self, input_param, input_shape, expected_shape):\n        net = test_pretrained_networks(FCN, input_param, device)\n        with eval_mode(net):\n            result = net.forward(torch.randn(input_shape).to(device))\n            self.assertEqual(result.shape, expected_shape)\n\n\nclass TestMCFCN(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_MCFCN_1, TEST_CASE_MCFCN_2, TEST_CASE_MCFCN_3])\n    def test_mcfcn_shape(self, input_param, input_shape, expected_shape):\n        net = MCFCN(**input_param).to(device)\n        with eval_mode(net):\n            result = net.forward(torch.randn(input_shape).to(device))\n            self.assertEqual(result.shape, expected_shape)\n\n\nclass TestMCFCNWithPretrain(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_MCFCN_WITH_PRETRAIN_1, TEST_CASE_MCFCN_WITH_PRETRAIN_2])\n    def test_mcfcn_shape(self, input_param, input_shape, expected_shape):\n        net = test_pretrained_networks(MCFCN, input_param, device)\n        with eval_mode(net):\n            result = net.forward(torch.randn(input_shape).to(device))\n            self.assertEqual(result.shape, expected_shape)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ahnet.py_TestAHNET.test_script_TestAHNET.test_script.None_1": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ahnet.py_TestAHNET.test_script_TestAHNET.test_script.None_1", "embedding": null, "metadata": {"file_path": "tests/test_ahnet.py", "file_name": "test_ahnet.py", "file_type": "text/x-python", "category": "test", "start_line": 192, "end_line": 201, "span_ids": ["TestAHNET.test_script"], "tokens": 137}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestAHNET(unittest.TestCase):\n\n    @skip_if_quick\n    def test_script(self):\n        # test 2D network\n        net = AHNet(spatial_dims=2, out_channels=2)\n        test_data = torch.randn(1, 1, 128, 64)\n        test_script_save(net, test_data)\n        # test 3D network\n        net = AHNet(spatial_dims=3, out_channels=2, psp_block_num=0, upsample_mode=\"nearest\")\n        test_data = torch.randn(1, 1, 32, 32, 64)\n        test_script_save(net, test_data)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ahnet.py_TestAHNETWithPretrain_TestAHNETWithPretrain.test_ahnet_shape.with_eval_mode_net_.self_assertEqual_result_s": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ahnet.py_TestAHNETWithPretrain_TestAHNETWithPretrain.test_ahnet_shape.with_eval_mode_net_.self_assertEqual_result_s", "embedding": null, "metadata": {"file_path": "tests/test_ahnet.py", "file_name": "test_ahnet.py", "file_type": "text/x-python", "category": "test", "start_line": 192, "end_line": 202, "span_ids": ["TestAHNETWithPretrain.test_ahnet_shape", "TestAHNETWithPretrain"], "tokens": 148}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestAHNETWithPretrain(unittest.TestCase):\n    @parameterized.expand(\n        [TEST_CASE_AHNET_3D_WITH_PRETRAIN_1, TEST_CASE_AHNET_3D_WITH_PRETRAIN_2, TEST_CASE_AHNET_3D_WITH_PRETRAIN_3]\n    )\n    def test_ahnet_shape(self, input_param, input_shape, expected_shape, fcn_input_param):\n        net = AHNet(**input_param).to(device)\n        net2d = FCN(**fcn_input_param).to(device)\n        net.copy_from(net2d)\n        with eval_mode(net):\n            result = net.forward(torch.randn(input_shape).to(device))\n            self.assertEqual(result.shape, expected_shape)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ahnet.py_TestAHNETWithPretrain.test_initialize_pretrained_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ahnet.py_TestAHNETWithPretrain.test_initialize_pretrained_", "embedding": null, "metadata": {"file_path": "tests/test_ahnet.py", "file_name": "test_ahnet.py", "file_type": "text/x-python", "category": "test", "start_line": 220, "end_line": 239, "span_ids": ["impl:41", "TestAHNETWithPretrain.test_initialize_pretrained"], "tokens": 147}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestAHNETWithPretrain(unittest.TestCase):\n\n    @skip_if_quick\n    def test_initialize_pretrained(self):\n        net = AHNet(\n            spatial_dims=3,\n            upsample_mode=\"transpose\",\n            in_channels=2,\n            out_channels=3,\n            psp_block_num=2,\n            pretrained=True,\n            progress=True,\n        ).to(device)\n        input_data = torch.randn(2, 2, 32, 32, 64).to(device)\n        with eval_mode(net):\n            result = net.forward(input_data)\n            self.assertEqual(result.shape, (2, 3, 32, 32, 64))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_arraydataset.py_os_TEST_CASE_4._Compose_LoadImage_image": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_arraydataset.py_os_TEST_CASE_4._Compose_LoadImage_image", "embedding": null, "metadata": {"file_path": "tests/test_arraydataset.py", "file_name": "test_arraydataset.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 55, "span_ids": ["impl:5", "TestCompose", "TestCompose.__call__", "docstring"], "tokens": 432}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport sys\nimport tempfile\nimport unittest\n\nimport nibabel as nib\nimport numpy as np\nfrom parameterized import parameterized\nfrom torch.utils.data import DataLoader\n\nfrom monai.data import ArrayDataset\nfrom monai.transforms import AddChannel, Compose, LoadImage, RandAdjustContrast, RandGaussianNoise, Spacing\n\nTEST_CASE_1 = [\n    Compose([LoadImage(image_only=True), AddChannel(), RandGaussianNoise(prob=1.0)]),\n    Compose([LoadImage(image_only=True), AddChannel(), RandGaussianNoise(prob=1.0)]),\n    (0, 1),\n    (1, 128, 128, 128),\n]\n\nTEST_CASE_2 = [\n    Compose([LoadImage(image_only=True), AddChannel(), RandAdjustContrast(prob=1.0)]),\n    Compose([LoadImage(image_only=True), AddChannel(), RandAdjustContrast(prob=1.0)]),\n    (0, 1),\n    (1, 128, 128, 128),\n]\n\n\nclass TestCompose(Compose):\n    def __call__(self, input_):\n        img, metadata = self.transforms[0](input_)\n        img = self.transforms[1](img)\n        img, _, _ = self.transforms[2](img, metadata[\"affine\"])\n        return self.transforms[3](img), metadata\n\n\nTEST_CASE_3 = [\n    TestCompose([LoadImage(image_only=False), AddChannel(), Spacing(pixdim=(2, 2, 4)), RandAdjustContrast(prob=1.0)]),\n    TestCompose([LoadImage(image_only=False), AddChannel(), Spacing(pixdim=(2, 2, 4)), RandAdjustContrast(prob=1.0)]),\n    (0, 2),\n    (1, 64, 64, 33),\n]\n\nTEST_CASE_4 = [Compose([LoadImage(image_only=True), AddChannel(), RandGaussianNoise(prob=1.0)]), (1, 128, 128, 128)]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_autoencoder.py_unittest_TEST_CASE_FAIL._2_channel_2D_should": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_autoencoder.py_unittest_TEST_CASE_FAIL._2_channel_2D_should", "embedding": null, "metadata": {"file_path": "tests/test_autoencoder.py", "file_name": "test_autoencoder.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 71, "span_ids": ["impl:9", "docstring"], "tokens": 599}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks import eval_mode\nfrom monai.networks.layers import Act\nfrom monai.networks.nets import AutoEncoder\nfrom tests.utils import test_script_save\n\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n\nTEST_CASE_0 = [  # single channel 2D, batch 4, no residual\n    {\n        \"spatial_dims\": 2,\n        \"in_channels\": 1,\n        \"out_channels\": 1,\n        \"channels\": (4, 8, 16),\n        \"strides\": (2, 2, 2),\n        \"num_res_units\": 0,\n    },\n    (1, 1, 128, 128),\n    (1, 1, 128, 128),\n]\n\nTEST_CASE_1 = [  # single channel 2D, batch 4\n    {\"spatial_dims\": 2, \"in_channels\": 1, \"out_channels\": 1, \"channels\": (4, 8, 16), \"strides\": (2, 2, 2)},\n    (1, 1, 128, 128),\n    (1, 1, 128, 128),\n]\n\nTEST_CASE_2 = [  # 3-channel 2D, batch 4, LeakyReLU activation\n    {\n        \"spatial_dims\": 2,\n        \"in_channels\": 3,\n        \"out_channels\": 3,\n        \"channels\": (4, 8, 16),\n        \"strides\": (2, 2, 2),\n        \"act\": (Act.LEAKYRELU, {\"negative_slope\": 0.2}),\n    },\n    (1, 3, 128, 128),\n    (1, 3, 128, 128),\n]\n\nTEST_CASE_3 = [  # 4-channel 3D, batch 4\n    {\"spatial_dims\": 3, \"in_channels\": 4, \"out_channels\": 3, \"channels\": (4, 8, 16), \"strides\": (2, 2, 2)},\n    (1, 4, 128, 128, 128),\n    (1, 3, 128, 128, 128),\n]\n\nCASES = [TEST_CASE_0, TEST_CASE_1, TEST_CASE_2, TEST_CASE_3]\n\n\nTEST_CASE_FAIL = {  # 2-channel 2D, should fail because of stride/channel mismatch.\n    \"spatial_dims\": 2,\n    \"in_channels\": 2,\n    \"out_channels\": 2,\n    \"channels\": (4, 8, 16),\n    \"strides\": (2, 2),\n}", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_autoencoder.py_TestAutoEncoder_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_autoencoder.py_TestAutoEncoder_", "embedding": null, "metadata": {"file_path": "tests/test_autoencoder.py", "file_name": "test_autoencoder.py", "file_type": "text/x-python", "category": "test", "start_line": 74, "end_line": 94, "span_ids": ["TestAutoEncoder", "TestAutoEncoder.test_shape", "TestAutoEncoder.test_channel_stride_difference", "TestAutoEncoder.test_script", "impl:15"], "tokens": 173}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestAutoEncoder(unittest.TestCase):\n    @parameterized.expand(CASES)\n    def test_shape(self, input_param, input_shape, expected_shape):\n        net = AutoEncoder(**input_param).to(device)\n        with eval_mode(net):\n            result = net.forward(torch.randn(input_shape).to(device))\n            self.assertEqual(result.shape, expected_shape)\n\n    def test_script(self):\n        net = AutoEncoder(spatial_dims=2, in_channels=1, out_channels=1, channels=(4, 8), strides=(2, 2))\n        test_data = torch.randn(2, 1, 32, 32)\n        test_script_save(net, test_data)\n\n    def test_channel_stride_difference(self):\n        with self.assertRaises(ValueError):\n            AutoEncoder(**TEST_CASE_FAIL)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_basic_unet.py_unittest_CASES_3D._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_basic_unet.py_unittest_CASES_3D._", "embedding": null, "metadata": {"file_path": "tests/test_basic_unet.py", "file_name": "test_basic_unet.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 80, "span_ids": ["docstring"], "tokens": 640}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks import eval_mode\nfrom monai.networks.nets import BasicUNet\nfrom tests.utils import test_script_save\n\nCASES_1D = []\nfor mode in [\"pixelshuffle\", \"nontrainable\", \"deconv\", None]:\n    kwargs = {\"spatial_dims\": 1, \"in_channels\": 5, \"out_channels\": 8}\n    if mode is not None:\n        kwargs[\"upsample\"] = mode  # type: ignore\n    CASES_1D.append([kwargs, (10, 5, 33), (10, 8, 33)])\n\nCASES_2D = []\nfor mode in [\"pixelshuffle\", \"nontrainable\", \"deconv\"]:\n    for d1 in range(33, 64, 14):\n        for d2 in range(63, 33, -21):\n            in_channels, out_channels = 2, 3\n            CASES_2D.append(\n                [\n                    {\n                        \"spatial_dims\": 2,\n                        \"in_channels\": in_channels,\n                        \"out_channels\": out_channels,\n                        \"features\": (12, 12, 13, 14, 15, 16),\n                        \"upsample\": mode,\n                    },\n                    (2, in_channels, d1, d2),\n                    (2, out_channels, d1, d2),\n                ]\n            )\nCASES_3D = [\n    [  # single channel 3D, batch 2\n        {\n            \"spatial_dims\": 3,\n            \"in_channels\": 1,\n            \"out_channels\": 2,\n            \"features\": (16, 20, 21, 22, 23, 11),\n            \"upsample\": \"pixelshuffle\",\n        },\n        (2, 1, 33, 34, 35),\n        (2, 2, 33, 34, 35),\n    ],\n    [  # 2-channel 3D, batch 3\n        {\n            \"spatial_dims\": 3,\n            \"in_channels\": 2,\n            \"out_channels\": 7,\n            \"features\": (14, 15, 16, 17, 18, 11),\n            \"upsample\": \"deconv\",\n        },\n        (3, 2, 33, 37, 34),\n        (3, 7, 33, 37, 34),\n    ],\n    [  # 4-channel 3D, batch 5\n        {\n            \"spatial_dims\": 3,\n            \"in_channels\": 4,\n            \"out_channels\": 2,\n            \"features\": (14, 15, 16, 17, 18, 10),\n            \"upsample\": \"nontrainable\",\n        },\n        (5, 4, 34, 35, 37),\n        (5, 2, 34, 35, 37),\n    ],\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_basic_unet.py_TestBasicUNET_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_basic_unet.py_TestBasicUNET_", "embedding": null, "metadata": {"file_path": "tests/test_basic_unet.py", "file_name": "test_basic_unet.py", "file_type": "text/x-python", "category": "test", "start_line": 83, "end_line": 101, "span_ids": ["TestBasicUNET.test_script", "impl:18", "TestBasicUNET.test_shape", "TestBasicUNET"], "tokens": 174}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestBasicUNET(unittest.TestCase):\n    @parameterized.expand(CASES_1D + CASES_2D + CASES_3D)\n    def test_shape(self, input_param, input_shape, expected_shape):\n        device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n        print(input_param)\n        net = BasicUNet(**input_param).to(device)\n        with eval_mode(net):\n            result = net(torch.randn(input_shape).to(device))\n        self.assertEqual(result.shape, expected_shape)\n\n    def test_script(self):\n        net = BasicUNet(spatial_dims=2, in_channels=1, out_channels=3)\n        test_data = torch.randn(16, 1, 32, 32)\n        test_script_save(net, test_data)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_bending_energy.py_unittest_TEST_CASES._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_bending_energy.py_unittest_TEST_CASES._", "embedding": null, "metadata": {"file_path": "tests/test_bending_energy.py", "file_name": "test_bending_energy.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 47, "span_ids": ["docstring"], "tokens": 455}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.losses.deform import BendingEnergyLoss\n\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\nTEST_CASES = [\n    [{}, {\"pred\": torch.ones((1, 3, 5, 5, 5), device=device)}, 0.0],\n    [{}, {\"pred\": torch.arange(0, 5, device=device)[None, None, None, None, :].expand(1, 3, 5, 5, 5)}, 0.0],\n    [\n        {\"normalize\": False},\n        {\"pred\": torch.arange(0, 5, device=device)[None, None, None, None, :].expand(1, 3, 5, 5, 5) ** 2},\n        4.0,\n    ],\n    [\n        {\"normalize\": False},\n        {\"pred\": torch.arange(0, 5, device=device)[None, None, None, :].expand(1, 2, 5, 5) ** 2},\n        4.0,\n    ],\n    [{\"normalize\": False}, {\"pred\": torch.arange(0, 5, device=device)[None, None, :].expand(1, 1, 5) ** 2}, 4.0],\n    [\n        {\"normalize\": True},\n        {\"pred\": torch.arange(0, 5, device=device)[None, None, None, None, :].expand(1, 3, 5, 5, 5) ** 2},\n        100.0,\n    ],\n    [\n        {\"normalize\": True},\n        {\"pred\": torch.arange(0, 5, device=device)[None, None, None, :].expand(1, 2, 5, 5) ** 2},\n        100.0,\n    ],\n    [{\"normalize\": True}, {\"pred\": torch.arange(0, 5, device=device)[None, None, :].expand(1, 1, 5) ** 2}, 100.0],\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_bending_energy.py_TestBendingEnergy_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_bending_energy.py_TestBendingEnergy_", "embedding": null, "metadata": {"file_path": "tests/test_bending_energy.py", "file_name": "test_bending_energy.py", "file_type": "text/x-python", "category": "test", "start_line": 50, "end_line": 87, "span_ids": ["TestBendingEnergy.test_ill_shape", "TestBendingEnergy.test_shape", "TestBendingEnergy", "impl:5", "TestBendingEnergy.test_ill_opts"], "tokens": 459}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestBendingEnergy(unittest.TestCase):\n    @parameterized.expand(TEST_CASES)\n    def test_shape(self, input_param, input_data, expected_val):\n        result = BendingEnergyLoss(**input_param).forward(**input_data)\n        np.testing.assert_allclose(result.detach().cpu().numpy(), expected_val, rtol=1e-5)\n\n    def test_ill_shape(self):\n        loss = BendingEnergyLoss()\n        # not in 3-d, 4-d, 5-d\n        with self.assertRaisesRegex(ValueError, \"Expecting 3-d, 4-d or 5-d\"):\n            loss.forward(torch.ones((1, 3), device=device))\n        with self.assertRaisesRegex(ValueError, \"Expecting 3-d, 4-d or 5-d\"):\n            loss.forward(torch.ones((1, 4, 5, 5, 5, 5), device=device))\n        # spatial_dim < 5\n        with self.assertRaisesRegex(ValueError, \"All spatial dimensions\"):\n            loss.forward(torch.ones((1, 3, 4, 5, 5), device=device))\n        with self.assertRaisesRegex(ValueError, \"All spatial dimensions\"):\n            loss.forward(torch.ones((1, 3, 5, 4, 5)))\n        with self.assertRaisesRegex(ValueError, \"All spatial dimensions\"):\n            loss.forward(torch.ones((1, 3, 5, 5, 4)))\n\n        # number of vector components unequal to number of spatial dims\n        with self.assertRaisesRegex(ValueError, \"Number of vector components\"):\n            loss.forward(torch.ones((1, 2, 5, 5, 5)))\n        with self.assertRaisesRegex(ValueError, \"Number of vector components\"):\n            loss.forward(torch.ones((1, 2, 5, 5, 5)))\n\n    def test_ill_opts(self):\n        pred = torch.rand(1, 3, 5, 5, 5).to(device=device)\n        with self.assertRaisesRegex(ValueError, \"\"):\n            BendingEnergyLoss(reduction=\"unknown\")(pred)\n        with self.assertRaisesRegex(ValueError, \"\"):\n            BendingEnergyLoss(reduction=None)(pred)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_bilateral_approx_cpu.py_unittest_TEST_CASES": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_bilateral_approx_cpu.py_unittest_TEST_CASES", "embedding": null, "metadata": {"file_path": "tests/test_bilateral_approx_cpu.py", "file_name": "test_bilateral_approx_cpu.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 361, "span_ids": ["docstring"], "tokens": 57}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\nfrom torch.autograd import gradcheck\n\nfrom monai.networks.layers.filtering import BilateralFilter\nfrom tests.utils import skip_if_no_cpp_extension\n\nTEST_CASES =\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_bilateral_approx_cpu.py_BilateralFilterTestCaseCpuApprox_BilateralFilterTestCaseCpuApprox.test_cpu_approx.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_bilateral_approx_cpu.py_BilateralFilterTestCaseCpuApprox_BilateralFilterTestCaseCpuApprox.test_cpu_approx.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_bilateral_approx_cpu.py", "file_name": "test_bilateral_approx_cpu.py", "file_type": "text/x-python", "category": "test", "start_line": 364, "end_line": 378, "span_ids": ["BilateralFilterTestCaseCpuApprox", "BilateralFilterTestCaseCpuApprox.test_cpu_approx"], "tokens": 143}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_no_cpp_extension\nclass BilateralFilterTestCaseCpuApprox(unittest.TestCase):\n    @parameterized.expand(TEST_CASES)\n    def test_cpu_approx(self, test_case_description, sigmas, input, expected):\n\n        # Params to determine the implementation to test\n        device = torch.device(\"cpu\")\n        fast_approx = True\n\n        # Create input tensor and apply filter\n        input_tensor = torch.from_numpy(np.array(input)).to(dtype=torch.float, device=device)\n        output = BilateralFilter.apply(input_tensor, *sigmas, fast_approx).cpu().numpy()\n\n        # Ensure result are as expected\n        np.testing.assert_allclose(output, expected, atol=1e-5)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_bilateral_approx_cpu.py_BilateralFilterTestCaseCpuApprox.test_cpu_approx_backwards_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_bilateral_approx_cpu.py_BilateralFilterTestCaseCpuApprox.test_cpu_approx_backwards_", "embedding": null, "metadata": {"file_path": "tests/test_bilateral_approx_cpu.py", "file_name": "test_bilateral_approx_cpu.py", "file_type": "text/x-python", "category": "test", "start_line": 380, "end_line": 400, "span_ids": ["impl:3", "BilateralFilterTestCaseCpuApprox.test_cpu_approx_backwards"], "tokens": 155}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_no_cpp_extension\nclass BilateralFilterTestCaseCpuApprox(unittest.TestCase):\n\n    @parameterized.expand(TEST_CASES)\n    def test_cpu_approx_backwards(self, test_case_description, sigmas, input, expected):\n\n        # Params to determine the implementation to test\n        device = torch.device(\"cpu\")\n        fast_approx = True\n\n        # Prepare input tensor\n        input_tensor = torch.from_numpy(np.array(input)).to(dtype=torch.float, device=device)\n        input_tensor.requires_grad = True\n\n        # Prepare args\n        args = (input_tensor, *sigmas, fast_approx)\n\n        # Run grad check\n        gradcheck(BilateralFilter.apply, args, raise_exception=False)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_bilateral_approx_cuda.py_unittest_TEST_CASES": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_bilateral_approx_cuda.py_unittest_TEST_CASES", "embedding": null, "metadata": {"file_path": "tests/test_bilateral_approx_cuda.py", "file_name": "test_bilateral_approx_cuda.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 361, "span_ids": ["docstring"], "tokens": 62}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\nfrom torch.autograd import gradcheck\n\nfrom monai.networks.layers.filtering import BilateralFilter\nfrom tests.utils import skip_if_no_cpp_extension, skip_if_no_cuda\n\nTEST_CASES =\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_bilateral_approx_cuda.py_BilateralFilterTestCaseCudaApprox_BilateralFilterTestCaseCudaApprox.test_cuda_approx.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_bilateral_approx_cuda.py_BilateralFilterTestCaseCudaApprox_BilateralFilterTestCaseCudaApprox.test_cuda_approx.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_bilateral_approx_cuda.py", "file_name": "test_bilateral_approx_cuda.py", "file_type": "text/x-python", "category": "test", "start_line": 364, "end_line": 383, "span_ids": ["BilateralFilterTestCaseCudaApprox.test_cuda_approx", "BilateralFilterTestCaseCudaApprox"], "tokens": 166}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_no_cuda\n@skip_if_no_cpp_extension\nclass BilateralFilterTestCaseCudaApprox(unittest.TestCase):\n    @parameterized.expand(TEST_CASES)\n    def test_cuda_approx(self, test_case_description, sigmas, input, expected):\n\n        # Skip this test\n        if not torch.cuda.is_available():\n            return\n\n        # Params to determine the implementation to test\n        device = torch.device(\"cuda\")\n        fast_approx = True\n\n        # Create input tensor and apply filter\n        input_tensor = torch.from_numpy(np.array(input)).to(dtype=torch.float, device=device)\n        output = BilateralFilter.apply(input_tensor, *sigmas, fast_approx).cpu().numpy()\n\n        # Ensure result are as expected\n        np.testing.assert_allclose(output, expected, atol=1e-2)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_bilateral_approx_cuda.py_BilateralFilterTestCaseCudaApprox.test_cpu_approx_backwards_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_bilateral_approx_cuda.py_BilateralFilterTestCaseCudaApprox.test_cpu_approx_backwards_", "embedding": null, "metadata": {"file_path": "tests/test_bilateral_approx_cuda.py", "file_name": "test_bilateral_approx_cuda.py", "file_type": "text/x-python", "category": "test", "start_line": 385, "end_line": 405, "span_ids": ["impl:3", "BilateralFilterTestCaseCudaApprox.test_cpu_approx_backwards"], "tokens": 161}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_no_cuda\n@skip_if_no_cpp_extension\nclass BilateralFilterTestCaseCudaApprox(unittest.TestCase):\n\n    @parameterized.expand(TEST_CASES)\n    def test_cpu_approx_backwards(self, test_case_description, sigmas, input, expected):\n\n        # Params to determine the implementation to test\n        device = torch.device(\"cuda\")\n        fast_approx = True\n\n        # Prepare input tensor\n        input_tensor = torch.from_numpy(np.array(input)).to(dtype=torch.float, device=device)\n        input_tensor.requires_grad = True\n\n        # Prepare args\n        args = (input_tensor, *sigmas, fast_approx)\n\n        # Run grad check\n        gradcheck(BilateralFilter.apply, args, raise_exception=False)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_bilateral_precise.py_unittest_TEST_CASES": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_bilateral_precise.py_unittest_TEST_CASES", "embedding": null, "metadata": {"file_path": "tests/test_bilateral_precise.py", "file_name": "test_bilateral_precise.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 361, "span_ids": ["docstring"], "tokens": 62}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\nfrom torch.autograd import gradcheck\n\nfrom monai.networks.layers.filtering import BilateralFilter\nfrom tests.utils import skip_if_no_cpp_extension, skip_if_no_cuda\n\nTEST_CASES =\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_bilateral_precise.py_BilateralFilterTestCaseCpuPrecise_BilateralFilterTestCaseCpuPrecise.test_cpu_precise.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_bilateral_precise.py_BilateralFilterTestCaseCpuPrecise_BilateralFilterTestCaseCpuPrecise.test_cpu_precise.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_bilateral_precise.py", "file_name": "test_bilateral_precise.py", "file_type": "text/x-python", "category": "test", "start_line": 364, "end_line": 378, "span_ids": ["BilateralFilterTestCaseCpuPrecise", "BilateralFilterTestCaseCpuPrecise.test_cpu_precise"], "tokens": 145}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_no_cpp_extension\nclass BilateralFilterTestCaseCpuPrecise(unittest.TestCase):\n    @parameterized.expand(TEST_CASES)\n    def test_cpu_precise(self, test_case_description, sigmas, input, expected):\n\n        # Params to determine the implementation to test\n        device = torch.device(\"cpu\")\n        fast_approx = False\n\n        # Create input tensor and apply filter\n        input_tensor = torch.from_numpy(np.array(input)).to(dtype=torch.float, device=device)\n        output = BilateralFilter.apply(input_tensor, *sigmas, fast_approx).cpu().numpy()\n\n        # Ensure result are as expected\n        np.testing.assert_allclose(output, expected, atol=1e-5)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_bilateral_precise.py_BilateralFilterTestCaseCpuPrecise.test_cpu_precise_backwards_BilateralFilterTestCaseCpuPrecise.test_cpu_precise_backwards.gradcheck_BilateralFilter": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_bilateral_precise.py_BilateralFilterTestCaseCpuPrecise.test_cpu_precise_backwards_BilateralFilterTestCaseCpuPrecise.test_cpu_precise_backwards.gradcheck_BilateralFilter", "embedding": null, "metadata": {"file_path": "tests/test_bilateral_precise.py", "file_name": "test_bilateral_precise.py", "file_type": "text/x-python", "category": "test", "start_line": 380, "end_line": 395, "span_ids": ["BilateralFilterTestCaseCpuPrecise.test_cpu_precise_backwards"], "tokens": 145}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_no_cpp_extension\nclass BilateralFilterTestCaseCpuPrecise(unittest.TestCase):\n\n    @parameterized.expand(TEST_CASES)\n    def test_cpu_precise_backwards(self, test_case_description, sigmas, input, expected):\n\n        # Params to determine the implementation to test\n        device = torch.device(\"cpu\")\n        fast_approx = False\n\n        # Prepare input tensor\n        input_tensor = torch.from_numpy(np.array(input)).to(dtype=torch.float, device=device)\n        input_tensor.requires_grad = True\n\n        # Prepare args\n        args = (input_tensor, *sigmas, fast_approx)\n\n        # Run grad check\n        gradcheck(BilateralFilter.apply, args, raise_exception=False)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_bilateral_precise.py_BilateralFilterTestCaseCudaPrecise_BilateralFilterTestCaseCudaPrecise.test_cuda_precise.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_bilateral_precise.py_BilateralFilterTestCaseCudaPrecise_BilateralFilterTestCaseCudaPrecise.test_cuda_precise.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_bilateral_precise.py", "file_name": "test_bilateral_precise.py", "file_type": "text/x-python", "category": "test", "start_line": 398, "end_line": 417, "span_ids": ["BilateralFilterTestCaseCudaPrecise", "BilateralFilterTestCaseCudaPrecise.test_cuda_precise"], "tokens": 168}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_no_cuda\n@skip_if_no_cpp_extension\nclass BilateralFilterTestCaseCudaPrecise(unittest.TestCase):\n    @parameterized.expand(TEST_CASES)\n    def test_cuda_precise(self, test_case_description, sigmas, input, expected):\n\n        # Skip this test\n        if not torch.cuda.is_available():\n            return\n\n        # Params to determine the implementation to test\n        device = torch.device(\"cuda\")\n        fast_approx = False\n\n        # Create input tensor and apply filter\n        input_tensor = torch.from_numpy(np.array(input)).to(dtype=torch.float, device=device)\n        output = BilateralFilter.apply(input_tensor, *sigmas, fast_approx).cpu().numpy()\n\n        # Ensure result are as expected\n        np.testing.assert_allclose(output, expected, atol=1e-5)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_bilateral_precise.py_BilateralFilterTestCaseCudaPrecise.test_cuda_precise_backwards_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_bilateral_precise.py_BilateralFilterTestCaseCudaPrecise.test_cuda_precise_backwards_", "embedding": null, "metadata": {"file_path": "tests/test_bilateral_precise.py", "file_name": "test_bilateral_precise.py", "file_type": "text/x-python", "category": "test", "start_line": 419, "end_line": 439, "span_ids": ["impl:3", "BilateralFilterTestCaseCudaPrecise.test_cuda_precise_backwards"], "tokens": 163}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_no_cuda\n@skip_if_no_cpp_extension\nclass BilateralFilterTestCaseCudaPrecise(unittest.TestCase):\n\n    @parameterized.expand(TEST_CASES)\n    def test_cuda_precise_backwards(self, test_case_description, sigmas, input, expected):\n\n        # Params to determine the implementation to test\n        device = torch.device(\"cuda\")\n        fast_approx = False\n\n        # Prepare input tensor\n        input_tensor = torch.from_numpy(np.array(input)).to(dtype=torch.float, device=device)\n        input_tensor.requires_grad = True\n\n        # Prepare args\n        args = (input_tensor, *sigmas, fast_approx)\n\n        # Run grad check\n        gradcheck(BilateralFilter.apply, args, raise_exception=False)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_bounding_rect.py_unittest_TEST_CASE_3._2_16_20_18_0_16": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_bounding_rect.py_unittest_TEST_CASE_3._2_16_20_18_0_16", "embedding": null, "metadata": {"file_path": "tests/test_bounding_rect.py", "file_name": "test_bounding_rect.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 25, "span_ids": ["docstring"], "tokens": 143}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nimport monai\nfrom monai.transforms import BoundingRect\nfrom tests.utils import TEST_NDARRAYS\n\nTEST_CASE_1 = [(2, 3), [[0, 0], [1, 2]]]\n\nTEST_CASE_2 = [(1, 8, 10), [[0, 7, 1, 9]]]\n\nTEST_CASE_3 = [(2, 16, 20, 18), [[0, 16, 0, 20, 0, 18], [0, 16, 0, 20, 0, 18]]]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_bounding_rect.py_TestBoundingRect_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_bounding_rect.py_TestBoundingRect_", "embedding": null, "metadata": {"file_path": "tests/test_bounding_rect.py", "file_name": "test_bounding_rect.py", "file_type": "text/x-python", "category": "test", "start_line": 28, "end_line": 53, "span_ids": ["TestBoundingRect", "impl:7", "TestBoundingRect.test_shape", "TestBoundingRect.test_select_fn", "TestBoundingRect.tearDown", "TestBoundingRect.setUp"], "tokens": 228}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestBoundingRect(unittest.TestCase):\n    def setUp(self):\n        monai.utils.set_determinism(1)\n\n    def tearDown(self):\n        monai.utils.set_determinism(None)\n\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3])\n    def test_shape(self, input_shape, expected):\n        test_data = np.random.randint(0, 8, size=input_shape)\n        test_data = test_data == 7\n        for p in TEST_NDARRAYS:\n            result = BoundingRect()(p(test_data))\n            np.testing.assert_allclose(result, expected)\n\n    def test_select_fn(self):\n        test_data = np.random.randint(0, 8, size=(2, 3))\n        test_data = test_data == 7\n        for p in TEST_NDARRAYS:\n            bbox = BoundingRect(select_fn=lambda x: x < 1)(p(test_data))\n            np.testing.assert_allclose(bbox, [[0, 3], [0, 3]])\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_bounding_rectd.py_unittest_TEST_CASE_3._2_16_20_18_0_16": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_bounding_rectd.py_unittest_TEST_CASE_3._2_16_20_18_0_16", "embedding": null, "metadata": {"file_path": "tests/test_bounding_rectd.py", "file_name": "test_bounding_rectd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 25, "span_ids": ["docstring"], "tokens": 144}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nimport monai\nfrom monai.transforms import BoundingRectD\nfrom tests.utils import TEST_NDARRAYS\n\nTEST_CASE_1 = [(2, 3), [[0, 0], [1, 2]]]\n\nTEST_CASE_2 = [(1, 8, 10), [[0, 7, 1, 9]]]\n\nTEST_CASE_3 = [(2, 16, 20, 18), [[0, 16, 0, 20, 0, 18], [0, 16, 0, 20, 0, 18]]]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_bounding_rectd.py_TestBoundingRectD_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_bounding_rectd.py_TestBoundingRectD_", "embedding": null, "metadata": {"file_path": "tests/test_bounding_rectd.py", "file_name": "test_bounding_rectd.py", "file_type": "text/x-python", "category": "test", "start_line": 28, "end_line": 52, "span_ids": ["TestBoundingRectD.test_shape", "impl:7", "TestBoundingRectD.setUp", "TestBoundingRectD.tearDown", "TestBoundingRectD"], "tokens": 214}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestBoundingRectD(unittest.TestCase):\n    def setUp(self):\n        monai.utils.set_determinism(1)\n\n    def tearDown(self):\n        monai.utils.set_determinism(None)\n\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3])\n    def test_shape(self, input_shape, expected):\n        test_data = np.random.randint(0, 8, size=input_shape)\n        test_data = test_data == 7\n        for p in TEST_NDARRAYS:\n            result = BoundingRectD(\"image\")({\"image\": p(test_data)})\n            np.testing.assert_allclose(result[\"image_bbox\"], expected)\n\n            result = BoundingRectD(\"image\", \"cc\")({\"image\": p(test_data)})\n            np.testing.assert_allclose(result[\"image_cc\"], expected)\n\n            with self.assertRaises(KeyError):\n                BoundingRectD(\"image\", \"cc\")({\"image\": p(test_data), \"image_cc\": None})\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cachedataset_parallel.py_os_TEST_CASE_3._4_5_None_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cachedataset_parallel.py_os_TEST_CASE_3._4_5_None_", "embedding": null, "metadata": {"file_path": "tests/test_cachedataset_parallel.py", "file_name": "test_cachedataset_parallel.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 27, "span_ids": ["docstring"], "tokens": 118}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport tempfile\nimport unittest\n\nimport nibabel as nib\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.data import CacheDataset\nfrom monai.transforms import Compose, LoadImaged\n\nTEST_CASE_1 = [0, 5, Compose([LoadImaged(keys=[\"image\", \"label\", \"extra\"])])]\n\nTEST_CASE_2 = [4, 5, Compose([LoadImaged(keys=[\"image\", \"label\", \"extra\"])])]\n\nTEST_CASE_3 = [4, 5, None]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cachentransdataset.py_os_TEST_CASE_1._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cachentransdataset.py_os_TEST_CASE_1._", "embedding": null, "metadata": {"file_path": "tests/test_cachentransdataset.py", "file_name": "test_cachentransdataset.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 31, "span_ids": ["docstring"], "tokens": 121}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport tempfile\nimport unittest\n\nimport nibabel as nib\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.data import CacheNTransDataset\nfrom monai.transforms import LoadImaged, ShiftIntensityd\n\nTEST_CASE_1 = [\n    [\n        LoadImaged(keys=\"image\"),\n        ShiftIntensityd(keys=\"image\", offset=1.0),\n        ShiftIntensityd(keys=\"image\", offset=2.0),\n        ShiftIntensityd(keys=\"image\", offset=3.0),\n    ],\n    (128, 128, 128),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cachentransdataset.py_TestCacheNTransDataset_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cachentransdataset.py_TestCacheNTransDataset_", "embedding": null, "metadata": {"file_path": "tests/test_cachentransdataset.py", "file_name": "test_cachentransdataset.py", "file_type": "text/x-python", "category": "test", "start_line": 34, "end_line": 61, "span_ids": ["impl:3", "TestCacheNTransDataset", "TestCacheNTransDataset.test_n_trans"], "tokens": 312}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestCacheNTransDataset(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1])\n    def test_n_trans(self, transform, expected_shape):\n        data_array = np.random.randint(0, 2, size=[128, 128, 128])\n        test_image = nib.Nifti1Image(data_array, np.eye(4))\n        with tempfile.TemporaryDirectory() as tempdir:\n            nib.save(test_image, os.path.join(tempdir, \"test_image.nii.gz\"))\n            test_data = [{\"image\": os.path.join(tempdir, \"test_image.nii.gz\")}]\n\n            cache_dir = os.path.join(os.path.join(tempdir, \"cache\"), \"data\")\n            dataset_precached = CacheNTransDataset(\n                data=test_data, transform=transform, cache_dir=cache_dir, cache_n_trans=2\n            )\n            data_precached = dataset_precached[0]\n            self.assertTupleEqual(data_precached[\"image\"].shape, expected_shape)\n\n            dataset_postcached = CacheNTransDataset(\n                data=test_data, transform=transform, cache_dir=cache_dir, cache_n_trans=2\n            )\n            data_postcached = dataset_postcached[0]\n            self.assertTupleEqual(data_postcached[\"image\"].shape, expected_shape)\n            np.testing.assert_allclose(data_array + 6.0, data_postcached[\"image\"])\n            np.testing.assert_allclose(data_precached[\"image\"], data_postcached[\"image\"])\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_center_spatial_crop.py_unittest_TEST_CASE_3._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_center_spatial_crop.py_unittest_TEST_CASE_3._", "embedding": null, "metadata": {"file_path": "tests/test_center_spatial_crop.py", "file_name": "test_center_spatial_crop.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 34, "span_ids": ["docstring"], "tokens": 316}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import CenterSpatialCrop\n\nTEST_CASE_0 = [{\"roi_size\": [2, 2, -1]}, np.random.randint(0, 2, size=[3, 3, 3, 3]), (3, 2, 2, 3)]\n\nTEST_CASE_1 = [{\"roi_size\": [2, 2, 2]}, np.random.randint(0, 2, size=[3, 3, 3, 3]), (3, 2, 2, 2)]\n\nTEST_CASE_2 = [\n    {\"roi_size\": [2, 2]},\n    np.array([[[0, 0, 0, 0, 0], [0, 1, 2, 1, 0], [0, 2, 3, 2, 0], [0, 1, 2, 1, 0], [0, 0, 0, 0, 0]]]),\n    np.array([[[1, 2], [2, 3]]]),\n]\n\nTEST_CASE_3 = [\n    {\"roi_size\": [2, 2, 2]},\n    torch.randint(0, 2, size=[3, 3, 3, 3], device=\"cuda\" if torch.cuda.is_available() else \"cpu\"),\n    (3, 2, 2, 2),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_channel_pad.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_channel_pad.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_channel_pad.py", "file_name": "test_channel_pad.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 49, "span_ids": ["TestChannelPad", "impl:14", "docstring", "TestChannelPad.test_shape", "TestChannelPad.test_wrong_mode"], "tokens": 275}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks import eval_mode\nfrom monai.networks.layers import ChannelPad\n\nTEST_CASES_3D = []\nfor type_1 in (\"pad\", \"project\"):\n    input_shape = (16, 10, 32, 24, 48)\n    out_chns = 13\n    result_shape = list(input_shape)\n    result_shape[1] = out_chns\n    test_case = [\n        {\"spatial_dims\": 3, \"in_channels\": 10, \"out_channels\": out_chns, \"mode\": type_1},\n        input_shape,\n        result_shape,\n    ]\n    TEST_CASES_3D.append(test_case)\n\n\nclass TestChannelPad(unittest.TestCase):\n    @parameterized.expand(TEST_CASES_3D)\n    def test_shape(self, input_param, input_shape, expected_shape):\n        net = ChannelPad(**input_param)\n        with eval_mode(net):\n            result = net(torch.randn(input_shape))\n            self.assertEqual(list(result.shape), list(expected_shape))\n\n    def test_wrong_mode(self):\n        with self.assertRaises(ValueError):\n            ChannelPad(3, 10, 20, mode=\"test\")(torch.randn(10, 10))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compose.py_TestCompose.test_data_loader_2_TestCompose.test_data_loader_2.set_determinism_None_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compose.py_TestCompose.test_data_loader_2_TestCompose.test_data_loader_2.set_determinism_None_", "embedding": null, "metadata": {"file_path": "tests/test_compose.py", "file_name": "test_compose.py", "file_type": "text/x-python", "category": "test", "start_line": 141, "end_line": 161, "span_ids": ["TestCompose.test_data_loader_2"], "tokens": 228}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestCompose(unittest.TestCase):\n\n    def test_data_loader_2(self):\n        set_determinism(seed=123)\n        xform_2 = Compose([_RandXform(), _RandXform()])\n        train_ds = Dataset([1], transform=xform_2)\n\n        out_2 = train_ds[0]\n        self.assertAlmostEqual(out_2, 0.4092510)\n\n        train_loader = DataLoader(train_ds, num_workers=0)\n        out_2 = next(iter(train_loader))\n        self.assertAlmostEqual(out_2.cpu().item(), 0.7858843729)\n\n        if sys.platform != \"win32\":  # skip multi-worker tests on win32\n            train_loader = DataLoader(train_ds, num_workers=1)\n            out_2 = next(iter(train_loader))\n            self.assertAlmostEqual(out_2.cpu().item(), 0.305763411)\n\n            train_loader = DataLoader(train_ds, num_workers=2)\n            out_1 = next(iter(train_loader))\n            self.assertAlmostEqual(out_1.cpu().item(), 0.131966779)\n        set_determinism(None)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compose.py_TestCompose.test_flatten_and_len_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compose.py_TestCompose.test_flatten_and_len_", "embedding": null, "metadata": {"file_path": "tests/test_compose.py", "file_name": "test_compose.py", "file_type": "text/x-python", "category": "test", "start_line": 163, "end_line": 180, "span_ids": ["impl", "TestCompose.test_backwards_compatible_imports", "TestCompose.test_flatten_and_len"], "tokens": 136}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestCompose(unittest.TestCase):\n\n    def test_flatten_and_len(self):\n        x = AddChannel()\n        t1 = Compose([x, x, x, x, Compose([Compose([x, x]), x, x])])\n\n        t2 = t1.flatten()\n        for t in t2.transforms:\n            self.assertNotIsInstance(t, Compose)\n\n        # test len\n        self.assertEqual(len(t1), 8)\n\n    def test_backwards_compatible_imports(self):\n        from monai.transforms.compose import MapTransform, RandomizableTransform, Transform  # noqa: F401\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_confusion_matrix.py_unittest_data._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_confusion_matrix.py_unittest_data._", "embedding": null, "metadata": {"file_path": "tests/test_compute_confusion_matrix.py", "file_name": "test_compute_confusion_matrix.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 40, "span_ids": ["docstring"], "tokens": 347}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom typing import Any, Dict, List\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.metrics import (\n    ConfusionMatrixMetric,\n    compute_confusion_matrix_metric,\n    do_metric_reduction,\n    get_confusion_matrix,\n)\n\n# input data\ndata: Dict[Any, Any] = {\n    \"y_pred\": torch.tensor(\n        [\n            [[[0.0, 1.0], [0.0, 0.0]], [[0.0, 0.0], [1.0, 1.0]], [[1.0, 0.0], [0.0, 0.0]]],\n            [[[0.0, 0.0], [0.0, 1.0]], [[1.0, 0.0], [0.0, 0.0]], [[0.0, 1.0], [1.0, 0.0]]],\n        ]\n    ),\n    \"y\": torch.tensor(\n        [\n            [[[0.0, 0.0], [0.0, 1.0]], [[1.0, 0.0], [1.0, 0.0]], [[0.0, 1.0], [0.0, 0.0]]],\n            [[[0.0, 0.0], [0.0, 1.0]], [[1.0, 1.0], [0.0, 0.0]], [[0.0, 0.0], [1.0, 0.0]]],\n        ]\n    ),\n}", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_froc.py_unittest_TEST_CASE_5._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_froc.py_unittest_TEST_CASE_5._", "embedding": null, "metadata": {"file_path": "tests/test_compute_froc.py", "file_name": "test_compute_froc.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 80, "span_ids": ["impl:7", "docstring"], "tokens": 702}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.metrics import compute_fp_tp_probs, compute_froc_curve_data, compute_froc_score\n\nTEST_CASE_1 = [\n    {\n        \"probs\": torch.tensor([1, 0.6, 0.8]),\n        \"y_coord\": torch.tensor([0, 2, 3]),\n        \"x_coord\": torch.tensor([3, 0, 1]),\n        \"evaluation_mask\": np.array([[0, 0, 1, 1], [2, 2, 0, 0], [0, 3, 3, 0], [0, 3, 3, 3]]),\n        \"labels_to_exclude\": [2],\n        \"resolution_level\": 0,\n    },\n    np.array([0.6]),\n    np.array([1, 0, 0.8]),\n    2,\n]\n\nTEST_CASE_2 = [\n    {\n        \"probs\": torch.tensor([1, 0.6, 0.8]),\n        \"y_coord\": torch.tensor([0, 2, 3]),\n        \"x_coord\": torch.tensor([3, 0, 1]),\n        \"evaluation_mask\": np.array([[0, 0, 1, 1], [2, 2, 0, 0], [0, 3, 3, 0], [0, 3, 3, 3]]),\n        \"resolution_level\": 0,\n    },\n    np.array([0.6]),\n    np.array([1, 0, 0.8]),\n    3,\n]\n\nTEST_CASE_3 = [\n    {\n        \"probs\": torch.tensor([1, 0.6, 0.8]),\n        \"y_coord\": torch.tensor([0, 4, 6]),\n        \"x_coord\": torch.tensor([6, 0, 2]),\n        \"evaluation_mask\": np.array([[0, 0, 1, 1], [2, 2, 0, 0], [0, 3, 3, 0], [0, 3, 3, 3]]),\n        \"resolution_level\": 1,\n    },\n    np.array([0.6]),\n    np.array([1, 0, 0.8]),\n    3,\n]\n\nTEST_CASE_4 = [\n    {\n        \"fp_probs\": np.array([0.8, 0.6]),\n        \"tp_probs\": np.array([1, 1, 0, 0, 0.8, 0.8, 0]),\n        \"num_targets\": 4,\n        \"num_images\": 2,\n    },\n    (0.25, 0.5, 1, 2, 4, 8),\n    0.95833333,\n]\n\nTEST_CASE_5 = [\n    {\n        \"fp_probs\": torch.tensor([0.8, 0.6]),\n        \"tp_probs\": torch.tensor([1, 1, 0, 0, 0.8, 0.8, 0]),\n        \"num_targets\": 4,\n        \"num_images\": 2,\n    },\n    (0.25),\n    0.75,\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_froc.py_TestComputeFpTp_TestComputeFpTp.test_value.np_testing_assert_equal_n": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_froc.py_TestComputeFpTp_TestComputeFpTp.test_value.np_testing_assert_equal_n", "embedding": null, "metadata": {"file_path": "tests/test_compute_froc.py", "file_name": "test_compute_froc.py", "file_type": "text/x-python", "category": "test", "start_line": 83, "end_line": 89, "span_ids": ["TestComputeFpTp", "TestComputeFpTp.test_value"], "tokens": 121}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestComputeFpTp(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3])\n    def test_value(self, input_data, expected_fp, expected_tp, expected_num):\n        fp_probs, tp_probs, num_tumors = compute_fp_tp_probs(**input_data)\n        np.testing.assert_allclose(fp_probs, expected_fp, rtol=1e-5)\n        np.testing.assert_allclose(tp_probs, expected_tp, rtol=1e-5)\n        np.testing.assert_equal(num_tumors, expected_num)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_froc.py_TestComputeFrocScore_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_froc.py_TestComputeFrocScore_", "embedding": null, "metadata": {"file_path": "tests/test_compute_froc.py", "file_name": "test_compute_froc.py", "file_type": "text/x-python", "category": "test", "start_line": 92, "end_line": 102, "span_ids": ["impl:11", "TestComputeFrocScore.test_value", "TestComputeFrocScore"], "tokens": 106}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestComputeFrocScore(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_4, TEST_CASE_5])\n    def test_value(self, input_data, thresholds, expected_score):\n        fps_per_image, total_sensitivity = compute_froc_curve_data(**input_data)\n        score = compute_froc_score(fps_per_image, total_sensitivity, thresholds)\n        np.testing.assert_allclose(score, expected_score, rtol=1e-5)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_meandice.py_unittest__should_return_Nan_for_a": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_meandice.py_unittest__should_return_Nan_for_a", "embedding": null, "metadata": {"file_path": "tests/test_compute_meandice.py", "file_name": "test_compute_meandice.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 50, "span_ids": ["docstring:13", "docstring"], "tokens": 541}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.metrics import DiceMetric, compute_meandice\n\n# keep background\nTEST_CASE_1 = [  # y (1, 1, 2, 2), y_pred (1, 1, 2, 2), expected out (1, 1)\n    {\n        \"y_pred\": torch.tensor([[[[1.0, 0.0], [0.0, 1.0]]]]),\n        \"y\": torch.tensor([[[[1.0, 0.0], [1.0, 1.0]]]]),\n        \"include_background\": True,\n    },\n    [[0.8]],\n]\n\n# remove background and not One-Hot target\nTEST_CASE_2 = [  # y (2, 1, 2, 2), y_pred (2, 3, 2, 2), expected out (2, 2) (no background)\n    {\n        \"y_pred\": torch.tensor(\n            [\n                [[[0.0, 1.0], [0.0, 0.0]], [[0.0, 0.0], [1.0, 1.0]], [[1.0, 0.0], [0.0, 0.0]]],\n                [[[0.0, 0.0], [0.0, 1.0]], [[1.0, 0.0], [0.0, 0.0]], [[0.0, 1.0], [1.0, 0.0]]],\n            ]\n        ),\n        \"y\": torch.tensor(\n            [\n                [[[0.0, 0.0], [0.0, 1.0]], [[1.0, 0.0], [1.0, 0.0]], [[0.0, 1.0], [0.0, 0.0]]],\n                [[[0.0, 0.0], [0.0, 1.0]], [[1.0, 1.0], [0.0, 0.0]], [[0.0, 0.0], [1.0, 0.0]]],\n            ]\n        ),\n        \"include_background\": False,\n    },\n    [[0.5000, 0.0000], [0.6667, 0.6667]],\n]\n\n# should return Nan for all labels=0 case and skip for MeanDice", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_meandice.py_TEST_CASE_3_TEST_CASE_4._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_meandice.py_TEST_CASE_3_TEST_CASE_4._", "embedding": null, "metadata": {"file_path": "tests/test_compute_meandice.py", "file_name": "test_compute_meandice.py", "file_type": "text/x-python", "category": "test", "start_line": 51, "end_line": 87, "span_ids": ["docstring:13"], "tokens": 636}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "TEST_CASE_3 = [\n    {\n        \"y_pred\": torch.tensor(\n            [\n                [[[0.0, 0.0], [0.0, 0.0]], [[0.0, 0.0], [0.0, 0.0]], [[1.0, 1.0], [1.0, 1.0]]],\n                [[[0.0, 0.0], [0.0, 0.0]], [[0.0, 0.0], [0.0, 0.0]], [[1.0, 1.0], [1.0, 1.0]]],\n            ]\n        ),\n        \"y\": torch.tensor(\n            [\n                [[[1.0, 1.0], [1.0, 1.0]], [[0.0, 0.0], [0.0, 0.0]], [[0.0, 0.0], [0.0, 0.0]]],\n                [[[0.0, 1.0], [1.0, 0.0]], [[1.0, 0.0], [0.0, 1.0]], [[0.0, 0.0], [0.0, 0.0]]],\n            ]\n        ),\n        \"include_background\": True,\n    },\n    [[False, True, True], [False, False, True]],\n]\n\nTEST_CASE_4 = [\n    {\"include_background\": True, \"reduction\": \"mean_batch\", \"get_not_nans\": True},\n    {\n        \"y_pred\": torch.tensor(\n            [\n                [[[1.0, 1.0], [1.0, 0.0]], [[0.0, 1.0], [0.0, 0.0]], [[0.0, 1.0], [1.0, 1.0]]],\n                [[[1.0, 0.0], [1.0, 1.0]], [[0.0, 1.0], [1.0, 1.0]], [[0.0, 1.0], [1.0, 0.0]]],\n            ]\n        ),\n        \"y\": torch.tensor(\n            [\n                [[[1.0, 1.0], [1.0, 1.0]], [[0.0, 0.0], [0.0, 0.0]], [[0.0, 0.0], [0.0, 0.0]]],\n                [[[0.0, 0.0], [0.0, 1.0]], [[1.0, 1.0], [0.0, 0.0]], [[0.0, 0.0], [1.0, 0.0]]],\n            ]\n        ),\n    },\n    [0.6786, 0.4000, 0.6667],\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_meandice.py_TEST_CASE_5_TEST_CASE_6._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_meandice.py_TEST_CASE_5_TEST_CASE_6._", "embedding": null, "metadata": {"file_path": "tests/test_compute_meandice.py", "file_name": "test_compute_meandice.py", "file_type": "text/x-python", "category": "test", "start_line": 89, "end_line": 125, "span_ids": ["impl:9"], "tokens": 643}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "TEST_CASE_5 = [\n    {\"include_background\": True, \"reduction\": \"mean\", \"get_not_nans\": True},\n    {\n        \"y_pred\": torch.tensor(\n            [\n                [[[1.0, 1.0], [1.0, 0.0]], [[0.0, 1.0], [0.0, 0.0]], [[0.0, 1.0], [1.0, 1.0]]],\n                [[[1.0, 0.0], [1.0, 1.0]], [[0.0, 1.0], [1.0, 1.0]], [[0.0, 1.0], [1.0, 0.0]]],\n            ]\n        ),\n        \"y\": torch.tensor(\n            [\n                [[[1.0, 1.0], [1.0, 1.0]], [[0.0, 0.0], [0.0, 0.0]], [[0.0, 0.0], [0.0, 0.0]]],\n                [[[0.0, 0.0], [0.0, 1.0]], [[1.0, 1.0], [0.0, 0.0]], [[0.0, 0.0], [1.0, 0.0]]],\n            ]\n        ),\n    },\n    0.689683,\n]\n\nTEST_CASE_6 = [\n    {\"include_background\": True, \"reduction\": \"sum_batch\", \"get_not_nans\": True},\n    {\n        \"y_pred\": torch.tensor(\n            [\n                [[[1.0, 1.0], [1.0, 0.0]], [[0.0, 1.0], [0.0, 0.0]], [[0.0, 1.0], [1.0, 1.0]]],\n                [[[1.0, 0.0], [1.0, 1.0]], [[0.0, 1.0], [1.0, 1.0]], [[0.0, 1.0], [1.0, 0.0]]],\n            ]\n        ),\n        \"y\": torch.tensor(\n            [\n                [[[1.0, 1.0], [1.0, 1.0]], [[0.0, 0.0], [0.0, 0.0]], [[0.0, 0.0], [0.0, 0.0]]],\n                [[[1.0, 1.0], [1.0, 1.0]], [[0.0, 0.0], [0.0, 0.0]], [[0.0, 0.0], [0.0, 0.0]]],\n            ]\n        ),\n    },\n    [1.7143, 0.0000, 0.0000],\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_meandice.py_TEST_CASE_7_TEST_CASE_9._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_meandice.py_TEST_CASE_7_TEST_CASE_9._", "embedding": null, "metadata": {"file_path": "tests/test_compute_meandice.py", "file_name": "test_compute_meandice.py", "file_type": "text/x-python", "category": "test", "start_line": 127, "end_line": 168, "span_ids": ["impl:13", "impl:17"], "tokens": 706}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "TEST_CASE_7 = [\n    {\"include_background\": True, \"reduction\": \"mean\", \"get_not_nans\": True},\n    {\n        \"y_pred\": torch.tensor(\n            [\n                [[[1.0, 1.0], [1.0, 0.0]], [[0.0, 1.0], [0.0, 0.0]], [[0.0, 1.0], [1.0, 1.0]]],\n                [[[1.0, 0.0], [1.0, 1.0]], [[0.0, 1.0], [1.0, 1.0]], [[0.0, 1.0], [1.0, 0.0]]],\n            ]\n        ),\n        \"y\": torch.tensor(\n            [\n                [[[1.0, 1.0], [1.0, 1.0]], [[0.0, 0.0], [0.0, 0.0]], [[0.0, 0.0], [0.0, 0.0]]],\n                [[[1.0, 1.0], [1.0, 1.0]], [[0.0, 0.0], [0.0, 0.0]], [[0.0, 0.0], [0.0, 0.0]]],\n            ]\n        ),\n    },\n    0.857143,\n]\n\nTEST_CASE_8 = [\n    {\"include_background\": False, \"reduction\": \"sum_batch\", \"get_not_nans\": True},\n    {\n        \"y_pred\": torch.tensor(\n            [\n                [[[1.0, 1.0], [1.0, 0.0]], [[0.0, 1.0], [0.0, 0.0]], [[0.0, 1.0], [1.0, 1.0]]],\n                [[[1.0, 0.0], [1.0, 1.0]], [[0.0, 1.0], [1.0, 1.0]], [[0.0, 1.0], [1.0, 0.0]]],\n            ]\n        ),\n        \"y\": torch.tensor(\n            [\n                [[[1.0, 1.0], [1.0, 1.0]], [[0.0, 0.0], [0.0, 0.0]], [[0.0, 0.0], [0.0, 0.0]]],\n                [[[1.0, 1.0], [1.0, 1.0]], [[0.0, 0.0], [0.0, 0.0]], [[0.0, 0.0], [0.0, 0.0]]],\n            ]\n        ),\n    },\n    [0.0000, 0.0000],\n]\n\nTEST_CASE_9 = [\n    {\"y\": torch.ones((2, 2, 3, 3)), \"y_pred\": torch.ones((2, 2, 3, 3))},\n    [[1.0000, 1.0000], [1.0000, 1.0000]],\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_concat_itemsd.py_TestConcatItemsd.test_numpy_values_TestConcatItemsd.test_numpy_values.None_2": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_concat_itemsd.py_TestConcatItemsd.test_numpy_values_TestConcatItemsd.test_numpy_values.None_2", "embedding": null, "metadata": {"file_path": "tests/test_concat_itemsd.py", "file_name": "test_concat_itemsd.py", "file_type": "text/x-python", "category": "test", "start_line": 33, "end_line": 39, "span_ids": ["TestConcatItemsd.test_numpy_values"], "tokens": 164}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestConcatItemsd(unittest.TestCase):\n\n    def test_numpy_values(self):\n        input_data = {\"img1\": np.array([[0, 1], [1, 2]]), \"img2\": np.array([[0, 1], [1, 2]])}\n        result = ConcatItemsd(keys=[\"img1\", \"img2\"], name=\"cat_img\")(input_data)\n        self.assertTrue(\"cat_img\" in result)\n        result[\"cat_img\"] += 1\n        np.testing.assert_allclose(result[\"img1\"], np.array([[0, 1], [1, 2]]))\n        np.testing.assert_allclose(result[\"cat_img\"], np.array([[1, 2], [2, 3], [1, 2], [2, 3]]))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_concat_itemsd.py_TestConcatItemsd.test_single_numpy_TestConcatItemsd.test_single_numpy.None_1": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_concat_itemsd.py_TestConcatItemsd.test_single_numpy_TestConcatItemsd.test_single_numpy.None_1", "embedding": null, "metadata": {"file_path": "tests/test_concat_itemsd.py", "file_name": "test_concat_itemsd.py", "file_type": "text/x-python", "category": "test", "start_line": 41, "end_line": 46, "span_ids": ["TestConcatItemsd.test_single_numpy"], "tokens": 116}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestConcatItemsd(unittest.TestCase):\n\n    def test_single_numpy(self):\n        input_data = {\"img\": np.array([[0, 1], [1, 2]])}\n        result = ConcatItemsd(keys=\"img\", name=\"cat_img\")(input_data)\n        result[\"cat_img\"] += 1\n        np.testing.assert_allclose(result[\"img\"], np.array([[0, 1], [1, 2]]))\n        np.testing.assert_allclose(result[\"cat_img\"], np.array([[1, 2], [2, 3]]))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_concat_itemsd.py_TestConcatItemsd.test_single_tensor_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_concat_itemsd.py_TestConcatItemsd.test_single_tensor_", "embedding": null, "metadata": {"file_path": "tests/test_concat_itemsd.py", "file_name": "test_concat_itemsd.py", "file_type": "text/x-python", "category": "test", "start_line": 48, "end_line": 58, "span_ids": ["TestConcatItemsd.test_single_tensor", "impl"], "tokens": 128}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestConcatItemsd(unittest.TestCase):\n\n    def test_single_tensor(self):\n        input_data = {\"img\": torch.tensor([[0, 1], [1, 2]])}\n        result = ConcatItemsd(keys=\"img\", name=\"cat_img\")(input_data)\n        result[\"cat_img\"] += 1\n        torch.testing.assert_allclose(result[\"img\"], torch.tensor([[0, 1], [1, 2]]))\n        torch.testing.assert_allclose(result[\"cat_img\"], torch.tensor([[1, 2], [2, 3]]))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_convert_to_multi_channel.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_convert_to_multi_channel.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_convert_to_multi_channel.py", "file_name": "test_convert_to_multi_channel.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 58, "span_ids": ["TestConvertToMultiChannel", "impl:5", "TestConvertToMultiChannel.test_type_shape", "docstring"], "tokens": 391}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import ConvertToMultiChannelBasedOnBratsClasses\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    TESTS.extend(\n        [\n            [\n                p([[0, 1, 2], [1, 2, 4], [0, 1, 4]]),\n                p(\n                    [\n                        [[0, 1, 0], [1, 0, 1], [0, 1, 1]],\n                        [[0, 1, 1], [1, 1, 1], [0, 1, 1]],\n                        [[0, 0, 0], [0, 0, 1], [0, 0, 1]],\n                    ]\n                ),\n            ],\n            [\n                p([[[[0, 1], [1, 2]], [[2, 4], [4, 4]]]]),\n                p(\n                    [\n                        [[[0, 1], [1, 0]], [[0, 1], [1, 1]]],\n                        [[[0, 1], [1, 1]], [[1, 1], [1, 1]]],\n                        [[[0, 0], [0, 0]], [[0, 1], [1, 1]]],\n                    ]\n                ),\n            ],\n        ]\n    )\n\n\nclass TestConvertToMultiChannel(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_type_shape(self, data, expected_result):\n        result = ConvertToMultiChannelBasedOnBratsClasses()(data)\n        assert_allclose(result, expected_result)\n        self.assertTrue(result.dtype in (bool, torch.bool))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_convert_to_multi_channeld.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_convert_to_multi_channeld.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_convert_to_multi_channeld.py", "file_name": "test_convert_to_multi_channeld.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 35, "span_ids": ["TestConvertToMultiChanneld", "impl:3", "TestConvertToMultiChanneld.test_type_shape", "docstring"], "tokens": 239}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import ConvertToMultiChannelBasedOnBratsClassesd\n\nTEST_CASE = [\n    {\"keys\": \"label\"},\n    {\"label\": np.array([[0, 1, 2], [1, 2, 4], [0, 1, 4]])},\n    np.array([[[0, 1, 0], [1, 0, 1], [0, 1, 1]], [[0, 1, 1], [1, 1, 1], [0, 1, 1]], [[0, 0, 0], [0, 0, 1], [0, 0, 1]]]),\n]\n\n\nclass TestConvertToMultiChanneld(unittest.TestCase):\n    @parameterized.expand([TEST_CASE])\n    def test_type_shape(self, keys, data, expected_result):\n        result = ConvertToMultiChannelBasedOnBratsClassesd(**keys)(data)\n        np.testing.assert_equal(result[\"label\"], expected_result)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_copy_itemsd.py_TestCopyItemsd.test_array_values_TestCopyItemsd.test_array_values.None_2": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_copy_itemsd.py_TestCopyItemsd.test_array_values_TestCopyItemsd.test_array_values.None_2", "embedding": null, "metadata": {"file_path": "tests/test_copy_itemsd.py", "file_name": "test_copy_itemsd.py", "file_type": "text/x-python", "category": "test", "start_line": 54, "end_line": 60, "span_ids": ["TestCopyItemsd.test_array_values"], "tokens": 145}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestCopyItemsd(unittest.TestCase):\n\n    def test_array_values(self):\n        input_data = {\"img\": [[0, 1], [1, 2]], \"seg\": [[0, 1], [1, 2]]}\n        result = CopyItemsd(keys=\"img\", times=1, names=\"img_1\")(input_data)\n        self.assertTrue(\"img_1\" in result)\n        result[\"img_1\"][0][0] += 1\n        np.testing.assert_allclose(result[\"img\"], [[0, 1], [1, 2]])\n        np.testing.assert_allclose(result[\"img_1\"], [[1, 1], [1, 2]])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_copy_itemsd.py_TestCopyItemsd.test_graph_tensor_values_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_copy_itemsd.py_TestCopyItemsd.test_graph_tensor_values_", "embedding": null, "metadata": {"file_path": "tests/test_copy_itemsd.py", "file_name": "test_copy_itemsd.py", "file_type": "text/x-python", "category": "test", "start_line": 62, "end_line": 77, "span_ids": ["impl:9", "TestCopyItemsd.test_graph_tensor_values"], "tokens": 254}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestCopyItemsd(unittest.TestCase):\n\n    def test_graph_tensor_values(self):\n        device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu:0\")\n        net = torch.nn.PReLU().to(device)\n        with eval_mode(net):\n            pred = net(torch.tensor([[0.0, 1.0], [1.0, 2.0]], device=device))\n        input_data = {\"pred\": pred, \"seg\": torch.tensor([[0.0, 1.0], [1.0, 2.0]], device=device)}\n        result = CopyItemsd(keys=\"pred\", times=1, names=\"pred_1\")(input_data)\n        self.assertTrue(\"pred_1\" in result)\n        result[\"pred_1\"] += 1.0\n        torch.testing.assert_allclose(result[\"pred\"], torch.tensor([[0.0, 1.0], [1.0, 2.0]], device=device))\n        torch.testing.assert_allclose(result[\"pred_1\"], torch.tensor([[1.0, 2.0], [2.0, 3.0]], device=device))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_crf_cpu.py_unittest_TEST_CASES": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_crf_cpu.py_unittest_TEST_CASES", "embedding": null, "metadata": {"file_path": "tests/test_crf_cpu.py", "file_name": "test_crf_cpu.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 429, "span_ids": ["docstring"], "tokens": 46}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks.blocks import CRF\nfrom tests.utils import skip_if_no_cpp_extension\n\nTEST_CASES =\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_crf_cpu.py_CRFTestCaseCpu_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_crf_cpu.py_CRFTestCaseCpu_", "embedding": null, "metadata": {"file_path": "tests/test_crf_cpu.py", "file_name": "test_crf_cpu.py", "file_type": "text/x-python", "category": "test", "start_line": 432, "end_line": 451, "span_ids": ["CRFTestCaseCpu.test", "impl:3", "CRFTestCaseCpu"], "tokens": 158}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_no_cpp_extension\nclass CRFTestCaseCpu(unittest.TestCase):\n    @parameterized.expand(TEST_CASES)\n    def test(self, test_case_description, params, input, features, expected):\n\n        # Create input tensors\n        input_tensor = torch.from_numpy(np.array(input)).to(dtype=torch.float, device=torch.device(\"cpu\"))\n        feature_tensor = torch.from_numpy(np.array(features)).to(dtype=torch.float, device=torch.device(\"cpu\"))\n\n        # apply filter\n        crf = CRF(*params)\n        output = crf(input_tensor, feature_tensor).cpu().numpy()\n\n        # Ensure result are as expected\n        np.testing.assert_allclose(output, expected, atol=1e-4)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_crf_cuda.py_unittest_TEST_CASES": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_crf_cuda.py_unittest_TEST_CASES", "embedding": null, "metadata": {"file_path": "tests/test_crf_cuda.py", "file_name": "test_crf_cuda.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 429, "span_ids": ["docstring"], "tokens": 51}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks.blocks import CRF\nfrom tests.utils import skip_if_no_cpp_extension, skip_if_no_cuda\n\nTEST_CASES =\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_crf_cuda.py_CRFTestCaseCuda_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_crf_cuda.py_CRFTestCaseCuda_", "embedding": null, "metadata": {"file_path": "tests/test_crf_cuda.py", "file_name": "test_crf_cuda.py", "file_type": "text/x-python", "category": "test", "start_line": 494, "end_line": 528, "span_ids": ["CRFTestCaseCuda", "impl:3", "CRFTestCaseCuda.test"], "tokens": 288}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_no_cpp_extension\n@skip_if_no_cuda\nclass CRFTestCaseCuda(unittest.TestCase):\n    @parameterized.expand(TEST_CASES)\n    def test(self, test_case_description, params, input, features, expected):\n\n        # Create input tensors\n        input_tensor = torch.from_numpy(np.array(input)).to(dtype=torch.float, device=torch.device(\"cuda\"))\n        feature_tensor = torch.from_numpy(np.array(features)).to(dtype=torch.float, device=torch.device(\"cuda\"))\n\n        params[-1] = None if params[-1] is None else params[-1].cuda()\n\n        # apply filter\n        crf = CRF(*params)\n        output = crf(input_tensor, feature_tensor).cpu().numpy()\n\n        # Ensure result are as expected\n        # np.testing.assert_allclose(output, expected, atol=1e-4)\n\n        # Temporarily allowing some (10%) mismatched elements due to non determinism.\n        absolute_diff_tolerance = 5e-2\n        mismatch_ratio_tolerance = 0.1\n\n        output = np.array(output).flatten()\n        expected = np.array(expected).flatten()\n\n        abs_diff = abs(output - expected)\n        mismatch_count = sum(np.where(abs_diff > absolute_diff_tolerance, 1, 0))\n\n        self.assertLessEqual(mismatch_count / len(output), mismatch_ratio_tolerance)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_crop_foreground.py_TestCropForeground_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_crop_foreground.py_TestCropForeground_", "embedding": null, "metadata": {"file_path": "tests/test_crop_foreground.py", "file_name": "test_crop_foreground.py", "file_type": "text/x-python", "category": "test", "start_line": 81, "end_line": 98, "span_ids": ["TestCropForeground.test_return_coords", "TestCropForeground.test_value", "TestCropForeground", "impl:10"], "tokens": 173}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestCropForeground(unittest.TestCase):\n    @parameterized.expand(TEST_COORDS + TESTS)\n    def test_value(self, argments, image, expected_data):\n        result = CropForeground(**argments)(image)\n        torch.testing.assert_allclose(result, expected_data, rtol=1e-7, atol=0)\n\n    @parameterized.expand(TEST_COORDS)\n    def test_return_coords(self, argments, image, _):\n        argments[\"return_coords\"] = True\n        _, start_coord, end_coord = CropForeground(**argments)(image)\n        argments[\"return_coords\"] = False\n        np.testing.assert_allclose(start_coord, np.asarray([1, 1]))\n        np.testing.assert_allclose(end_coord, np.asarray([4, 4]))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_crop_foregroundd.py_TestCropForegroundd_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_crop_foregroundd.py_TestCropForegroundd_", "embedding": null, "metadata": {"file_path": "tests/test_crop_foregroundd.py", "file_name": "test_crop_foregroundd.py", "file_type": "text/x-python", "category": "test", "start_line": 118, "end_line": 143, "span_ids": ["impl:9", "TestCropForegroundd.test_foreground_position", "TestCropForegroundd", "TestCropForegroundd.test_value"], "tokens": 267}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestCropForegroundd(unittest.TestCase):\n    @parameterized.expand(TEST_POSITION + TESTS)\n    def test_value(self, argments, input_data, expected_data):\n        result = CropForegroundd(**argments)(input_data)\n        r, i = result[\"img\"], input_data[\"img\"]\n        self.assertEqual(type(r), type(i))\n        if isinstance(r, torch.Tensor):\n            self.assertEqual(r.device, i.device)\n        assert_allclose(r, expected_data)\n\n    @parameterized.expand(TEST_POSITION)\n    def test_foreground_position(self, argments, input_data, _):\n        result = CropForegroundd(**argments)(input_data)\n        np.testing.assert_allclose(result[\"foreground_start_coord\"], np.array([1, 1]))\n        np.testing.assert_allclose(result[\"foreground_end_coord\"], np.array([4, 4]))\n\n        argments[\"start_coord_key\"] = \"test_start_coord\"\n        argments[\"end_coord_key\"] = \"test_end_coord\"\n        result = CropForegroundd(**argments)(input_data)\n        np.testing.assert_allclose(result[\"test_start_coord\"], np.array([1, 1]))\n        np.testing.assert_allclose(result[\"test_end_coord\"], np.array([4, 4]))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cross_validation.py_os_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cross_validation.py_os_", "embedding": null, "metadata": {"file_path": "tests/test_cross_validation.py", "file_name": "test_cross_validation.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 80, "span_ids": ["TestCrossValidation.test_values", "TestCrossValidation", "impl", "docstring"], "tokens": 639}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport unittest\nfrom urllib.error import ContentTooShortError, HTTPError\n\nfrom monai.apps import CrossValidation, DecathlonDataset\nfrom monai.transforms import AddChanneld, Compose, LoadImaged, ScaleIntensityd, ToTensord\nfrom tests.utils import skip_if_quick\n\n\nclass TestCrossValidation(unittest.TestCase):\n    @skip_if_quick\n    def test_values(self):\n        testing_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"testing_data\")\n        train_transform = Compose(\n            [\n                LoadImaged(keys=[\"image\", \"label\"]),\n                AddChanneld(keys=[\"image\", \"label\"]),\n                ScaleIntensityd(keys=\"image\"),\n                ToTensord(keys=[\"image\", \"label\"]),\n            ]\n        )\n        val_transform = LoadImaged(keys=[\"image\", \"label\"])\n\n        def _test_dataset(dataset):\n            self.assertEqual(len(dataset), 52)\n            self.assertTrue(\"image\" in dataset[0])\n            self.assertTrue(\"label\" in dataset[0])\n            self.assertTrue(\"image_meta_dict\" in dataset[0])\n            self.assertTupleEqual(dataset[0][\"image\"].shape, (1, 34, 49, 41))\n\n        cvdataset = CrossValidation(\n            dataset_cls=DecathlonDataset,\n            nfolds=5,\n            seed=12345,\n            root_dir=testing_dir,\n            task=\"Task04_Hippocampus\",\n            section=\"validation\",\n            transform=train_transform,\n            download=True,\n        )\n\n        try:  # will start downloading if testing_dir doesn't have the Decathlon files\n            data = cvdataset.get_dataset(folds=0)\n        except (ContentTooShortError, HTTPError, RuntimeError) as e:\n            print(str(e))\n            if isinstance(e, RuntimeError):\n                # FIXME: skip MD5 check as current downloading method may fail\n                self.assertTrue(str(e).startswith(\"md5 check\"))\n            return  # skipping this test due the network connection errors\n\n        _test_dataset(data)\n\n        # test training data for fold [1, 2, 3, 4] of 5 splits\n        data = cvdataset.get_dataset(folds=[1, 2, 3, 4])\n        self.assertTupleEqual(data[0][\"image\"].shape, (1, 35, 52, 33))\n        self.assertEqual(len(data), 208)\n        # test train / validation for fold 4 of 5 splits\n        data = cvdataset.get_dataset(folds=[4], transform=val_transform, download=False)\n        # val_transform doesn't add the channel dim to shape\n        self.assertTupleEqual(data[0][\"image\"].shape, (38, 53, 30))\n        self.assertEqual(len(data), 52)\n        data = cvdataset.get_dataset(folds=[0, 1, 2, 3])\n        self.assertTupleEqual(data[0][\"image\"].shape, (1, 34, 49, 41))\n        self.assertEqual(len(data), 208)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_data_stats.py_logging_TEST_CASE_6._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_data_stats.py_logging_TEST_CASE_6._", "embedding": null, "metadata": {"file_path": "tests/test_data_stats.py", "file_name": "test_data_stats.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 109, "span_ids": ["impl:9", "docstring"], "tokens": 699}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import logging\nimport os\nimport sys\nimport tempfile\nimport unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import DataStats\n\nTEST_CASE_1 = [\n    {\n        \"prefix\": \"test data\",\n        \"data_type\": False,\n        \"data_shape\": False,\n        \"value_range\": False,\n        \"data_value\": False,\n        \"additional_info\": None,\n        \"logger_handler\": None,\n    },\n    np.array([[0, 1], [1, 2]]),\n    \"test data statistics:\",\n]\n\nTEST_CASE_2 = [\n    {\n        \"prefix\": \"test data\",\n        \"data_type\": True,\n        \"data_shape\": False,\n        \"value_range\": False,\n        \"data_value\": False,\n        \"additional_info\": None,\n        \"logger_handler\": None,\n    },\n    np.array([[0, 1], [1, 2]]),\n    \"test data statistics:\\nType: <class 'numpy.ndarray'>\",\n]\n\nTEST_CASE_3 = [\n    {\n        \"prefix\": \"test data\",\n        \"data_type\": True,\n        \"data_shape\": True,\n        \"value_range\": False,\n        \"data_value\": False,\n        \"additional_info\": None,\n        \"logger_handler\": None,\n    },\n    np.array([[0, 1], [1, 2]]),\n    \"test data statistics:\\nType: <class 'numpy.ndarray'>\\nShape: (2, 2)\",\n]\n\nTEST_CASE_4 = [\n    {\n        \"prefix\": \"test data\",\n        \"data_type\": True,\n        \"data_shape\": True,\n        \"value_range\": True,\n        \"data_value\": False,\n        \"additional_info\": None,\n        \"logger_handler\": None,\n    },\n    np.array([[0, 1], [1, 2]]),\n    \"test data statistics:\\nType: <class 'numpy.ndarray'>\\nShape: (2, 2)\\nValue range: (0, 2)\",\n]\n\nTEST_CASE_5 = [\n    {\n        \"prefix\": \"test data\",\n        \"data_type\": True,\n        \"data_shape\": True,\n        \"value_range\": True,\n        \"data_value\": True,\n        \"additional_info\": None,\n        \"logger_handler\": None,\n    },\n    np.array([[0, 1], [1, 2]]),\n    \"test data statistics:\\nType: <class 'numpy.ndarray'>\\nShape: (2, 2)\\nValue range: (0, 2)\\nValue: [[0 1]\\n [1 2]]\",\n]\n\nTEST_CASE_6 = [\n    {\n        \"prefix\": \"test data\",\n        \"data_type\": True,\n        \"data_shape\": True,\n        \"value_range\": True,\n        \"data_value\": True,\n        \"additional_info\": np.mean,\n        \"logger_handler\": None,\n    },\n    np.array([[0, 1], [1, 2]]),\n    (\n        \"test data statistics:\\nType: <class 'numpy.ndarray'>\\nShape: (2, 2)\\n\"\n        \"Value range: (0, 2)\\nValue: [[0 1]\\n [1 2]]\\nAdditional info: 1.0\"\n    ),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_data_stats.py_TEST_CASE_7_TEST_CASE_8._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_data_stats.py_TEST_CASE_7_TEST_CASE_8._", "embedding": null, "metadata": {"file_path": "tests/test_data_stats.py", "file_name": "test_data_stats.py", "file_type": "text/x-python", "category": "test", "start_line": 111, "end_line": 132, "span_ids": ["impl:9"], "tokens": 258}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "TEST_CASE_7 = [\n    {\n        \"prefix\": \"test data\",\n        \"data_type\": True,\n        \"data_shape\": True,\n        \"value_range\": True,\n        \"data_value\": True,\n        \"additional_info\": lambda x: torch.mean(x.float()),\n        \"logger_handler\": None,\n    },\n    torch.tensor([[0, 1], [1, 2]]).to(\"cuda\" if torch.cuda.is_available() else \"cpu\"),\n    (\n        \"test data statistics:\\nType: <class 'torch.Tensor'>\\nShape: torch.Size([2, 2])\\nValue range: (0, 2)\\n\"\n        \"Value: tensor([[0, 1],\\n        [1, 2]])\\nAdditional info: 1.0\"\n    ),\n]\n\nTEST_CASE_8 = [\n    np.array([[0, 1], [1, 2]]),\n    \"test data statistics:\\nType: <class 'numpy.ndarray'> int64\\nShape: (2, 2)\\nValue range: (0, 2)\\n\"\n    \"Value: [[0 1]\\n [1 2]]\\nAdditional info: 1.0\\n\",\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_decollate.py_TestDeCollate_TestDeCollate.check_match.if_isinstance_in1_dict_.else_.raise_RuntimeError_f_Not_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_decollate.py_TestDeCollate_TestDeCollate.check_match.if_isinstance_in1_dict_.else_.raise_RuntimeError_f_Not_", "embedding": null, "metadata": {"file_path": "tests/test_decollate.py", "file_name": "test_decollate.py", "file_type": "text/x-python", "category": "test", "start_line": 89, "end_line": 119, "span_ids": ["TestDeCollate.tearDown", "TestDeCollate", "TestDeCollate.setUp", "TestDeCollate.check_match"], "tokens": 371}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestDeCollate(unittest.TestCase):\n    def setUp(self) -> None:\n        set_determinism(seed=0)\n\n        im = create_test_image_2d(100, 101)[0]\n        self.data_dict = [{\"image\": make_nifti_image(im) if has_nib else im} for _ in range(6)]\n        self.data_list = [make_nifti_image(im) if has_nib else im for _ in range(6)]\n\n    def tearDown(self) -> None:\n        set_determinism(None)\n\n    def check_match(self, in1, in2):\n        if isinstance(in1, dict):\n            self.assertTrue(isinstance(in2, dict))\n            for (k1, v1), (k2, v2) in zip(in1.items(), in2.items()):\n                if isinstance(k1, Enum) and isinstance(k2, Enum):\n                    k1, k2 = k1.value, k2.value\n                self.check_match(k1, k2)\n                # Transform ids won't match for windows with multiprocessing, so don't check values\n                if k1 == TraceKeys.ID and sys.platform in [\"darwin\", \"win32\"]:\n                    continue\n                self.check_match(v1, v2)\n        elif isinstance(in1, (list, tuple)):\n            for l1, l2 in zip(in1, in2):\n                self.check_match(l1, l2)\n        elif isinstance(in1, (str, int)):\n            self.assertEqual(in1, in2)\n        elif isinstance(in1, (torch.Tensor, np.ndarray)):\n            np.testing.assert_array_equal(in1, in2)\n        else:\n            raise RuntimeError(f\"Not sure how to compare types. type(in1): {type(in1)}, type(in2): {type(in2)}\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deepgrow_dataset.py_os_TEST_CASE_8._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deepgrow_dataset.py_os_TEST_CASE_8._", "embedding": null, "metadata": {"file_path": "tests/test_deepgrow_dataset.py", "file_name": "test_deepgrow_dataset.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 48, "span_ids": ["docstring"], "tokens": 392}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport shutil\nimport tempfile\nimport unittest\n\nimport nibabel as nib\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.apps.deepgrow.dataset import create_dataset\nfrom monai.utils import set_determinism\n\nTEST_CASE_1 = [{\"dimension\": 2, \"pixdim\": (1, 1)}, {\"length\": 3}, 9, 1]\n\nTEST_CASE_2 = [{\"dimension\": 2, \"pixdim\": (1, 1), \"limit\": 1}, {\"length\": 3}, 3, 1]\n\nTEST_CASE_3 = [{\"dimension\": 2, \"pixdim\": (1, 1)}, {\"length\": 1}, 3, 1]\n\nTEST_CASE_4 = [{\"dimension\": 3, \"pixdim\": (1, 1, 1)}, {\"length\": 1}, 1, 1]\n\nTEST_CASE_5 = [{\"dimension\": 3, \"pixdim\": (1, 1, 1)}, {\"length\": 1, \"image_channel\": 4}, 1, 1]\n\nTEST_CASE_6 = [{\"dimension\": 2, \"pixdim\": (1, 1)}, {\"length\": 1, \"image_channel\": 4}, 3, 1]\n\nTEST_CASE_7 = [\n    {\"dimension\": 2, \"pixdim\": (1, 1), \"label_key\": None},\n    {\"length\": 1, \"image_channel\": 4, \"with_label\": False},\n    40,\n    None,\n]\n\nTEST_CASE_8 = [\n    {\"dimension\": 3, \"pixdim\": (1, 1, 1), \"label_key\": None},\n    {\"length\": 1, \"image_channel\": 4, \"with_label\": False},\n    1,\n    None,\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deepgrow_dataset.py_TestCreateDataset_TestCreateDataset._create_data.return.datalist": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deepgrow_dataset.py_TestCreateDataset_TestCreateDataset._create_data.return.datalist", "embedding": null, "metadata": {"file_path": "tests/test_deepgrow_dataset.py", "file_name": "test_deepgrow_dataset.py", "file_type": "text/x-python", "category": "test", "start_line": 51, "end_line": 80, "span_ids": ["TestCreateDataset", "TestCreateDataset._create_data", "TestCreateDataset.setUp"], "tokens": 313}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestCreateDataset(unittest.TestCase):\n    def setUp(self):\n        set_determinism(1)\n        self.tempdir = tempfile.mkdtemp()\n\n    def _create_data(self, length=1, image_channel=1, with_label=True):\n        affine = np.eye(4)\n        datalist = []\n        for i in range(length):\n            if image_channel == 1:\n                image = np.random.randint(0, 2, size=(128, 128, 40))\n            else:\n                image = np.random.randint(0, 2, size=(128, 128, 40, image_channel))\n            image_file = os.path.join(self.tempdir, f\"image{i}.nii.gz\")\n            nib.save(nib.Nifti1Image(image, affine), image_file)\n\n            if with_label:\n                # 3 slices has label\n                label = np.zeros((128, 128, 40))\n                label[0][1][0] = 1\n                label[0][1][1] = 1\n                label[0][0][2] = 1\n                label[0][1][2] = 1\n                label_file = os.path.join(self.tempdir, f\"label{i}.nii.gz\")\n                nib.save(nib.Nifti1Image(label, affine), label_file)\n                datalist.append({\"image\": image_file, \"label\": label_file})\n            else:\n                datalist.append({\"image\": image_file})\n\n        return datalist", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deepgrow_dataset.py_TestCreateDataset.test_create_dataset_TestCreateDataset.test_create_dataset.if_expected_region_is_not.self_assertEqual_deepgrow": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deepgrow_dataset.py_TestCreateDataset.test_create_dataset_TestCreateDataset.test_create_dataset.if_expected_region_is_not.self_assertEqual_deepgrow", "embedding": null, "metadata": {"file_path": "tests/test_deepgrow_dataset.py", "file_name": "test_deepgrow_dataset.py", "file_type": "text/x-python", "category": "test", "start_line": 82, "end_line": 90, "span_ids": ["TestCreateDataset.test_create_dataset"], "tokens": 151}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestCreateDataset(unittest.TestCase):\n\n    @parameterized.expand(\n        [TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5, TEST_CASE_6, TEST_CASE_7, TEST_CASE_8]\n    )\n    def test_create_dataset(self, args, data_args, expected_length, expected_region):\n        datalist = self._create_data(**data_args)\n        deepgrow_datalist = create_dataset(datalist=datalist, output_dir=self.tempdir, **args)\n        self.assertEqual(len(deepgrow_datalist), expected_length)\n        if expected_region is not None:\n            self.assertEqual(deepgrow_datalist[0][\"region\"], expected_region)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deepgrow_dataset.py_TestCreateDataset.test_invalid_dim_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deepgrow_dataset.py_TestCreateDataset.test_invalid_dim_", "embedding": null, "metadata": {"file_path": "tests/test_deepgrow_dataset.py", "file_name": "test_deepgrow_dataset.py", "file_type": "text/x-python", "category": "test", "start_line": 92, "end_line": 107, "span_ids": ["impl:17", "TestCreateDataset.test_empty_datalist", "TestCreateDataset.tearDown", "TestCreateDataset.test_invalid_dim"], "tokens": 129}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestCreateDataset(unittest.TestCase):\n\n    def test_invalid_dim(self):\n        with self.assertRaises(ValueError):\n            create_dataset(datalist=self._create_data(), output_dir=self.tempdir, dimension=4, pixdim=(1, 1, 1, 1))\n\n    def test_empty_datalist(self):\n        with self.assertRaises(ValueError):\n            create_dataset(datalist=[], output_dir=self.tempdir, dimension=3, pixdim=(1, 1, 1))\n\n    def tearDown(self):\n        shutil.rmtree(self.tempdir)\n        set_determinism(None)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deepgrow_transforms.py_ADD_GUIDANCE_TEST_CASE_1_ADD_GUIDANCE_TEST_CASE_1._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deepgrow_transforms.py_ADD_GUIDANCE_TEST_CASE_1_ADD_GUIDANCE_TEST_CASE_1._", "embedding": null, "metadata": {"file_path": "tests/test_deepgrow_transforms.py", "file_name": "test_deepgrow_transforms.py", "file_type": "text/x-python", "category": "test", "start_line": 177, "end_line": 208, "span_ids": ["impl:27"], "tokens": 704}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "ADD_GUIDANCE_TEST_CASE_1 = [\n    {\"image\": \"image\", \"guidance\": \"guidance\"},\n    DATA_2,\n    np.array(\n        [\n            [\n                [[1, 2, 3, 2, 1], [1, 1, 3, 2, 1], [0, 0, 0, 0, 0], [1, 1, 1, 2, 1], [0, 2, 2, 2, 1]],\n                [[1, 0, 2, 0, 1], [0, 1, 2, 1, 0], [2, 2, 3, 2, 2], [0, 1, 2, 1, 0], [1, 0, 2, 0, 1]],\n            ],\n            [\n                [\n                    [0.0, 0.26689214, 0.37996644, 0.26689214, 0.0],\n                    [0.26689214, 0.65222847, 0.81548417, 0.65222847, 0.26689214],\n                    [0.37996635, 0.81548399, 1.0, 0.81548399, 0.37996635],\n                    [0.26689214, 0.65222847, 0.81548417, 0.65222847, 0.26689214],\n                    [0.0, 0.26689214, 0.37996644, 0.26689214, 0.0],\n                ],\n                [\n                    [0.0, 0.26689214, 0.37996644, 0.26689214, 0.0],\n                    [0.26689214, 0.65222847, 0.81548417, 0.65222847, 0.26689214],\n                    [0.37996635, 0.81548399, 1.0, 0.81548399, 0.37996635],\n                    [0.26689214, 0.65222847, 0.81548417, 0.65222847, 0.26689214],\n                    [0.0, 0.26689214, 0.37996644, 0.26689214, 0.0],\n                ],\n            ],\n            [\n                [[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]],\n                [[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]],\n            ],\n        ]\n    ),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deepgrow_transforms.py_RESULT_4_8_4_8_4_8_n_TestFindAllValidSlicesd.test_correct_results.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deepgrow_transforms.py_RESULT_4_8_4_8_4_8_n_TestFindAllValidSlicesd.test_correct_results.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_deepgrow_transforms.py", "file_name": "test_deepgrow_transforms.py", "file_type": "text/x-python", "category": "test", "start_line": 318, "end_line": 340, "span_ids": ["TestFindAllValidSlicesd.test_correct_results", "TestFindAllValidSlicesd", "impl:72", "impl:63"], "tokens": 527}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "RESULT[4:8, 4:8, 4:8] = np.array(\n    [\n        [[1.0, 1.0, 2.0, 2.0], [1.0, 1.0, 2.0, 2.0], [3.0, 3.0, 4.0, 4.0], [3.0, 3.0, 4.0, 4.0]],\n        [[1.0, 1.0, 2.0, 2.0], [1.0, 1.0, 2.0, 2.0], [3.0, 3.0, 4.0, 4.0], [3.0, 3.0, 4.0, 4.0]],\n        [[5.0, 5.0, 6.0, 6.0], [5.0, 5.0, 6.0, 6.0], [7.0, 7.0, 8.0, 8.0], [7.0, 7.0, 8.0, 8.0]],\n        [[5.0, 5.0, 6.0, 6.0], [5.0, 5.0, 6.0, 6.0], [7.0, 7.0, 8.0, 8.0], [7.0, 7.0, 8.0, 8.0]],\n    ]\n)\n\nRESTORE_LABEL_TEST_CASE_2 = [{\"keys\": [\"pred\"], \"ref_image\": \"image\", \"mode\": \"nearest\"}, DATA_11, RESULT]\n\nFETCH_2D_SLICE_TEST_CASE_1 = [\n    {\"keys\": [\"image\"], \"guidance\": \"guidance\"},\n    DATA_12,\n    np.array([[9, 10, 11], [12, 13, 14], [15, 16, 17]]),\n]\n\n\nclass TestFindAllValidSlicesd(unittest.TestCase):\n    @parameterized.expand([FIND_SLICE_TEST_CASE_1, FIND_SLICE_TEST_CASE_2])\n    def test_correct_results(self, arguments, input_data, expected_result):\n        result = FindAllValidSlicesd(**arguments)(input_data)\n        np.testing.assert_allclose(result[arguments[\"sids\"]], expected_result)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deepgrow_transforms.py_TestAddGuidanceFromPointsd_TestAddGuidanceFromPointsd.test_correct_results.None_1": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deepgrow_transforms.py_TestAddGuidanceFromPointsd_TestAddGuidanceFromPointsd.test_correct_results.None_1", "embedding": null, "metadata": {"file_path": "tests/test_deepgrow_transforms.py", "file_name": "test_deepgrow_transforms.py", "file_type": "text/x-python", "category": "test", "start_line": 410, "end_line": 424, "span_ids": ["TestAddGuidanceFromPointsd.test_correct_results", "TestAddGuidanceFromPointsd"], "tokens": 154}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestAddGuidanceFromPointsd(unittest.TestCase):\n    @parameterized.expand(\n        [\n            ADD_GUIDANCE_FROM_POINTS_TEST_CASE_1,\n            ADD_GUIDANCE_FROM_POINTS_TEST_CASE_2,\n            ADD_GUIDANCE_FROM_POINTS_TEST_CASE_3,\n            ADD_GUIDANCE_FROM_POINTS_TEST_CASE_4,\n            ADD_GUIDANCE_FROM_POINTS_TEST_CASE_5,\n            ADD_GUIDANCE_FROM_POINTS_TEST_CASE_6,\n        ]\n    )\n    def test_correct_results(self, arguments, input_data, expected_pos, expected_neg):\n        result = AddGuidanceFromPointsd(**arguments)(input_data)\n        self.assertEqual(result[arguments[\"guidance\"]][0], expected_pos)\n        self.assertEqual(result[arguments[\"guidance\"]][1], expected_neg)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deepgrow_transforms.py_TestSpatialCropGuidanced_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deepgrow_transforms.py_TestSpatialCropGuidanced_", "embedding": null, "metadata": {"file_path": "tests/test_deepgrow_transforms.py", "file_name": "test_deepgrow_transforms.py", "file_type": "text/x-python", "category": "test", "start_line": 418, "end_line": 450, "span_ids": ["impl:74", "TestFetch2DSliced", "TestRestoreLabeld.test_correct_results", "TestSpatialCropGuidanced.test_correct_results", "TestResizeGuidanced", "TestRestoreLabeld", "TestFetch2DSliced.test_correct_results", "TestSpatialCropGuidanced", "TestResizeGuidanced.test_correct_results"], "tokens": 309}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestSpatialCropGuidanced(unittest.TestCase):\n    @parameterized.expand(\n        [SPATIAL_CROP_GUIDANCE_TEST_CASE_1, SPATIAL_CROP_GUIDANCE_TEST_CASE_2, SPATIAL_CROP_GUIDANCE_TEST_CASE_3]\n    )\n    def test_correct_results(self, arguments, input_data, expected_result):\n        result = SpatialCropGuidanced(**arguments)(input_data)\n        np.testing.assert_allclose(result[\"image\"], expected_result)\n\n\nclass TestResizeGuidanced(unittest.TestCase):\n    @parameterized.expand([RESIZE_GUIDANCE_TEST_CASE_1])\n    def test_correct_results(self, arguments, input_data, expected_result):\n        result = ResizeGuidanced(**arguments)(input_data)\n        self.assertEqual(result[arguments[\"guidance\"]], expected_result)\n\n\nclass TestRestoreLabeld(unittest.TestCase):\n    @parameterized.expand([RESTORE_LABEL_TEST_CASE_1, RESTORE_LABEL_TEST_CASE_2])\n    def test_correct_results(self, arguments, input_data, expected_result):\n        result = RestoreLabeld(**arguments)(input_data)\n        np.testing.assert_allclose(result[\"pred\"], expected_result)\n\n\nclass TestFetch2DSliced(unittest.TestCase):\n    @parameterized.expand([FETCH_2D_SLICE_TEST_CASE_1])\n    def test_correct_results(self, arguments, input_data, expected_result):\n        result = Fetch2DSliced(**arguments)(input_data)\n        np.testing.assert_allclose(result[\"image\"], expected_result)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_densenet.py_unittest_TEST_PRETRAINED_2D_CASE_3._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_densenet.py_unittest_TEST_PRETRAINED_2D_CASE_3._", "embedding": null, "metadata": {"file_path": "tests/test_densenet.py", "file_name": "test_densenet.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 79, "span_ids": ["impl:19", "docstring"], "tokens": 691}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom typing import TYPE_CHECKING\nfrom unittest import skipUnless\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks import eval_mode\nfrom monai.networks.nets import DenseNet121, Densenet169, DenseNet264, densenet201\nfrom monai.utils import optional_import\nfrom tests.utils import skip_if_quick, test_script_save\n\nif TYPE_CHECKING:\n    import torchvision\n\n    has_torchvision = True\nelse:\n    torchvision, has_torchvision = optional_import(\"torchvision\")\n\n\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\nTEST_CASE_1 = [  # 4-channel 3D, batch 2\n    {\"pretrained\": False, \"spatial_dims\": 3, \"in_channels\": 2, \"out_channels\": 3, \"norm\": (\"instance\", {\"eps\": 1e-5})},\n    (2, 2, 32, 64, 48),\n    (2, 3),\n]\n\nTEST_CASE_2 = [  # 4-channel 2D, batch 2\n    {\"pretrained\": False, \"spatial_dims\": 2, \"in_channels\": 2, \"out_channels\": 3, \"act\": \"PRELU\"},\n    (2, 2, 32, 64),\n    (2, 3),\n]\n\nTEST_CASE_3 = [  # 4-channel 1D, batch 1\n    {\"pretrained\": False, \"spatial_dims\": 1, \"in_channels\": 2, \"out_channels\": 3},\n    (1, 2, 32),\n    (1, 3),\n]\n\nTEST_CASES = []\nfor case in [TEST_CASE_1, TEST_CASE_2, TEST_CASE_3]:\n    for model in [DenseNet121, Densenet169, densenet201, DenseNet264]:\n        TEST_CASES.append([model, *case])\n\n\nTEST_SCRIPT_CASES = [[model, *TEST_CASE_1] for model in [DenseNet121, Densenet169, densenet201, DenseNet264]]\n\n\nTEST_PRETRAINED_2D_CASE_1 = [  # 4-channel 2D, batch 2\n    DenseNet121,\n    {\"pretrained\": True, \"progress\": True, \"spatial_dims\": 2, \"in_channels\": 2, \"out_channels\": 3},\n    (1, 2, 32, 64),\n    (1, 3),\n]\n\nTEST_PRETRAINED_2D_CASE_2 = [  # 4-channel 2D, batch 2\n    DenseNet121,\n    {\"pretrained\": True, \"progress\": False, \"spatial_dims\": 2, \"in_channels\": 2, \"out_channels\": 1},\n    (1, 2, 32, 64),\n    (1, 1),\n]\n\nTEST_PRETRAINED_2D_CASE_3 = [\n    DenseNet121,\n    {\"pretrained\": True, \"progress\": False, \"spatial_dims\": 2, \"in_channels\": 3, \"out_channels\": 1},\n    (1, 3, 32, 32),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_densenet.py_TestPretrainedDENSENET_TestPretrainedDENSENET.test_pretrain_consistency.self_assertTrue_torch_all": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_densenet.py_TestPretrainedDENSENET_TestPretrainedDENSENET.test_pretrain_consistency.self_assertTrue_torch_all", "embedding": null, "metadata": {"file_path": "tests/test_densenet.py", "file_name": "test_densenet.py", "file_type": "text/x-python", "category": "test", "start_line": 82, "end_line": 101, "span_ids": ["TestPretrainedDENSENET.test_pretrain_consistency", "TestPretrainedDENSENET", "TestPretrainedDENSENET.test_121_2d_shape_pretrain"], "tokens": 239}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestPretrainedDENSENET(unittest.TestCase):\n    @parameterized.expand([TEST_PRETRAINED_2D_CASE_1, TEST_PRETRAINED_2D_CASE_2])\n    @skip_if_quick\n    def test_121_2d_shape_pretrain(self, model, input_param, input_shape, expected_shape):\n        net = model(**input_param).to(device)\n        with eval_mode(net):\n            result = net.forward(torch.randn(input_shape).to(device))\n            self.assertEqual(result.shape, expected_shape)\n\n    @parameterized.expand([TEST_PRETRAINED_2D_CASE_3])\n    @skipUnless(has_torchvision, \"Requires `torchvision` package.\")\n    def test_pretrain_consistency(self, model, input_param, input_shape):\n        example = torch.randn(input_shape).to(device)\n        net = model(**input_param).to(device)\n        with eval_mode(net):\n            result = net.features.forward(example)\n        torchvision_net = torchvision.models.densenet121(pretrained=True).to(device)\n        with eval_mode(torchvision_net):\n            expected_result = torchvision_net.features.forward(example)\n        self.assertTrue(torch.all(result == expected_result))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_detect_envelope.py_unittest__MULTI_CHANNEL_VALUE_TES": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_detect_envelope.py_unittest__MULTI_CHANNEL_VALUE_TES", "embedding": null, "metadata": {"file_path": "tests/test_detect_envelope.py", "file_name": "test_detect_envelope.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 69, "span_ids": ["impl:11", "docstring"], "tokens": 761}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import DetectEnvelope\nfrom monai.utils import InvalidPyTorchVersionError, OptionalImportError\nfrom tests.utils import SkipIfAtLeastPyTorchVersion, SkipIfBeforePyTorchVersion, SkipIfModule, SkipIfNoModule\n\nn_samples = 500\nhann_windowed_sine = np.sin(2 * np.pi * 10 * np.linspace(0, 1, n_samples)) * np.hanning(n_samples)\n\n# SINGLE-CHANNEL VALUE TESTS\n# using np.expand_dims() to add length 1 channel dimension at dimension 0\n\nTEST_CASE_1D_SINE = [\n    {},  # args (empty, so use default)\n    np.expand_dims(hann_windowed_sine, 0),  # Input data: Hann windowed sine wave\n    np.expand_dims(np.hanning(n_samples), 0),  # Expected output: the Hann window\n    1e-4,  # absolute tolerance\n]\n\nTEST_CASE_2D_SINE = [\n    {},  # args (empty, so use default (i.e. process along first spatial dimension, axis=1)\n    # Create 10 identical windowed sine waves as a 2D numpy array\n    np.expand_dims(np.stack([hann_windowed_sine] * 10, axis=1), 0),\n    # Expected output: Set of 10 identical Hann windows\n    np.expand_dims(np.stack([np.hanning(n_samples)] * 10, axis=1), 0),\n    1e-4,  # absolute tolerance\n]\n\nTEST_CASE_3D_SINE = [\n    {},  # args (empty, so use default (i.e. process along first spatial dimension, axis=1)\n    # Create 100 identical windowed sine waves as a (n_samples x 10 x 10) 3D numpy array\n    np.expand_dims(np.stack([np.stack([hann_windowed_sine] * 10, axis=1)] * 10, axis=2), 0),\n    # Expected output: Set of 100 identical Hann windows in (n_samples x 10 x 10) 3D numpy array\n    np.expand_dims(np.stack([np.stack([np.hanning(n_samples)] * 10, axis=1)] * 10, axis=2), 0),\n    1e-4,  # absolute tolerance\n]\n\nTEST_CASE_2D_SINE_AXIS_1 = [\n    {\"axis\": 2},  # set axis argument to 1\n    # Create 10 identical windowed sine waves as a 2D numpy array\n    np.expand_dims(np.stack([hann_windowed_sine] * 10, axis=1), 0),\n    # Expected output: absolute value of each sample of the waveform, repeated (i.e. flat envelopes)\n    np.expand_dims(np.abs(np.repeat(hann_windowed_sine, 10).reshape((n_samples, 10))), 0),\n    1e-4,  # absolute tolerance\n]\n\nTEST_CASE_1D_SINE_PADDING_N = [\n    {\"n\": 512},  # args (empty, so use default)\n    np.expand_dims(hann_windowed_sine, 0),  # Input data: Hann windowed sine wave\n    np.expand_dims(np.concatenate([np.hanning(500), np.zeros(12)]), 0),  # Expected output: the Hann window\n    1e-3,  # absolute tolerance\n]\n\n# MULTI-CHANNEL VALUE TEST", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_detect_envelope.py_TEST_CASE_2_CHAN_3D_SINE_TEST_CASE_INVALID_OBJ._a_string___call_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_detect_envelope.py_TEST_CASE_2_CHAN_3D_SINE_TEST_CASE_INVALID_OBJ._a_string___call_", "embedding": null, "metadata": {"file_path": "tests/test_detect_envelope.py", "file_name": "test_detect_envelope.py", "file_type": "text/x-python", "category": "test", "start_line": 71, "end_line": 112, "span_ids": ["impl:11", "impl:19"], "tokens": 535}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "TEST_CASE_2_CHAN_3D_SINE = [\n    {},  # args (empty, so use default (i.e. process along first spatial dimension, axis=1)\n    # Create 100 identical windowed sine waves as a (n_samples x 10 x 10) 3D numpy array, twice (2 channels)\n    torch.as_tensor(np.stack([np.stack([np.stack([hann_windowed_sine] * 10, axis=1)] * 10, axis=2)] * 2, axis=0)),\n    # Expected output: Set of 100 identical Hann windows in (n_samples x 10 x 10) 3D numpy array, twice (2 channels)\n    torch.as_tensor(np.stack([np.stack([np.stack([np.hanning(n_samples)] * 10, axis=1)] * 10, axis=2)] * 2, axis=0)),\n    1e-4,  # absolute tolerance\n]\n\n# EXCEPTION TESTS\n\nTEST_CASE_INVALID_AXIS_1 = [\n    {\"axis\": 3},  # set axis argument to 3 when only 3 dimensions (1 channel + 2 spatial)\n    np.expand_dims(np.stack([hann_windowed_sine] * 10, axis=1), 0),  # Create 2D dataset\n    \"__call__\",  # method expected to raise exception\n]\n\nTEST_CASE_INVALID_AXIS_2 = [\n    {\"axis\": -1},  # set axis argument negative\n    np.expand_dims(np.stack([hann_windowed_sine] * 10, axis=1), 0),  # Create 2D dataset\n    \"__init__\",  # method expected to raise exception\n]\n\nTEST_CASE_INVALID_N = [\n    {\"n\": 0},  # set FFT length to zero\n    np.expand_dims(np.stack([hann_windowed_sine] * 10, axis=1), 0),  # Create 2D dataset\n    \"__call__\",  # method expected to raise exception\n]\n\nTEST_CASE_INVALID_DTYPE = [\n    {},\n    np.expand_dims(np.array(hann_windowed_sine, dtype=complex), 0),  # complex numbers are invalid\n    \"__call__\",  # method expected to raise exception\n]\n\nTEST_CASE_INVALID_IMG_LEN = [\n    {},\n    np.expand_dims(np.array([]), 0),  # empty array is invalid\n    \"__call__\",  # method expected to raise exception\n]\n\nTEST_CASE_INVALID_OBJ = [{}, \"a string\", \"__call__\"]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_detect_envelope.py__method_expected_to_rais_TestDetectEnvelope.test_value.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_detect_envelope.py__method_expected_to_rais_TestDetectEnvelope.test_value.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_detect_envelope.py", "file_name": "test_detect_envelope.py", "file_type": "text/x-python", "category": "test", "start_line": 111, "end_line": 129, "span_ids": ["TestDetectEnvelope", "impl:19", "TestDetectEnvelope.test_value"], "tokens": 153}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "  # method expected to raise exception\n\n\n@SkipIfBeforePyTorchVersion((1, 7))\n@SkipIfNoModule(\"torch.fft\")\nclass TestDetectEnvelope(unittest.TestCase):\n    @parameterized.expand(\n        [\n            TEST_CASE_1D_SINE,\n            TEST_CASE_2D_SINE,\n            TEST_CASE_3D_SINE,\n            TEST_CASE_2D_SINE_AXIS_1,\n            TEST_CASE_1D_SINE_PADDING_N,\n            TEST_CASE_2_CHAN_3D_SINE,\n        ]\n    )\n    def test_value(self, arguments, image, expected_data, atol):\n        result = DetectEnvelope(**arguments)(image)\n        np.testing.assert_allclose(result, expected_data, atol=atol)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_detect_envelope.py_TestDetectEnvelope.test_value_error_TestDetectEnvelope.test_value_error.if_method___init___.else_.raise_ValueError_Expecte": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_detect_envelope.py_TestDetectEnvelope.test_value_error_TestDetectEnvelope.test_value_error.if_method___init___.else_.raise_ValueError_Expecte", "embedding": null, "metadata": {"file_path": "tests/test_detect_envelope.py", "file_name": "test_detect_envelope.py", "file_type": "text/x-python", "category": "test", "start_line": 131, "end_line": 146, "span_ids": ["TestDetectEnvelope.test_value_error"], "tokens": 150}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@SkipIfBeforePyTorchVersion((1, 7))\n@SkipIfNoModule(\"torch.fft\")\nclass TestDetectEnvelope(unittest.TestCase):\n\n    @parameterized.expand(\n        [\n            TEST_CASE_INVALID_AXIS_1,\n            TEST_CASE_INVALID_AXIS_2,\n            TEST_CASE_INVALID_N,\n            TEST_CASE_INVALID_DTYPE,\n            TEST_CASE_INVALID_IMG_LEN,\n        ]\n    )\n    def test_value_error(self, arguments, image, method):\n        if method == \"__init__\":\n            self.assertRaises(ValueError, DetectEnvelope, **arguments)\n        elif method == \"__call__\":\n            self.assertRaises(ValueError, DetectEnvelope(**arguments), image)\n        else:\n            raise ValueError(\"Expected raising method invalid. Should be __init__ or __call__.\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_detect_envelope.py_TestHilbertTransformNoFFTMod_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_detect_envelope.py_TestHilbertTransformNoFFTMod_", "embedding": null, "metadata": {"file_path": "tests/test_detect_envelope.py", "file_name": "test_detect_envelope.py", "file_type": "text/x-python", "category": "test", "start_line": 149, "end_line": 165, "span_ids": ["TestHilbertTransformNoFFTMod", "TestDetectEnvelopeInvalidPyTorch", "impl:29", "TestHilbertTransformNoFFTMod.test_no_fft_module_error", "TestDetectEnvelopeInvalidPyTorch.test_invalid_pytorch_error"], "tokens": 129}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@SkipIfBeforePyTorchVersion((1, 7))\n@SkipIfModule(\"torch.fft\")\nclass TestHilbertTransformNoFFTMod(unittest.TestCase):\n    def test_no_fft_module_error(self):\n        self.assertRaises(OptionalImportError, DetectEnvelope(), np.random.rand(1, 10))\n\n\n@SkipIfAtLeastPyTorchVersion((1, 7))\nclass TestDetectEnvelopeInvalidPyTorch(unittest.TestCase):\n    def test_invalid_pytorch_error(self):\n        with self.assertRaisesRegex(InvalidPyTorchVersionError, \"version\"):\n            DetectEnvelope()\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dice_ce_loss.py_unittest_TEST_CASES._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dice_ce_loss.py_unittest_TEST_CASES._", "embedding": null, "metadata": {"file_path": "tests/test_dice_ce_loss.py", "file_name": "test_dice_ce_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 68, "span_ids": ["docstring"], "tokens": 980}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.losses import DiceCELoss\nfrom tests.utils import SkipIfBeforePyTorchVersion, test_script_save\n\nTEST_CASES = [\n    [  # shape: (2, 2, 3), (2, 1, 3)\n        {\"to_onehot_y\": True},\n        {\n            \"input\": torch.tensor([[[1.0, 1.0, 0.0], [0.0, 0.0, 1.0]], [[1.0, 0.0, 1.0], [0.0, 1.0, 0.0]]]),\n            \"target\": torch.tensor([[[0.0, 0.0, 1.0]], [[0.0, 1.0, 0.0]]]),\n        },\n        0.3133,  # the result equals to -1 + np.log(1 + np.exp(1))\n    ],\n    [  # shape: (2, 2, 3), (2, 2, 3), one-hot target\n        {\"to_onehot_y\": False},\n        {\n            \"input\": torch.tensor([[[1.0, 1.0, 0.0], [0.0, 0.0, 1.0]], [[1.0, 0.0, 1.0], [0.0, 1.0, 0.0]]]),\n            \"target\": torch.tensor([[[1.0, 1.0, 0.0], [0.0, 0.0, 1.0]], [[1.0, 0.0, 1.0], [0.0, 1.0, 0.0]]]),\n        },\n        0.3133,\n    ],\n    [  # shape: (2, 2, 3), (2, 1, 3)\n        {\"include_background\": False, \"to_onehot_y\": True, \"ce_weight\": torch.tensor([1.0, 1.0])},\n        {\n            \"input\": torch.tensor([[[100.0, 100.0, 0.0], [0.0, 0.0, 1.0]], [[1.0, 0.0, 1.0], [0.0, 1.0, 0.0]]]),\n            \"target\": torch.tensor([[[0.0, 0.0, 1.0]], [[0.0, 1.0, 0.0]]]),\n        },\n        0.2088,\n    ],\n    [  # shape: (2, 2, 3), (2, 1, 3) lambda_dice: 1.0, lambda_ce: 2.0\n        {\n            \"include_background\": False,\n            \"to_onehot_y\": True,\n            \"ce_weight\": torch.tensor([1.0, 1.0]),\n            \"lambda_dice\": 1.0,\n            \"lambda_ce\": 2.0,\n        },\n        {\n            \"input\": torch.tensor([[[100.0, 100.0, 0.0], [0.0, 0.0, 1.0]], [[1.0, 0.0, 1.0], [0.0, 1.0, 0.0]]]),\n            \"target\": torch.tensor([[[0.0, 0.0, 1.0]], [[0.0, 1.0, 0.0]]]),\n        },\n        0.4176,\n    ],\n    [  # shape: (2, 2, 3), (2, 1, 3), do not include class 0\n        {\"include_background\": False, \"to_onehot_y\": True, \"ce_weight\": torch.tensor([0.0, 1.0])},\n        {\n            \"input\": torch.tensor([[[100.0, 100.0, 0.0], [0.0, 0.0, 1.0]], [[1.0, 0.0, 1.0], [0.0, 1.0, 0.0]]]),\n            \"target\": torch.tensor([[[0.0, 0.0, 1.0]], [[0.0, 1.0, 0.0]]]),\n        },\n        0.3133,\n    ],\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dice_ce_loss.py_TestDiceCELoss_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dice_ce_loss.py_TestDiceCELoss_", "embedding": null, "metadata": {"file_path": "tests/test_dice_ce_loss.py", "file_name": "test_dice_ce_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 71, "end_line": 92, "span_ids": ["impl:3", "TestDiceCELoss.test_ill_shape", "TestDiceCELoss.test_script", "TestDiceCELoss", "TestDiceCELoss.test_result"], "tokens": 209}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestDiceCELoss(unittest.TestCase):\n    @parameterized.expand(TEST_CASES)\n    def test_result(self, input_param, input_data, expected_val):\n        diceceloss = DiceCELoss(**input_param)\n        result = diceceloss(**input_data)\n        np.testing.assert_allclose(result.detach().cpu().numpy(), expected_val, atol=1e-4, rtol=1e-4)\n\n    def test_ill_shape(self):\n        loss = DiceCELoss()\n        with self.assertRaisesRegex(ValueError, \"\"):\n            loss(torch.ones((1, 2, 3)), torch.ones((1, 1, 2, 3)))\n\n    @SkipIfBeforePyTorchVersion((1, 7, 0))\n    def test_script(self):\n        loss = DiceCELoss()\n        test_input = torch.ones(2, 1, 8, 8)\n        test_script_save(loss, test_input, test_input)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dice_focal_loss.py_unittest_TestDiceFocalLoss.test_result_onehot_target_include_bg.for_reduction_in_sum_.for_focal_weight_in_None.for_lambda_focal_in_0_5_.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dice_focal_loss.py_unittest_TestDiceFocalLoss.test_result_onehot_target_include_bg.for_reduction_in_sum_.for_focal_weight_in_None.for_lambda_focal_in_0_5_.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_dice_focal_loss.py", "file_name": "test_dice_focal_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 37, "span_ids": ["TestDiceFocalLoss", "TestDiceFocalLoss.test_result_onehot_target_include_bg", "docstring"], "tokens": 304}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\n\nfrom monai.losses import DiceFocalLoss, DiceLoss, FocalLoss\nfrom tests.utils import SkipIfBeforePyTorchVersion, test_script_save\n\n\nclass TestDiceFocalLoss(unittest.TestCase):\n    def test_result_onehot_target_include_bg(self):\n        size = [3, 3, 5, 5]\n        label = torch.randint(low=0, high=2, size=size)\n        pred = torch.randn(size)\n        for reduction in [\"sum\", \"mean\", \"none\"]:\n            common_params = {\"include_background\": True, \"to_onehot_y\": False, \"reduction\": reduction}\n            for focal_weight in [None, torch.tensor([1.0, 1.0, 2.0]), (3, 2.0, 1)]:\n                for lambda_focal in [0.5, 1.0, 1.5]:\n                    dice_focal = DiceFocalLoss(\n                        focal_weight=focal_weight, gamma=1.0, lambda_focal=lambda_focal, **common_params\n                    )\n                    dice = DiceLoss(**common_params)\n                    focal = FocalLoss(weight=focal_weight, gamma=1.0, **common_params)\n                    result = dice_focal(pred, label)\n                    expected_val = dice(pred, label) + lambda_focal * focal(pred, label)\n                    np.testing.assert_allclose(result, expected_val)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dice_focal_loss.py_TestDiceFocalLoss.test_result_no_onehot_no_bg_TestDiceFocalLoss.test_result_no_onehot_no_bg.for_reduction_in_sum_.for_focal_weight_in_2_0_.for_lambda_focal_in_0_5_.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dice_focal_loss.py_TestDiceFocalLoss.test_result_no_onehot_no_bg_TestDiceFocalLoss.test_result_no_onehot_no_bg.for_reduction_in_sum_.for_focal_weight_in_2_0_.for_lambda_focal_in_0_5_.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_dice_focal_loss.py", "file_name": "test_dice_focal_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 39, "end_line": 53, "span_ids": ["TestDiceFocalLoss.test_result_no_onehot_no_bg"], "tokens": 253}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestDiceFocalLoss(unittest.TestCase):\n\n    def test_result_no_onehot_no_bg(self):\n        size = [3, 3, 5, 5]\n        label = torch.randint(low=0, high=2, size=size)\n        label = torch.argmax(label, dim=1, keepdim=True)\n        pred = torch.randn(size)\n        for reduction in [\"sum\", \"mean\", \"none\"]:\n            common_params = {\"include_background\": False, \"to_onehot_y\": True, \"reduction\": reduction}\n            for focal_weight in [2.0, torch.tensor([1.0, 2.0]), (2.0, 1)]:\n                for lambda_focal in [0.5, 1.0, 1.5]:\n                    dice_focal = DiceFocalLoss(focal_weight=focal_weight, lambda_focal=lambda_focal, **common_params)\n                    dice = DiceLoss(**common_params)\n                    focal = FocalLoss(weight=focal_weight, **common_params)\n                    result = dice_focal(pred, label)\n                    expected_val = dice(pred, label) + lambda_focal * focal(pred, label)\n                    np.testing.assert_allclose(result, expected_val)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dice_focal_loss.py_TestDiceFocalLoss.test_ill_shape_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dice_focal_loss.py_TestDiceFocalLoss.test_ill_shape_", "embedding": null, "metadata": {"file_path": "tests/test_dice_focal_loss.py", "file_name": "test_dice_focal_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 63, "end_line": 81, "span_ids": ["TestDiceFocalLoss.test_script", "TestDiceFocalLoss.test_ill_shape", "impl", "TestDiceFocalLoss.test_ill_lambda"], "tokens": 164}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestDiceFocalLoss(unittest.TestCase):\n\n    def test_ill_shape(self):\n        loss = DiceFocalLoss()\n        with self.assertRaisesRegex(ValueError, \"\"):\n            loss(torch.ones((1, 2, 3)), torch.ones((1, 1, 2, 3)))\n\n    def test_ill_lambda(self):\n        with self.assertRaisesRegex(ValueError, \"\"):\n            DiceFocalLoss(lambda_dice=-1.0)\n\n    @SkipIfBeforePyTorchVersion((1, 7, 0))\n    def test_script(self):\n        loss = DiceFocalLoss()\n        test_input = torch.ones(2, 1, 8, 8)\n        test_script_save(loss, test_input, test_input)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_discriminator.py_unittest_CASES._TEST_CASE_0_TEST_CASE_1": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_discriminator.py_unittest_CASES._TEST_CASE_0_TEST_CASE_1", "embedding": null, "metadata": {"file_path": "tests/test_discriminator.py", "file_name": "test_discriminator.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 39, "span_ids": ["docstring"], "tokens": 282}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks import eval_mode\nfrom monai.networks.nets import Discriminator\nfrom tests.utils import test_script_save\n\nTEST_CASE_0 = [\n    {\"in_shape\": (1, 64, 64), \"channels\": (2, 4, 8), \"strides\": (2, 2, 2), \"num_res_units\": 0},\n    torch.rand(16, 1, 64, 64),\n    (16, 1),\n]\n\nTEST_CASE_1 = [\n    {\"in_shape\": (1, 64, 64), \"channels\": (2, 4, 8), \"strides\": (2, 2, 2), \"num_res_units\": 2},\n    torch.rand(16, 1, 64, 64),\n    (16, 1),\n]\n\nTEST_CASE_2 = [\n    {\"in_shape\": (1, 64, 64), \"channels\": (2, 4), \"strides\": (2, 2), \"num_res_units\": 0},\n    torch.rand(16, 1, 64, 64),\n    (16, 1),\n]\n\nCASES = [TEST_CASE_0, TEST_CASE_1, TEST_CASE_2]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_discriminator.py_TestDiscriminator_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_discriminator.py_TestDiscriminator_", "embedding": null, "metadata": {"file_path": "tests/test_discriminator.py", "file_name": "test_discriminator.py", "file_type": "text/x-python", "category": "test", "start_line": 42, "end_line": 58, "span_ids": ["impl:9", "TestDiscriminator.test_script", "TestDiscriminator.test_shape", "TestDiscriminator"], "tokens": 144}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestDiscriminator(unittest.TestCase):\n    @parameterized.expand(CASES)\n    def test_shape(self, input_param, input_data, expected_shape):\n        net = Discriminator(**input_param)\n        with eval_mode(net):\n            result = net.forward(input_data)\n            self.assertEqual(result.shape, expected_shape)\n\n    def test_script(self):\n        net = Discriminator(in_shape=(1, 64, 64), channels=(2, 4), strides=(2, 2), num_res_units=0)\n        test_data = torch.rand(16, 1, 64, 64)\n        test_script_save(net, test_data)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dvf2ddf.py_unittest_TEST_CASES._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dvf2ddf.py_unittest_TEST_CASES._", "embedding": null, "metadata": {"file_path": "tests/test_dvf2ddf.py", "file_name": "test_dvf2ddf.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 39, "span_ids": ["docstring"], "tokens": 346}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\nfrom torch import nn\nfrom torch.optim import SGD\n\nfrom monai.networks.blocks.warp import DVF2DDF\nfrom monai.utils import set_determinism\n\nTEST_CASES = [\n    [{\"num_steps\": 1}, {\"dvf\": torch.zeros(1, 2, 2, 2)}, torch.zeros(1, 2, 2, 2)],\n    [\n        {\"num_steps\": 1},\n        {\"dvf\": torch.ones(1, 3, 2, 2, 2)},\n        torch.tensor([[[1.0000, 0.7500], [0.7500, 0.6250]], [[0.7500, 0.6250], [0.6250, 0.5625]]])\n        .reshape(1, 1, 2, 2, 2)\n        .expand(-1, 3, -1, -1, -1),\n    ],\n    [\n        {\"num_steps\": 2},\n        {\"dvf\": torch.ones(1, 3, 2, 2, 2)},\n        torch.tensor([[[0.9175, 0.6618], [0.6618, 0.5306]], [[0.6618, 0.5306], [0.5306, 0.4506]]])\n        .reshape(1, 1, 2, 2, 2)\n        .expand(-1, 3, -1, -1, -1),\n    ],\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dvf2ddf.py_TestDVF2DDF_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dvf2ddf.py_TestDVF2DDF_", "embedding": null, "metadata": {"file_path": "tests/test_dvf2ddf.py", "file_name": "test_dvf2ddf.py", "file_type": "text/x-python", "category": "test", "start_line": 42, "end_line": 70, "span_ids": ["impl:3", "TestDVF2DDF.test_gradient", "TestDVF2DDF.tearDown", "TestDVF2DDF.setUp", "TestDVF2DDF", "TestDVF2DDF.test_value"], "tokens": 259}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestDVF2DDF(unittest.TestCase):\n    def setUp(self):\n        set_determinism(0)\n\n    def tearDown(self):\n        set_determinism(None)\n\n    @parameterized.expand(TEST_CASES)\n    def test_value(self, input_param, input_data, expected_val):\n        layer = DVF2DDF(**input_param)\n        result = layer(**input_data)\n        np.testing.assert_allclose(result.cpu().numpy(), expected_val.cpu().numpy(), rtol=1e-4, atol=1e-4)\n\n    def test_gradient(self):\n        network = nn.Conv2d(in_channels=1, out_channels=2, kernel_size=1)\n        dvf2ddf = DVF2DDF(num_steps=1)\n        optimizer = SGD(network.parameters(), lr=0.01)\n        x = torch.ones((1, 1, 5, 5))\n        x = network(x)\n        x = dvf2ddf(x)\n        loss = torch.sum(x)\n        loss.backward()\n        optimizer.step()\n        np.testing.assert_allclose(network.weight.grad.cpu().numpy(), np.array([[[[22.471329]]], [[[22.552576]]]]))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dynunet.py_for_spatial_dims_in_2_3_for_spatial_dims_in_2_3.for_res_block_in_True_F.for_deep_supr_num_in_1_.for_strides_in_1_2_1_.TEST_CASE_DEEP_SUPERVISIO": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dynunet.py_for_spatial_dims_in_2_3_for_spatial_dims_in_2_3.for_res_block_in_True_F.for_deep_supr_num_in_1_.for_strides_in_1_2_1_.TEST_CASE_DEEP_SUPERVISIO", "embedding": null, "metadata": {"file_path": "tests/test_dynunet.py", "file_name": "test_dynunet.py", "file_type": "text/x-python", "category": "test", "start_line": 82, "end_line": 103, "span_ids": ["docstring:11"], "tokens": 250}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "for spatial_dims in [2, 3]:\n    for res_block in [True, False]:\n        for deep_supr_num in [1, 2]:\n            for strides in [(1, 2, 1, 2, 1), (2, 2, 2, 1), (2, 1, 1, 2, 2)]:\n                scale = strides[0]\n                test_case = [\n                    {\n                        \"spatial_dims\": spatial_dims,\n                        \"in_channels\": 1,\n                        \"out_channels\": 2,\n                        \"kernel_size\": [3] * len(strides),\n                        \"strides\": strides,\n                        \"upsample_kernel_size\": strides[1:],\n                        \"norm_name\": (\"group\", {\"num_groups\": 16}),\n                        \"deep_supervision\": True,\n                        \"deep_supr_num\": deep_supr_num,\n                        \"res_block\": res_block,\n                    },\n                    (1, 1, *[in_size] * spatial_dims),\n                    (1, 1 + deep_supr_num, 2, *[in_size // scale] * spatial_dims),\n                ]\n                TEST_CASE_DEEP_SUPERVISION.append(test_case)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dynunet.py_TestDynUNet_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dynunet.py_TestDynUNet_", "embedding": null, "metadata": {"file_path": "tests/test_dynunet.py", "file_name": "test_dynunet.py", "file_type": "text/x-python", "category": "test", "start_line": 102, "end_line": 128, "span_ids": ["TestDynUNet", "TestDynUNetDeepSupervision", "TestDynUNet.test_script", "TestDynUNetDeepSupervision.test_shape", "impl:47", "TestDynUNet.test_shape"], "tokens": 231}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestDynUNet(unittest.TestCase):\n    @parameterized.expand(TEST_CASE_DYNUNET_2D + TEST_CASE_DYNUNET_3D)\n    def test_shape(self, input_param, input_shape, expected_shape):\n        net = DynUNet(**input_param).to(device)\n        with eval_mode(net):\n            result = net(torch.randn(input_shape).to(device))\n            self.assertEqual(result.shape, expected_shape)\n\n    def test_script(self):\n        input_param, input_shape, _ = TEST_CASE_DYNUNET_2D[0]\n        net = DynUNet(**input_param)\n        test_data = torch.randn(input_shape)\n        test_script_save(net, test_data)\n\n\nclass TestDynUNetDeepSupervision(unittest.TestCase):\n    @parameterized.expand(TEST_CASE_DEEP_SUPERVISION)\n    def test_shape(self, input_param, input_shape, expected_shape):\n        net = DynUNet(**input_param).to(device)\n        with torch.no_grad():\n            results = net(torch.randn(input_shape).to(device))\n            self.assertEqual(results.shape, expected_shape)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dynunet_block.py_TestResBasicBlock_TestResBasicBlock.test_script.for_net_type_in_UnetResB.test_script_save_net_tes": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dynunet_block.py_TestResBasicBlock_TestResBasicBlock.test_script.for_net_type_in_UnetResB.test_script_save_net_tes", "embedding": null, "metadata": {"file_path": "tests/test_dynunet_block.py", "file_name": "test_dynunet_block.py", "file_type": "text/x-python", "category": "test", "start_line": 70, "end_line": 90, "span_ids": ["TestResBasicBlock.test_ill_arg", "TestResBasicBlock", "TestResBasicBlock.test_shape", "TestResBasicBlock.test_script"], "tokens": 226}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestResBasicBlock(unittest.TestCase):\n    @parameterized.expand(TEST_CASE_RES_BASIC_BLOCK)\n    def test_shape(self, input_param, input_shape, expected_shape):\n        for net in [UnetResBlock(**input_param), UnetBasicBlock(**input_param)]:\n            with eval_mode(net):\n                result = net(torch.randn(input_shape))\n                self.assertEqual(result.shape, expected_shape)\n\n    def test_ill_arg(self):\n        with self.assertRaises(ValueError):\n            UnetBasicBlock(3, 4, 2, kernel_size=3, stride=1, norm_name=\"norm\")\n        with self.assertRaises(AssertionError):\n            UnetResBlock(3, 4, 2, kernel_size=1, stride=4, norm_name=\"batch\")\n\n    def test_script(self):\n        input_param, input_shape, _ = TEST_CASE_RES_BASIC_BLOCK[0]\n\n        for net_type in (UnetResBlock, UnetBasicBlock):\n            net = net_type(**input_param)\n            test_data = torch.randn(input_shape)\n            test_script_save(net, test_data)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dynunet_block.py_TestUpBlock_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dynunet_block.py_TestUpBlock_", "embedding": null, "metadata": {"file_path": "tests/test_dynunet_block.py", "file_name": "test_dynunet_block.py", "file_type": "text/x-python", "category": "test", "start_line": 96, "end_line": 115, "span_ids": ["TestUpBlock.test_shape", "impl:32", "TestUpBlock.test_script", "TestUpBlock"], "tokens": 150}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestUpBlock(unittest.TestCase):\n    @parameterized.expand(TEST_UP_BLOCK)\n    def test_shape(self, input_param, input_shape, expected_shape, skip_shape):\n        net = UnetUpBlock(**input_param)\n        with eval_mode(net):\n            result = net(torch.randn(input_shape), torch.randn(skip_shape))\n            self.assertEqual(result.shape, expected_shape)\n\n    def test_script(self):\n        input_param, input_shape, _, skip_shape = TEST_UP_BLOCK[0]\n\n        net = UnetUpBlock(**input_param)\n        test_data = torch.randn(input_shape)\n        skip_data = torch.randn(skip_shape)\n        test_script_save(net, test_data, skip_data)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_efficientnet.py_get_expected_model_shape_get_expected_model_shape.return.model_input_shapes_model_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_efficientnet.py_get_expected_model_shape_get_expected_model_shape.return.model_input_shapes_model_", "embedding": null, "metadata": {"file_path": "tests/test_efficientnet.py", "file_name": "test_efficientnet.py", "file_type": "text/x-python", "category": "test", "start_line": 44, "end_line": 55, "span_ids": ["get_expected_model_shape"], "tokens": 104}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_expected_model_shape(model_name):\n    model_input_shapes = {\n        \"efficientnet-b0\": 224,\n        \"efficientnet-b1\": 240,\n        \"efficientnet-b2\": 260,\n        \"efficientnet-b3\": 300,\n        \"efficientnet-b4\": 380,\n        \"efficientnet-b5\": 456,\n        \"efficientnet-b6\": 528,\n        \"efficientnet-b7\": 600,\n    }\n    return model_input_shapes[model_name]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_efficientnet.py_make_shape_cases_make_shape_cases.return.ret_tests": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_efficientnet.py_make_shape_cases_make_shape_cases.return.ret_tests", "embedding": null, "metadata": {"file_path": "tests/test_efficientnet.py", "file_name": "test_efficientnet.py", "file_type": "text/x-python", "category": "test", "start_line": 84, "end_line": 114, "span_ids": ["make_shape_cases"], "tokens": 222}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def make_shape_cases(\n    models,\n    spatial_dims,\n    batches,\n    pretrained,\n    in_channels=3,\n    num_classes=1000,\n    norm=(\"batch\", {\"eps\": 1e-3, \"momentum\": 0.01}),\n):\n    ret_tests = []\n    for spatial_dim in spatial_dims:  # selected spatial_dims\n        for batch in batches:  # check single batch as well as multiple batch input\n            for model in models:  # selected models\n                for is_pretrained in pretrained:  # pretrained or not pretrained\n                    kwargs = {\n                        \"model_name\": model,\n                        \"pretrained\": is_pretrained,\n                        \"progress\": False,\n                        \"spatial_dims\": spatial_dim,\n                        \"in_channels\": in_channels,\n                        \"num_classes\": num_classes,\n                        \"norm\": norm,\n                    }\n                    ret_tests.append(\n                        [\n                            kwargs,\n                            (batch, in_channels) + (get_expected_model_shape(model),) * spatial_dim,\n                            (batch, num_classes),\n                        ]\n                    )\n    return ret_tests", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_efficientnet.py_TestEFFICIENTNET_TestEFFICIENTNET.test_shape.self_assertEqual_result_s": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_efficientnet.py_TestEFFICIENTNET_TestEFFICIENTNET.test_shape.self_assertEqual_result_s", "embedding": null, "metadata": {"file_path": "tests/test_efficientnet.py", "file_name": "test_efficientnet.py", "file_type": "text/x-python", "category": "test", "start_line": 248, "end_line": 265, "span_ids": ["TestEFFICIENTNET.test_shape", "TestEFFICIENTNET"], "tokens": 164}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestEFFICIENTNET(unittest.TestCase):\n    @parameterized.expand(CASES_1D + CASES_2D + CASES_3D + CASES_VARIATIONS)\n    def test_shape(self, input_param, input_shape, expected_shape):\n        device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\n        try:\n            # initialize model\n            net = EfficientNetBN(**input_param).to(device)\n        except (ContentTooShortError, HTTPError, RuntimeError) as e:\n            print(str(e))\n            return  # skipping the tests because of http errors\n\n        # run inference with random tensor\n        with eval_mode(net):\n            result = net(torch.randn(input_shape).to(device))\n\n        # check output shape\n        self.assertEqual(result.shape, expected_shape)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_efficientnet.py_TestEFFICIENTNET.test_non_default_shapes_TestEFFICIENTNET.test_non_default_shapes.for_candidate_size_in_non.self_assertEqual_result_s": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_efficientnet.py_TestEFFICIENTNET.test_non_default_shapes_TestEFFICIENTNET.test_non_default_shapes.for_candidate_size_in_non.self_assertEqual_result_s", "embedding": null, "metadata": {"file_path": "tests/test_efficientnet.py", "file_name": "test_efficientnet.py", "file_type": "text/x-python", "category": "test", "start_line": 267, "end_line": 288, "span_ids": ["TestEFFICIENTNET.test_non_default_shapes"], "tokens": 218}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestEFFICIENTNET(unittest.TestCase):\n\n    @parameterized.expand(CASES_1D + CASES_2D)\n    def test_non_default_shapes(self, input_param, input_shape, expected_shape):\n        device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\n        try:\n            # initialize model\n            net = EfficientNetBN(**input_param).to(device)\n        except (ContentTooShortError, HTTPError, RuntimeError) as e:\n            print(str(e))\n            return  # skipping the tests because of http errors\n\n        # override input shape with different variations\n        num_dims = len(input_shape) - 2\n        non_default_sizes = [128, 256, 512]\n        for candidate_size in non_default_sizes:\n            input_shape = input_shape[0:2] + (candidate_size,) * num_dims\n            # run inference with random tensor\n            with eval_mode(net):\n                result = net(torch.randn(input_shape).to(device))\n\n            # check output shape\n            self.assertEqual(result.shape, expected_shape)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_efficientnet.py_TestEFFICIENTNET.test_kitty_pretrained_TestEFFICIENTNET.test_kitty_pretrained.self_assertEqual_pred_lab": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_efficientnet.py_TestEFFICIENTNET.test_kitty_pretrained_TestEFFICIENTNET.test_kitty_pretrained.self_assertEqual_pred_lab", "embedding": null, "metadata": {"file_path": "tests/test_efficientnet.py", "file_name": "test_efficientnet.py", "file_type": "text/x-python", "category": "test", "start_line": 245, "end_line": 278, "span_ids": ["TestEFFICIENTNET.test_kitty_pretrained"], "tokens": 293}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestEFFICIENTNET(unittest.TestCase):\n\n    @parameterized.expand(CASES_KITTY_TRAINED)\n    @skip_if_quick\n    @skipUnless(has_torchvision, \"Requires `torchvision` package.\")\n    @skipUnless(has_pil, \"Requires `pillow` package.\")\n    def test_kitty_pretrained(self, input_param, image_path, expected_label):\n        device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\n        # open image\n        image_size = get_efficientnet_image_size(input_param[\"model_name\"])\n        img = PIL.Image.open(image_path)\n\n        # define ImageNet transforms\n        tfms = torchvision.transforms.Compose(\n            [\n                torchvision.transforms.Resize(image_size),\n                torchvision.transforms.CenterCrop(image_size),\n                torchvision.transforms.ToTensor(),\n                torchvision.transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n            ]\n        )\n\n        # preprocess and prepare image tensor\n        img = tfms(img).unsqueeze(0).to(device)\n\n        # initialize a pretrained model\n        net = test_pretrained_networks(EfficientNetBN, input_param, device)\n\n        # run inference\n        with eval_mode(net):\n            result = net(img)\n        pred_label = torch.argmax(result, dim=-1)\n\n        # check output label\n        self.assertEqual(pred_label, expected_label)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_efficientnet.py_TestEFFICIENTNET.test_drop_connect_layer_TestEFFICIENTNET.test_drop_connect_layer.for_rand_tensor_shape_in_.None_1.self_assertTrue_abs_p_cal": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_efficientnet.py_TestEFFICIENTNET.test_drop_connect_layer_TestEFFICIENTNET.test_drop_connect_layer.for_rand_tensor_shape_in_.None_1.self_assertTrue_abs_p_cal", "embedding": null, "metadata": {"file_path": "tests/test_efficientnet.py", "file_name": "test_efficientnet.py", "file_type": "text/x-python", "category": "test", "start_line": 280, "end_line": 306, "span_ids": ["TestEFFICIENTNET.test_drop_connect_layer"], "tokens": 329}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestEFFICIENTNET(unittest.TestCase):\n\n    def test_drop_connect_layer(self):\n        p_list = [float(d + 1) / 10.0 for d in range(9)]\n\n        # testing 1D, 2D and 3D shape\n        for rand_tensor_shape in [(512, 16, 4), (384, 16, 4, 4), (256, 16, 4, 4, 4)]:\n\n            # test validation mode, out tensor == in tensor\n            training = False\n            for p in p_list:\n                in_tensor = torch.rand(rand_tensor_shape) + 0.1\n                out_tensor = drop_connect(in_tensor, p, training=training)\n                self.assertTrue(torch.equal(out_tensor, in_tensor))\n\n            # test training mode, sum((out tensor * (1.0 - p)) != in tensor)/out_tensor.size() == p\n            # use tolerance of 0.175 to account for rounding errors due to finite set in/out\n            tol = 0.175\n            training = True\n            for p in p_list:\n                in_tensor = torch.rand(rand_tensor_shape) + 0.1\n                out_tensor = drop_connect(in_tensor, p, training=training)\n\n                p_calculated = 1.0 - torch.sum(torch.isclose(in_tensor, out_tensor * (1.0 - p))) / float(\n                    in_tensor.numel()\n                )\n                p_calculated = p_calculated.cpu().numpy()\n\n                self.assertTrue(abs(p_calculated - p) < tol)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_enum_bound_interp.py_unittest_TestEnumBoundInterp.test_bound.self_assertEqual_int_b_ze": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_enum_bound_interp.py_unittest_TestEnumBoundInterp.test_bound.self_assertEqual_int_b_ze", "embedding": null, "metadata": {"file_path": "tests/test_enum_bound_interp.py", "file_name": "test_enum_bound_interp.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 50, "span_ids": ["TestEnumBoundInterp.test_bound", "TestEnumBoundInterp", "docstring"], "tokens": 410}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nfrom monai.utils import optional_import\nfrom tests.utils import skip_if_no_cpp_extension\n\nb, _ = optional_import(\"monai._C\", name=\"BoundType\")\np, _ = optional_import(\"monai._C\", name=\"InterpolationType\")\n\n\n@skip_if_no_cpp_extension\nclass TestEnumBoundInterp(unittest.TestCase):\n    def test_bound(self):\n        self.assertEqual(str(b.replicate), \"BoundType.replicate\")\n        self.assertEqual(str(b.nearest), \"BoundType.replicate\")\n        self.assertEqual(str(b.dct1), \"BoundType.dct1\")\n        self.assertEqual(str(b.mirror), \"BoundType.dct1\")\n        self.assertEqual(str(b.dct2), \"BoundType.dct2\")\n        self.assertEqual(str(b.reflect), \"BoundType.dct2\")\n        self.assertEqual(str(b.dst1), \"BoundType.dst1\")\n        self.assertEqual(str(b.antimirror), \"BoundType.dst1\")\n        self.assertEqual(str(b.dst2), \"BoundType.dst2\")\n        self.assertEqual(str(b.antireflect), \"BoundType.dst2\")\n        self.assertEqual(str(b.dft), \"BoundType.dft\")\n        self.assertEqual(str(b.wrap), \"BoundType.dft\")\n        self.assertEqual(str(b.zero), \"BoundType.zero\")\n\n        self.assertEqual(int(b.replicate), 0)\n        self.assertEqual(int(b.nearest), 0)\n        self.assertEqual(int(b.dct1), 1)\n        self.assertEqual(int(b.mirror), 1)\n        self.assertEqual(int(b.dct2), 2)\n        self.assertEqual(int(b.reflect), 2)\n        self.assertEqual(int(b.dst1), 3)\n        self.assertEqual(int(b.antimirror), 3)\n        self.assertEqual(int(b.dst2), 4)\n        self.assertEqual(int(b.antireflect), 4)\n        self.assertEqual(int(b.dft), 5)\n        self.assertEqual(int(b.wrap), 5)\n        self.assertEqual(int(b.zero), 7)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_enum_bound_interp.py_TestEnumBoundInterp.test_interp_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_enum_bound_interp.py_TestEnumBoundInterp.test_interp_", "embedding": null, "metadata": {"file_path": "tests/test_enum_bound_interp.py", "file_name": "test_enum_bound_interp.py", "file_type": "text/x-python", "category": "test", "start_line": 52, "end_line": 74, "span_ids": ["impl:3", "TestEnumBoundInterp.test_interp"], "tokens": 245}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_no_cpp_extension\nclass TestEnumBoundInterp(unittest.TestCase):\n\n    def test_interp(self):\n        self.assertEqual(str(p.nearest), \"InterpolationType.nearest\")\n        self.assertEqual(str(p.linear), \"InterpolationType.linear\")\n        self.assertEqual(str(p.quadratic), \"InterpolationType.quadratic\")\n        self.assertEqual(str(p.cubic), \"InterpolationType.cubic\")\n        self.assertEqual(str(p.fourth), \"InterpolationType.fourth\")\n        self.assertEqual(str(p.fifth), \"InterpolationType.fifth\")\n        self.assertEqual(str(p.sixth), \"InterpolationType.sixth\")\n        self.assertEqual(str(p.seventh), \"InterpolationType.seventh\")\n\n        self.assertEqual(int(p.nearest), 0)\n        self.assertEqual(int(p.linear), 1)\n        self.assertEqual(int(p.quadratic), 2)\n        self.assertEqual(int(p.cubic), 3)\n        self.assertEqual(int(p.fourth), 4)\n        self.assertEqual(int(p.fifth), 5)\n        self.assertEqual(int(p.sixth), 6)\n        self.assertEqual(int(p.seventh), 7)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_eval_mode.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_eval_mode.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_eval_mode.py", "file_name": "test_eval_mode.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 32, "span_ids": ["TestEvalMode", "TestEvalMode.test_eval_mode", "impl", "docstring"], "tokens": 117}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\n\nfrom monai.networks.utils import eval_mode\n\n\nclass TestEvalMode(unittest.TestCase):\n    def test_eval_mode(self):\n        t = torch.rand(1, 1, 4, 4)\n        p = torch.nn.Conv2d(1, 1, 3)\n        self.assertTrue(p.training)  # True\n        with eval_mode(p):\n            self.assertFalse(p.training)  # False\n            with self.assertRaises(RuntimeError):\n                p(t).sum().backward()\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_evenly_divisible_all_gather_dist.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_evenly_divisible_all_gather_dist.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_evenly_divisible_all_gather_dist.py", "file_name": "test_evenly_divisible_all_gather_dist.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 49, "span_ids": ["DistributedEvenlyDivisibleAllGather", "impl", "DistributedEvenlyDivisibleAllGather.test_data", "DistributedEvenlyDivisibleAllGather._run", "docstring"], "tokens": 358}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nimport torch.distributed as dist\n\nfrom monai.utils import evenly_divisible_all_gather\nfrom tests.utils import DistCall, DistTestCase\n\n\nclass DistributedEvenlyDivisibleAllGather(DistTestCase):\n    @DistCall(nnodes=1, nproc_per_node=2)\n    def test_data(self):\n        self._run()\n\n    def _run(self):\n        if dist.get_rank() == 0:\n            data1 = torch.tensor([[1, 2], [3, 4]])\n            data2 = torch.tensor([[1.0, 2.0]])\n            data3 = torch.tensor(7)\n\n        if dist.get_rank() == 1:\n            data1 = torch.tensor([[5, 6]])\n            data2 = torch.tensor([[3.0, 4.0], [5.0, 6.0]])\n            data3 = torch.tensor(8)\n\n        result1 = evenly_divisible_all_gather(data=data1, concat=True)\n        torch.testing.assert_allclose(result1, torch.tensor([[1, 2], [3, 4], [5, 6]]))\n        result2 = evenly_divisible_all_gather(data=data2, concat=False)\n        for r, e in zip(result2, [torch.tensor([[1.0, 2.0]]), torch.tensor([[3.0, 4.0], [5.0, 6.0]])]):\n            torch.testing.assert_allclose(r, e)\n        result3 = evenly_divisible_all_gather(data=data3, concat=False)\n        for r in result3:\n            self.assertEqual(r.ndimension(), 0)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_file_basename.py_os_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_file_basename.py_os_", "embedding": null, "metadata": {"file_path": "tests/test_file_basename.py", "file_name": "test_file_basename.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 85, "span_ids": ["impl", "TestFilename.test_relative_path", "TestFilename.test_value", "TestFilename", "docstring"], "tokens": 703}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport tempfile\nimport unittest\nfrom pathlib import Path\n\nfrom monai.data.utils import create_file_basename\n\n\nclass TestFilename(unittest.TestCase):\n    def test_value(self):\n        with tempfile.TemporaryDirectory() as tempdir:\n            output_tmp = os.path.join(tempdir, \"output\")\n            result = create_file_basename(\"\", \"test.txt\", output_tmp, \"\")\n            expected = os.path.join(output_tmp, \"test\", \"test\")\n            self.assertEqual(result, expected)\n\n            result = create_file_basename(\"\", os.path.join(\"foo\", \"test.txt\"), output_tmp, \"\")\n            expected = os.path.join(output_tmp, \"test\", \"test\")\n            self.assertEqual(result, expected)\n\n            result = create_file_basename(\"\", os.path.join(\"foo\", \"test.txt\"), output_tmp, \"foo\")\n            expected = os.path.join(output_tmp, \"test\", \"test\")\n            self.assertEqual(result, expected)\n\n            result = create_file_basename(\"\", os.path.join(\"foo\", \"bar\", \"test.txt\"), output_tmp, \"foo\")\n            expected = os.path.join(output_tmp, \"bar\", \"test\", \"test\")\n            self.assertEqual(result, expected)\n\n            result = create_file_basename(\n                postfix=\"\",\n                input_file_name=os.path.join(\"foo\", \"bar\", \"data\", \"test.txt\"),\n                folder_path=output_tmp,\n                data_root_dir=os.path.join(\"foo\", \"bar\"),\n            )\n            expected = os.path.join(output_tmp, \"data\", \"test\", \"test\")\n            self.assertEqual(result, expected)\n\n            result = create_file_basename(\"\", os.path.join(\"foo\", \"bar\", \"test.txt\"), output_tmp, \"bar\")\n            expected = os.path.join(tempdir, \"foo\", \"bar\", \"test\", \"test\")\n            self.assertEqual(result, expected)\n\n            result = create_file_basename(\"\", os.path.join(\"rest\", \"test.txt\"), output_tmp, \"rest\")\n            expected = os.path.join(tempdir, \"output\", \"test\", \"test\")\n            self.assertEqual(result, expected)\n\n            result = create_file_basename(\"\", \"test.txt\", output_tmp, \"foo\")\n            expected = os.path.join(output_tmp, \"test\", \"test\")\n            self.assertEqual(result, expected)\n\n            result = create_file_basename(\"\", \"test.txt\", output_tmp, \"foo\", False, 5)\n            expected = os.path.join(output_tmp, \"test_5\")\n            self.assertEqual(result, expected)\n\n            result = create_file_basename(\"post\", \"test.tar.gz\", output_tmp, \"foo\")\n            expected = os.path.join(output_tmp, \"test\", \"test_post\")\n            self.assertEqual(result, expected)\n\n            result = create_file_basename(\"post\", \"test.tar.gz\", output_tmp, \"foo\", True, 8)\n            expected = os.path.join(output_tmp, \"test\", \"test_post_8\")\n            self.assertEqual(result, expected)\n\n            result = create_file_basename(\"post\", Path(\"test.tar.gz\"), Path(output_tmp), Path(\"foo\"), True, 8)\n            expected = os.path.join(output_tmp, \"test\", \"test_post_8\")\n            self.assertEqual(result, expected)\n\n    def test_relative_path(self):\n        output = create_file_basename(\"\", \"test.txt\", \"output\", \"\", makedirs=False)\n        expected = os.path.join(\"output\", \"test\", \"test\")\n        self.assertEqual(output, expected)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_focal_loss.py_TestFocalLoss.test_consistency_with_cross_entropy_2d_onehot_label_TestFocalLoss.test_consistency_with_cross_entropy_2d_onehot_label.self_assertAlmostEqual_ma": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_focal_loss.py_TestFocalLoss.test_consistency_with_cross_entropy_2d_onehot_label_TestFocalLoss.test_consistency_with_cross_entropy_2d_onehot_label.self_assertAlmostEqual_ma", "embedding": null, "metadata": {"file_path": "tests/test_focal_loss.py", "file_name": "test_focal_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 47, "end_line": 68, "span_ids": ["TestFocalLoss.test_consistency_with_cross_entropy_2d_onehot_label"], "tokens": 287}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestFocalLoss(unittest.TestCase):\n\n    def test_consistency_with_cross_entropy_2d_onehot_label(self):\n        \"\"\"For gamma=0 the focal loss reduces to the cross entropy loss\"\"\"\n        focal_loss = FocalLoss(to_onehot_y=True, gamma=0.0, reduction=\"mean\")\n        ce = nn.BCEWithLogitsLoss(reduction=\"mean\")\n        max_error = 0\n        class_num = 10\n        batch_size = 128\n        for _ in range(100):\n            # Create a random tensor of shape (batch_size, class_num, 8, 4)\n            x = torch.rand(batch_size, class_num, 8, 4, requires_grad=True)\n            # Create a random batch of classes\n            l = torch.randint(low=0, high=class_num, size=(batch_size, 1, 8, 4))\n            if torch.cuda.is_available():\n                x = x.cuda()\n                l = l.cuda()\n            output0 = focal_loss(x, l)\n            output1 = ce(x, one_hot(l, num_classes=class_num))\n            a = float(output0.cpu().detach())\n            b = float(output1.cpu().detach())\n            if abs(a - b) > max_error:\n                max_error = abs(a - b)\n        self.assertAlmostEqual(max_error, 0.0, places=3)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_focal_loss.py_TestFocalLoss.test_ill_opts_TestFocalLoss.test_ill_shape.with_self_assertRaisesReg.FocalLoss_reduction_mean": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_focal_loss.py_TestFocalLoss.test_ill_opts_TestFocalLoss.test_ill_shape.with_self_assertRaisesReg.FocalLoss_reduction_mean", "embedding": null, "metadata": {"file_path": "tests/test_focal_loss.py", "file_name": "test_focal_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 213, "end_line": 223, "span_ids": ["TestFocalLoss.test_ill_shape", "TestFocalLoss.test_ill_opts"], "tokens": 135}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestFocalLoss(unittest.TestCase):\n\n    def test_ill_opts(self):\n        chn_input = torch.ones((1, 2, 3))\n        chn_target = torch.ones((1, 2, 3))\n        with self.assertRaisesRegex(ValueError, \"\"):\n            FocalLoss(reduction=\"unknown\")(chn_input, chn_target)\n\n    def test_ill_shape(self):\n        chn_input = torch.ones((1, 2, 3))\n        chn_target = torch.ones((1, 3))\n        with self.assertRaisesRegex(ValueError, \"\"):\n            FocalLoss(reduction=\"mean\")(chn_input, chn_target)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_focal_loss.py_TestFocalLoss.test_ill_class_weight_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_focal_loss.py_TestFocalLoss.test_ill_class_weight_", "embedding": null, "metadata": {"file_path": "tests/test_focal_loss.py", "file_name": "test_focal_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 190, "end_line": 209, "span_ids": ["TestFocalLoss.test_script", "TestFocalLoss.test_ill_class_weight", "impl"], "tokens": 253}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestFocalLoss(unittest.TestCase):\n\n    def test_ill_class_weight(self):\n        chn_input = torch.ones((1, 4, 3, 3))\n        chn_target = torch.ones((1, 4, 3, 3))\n        with self.assertRaisesRegex(ValueError, \"\"):\n            FocalLoss(include_background=True, weight=(1.0, 1.0, 2.0))(chn_input, chn_target)\n        with self.assertRaisesRegex(ValueError, \"\"):\n            FocalLoss(include_background=False, weight=(1.0, 1.0, 1.0, 1.0))(chn_input, chn_target)\n        with self.assertRaisesRegex(ValueError, \"\"):\n            FocalLoss(include_background=False, weight=(1.0, 1.0, -1.0))(chn_input, chn_target)\n\n    @SkipIfBeforePyTorchVersion((1, 7, 0))\n    def test_script(self):\n        loss = FocalLoss()\n        test_input = torch.ones(2, 2, 8, 8)\n        test_script_save(loss, test_input, test_input)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_fullyconnectednet.py_unittest_VFC_CASES._VFC_TEST_CASE_0_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_fullyconnectednet.py_unittest_VFC_CASES._VFC_TEST_CASE_0_", "embedding": null, "metadata": {"file_path": "tests/test_fullyconnectednet.py", "file_name": "test_fullyconnectednet.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 39, "span_ids": ["docstring"], "tokens": 187}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks import eval_mode\nfrom monai.networks.nets import FullyConnectedNet, VarFullyConnectedNet\n\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n\nFC_TEST_CASE_0 = [0]\nFC_TEST_CASE_1 = [0.15]\n\nFC_CASES = [FC_TEST_CASE_0, FC_TEST_CASE_1]\n\nVFC_TEST_CASE_0 = [\n    {\n        \"in_channels\": 10,\n        \"out_channels\": 10,\n        \"latent_size\": 30,\n        \"encode_channels\": (15, 20, 25),\n        \"decode_channels\": (15, 20, 25),\n    },\n    (3, 10),\n    (3, 10),\n]\n\nVFC_CASES = [VFC_TEST_CASE_0]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_fullyconnectednet.py_TestFullyConnectedNet_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_fullyconnectednet.py_TestFullyConnectedNet_", "embedding": null, "metadata": {"file_path": "tests/test_fullyconnectednet.py", "file_name": "test_fullyconnectednet.py", "file_type": "text/x-python", "category": "test", "start_line": 42, "end_line": 67, "span_ids": ["TestFullyConnectedNet.test_vfc_shape", "TestFullyConnectedNet.test_fc_shape", "TestFullyConnectedNet", "impl:13", "TestFullyConnectedNet.setUp"], "tokens": 225}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestFullyConnectedNet(unittest.TestCase):\n    def setUp(self):\n        self.batch_size = 10\n        self.inSize = 10\n        self.arrShape = (self.batch_size, self.inSize)\n        self.outSize = 3\n        self.channels = [8, 16]\n        self.arr = torch.randn(self.arrShape, dtype=torch.float32).to(device)\n\n    @parameterized.expand(FC_CASES)\n    def test_fc_shape(self, dropout):\n        net = FullyConnectedNet(self.inSize, self.outSize, self.channels, dropout).to(device)\n        out = net(self.arr)\n        self.assertEqual(out.shape, (self.batch_size, self.outSize))\n\n    @parameterized.expand(VFC_CASES)\n    def test_vfc_shape(self, input_param, input_shape, expected_shape):\n        net = VarFullyConnectedNet(**input_param).to(device)\n        with eval_mode(net):\n            result = net.forward(torch.randn(input_shape).to(device))[0]\n            self.assertEqual(result.shape, expected_shape)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gaussian_filter.py_unittest_TEST_CASES_SLOW._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gaussian_filter.py_unittest_TEST_CASES_SLOW._", "embedding": null, "metadata": {"file_path": "tests/test_gaussian_filter.py", "file_name": "test_gaussian_filter.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 32, "span_ids": ["docstring"], "tokens": 415}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks.layers import GaussianFilter\nfrom tests.utils import SkipIfBeforePyTorchVersion, skip_if_quick\n\nTEST_CASES = [[{\"type\": \"erf\", \"gt\": 2.0}], [{\"type\": \"scalespace\", \"gt\": 3.0}], [{\"type\": \"sampled\", \"gt\": 5.0}]]\nTEST_CASES_GPU = [[{\"type\": \"erf\", \"gt\": 0.8, \"device\": \"cuda\"}], [{\"type\": \"sampled\", \"gt\": 5.0, \"device\": \"cuda\"}]]\nTEST_CASES_3d = [\n    [{\"type\": \"scalespace\", \"gt\": 0.5, \"dims\": (2, 3, 8, 9, 10), \"lr\": 0.01, \"device\": \"cuda\"}],\n    [{\"type\": \"erf\", \"gt\": 3.8, \"dims\": (2, 3, 8, 9, 10), \"lr\": 0.1, \"device\": \"cuda\"}],\n]\nTEST_CASES_SLOW = [\n    [{\"type\": \"erf\", \"gt\": 2.0, \"dims\": (2, 3, 8, 9, 10)}],\n    [{\"type\": \"scalespace\", \"gt\": 3.0, \"dims\": (2, 3, 8, 9, 10), \"device\": \"cuda\"}],\n    [{\"type\": \"sampled\", \"gt\": (0.5, 0.8, 3.0), \"dims\": (2, 3, 8, 9, 10), \"lr\": 0.1}],\n    [{\"type\": \"scalespace\", \"gt\": 3.0, \"device\": \"cuda\"}],\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gaussian_filter.py_TestGaussianFilterBackprop_TestGaussianFilterBackprop.test_train_slow.self_code_to_run_input_ar": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gaussian_filter.py_TestGaussianFilterBackprop_TestGaussianFilterBackprop.test_train_slow.self_code_to_run_input_ar", "embedding": null, "metadata": {"file_path": "tests/test_gaussian_filter.py", "file_name": "test_gaussian_filter.py", "file_type": "text/x-python", "category": "test", "start_line": 35, "end_line": 92, "span_ids": ["TestGaussianFilterBackprop.test_train_slow", "TestGaussianFilterBackprop", "TestGaussianFilterBackprop.code_to_run", "TestGaussianFilterBackprop.test_train_quick"], "tokens": 548}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestGaussianFilterBackprop(unittest.TestCase):\n    def code_to_run(self, input_args):\n        input_dims = input_args.get(\"dims\", (2, 3, 8))\n        device = (\n            torch.device(\"cuda\")\n            if (input_args.get(\"device\") == \"cuda\" and torch.cuda.is_available())\n            else torch.device(\"cpu:0\")\n        )\n\n        base = torch.ones(*input_dims).to(device)\n        gt = torch.tensor(input_args[\"gt\"], requires_grad=False).to(device)\n        g_type = input_args[\"type\"]\n        lr = input_args.get(\"lr\", 0.1)\n        init_sigma = input_args.get(\"init\", 1.0)\n\n        # static filter to generate a target\n        spatial_dims = len(base.shape) - 2\n        filtering = GaussianFilter(spatial_dims=spatial_dims, sigma=gt, approx=g_type, requires_grad=False)\n        filtering.to(device)\n        target = filtering(base)\n        self.assertFalse(filtering.sigma[0].requires_grad)\n\n        # build trainable\n        init_sigma = torch.tensor(init_sigma).to(device)\n        trainable = GaussianFilter(spatial_dims=spatial_dims, sigma=init_sigma, approx=g_type, requires_grad=True)\n        trainable.to(device)\n        self.assertTrue(trainable.sigma[0].requires_grad)\n\n        # train\n        optimizer = torch.optim.Adam(trainable.parameters(), lr=lr)\n        for s in range(1000):\n            optimizer.zero_grad()\n            pred = trainable(base)\n            loss = torch.pow(pred - target, 2).mean()\n            loss.backward()\n            if (s + 1) % 50 == 0:\n                var = list(trainable.parameters())[0]\n                print(f\"step {s} loss {loss}\")\n                print(var, var.grad)\n            if loss.item() < 1e-7:\n                break\n            optimizer.step()\n        # check the result\n        print(s, gt)\n        for idx, s in enumerate(trainable.sigma):\n            np.testing.assert_allclose(\n                s.cpu().item(), gt.cpu() if len(gt.shape) == 0 else gt[idx].cpu().item(), rtol=1e-2\n            )\n\n    @parameterized.expand(TEST_CASES + TEST_CASES_GPU + TEST_CASES_3d)\n    @SkipIfBeforePyTorchVersion((1, 7))\n    def test_train_quick(self, input_args):\n        self.code_to_run(input_args)\n\n    @parameterized.expand(TEST_CASES_SLOW)\n    @skip_if_quick\n    def test_train_slow(self, input_args):\n        self.code_to_run(input_args)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_generalized_wasserstein_dice_loss.py_unittest_TestGeneralizedWassersteinDiceLoss.test_bin_seg_2d.for_weight_mode_in_defa.None_2": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_generalized_wasserstein_dice_loss.py_unittest_TestGeneralizedWassersteinDiceLoss.test_bin_seg_2d.for_weight_mode_in_defa.None_2", "embedding": null, "metadata": {"file_path": "tests/test_generalized_wasserstein_dice_loss.py", "file_name": "test_generalized_wasserstein_dice_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 50, "span_ids": ["TestGeneralizedWassersteinDiceLoss", "TestGeneralizedWassersteinDiceLoss.test_bin_seg_2d", "docstring"], "tokens": 449}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\n\nfrom monai.losses import GeneralizedWassersteinDiceLoss\nfrom tests.utils import SkipIfBeforePyTorchVersion, test_script_save\n\n\nclass TestGeneralizedWassersteinDiceLoss(unittest.TestCase):\n    def test_bin_seg_2d(self):\n        target = torch.tensor([[0, 0, 0, 0], [0, 1, 1, 0], [0, 1, 1, 0], [0, 0, 0, 0]])\n\n        # add another dimension corresponding to the batch (batch size = 1 here)\n        target = target.unsqueeze(0)\n        pred_very_good = 1000 * F.one_hot(target, num_classes=2).permute(0, 3, 1, 2).float()\n        pred_very_poor = 1000 * F.one_hot(1 - target, num_classes=2).permute(0, 3, 1, 2).float()\n\n        for weight_mode in [\"default\", \"GDL\"]:\n            # initialize the loss\n            loss = GeneralizedWassersteinDiceLoss(\n                dist_matrix=np.array([[0.0, 1.0], [1.0, 0.0]]), weighting_mode=weight_mode\n            )\n\n            # the loss for pred_very_good should be close to 0\n            loss_good = float(loss.forward(pred_very_good, target))\n            self.assertAlmostEqual(loss_good, 0.0, places=3)\n\n            # same test, but with target with a class dimension\n            target_4dim = target.unsqueeze(1)\n            loss_good = float(loss.forward(pred_very_good, target_4dim))\n            self.assertAlmostEqual(loss_good, 0.0, places=3)\n\n            # the loss for pred_very_poor should be close to 1\n            loss_poor = float(loss.forward(pred_very_poor, target))\n            self.assertAlmostEqual(loss_poor, 1.0, places=3)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_generalized_wasserstein_dice_loss.py_TestGeneralizedWassersteinDiceLoss.test_different_target_data_type_TestGeneralizedWassersteinDiceLoss.test_different_target_data_type.for_w_mode_in_default_.for_t_in_target_list_.self_assertAlmostEqual_lo": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_generalized_wasserstein_dice_loss.py_TestGeneralizedWassersteinDiceLoss.test_different_target_data_type_TestGeneralizedWassersteinDiceLoss.test_different_target_data_type.for_w_mode_in_default_.for_t_in_target_list_.self_assertAlmostEqual_lo", "embedding": null, "metadata": {"file_path": "tests/test_generalized_wasserstein_dice_loss.py", "file_name": "test_generalized_wasserstein_dice_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 52, "end_line": 79, "span_ids": ["TestGeneralizedWassersteinDiceLoss.test_different_target_data_type"], "tokens": 366}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestGeneralizedWassersteinDiceLoss(unittest.TestCase):\n\n    def test_different_target_data_type(self):\n        \"\"\"\n        Test if the loss is compatible with all the integer types\n        for the target segmentation.\n        \"\"\"\n        # define 2d examples\n        target = torch.tensor([[0, 0, 0, 0], [0, 1, 1, 0], [0, 1, 1, 0], [0, 0, 0, 0]])\n\n        # add another dimension corresponding to the batch (batch size = 1 here)\n        target = target.unsqueeze(0)  # shape (1, H, W)\n        pred_very_good = 1000 * F.one_hot(target, num_classes=2).permute(0, 3, 1, 2).float()\n\n        target_uint8 = target.to(torch.uint8)\n        target_int8 = target.to(torch.int8)\n        target_short = target.short()\n        target_int = target.int()\n        target_long = target.long()\n        target_list = [target_uint8, target_int8, target_short, target_int, target_long]\n\n        for w_mode in [\"default\", \"GDL\"]:\n            # initialize the loss\n            loss = GeneralizedWassersteinDiceLoss(dist_matrix=np.array([[0.0, 1.0], [1.0, 0.0]]), weighting_mode=w_mode)\n\n            # The test should pass irrespectively of the integer type used\n            for t in target_list:\n                # the loss for pred_very_good should be close to 0\n                loss_good = float(loss.forward(pred_very_good, t))\n                self.assertAlmostEqual(loss_good, 0.0, places=3)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_generalized_wasserstein_dice_loss.py_TestGeneralizedWassersteinDiceLoss.test_empty_class_2d_TestGeneralizedWassersteinDiceLoss.test_empty_class_2d.for_w_mode_in_default_.None_1": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_generalized_wasserstein_dice_loss.py_TestGeneralizedWassersteinDiceLoss.test_empty_class_2d_TestGeneralizedWassersteinDiceLoss.test_empty_class_2d.for_w_mode_in_default_.None_1", "embedding": null, "metadata": {"file_path": "tests/test_generalized_wasserstein_dice_loss.py", "file_name": "test_generalized_wasserstein_dice_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 81, "end_line": 100, "span_ids": ["TestGeneralizedWassersteinDiceLoss.test_empty_class_2d"], "tokens": 333}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestGeneralizedWassersteinDiceLoss(unittest.TestCase):\n\n    def test_empty_class_2d(self):\n        num_classes = 2\n        target = torch.tensor([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]])\n\n        # add another dimension corresponding to the batch (batch size = 1 here)\n        target = target.unsqueeze(0)\n        pred_very_good = 1000 * F.one_hot(target, num_classes=num_classes).permute(0, 3, 1, 2).float()\n        pred_very_poor = 1000 * F.one_hot(1 - target, num_classes=num_classes).permute(0, 3, 1, 2).float()\n\n        for w_mode in [\"default\", \"GDL\"]:\n            # initialize the loss\n            loss = GeneralizedWassersteinDiceLoss(dist_matrix=np.array([[0.0, 1.0], [1.0, 0.0]]), weighting_mode=w_mode)\n\n            # loss for pred_very_good should be close to 0\n            loss_good = float(loss.forward(pred_very_good, target))\n            self.assertAlmostEqual(loss_good, 0.0, places=3)\n\n            # loss for pred_very_poor should be close to 1\n            loss_poor = float(loss.forward(pred_very_poor, target))\n            self.assertAlmostEqual(loss_poor, 1.0, places=3)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_generalized_wasserstein_dice_loss.py_TestGeneralizedWassersteinDiceLoss.test_bin_seg_3d_TestGeneralizedWassersteinDiceLoss.test_bin_seg_3d.for_w_mode_in_default_.None_1": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_generalized_wasserstein_dice_loss.py_TestGeneralizedWassersteinDiceLoss.test_bin_seg_3d_TestGeneralizedWassersteinDiceLoss.test_bin_seg_3d.for_w_mode_in_default_.None_1", "embedding": null, "metadata": {"file_path": "tests/test_generalized_wasserstein_dice_loss.py", "file_name": "test_generalized_wasserstein_dice_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 102, "end_line": 130, "span_ids": ["TestGeneralizedWassersteinDiceLoss.test_bin_seg_3d"], "tokens": 480}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestGeneralizedWassersteinDiceLoss(unittest.TestCase):\n\n    def test_bin_seg_3d(self):\n        # define 3d examples\n        target = torch.tensor(\n            [\n                # raw 0\n                [[0, 0, 0, 0], [0, 1, 1, 0], [0, 1, 1, 0], [0, 0, 0, 0]],\n                # raw 1\n                [[0, 0, 0, 0], [0, 1, 1, 0], [0, 1, 1, 0], [0, 0, 0, 0]],\n                # raw 2\n                [[0, 0, 0, 0], [0, 1, 1, 0], [0, 1, 1, 0], [0, 0, 0, 0]],\n            ]\n        )\n\n        # add another dimension corresponding to the batch (batch size = 1 here)\n        target = target.unsqueeze(0)  # shape (1, H, W, D)\n        pred_very_good = 1000 * F.one_hot(target, num_classes=2).permute(0, 4, 1, 2, 3).float()\n        pred_very_poor = 1000 * F.one_hot(1 - target, num_classes=2).permute(0, 4, 1, 2, 3).float()\n\n        for w_mode in [\"default\", \"GDL\"]:\n            # initialize the loss\n            loss = GeneralizedWassersteinDiceLoss(dist_matrix=np.array([[0.0, 1.0], [1.0, 0.0]]), weighting_mode=w_mode)\n\n            # mean dice loss for pred_very_good should be close to 0\n            loss_good = float(loss.forward(pred_very_good, target))\n            self.assertAlmostEqual(loss_good, 0.0, places=3)\n\n            # mean dice loss for pred_very_poor should be close to 1\n            loss_poor = float(loss.forward(pred_very_poor, target))\n            self.assertAlmostEqual(loss_poor, 1.0, places=3)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_generalized_wasserstein_dice_loss.py_TestGeneralizedWassersteinDiceLoss.test_convergence_TestGeneralizedWassersteinDiceLoss.test_convergence.for_w_mode_in_default_.self_assertGreater_diff_s": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_generalized_wasserstein_dice_loss.py_TestGeneralizedWassersteinDiceLoss.test_convergence_TestGeneralizedWassersteinDiceLoss.test_convergence.for_w_mode_in_default_.self_assertGreater_diff_s", "embedding": null, "metadata": {"file_path": "tests/test_generalized_wasserstein_dice_loss.py", "file_name": "test_generalized_wasserstein_dice_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 132, "end_line": 217, "span_ids": ["TestGeneralizedWassersteinDiceLoss.test_convergence"], "tokens": 810}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestGeneralizedWassersteinDiceLoss(unittest.TestCase):\n\n    def test_convergence(self):\n        \"\"\"\n        The goal of this test is to assess if the gradient of the loss function\n        is correct by testing if we can train a one layer neural network\n        to segment one image.\n        We verify that the loss is decreasing in almost all SGD steps.\n        \"\"\"\n        learning_rate = 0.001\n        max_iter = 50\n\n        # define a simple 3d example\n        target_seg = torch.tensor(\n            [\n                # raw 0\n                [[0, 0, 0, 0], [0, 1, 1, 0], [0, 1, 1, 0], [0, 0, 0, 0]],\n                # raw 1\n                [[0, 0, 0, 0], [0, 1, 1, 0], [0, 1, 1, 0], [0, 0, 0, 0]],\n                # raw 2\n                [[0, 0, 0, 0], [0, 1, 1, 0], [0, 1, 1, 0], [0, 0, 0, 0]],\n            ]\n        )\n        target_seg = torch.unsqueeze(target_seg, dim=0)\n        image = 12 * target_seg + 27  # dummy image to segment\n        image = image.float()\n        num_classes = 2\n        num_voxels = 3 * 4 * 4\n\n        # define a model with one layer\n        class OnelayerNet(nn.Module):\n            def __init__(self):\n                super().__init__()\n                self.layer = nn.Linear(num_voxels, num_voxels * num_classes)\n\n            def forward(self, x):\n                x = x.view(-1, num_voxels)\n                x = self.layer(x)\n                x = x.view(-1, num_classes, 3, 4, 4)\n                return x\n\n        for w_mode in [\"default\", \"GDL\"]:\n            # initialise the network\n            net = OnelayerNet()\n\n            # initialize the loss\n            loss = GeneralizedWassersteinDiceLoss(dist_matrix=np.array([[0.0, 1.0], [1.0, 0.0]]), weighting_mode=w_mode)\n\n            # initialize an optimizer\n            optimizer = optim.Adam(net.parameters(), lr=learning_rate)\n\n            # initial difference between pred and target\n            pred_start = torch.argmax(net(image), dim=1)\n            diff_start = torch.norm(pred_start.float() - target_seg.float())\n\n            loss_history = []\n            # train the network\n            for _ in range(max_iter):\n                # set the gradient to zero\n                optimizer.zero_grad()\n\n                # forward pass\n                output = net(image)\n                loss_val = loss(output, target_seg)\n\n                # backward pass\n                loss_val.backward()\n                optimizer.step()\n\n                # stats\n                loss_history.append(loss_val.item())\n\n            # difference between pred and target after training\n            pred_end = torch.argmax(net(image), dim=1)\n            diff_end = torch.norm(pred_end.float() - target_seg.float())\n\n            # count the number of SGD steps in which the loss decreases\n            num_decreasing_steps = 0\n            for i in range(len(loss_history) - 1):\n                if loss_history[i] > loss_history[i + 1]:\n                    num_decreasing_steps += 1\n            decreasing_steps_ratio = float(num_decreasing_steps) / (len(loss_history) - 1)\n\n            # verify that the loss is decreasing for sufficiently many SGD steps\n            self.assertTrue(decreasing_steps_ratio > 0.75)\n\n            # check that the predicted segmentation has improved\n            self.assertGreater(diff_start, diff_end)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_generalized_wasserstein_dice_loss.py_TestGeneralizedWassersteinDiceLoss.test_script_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_generalized_wasserstein_dice_loss.py_TestGeneralizedWassersteinDiceLoss.test_script_", "embedding": null, "metadata": {"file_path": "tests/test_generalized_wasserstein_dice_loss.py", "file_name": "test_generalized_wasserstein_dice_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 219, "end_line": 234, "span_ids": ["TestGeneralizedWassersteinDiceLoss.test_script", "impl"], "tokens": 215}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestGeneralizedWassersteinDiceLoss(unittest.TestCase):\n\n    @SkipIfBeforePyTorchVersion((1, 7, 0))\n    def test_script(self):\n        target = torch.tensor([[0, 0, 0, 0], [0, 1, 1, 0], [0, 1, 1, 0], [0, 0, 0, 0]])\n\n        # add another dimension corresponding to the batch (batch size = 1 here)\n        target = target.unsqueeze(0)\n        pred_very_good = 1000 * F.one_hot(target, num_classes=2).permute(0, 3, 1, 2).float()\n\n        loss = GeneralizedWassersteinDiceLoss(dist_matrix=np.array([[0.0, 1.0], [1.0, 0.0]]), weighting_mode=\"default\")\n\n        test_script_save(loss, pred_very_good, target)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_generate_param_groups.py_TestGenerateParamGroups.test_wrong_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_generate_param_groups.py_TestGenerateParamGroups.test_wrong_", "embedding": null, "metadata": {"file_path": "tests/test_generate_param_groups.py", "file_name": "test_generate_param_groups.py", "file_type": "text/x-python", "category": "test", "start_line": 84, "end_line": 103, "span_ids": ["impl:13", "TestGenerateParamGroups.test_wrong"], "tokens": 161}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestGenerateParamGroups(unittest.TestCase):\n\n    def test_wrong(self):\n        \"\"\"overlapped\"\"\"\n        device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n        net = Unet(\n            spatial_dims=3, in_channels=1, out_channels=3, channels=(16, 32, 64), strides=(2, 2), num_res_units=1\n        ).to(device)\n\n        params = generate_param_groups(\n            network=net,\n            layer_matches=[lambda x: x.model[-1], lambda x: x.model[-1]],\n            match_types=\"select\",\n            lr_values=0.1,\n        )\n        with self.assertRaises(ValueError):\n            torch.optim.Adam(params, 100)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_generator.py_unittest_CASES._TEST_CASE_0_TEST_CASE_1": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_generator.py_unittest_CASES._TEST_CASE_0_TEST_CASE_1", "embedding": null, "metadata": {"file_path": "tests/test_generator.py", "file_name": "test_generator.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 39, "span_ids": ["docstring"], "tokens": 302}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks import eval_mode\nfrom monai.networks.nets import Generator\nfrom tests.utils import test_script_save\n\nTEST_CASE_0 = [\n    {\"latent_shape\": (64,), \"start_shape\": (8, 8, 8), \"channels\": (8, 4, 1), \"strides\": (2, 2, 2), \"num_res_units\": 0},\n    torch.rand(16, 64),\n    (16, 1, 64, 64),\n]\n\nTEST_CASE_1 = [\n    {\"latent_shape\": (64,), \"start_shape\": (8, 8, 8), \"channels\": (8, 4, 1), \"strides\": (2, 2, 2), \"num_res_units\": 2},\n    torch.rand(16, 64),\n    (16, 1, 64, 64),\n]\n\nTEST_CASE_2 = [\n    {\"latent_shape\": (64,), \"start_shape\": (8, 8, 8), \"channels\": (8, 1), \"strides\": (2, 2), \"num_res_units\": 2},\n    torch.rand(16, 64),\n    (16, 1, 32, 32),\n]\n\nCASES = [TEST_CASE_0, TEST_CASE_1, TEST_CASE_2]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_generator.py_TestGenerator_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_generator.py_TestGenerator_", "embedding": null, "metadata": {"file_path": "tests/test_generator.py", "file_name": "test_generator.py", "file_type": "text/x-python", "category": "test", "start_line": 42, "end_line": 58, "span_ids": ["TestGenerator", "impl:9", "TestGenerator.test_shape", "TestGenerator.test_script"], "tokens": 141}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestGenerator(unittest.TestCase):\n    @parameterized.expand(CASES)\n    def test_shape(self, input_param, input_data, expected_shape):\n        net = Generator(**input_param)\n        with eval_mode(net):\n            result = net.forward(input_data)\n            self.assertEqual(result.shape, expected_shape)\n\n    def test_script(self):\n        net = Generator(latent_shape=(64,), start_shape=(8, 8, 8), channels=(8, 1), strides=(2, 2), num_res_units=2)\n        test_data = torch.rand(16, 64)\n        test_script_save(net, test_data)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_get_extreme_points.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_get_extreme_points.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_get_extreme_points.py", "file_name": "test_get_extreme_points.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 56, "span_ids": ["TestGetExtremePoints.test_type_shape", "impl:6", "TestGetExtremePoints", "docstring"], "tokens": 322}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import get_extreme_points\nfrom tests.utils import TEST_NDARRAYS\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    TESTS.append(\n        [\n            {\n                \"img\": p(np.array([[0, 1, 0], [0, 0, 1], [0, 1, 0], [1, 0, 0]])),\n                \"rand_state\": np.random,\n                \"background\": 0,\n                \"pert\": 0.0,\n            },\n            [(0, 1), (3, 0), (3, 0), (1, 2)],\n        ]\n    )\n\n    TESTS.append(\n        [\n            {\n                \"img\": p(np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0], [0, 1, 0]])),\n                \"rand_state\": np.random,\n                \"background\": 0,\n                \"pert\": 0.0,\n            },\n            [(0, 1), (3, 1), (1, 0), (1, 2)],\n        ]\n    )\n\n\nclass TestGetExtremePoints(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_type_shape(self, input_data, expected):\n        result = get_extreme_points(**input_data)\n        self.assertEqual(result, expected)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_get_package_version.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_get_package_version.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_get_package_version.py", "file_name": "test_get_package_version.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 32, "span_ids": ["impl", "TestGetVersion.test_msg", "TestGetVersion.test_default", "TestGetVersion", "docstring"], "tokens": 111}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nfrom monai.utils.module import get_package_version\n\n\nclass TestGetVersion(unittest.TestCase):\n    def test_default(self):\n        output = get_package_version(\"42foobarnoexist\")\n        self.assertTrue(\"UNKNOWN\" in output)\n\n        output = get_package_version(\"numpy\")\n        self.assertFalse(\"UNKNOWN\" in output)\n\n    def test_msg(self):\n        output = get_package_version(\"42foobarnoexist\", \"test\")\n        self.assertTrue(\"test\" in output)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_globalnet.py_unittest_device._cuda_if_torch_cuda_is_a": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_globalnet.py_unittest_device._cuda_if_torch_cuda_is_a", "embedding": null, "metadata": {"file_path": "tests/test_globalnet.py", "file_name": "test_globalnet.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 72, "span_ids": ["impl:3", "TestAffineHead", "impl:5", "docstring", "TestAffineHead.test_shape"], "tokens": 624}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks import eval_mode\nfrom monai.networks.blocks import Warp\nfrom monai.networks.nets import GlobalNet\nfrom monai.networks.nets.regunet import AffineHead\nfrom tests.utils import test_script_save\n\nTEST_CASES_AFFINE_TRANSFORM = [\n    [\n        {\"spatial_dims\": 3, \"image_size\": (2, 2, 2), \"decode_size\": (2, 2, 2), \"in_channels\": 1},\n        torch.ones(2, 12),\n        torch.tensor([[[1, 2], [2, 3]], [[2, 3], [3, 4]]]).unsqueeze(0).unsqueeze(0).expand(2, 3, 2, 2, 2),\n    ],\n    [\n        {\"spatial_dims\": 3, \"image_size\": (2, 2, 2), \"decode_size\": (2, 2, 2), \"in_channels\": 1},\n        torch.arange(1, 13).reshape(1, 12).to(torch.float),\n        torch.tensor(\n            [\n                [[[4.0, 7.0], [6.0, 9.0]], [[5.0, 8.0], [7.0, 10.0]]],\n                [[[8.0, 15.0], [14.0, 21.0]], [[13.0, 20.0], [19.0, 26.0]]],\n                [[[12.0, 23.0], [22.0, 33.0]], [[21.0, 32.0], [31.0, 42.0]]],\n            ]\n        ).unsqueeze(0),\n    ],\n]\n\n\nTEST_CASES_GLOBAL_NET = [\n    [\n        {\n            \"image_size\": (16, 16),\n            \"spatial_dims\": 2,\n            \"in_channels\": 1,\n            \"num_channel_initial\": 16,\n            \"depth\": 1,\n            \"out_kernel_initializer\": \"kaiming_uniform\",\n            \"out_activation\": None,\n            \"pooling\": True,\n            \"concat_skip\": True,\n            \"encode_kernel_sizes\": 3,\n        },\n        (1, 1, 16, 16),\n        (1, 2, 16, 16),\n    ]\n]\n\n\nclass TestAffineHead(unittest.TestCase):\n    @parameterized.expand(TEST_CASES_AFFINE_TRANSFORM)\n    def test_shape(self, input_param, theta, expected_val):\n        layer = AffineHead(**input_param)\n        result = layer.affine_transform(theta)\n        np.testing.assert_allclose(result.cpu().numpy(), expected_val.cpu().numpy(), rtol=1e-4, atol=1e-4)\n\n\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_globalnet.py_TestGlobalNet_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_globalnet.py_TestGlobalNet_", "embedding": null, "metadata": {"file_path": "tests/test_globalnet.py", "file_name": "test_globalnet.py", "file_type": "text/x-python", "category": "test", "start_line": 75, "end_line": 97, "span_ids": ["TestGlobalNet.test_shape", "TestGlobalNet.test_script", "TestGlobalNet", "impl:7"], "tokens": 199}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestGlobalNet(unittest.TestCase):\n    @parameterized.expand(TEST_CASES_GLOBAL_NET)\n    def test_shape(self, input_param, input_shape, expected_shape):\n        net = GlobalNet(**input_param).to(device)\n        warp_layer = Warp()\n        with eval_mode(net):\n            img = torch.randn(input_shape)\n            result = net(img.to(device))\n            warped = warp_layer(img.to(device), result)\n            self.assertEqual(result.shape, expected_shape)\n            # testing initial pred identity\n            np.testing.assert_allclose(warped.detach().cpu().numpy(), img.detach().cpu().numpy(), rtol=1e-4, atol=1e-4)\n\n    def test_script(self):\n        input_param, input_shape, _ = TEST_CASES_GLOBAL_NET[0]\n        net = GlobalNet(**input_param)\n        test_data = torch.randn(input_shape)\n        test_script_save(net, test_data)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_grid_dataset.py_sys_TestGridPatchDataset.test_shape.self_assertEqual_len_j": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_grid_dataset.py_sys_TestGridPatchDataset.test_shape.self_assertEqual_len_j", "embedding": null, "metadata": {"file_path": "tests/test_grid_dataset.py", "file_name": "test_grid_dataset.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 44, "span_ids": ["TestGridPatchDataset", "TestGridPatchDataset.setUp", "TestGridPatchDataset.test_shape", "TestGridPatchDataset.tearDown", "identity_generator", "docstring"], "tokens": 258}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import sys\nimport unittest\n\nimport numpy as np\n\nfrom monai.data import DataLoader, GridPatchDataset, PatchIter\nfrom monai.transforms import RandShiftIntensity\nfrom monai.utils import set_determinism\n\n\ndef identity_generator(x):\n    # simple transform that returns the input itself\n    for idx, item in enumerate(x):\n        yield item, idx\n\n\nclass TestGridPatchDataset(unittest.TestCase):\n    def setUp(self):\n        set_determinism(seed=1234)\n\n    def tearDown(self):\n        set_determinism(None)\n\n    def test_shape(self):\n        test_dataset = [\"vwxyz\", \"helloworld\", \"worldfoobar\"]\n        result = GridPatchDataset(dataset=test_dataset, patch_iter=identity_generator, with_coordinates=False)\n        output = []\n        n_workers = 0 if sys.platform == \"win32\" else 2\n        for item in DataLoader(result, batch_size=3, num_workers=n_workers):\n            output.append(\"\".join(item))\n        expected = [\"vwx\", \"wor\", \"yzh\", \"ldf\", \"ell\", \"oob\", \"owo\", \"ar\", \"rld\"]\n        self.assertEqual(sorted(output), sorted(expected))\n        self.assertEqual(len(\"\".join(expected)), len(\"\".join(test_dataset)))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_grid_dataset.py_TestGridPatchDataset.test_loading_array_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_grid_dataset.py_TestGridPatchDataset.test_loading_array_", "embedding": null, "metadata": {"file_path": "tests/test_grid_dataset.py", "file_name": "test_grid_dataset.py", "file_type": "text/x-python", "category": "test", "start_line": 46, "end_line": 78, "span_ids": ["TestGridPatchDataset.test_loading_array", "impl"], "tokens": 533}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestGridPatchDataset(unittest.TestCase):\n\n    def test_loading_array(self):\n        set_determinism(seed=1234)\n        # image dataset\n        images = [np.arange(16, dtype=float).reshape(1, 4, 4), np.arange(16, dtype=float).reshape(1, 4, 4)]\n        # image level\n        patch_intensity = RandShiftIntensity(offsets=1.0, prob=1.0)\n        patch_iter = PatchIter(patch_size=(2, 2), start_pos=(0, 0))\n        ds = GridPatchDataset(dataset=images, patch_iter=patch_iter, transform=patch_intensity)\n        # use the grid patch dataset\n        for item in DataLoader(ds, batch_size=2, shuffle=False, num_workers=0):\n            np.testing.assert_equal(tuple(item[0].shape), (2, 1, 2, 2))\n        np.testing.assert_allclose(\n            item[0],\n            np.array([[[[2.0577, 3.0577], [6.0577, 7.0577]]], [[[10.5540, 11.5540], [14.5540, 15.5540]]]]),\n            rtol=1e-5,\n        )\n        np.testing.assert_allclose(item[1], np.array([[[0, 1], [0, 2], [2, 4]], [[0, 1], [2, 4], [2, 4]]]), rtol=1e-5)\n        if sys.platform != \"win32\":\n            for item in DataLoader(ds, batch_size=2, shuffle=False, num_workers=2):\n                np.testing.assert_equal(tuple(item[0].shape), (2, 1, 2, 2))\n            np.testing.assert_allclose(\n                item[0],\n                np.array([[[[1.6533, 2.6533], [5.6533, 6.6533]]], [[[9.8524, 10.8524], [13.8524, 14.8524]]]]),\n                rtol=1e-3,\n            )\n            np.testing.assert_allclose(\n                item[1], np.array([[[0, 1], [0, 2], [2, 4]], [[0, 1], [2, 4], [2, 4]]]), rtol=1e-5\n            )\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_grid_pull.py_unittest_for_bound_in_bounds_.for_interp_in_interps_.for_input_g_in_True_Fal.for_grid_g_in_True_Fals.TEST_1D_GP_append_test_ca": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_grid_pull.py_unittest_for_bound_in_bounds_.for_interp_in_interps_.for_input_g_in_True_Fal.for_grid_g_in_True_Fals.TEST_1D_GP_append_test_ca", "embedding": null, "metadata": {"file_path": "tests/test_grid_pull.py", "file_name": "test_grid_pull.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 58, "span_ids": ["impl:3", "make_grid", "docstring"], "tokens": 454}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks.layers import grid_pull\nfrom monai.utils import optional_import\nfrom tests.testing_data.cpp_resample_answers import Expected_1D_GP_bwd, Expected_1D_GP_fwd\nfrom tests.utils import skip_if_no_cpp_extension\n\nBType, has_b_type = optional_import(\"monai._C\", name=\"BoundType\")\nPType, has_p_type = optional_import(\"monai._C\", name=\"InterpolationType\")\n\n\ndef make_grid(shape, dtype=None, device=None, requires_grad=True):\n    ranges = [torch.arange(float(s), dtype=dtype, device=device, requires_grad=requires_grad) for s in shape]\n    grid = torch.stack(torch.meshgrid(*ranges), dim=-1)\n    return grid[None]\n\n\n# 1D combinations of bounds/interpolations\nbounds = set(BType.__members__.values()) if has_b_type else []\ninterps = set(PType.__members__.values()) if has_p_type else []\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\nTEST_1D_GP = []\nfor bound in bounds:\n    for interp in interps:\n        if not Expected_1D_GP_fwd or not Expected_1D_GP_bwd:\n            break  # skip if the testing data are unavailable\n        expected_val = Expected_1D_GP_fwd.pop(0)\n\n        for input_g in (True, False):\n            for grid_g in (True, False):\n                expected_grad = Expected_1D_GP_bwd.pop(0)\n                test_case = [\n                    {\n                        \"input\": torch.arange(10, dtype=torch.float, requires_grad=input_g, device=device).reshape(\n                            (1, 1, 10)\n                        ),\n                        \"grid\": make_grid((20,), dtype=torch.float, device=device, requires_grad=grid_g) + 0.5,\n                        \"interpolation\": interp,\n                        \"bound\": bound,\n                    },\n                    {\"val\": torch.tensor([[expected_val]]), \"device\": device, \"grad\": torch.tensor(expected_grad)},\n                ]\n                TEST_1D_GP.append(test_case)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_grid_pull.py_TestGridPull_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_grid_pull.py_TestGridPull_", "embedding": null, "metadata": {"file_path": "tests/test_grid_pull.py", "file_name": "test_grid_pull.py", "file_type": "text/x-python", "category": "test", "start_line": 61, "end_line": 91, "span_ids": ["impl:24", "TestGridPull", "TestGridPull.test_grid_pull"], "tokens": 312}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_no_cpp_extension\nclass TestGridPull(unittest.TestCase):\n    @parameterized.expand(TEST_1D_GP, skip_on_empty=True)\n    def test_grid_pull(self, input_param, expected):\n        result = grid_pull(**input_param)\n        if input_param[\"input\"].requires_grad:\n            input_param[\"input\"].retain_grad()\n        if input_param[\"grid\"].requires_grad:\n            input_param[\"grid\"].retain_grad()\n        if input_param[\"input\"].requires_grad or input_param[\"grid\"].requires_grad:\n            result.sum().backward()\n\n        grads = []\n        if input_param[\"input\"].requires_grad:\n            grads.append(input_param[\"input\"].grad.view(-1))\n        if input_param[\"grid\"].requires_grad:\n            grads.append(input_param[\"grid\"].grad.view(-1))\n        if not grads:\n            grads = torch.tensor(0.0, device=result.device)\n        elif len(grads) == 1:\n            grads = grads[0]\n        else:\n            grads = torch.cat(grads, dim=0)\n        self.assertTrue(f\"{result.device}\".startswith(expected[\"device\"]))\n        np.testing.assert_allclose(result.detach().cpu().numpy(), expected[\"val\"].cpu().numpy(), rtol=1e-4, atol=1e-4)\n        np.testing.assert_allclose(grads.detach().cpu().numpy(), expected[\"grad\"].cpu().numpy(), rtol=1e-4, atol=1e-4)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_checkpoint_loader.py_logging_TestHandlerCheckpointLoader.test_one_save_one_load.with_tempfile_TemporaryDi.try_.except_ValueError_.None_1": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_checkpoint_loader.py_logging_TestHandlerCheckpointLoader.test_one_save_one_load.with_tempfile_TemporaryDi.try_.except_ValueError_.None_1", "embedding": null, "metadata": {"file_path": "tests/test_handler_checkpoint_loader.py", "file_name": "test_handler_checkpoint_loader.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 58, "span_ids": ["TestHandlerCheckpointLoader.test_one_save_one_load", "TestHandlerCheckpointLoader", "docstring"], "tokens": 443}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import logging\nimport sys\nimport tempfile\nimport unittest\n\nimport torch\nimport torch.optim as optim\nfrom ignite.engine import Engine, Events\n\nfrom monai.handlers import CheckpointLoader, CheckpointSaver\n\n\nclass TestHandlerCheckpointLoader(unittest.TestCase):\n    def test_one_save_one_load(self):\n        logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n        net1 = torch.nn.PReLU()\n        data1 = net1.state_dict()\n        data1[\"weight\"] = torch.tensor([0.1])\n        net1.load_state_dict(data1)\n        net2 = torch.nn.PReLU()\n        data2 = net2.state_dict()\n        data2[\"weight\"] = torch.tensor([0.2])\n        net2.load_state_dict(data2)\n        with tempfile.TemporaryDirectory() as tempdir:\n            engine1 = Engine(lambda e, b: None)\n            CheckpointSaver(save_dir=tempdir, save_dict={\"net\": net1, \"eng\": engine1}, save_final=True).attach(engine1)\n            engine1.run([0] * 8, max_epochs=5)\n            path = tempdir + \"/checkpoint_final_iteration=40.pt\"\n            engine2 = Engine(lambda e, b: None)\n            CheckpointLoader(load_path=path, load_dict={\"net\": net2, \"eng\": engine2}, strict=True).attach(engine2)\n\n            @engine2.on(Events.STARTED)\n            def check_epoch(engine: Engine):\n                self.assertEqual(engine.state.epoch, 5)\n\n            engine2.run([0] * 8, max_epochs=8)\n            torch.testing.assert_allclose(net2.state_dict()[\"weight\"], torch.tensor([0.1]))\n\n            # test bad case with max_epochs smaller than current epoch\n            engine3 = Engine(lambda e, b: None)\n            CheckpointLoader(load_path=path, load_dict={\"net\": net2, \"eng\": engine3}, strict=True).attach(engine3)\n\n            try:\n                engine3.run([0] * 8, max_epochs=3)\n            except ValueError:\n                self.assertEqual(engine3.state.epoch, 5)\n                self.assertEqual(engine3.state.max_epochs, 5)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_checkpoint_loader.py_TestHandlerCheckpointLoader.test_save_single_device_load_multi_devices_TestHandlerCheckpointLoader.test_save_single_device_load_multi_devices.with_tempfile_TemporaryDi.torch_testing_assert_allc": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_checkpoint_loader.py_TestHandlerCheckpointLoader.test_save_single_device_load_multi_devices_TestHandlerCheckpointLoader.test_save_single_device_load_multi_devices.with_tempfile_TemporaryDi.torch_testing_assert_allc", "embedding": null, "metadata": {"file_path": "tests/test_handler_checkpoint_loader.py", "file_name": "test_handler_checkpoint_loader.py", "file_type": "text/x-python", "category": "test", "start_line": 82, "end_line": 101, "span_ids": ["TestHandlerCheckpointLoader.test_save_single_device_load_multi_devices"], "tokens": 270}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestHandlerCheckpointLoader(unittest.TestCase):\n\n    def test_save_single_device_load_multi_devices(self):\n        logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n        net1 = torch.nn.PReLU()\n        data1 = net1.state_dict()\n        data1[\"weight\"] = torch.tensor([0.1])\n        net1.load_state_dict(data1)\n        net2 = torch.nn.PReLU()\n        data2 = net2.state_dict()\n        data2[\"weight\"] = torch.tensor([0.2])\n        net2.load_state_dict(data2)\n        net2 = torch.nn.DataParallel(net2)\n        with tempfile.TemporaryDirectory() as tempdir:\n            engine = Engine(lambda e, b: None)\n            CheckpointSaver(save_dir=tempdir, save_dict={\"net\": net1}, save_final=True).attach(engine)\n            engine.run([0] * 8, max_epochs=5)\n            path = tempdir + \"/net_final_iteration=40.pt\"\n            engine = Engine(lambda e, b: None)\n            CheckpointLoader(load_path=path, load_dict={\"net\": net2}, strict=True).attach(engine)\n            engine.run([0] * 8, max_epochs=1)\n            torch.testing.assert_allclose(net2.state_dict()[\"module.weight\"].cpu(), torch.tensor([0.1]))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_checkpoint_loader.py_TestHandlerCheckpointLoader.test_partial_under_load_TestHandlerCheckpointLoader.test_partial_under_load.with_tempfile_TemporaryDi.torch_testing_assert_allc": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_checkpoint_loader.py_TestHandlerCheckpointLoader.test_partial_under_load_TestHandlerCheckpointLoader.test_partial_under_load.with_tempfile_TemporaryDi.torch_testing_assert_allc", "embedding": null, "metadata": {"file_path": "tests/test_handler_checkpoint_loader.py", "file_name": "test_handler_checkpoint_loader.py", "file_type": "text/x-python", "category": "test", "start_line": 103, "end_line": 124, "span_ids": ["TestHandlerCheckpointLoader.test_partial_under_load"], "tokens": 288}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestHandlerCheckpointLoader(unittest.TestCase):\n\n    def test_partial_under_load(self):\n        logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n        net1 = torch.nn.Sequential(*[torch.nn.PReLU(), torch.nn.PReLU()])\n        data1 = net1.state_dict()\n        data1[\"0.weight\"] = torch.tensor([0.1])\n        data1[\"1.weight\"] = torch.tensor([0.2])\n        net1.load_state_dict(data1)\n\n        net2 = torch.nn.Sequential(*[torch.nn.PReLU()])\n        data2 = net2.state_dict()\n        data2[\"0.weight\"] = torch.tensor([0.3])\n        net2.load_state_dict(data2)\n\n        with tempfile.TemporaryDirectory() as tempdir:\n            engine = Engine(lambda e, b: None)\n            CheckpointSaver(save_dir=tempdir, save_dict={\"net\": net1}, save_final=True).attach(engine)\n            engine.run([0] * 8, max_epochs=5)\n            path = tempdir + \"/net_final_iteration=40.pt\"\n            engine = Engine(lambda e, b: None)\n            CheckpointLoader(load_path=path, load_dict={\"net\": net2}, strict=False).attach(engine)\n            engine.run([0] * 8, max_epochs=1)\n            torch.testing.assert_allclose(net2.state_dict()[\"0.weight\"].cpu(), torch.tensor([0.1]))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_checkpoint_loader.py_TestHandlerCheckpointLoader.test_partial_over_load_TestHandlerCheckpointLoader.test_partial_over_load.with_tempfile_TemporaryDi.torch_testing_assert_allc": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_checkpoint_loader.py_TestHandlerCheckpointLoader.test_partial_over_load_TestHandlerCheckpointLoader.test_partial_over_load.with_tempfile_TemporaryDi.torch_testing_assert_allc", "embedding": null, "metadata": {"file_path": "tests/test_handler_checkpoint_loader.py", "file_name": "test_handler_checkpoint_loader.py", "file_type": "text/x-python", "category": "test", "start_line": 126, "end_line": 147, "span_ids": ["TestHandlerCheckpointLoader.test_partial_over_load"], "tokens": 288}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestHandlerCheckpointLoader(unittest.TestCase):\n\n    def test_partial_over_load(self):\n        logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n        net1 = torch.nn.Sequential(*[torch.nn.PReLU()])\n        data1 = net1.state_dict()\n        data1[\"0.weight\"] = torch.tensor([0.1])\n        net1.load_state_dict(data1)\n\n        net2 = torch.nn.Sequential(*[torch.nn.PReLU(), torch.nn.PReLU()])\n        data2 = net2.state_dict()\n        data2[\"0.weight\"] = torch.tensor([0.2])\n        data2[\"1.weight\"] = torch.tensor([0.3])\n        net2.load_state_dict(data2)\n\n        with tempfile.TemporaryDirectory() as tempdir:\n            engine = Engine(lambda e, b: None)\n            CheckpointSaver(save_dir=tempdir, save_dict={\"net\": net1}, save_final=True).attach(engine)\n            engine.run([0] * 8, max_epochs=5)\n            path = tempdir + \"/net_final_iteration=40.pt\"\n            engine = Engine(lambda e, b: None)\n            CheckpointLoader(load_path=path, load_dict={\"net\": net2}, strict=False).attach(engine)\n            engine.run([0] * 8, max_epochs=1)\n            torch.testing.assert_allclose(net2.state_dict()[\"0.weight\"].cpu(), torch.tensor([0.1]))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_checkpoint_loader.py_TestHandlerCheckpointLoader.test_strict_shape_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_checkpoint_loader.py_TestHandlerCheckpointLoader.test_strict_shape_", "embedding": null, "metadata": {"file_path": "tests/test_handler_checkpoint_loader.py", "file_name": "test_handler_checkpoint_loader.py", "file_type": "text/x-python", "category": "test", "start_line": 149, "end_line": 188, "span_ids": ["TestHandlerCheckpointLoader.test_strict_shape", "impl"], "tokens": 501}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestHandlerCheckpointLoader(unittest.TestCase):\n\n    def test_strict_shape(self):\n        logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n        net1 = torch.nn.Sequential(*[torch.nn.PReLU(num_parameters=5)])\n        data1 = net1.state_dict()\n        data1[\"0.weight\"] = torch.tensor([1, 2, 3, 4, 5])\n        data1[\"new\"] = torch.tensor(0.1)\n        net1.load_state_dict(data1, strict=False)\n        opt1 = optim.SGD(net1.parameters(), lr=0.02)\n\n        net2 = torch.nn.Sequential(*[torch.nn.PReLU(), torch.nn.PReLU()])\n        data2 = net2.state_dict()\n        data2[\"0.weight\"] = torch.tensor([0.2])\n        data2[\"1.weight\"] = torch.tensor([0.3])\n        net2.load_state_dict(data2)\n        opt2 = optim.SGD(net2.parameters(), lr=0.02)\n\n        with tempfile.TemporaryDirectory() as tempdir:\n            engine = Engine(lambda e, b: None)\n            CheckpointSaver(save_dir=tempdir, save_dict={\"net\": net1, \"opt\": opt1}, save_final=True).attach(engine)\n            engine.run([0] * 8, max_epochs=5)\n            path = tempdir + \"/checkpoint_final_iteration=40.pt\"\n            engine = Engine(lambda e, b: None)\n            CheckpointLoader(\n                load_path=path,\n                # expect to print a warning because it loads not only `net` but also `opt` with `strict_shape=False`\n                load_dict={\"net\": net2, \"opt\": opt2},\n                strict=False,\n                strict_shape=False,\n            ).attach(engine)\n            engine.run([0] * 8, max_epochs=1)\n            torch.testing.assert_allclose(net2.state_dict()[\"0.weight\"].cpu(), torch.tensor([0.2]))\n            # test whether `opt2` had been skipped when loading with `strict_shape=False`,\n            # it should have 2 items in `params`(0.weight and 1.weight) while the checkpoint has 1 item(0.weight)\n            self.assertEqual(len(opt1.state_dict()[\"param_groups\"][0][\"params\"]), 1)\n            self.assertEqual(len(opt2.state_dict()[\"param_groups\"][0][\"params\"]), 2)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_checkpoint_saver.py_logging_TEST_CASE_8._False_None_True_val_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_checkpoint_saver.py_logging_TEST_CASE_8._False_None_True_val_", "embedding": null, "metadata": {"file_path": "tests/test_handler_checkpoint_saver.py", "file_name": "test_handler_checkpoint_saver.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 110, "span_ids": ["impl:13", "docstring"], "tokens": 478}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import logging\nimport os\nimport sys\nimport tempfile\nimport unittest\n\nimport torch\nimport torch.optim as optim\nfrom ignite.engine import Engine\nfrom parameterized import parameterized\n\nfrom monai.handlers import CheckpointLoader, CheckpointSaver\n\nTEST_CASE_1 = [\n    True,\n    None,\n    False,\n    None,\n    1,\n    None,\n    False,\n    False,\n    False,\n    True,\n    0,\n    None,\n    [\"test_checkpoint_final_iteration=40.pt\"],\n]\n\nTEST_CASE_2 = [\n    False,\n    None,\n    True,\n    \"val_loss\",\n    2,\n    None,\n    False,\n    True,\n    False,\n    False,\n    0,\n    None,\n    [\"test_checkpoint_key_metric=32.pt\", \"test_checkpoint_key_metric=40.pt\"],\n]\n\nTEST_CASE_3 = [\n    False,\n    None,\n    False,\n    None,\n    1,\n    None,\n    False,\n    True,\n    False,\n    True,\n    2,\n    2,\n    [\"test_checkpoint_epoch=2.pt\", \"test_checkpoint_epoch=4.pt\"],\n]\n\nTEST_CASE_4 = [\n    False,\n    None,\n    False,\n    None,\n    1,\n    None,\n    False,\n    False,\n    False,\n    False,\n    10,\n    2,\n    [\"test_checkpoint_iteration=30.pt\", \"test_checkpoint_iteration=40.pt\"],\n]\n\nTEST_CASE_5 = [\n    True,\n    None,\n    False,\n    None,\n    1,\n    None,\n    False,\n    False,\n    False,\n    True,\n    0,\n    None,\n    [\"test_checkpoint_final_iteration=40.pt\"],\n    True,\n]\n\nTEST_CASE_6 = [True, \"final_model.pt\", False, None, 1, None, False, False, False, True, 0, None, [\"final_model.pt\"]]\n\nTEST_CASE_7 = [False, None, True, \"val_loss\", 1, \"model.pt\", False, False, False, True, 0, None, [\"model.pt\"]]\n\nTEST_CASE_8 = [False, None, True, \"val_loss\", 1, \"model.pt\", False, True, False, True, 0, None, [\"model.pt\"]]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_checkpoint_saver.py_TestHandlerCheckpointSaver.test_exception_TestHandlerCheckpointSaver.test_exception.with_tempfile_TemporaryDi.self_assertTrue_os_path_e": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_checkpoint_saver.py_TestHandlerCheckpointSaver.test_exception_TestHandlerCheckpointSaver.test_exception.with_tempfile_TemporaryDi.self_assertTrue_os_path_e", "embedding": null, "metadata": {"file_path": "tests/test_handler_checkpoint_saver.py", "file_name": "test_handler_checkpoint_saver.py", "file_type": "text/x-python", "category": "test", "start_line": 175, "end_line": 192, "span_ids": ["TestHandlerCheckpointSaver.test_exception"], "tokens": 147}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestHandlerCheckpointSaver(unittest.TestCase):\n\n    def test_exception(self):\n        logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n        net = torch.nn.PReLU()\n\n        # set up engine\n        def _train_func(engine, batch):\n            raise RuntimeError(\"test exception.\")\n\n        engine = Engine(_train_func)\n\n        # set up testing handler\n        with tempfile.TemporaryDirectory() as tempdir:\n            stats_handler = CheckpointSaver(tempdir, {\"net\": net}, save_final=True)\n            stats_handler.attach(engine)\n\n            with self.assertRaises(RuntimeError):\n                engine.run(range(3), max_epochs=2)\n            self.assertTrue(os.path.exists(os.path.join(tempdir, \"net_final_iteration=1.pt\")))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_checkpoint_saver.py_TestHandlerCheckpointSaver.test_load_state_dict_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_checkpoint_saver.py_TestHandlerCheckpointSaver.test_load_state_dict_", "embedding": null, "metadata": {"file_path": "tests/test_handler_checkpoint_saver.py", "file_name": "test_handler_checkpoint_saver.py", "file_type": "text/x-python", "category": "test", "start_line": 192, "end_line": 236, "span_ids": ["TestHandlerCheckpointSaver.test_load_state_dict", "impl:17"], "tokens": 352}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestHandlerCheckpointSaver(unittest.TestCase):\n\n    def test_load_state_dict(self):\n        logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n        net = torch.nn.PReLU()\n\n        # set up engine\n        def _train_func(engine, batch):\n            engine.state.metrics[\"val_loss\"] = engine.state.iteration\n\n        engine = Engine(_train_func)\n\n        # set up testing handler\n        with tempfile.TemporaryDirectory() as tempdir:\n            engine = Engine(_train_func)\n            CheckpointSaver(\n                save_dir=tempdir,\n                save_dict={\"net\": net},\n                save_key_metric=True,\n                key_metric_name=\"val_loss\",\n                key_metric_n_saved=2,\n                key_metric_save_state=True,\n                key_metric_negative_sign=True,\n            ).attach(engine)\n            engine.run(range(3), max_epochs=3)\n\n            saver = CheckpointSaver(\n                save_dir=tempdir,\n                save_dict={\"net\": net},\n                save_key_metric=True,\n                key_metric_name=\"val_loss\",\n                key_metric_n_saved=2,\n                key_metric_negative_sign=True,\n            )\n            engine = Engine(_train_func)\n            CheckpointLoader(os.path.join(tempdir, \"net_key_metric=-6.pt\"), {\"checkpointer\": saver}).attach(engine)\n            engine.run(range(1), max_epochs=1)\n\n            resumed = saver._key_metric_checkpoint._saved\n            for i in range(2):\n                self.assertEqual(resumed[1 - i].priority, -3 * (i + 1))\n                self.assertEqual(resumed[1 - i].filename, f\"net_key_metric=-{3 * (i + 1)}.pt\")\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_classification_saver_dist.py_csv_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_classification_saver_dist.py_csv_", "embedding": null, "metadata": {"file_path": "tests/test_handler_classification_saver_dist.py", "file_name": "test_handler_classification_saver_dist.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 76, "span_ids": ["DistributedHandlerClassificationSaver", "DistributedHandlerClassificationSaver.test_saved_content", "impl", "docstring"], "tokens": 477}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import csv\nimport os\nimport tempfile\nimport unittest\n\nimport numpy as np\nimport torch\nimport torch.distributed as dist\nfrom ignite.engine import Engine\n\nfrom monai.data import decollate_batch\nfrom monai.handlers import ClassificationSaver\nfrom tests.utils import DistCall, DistTestCase\n\n\nclass DistributedHandlerClassificationSaver(DistTestCase):\n    @DistCall(nnodes=1, nproc_per_node=2)\n    def test_saved_content(self):\n        with tempfile.TemporaryDirectory() as tempdir:\n            rank = dist.get_rank()\n\n            # set up engine\n            def _train_func(engine, batch):\n                engine.state.batch = decollate_batch(batch)\n                return [torch.zeros(1) for _ in range(8 + rank * 2)]\n\n            engine = Engine(_train_func)\n\n            # set up testing handler\n            saver = ClassificationSaver(output_dir=tempdir, filename=\"predictions.csv\", save_rank=1)\n            saver.attach(engine)\n\n            # rank 0 has 8 images, rank 1 has 10 images\n            data = [\n                {\n                    \"filename_or_obj\": [\"testfile\" + str(i) for i in range(8 * rank, (8 + rank) * (rank + 1))],\n                    \"data_shape\": torch.ones((8 + rank * 2, 1, 1)),\n                }\n            ]\n            # rank 1 has more iterations\n            if rank == 1:\n                data.append(\n                    {\n                        \"filename_or_obj\": [\"testfile\" + str(i) for i in range(18, 28)],\n                        \"data_shape\": torch.ones((10, 1, 1)),\n                    }\n                )\n\n            engine.run(data, max_epochs=1)\n            filepath = os.path.join(tempdir, \"predictions.csv\")\n            if rank == 1:\n                self.assertTrue(os.path.exists(filepath))\n                with open(filepath) as f:\n                    reader = csv.reader(f)\n                    i = 0\n                    for row in reader:\n                        self.assertEqual(row[0], \"testfile\" + str(i))\n                        self.assertEqual(np.array(row[1:]).astype(np.float32), 0.0)\n                        i += 1\n                    self.assertEqual(i, 28)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_confusion_matrix.py_unittest_data_2._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_confusion_matrix.py_unittest_data_2._", "embedding": null, "metadata": {"file_path": "tests/test_handler_confusion_matrix.py", "file_name": "test_handler_confusion_matrix.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 44, "span_ids": ["impl:11", "docstring"], "tokens": 600}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom typing import Any, Dict\n\nimport torch\nfrom ignite.engine import Engine\nfrom parameterized import parameterized\n\nfrom monai.handlers import ConfusionMatrix\n\nTEST_CASE_1 = [{\"include_background\": True, \"save_details\": False, \"metric_name\": \"f1\"}, 0.75]\nTEST_CASE_2 = [{\"include_background\": False, \"save_details\": False, \"metric_name\": \"ppv\"}, 1.0]\nTEST_CASE_3 = [{\"save_details\": False, \"metric_name\": \"f1\", \"reduction\": \"mean_batch\"}, torch.tensor([0.6667, 0.8000])]\nTEST_CASE_SEG_1 = [{\"include_background\": True, \"metric_name\": \"tpr\"}, 0.7]\n\ndata_1: Dict[Any, Any] = {\n    \"y_pred\": torch.tensor(\n        [\n            [[[0.0, 1.0], [0.0, 0.0]], [[0.0, 0.0], [1.0, 1.0]], [[1.0, 0.0], [0.0, 0.0]]],\n            [[[0.0, 0.0], [0.0, 1.0]], [[1.0, 0.0], [0.0, 0.0]], [[0.0, 1.0], [1.0, 0.0]]],\n        ]\n    ),\n    \"y\": torch.tensor(\n        [\n            [[[0.0, 1.0], [0.0, 0.0]], [[0.0, 0.0], [1.0, 1.0]], [[1.0, 0.0], [0.0, 0.0]]],\n            [[[0.0, 0.0], [0.0, 1.0]], [[1.0, 0.0], [0.0, 0.0]], [[0.0, 1.0], [1.0, 0.0]]],\n        ]\n    ),\n}\n\ndata_2: Dict[Any, Any] = {\n    \"y_pred\": torch.tensor([[[[0.0, 1.0], [1.0, 0.0]], [[1.0, 0.0], [1.0, 1.0]], [[0.0, 1.0], [0.0, 0.0]]]]),\n    \"y\": torch.tensor([[[[1.0, 1.0], [1.0, 1.0]], [[1.0, 1.0], [1.0, 1.0]], [[1.0, 1.0], [1.0, 1.0]]]]),\n}", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_confusion_matrix.py_TestHandlerConfusionMatrix.test_compute_seg_TestHandlerConfusionMatrix.test_compute_seg.self_assertAlmostEqual_av": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_confusion_matrix.py_TestHandlerConfusionMatrix.test_compute_seg_TestHandlerConfusionMatrix.test_compute_seg.self_assertAlmostEqual_av", "embedding": null, "metadata": {"file_path": "tests/test_handler_confusion_matrix.py", "file_name": "test_handler_confusion_matrix.py", "file_type": "text/x-python", "category": "test", "start_line": 73, "end_line": 92, "span_ids": ["TestHandlerConfusionMatrix.test_compute_seg"], "tokens": 152}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestHandlerConfusionMatrix(unittest.TestCase):\n\n    @parameterized.expand([TEST_CASE_SEG_1])\n    def test_compute_seg(self, input_params, expected_avg):\n        metric = ConfusionMatrix(**input_params)\n\n        def _val_func(engine, batch):\n            pass\n\n        engine = Engine(_val_func)\n        metric.attach(engine, \"confusion_matrix\")\n\n        y_pred = data_1[\"y_pred\"]\n        y = data_1[\"y\"]\n        metric.update([y_pred, y])\n\n        y_pred = data_2[\"y_pred\"]\n        y = data_2[\"y\"]\n        metric.update([y_pred, y])\n\n        avg_metric = metric.compute()\n        self.assertAlmostEqual(avg_metric, expected_avg, places=4)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_confusion_matrix.py_TestHandlerConfusionMatrix.test_shape_mismatch_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_confusion_matrix.py_TestHandlerConfusionMatrix.test_shape_mismatch_", "embedding": null, "metadata": {"file_path": "tests/test_handler_confusion_matrix.py", "file_name": "test_handler_confusion_matrix.py", "file_type": "text/x-python", "category": "test", "start_line": 86, "end_line": 102, "span_ids": ["impl:13", "TestHandlerConfusionMatrix.test_shape_mismatch"], "tokens": 157}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestHandlerConfusionMatrix(unittest.TestCase):\n\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2])\n    def test_shape_mismatch(self, input_params, _expected):\n        metric = ConfusionMatrix(**input_params)\n        with self.assertRaises((AssertionError, ValueError)):\n            y_pred = torch.Tensor([[0, 1], [1, 0]])\n            y = torch.ones((2, 3))\n            metric.update([y_pred, y])\n\n        with self.assertRaises((AssertionError, ValueError)):\n            y_pred = torch.Tensor([[0, 1], [1, 0]])\n            y = torch.ones((3, 2))\n            metric.update([y_pred, y])\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_confusion_matrix_dist.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_confusion_matrix_dist.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_handler_confusion_matrix_dist.py", "file_name": "test_handler_confusion_matrix_dist.py", "file_type": "text/x-python", "category": "test", "start_line": 13, "end_line": 70, "span_ids": ["impl", "DistributedConfusionMatrix._compute", "DistributedConfusionMatrix.test_compute", "DistributedConfusionMatrix", "docstring"], "tokens": 669}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nimport torch.distributed as dist\nfrom ignite.engine import Engine\n\nfrom monai.handlers import ConfusionMatrix\nfrom tests.utils import DistCall, DistTestCase\n\n\nclass DistributedConfusionMatrix(DistTestCase):\n    @DistCall(nnodes=1, nproc_per_node=2)\n    def test_compute(self):\n        self._compute()\n\n    def _compute(self):\n        device = f\"cuda:{dist.get_rank()}\" if torch.cuda.is_available() else \"cpu\"\n        metric = ConfusionMatrix(include_background=True, metric_name=\"tpr\")\n\n        def _val_func(engine, batch):\n            pass\n\n        engine = Engine(_val_func)\n        metric.attach(engine, \"confusion_matrix\")\n        if dist.get_rank() == 0:\n            y_pred = torch.tensor(\n                [\n                    [[[0.0, 1.0], [0.0, 0.0]], [[0.0, 0.0], [1.0, 1.0]], [[1.0, 0.0], [0.0, 0.0]]],\n                    [[[0.0, 0.0], [0.0, 1.0]], [[1.0, 0.0], [0.0, 0.0]], [[0.0, 1.0], [1.0, 0.0]]],\n                ],\n                device=device,\n            )\n            y = torch.tensor(\n                [\n                    [[[0.0, 1.0], [0.0, 0.0]], [[0.0, 0.0], [1.0, 1.0]], [[1.0, 0.0], [0.0, 0.0]]],\n                    [[[0.0, 0.0], [0.0, 1.0]], [[1.0, 0.0], [0.0, 0.0]], [[0.0, 1.0], [1.0, 0.0]]],\n                ],\n                device=device,\n            )\n            metric.update([y_pred, y])\n\n        if dist.get_rank() == 1:\n            y_pred = torch.tensor(\n                [[[[0.0, 1.0], [1.0, 0.0]], [[1.0, 0.0], [1.0, 1.0]], [[0.0, 1.0], [0.0, 0.0]]]], device=device\n            )\n            y = torch.tensor(\n                [[[[1.0, 1.0], [1.0, 1.0]], [[1.0, 1.0], [1.0, 1.0]], [[1.0, 1.0], [1.0, 1.0]]]], device=device\n            )\n            metric.update([y_pred, y])\n\n        avg_metric = metric.compute()\n        np.testing.assert_allclose(avg_metric, 0.7, rtol=1e-04, atol=1e-04)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_garbage_collector.py_gc_TEST_CASE_2._0_1_2_Events_EPOCH_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_garbage_collector.py_gc_TEST_CASE_2._0_1_2_Events_EPOCH_", "embedding": null, "metadata": {"file_path": "tests/test_handler_garbage_collector.py", "file_name": "test_handler_garbage_collector.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 32, "span_ids": ["docstring"], "tokens": 143}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import gc\nimport unittest\nfrom unittest import skipUnless\n\nimport torch\nfrom ignite.engine import Engine\nfrom parameterized import parameterized\n\nfrom monai.config import IgniteInfo\nfrom monai.data import Dataset\nfrom monai.handlers import GarbageCollector\nfrom monai.utils import min_version, optional_import\n\nEvents, has_ignite = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Events\")\n\n\nTEST_CASE_0 = [[0, 1, 2], \"epoch\"]\n\nTEST_CASE_1 = [[0, 1, 2], \"iteration\"]\n\nTEST_CASE_2 = [[0, 1, 2], Events.EPOCH_COMPLETED]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_garbage_collector.py_TestHandlerGarbageCollector_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_garbage_collector.py_TestHandlerGarbageCollector_", "embedding": null, "metadata": {"file_path": "tests/test_handler_garbage_collector.py", "file_name": "test_handler_garbage_collector.py", "file_type": "text/x-python", "category": "test", "start_line": 35, "end_line": 73, "span_ids": ["TestHandlerGarbageCollector.test_content", "impl:8", "TestHandlerGarbageCollector"], "tokens": 355}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestHandlerGarbageCollector(unittest.TestCase):\n    @skipUnless(has_ignite, \"Requires ignite\")\n    @parameterized.expand([TEST_CASE_0, TEST_CASE_1, TEST_CASE_2])\n    def test_content(self, data, trigger_event):\n        # set up engine\n        gb_count_dict = {}\n\n        def _train_func(engine, batch):\n            # store garbage collection counts\n            if trigger_event == Events.EPOCH_COMPLETED or trigger_event.lower() == \"epoch\":\n                if engine.state.iteration % engine.state.epoch_length == 1:\n                    gb_count_dict[engine.state.epoch] = gc.get_count()\n            elif trigger_event.lower() == \"iteration\":\n                gb_count_dict[engine.state.iteration] = gc.get_count()\n\n        engine = Engine(_train_func)\n\n        # set up testing handler\n        dataset = Dataset(data, transform=None)\n        data_loader = torch.utils.data.DataLoader(dataset, batch_size=1)\n        GarbageCollector(trigger_event=trigger_event, log_level=30).attach(engine)\n\n        engine.run(data_loader, max_epochs=5)\n\n        first_count = 0\n        for iter, gb_count in gb_count_dict.items():\n            # At least one zero-generation object is collected\n            # self.assertGreaterEqual(gb_count[0], 0)\n            if iter > 1:\n                # Since we are collecting all objects from all generations manually at each call,\n                # starting from the second call, there shouldn't be any 1st and 2nd\n                # generation objects available to collect.\n                self.assertEqual(gb_count[1], first_count)\n                self.assertEqual(gb_count[2], first_count)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_hausdorff_distance.py_unittest_create_spherical_seg_3d.return.image": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_hausdorff_distance.py_unittest_create_spherical_seg_3d.return.image", "embedding": null, "metadata": {"file_path": "tests/test_handler_hausdorff_distance.py", "file_name": "test_handler_hausdorff_distance.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 46, "span_ids": ["create_spherical_seg_3d", "docstring"], "tokens": 317}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom typing import Tuple\n\nimport numpy as np\nimport torch\nfrom ignite.engine import Engine\n\nfrom monai.handlers import HausdorffDistance\n\n\ndef create_spherical_seg_3d(\n    radius: float = 20.0, centre: Tuple[int, int, int] = (49, 49, 49), im_shape: Tuple[int, int, int] = (99, 99, 99)\n) -> np.ndarray:\n    \"\"\"\n    Return a 3D image with a sphere inside. Voxel values will be\n    1 inside the sphere, and 0 elsewhere.\n\n    Args:\n        radius: radius of sphere (in terms of number of voxels, can be partial)\n        centre: location of sphere centre.\n        im_shape: shape of image to create\n\n    See also:\n        :py:meth:`~create_test_image_3d`\n    \"\"\"\n    # Create image\n    image = np.zeros(im_shape, dtype=np.int32)\n    spy, spx, spz = np.ogrid[\n        -centre[0] : im_shape[0] - centre[0], -centre[1] : im_shape[1] - centre[1], -centre[2] : im_shape[2] - centre[2]\n    ]\n    circle = (spx * spx + spy * spy + spz * spz) <= radius * radius\n\n    image[circle] = 1\n    image[~circle] = 0\n    return image", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_hausdorff_distance.py_sampler_sphere_TEST_SAMPLE_4._sampler_sphere_zeros_sa": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_hausdorff_distance.py_sampler_sphere_TEST_SAMPLE_4._sampler_sphere_zeros_sa", "embedding": null, "metadata": {"file_path": "tests/test_handler_hausdorff_distance.py", "file_name": "test_handler_hausdorff_distance.py", "file_type": "text/x-python", "category": "test", "start_line": 51, "end_line": 59, "span_ids": ["impl"], "tokens": 149}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "sampler_sphere = torch.Tensor(create_spherical_seg_3d(radius=20, centre=(20, 20, 20))).unsqueeze(0).unsqueeze(0)\n# test input a list of channel-first tensor\nsampler_sphere_gt = [torch.Tensor(create_spherical_seg_3d(radius=20, centre=(10, 20, 20))).unsqueeze(0)]\nsampler_sphere_zeros = torch.zeros_like(sampler_sphere)\n\nTEST_SAMPLE_1 = [sampler_sphere, sampler_sphere_gt]\nTEST_SAMPLE_2 = [sampler_sphere_gt, sampler_sphere_gt]\nTEST_SAMPLE_3 = [sampler_sphere_zeros, sampler_sphere_gt]\nTEST_SAMPLE_4 = [sampler_sphere_zeros, sampler_sphere_zeros]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_mean_dice.py_TestHandlerMeanDice.test_shape_mismatch_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_mean_dice.py_TestHandlerMeanDice.test_shape_mismatch_", "embedding": null, "metadata": {"file_path": "tests/test_handler_mean_dice.py", "file_name": "test_handler_mean_dice.py", "file_type": "text/x-python", "category": "test", "start_line": 57, "end_line": 73, "span_ids": ["impl:7", "TestHandlerMeanDice.test_shape_mismatch"], "tokens": 163}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestHandlerMeanDice(unittest.TestCase):\n\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2])\n    def test_shape_mismatch(self, input_params, _expected_avg, _details_shape):\n        dice_metric = MeanDice(**input_params)\n        with self.assertRaises((AssertionError, ValueError)):\n            y_pred = torch.Tensor([[0, 1], [1, 0]])\n            y = torch.ones((2, 3))\n            dice_metric.update([y_pred, y])\n\n        with self.assertRaises((AssertionError, ValueError)):\n            y_pred = torch.Tensor([[0, 1], [1, 0]])\n            y = torch.ones((3, 2))\n            dice_metric.update([y_pred, y])\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_metric_logger.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_metric_logger.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_handler_metric_logger.py", "file_name": "test_handler_metric_logger.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 61, "span_ids": ["TestHandlerMetricLogger.test_metric_logging", "TestHandlerMetricLogger", "impl:6", "docstring"], "tokens": 324}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\n\nfrom monai.utils import optional_import\nfrom tests.utils import SkipIfNoModule\n\ntry:\n    _, has_ignite = optional_import(\"ignite\")\n    from ignite.engine import Engine, Events\n\n    from monai.handlers import MetricLogger\nexcept ImportError:\n    has_ignite = False\n\n\nclass TestHandlerMetricLogger(unittest.TestCase):\n    @SkipIfNoModule(\"ignite\")\n    def test_metric_logging(self):\n        dummy_name = \"dummy\"\n\n        # set up engine\n        def _train_func(engine, batch):\n            return torch.tensor(0.0)\n\n        engine = Engine(_train_func)\n\n        # set up dummy metric\n        @engine.on(Events.EPOCH_COMPLETED)\n        def _update_metric(engine):\n            engine.state.metrics[dummy_name] = 1\n\n        # set up testing handler\n        handler = MetricLogger(loss_transform=lambda output: output.item())\n        handler.attach(engine)\n\n        engine.run(range(3), max_epochs=2)\n\n        expected_loss = [(1, 0.0), (2, 0.0), (3, 0.0), (4, 0.0), (5, 0.0), (6, 0.0)]\n        expected_metric = [(4, 1), (5, 1), (6, 1)]\n\n        self.assertSetEqual({dummy_name}, set(handler.metrics))\n\n        self.assertListEqual(expected_loss, handler.loss)\n        self.assertListEqual(expected_metric, handler.metrics[dummy_name])\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_metrics_saver.py_csv_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_metrics_saver.py_csv_", "embedding": null, "metadata": {"file_path": "tests/test_handler_metrics_saver.py", "file_name": "test_handler_metrics_saver.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 85, "span_ids": ["impl", "TestHandlerMetricsSaver.test_content", "TestHandlerMetricsSaver", "docstring"], "tokens": 741}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import csv\nimport os\nimport tempfile\nimport unittest\n\nimport torch\nfrom ignite.engine import Engine, Events\n\nfrom monai.handlers import MetricsSaver\n\n\nclass TestHandlerMetricsSaver(unittest.TestCase):\n    def test_content(self):\n        with tempfile.TemporaryDirectory() as tempdir:\n            metrics_saver = MetricsSaver(\n                save_dir=tempdir,\n                metrics=[\"metric1\", \"metric2\"],\n                metric_details=[\"metric3\", \"metric4\"],\n                batch_transform=lambda x: x[\"image_meta_dict\"],\n                summary_ops=[\"mean\", \"median\", \"max\", \"5percentile\", \"95percentile\", \"notnans\"],\n            )\n            # set up engine\n            data = [\n                {\"image_meta_dict\": {\"filename_or_obj\": [\"filepath1\"]}},\n                {\"image_meta_dict\": {\"filename_or_obj\": [\"filepath2\"]}},\n            ]\n\n            def _val_func(engine, batch):\n                pass\n\n            engine = Engine(_val_func)\n\n            @engine.on(Events.EPOCH_COMPLETED)\n            def _save_metrics(engine):\n                engine.state.metrics = {\"metric1\": 1, \"metric2\": 2}\n                engine.state.metric_details = {\n                    \"metric3\": torch.tensor([[1, 2], [2, 3]]),\n                    \"metric4\": torch.tensor([[5, 6], [7, torch.tensor(float(\"nan\"))]]),\n                }\n\n            metrics_saver.attach(engine)\n            engine.run(data, max_epochs=1)\n\n            # check the metrics.csv and content\n            self.assertTrue(os.path.exists(os.path.join(tempdir, \"metrics.csv\")))\n            with open(os.path.join(tempdir, \"metrics.csv\")) as f:\n                f_csv = csv.reader(f)\n                for i, row in enumerate(f_csv):\n                    self.assertEqual(row, [f\"metric{i + 1}\\t{i + 1}\"])\n            self.assertTrue(os.path.exists(os.path.join(tempdir, \"metric3_raw.csv\")))\n            # check the metric_raw.csv and content\n            with open(os.path.join(tempdir, \"metric3_raw.csv\")) as f:\n                f_csv = csv.reader(f)\n                for i, row in enumerate(f_csv):\n                    if i > 0:\n                        self.assertEqual(row, [f\"filepath{i}\\t{float(i)}\\t{float(i + 1)}\\t{i + 0.5}\"])\n            self.assertTrue(os.path.exists(os.path.join(tempdir, \"metric3_summary.csv\")))\n            # check the metric_summary.csv and content\n            with open(os.path.join(tempdir, \"metric4_summary.csv\")) as f:\n                f_csv = csv.reader(f)\n                for i, row in enumerate(f_csv):\n                    if i == 1:\n                        self.assertEqual(row, [\"class0\\t6.0000\\t6.0000\\t7.0000\\t5.1000\\t6.9000\\t2.0000\"])\n                    elif i == 2:\n                        self.assertEqual(row, [\"class1\\t6.0000\\t6.0000\\t6.0000\\t6.0000\\t6.0000\\t1.0000\"])\n                    elif i == 3:\n                        self.assertEqual(row, [\"mean\\t6.2500\\t6.2500\\t7.0000\\t5.5750\\t6.9250\\t2.0000\"])\n            self.assertTrue(os.path.exists(os.path.join(tempdir, \"metric4_raw.csv\")))\n            self.assertTrue(os.path.exists(os.path.join(tempdir, \"metric3_summary.csv\")))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_metrics_saver_dist.py_csv_DistributedMetricsSaver._run.engine_run_data_max_epoc": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_metrics_saver_dist.py_csv_DistributedMetricsSaver._run.engine_run_data_max_epoc", "embedding": null, "metadata": {"file_path": "tests/test_handler_metrics_saver_dist.py", "file_name": "test_handler_metrics_saver_dist.py", "file_type": "text/x-python", "category": "test", "start_line": 13, "end_line": 80, "span_ids": ["DistributedMetricsSaver._run", "DistributedMetricsSaver.test_content", "DistributedMetricsSaver", "docstring"], "tokens": 575}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import csv\nimport os\nimport tempfile\nimport unittest\n\nimport torch\nimport torch.distributed as dist\nfrom ignite.engine import Engine, Events\n\nfrom monai.handlers import MetricsSaver\nfrom monai.utils import evenly_divisible_all_gather\nfrom tests.utils import DistCall, DistTestCase\n\n\nclass DistributedMetricsSaver(DistTestCase):\n    @DistCall(nnodes=1, nproc_per_node=2)\n    def test_content(self):\n        with tempfile.TemporaryDirectory() as tempdir:\n            self._run(tempdir)\n\n    def _run(self, tempdir):\n        fnames = [\"aaa\" * 300, \"bbb\" * 301, \"ccc\" * 302]\n\n        metrics_saver = MetricsSaver(\n            save_dir=tempdir,\n            metrics=[\"metric1\", \"metric2\"],\n            metric_details=[\"metric3\", \"metric4\"],\n            batch_transform=lambda x: x[\"image_meta_dict\"],\n            summary_ops=\"*\",\n        )\n\n        def _val_func(engine, batch):\n            pass\n\n        engine = Engine(_val_func)\n\n        if dist.get_rank() == 0:\n            data = [{\"image_meta_dict\": {\"filename_or_obj\": [fnames[0]]}}]\n\n            @engine.on(Events.EPOCH_COMPLETED)\n            def _save_metrics0(engine):\n                engine.state.metrics = {\"metric1\": 1, \"metric2\": 2}\n                engine.state.metric_details = {\"metric3\": torch.tensor([[1, 2]]), \"metric4\": torch.tensor([[5, 6]])}\n\n        if dist.get_rank() == 1:\n            # different ranks have different data length\n            data = [\n                {\"image_meta_dict\": {\"filename_or_obj\": [fnames[1]]}},\n                {\"image_meta_dict\": {\"filename_or_obj\": [fnames[2]]}},\n            ]\n\n            @engine.on(Events.EPOCH_COMPLETED)\n            def _save_metrics1(engine):\n                engine.state.metrics = {\"metric1\": 1, \"metric2\": 2}\n                engine.state.metric_details = {\n                    \"metric3\": torch.tensor([[2, 3], [3, 4]]),\n                    \"metric4\": torch.tensor([[6, 7], [7, 8]]),\n                }\n\n        @engine.on(Events.EPOCH_COMPLETED)\n        def _all_gather(engine):\n            scores = engine.state.metric_details[\"metric3\"]\n            engine.state.metric_details[\"metric3\"] = evenly_divisible_all_gather(data=scores, concat=True)\n            scores = engine.state.metric_details[\"metric4\"]\n            engine.state.metric_details[\"metric4\"] = evenly_divisible_all_gather(data=scores, concat=True)\n\n        metrics_saver.attach(engine)\n        engine.run(data, max_epochs=1)\n        # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_metrics_saver_dist.py_DistributedMetricsSaver._run.None_2_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_metrics_saver_dist.py_DistributedMetricsSaver._run.None_2_", "embedding": null, "metadata": {"file_path": "tests/test_handler_metrics_saver_dist.py", "file_name": "test_handler_metrics_saver_dist.py", "file_type": "text/x-python", "category": "test", "start_line": 85, "end_line": 117, "span_ids": ["impl", "DistributedMetricsSaver._run"], "tokens": 494}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DistributedMetricsSaver(DistTestCase):\n\n    def _run(self, tempdir):\n        # ... other code\n\n        if dist.get_rank() == 0:\n            # check the metrics.csv and content\n            self.assertTrue(os.path.exists(os.path.join(tempdir, \"metrics.csv\")))\n            with open(os.path.join(tempdir, \"metrics.csv\")) as f:\n                f_csv = csv.reader(f)\n                for i, row in enumerate(f_csv):\n                    self.assertEqual(row, [f\"metric{i + 1}\\t{i + 1}\"])\n            self.assertTrue(os.path.exists(os.path.join(tempdir, \"metric3_raw.csv\")))\n            # check the metric_raw.csv and content\n            with open(os.path.join(tempdir, \"metric3_raw.csv\")) as f:\n                f_csv = csv.reader(f)\n                for i, row in enumerate(f_csv):\n                    if i > 0:\n                        expected = [f\"{fnames[i-1]}\\t{float(i)}\\t{float(i + 1)}\\t{i + 0.5}\"]\n                        self.assertEqual(row, expected)\n            self.assertTrue(os.path.exists(os.path.join(tempdir, \"metric3_summary.csv\")))\n            # check the metric_summary.csv and content\n            with open(os.path.join(tempdir, \"metric3_summary.csv\")) as f:\n                f_csv = csv.reader(f)\n                for i, row in enumerate(f_csv):\n                    if i == 1:\n                        self.assertEqual(row, [\"class0\\t2.0000\\t2.0000\\t3.0000\\t1.0000\\t2.8000\\t0.8165\\t3.0000\"])\n                    elif i == 2:\n                        self.assertEqual(row, [\"class1\\t3.0000\\t3.0000\\t4.0000\\t2.0000\\t3.8000\\t0.8165\\t3.0000\"])\n                    elif i == 3:\n                        self.assertEqual(row, [\"mean\\t2.5000\\t2.5000\\t3.5000\\t1.5000\\t3.3000\\t0.8165\\t3.0000\"])\n            self.assertTrue(os.path.exists(os.path.join(tempdir, \"metric4_raw.csv\")))\n            self.assertTrue(os.path.exists(os.path.join(tempdir, \"metric4_summary.csv\")))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_parameter_scheduler.py_unittest_TestHandlerParameterScheduler.test_linear_scheduler.None_8": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_parameter_scheduler.py_unittest_TestHandlerParameterScheduler.test_linear_scheduler.None_8", "embedding": null, "metadata": {"file_path": "tests/test_handler_parameter_scheduler.py", "file_name": "test_handler_parameter_scheduler.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 75, "span_ids": ["ToyNet.__init__", "TestHandlerParameterScheduler", "ToyNet.set_value", "ToyNet.forward", "ToyNet.get_value", "docstring", "ToyNet", "TestHandlerParameterScheduler.test_linear_scheduler"], "tokens": 498}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom ignite.engine import Engine, Events\nfrom torch.nn import Module\n\nfrom monai.handlers.parameter_scheduler import ParamSchedulerHandler\n\n\nclass ToyNet(Module):\n    def __init__(self, value):\n        super().__init__()\n        self.value = value\n\n    def forward(self, input):\n        return input\n\n    def get_value(self):\n        return self.value\n\n    def set_value(self, value):\n        self.value = value\n\n\nclass TestHandlerParameterScheduler(unittest.TestCase):\n    def test_linear_scheduler(self):\n        # Testing step_constant\n        net = ToyNet(value=-1)\n        engine = Engine(lambda e, b: None)\n        ParamSchedulerHandler(\n            parameter_setter=net.set_value,\n            value_calculator=\"linear\",\n            vc_kwargs={\"initial_value\": 0, \"step_constant\": 2, \"step_max_value\": 5, \"max_value\": 10},\n            epoch_level=True,\n            event=Events.EPOCH_COMPLETED,\n        ).attach(engine)\n        engine.run([0] * 8, max_epochs=2)\n        torch.testing.assert_allclose(net.get_value(), 0)\n\n        # Testing linear increase\n        net = ToyNet(value=-1)\n        engine = Engine(lambda e, b: None)\n        ParamSchedulerHandler(\n            parameter_setter=net.set_value,\n            value_calculator=\"linear\",\n            vc_kwargs={\"initial_value\": 0, \"step_constant\": 2, \"step_max_value\": 5, \"max_value\": 10},\n            epoch_level=True,\n            event=Events.EPOCH_COMPLETED,\n        ).attach(engine)\n        engine.run([0] * 8, max_epochs=3)\n        torch.testing.assert_allclose(net.get_value(), 3.333333, atol=0.001, rtol=0.0)\n\n        # Testing max_value\n        net = ToyNet(value=-1)\n        engine = Engine(lambda e, b: None)\n        ParamSchedulerHandler(\n            parameter_setter=net.set_value,\n            value_calculator=\"linear\",\n            vc_kwargs={\"initial_value\": 0, \"step_constant\": 2, \"step_max_value\": 5, \"max_value\": 10},\n            epoch_level=True,\n            event=Events.EPOCH_COMPLETED,\n        ).attach(engine)\n        engine.run([0] * 8, max_epochs=10)\n        torch.testing.assert_allclose(net.get_value(), 10)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_parameter_scheduler.py_TestHandlerParameterScheduler.test_exponential_scheduler_TestHandlerParameterScheduler.test_exponential_scheduler.torch_testing_assert_allc": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_parameter_scheduler.py_TestHandlerParameterScheduler.test_exponential_scheduler_TestHandlerParameterScheduler.test_exponential_scheduler.torch_testing_assert_allc", "embedding": null, "metadata": {"file_path": "tests/test_handler_parameter_scheduler.py", "file_name": "test_handler_parameter_scheduler.py", "file_type": "text/x-python", "category": "test", "start_line": 66, "end_line": 77, "span_ids": ["TestHandlerParameterScheduler.test_exponential_scheduler"], "tokens": 132}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestHandlerParameterScheduler(unittest.TestCase):\n\n    def test_exponential_scheduler(self):\n        net = ToyNet(value=-1)\n        engine = Engine(lambda e, b: None)\n        ParamSchedulerHandler(\n            parameter_setter=net.set_value,\n            value_calculator=\"exponential\",\n            vc_kwargs={\"initial_value\": 10, \"gamma\": 0.99},\n            epoch_level=True,\n            event=Events.EPOCH_COMPLETED,\n        ).attach(engine)\n        engine.run([0] * 8, max_epochs=2)\n        torch.testing.assert_allclose(net.get_value(), 10 * 0.99 * 0.99)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_parameter_scheduler.py_TestHandlerParameterScheduler.test_step_scheduler_TestHandlerParameterScheduler.test_step_scheduler.torch_testing_assert_allc": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_parameter_scheduler.py_TestHandlerParameterScheduler.test_step_scheduler_TestHandlerParameterScheduler.test_step_scheduler.torch_testing_assert_allc", "embedding": null, "metadata": {"file_path": "tests/test_handler_parameter_scheduler.py", "file_name": "test_handler_parameter_scheduler.py", "file_type": "text/x-python", "category": "test", "start_line": 79, "end_line": 90, "span_ids": ["TestHandlerParameterScheduler.test_step_scheduler"], "tokens": 137}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestHandlerParameterScheduler(unittest.TestCase):\n\n    def test_step_scheduler(self):\n        net = ToyNet(value=-1)\n        engine = Engine(lambda e, b: None)\n        ParamSchedulerHandler(\n            parameter_setter=net.set_value,\n            value_calculator=\"step\",\n            vc_kwargs={\"initial_value\": 10, \"gamma\": 0.99, \"step_size\": 5},\n            epoch_level=True,\n            event=Events.EPOCH_COMPLETED,\n        ).attach(engine)\n        engine.run([0] * 8, max_epochs=10)\n        torch.testing.assert_allclose(net.get_value(), 10 * 0.99 * 0.99)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_parameter_scheduler.py_TestHandlerParameterScheduler.test_multistep_scheduler_TestHandlerParameterScheduler.test_multistep_scheduler.torch_testing_assert_allc": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_parameter_scheduler.py_TestHandlerParameterScheduler.test_multistep_scheduler_TestHandlerParameterScheduler.test_multistep_scheduler.torch_testing_assert_allc", "embedding": null, "metadata": {"file_path": "tests/test_handler_parameter_scheduler.py", "file_name": "test_handler_parameter_scheduler.py", "file_type": "text/x-python", "category": "test", "start_line": 92, "end_line": 103, "span_ids": ["TestHandlerParameterScheduler.test_multistep_scheduler"], "tokens": 144}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestHandlerParameterScheduler(unittest.TestCase):\n\n    def test_multistep_scheduler(self):\n        net = ToyNet(value=-1)\n        engine = Engine(lambda e, b: None)\n        ParamSchedulerHandler(\n            parameter_setter=net.set_value,\n            value_calculator=\"multistep\",\n            vc_kwargs={\"initial_value\": 10, \"gamma\": 0.99, \"milestones\": [3, 6]},\n            epoch_level=True,\n            event=Events.EPOCH_COMPLETED,\n        ).attach(engine)\n        engine.run([0] * 8, max_epochs=10)\n        torch.testing.assert_allclose(net.get_value(), 10 * 0.99 * 0.99)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_parameter_scheduler.py_TestHandlerParameterScheduler.test_custom_scheduler_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_parameter_scheduler.py_TestHandlerParameterScheduler.test_custom_scheduler_", "embedding": null, "metadata": {"file_path": "tests/test_handler_parameter_scheduler.py", "file_name": "test_handler_parameter_scheduler.py", "file_type": "text/x-python", "category": "test", "start_line": 105, "end_line": 124, "span_ids": ["TestHandlerParameterScheduler.test_custom_scheduler", "impl"], "tokens": 169}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestHandlerParameterScheduler(unittest.TestCase):\n\n    def test_custom_scheduler(self):\n        def custom_logic(initial_value, gamma, current_step):\n            return initial_value * gamma ** (current_step % 9)\n\n        net = ToyNet(value=-1)\n        engine = Engine(lambda e, b: None)\n        ParamSchedulerHandler(\n            parameter_setter=net.set_value,\n            value_calculator=custom_logic,\n            vc_kwargs={\"initial_value\": 10, \"gamma\": 0.99},\n            epoch_level=True,\n            event=Events.EPOCH_COMPLETED,\n        ).attach(engine)\n        engine.run([0] * 8, max_epochs=2)\n        torch.testing.assert_allclose(net.get_value(), 10 * 0.99 * 0.99)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_prob_map_producer.py_os_TEST_CASE_2._temp_image_inference_ou": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_prob_map_producer.py_os_TEST_CASE_2._temp_image_inference_ou", "embedding": null, "metadata": {"file_path": "tests/test_handler_prob_map_producer.py", "file_name": "test_handler_prob_map_producer.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 28, "span_ids": ["docstring"], "tokens": 123}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport unittest\n\nimport numpy as np\nimport torch\nfrom ignite.engine import Engine\nfrom parameterized import parameterized\nfrom torch.utils.data import DataLoader\n\nfrom monai.apps.pathology.handlers import ProbMapProducer\nfrom monai.data.dataset import Dataset\nfrom monai.engines import Evaluator\nfrom monai.handlers import ValidationHandler\n\nTEST_CASE_0 = [\"temp_image_inference_output_1\", 2]\nTEST_CASE_1 = [\"temp_image_inference_output_2\", 9]\nTEST_CASE_2 = [\"temp_image_inference_output_3\", 1000]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_prob_map_producer.py_TestDataset_TestEvaluator._iteration.return.batchdata": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_prob_map_producer.py_TestDataset_TestEvaluator._iteration.return.batchdata", "embedding": null, "metadata": {"file_path": "tests/test_handler_prob_map_producer.py", "file_name": "test_handler_prob_map_producer.py", "file_type": "text/x-python", "category": "test", "start_line": 31, "end_line": 49, "span_ids": ["TestDataset.__len__", "TestEvaluator", "TestDataset.__getitem__", "TestEvaluator._iteration", "TestDataset", "TestDataset.__init__"], "tokens": 147}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestDataset(Dataset):\n    def __init__(self, name, size):\n        super().__init__(\n            data=[\n                {\"name\": name, \"mask_shape\": (size, size), \"mask_locations\": [[i, i] for i in range(size)], \"level\": 0}\n            ]\n        )\n        self.len = size\n\n    def __len__(self):\n        return self.len\n\n    def __getitem__(self, index):\n        return {\"name\": self.data[0][\"name\"], \"mask_location\": self.data[0][\"mask_locations\"][index], \"pred\": index + 1}\n\n\nclass TestEvaluator(Evaluator):\n    def _iteration(self, engine, batchdata):\n        return batchdata", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_prob_map_producer.py_TestHandlerProbMapGenerator_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_prob_map_producer.py_TestHandlerProbMapGenerator_", "embedding": null, "metadata": {"file_path": "tests/test_handler_prob_map_producer.py", "file_name": "test_handler_prob_map_producer.py", "file_type": "text/x-python", "category": "test", "start_line": 52, "end_line": 84, "span_ids": ["TestHandlerProbMapGenerator.test_prob_map_generator", "TestHandlerProbMapGenerator", "impl:7"], "tokens": 253}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestHandlerProbMapGenerator(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_0, TEST_CASE_1, TEST_CASE_2])\n    def test_prob_map_generator(self, name, size):\n        # set up dataset\n        dataset = TestDataset(name, size)\n        data_loader = DataLoader(dataset, batch_size=1)\n\n        # set up engine\n        def inference(enging, batch):\n            pass\n\n        engine = Engine(inference)\n\n        # add ProbMapGenerator() to evaluator\n        output_dir = os.path.join(os.path.dirname(__file__), \"testing_data\")\n        prob_map_gen = ProbMapProducer(output_dir=output_dir)\n\n        evaluator = TestEvaluator(torch.device(\"cpu:0\"), data_loader, size, val_handlers=[prob_map_gen])\n\n        # set up validation handler\n        validation = ValidationHandler(interval=1, validator=None)\n        validation.attach(engine)\n        validation.set_validator(validator=evaluator)\n\n        engine.run(data_loader)\n\n        prob_map = np.load(os.path.join(output_dir, name + \".npy\"))\n        self.assertListEqual(np.diag(prob_map).astype(int).tolist(), list(range(1, size + 1)))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_rocauc_dist.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_rocauc_dist.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_handler_rocauc_dist.py", "file_name": "test_handler_rocauc_dist.py", "file_type": "text/x-python", "category": "test", "start_line": 13, "end_line": 54, "span_ids": ["DistributedROCAUC.test_compute", "impl", "DistributedROCAUC", "docstring"], "tokens": 376}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nimport torch.distributed as dist\n\nfrom monai.handlers import ROCAUC\nfrom monai.transforms import Activations, AsDiscrete\nfrom tests.utils import DistCall, DistTestCase\n\n\nclass DistributedROCAUC(DistTestCase):\n    @DistCall(nnodes=1, nproc_per_node=2, node_rank=0)\n    def test_compute(self):\n        auc_metric = ROCAUC()\n        act = Activations(softmax=True)\n        to_onehot = AsDiscrete(to_onehot=2)\n\n        device = f\"cuda:{dist.get_rank()}\" if torch.cuda.is_available() else \"cpu\"\n        if dist.get_rank() == 0:\n            y_pred = [torch.tensor([0.1, 0.9], device=device), torch.tensor([0.3, 1.4], device=device)]\n            y = [torch.tensor([0], device=device), torch.tensor([1], device=device)]\n\n        if dist.get_rank() == 1:\n            y_pred = [\n                torch.tensor([0.2, 0.1], device=device),\n                torch.tensor([0.1, 0.5], device=device),\n                torch.tensor([0.3, 0.4], device=device),\n            ]\n            y = [torch.tensor([0], device=device), torch.tensor([1], device=device), torch.tensor([1], device=device)]\n\n        y_pred = [act(p) for p in y_pred]\n        y = [to_onehot(y_) for y_ in y]\n        auc_metric.update([y_pred, y])\n\n        result = auc_metric.compute()\n        np.testing.assert_allclose(0.66667, result, rtol=1e-4)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_surface_distance.py_unittest_create_spherical_seg_3d.return.image": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_surface_distance.py_unittest_create_spherical_seg_3d.return.image", "embedding": null, "metadata": {"file_path": "tests/test_handler_surface_distance.py", "file_name": "test_handler_surface_distance.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 46, "span_ids": ["create_spherical_seg_3d", "docstring"], "tokens": 314}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom typing import Tuple\n\nimport numpy as np\nimport torch\nfrom ignite.engine import Engine\n\nfrom monai.handlers import SurfaceDistance\n\n\ndef create_spherical_seg_3d(\n    radius: float = 20.0, centre: Tuple[int, int, int] = (49, 49, 49), im_shape: Tuple[int, int, int] = (99, 99, 99)\n) -> np.ndarray:\n    \"\"\"\n    Return a 3D image with a sphere inside. Voxel values will be\n    1 inside the sphere, and 0 elsewhere.\n\n    Args:\n        radius: radius of sphere (in terms of number of voxels, can be partial)\n        centre: location of sphere centre.\n        im_shape: shape of image to create\n\n    See also:\n        :py:meth:`~create_test_image_3d`\n    \"\"\"\n    # Create image\n    image = np.zeros(im_shape, dtype=np.int32)\n    spy, spx, spz = np.ogrid[\n        -centre[0] : im_shape[0] - centre[0], -centre[1] : im_shape[1] - centre[1], -centre[2] : im_shape[2] - centre[2]\n    ]\n    circle = (spx * spx + spy * spy + spz * spz) <= radius * radius\n\n    image[circle] = 1\n    image[~circle] = 0\n    return image", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_surface_distance.py_sampler_sphere_TEST_SAMPLE_4._sampler_sphere_zeros_sa": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_surface_distance.py_sampler_sphere_TEST_SAMPLE_4._sampler_sphere_zeros_sa", "embedding": null, "metadata": {"file_path": "tests/test_handler_surface_distance.py", "file_name": "test_handler_surface_distance.py", "file_type": "text/x-python", "category": "test", "start_line": 51, "end_line": 59, "span_ids": ["impl"], "tokens": 149}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "sampler_sphere = torch.Tensor(create_spherical_seg_3d(radius=20, centre=(20, 20, 20))).unsqueeze(0).unsqueeze(0)\n# test input a list of channel-first tensor\nsampler_sphere_gt = [torch.Tensor(create_spherical_seg_3d(radius=20, centre=(10, 20, 20))).unsqueeze(0)]\nsampler_sphere_zeros = torch.zeros_like(sampler_sphere)\n\nTEST_SAMPLE_1 = [sampler_sphere, sampler_sphere_gt]\nTEST_SAMPLE_2 = [sampler_sphere_gt, sampler_sphere_gt]\nTEST_SAMPLE_3 = [sampler_sphere_zeros, sampler_sphere_gt]\nTEST_SAMPLE_4 = [sampler_sphere_zeros, sampler_sphere_zeros]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_hashing.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_hashing.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_hashing.py", "file_name": "test_hashing.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 57, "span_ids": ["TestPickleHashing", "TestJSONHashing", "impl", "TestJSONHashing.test_json", "TestPickleHashing.test_pickle", "docstring"], "tokens": 312}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\n\nfrom monai.data import json_hashing, pickle_hashing\nfrom monai.utils import set_determinism\n\n\nclass TestPickleHashing(unittest.TestCase):\n    def test_pickle(self):\n        set_determinism(0)\n        data1 = np.random.rand(10)\n        data2 = np.random.rand(10)\n        set_determinism(0)\n        data3 = np.random.rand(10)\n        data4 = np.random.rand(10)\n        set_determinism(None)\n\n        h1 = pickle_hashing(data1)\n        h2 = pickle_hashing(data3)\n        self.assertEqual(h1, h2)\n\n        data_dict1 = {\"b\": data2, \"a\": data1}\n        data_dict2 = {\"a\": data3, \"b\": data4}\n\n        h1 = pickle_hashing(data_dict1)\n        h2 = pickle_hashing(data_dict2)\n        self.assertEqual(h1, h2)\n\n        with self.assertRaises(TypeError):\n            json_hashing(data_dict1)\n\n\nclass TestJSONHashing(unittest.TestCase):\n    def test_json(self):\n        data_dict1 = {\"b\": \"str2\", \"a\": \"str1\"}\n        data_dict2 = {\"a\": \"str1\", \"b\": \"str2\"}\n\n        h1 = json_hashing(data_dict1)\n        h2 = json_hashing(data_dict2)\n        self.assertEqual(h1, h2)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_hausdorff_distance.py_TEST_CASES_TEST_CASES_NANS._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_hausdorff_distance.py_TEST_CASES_TEST_CASES_NANS._", "embedding": null, "metadata": {"file_path": "tests/test_hausdorff_distance.py", "file_name": "test_hausdorff_distance.py", "file_type": "text/x-python", "category": "test", "start_line": 49, "end_line": 106, "span_ids": ["impl:3", "impl"], "tokens": 579}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "TEST_CASES = [\n    [[create_spherical_seg_3d(), create_spherical_seg_3d(), 1], [0, 0, 0, 0, 0, 0]],\n    [\n        [\n            create_spherical_seg_3d(radius=20, centre=(20, 20, 20)),\n            create_spherical_seg_3d(radius=20, centre=(19, 19, 19)),\n        ],\n        [1.7320508075688772, 1.7320508075688772, 1, 1, 3, 3],\n    ],\n    [\n        [\n            create_spherical_seg_3d(radius=33, centre=(19, 33, 22)),\n            create_spherical_seg_3d(radius=33, centre=(20, 33, 22)),\n        ],\n        [1, 1, 1, 1, 1, 1],\n    ],\n    [\n        [\n            create_spherical_seg_3d(radius=20, centre=(20, 33, 22)),\n            create_spherical_seg_3d(radius=40, centre=(20, 33, 22)),\n        ],\n        [20.09975124224178, 20.223748416156685, 15, 20, 24, 35],\n    ],\n    [\n        [\n            # pred does not have foreground (but gt has), the metric should be inf\n            np.zeros([99, 99, 99]),\n            create_spherical_seg_3d(radius=40, centre=(20, 33, 22)),\n        ],\n        [np.inf, np.inf, np.inf, np.inf, np.inf, np.inf],\n    ],\n    [\n        [\n            # gt does not have foreground (but pred has), the metric should be inf\n            create_spherical_seg_3d(),\n            np.zeros([99, 99, 99]),\n        ],\n        [np.inf, np.inf, np.inf, np.inf, np.inf, np.inf],\n    ],\n    [\n        [\n            create_spherical_seg_3d(radius=20, centre=(20, 33, 22)),\n            create_spherical_seg_3d(radius=40, centre=(20, 33, 22)),\n            95,\n        ],\n        [19.924858845171276, 20.09975124224178, 14, 18, 22, 33],\n    ],\n]\n\nTEST_CASES_NANS = [\n    [\n        [\n            # both pred and gt do not have foreground, metric and not_nans should be 0\n            np.zeros([99, 99, 99]),\n            np.zeros([99, 99, 99]),\n        ]\n    ]\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_hausdorff_distance.py_TestHausdorffDistance_TestHausdorffDistance.test_value.for_metric_in_euclidean.for_directed_in_True_Fa.ct_1": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_hausdorff_distance.py_TestHausdorffDistance_TestHausdorffDistance.test_value.for_metric_in_euclidean.for_directed_in_True_Fa.ct_1", "embedding": null, "metadata": {"file_path": "tests/test_hausdorff_distance.py", "file_name": "test_hausdorff_distance.py", "file_type": "text/x-python", "category": "test", "start_line": 114, "end_line": 138, "span_ids": ["TestHausdorffDistance.test_value", "TestHausdorffDistance"], "tokens": 323}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestHausdorffDistance(unittest.TestCase):\n    @parameterized.expand(TEST_CASES)\n    def test_value(self, input_data, expected_value):\n        percentile = None\n        if len(input_data) == 3:\n            [seg_1, seg_2, percentile] = input_data\n        else:\n            [seg_1, seg_2] = input_data\n        ct = 0\n        seg_1 = torch.tensor(seg_1)\n        seg_2 = torch.tensor(seg_2)\n        for metric in [\"euclidean\", \"chessboard\", \"taxicab\"]:\n            for directed in [True, False]:\n                hd_metric = HausdorffDistanceMetric(\n                    include_background=False, distance_metric=metric, percentile=percentile, directed=directed\n                )\n                # shape of seg_1, seg_2 are: HWD, converts to BNHWD\n                batch, n_class = 2, 3\n                batch_seg_1 = seg_1.unsqueeze(0).unsqueeze(0).repeat([batch, n_class, 1, 1, 1])\n                batch_seg_2 = seg_2.unsqueeze(0).unsqueeze(0).repeat([batch, n_class, 1, 1, 1])\n                hd_metric(batch_seg_1, batch_seg_2)\n                result = hd_metric.aggregate()\n                expected_value_curr = expected_value[ct]\n                np.testing.assert_allclose(expected_value_curr, result, rtol=1e-7)\n                ct += 1", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_hausdorff_distance.py_TestHausdorffDistance.test_nans_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_hausdorff_distance.py_TestHausdorffDistance.test_nans_", "embedding": null, "metadata": {"file_path": "tests/test_hausdorff_distance.py", "file_name": "test_hausdorff_distance.py", "file_type": "text/x-python", "category": "test", "start_line": 140, "end_line": 156, "span_ids": ["TestHausdorffDistance.test_nans", "impl:5"], "tokens": 198}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestHausdorffDistance(unittest.TestCase):\n\n    @parameterized.expand(TEST_CASES_NANS)\n    def test_nans(self, input_data):\n        [seg_1, seg_2] = input_data\n        seg_1 = torch.tensor(seg_1)\n        seg_2 = torch.tensor(seg_2)\n        hd_metric = HausdorffDistanceMetric(include_background=False, get_not_nans=True)\n        batch_seg_1 = seg_1.unsqueeze(0).unsqueeze(0)\n        batch_seg_2 = seg_2.unsqueeze(0).unsqueeze(0)\n        hd_metric(batch_seg_1, batch_seg_2)\n        result, not_nans = hd_metric.aggregate()\n        np.testing.assert_allclose(0, result, rtol=1e-7)\n        np.testing.assert_allclose(0, not_nans, rtol=1e-7)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_highresnet.py_unittest_TEST_CASE_4._4_channel_1D_batch_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_highresnet.py_unittest_TEST_CASE_4._4_channel_1D_batch_", "embedding": null, "metadata": {"file_path": "tests/test_highresnet.py", "file_name": "test_highresnet.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 45, "span_ids": ["docstring"], "tokens": 370}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks import eval_mode\nfrom monai.networks.nets import HighResNet\nfrom tests.utils import DistTestCase, TimedCall, test_script_save\n\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\nTEST_CASE_1 = [  # single channel 3D, batch 16\n    {\"spatial_dims\": 3, \"in_channels\": 1, \"out_channels\": 3, \"norm_type\": \"instance\"},\n    (16, 1, 32, 24, 48),\n    (16, 3, 32, 24, 48),\n]\n\nTEST_CASE_2 = [  # 4-channel 3D, batch 1\n    {\"spatial_dims\": 3, \"in_channels\": 4, \"out_channels\": 3, \"acti_type\": \"relu6\"},\n    (1, 4, 17, 64, 48),\n    (1, 3, 17, 64, 48),\n]\n\nTEST_CASE_3 = [  # 4-channel 2D, batch 7\n    {\"spatial_dims\": 2, \"in_channels\": 4, \"out_channels\": 3},\n    (7, 4, 64, 48),\n    (7, 3, 64, 48),\n]\n\nTEST_CASE_4 = [  # 4-channel 1D, batch 16\n    {\"spatial_dims\": 1, \"in_channels\": 4, \"out_channels\": 3, \"dropout_prob\": 0.1},\n    (16, 4, 63),\n    (16, 3, 63),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_highresnet.py_TestHighResNet_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_highresnet.py_TestHighResNet_", "embedding": null, "metadata": {"file_path": "tests/test_highresnet.py", "file_name": "test_highresnet.py", "file_type": "text/x-python", "category": "test", "start_line": 48, "end_line": 66, "span_ids": ["TestHighResNet.test_shape", "TestHighResNet", "impl:11", "TestHighResNet.test_script"], "tokens": 165}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestHighResNet(DistTestCase):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4])\n    def test_shape(self, input_param, input_shape, expected_shape):\n        net = HighResNet(**input_param).to(device)\n        with eval_mode(net):\n            result = net.forward(torch.randn(input_shape).to(device))\n            self.assertEqual(result.shape, expected_shape)\n\n    @TimedCall(seconds=800, force_quit=True)\n    def test_script(self):\n        input_param, input_shape, expected_shape = TEST_CASE_1\n        net = HighResNet(**input_param)\n        test_data = torch.randn(input_shape)\n        test_script_save(net, test_data)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_hilbert_transform.py_unittest_create_expected_numpy_output.return.ht": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_hilbert_transform.py_unittest_create_expected_numpy_output.return.ht", "embedding": null, "metadata": {"file_path": "tests/test_hilbert_transform.py", "file_name": "test_hilbert_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 42, "span_ids": ["create_expected_numpy_output", "docstring"], "tokens": 251}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks.layers import HilbertTransform\nfrom monai.utils import InvalidPyTorchVersionError, OptionalImportError\nfrom tests.utils import (\n    SkipIfAtLeastPyTorchVersion,\n    SkipIfBeforePyTorchVersion,\n    SkipIfModule,\n    SkipIfNoModule,\n    skip_if_no_cuda,\n)\n\n\ndef create_expected_numpy_output(input_datum, **kwargs):\n\n    x = np.fft.fft(input_datum.cpu().numpy() if input_datum.device.type == \"cuda\" else input_datum.numpy(), **kwargs)\n    f = np.fft.fftfreq(x.shape[kwargs[\"axis\"]])\n    u = np.heaviside(f, 0.5)\n    new_dims_before = kwargs[\"axis\"]\n    new_dims_after = len(x.shape) - kwargs[\"axis\"] - 1\n    for _ in range(new_dims_before):\n        u = np.expand_dims(u, 0)\n    for _ in range(new_dims_after):\n        u = np.expand_dims(u, -1)\n    ht = np.fft.ifft(x * 2 * u, axis=kwargs[\"axis\"])\n\n    return ht", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_hilbert_transform.py_cpu__GPU_TEST_DATA": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_hilbert_transform.py_cpu__GPU_TEST_DATA", "embedding": null, "metadata": {"file_path": "tests/test_hilbert_transform.py", "file_name": "test_hilbert_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 48, "end_line": 108, "span_ids": ["impl:20", "impl"], "tokens": 703}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "cpu = torch.device(\"cpu\")\nn_samples = 500\nhann_windowed_sine = np.sin(2 * np.pi * 10 * np.linspace(0, 1, n_samples)) * np.hanning(n_samples)\n\n# CPU TEST DATA\n\ncpu_input_data = {}\ncpu_input_data[\"1D\"] = torch.as_tensor(hann_windowed_sine, device=cpu).unsqueeze(0).unsqueeze(0)\ncpu_input_data[\"2D\"] = (\n    torch.as_tensor(np.stack([hann_windowed_sine] * 10, axis=1), device=cpu).unsqueeze(0).unsqueeze(0)\n)\ncpu_input_data[\"3D\"] = (\n    torch.as_tensor(np.stack([np.stack([hann_windowed_sine] * 10, axis=1)] * 10, axis=2), device=cpu)\n    .unsqueeze(0)\n    .unsqueeze(0)\n)\ncpu_input_data[\"1D 2CH\"] = torch.as_tensor(np.stack([hann_windowed_sine] * 10, axis=1), device=cpu).unsqueeze(0)\ncpu_input_data[\"2D 2CH\"] = torch.as_tensor(\n    np.stack([np.stack([hann_windowed_sine] * 10, axis=1)] * 10, axis=2), device=cpu\n).unsqueeze(0)\n\n# SINGLE-CHANNEL CPU VALUE TESTS\n\nTEST_CASE_1D_SINE_CPU = [\n    {},  # args (empty, so use default)\n    cpu_input_data[\"1D\"],  # Input data: Random 1D signal\n    create_expected_numpy_output(cpu_input_data[\"1D\"], axis=2),  # Expected output: FFT of signal\n    1e-5,  # absolute tolerance\n]\n\nTEST_CASE_2D_SINE_CPU = [\n    {},  # args (empty, so use default)\n    cpu_input_data[\"2D\"],  # Input data: Random 1D signal\n    create_expected_numpy_output(cpu_input_data[\"2D\"], axis=2),  # Expected output: FFT of signal\n    1e-5,  # absolute tolerance\n]\n\nTEST_CASE_3D_SINE_CPU = [\n    {},  # args (empty, so use default)\n    cpu_input_data[\"3D\"],  # Input data: Random 1D signal\n    create_expected_numpy_output(cpu_input_data[\"3D\"], axis=2),\n    1e-5,  # absolute tolerance\n]\n\n# MULTICHANNEL CPU VALUE TESTS, PROCESS ALONG FIRST SPATIAL AXIS\n\nTEST_CASE_1D_2CH_SINE_CPU = [\n    {},  # args (empty, so use default)\n    cpu_input_data[\"1D 2CH\"],  # Input data: Random 1D signal\n    create_expected_numpy_output(cpu_input_data[\"1D 2CH\"], axis=2),\n    1e-5,  # absolute tolerance\n]\n\nTEST_CASE_2D_2CH_SINE_CPU = [\n    {},  # args (empty, so use default)\n    cpu_input_data[\"2D 2CH\"],  # Input data: Random 1D signal\n    create_expected_numpy_output(cpu_input_data[\"2D 2CH\"], axis=2),\n    1e-5,  # absolute tolerance\n]\n\n# GPU TEST DATA", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_hilbert_transform.py_if_torch_cuda_is_availabl_if_torch_cuda_is_availabl.TEST_CASE_2D_2CH_SINE_GPU._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_hilbert_transform.py_if_torch_cuda_is_availabl_if_torch_cuda_is_availabl.TEST_CASE_2D_2CH_SINE_GPU._", "embedding": null, "metadata": {"file_path": "tests/test_hilbert_transform.py", "file_name": "test_hilbert_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 110, "end_line": 165, "span_ids": ["impl:20"], "tokens": 693}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "if torch.cuda.is_available():\n    gpu = torch.device(\"cuda\")\n\n    gpu_input_data = {}\n    gpu_input_data[\"1D\"] = torch.as_tensor(hann_windowed_sine, device=gpu).unsqueeze(0).unsqueeze(0)\n    gpu_input_data[\"2D\"] = (\n        torch.as_tensor(np.stack([hann_windowed_sine] * 10, axis=1), device=gpu).unsqueeze(0).unsqueeze(0)\n    )\n    gpu_input_data[\"3D\"] = (\n        torch.as_tensor(np.stack([np.stack([hann_windowed_sine] * 10, axis=1)] * 10, axis=2), device=gpu)\n        .unsqueeze(0)\n        .unsqueeze(0)\n    )\n    gpu_input_data[\"1D 2CH\"] = torch.as_tensor(np.stack([hann_windowed_sine] * 10, axis=1), device=gpu).unsqueeze(0)\n    gpu_input_data[\"2D 2CH\"] = torch.as_tensor(\n        np.stack([np.stack([hann_windowed_sine] * 10, axis=1)] * 10, axis=2), device=gpu\n    ).unsqueeze(0)\n\n    # SINGLE CHANNEL GPU VALUE TESTS\n\n    TEST_CASE_1D_SINE_GPU = [\n        {},  # args (empty, so use default)\n        gpu_input_data[\"1D\"],  # Input data: Random 1D signal\n        create_expected_numpy_output(gpu_input_data[\"1D\"], axis=2),  # Expected output: FFT of signal\n        1e-5,  # absolute tolerance\n    ]\n\n    TEST_CASE_2D_SINE_GPU = [\n        {},  # args (empty, so use default)\n        gpu_input_data[\"2D\"],  # Input data: Random 1D signal\n        create_expected_numpy_output(gpu_input_data[\"2D\"], axis=2),  # Expected output: FFT of signal\n        1e-5,  # absolute tolerance\n    ]\n\n    TEST_CASE_3D_SINE_GPU = [\n        {},  # args (empty, so use default)\n        gpu_input_data[\"3D\"],  # Input data: Random 1D signal\n        create_expected_numpy_output(gpu_input_data[\"3D\"], axis=2),  # Expected output: FFT of signal\n        1e-5,  # absolute tolerance\n    ]\n\n    # MULTICHANNEL GPU VALUE TESTS, PROCESS ALONG FIRST SPATIAL AXIS\n\n    TEST_CASE_1D_2CH_SINE_GPU = [\n        {},  # args (empty, so use default)\n        gpu_input_data[\"1D 2CH\"],  # Input data: Random 1D signal\n        create_expected_numpy_output(gpu_input_data[\"1D 2CH\"], axis=2),\n        1e-5,  # absolute tolerance\n    ]\n\n    TEST_CASE_2D_2CH_SINE_GPU = [\n        {},  # args (empty, so use default)\n        gpu_input_data[\"2D 2CH\"],  # Input data: Random 1D signal\n        create_expected_numpy_output(gpu_input_data[\"2D 2CH\"], axis=2),\n        1e-5,  # absolute tolerance\n    ]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_hilbert_transform.py__TESTS_CHECKING_PADDING__TestHilbertTransformCPU.test_value.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_hilbert_transform.py__TESTS_CHECKING_PADDING__TestHilbertTransformCPU.test_value.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_hilbert_transform.py", "file_name": "test_hilbert_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 167, "end_line": 185, "span_ids": ["TestHilbertTransformCPU", "impl:20", "TestHilbertTransformCPU.test_value"], "tokens": 181}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "# TESTS CHECKING PADDING, AXIS SELECTION ETC ARE COVERED BY test_detect_envelope.py\n\n\n@SkipIfBeforePyTorchVersion((1, 7))\n@SkipIfNoModule(\"torch.fft\")\nclass TestHilbertTransformCPU(unittest.TestCase):\n    @parameterized.expand(\n        [\n            TEST_CASE_1D_SINE_CPU,\n            TEST_CASE_2D_SINE_CPU,\n            TEST_CASE_3D_SINE_CPU,\n            TEST_CASE_1D_2CH_SINE_CPU,\n            TEST_CASE_2D_2CH_SINE_CPU,\n        ]\n    )\n    def test_value(self, arguments, image, expected_data, atol):\n        result = HilbertTransform(**arguments)(image)\n        result = result.squeeze(0).squeeze(0).numpy()\n        np.testing.assert_allclose(result, expected_data.squeeze(), atol=atol)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_hilbert_transform.py_TestHilbertTransformGPU_TestHilbertTransformGPU.test_value.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_hilbert_transform.py_TestHilbertTransformGPU_TestHilbertTransformGPU.test_value.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_hilbert_transform.py", "file_name": "test_hilbert_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 188, "end_line": 207, "span_ids": ["TestHilbertTransformGPU.test_value", "TestHilbertTransformGPU"], "tokens": 183}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_no_cuda\n@SkipIfBeforePyTorchVersion((1, 7))\n@SkipIfNoModule(\"torch.fft\")\nclass TestHilbertTransformGPU(unittest.TestCase):\n    @parameterized.expand(\n        []\n        if not torch.cuda.is_available()\n        else [\n            TEST_CASE_1D_SINE_GPU,\n            TEST_CASE_2D_SINE_GPU,\n            TEST_CASE_3D_SINE_GPU,\n            TEST_CASE_1D_2CH_SINE_GPU,\n            TEST_CASE_2D_2CH_SINE_GPU,\n        ],\n        skip_on_empty=True,\n    )\n    def test_value(self, arguments, image, expected_data, atol):\n        result = HilbertTransform(**arguments)(image)\n        result = result.squeeze(0).squeeze(0).cpu().numpy()\n        np.testing.assert_allclose(result, expected_data.squeeze(), atol=atol)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_hilbert_transform.py_TestHilbertTransformNoFFTMod_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_hilbert_transform.py_TestHilbertTransformNoFFTMod_", "embedding": null, "metadata": {"file_path": "tests/test_hilbert_transform.py", "file_name": "test_hilbert_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 210, "end_line": 226, "span_ids": ["TestHilbertTransformNoFFTMod", "TestHilbertTransformNoFFTMod.test_no_fft_module_error", "TestHilbertTransformInvalidPyTorch.test_invalid_pytorch_error", "impl:44", "TestHilbertTransformInvalidPyTorch"], "tokens": 135}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@SkipIfBeforePyTorchVersion((1, 7))\n@SkipIfModule(\"torch.fft\")\nclass TestHilbertTransformNoFFTMod(unittest.TestCase):\n    def test_no_fft_module_error(self):\n        self.assertRaises(OptionalImportError, HilbertTransform(), torch.randn(1, 1, 10))\n\n\n@SkipIfAtLeastPyTorchVersion((1, 7))\nclass TestHilbertTransformInvalidPyTorch(unittest.TestCase):\n    def test_invalid_pytorch_error(self):\n        with self.assertRaisesRegex(InvalidPyTorchVersionError, \"version\"):\n            HilbertTransform()\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_init_reader.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_init_reader.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_init_reader.py", "file_name": "test_init_reader.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 53, "span_ids": ["TestInitLoadImage.test_readers", "TestInitLoadImage.test_load_image", "impl", "docstring", "TestInitLoadImage"], "tokens": 310}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nfrom monai.data import ITKReader, NibabelReader, NumpyReader, PILReader\nfrom monai.transforms import LoadImage, LoadImaged\nfrom tests.utils import SkipIfNoModule\n\n\nclass TestInitLoadImage(unittest.TestCase):\n    def test_load_image(self):\n        instance1 = LoadImage(image_only=False, dtype=None)\n        instance2 = LoadImage(image_only=True, dtype=None)\n        self.assertIsInstance(instance1, LoadImage)\n        self.assertIsInstance(instance2, LoadImage)\n\n        for r in [\"NibabelReader\", \"PILReader\", \"ITKReader\", \"NumpyReader\", None]:\n            inst = LoadImaged(\"image\", reader=r)\n            self.assertIsInstance(inst, LoadImaged)\n\n    @SkipIfNoModule(\"itk\")\n    @SkipIfNoModule(\"nibabel\")\n    @SkipIfNoModule(\"PIL\")\n    def test_readers(self):\n        inst = ITKReader()\n        self.assertIsInstance(inst, ITKReader)\n\n        inst = NibabelReader()\n        self.assertIsInstance(inst, NibabelReader)\n        inst = NibabelReader(as_closest_canonical=True)\n        self.assertIsInstance(inst, NibabelReader)\n\n        inst = NumpyReader()\n        self.assertIsInstance(inst, NumpyReader)\n        inst = NumpyReader(npz_keys=\"test\")\n        self.assertIsInstance(inst, NumpyReader)\n\n        inst = PILReader()\n        self.assertIsInstance(inst, PILReader)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_classification_2d.py_run_training_test_run_training_test.model_filename.os_path_join_root_dir_b": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_classification_2d.py_run_training_test_run_training_test.model_filename.os_path_join_root_dir_b", "embedding": null, "metadata": {"file_path": "tests/test_integration_classification_2d.py", "file_name": "test_integration_classification_2d.py", "file_type": "text/x-python", "category": "test", "start_line": 62, "end_line": 104, "span_ids": ["run_training_test"], "tokens": 454}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def run_training_test(root_dir, train_x, train_y, val_x, val_y, device=\"cuda:0\", num_workers=10):\n\n    monai.config.print_config()\n    # define transforms for image and classification\n    train_transforms = Compose(\n        [\n            LoadImage(image_only=True),\n            AddChannel(),\n            Transpose(indices=[0, 2, 1]),\n            ScaleIntensity(),\n            RandRotate(range_x=np.pi / 12, prob=0.5, keep_size=True),\n            RandFlip(spatial_axis=0, prob=0.5),\n            RandZoom(min_zoom=0.9, max_zoom=1.1, prob=0.5),\n            ToTensor(),\n        ]\n    )\n    train_transforms.set_random_state(1234)\n    val_transforms = Compose(\n        [LoadImage(image_only=True), AddChannel(), Transpose(indices=[0, 2, 1]), ScaleIntensity(), ToTensor()]\n    )\n    y_pred_trans = Compose([ToTensor(), Activations(softmax=True)])\n    y_trans = Compose([ToTensor(), AsDiscrete(to_onehot=len(np.unique(train_y)))])\n    auc_metric = ROCAUCMetric()\n\n    # create train, val data loaders\n    train_ds = MedNISTDataset(train_x, train_y, train_transforms)\n    train_loader = DataLoader(train_ds, batch_size=300, shuffle=True, num_workers=num_workers)\n\n    val_ds = MedNISTDataset(val_x, val_y, val_transforms)\n    val_loader = DataLoader(val_ds, batch_size=300, num_workers=num_workers)\n\n    model = DenseNet121(spatial_dims=2, in_channels=1, out_channels=len(np.unique(train_y))).to(device)\n    loss_function = torch.nn.CrossEntropyLoss()\n    optimizer = torch.optim.Adam(model.parameters(), 1e-5)\n    epoch_num = 4\n    val_interval = 1\n\n    # start training validation\n    best_metric = -1\n    best_metric_epoch = -1\n    epoch_loss_values = []\n    metric_values = []\n    model_filename = os.path.join(root_dir, \"best_metric_model.pth\")\n    # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_classification_2d.py_run_training_test.for_epoch_in_range_epoch__run_training_test.return.epoch_loss_values_best_m": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_classification_2d.py_run_training_test.for_epoch_in_range_epoch__run_training_test.return.epoch_loss_values_best_m", "embedding": null, "metadata": {"file_path": "tests/test_integration_classification_2d.py", "file_name": "test_integration_classification_2d.py", "file_type": "text/x-python", "category": "test", "start_line": 105, "end_line": 154, "span_ids": ["run_training_test"], "tokens": 570}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def run_training_test(root_dir, train_x, train_y, val_x, val_y, device=\"cuda:0\", num_workers=10):\n    # ... other code\n    for epoch in range(epoch_num):\n        print(\"-\" * 10)\n        print(f\"Epoch {epoch + 1}/{epoch_num}\")\n        model.train()\n        epoch_loss = 0\n        step = 0\n        for batch_data in train_loader:\n            step += 1\n            inputs, labels = batch_data[0].to(device), batch_data[1].to(device)\n            optimizer.zero_grad()\n            outputs = model(inputs)\n            loss = loss_function(outputs, labels)\n            loss.backward()\n            optimizer.step()\n            epoch_loss += loss.item()\n        epoch_loss /= step\n        epoch_loss_values.append(epoch_loss)\n        print(f\"epoch {epoch + 1} average loss:{epoch_loss:0.4f}\")\n\n        if (epoch + 1) % val_interval == 0:\n            with eval_mode(model):\n                y_pred = torch.tensor([], dtype=torch.float32, device=device)\n                y = torch.tensor([], dtype=torch.long, device=device)\n                for val_data in val_loader:\n                    val_images, val_labels = val_data[0].to(device), val_data[1].to(device)\n                    y_pred = torch.cat([y_pred, model(val_images)], dim=0)\n                    y = torch.cat([y, val_labels], dim=0)\n\n                # compute accuracy\n                acc_value = torch.eq(y_pred.argmax(dim=1), y)\n                acc_metric = acc_value.sum().item() / len(acc_value)\n                # decollate prediction and label and execute post processing\n                y_pred = [y_pred_trans(i) for i in decollate_batch(y_pred)]\n                y = [y_trans(i) for i in decollate_batch(y)]\n                # compute AUC\n                auc_metric(y_pred, y)\n                auc_value = auc_metric.aggregate()\n                auc_metric.reset()\n                metric_values.append(auc_value)\n                if auc_value > best_metric:\n                    best_metric = auc_value\n                    best_metric_epoch = epoch + 1\n                    torch.save(model.state_dict(), model_filename)\n                    print(\"saved new best metric model\")\n                print(\n                    f\"current epoch {epoch +1} current AUC: {auc_value:0.4f} \"\n                    f\"current accuracy: {acc_metric:0.4f} best AUC: {best_metric:0.4f} at epoch {best_metric_epoch}\"\n                )\n    print(f\"train completed, best_metric: {best_metric:0.4f}  at epoch: {best_metric_epoch}\")\n    return epoch_loss_values, best_metric, best_metric_epoch", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_classification_2d.py_IntegrationClassification2D_IntegrationClassification2D.setUp.self.device._cuda_0_if_torch_cuda_is": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_classification_2d.py_IntegrationClassification2D_IntegrationClassification2D.setUp.self.device._cuda_0_if_torch_cuda_is", "embedding": null, "metadata": {"file_path": "tests/test_integration_classification_2d.py", "file_name": "test_integration_classification_2d.py", "file_type": "text/x-python", "category": "test", "start_line": 180, "end_line": 227, "span_ids": ["IntegrationClassification2D", "IntegrationClassification2D.setUp"], "tokens": 479}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_quick\nclass IntegrationClassification2D(DistTestCase):\n    def setUp(self):\n        set_determinism(seed=0)\n        self.data_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"testing_data\")\n        data_dir = os.path.join(self.data_dir, \"MedNIST\")\n        dataset_file = os.path.join(self.data_dir, \"MedNIST.tar.gz\")\n\n        if not os.path.exists(data_dir):\n            try:\n                download_and_extract(TEST_DATA_URL, dataset_file, self.data_dir, MD5_VALUE)\n            except (ContentTooShortError, HTTPError, RuntimeError) as e:\n                print(str(e))\n                if isinstance(e, RuntimeError):\n                    # FIXME: skip MD5 check as current downloading method may fail\n                    self.assertTrue(str(e).startswith(\"md5 check\"))\n                return  # skipping this test due the network connection errors\n\n        assert os.path.exists(data_dir)\n\n        class_names = sorted(x for x in os.listdir(data_dir) if os.path.isdir(os.path.join(data_dir, x)))\n        image_files = [\n            [os.path.join(data_dir, class_name, x) for x in sorted(os.listdir(os.path.join(data_dir, class_name)))]\n            for class_name in class_names\n        ]\n        image_file_list, image_classes = [], []\n        for i, _ in enumerate(class_names):\n            image_file_list.extend(image_files[i])\n            image_classes.extend([i] * len(image_files[i]))\n\n        # split train, val, test\n        valid_frac, test_frac = 0.1, 0.1\n        self.train_x, self.train_y = [], []\n        self.val_x, self.val_y = [], []\n        self.test_x, self.test_y = [], []\n        for i in range(len(image_classes)):\n            rann = np.random.random()\n            if rann < valid_frac:\n                self.val_x.append(image_file_list[i])\n                self.val_y.append(image_classes[i])\n            elif rann < test_frac + valid_frac:\n                self.test_x.append(image_file_list[i])\n                self.test_y.append(image_classes[i])\n            else:\n                self.train_x.append(image_file_list[i])\n                self.train_y.append(image_classes[i])\n\n        self.device = \"cuda:0\" if torch.cuda.is_available() else \"cpu:0\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_segmentation_3d.py_os_TASK._integration_segmentation": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_segmentation_3d.py_os_TASK._integration_segmentation", "embedding": null, "metadata": {"file_path": "tests/test_integration_segmentation_3d.py", "file_name": "test_integration_segmentation_3d.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 47, "span_ids": ["docstring"], "tokens": 235}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport shutil\nimport tempfile\nimport unittest\nfrom glob import glob\n\nimport nibabel as nib\nimport numpy as np\nimport torch\nfrom torch.utils.tensorboard import SummaryWriter\n\nimport monai\nfrom monai.data import NiftiSaver, create_test_image_3d, decollate_batch\nfrom monai.inferers import sliding_window_inference\nfrom monai.metrics import DiceMetric\nfrom monai.networks import eval_mode\nfrom monai.networks.nets import UNet\nfrom monai.transforms import (\n    Activations,\n    AsDiscrete,\n    Compose,\n    EnsureChannelFirstd,\n    LoadImaged,\n    RandCropByPosNegLabeld,\n    RandRotate90d,\n    ScaleIntensityd,\n    Spacingd,\n    ToTensor,\n    ToTensord,\n)\nfrom monai.utils import set_determinism\nfrom monai.visualize import plot_2d_or_3d_image\nfrom tests.testing_data.integration_answers import test_integration_value\nfrom tests.utils import DistTestCase, TimedCall, skip_if_quick\n\nTASK = \"integration_segmentation_3d\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_segmentation_3d.py_run_training_test_run_training_test._create_UNet_DiceLoss_a": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_segmentation_3d.py_run_training_test_run_training_test._create_UNet_DiceLoss_a", "embedding": null, "metadata": {"file_path": "tests/test_integration_segmentation_3d.py", "file_name": "test_integration_segmentation_3d.py", "file_type": "text/x-python", "category": "test", "start_line": 50, "end_line": 101, "span_ids": ["run_training_test"], "tokens": 759}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def run_training_test(root_dir, device=\"cuda:0\", cachedataset=0, readers=(None, None)):\n    monai.config.print_config()\n    images = sorted(glob(os.path.join(root_dir, \"img*.nii.gz\")))\n    segs = sorted(glob(os.path.join(root_dir, \"seg*.nii.gz\")))\n    train_files = [{\"img\": img, \"seg\": seg} for img, seg in zip(images[:20], segs[:20])]\n    val_files = [{\"img\": img, \"seg\": seg} for img, seg in zip(images[-20:], segs[-20:])]\n\n    # define transforms for image and segmentation\n    train_transforms = Compose(\n        [\n            LoadImaged(keys=[\"img\", \"seg\"], reader=readers[0]),\n            EnsureChannelFirstd(keys=[\"img\", \"seg\"]),\n            # resampling with align_corners=True or dtype=float64 will generate\n            # slight different results between PyTorch 1.5 an 1.6\n            Spacingd(keys=[\"img\", \"seg\"], pixdim=[1.2, 0.8, 0.7], mode=[\"bilinear\", \"nearest\"], dtype=np.float32),\n            ScaleIntensityd(keys=\"img\"),\n            RandCropByPosNegLabeld(\n                keys=[\"img\", \"seg\"], label_key=\"seg\", spatial_size=[96, 96, 96], pos=1, neg=1, num_samples=4\n            ),\n            RandRotate90d(keys=[\"img\", \"seg\"], prob=0.8, spatial_axes=[0, 2]),\n            ToTensord(keys=[\"img\", \"seg\"]),\n        ]\n    )\n    train_transforms.set_random_state(1234)\n    val_transforms = Compose(\n        [\n            LoadImaged(keys=[\"img\", \"seg\"], reader=readers[1]),\n            EnsureChannelFirstd(keys=[\"img\", \"seg\"]),\n            # resampling with align_corners=True or dtype=float64 will generate\n            # slight different results between PyTorch 1.5 an 1.6\n            Spacingd(keys=[\"img\", \"seg\"], pixdim=[1.2, 0.8, 0.7], mode=[\"bilinear\", \"nearest\"], dtype=np.float32),\n            ScaleIntensityd(keys=\"img\"),\n            ToTensord(keys=[\"img\", \"seg\"]),\n        ]\n    )\n\n    # create a training data loader\n    if cachedataset == 2:\n        train_ds = monai.data.CacheDataset(data=train_files, transform=train_transforms, cache_rate=0.8)\n    elif cachedataset == 3:\n        train_ds = monai.data.LMDBDataset(data=train_files, transform=train_transforms, cache_dir=root_dir)\n    else:\n        train_ds = monai.data.Dataset(data=train_files, transform=train_transforms)\n    # use batch_size=2 to load images and use RandCropByPosNegLabeld to generate 2 x 4 images for network training\n    train_loader = monai.data.DataLoader(train_ds, batch_size=2, shuffle=True, num_workers=4)\n    # create a validation data loader\n    val_ds = monai.data.Dataset(data=val_files, transform=val_transforms)\n    val_loader = monai.data.DataLoader(val_ds, batch_size=1, num_workers=4)\n    val_post_tran = Compose([ToTensor(), Activations(sigmoid=True), AsDiscrete(threshold=0.5)])\n    dice_metric = DiceMetric(include_background=True, reduction=\"mean\", get_not_nans=False)\n\n    # create UNet, DiceLoss and Adam optimizer\n    # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_segmentation_3d.py_run_training_test.model_run_training_test.return.epoch_loss_values_best_m": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_segmentation_3d.py_run_training_test.model_run_training_test.return.epoch_loss_values_best_m", "embedding": null, "metadata": {"file_path": "tests/test_integration_segmentation_3d.py", "file_name": "test_integration_segmentation_3d.py", "file_type": "text/x-python", "category": "test", "start_line": 102, "end_line": 175, "span_ids": ["run_training_test"], "tokens": 851}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def run_training_test(root_dir, device=\"cuda:0\", cachedataset=0, readers=(None, None)):\n    # ... other code\n    model = monai.networks.nets.UNet(\n        spatial_dims=3,\n        in_channels=1,\n        out_channels=1,\n        channels=(16, 32, 64, 128, 256),\n        strides=(2, 2, 2, 2),\n        num_res_units=2,\n    ).to(device)\n    loss_function = monai.losses.DiceLoss(sigmoid=True)\n    optimizer = torch.optim.Adam(model.parameters(), 5e-4)\n\n    # start a typical PyTorch training\n    val_interval = 2\n    best_metric, best_metric_epoch = -1, -1\n    epoch_loss_values = []\n    metric_values = []\n    writer = SummaryWriter(log_dir=os.path.join(root_dir, \"runs\"))\n    model_filename = os.path.join(root_dir, \"best_metric_model.pth\")\n    for epoch in range(6):\n        print(\"-\" * 10)\n        print(f\"Epoch {epoch + 1}/{6}\")\n        model.train()\n        epoch_loss = 0\n        step = 0\n        for batch_data in train_loader:\n            step += 1\n            inputs, labels = batch_data[\"img\"].to(device), batch_data[\"seg\"].to(device)\n            optimizer.zero_grad()\n            outputs = model(inputs)\n            loss = loss_function(outputs, labels)\n            loss.backward()\n            optimizer.step()\n            epoch_loss += loss.item()\n            epoch_len = len(train_ds) // train_loader.batch_size\n            print(f\"{step}/{epoch_len}, train_loss:{loss.item():0.4f}\")\n            writer.add_scalar(\"train_loss\", loss.item(), epoch_len * epoch + step)\n        epoch_loss /= step\n        epoch_loss_values.append(epoch_loss)\n        print(f\"epoch {epoch +1} average loss:{epoch_loss:0.4f}\")\n\n        if (epoch + 1) % val_interval == 0:\n            with eval_mode(model):\n                val_images = None\n                val_labels = None\n                val_outputs = None\n                for val_data in val_loader:\n                    val_images, val_labels = val_data[\"img\"].to(device), val_data[\"seg\"].to(device)\n                    sw_batch_size, roi_size = 4, (96, 96, 96)\n                    val_outputs = sliding_window_inference(val_images, roi_size, sw_batch_size, model)\n                    # decollate prediction into a list and execute post processing for every item\n                    val_outputs = [val_post_tran(i) for i in decollate_batch(val_outputs)]\n                    # compute metrics\n                    dice_metric(y_pred=val_outputs, y=val_labels)\n\n                metric = dice_metric.aggregate().item()\n                dice_metric.reset()\n                metric_values.append(metric)\n                if metric > best_metric:\n                    best_metric = metric\n                    best_metric_epoch = epoch + 1\n                    torch.save(model.state_dict(), model_filename)\n                    print(\"saved new best metric model\")\n                print(\n                    f\"current epoch {epoch +1} current mean dice: {metric:0.4f} \"\n                    f\"best mean dice: {best_metric:0.4f} at epoch {best_metric_epoch}\"\n                )\n                writer.add_scalar(\"val_mean_dice\", metric, epoch + 1)\n                # plot the last model output as GIF image in TensorBoard with the corresponding image and label\n                plot_2d_or_3d_image(val_images, epoch + 1, writer, index=0, tag=\"image\")\n                plot_2d_or_3d_image(val_labels, epoch + 1, writer, index=0, tag=\"label\")\n                plot_2d_or_3d_image(val_outputs, epoch + 1, writer, index=0, tag=\"output\")\n    print(f\"train completed, best_metric: {best_metric:0.4f}  at epoch: {best_metric_epoch}\")\n    writer.close()\n    return epoch_loss_values, best_metric, best_metric_epoch", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_segmentation_3d.py_IntegrationSegmentation3D.train_and_infer_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_segmentation_3d.py_IntegrationSegmentation3D.train_and_infer_", "embedding": null, "metadata": {"file_path": "tests/test_integration_segmentation_3d.py", "file_name": "test_integration_segmentation_3d.py", "file_type": "text/x-python", "category": "test", "start_line": 249, "end_line": 301, "span_ids": ["impl:3", "IntegrationSegmentation3D.test_training", "IntegrationSegmentation3D.train_and_infer", "IntegrationSegmentation3D.test_timing"], "tokens": 555}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_quick\nclass IntegrationSegmentation3D(DistTestCase):\n\n    def train_and_infer(self, idx=0):\n        results = []\n        set_determinism(0)\n        _readers = (None, None)\n        if idx == 1:\n            _readers = (\"itkreader\", \"itkreader\")\n        elif idx == 2:\n            _readers = (\"itkreader\", \"nibabelreader\")\n        losses, best_metric, best_metric_epoch = run_training_test(\n            self.data_dir, device=self.device, cachedataset=idx, readers=_readers\n        )\n        infer_metric = run_inference_test(self.data_dir, device=self.device)\n\n        # check training properties\n        print(\"losses\", losses)\n        print(\"best metric\", best_metric)\n        print(\"infer metric\", infer_metric)\n        self.assertTrue(len(glob(os.path.join(self.data_dir, \"runs\"))) > 0)\n        model_file = os.path.join(self.data_dir, \"best_metric_model.pth\")\n        self.assertTrue(os.path.exists(model_file))\n\n        # check inference properties\n        output_files = sorted(glob(os.path.join(self.data_dir, \"output\", \"img*\", \"*.nii.gz\")))\n        print([np.mean(nib.load(output).get_fdata()) for output in output_files])\n        results.extend(losses)\n        results.append(best_metric)\n        results.append(infer_metric)\n        for output in output_files:\n            ave = np.mean(nib.load(output).get_fdata())\n            results.append(ave)\n        self.assertTrue(test_integration_value(TASK, key=\"losses\", data=results[:6], rtol=1e-3))\n        self.assertTrue(test_integration_value(TASK, key=\"best_metric\", data=results[6], rtol=1e-2))\n        self.assertTrue(test_integration_value(TASK, key=\"infer_metric\", data=results[7], rtol=1e-2))\n        self.assertTrue(test_integration_value(TASK, key=\"output_sums\", data=results[8:], rtol=1e-2))\n        return results\n\n    def test_training(self):\n        repeated = []\n        for i in range(4):\n            results = self.train_and_infer(i)\n            repeated.append(results)\n        np.testing.assert_allclose(repeated[0], repeated[1])\n        np.testing.assert_allclose(repeated[0], repeated[2])\n        np.testing.assert_allclose(repeated[0], repeated[3])\n\n    @TimedCall(seconds=360, daemon=False)\n    def test_timing(self):\n        self.train_and_infer(idx=3)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_sliding_window.py_os_from_tests_utils_import_D": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_sliding_window.py_os_from_tests_utils_import_D", "embedding": null, "metadata": {"file_path": "tests/test_integration_sliding_window.py", "file_name": "test_integration_sliding_window.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 29, "span_ids": ["docstring"], "tokens": 135}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport tempfile\nimport unittest\n\nimport nibabel as nib\nimport numpy as np\nimport torch\nfrom ignite.engine import Engine\nfrom torch.utils.data import DataLoader\n\nfrom monai.data import ImageDataset, create_test_image_3d\nfrom monai.handlers import SegmentationSaver\nfrom monai.inferers import sliding_window_inference\nfrom monai.networks import eval_mode, predict_segmentation\nfrom monai.networks.nets import UNet\nfrom monai.transforms import AddChannel\nfrom monai.utils import set_determinism\nfrom tests.utils import DistTestCase, TimedCall, make_nifti_image, skip_if_quick", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_sliding_window.py_TestIntegrationSlidingWindow_TestIntegrationSlidingWindow.tearDown.if_os_path_exists_self_se.os_remove_self_seg_name_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_sliding_window.py_TestIntegrationSlidingWindow_TestIntegrationSlidingWindow.tearDown.if_os_path_exists_self_se.os_remove_self_seg_name_", "embedding": null, "metadata": {"file_path": "tests/test_integration_sliding_window.py", "file_name": "test_integration_sliding_window.py", "file_type": "text/x-python", "category": "test", "start_line": 61, "end_line": 76, "span_ids": ["TestIntegrationSlidingWindow.tearDown", "TestIntegrationSlidingWindow", "TestIntegrationSlidingWindow.setUp"], "tokens": 165}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_quick\nclass TestIntegrationSlidingWindow(DistTestCase):\n    def setUp(self):\n        set_determinism(seed=0)\n\n        im, seg = create_test_image_3d(25, 28, 63, rad_max=10, noise_max=1, num_objs=4, num_seg_classes=1)\n        self.img_name = make_nifti_image(im)\n        self.seg_name = make_nifti_image(seg)\n        self.device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu:0\")\n\n    def tearDown(self):\n        set_determinism(seed=None)\n        if os.path.exists(self.img_name):\n            os.remove(self.img_name)\n        if os.path.exists(self.seg_name):\n            os.remove(self.seg_name)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_sliding_window.py_TestIntegrationSlidingWindow.test_training_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_sliding_window.py_TestIntegrationSlidingWindow.test_training_", "embedding": null, "metadata": {"file_path": "tests/test_integration_sliding_window.py", "file_name": "test_integration_sliding_window.py", "file_type": "text/x-python", "category": "test", "start_line": 78, "end_line": 92, "span_ids": ["TestIntegrationSlidingWindow.test_training", "impl"], "tokens": 146}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_quick\nclass TestIntegrationSlidingWindow(DistTestCase):\n\n    @TimedCall(seconds=20)\n    def test_training(self):\n        set_determinism(seed=0)\n        with tempfile.TemporaryDirectory() as tempdir:\n            output_file = run_test(\n                batch_size=2, img_name=self.img_name, seg_name=self.seg_name, output_dir=tempdir, device=self.device\n            )\n            output_image = nib.load(output_file).get_fdata()\n            np.testing.assert_allclose(np.sum(output_image), 33621)\n            np.testing.assert_allclose(output_image.shape, (28, 25, 63))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_unet_2d.py_unittest_run_test.return.loss": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_unet_2d.py_unittest_run_test.return.loss", "embedding": null, "metadata": {"file_path": "tests/test_integration_unet_2d.py", "file_name": "test_integration_unet_2d.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 50, "span_ids": ["run_test", "docstring"], "tokens": 377}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom ignite.engine import create_supervised_trainer\nfrom torch.utils.data import DataLoader, Dataset\n\nfrom monai.data import create_test_image_2d\nfrom monai.losses import DiceLoss\nfrom monai.networks.nets import BasicUNet, UNet\nfrom tests.utils import DistTestCase, TimedCall, skip_if_quick\n\n\ndef run_test(net_name=\"basicunet\", batch_size=64, train_steps=100, device=\"cuda:0\"):\n    class _TestBatch(Dataset):\n        def __getitem__(self, _unused_id):\n            im, seg = create_test_image_2d(128, 128, noise_max=1, num_objs=4, num_seg_classes=1)\n            return im[None], seg[None].astype(np.float32)\n\n        def __len__(self):\n            return train_steps\n\n    if net_name == \"basicunet\":\n        net = BasicUNet(spatial_dims=2, in_channels=1, out_channels=1, features=(4, 8, 8, 16, 16, 32))\n    elif net_name == \"unet\":\n        net = UNet(\n            spatial_dims=2, in_channels=1, out_channels=1, channels=(4, 8, 16, 32), strides=(2, 2, 2), num_res_units=2\n        )\n    net.to(device)\n\n    loss = DiceLoss(sigmoid=True)\n    opt = torch.optim.Adam(net.parameters(), 1e-4)\n    src = DataLoader(_TestBatch(), batch_size=batch_size)\n\n    trainer = create_supervised_trainer(net, opt, loss, device, False)\n\n    trainer.run(src, 1)\n    loss = trainer.state.output\n    return loss", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_unet_2d.py_TestIntegrationUnet2D_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_unet_2d.py_TestIntegrationUnet2D_", "embedding": null, "metadata": {"file_path": "tests/test_integration_unet_2d.py", "file_name": "test_integration_unet_2d.py", "file_type": "text/x-python", "category": "test", "start_line": 53, "end_line": 65, "span_ids": ["TestIntegrationUnet2D.test_unet_training", "TestIntegrationUnet2D", "impl"], "tokens": 106}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_quick\nclass TestIntegrationUnet2D(DistTestCase):\n    @TimedCall(seconds=20, daemon=False)\n    def test_unet_training(self):\n        for n in [\"basicunet\", \"unet\"]:\n            loss = run_test(net_name=n, device=torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu:0\"))\n            print(loss)\n            self.assertGreaterEqual(0.85, loss)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_workflows.py_logging_TASK._integration_workflows_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_workflows.py_logging_TASK._integration_workflows_", "embedding": null, "metadata": {"file_path": "tests/test_integration_workflows.py", "file_name": "test_integration_workflows.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 60, "span_ids": ["docstring"], "tokens": 282}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import logging\nimport os\nimport shutil\nimport sys\nimport tempfile\nimport unittest\nimport warnings\nfrom glob import glob\n\nimport nibabel as nib\nimport numpy as np\nimport torch\nfrom ignite.metrics import Accuracy\nfrom torch.utils.tensorboard import SummaryWriter\n\nimport monai\nfrom monai.data import create_test_image_3d\nfrom monai.engines import IterationEvents, SupervisedEvaluator, SupervisedTrainer\nfrom monai.handlers import (\n    CheckpointLoader,\n    CheckpointSaver,\n    LrScheduleHandler,\n    MeanDice,\n    SegmentationSaver,\n    StatsHandler,\n    TensorBoardImageHandler,\n    TensorBoardStatsHandler,\n    ValidationHandler,\n    from_engine,\n)\nfrom monai.inferers import SimpleInferer, SlidingWindowInferer\nfrom monai.transforms import (\n    Activationsd,\n    AsChannelFirstd,\n    AsDiscreted,\n    Compose,\n    KeepLargestConnectedComponentd,\n    LoadImaged,\n    RandCropByPosNegLabeld,\n    RandRotate90d,\n    SaveImaged,\n    ScaleIntensityd,\n    ToTensord,\n)\nfrom monai.utils import set_determinism\nfrom tests.testing_data.integration_answers import test_integration_value\nfrom tests.utils import DistTestCase, TimedCall, skip_if_quick\n\nTASK = \"integration_workflows\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_workflows.py_run_training_test_run_training_test._TestEvalIterEvents._forward_completed.pass": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_workflows.py_run_training_test_run_training_test._TestEvalIterEvents._forward_completed.pass", "embedding": null, "metadata": {"file_path": "tests/test_integration_workflows.py", "file_name": "test_integration_workflows.py", "file_type": "text/x-python", "category": "test", "start_line": 63, "end_line": 127, "span_ids": ["run_training_test"], "tokens": 742}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def run_training_test(root_dir, device=\"cuda:0\", amp=False, num_workers=4):\n    images = sorted(glob(os.path.join(root_dir, \"img*.nii.gz\")))\n    segs = sorted(glob(os.path.join(root_dir, \"seg*.nii.gz\")))\n    train_files = [{\"image\": img, \"label\": seg} for img, seg in zip(images[:20], segs[:20])]\n    val_files = [{\"image\": img, \"label\": seg} for img, seg in zip(images[-20:], segs[-20:])]\n\n    # define transforms for image and segmentation\n    train_transforms = Compose(\n        [\n            LoadImaged(keys=[\"image\", \"label\"]),\n            AsChannelFirstd(keys=[\"image\", \"label\"], channel_dim=-1),\n            ScaleIntensityd(keys=[\"image\", \"label\"]),\n            RandCropByPosNegLabeld(\n                keys=[\"image\", \"label\"], label_key=\"label\", spatial_size=[96, 96, 96], pos=1, neg=1, num_samples=4\n            ),\n            RandRotate90d(keys=[\"image\", \"label\"], prob=0.5, spatial_axes=[0, 2]),\n            ToTensord(keys=[\"image\", \"label\"]),\n        ]\n    )\n    val_transforms = Compose(\n        [\n            LoadImaged(keys=[\"image\", \"label\"]),\n            AsChannelFirstd(keys=[\"image\", \"label\"], channel_dim=-1),\n            ScaleIntensityd(keys=[\"image\", \"label\"]),\n            ToTensord(keys=[\"image\", \"label\"]),\n        ]\n    )\n\n    # create a training data loader\n    train_ds = monai.data.CacheDataset(data=train_files, transform=train_transforms, cache_rate=0.5)\n    # use batch_size=2 to load images and use RandCropByPosNegLabeld to generate 2 x 4 images for network training\n    train_loader = monai.data.DataLoader(train_ds, batch_size=2, shuffle=True, num_workers=num_workers)\n    # create a validation data loader\n    val_ds = monai.data.CacheDataset(data=val_files, transform=val_transforms, cache_rate=1.0)\n    val_loader = monai.data.DataLoader(val_ds, batch_size=1, num_workers=num_workers)\n\n    # create UNet, DiceLoss and Adam optimizer\n    net = monai.networks.nets.UNet(\n        spatial_dims=3,\n        in_channels=1,\n        out_channels=1,\n        channels=(16, 32, 64, 128, 256),\n        strides=(2, 2, 2, 2),\n        num_res_units=2,\n    ).to(device)\n    loss = monai.losses.DiceLoss(sigmoid=True)\n    opt = torch.optim.Adam(net.parameters(), 1e-3)\n    lr_scheduler = torch.optim.lr_scheduler.StepLR(opt, step_size=2, gamma=0.1)\n    summary_writer = SummaryWriter(log_dir=root_dir)\n\n    val_postprocessing = Compose(\n        [\n            ToTensord(keys=[\"pred\", \"label\"]),\n            Activationsd(keys=\"pred\", sigmoid=True),\n            AsDiscreted(keys=\"pred\", threshold=0.5),\n            KeepLargestConnectedComponentd(keys=\"pred\", applied_labels=[1]),\n        ]\n    )\n\n    class _TestEvalIterEvents:\n        def attach(self, engine):\n            engine.add_event_handler(IterationEvents.FORWARD_COMPLETED, self._forward_completed)\n\n        def _forward_completed(self, engine):\n            pass\n    # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_workflows.py_run_training_test._TestTrainIterEvents_run_training_test._TestTrainIterEvents._model_completed.pass": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_workflows.py_run_training_test._TestTrainIterEvents_run_training_test._TestTrainIterEvents._model_completed.pass", "embedding": null, "metadata": {"file_path": "tests/test_integration_workflows.py", "file_name": "test_integration_workflows.py", "file_type": "text/x-python", "category": "test", "start_line": 158, "end_line": 175, "span_ids": ["run_training_test"], "tokens": 164}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def run_training_test(root_dir, device=\"cuda:0\", amp=False, num_workers=4):\n    # ... other code\n\n    class _TestTrainIterEvents:\n        def attach(self, engine):\n            engine.add_event_handler(IterationEvents.FORWARD_COMPLETED, self._forward_completed)\n            engine.add_event_handler(IterationEvents.LOSS_COMPLETED, self._loss_completed)\n            engine.add_event_handler(IterationEvents.BACKWARD_COMPLETED, self._backward_completed)\n            engine.add_event_handler(IterationEvents.MODEL_COMPLETED, self._model_completed)\n\n        def _forward_completed(self, engine):\n            pass\n\n        def _loss_completed(self, engine):\n            pass\n\n        def _backward_completed(self, engine):\n            pass\n\n        def _model_completed(self, engine):\n            pass\n    # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_workflows.py_run_training_test.train_handlers_run_training_test.return.evaluator_state_best_metr": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_workflows.py_run_training_test.train_handlers_run_training_test.return.evaluator_state_best_metr", "embedding": null, "metadata": {"file_path": "tests/test_integration_workflows.py", "file_name": "test_integration_workflows.py", "file_type": "text/x-python", "category": "test", "start_line": 182, "end_line": 209, "span_ids": ["run_training_test"], "tokens": 267}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def run_training_test(root_dir, device=\"cuda:0\", amp=False, num_workers=4):\n    # ... other code\n\n    train_handlers = [\n        LrScheduleHandler(lr_scheduler=lr_scheduler, print_lr=True),\n        ValidationHandler(validator=evaluator, interval=2, epoch_level=True),\n        StatsHandler(tag_name=\"train_loss\", output_transform=from_engine(\"loss\", first=True)),\n        TensorBoardStatsHandler(\n            summary_writer=summary_writer, tag_name=\"train_loss\", output_transform=from_engine(\"loss\", first=True)\n        ),\n        CheckpointSaver(save_dir=root_dir, save_dict={\"net\": net, \"opt\": opt}, save_interval=2, epoch_level=True),\n        _TestTrainIterEvents(),\n    ]\n\n    trainer = SupervisedTrainer(\n        device=device,\n        max_epochs=5,\n        train_data_loader=train_loader,\n        network=net,\n        optimizer=opt,\n        loss_function=loss,\n        inferer=SimpleInferer(),\n        postprocessing=train_postprocessing,\n        key_train_metric={\"train_acc\": Accuracy(output_transform=from_engine([\"pred\", \"label\"]))},\n        train_handlers=train_handlers,\n        amp=True if amp else False,\n        optim_set_to_none=True,\n    )\n    trainer.run()\n\n    return evaluator.state.best_metric", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_workflows_gan.py_logging_from_tests_utils_import_D": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_workflows_gan.py_logging_from_tests_utils_import_D", "embedding": null, "metadata": {"file_path": "tests/test_integration_workflows_gan.py", "file_name": "test_integration_workflows_gan.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 33, "span_ids": ["docstring"], "tokens": 169}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import logging\nimport os\nimport shutil\nimport sys\nimport tempfile\nimport unittest\nfrom glob import glob\n\nimport nibabel as nib\nimport numpy as np\nimport torch\n\nimport monai\nfrom monai.data import create_test_image_2d\nfrom monai.engines import GanTrainer\nfrom monai.engines.utils import GanKeys as Keys\nfrom monai.handlers import CheckpointSaver, StatsHandler, TensorBoardStatsHandler\nfrom monai.networks import normal_init\nfrom monai.networks.nets import Discriminator, Generator\nfrom monai.transforms import AsChannelFirstd, Compose, LoadImaged, RandFlipd, ScaleIntensityd, ToTensord\nfrom monai.utils import set_determinism\nfrom tests.utils import DistTestCase, TimedCall, skip_if_quick", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_inverse.py_random__non_sensical_tests_cro": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_inverse.py_random__non_sensical_tests_cro", "embedding": null, "metadata": {"file_path": "tests/test_inverse.py", "file_name": "test_inverse.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 102, "span_ids": ["docstring:11", "docstring"], "tokens": 705}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import random\nimport sys\nimport unittest\nfrom functools import partial\nfrom typing import TYPE_CHECKING, List, Tuple\nfrom unittest.case import skipUnless\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.data import CacheDataset, DataLoader, create_test_image_2d, create_test_image_3d\nfrom monai.data.utils import decollate_batch\nfrom monai.networks.nets import UNet\nfrom monai.transforms import (\n    AddChanneld,\n    Affined,\n    BatchInverseTransform,\n    BorderPadd,\n    CenterScaleCropd,\n    CenterSpatialCropd,\n    Compose,\n    CropForegroundd,\n    DivisiblePadd,\n    Flipd,\n    InvertibleTransform,\n    Lambdad,\n    LoadImaged,\n    Orientationd,\n    RandAffined,\n    RandAxisFlipd,\n    RandCropByLabelClassesd,\n    RandCropByPosNegLabeld,\n    RandFlipd,\n    RandLambdad,\n    Randomizable,\n    RandRotate90d,\n    RandRotated,\n    RandSpatialCropd,\n    RandSpatialCropSamplesd,\n    RandWeightedCropd,\n    RandZoomd,\n    Resized,\n    ResizeWithPadOrCrop,\n    ResizeWithPadOrCropd,\n    Rotate90d,\n    Rotated,\n    Spacingd,\n    SpatialCropd,\n    SpatialPadd,\n    TraceableTransform,\n    Transposed,\n    Zoomd,\n    allow_missing_keys_mode,\n    convert_inverse_interp_mode,\n)\nfrom monai.utils import first, get_seed, optional_import, set_determinism\nfrom tests.utils import make_nifti_image, make_rand_affine\n\nif TYPE_CHECKING:\n\n    has_nib = True\nelse:\n    _, has_nib = optional_import(\"nibabel\")\n\nKEYS = [\"image\", \"label\"]\n\nTESTS: List[Tuple] = []\n\n# For pad, start with odd/even images and add odd/even amounts\nfor name in (\"1D even\", \"1D odd\"):\n    for val in (3, 4):\n        for t in (\n            partial(SpatialPadd, spatial_size=val, method=\"symmetric\"),\n            partial(SpatialPadd, spatial_size=val, method=\"end\"),\n            partial(BorderPadd, spatial_border=[val, val + 1]),\n            partial(DivisiblePadd, k=val),\n            partial(ResizeWithPadOrCropd, spatial_size=20 + val),\n            partial(CenterSpatialCropd, roi_size=10 + val),\n            partial(CenterScaleCropd, roi_scale=0.8),\n            partial(CropForegroundd, source_key=\"label\"),\n            partial(SpatialCropd, roi_center=10, roi_size=10 + val),\n            partial(SpatialCropd, roi_center=11, roi_size=10 + val),\n            partial(SpatialCropd, roi_start=val, roi_end=17),\n            partial(SpatialCropd, roi_start=val, roi_end=16),\n            partial(RandSpatialCropd, roi_size=12 + val),\n            partial(ResizeWithPadOrCropd, spatial_size=21 - val),\n        ):\n            TESTS.append((t.func.__name__ + name, name, 0, t(KEYS)))  # type: ignore\n\n# non-sensical tests: crop bigger or pad smaller or -ve values", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_inverse.py_TestInverse_TestInverse._Test_inverse_methods_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_inverse.py_TestInverse_TestInverse._Test_inverse_methods_", "embedding": null, "metadata": {"file_path": "tests/test_inverse.py", "file_name": "test_inverse.py", "file_type": "text/x-python", "category": "test", "start_line": 424, "end_line": 461, "span_ids": ["TestInverse"], "tokens": 428}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestInverse(unittest.TestCase):\n    \"\"\"Test inverse methods.\n\n    If tests are failing, the following function might be useful for displaying\n    `x`, `fx`, `f\u207b\u00b9fx` and `x - f\u207b\u00b9fx`.\n\n    .. code-block:: python\n\n        def plot_im(orig, fwd_bck, fwd):\n            import matplotlib.pyplot as plt\n            diff_orig_fwd_bck = orig - fwd_bck\n            ims_to_show = [orig, fwd, fwd_bck, diff_orig_fwd_bck]\n            titles = [\"x\", \"fx\", \"f\u207b\u00b9fx\", \"x - f\u207b\u00b9fx\"]\n            fig, axes = plt.subplots(1, 4, gridspec_kw={\"width_ratios\": [i.shape[1] for i in ims_to_show]})\n            vmin = min(np.array(i).min() for i in [orig, fwd_bck, fwd])\n            vmax = max(np.array(i).max() for i in [orig, fwd_bck, fwd])\n            for im, title, ax in zip(ims_to_show, titles, axes):\n                _vmin, _vmax = (vmin, vmax) if id(im) != id(diff_orig_fwd_bck) else (None, None)\n                im = np.squeeze(np.array(im))\n                while im.ndim > 2:\n                    im = im[..., im.shape[-1] // 2]\n                im_show = ax.imshow(np.squeeze(im), vmin=_vmin, vmax=_vmax)\n                ax.set_title(title, fontsize=25)\n                ax.axis(\"off\")\n                fig.colorbar(im_show, ax=ax)\n            plt.show()\n\n    This can then be added to the exception:\n\n    .. code-block:: python\n\n        except AssertionError:\n            print(\n                f\"Failed: {name}. Mean diff = {mean_diff} (expected <= {acceptable_diff}), unmodified diff: {unmodded_diff}\"\n            )\n            if orig[0].ndim > 1:\n                plot_im(orig, fwd_bck, unmodified)\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_inverse.py_TestInverse.setUp_TestInverse.setUp.self_all_data_3D_loa": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_inverse.py_TestInverse.setUp_TestInverse.setUp.self_all_data_3D_loa", "embedding": null, "metadata": {"file_path": "tests/test_inverse.py", "file_name": "test_inverse.py", "file_type": "text/x-python", "category": "test", "start_line": 347, "end_line": 373, "span_ids": ["TestInverse.setUp"], "tokens": 338}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestInverse(unittest.TestCase):\n\n    def setUp(self):\n        if not has_nib:\n            self.skipTest(\"nibabel required for test_inverse\")\n\n        set_determinism(seed=0)\n\n        self.all_data = {}\n\n        affine = make_rand_affine()\n        affine[0] *= 2\n\n        for size in [10, 11]:\n            # pad 5 onto both ends so that cropping can be lossless\n            im_1d = np.pad(np.arange(size), 5)[None]\n            name = \"1D even\" if size % 2 == 0 else \"1D odd\"\n            self.all_data[name] = {\n                \"image\": np.array(im_1d, copy=True),\n                \"label\": np.array(im_1d, copy=True),\n                \"other\": np.array(im_1d, copy=True),\n            }\n\n        im_2d_fname, seg_2d_fname = (make_nifti_image(i) for i in create_test_image_2d(101, 100))\n        im_3d_fname, seg_3d_fname = (make_nifti_image(i, affine) for i in create_test_image_3d(100, 101, 107))\n\n        load_ims = Compose([LoadImaged(KEYS), AddChanneld(KEYS)])\n        self.all_data[\"2D\"] = load_ims({\"image\": im_2d_fname, \"label\": seg_2d_fname})\n        self.all_data[\"3D\"] = load_ims({\"image\": im_3d_fname, \"label\": seg_3d_fname})", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_inverse.py_TestInverse.tearDown_TestInverse.check_inverse.for_key_in_keys_.if_isinstance_orig_np_nd.try_.except_AssertionError_.raise": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_inverse.py_TestInverse.tearDown_TestInverse.check_inverse.for_key_in_keys_.if_isinstance_orig_np_nd.try_.except_AssertionError_.raise", "embedding": null, "metadata": {"file_path": "tests/test_inverse.py", "file_name": "test_inverse.py", "file_type": "text/x-python", "category": "test", "start_line": 491, "end_line": 517, "span_ids": ["TestInverse.check_inverse", "TestInverse.tearDown"], "tokens": 275}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestInverse(unittest.TestCase):\n\n    def tearDown(self):\n        set_determinism(seed=None)\n\n    def check_inverse(self, name, keys, orig_d, fwd_bck_d, unmodified_d, acceptable_diff):\n        for key in keys:\n            orig = orig_d[key]\n            fwd_bck = fwd_bck_d[key]\n            if isinstance(fwd_bck, torch.Tensor):\n                fwd_bck = fwd_bck.cpu().numpy()\n            unmodified = unmodified_d[key]\n            if isinstance(orig, np.ndarray):\n                mean_diff = np.mean(np.abs(orig - fwd_bck))\n                resized = ResizeWithPadOrCrop(orig.shape[1:])(unmodified)\n                if isinstance(resized, torch.Tensor):\n                    resized = resized.detach().cpu().numpy()\n                unmodded_diff = np.mean(np.abs(orig - resized))\n                try:\n                    self.assertLessEqual(mean_diff, acceptable_diff)\n                except AssertionError:\n                    print(\n                        f\"Failed: {name}. Mean diff = {mean_diff} (expected <= {acceptable_diff}), unmodified diff: {unmodded_diff}\"\n                    )\n                    if orig[0].ndim == 1:\n                        print(\"orig\", orig[0])\n                        print(\"fwd_bck\", fwd_bck[0])\n                        print(\"unmod\", unmodified[0])\n                    raise", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_inverse.py_TestInverse._skip_this_test_if_multi_TestInverse.test_fail.with_self_assertRaises_Ru.t2_inverse_data_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_inverse.py_TestInverse._skip_this_test_if_multi_TestInverse.test_fail.with_self_assertRaises_Ru.t2_inverse_data_", "embedding": null, "metadata": {"file_path": "tests/test_inverse.py", "file_name": "test_inverse.py", "file_type": "text/x-python", "category": "test", "start_line": 431, "end_line": 441, "span_ids": ["TestInverse.test_fail", "TestInverse.test_inverse"], "tokens": 126}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestInverse(unittest.TestCase):\n\n    # skip this test if multiprocessing uses 'spawn', as the check is only basic anyway\n    @skipUnless(torch.multiprocessing.get_start_method() == \"spawn\", \"requires spawn\")\n    def test_fail(self):\n\n        t1 = SpatialPadd(\"image\", [10, 5])\n        data = t1(self.all_data[\"2D\"])\n\n        # Check that error is thrown when inverse are used out of order.\n        t2 = ResizeWithPadOrCropd(\"image\", [10, 5])\n        with self.assertRaises(RuntimeError):\n            t2.inverse(data)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_inverse.py_TestInverse.test_inverse_inferred_seg_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_inverse.py_TestInverse.test_inverse_inferred_seg_", "embedding": null, "metadata": {"file_path": "tests/test_inverse.py", "file_name": "test_inverse.py", "file_type": "text/x-python", "category": "test", "start_line": 443, "end_line": 493, "span_ids": ["TestInverse.test_inverse_inferred_seg", "impl:74"], "tokens": 569}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestInverse(unittest.TestCase):\n\n    @parameterized.expand(N_SAMPLES_TESTS)\n    def test_inverse_inferred_seg(self, extra_transform):\n\n        test_data = []\n        for _ in range(20):\n            image, label = create_test_image_2d(100, 101)\n            test_data.append({\"image\": image, \"label\": label.astype(np.float32)})\n\n        batch_size = 10\n        # num workers = 0 for mac\n        num_workers = 2 if sys.platform == \"linux\" else 0\n        transforms = Compose([AddChanneld(KEYS), SpatialPadd(KEYS, (150, 153)), extra_transform])\n        num_invertible_transforms = sum(1 for i in transforms.transforms if isinstance(i, InvertibleTransform))\n\n        dataset = CacheDataset(test_data, transform=transforms, progress=False)\n        loader = DataLoader(dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers)\n\n        device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n        model = UNet(spatial_dims=2, in_channels=1, out_channels=1, channels=(2, 4), strides=(2,)).to(device)\n\n        data = first(loader)\n        self.assertEqual(len(data[\"label_transforms\"]), num_invertible_transforms)\n        self.assertEqual(data[\"image\"].shape[0], batch_size * NUM_SAMPLES)\n\n        labels = data[\"label\"].to(device)\n        segs = model(labels).detach().cpu()\n        label_transform_key = TraceableTransform.trace_key(\"label\")\n        segs_dict = {\"label\": segs, label_transform_key: data[label_transform_key]}\n\n        segs_dict_decollated = decollate_batch(segs_dict)\n        # inverse of individual segmentation\n        seg_dict = first(segs_dict_decollated)\n        # test to convert interpolation mode for 1 data of model output batch\n        convert_inverse_interp_mode(seg_dict, mode=\"nearest\", align_corners=None)\n\n        with allow_missing_keys_mode(transforms):\n            inv_seg = transforms.inverse(seg_dict)[\"label\"]\n        self.assertEqual(len(data[\"label_transforms\"]), num_invertible_transforms)\n        self.assertEqual(len(seg_dict[\"label_transforms\"]), num_invertible_transforms)\n        self.assertEqual(inv_seg.shape[1:], test_data[0][\"label\"].shape)\n\n        # Inverse of batch\n        batch_inverter = BatchInverseTransform(transforms, loader, collate_fn=no_collation, detach=True)\n        with allow_missing_keys_mode(transforms):\n            inv_batch = batch_inverter(segs_dict)\n        self.assertEqual(inv_batch[0][\"label\"].shape[1:], test_data[0][\"label\"].shape)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_inverse_collation.py_sys_TESTS_2D._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_inverse_collation.py_sys_TESTS_2D._", "embedding": null, "metadata": {"file_path": "tests/test_inverse_collation.py", "file_name": "test_inverse_collation.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 73, "span_ids": ["impl:10", "docstring"], "tokens": 644}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import sys\nimport unittest\nfrom typing import TYPE_CHECKING\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.data import CacheDataset, DataLoader, create_test_image_2d, create_test_image_3d, pad_list_data_collate\nfrom monai.transforms import (\n    AddChanneld,\n    Compose,\n    LoadImaged,\n    RandAffined,\n    RandAxisFlipd,\n    RandFlipd,\n    RandRotate90d,\n    RandRotated,\n    RandZoomd,\n    ResizeWithPadOrCropd,\n    ToTensord,\n)\nfrom monai.utils import optional_import, set_determinism\nfrom tests.utils import make_nifti_image\n\nif TYPE_CHECKING:\n\n    has_nib = True\nelse:\n    _, has_nib = optional_import(\"nibabel\")\n\nKEYS = [\"image\", \"label\"]\n\nTESTS_3D = [\n    (t.__class__.__name__ + (\" pad_list_data_collate\" if collate_fn else \" default_collate\"), t, collate_fn, 3)\n    for collate_fn in [None, pad_list_data_collate]\n    for t in [\n        RandFlipd(keys=KEYS, prob=0.5, spatial_axis=[1, 2]),\n        RandAxisFlipd(keys=KEYS, prob=0.5),\n        Compose([RandRotate90d(keys=KEYS, spatial_axes=(1, 2)), ToTensord(keys=KEYS)]),\n        RandZoomd(keys=KEYS, prob=0.5, min_zoom=0.5, max_zoom=1.1, keep_size=True),\n        RandRotated(keys=KEYS, prob=0.5, range_x=np.pi),\n        RandAffined(\n            keys=KEYS, prob=0.5, rotate_range=np.pi, device=torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n        ),\n    ]\n]\n\nTESTS_2D = [\n    (t.__class__.__name__ + (\" pad_list_data_collate\" if collate_fn else \" default_collate\"), t, collate_fn, 2)\n    for collate_fn in [None, pad_list_data_collate]\n    for t in [\n        RandFlipd(keys=KEYS, prob=0.5, spatial_axis=[1]),\n        RandAxisFlipd(keys=KEYS, prob=0.5),\n        Compose([RandRotate90d(keys=KEYS, prob=0.5, spatial_axes=(0, 1)), ToTensord(keys=KEYS)]),\n        RandZoomd(keys=KEYS, prob=0.5, min_zoom=0.5, max_zoom=1.1, keep_size=True),\n        RandRotated(keys=KEYS, prob=0.5, range_x=np.pi),\n        RandAffined(\n            keys=KEYS, prob=0.5, rotate_range=np.pi, device=torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n        ),\n    ]\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_inverse_collation.py_TestInverseCollation_TestInverseCollation.setUp.self.batch_size.7": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_inverse_collation.py_TestInverseCollation_TestInverseCollation.setUp.self.batch_size.7", "embedding": null, "metadata": {"file_path": "tests/test_inverse_collation.py", "file_name": "test_inverse_collation.py", "file_type": "text/x-python", "category": "test", "start_line": 76, "end_line": 95, "span_ids": ["TestInverseCollation", "TestInverseCollation.setUp"], "tokens": 251}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestInverseCollation(unittest.TestCase):\n    \"\"\"Test collation for of random transformations with prob == 0 and 1.\"\"\"\n\n    def setUp(self):\n        if not has_nib:\n            self.skipTest(\"nibabel required for test_inverse\")\n\n        set_determinism(seed=0)\n\n        b_size = 11\n        im_fname, seg_fname = (make_nifti_image(i) for i in create_test_image_3d(101, 100, 107))\n        load_ims = Compose([LoadImaged(KEYS), AddChanneld(KEYS)])\n        self.data_3d = [load_ims({\"image\": im_fname, \"label\": seg_fname}) for _ in range(b_size)]\n\n        b_size = 8\n        im_fname, seg_fname = (make_nifti_image(i) for i in create_test_image_2d(62, 37, rad_max=10))\n        load_ims = Compose([LoadImaged(KEYS), AddChanneld(KEYS)])\n        self.data_2d = [load_ims({\"image\": im_fname, \"label\": seg_fname}) for _ in range(b_size)]\n\n        self.batch_size = 7", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_inverse_collation.py_TestInverseCollation.tearDown_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_inverse_collation.py_TestInverseCollation.tearDown_", "embedding": null, "metadata": {"file_path": "tests/test_inverse_collation.py", "file_name": "test_inverse_collation.py", "file_type": "text/x-python", "category": "test", "start_line": 97, "end_line": 122, "span_ids": ["TestInverseCollation.tearDown", "impl:12", "TestInverseCollation.test_collation"], "tokens": 245}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestInverseCollation(unittest.TestCase):\n\n    def tearDown(self):\n        set_determinism(seed=None)\n\n    @parameterized.expand(TESTS_2D + TESTS_3D)\n    def test_collation(self, _, transform, collate_fn, ndim):\n        data = self.data_3d if ndim == 3 else self.data_2d\n        if collate_fn:\n            modified_transform = transform\n        else:\n            modified_transform = Compose([transform, ResizeWithPadOrCropd(KEYS, 100), ToTensord(KEYS)])\n\n        # num workers = 0 for mac or gpu transforms\n        num_workers = 0 if sys.platform != \"linux\" or torch.cuda.is_available() else 2\n\n        dataset = CacheDataset(data, transform=modified_transform, progress=False)\n        loader = DataLoader(dataset, num_workers, batch_size=self.batch_size, collate_fn=collate_fn)\n\n        for item in loader:\n            np.testing.assert_array_equal(\n                item[\"image_transforms\"][0][\"do_transforms\"], item[\"label_transforms\"][0][\"do_transforms\"]\n            )\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_iterable_dataset.py_os_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_iterable_dataset.py_os_", "embedding": null, "metadata": {"file_path": "tests/test_iterable_dataset.py", "file_name": "test_iterable_dataset.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 61, "span_ids": ["_Stream.__iter__", "impl", "_Stream.__init__", "_Stream", "TestIterableDataset", "docstring", "TestIterableDataset.test_shape"], "tokens": 395}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport sys\nimport tempfile\nimport unittest\n\nimport nibabel as nib\nimport numpy as np\n\nfrom monai.data import DataLoader, Dataset, IterableDataset\nfrom monai.transforms import Compose, LoadImaged, SimulateDelayd\n\n\nclass _Stream:\n    def __init__(self, data):\n        self.data = data\n\n    def __iter__(self):\n        return iter(self.data)\n\n\nclass TestIterableDataset(unittest.TestCase):\n    def test_shape(self):\n        expected_shape = (128, 128, 128)\n        test_image = nib.Nifti1Image(np.random.randint(0, 2, size=[128, 128, 128]), np.eye(4))\n        test_data = []\n        with tempfile.TemporaryDirectory() as tempdir:\n            for i in range(6):\n                nib.save(test_image, os.path.join(tempdir, f\"test_image{str(i)}.nii.gz\"))\n                test_data.append({\"image\": os.path.join(tempdir, f\"test_image{i}.nii.gz\")})\n\n            test_transform = Compose([LoadImaged(keys=\"image\"), SimulateDelayd(keys=\"image\", delay_time=1e-7)])\n\n            data_iterator = _Stream(test_data)\n            with self.assertRaises(TypeError):  # Dataset doesn't work\n                dataset = Dataset(data=data_iterator, transform=test_transform)\n                for _ in dataset:\n                    pass\n            dataset = IterableDataset(data=data_iterator, transform=test_transform)\n            for d in dataset:\n                self.assertTupleEqual(d[\"image\"].shape, expected_shape)\n\n            num_workers = 2 if sys.platform == \"linux\" else 0\n            dataloader = DataLoader(dataset=dataset, batch_size=3, num_workers=num_workers)\n            for d in dataloader:\n                self.assertTupleEqual(d[\"image\"].shape[1:], expected_shape)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_lesion_froc.py_os_WIDTH.800": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_lesion_froc.py_os_WIDTH.800", "embedding": null, "metadata": {"file_path": "tests/test_lesion_froc.py", "file_name": "test_lesion_froc.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 43, "span_ids": ["save_as_tif", "around", "impl:7", "docstring"], "tokens": 232}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport unittest\nfrom unittest import skipUnless\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.apps.pathology.metrics import LesionFROC\nfrom monai.utils import optional_import\n\n_cucim, has_cucim = optional_import(\"cucim\")\nhas_cucim = has_cucim and hasattr(_cucim, \"CuImage\")\n_, has_skimage = optional_import(\"skimage.measure\")\n_, has_sp = optional_import(\"scipy.ndimage\")\nimwrite, has_tif = optional_import(\"tifffile\", name=\"imwrite\")\n\n\ndef save_as_tif(filename, array):\n    array = array[::-1, ...]  # Upside-down\n    if not filename.endswith(\".tif\"):\n        filename += \".tif\"\n    file_path = os.path.join(\"tests\", \"testing_data\", filename)\n    imwrite(file_path, array, compress=\"jpeg\", tile=(16, 16))\n\n\ndef around(val, interval=3):\n    return slice(val - interval, val + interval)\n\n\n# mask and prediction image size\nHEIGHT = 101\nWIDTH = 800", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_lesion_froc.py_prepare_test_data_prepare_test_data.None_10": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_lesion_froc.py_prepare_test_data_prepare_test_data.None_10", "embedding": null, "metadata": {"file_path": "tests/test_lesion_froc.py", "file_name": "test_lesion_froc.py", "file_type": "text/x-python", "category": "test", "start_line": 45, "end_line": 97, "span_ids": ["prepare_test_data"], "tokens": 598}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def prepare_test_data():\n    # -------------------------------------\n    # Ground Truth - Binary Masks\n    # -------------------------------------\n    # ground truth with no tumor\n    ground_truth = np.zeros((HEIGHT, WIDTH), dtype=np.uint8)\n    save_as_tif(\"temp_ground_truth_0\", ground_truth)\n\n    # ground truth with one tumor\n    ground_truth[around(HEIGHT // 2), around(1 * WIDTH // 7)] = 1\n    save_as_tif(\"temp_ground_truth_1\", ground_truth)\n\n    # ground truth with two tumors\n    ground_truth[around(HEIGHT // 2), around(2 * WIDTH // 7)] = 1\n    save_as_tif(\"temp_ground_truth_2\", ground_truth)\n\n    # ground truth with three tumors\n    ground_truth[around(HEIGHT // 2), around(3 * WIDTH // 7)] = 1\n    save_as_tif(\"temp_ground_truth_3\", ground_truth)\n\n    # ground truth with four tumors\n    ground_truth[around(HEIGHT // 2), around(4 * WIDTH // 7)] = 1\n    save_as_tif(\"temp_ground_truth_4\", ground_truth)\n\n    # -------------------------------------\n    # predictions - Probability Maps\n    # -------------------------------------\n\n    # prediction with no tumor\n    prob_map = np.zeros((HEIGHT, WIDTH))\n    np.save(\"./tests/testing_data/temp_prob_map_0_0.npy\", prob_map)\n\n    # prediction with one incorrect tumor\n    prob_map[HEIGHT // 2, 5 * WIDTH // 7] = 0.6\n    np.save(\"./tests/testing_data/temp_prob_map_0_1.npy\", prob_map)\n\n    # prediction with correct first tumors and an incorrect tumor\n    prob_map[HEIGHT // 2, 1 * WIDTH // 7] = 0.8\n    np.save(\"./tests/testing_data/temp_prob_map_1_1.npy\", prob_map)\n\n    # prediction with correct firt two tumors and an incorrect tumor\n    prob_map[HEIGHT // 2, 2 * WIDTH // 7] = 0.8\n    np.save(\"./tests/testing_data/temp_prob_map_2_1.npy\", prob_map)\n\n    # prediction with two incorrect tumors\n    prob_map = np.zeros((HEIGHT, WIDTH))\n    prob_map[HEIGHT // 2, 5 * WIDTH // 7] = 0.6\n    prob_map[HEIGHT // 2, 6 * WIDTH // 7] = 0.4\n    np.save(\"./tests/testing_data/temp_prob_map_0_2.npy\", prob_map)\n\n    # prediction with correct first tumors and two incorrect tumors\n    prob_map[HEIGHT // 2, 1 * WIDTH // 7] = 0.8\n    np.save(\"./tests/testing_data/temp_prob_map_1_2.npy\", prob_map)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_lesion_froc.py_TEST_CASE_0_TEST_CASE_6._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_lesion_froc.py_TEST_CASE_0_TEST_CASE_6._", "embedding": null, "metadata": {"file_path": "tests/test_lesion_froc.py", "file_name": "test_lesion_froc.py", "file_type": "text/x-python", "category": "test", "start_line": 101, "end_line": 220, "span_ids": ["impl:11", "impl:23"], "tokens": 729}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "TEST_CASE_0 = [\n    {\n        \"data\": [\n            {\n                \"prob_map\": \"./tests/testing_data/temp_prob_map_0_0.npy\",\n                \"tumor_mask\": \"./tests/testing_data/temp_ground_truth_0.tif\",\n                \"level\": 0,\n                \"pixel_spacing\": 1,\n            }\n        ],\n        \"grow_distance\": 2,\n        \"itc_diameter\": 0,\n    },\n    np.nan,\n]\n\n\nTEST_CASE_1 = [\n    {\n        \"data\": [\n            {\n                \"prob_map\": \"./tests/testing_data/temp_prob_map_0_0.npy\",\n                \"tumor_mask\": \"./tests/testing_data/temp_ground_truth_1.tif\",\n                \"level\": 0,\n                \"pixel_spacing\": 1,\n            }\n        ],\n        \"grow_distance\": 2,\n        \"itc_diameter\": 0,\n    },\n    0.0,\n]\n\nTEST_CASE_2 = [\n    {\n        \"data\": [\n            {\n                \"prob_map\": \"./tests/testing_data/temp_prob_map_1_1.npy\",\n                \"tumor_mask\": \"./tests/testing_data/temp_ground_truth_1.tif\",\n                \"level\": 0,\n                \"pixel_spacing\": 1,\n            }\n        ],\n        \"grow_distance\": 2,\n        \"itc_diameter\": 0,\n    },\n    1.0,\n]\n\nTEST_CASE_3 = [\n    {\n        \"data\": [\n            {\n                \"prob_map\": \"./tests/testing_data/temp_prob_map_2_1.npy\",\n                \"tumor_mask\": \"./tests/testing_data/temp_ground_truth_1.tif\",\n                \"level\": 0,\n                \"pixel_spacing\": 1,\n            }\n        ],\n        \"grow_distance\": 2,\n        \"itc_diameter\": 0,\n    },\n    1.0,\n]\n\n\nTEST_CASE_4 = [\n    {\n        \"data\": [\n            {\n                \"prob_map\": \"./tests/testing_data/temp_prob_map_2_1.npy\",\n                \"tumor_mask\": \"./tests/testing_data/temp_ground_truth_2.tif\",\n                \"level\": 0,\n                \"pixel_spacing\": 1,\n            }\n        ],\n        \"grow_distance\": 2,\n        \"itc_diameter\": 0,\n    },\n    1.0,\n]\n\nTEST_CASE_5 = [\n    {\n        \"data\": [\n            {\n                \"prob_map\": \"./tests/testing_data/temp_prob_map_1_2.npy\",\n                \"tumor_mask\": \"./tests/testing_data/temp_ground_truth_2.tif\",\n                \"level\": 0,\n                \"pixel_spacing\": 1,\n            }\n        ],\n        \"grow_distance\": 2,\n        \"itc_diameter\": 0,\n    },\n    0.5,\n]\n\n\nTEST_CASE_6 = [\n    {\n        \"data\": [\n            {\n                \"prob_map\": \"./tests/testing_data/temp_prob_map_1_1.npy\",\n                \"tumor_mask\": \"./tests/testing_data/temp_ground_truth_1.tif\",\n                \"level\": 0,\n                \"pixel_spacing\": 1,\n            },\n            {\n                \"prob_map\": \"./tests/testing_data/temp_prob_map_1_2.npy\",\n                \"tumor_mask\": \"./tests/testing_data/temp_ground_truth_2.tif\",\n                \"level\": 0,\n                \"pixel_spacing\": 1,\n            },\n        ],\n        \"grow_distance\": 2,\n        \"itc_diameter\": 0,\n    },\n    2.0 / 3.0,\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_lesion_froc.py_TEST_CASE_7_TEST_CASE_9._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_lesion_froc.py_TEST_CASE_7_TEST_CASE_9._", "embedding": null, "metadata": {"file_path": "tests/test_lesion_froc.py", "file_name": "test_lesion_froc.py", "file_type": "text/x-python", "category": "test", "start_line": 222, "end_line": 298, "span_ids": ["impl:29", "impl:23"], "tokens": 568}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "TEST_CASE_7 = [\n    {\n        \"data\": [\n            {\n                \"prob_map\": \"./tests/testing_data/temp_prob_map_1_1.npy\",\n                \"tumor_mask\": \"./tests/testing_data/temp_ground_truth_3.tif\",\n                \"level\": 0,\n                \"pixel_spacing\": 1,\n            },\n            {\n                \"prob_map\": \"./tests/testing_data/temp_prob_map_1_2.npy\",\n                \"tumor_mask\": \"./tests/testing_data/temp_ground_truth_2.tif\",\n                \"level\": 0,\n                \"pixel_spacing\": 1,\n            },\n        ],\n        \"grow_distance\": 2,\n        \"itc_diameter\": 0,\n    },\n    0.4,\n]\n\nTEST_CASE_8 = [\n    {\n        \"data\": [\n            {\n                \"prob_map\": \"./tests/testing_data/temp_prob_map_0_1.npy\",\n                \"tumor_mask\": \"./tests/testing_data/temp_ground_truth_1.tif\",\n                \"level\": 0,\n                \"pixel_spacing\": 1,\n            },\n            {\n                \"prob_map\": \"./tests/testing_data/temp_prob_map_1_1.npy\",\n                \"tumor_mask\": \"./tests/testing_data/temp_ground_truth_3.tif\",\n                \"level\": 0,\n                \"pixel_spacing\": 1,\n            },\n            {\n                \"prob_map\": \"./tests/testing_data/temp_prob_map_1_2.npy\",\n                \"tumor_mask\": \"./tests/testing_data/temp_ground_truth_2.tif\",\n                \"level\": 0,\n                \"pixel_spacing\": 1,\n            },\n        ],\n        \"grow_distance\": 2,\n        \"itc_diameter\": 0,\n    },\n    1.0 / 3.0,\n]\n\nTEST_CASE_9 = [\n    {\n        \"data\": [\n            {\n                \"prob_map\": \"./tests/testing_data/temp_prob_map_0_2.npy\",\n                \"tumor_mask\": \"./tests/testing_data/temp_ground_truth_4.tif\",\n                \"level\": 0,\n                \"pixel_spacing\": 1,\n            },\n            {\n                \"prob_map\": \"./tests/testing_data/temp_prob_map_1_1.npy\",\n                \"tumor_mask\": \"./tests/testing_data/temp_ground_truth_3.tif\",\n                \"level\": 0,\n                \"pixel_spacing\": 1,\n            },\n            {\n                \"prob_map\": \"./tests/testing_data/temp_prob_map_1_2.npy\",\n                \"tumor_mask\": \"./tests/testing_data/temp_ground_truth_2.tif\",\n                \"level\": 0,\n                \"pixel_spacing\": 1,\n            },\n        ],\n        \"grow_distance\": 2,\n        \"itc_diameter\": 0,\n    },\n    2.0 / 9.0,\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_lesion_froc.py_TestEvaluateTumorFROC_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_lesion_froc.py_TestEvaluateTumorFROC_", "embedding": null, "metadata": {"file_path": "tests/test_lesion_froc.py", "file_name": "test_lesion_froc.py", "file_type": "text/x-python", "category": "test", "start_line": 301, "end_line": 334, "span_ids": ["TestEvaluateTumorFROC", "TestEvaluateTumorFROC.test_read_patches_cucim", "impl:31", "TestEvaluateTumorFROC.setUp"], "tokens": 219}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestEvaluateTumorFROC(unittest.TestCase):\n    @skipUnless(has_cucim, \"Requires cucim\")\n    @skipUnless(has_skimage, \"Requires skimage\")\n    @skipUnless(has_sp, \"Requires scipy\")\n    @skipUnless(has_tif, \"Requires tifffile\")\n    def setUp(self):\n        prepare_test_data()\n\n    @parameterized.expand(\n        [\n            TEST_CASE_0,\n            TEST_CASE_1,\n            TEST_CASE_2,\n            TEST_CASE_3,\n            TEST_CASE_4,\n            TEST_CASE_5,\n            TEST_CASE_6,\n            TEST_CASE_7,\n            TEST_CASE_8,\n            TEST_CASE_9,\n        ]\n    )\n    def test_read_patches_cucim(self, input_parameters, expected):\n        froc = LesionFROC(**input_parameters)\n        froc_score = froc.evaluate()\n        if np.isnan(expected):\n            self.assertTrue(np.isnan(froc_score))\n        else:\n            self.assertAlmostEqual(froc_score, expected)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_lmdbdataset.py_TestLMDBDataset_TestLMDBDataset.test_cache.self_assertTrue_isinstanc": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_lmdbdataset.py_TestLMDBDataset_TestLMDBDataset.test_cache.self_assertTrue_isinstanc", "embedding": null, "metadata": {"file_path": "tests/test_lmdbdataset.py", "file_name": "test_lmdbdataset.py", "file_type": "text/x-python", "category": "test", "start_line": 91, "end_line": 122, "span_ids": ["TestLMDBDataset.test_cache", "TestLMDBDataset"], "tokens": 408}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_windows\nclass TestLMDBDataset(unittest.TestCase):\n    def test_cache(self):\n        \"\"\"testing no inplace change to the hashed item\"\"\"\n        items = [[list(range(i))] for i in range(5)]\n\n        with tempfile.TemporaryDirectory() as tempdir:\n            ds = LMDBDataset(items, transform=_InplaceXform(), cache_dir=tempdir, lmdb_kwargs={\"map_size\": 10 * 1024})\n            self.assertEqual(items, [[[]], [[0]], [[0, 1]], [[0, 1, 2]], [[0, 1, 2, 3]]])\n            ds1 = LMDBDataset(items, transform=_InplaceXform(), cache_dir=tempdir, lmdb_kwargs={\"map_size\": 10 * 1024})\n            self.assertEqual(list(ds1), list(ds))\n            self.assertEqual(items, [[[]], [[0]], [[0, 1]], [[0, 1, 2]], [[0, 1, 2, 3]]])\n\n            ds = LMDBDataset(\n                items,\n                transform=_InplaceXform(),\n                cache_dir=tempdir,\n                lmdb_kwargs={\"map_size\": 10 * 1024},\n                hash_func=json_hashing,\n            )\n            self.assertEqual(items, [[[]], [[0]], [[0, 1]], [[0, 1, 2]], [[0, 1, 2, 3]]])\n            ds1 = LMDBDataset(\n                items,\n                transform=_InplaceXform(),\n                cache_dir=tempdir,\n                lmdb_kwargs={\"map_size\": 10 * 1024},\n                hash_func=json_hashing,\n            )\n            self.assertEqual(list(ds1), list(ds))\n            self.assertEqual(items, [[[]], [[0]], [[0, 1]], [[0, 1, 2]], [[0, 1, 2, 3]]])\n\n        self.assertTrue(isinstance(ds1.info(), dict))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_decathlon_datalist.py_TestLoadDecathlonDatalist.test_seg_no_labels_TestLoadDecathlonDatalist.test_seg_no_labels.with_tempfile_TemporaryDi.self_assertEqual_result_0": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_decathlon_datalist.py_TestLoadDecathlonDatalist.test_seg_no_labels_TestLoadDecathlonDatalist.test_seg_no_labels.with_tempfile_TemporaryDi.self_assertEqual_result_0", "embedding": null, "metadata": {"file_path": "tests/test_load_decathlon_datalist.py", "file_name": "test_load_decathlon_datalist.py", "file_type": "text/x-python", "category": "test", "start_line": 84, "end_line": 97, "span_ids": ["TestLoadDecathlonDatalist.test_seg_no_labels"], "tokens": 185}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestLoadDecathlonDatalist(unittest.TestCase):\n\n    def test_seg_no_labels(self):\n        with tempfile.TemporaryDirectory() as tempdir:\n            test_data = {\n                \"name\": \"Spleen\",\n                \"description\": \"Spleen Segmentation\",\n                \"labels\": {\"0\": \"background\", \"1\": \"spleen\"},\n                \"test\": [\"spleen_15.nii.gz\", \"spleen_23.nii.gz\"],\n            }\n            json_str = json.dumps(test_data)\n            file_path = os.path.join(tempdir, \"test_data.json\")\n            with open(file_path, \"w\") as json_file:\n                json_file.write(json_str)\n            result = load_decathlon_datalist(file_path, True, \"test\", tempdir)\n            self.assertEqual(result[0][\"image\"], os.path.join(tempdir, \"spleen_15.nii.gz\"))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_decathlon_datalist.py_TestLoadDecathlonDatalist.test_additional_items_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_decathlon_datalist.py_TestLoadDecathlonDatalist.test_additional_items_", "embedding": null, "metadata": {"file_path": "tests/test_load_decathlon_datalist.py", "file_name": "test_load_decathlon_datalist.py", "file_type": "text/x-python", "category": "test", "start_line": 100, "end_line": 128, "span_ids": ["TestLoadDecathlonDatalist.test_additional_items", "impl"], "tokens": 360}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestLoadDecathlonDatalist(unittest.TestCase):\n\n    def test_additional_items(self):\n        with tempfile.TemporaryDirectory() as tempdir:\n            with open(os.path.join(tempdir, \"mask31.txt\"), \"w\") as f:\n                f.write(\"spleen31 mask\")\n\n            test_data = {\n                \"name\": \"Spleen\",\n                \"description\": \"Spleen Segmentation\",\n                \"labels\": {\"0\": \"background\", \"1\": \"spleen\"},\n                \"training\": [\n                    {\"image\": \"spleen_19.nii.gz\", \"label\": \"spleen_19.nii.gz\", \"mask\": \"spleen mask\"},\n                    {\"image\": \"spleen_31.nii.gz\", \"label\": \"spleen_31.nii.gz\", \"mask\": \"mask31.txt\"},\n                ],\n                \"test\": [\"spleen_15.nii.gz\", \"spleen_23.nii.gz\"],\n            }\n            json_str = json.dumps(test_data)\n            file_path = os.path.join(tempdir, \"test_data.json\")\n            with open(file_path, \"w\") as json_file:\n                json_file.write(json_str)\n            result = load_decathlon_datalist(file_path, True, \"training\", Path(tempdir))\n            self.assertEqual(result[0][\"image\"], os.path.join(tempdir, \"spleen_19.nii.gz\"))\n            self.assertEqual(result[0][\"label\"], os.path.join(tempdir, \"spleen_19.nii.gz\"))\n            self.assertEqual(result[1][\"mask\"], os.path.join(tempdir, \"mask31.txt\"))\n            self.assertEqual(result[0][\"mask\"], \"spleen mask\")\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_image.py_os_TEST_CASE_11._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_image.py_os_TEST_CASE_11._", "embedding": null, "metadata": {"file_path": "tests/test_load_image.py", "file_name": "test_load_image.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 101, "span_ids": ["_MiniReader.__init__", "impl", "_MiniReader.read", "impl:25", "_MiniReader.get_data", "docstring", "_MiniReader", "_MiniReader.verify_suffix"], "tokens": 763}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport tempfile\nimport unittest\nfrom pathlib import Path\n\nimport itk\nimport nibabel as nib\nimport numpy as np\nfrom parameterized import parameterized\nfrom PIL import Image\n\nfrom monai.data import ITKReader, NibabelReader\nfrom monai.transforms import LoadImage\n\n\nclass _MiniReader:\n    \"\"\"a test case customised reader\"\"\"\n\n    def __init__(self, is_compatible=False):\n        self.is_compatible = is_compatible\n\n    def verify_suffix(self, _name):\n        return self.is_compatible\n\n    def read(self, name):\n        return name\n\n    def get_data(self, _obj):\n        return np.zeros((1, 1, 1)), {\"name\": \"my test\"}\n\n\nTEST_CASE_1 = [{\"image_only\": True}, [\"test_image.nii.gz\"], (128, 128, 128)]\n\nTEST_CASE_2 = [{\"image_only\": False}, [\"test_image.nii.gz\"], (128, 128, 128)]\n\nTEST_CASE_3 = [\n    {\"image_only\": True},\n    [\"test_image.nii.gz\", \"test_image2.nii.gz\", \"test_image3.nii.gz\"],\n    (3, 128, 128, 128),\n]\n\nTEST_CASE_3_1 = [  # .mgz format\n    {\"image_only\": True, \"reader\": \"nibabelreader\"},\n    [\"test_image.mgz\", \"test_image2.mgz\", \"test_image3.mgz\"],\n    (3, 128, 128, 128),\n]\n\nTEST_CASE_4 = [\n    {\"image_only\": False},\n    [\"test_image.nii.gz\", \"test_image2.nii.gz\", \"test_image3.nii.gz\"],\n    (3, 128, 128, 128),\n]\n\nTEST_CASE_4_1 = [  # additional parameter\n    {\"image_only\": False, \"mmap\": False},\n    [\"test_image.nii.gz\", \"test_image2.nii.gz\", \"test_image3.nii.gz\"],\n    (3, 128, 128, 128),\n]\n\nTEST_CASE_5 = [{\"reader\": NibabelReader(mmap=False), \"image_only\": False}, [\"test_image.nii.gz\"], (128, 128, 128)]\n\nTEST_CASE_6 = [{\"reader\": ITKReader(), \"image_only\": True}, [\"test_image.nii.gz\"], (128, 128, 128)]\n\nTEST_CASE_7 = [{\"reader\": ITKReader(), \"image_only\": False}, [\"test_image.nii.gz\"], (128, 128, 128)]\n\nTEST_CASE_8 = [\n    {\"reader\": ITKReader(), \"image_only\": True},\n    [\"test_image.nii.gz\", \"test_image2.nii.gz\", \"test_image3.nii.gz\"],\n    (3, 128, 128, 128),\n]\n\nTEST_CASE_9 = [\n    {\"reader\": ITKReader(), \"image_only\": False},\n    [\"test_image.nii.gz\", \"test_image2.nii.gz\", \"test_image3.nii.gz\"],\n    (3, 128, 128, 128),\n]\n\nTEST_CASE_10 = [\n    {\"image_only\": False, \"reader\": ITKReader(pixel_type=itk.UC)},\n    \"tests/testing_data/CT_DICOM\",\n    (16, 16, 4),\n    (16, 16, 4),\n]\n\nTEST_CASE_11 = [\n    {\"image_only\": False, \"reader\": \"ITKReader\", \"pixel_type\": itk.UC},\n    \"tests/testing_data/CT_DICOM\",\n    (16, 16, 4),\n    (16, 16, 4),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_image.py_TestLoadImage.test_load_png_TestLoadImage.test_load_png.with_tempfile_TemporaryDi.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_image.py_TestLoadImage.test_load_png_TestLoadImage.test_load_png.with_tempfile_TemporaryDi.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_load_image.py", "file_name": "test_load_image.py", "file_type": "text/x-python", "category": "test", "start_line": 188, "end_line": 197, "span_ids": ["TestLoadImage.test_load_png"], "tokens": 137}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestLoadImage(unittest.TestCase):\n\n    def test_load_png(self):\n        spatial_size = (256, 224)\n        test_image = np.random.randint(0, 256, size=spatial_size)\n        with tempfile.TemporaryDirectory() as tempdir:\n            filename = os.path.join(tempdir, \"test_image.png\")\n            Image.fromarray(test_image.astype(\"uint8\")).save(filename)\n            result, header = LoadImage(image_only=False)(filename)\n            self.assertTupleEqual(tuple(header[\"spatial_shape\"]), spatial_size[::-1])\n            self.assertTupleEqual(result.shape, spatial_size[::-1])\n            np.testing.assert_allclose(result.T, test_image)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_imaged.py_os_TEST_CASE_2._keys_KEYS_reader_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_imaged.py_os_TEST_CASE_2._keys_KEYS_reader_", "embedding": null, "metadata": {"file_path": "tests/test_load_imaged.py", "file_name": "test_load_imaged.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 29, "span_ids": ["docstring"], "tokens": 131}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport tempfile\nimport unittest\nfrom pathlib import Path\n\nimport itk\nimport nibabel as nib\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.data import ITKReader\nfrom monai.transforms import Compose, EnsureChannelFirstD, LoadImaged, SaveImageD\n\nKEYS = [\"image\", \"label\", \"extra\"]\n\nTEST_CASE_1 = [{\"keys\": KEYS}, (128, 128, 128)]\n\nTEST_CASE_2 = [{\"keys\": KEYS, \"reader\": \"ITKReader\", \"fallback_only\": False}, (128, 128, 128)]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_imaged.py_TestLoadImaged_TestLoadImaged.test_shape.for_key_in_KEYS_.self_assertTupleEqual_res": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_imaged.py_TestLoadImaged_TestLoadImaged.test_shape.for_key_in_KEYS_.self_assertTupleEqual_res", "embedding": null, "metadata": {"file_path": "tests/test_load_imaged.py", "file_name": "test_load_imaged.py", "file_type": "text/x-python", "category": "test", "start_line": 31, "end_line": 43, "span_ids": ["TestLoadImaged", "TestLoadImaged.test_shape"], "tokens": 159}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestLoadImaged(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2])\n    def test_shape(self, input_param, expected_shape):\n        test_image = nib.Nifti1Image(np.random.rand(128, 128, 128), np.eye(4))\n        test_data = {}\n        with tempfile.TemporaryDirectory() as tempdir:\n            for key in KEYS:\n                nib.save(test_image, os.path.join(tempdir, key + \".nii.gz\"))\n                test_data.update({key: os.path.join(tempdir, key + \".nii.gz\")})\n            result = LoadImaged(**input_param)(test_data)\n\n        for key in KEYS:\n            self.assertTupleEqual(result[key].shape, expected_shape)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_local_normalized_cross_correlation_loss.py_unittest_TEST_CASES._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_local_normalized_cross_correlation_loss.py_unittest_TEST_CASES._", "embedding": null, "metadata": {"file_path": "tests/test_local_normalized_cross_correlation_loss.py", "file_name": "test_local_normalized_cross_correlation_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 106, "span_ids": ["docstring"], "tokens": 980}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.losses.image_dissimilarity import LocalNormalizedCrossCorrelationLoss\n\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\nTEST_CASES = [\n    [\n        {\"ndim\": 1, \"kernel_type\": \"rectangular\", \"reduction\": \"sum\"},\n        {\n            \"pred\": torch.arange(0, 3).reshape(1, 1, -1).to(dtype=torch.float, device=device),\n            \"target\": torch.arange(0, 3).reshape(1, 1, -1).to(dtype=torch.float, device=device),\n        },\n        -1.0 * 3,\n    ],\n    [\n        {\"ndim\": 1, \"kernel_type\": \"rectangular\"},\n        {\n            \"pred\": torch.arange(0, 3).reshape(1, 1, -1).to(dtype=torch.float, device=device),\n            \"target\": torch.arange(0, 3).reshape(1, 1, -1).to(dtype=torch.float, device=device),\n        },\n        -1.0,\n    ],\n    [\n        {\"ndim\": 2, \"kernel_type\": \"rectangular\"},\n        {\n            \"pred\": torch.arange(0, 3).reshape(1, 1, -1, 1).expand(1, 1, 3, 3).to(dtype=torch.float, device=device),\n            \"target\": torch.arange(0, 3).reshape(1, 1, -1, 1).expand(1, 1, 3, 3).to(dtype=torch.float, device=device),\n        },\n        -1.0,\n    ],\n    [\n        {\"ndim\": 3, \"kernel_type\": \"rectangular\"},\n        {\n            \"pred\": torch.arange(0, 3)\n            .reshape(1, 1, -1, 1, 1)\n            .expand(1, 1, 3, 3, 3)\n            .to(dtype=torch.float, device=device),\n            \"target\": torch.arange(0, 3)\n            .reshape(1, 1, -1, 1, 1)\n            .expand(1, 1, 3, 3, 3)\n            .to(dtype=torch.float, device=device),\n        },\n        -1.0,\n    ],\n    [\n        {\"ndim\": 3, \"kernel_type\": \"rectangular\"},\n        {\n            \"pred\": torch.arange(0, 3)\n            .reshape(1, 1, -1, 1, 1)\n            .expand(1, 3, 3, 3, 3)\n            .to(dtype=torch.float, device=device),\n            \"target\": torch.arange(0, 3)\n            .reshape(1, 1, -1, 1, 1)\n            .expand(1, 3, 3, 3, 3)\n            .to(dtype=torch.float, device=device)\n            ** 2,\n        },\n        -0.95801723,\n    ],\n    [\n        {\"ndim\": 3, \"kernel_type\": \"triangular\", \"kernel_size\": 5},\n        {\n            \"pred\": torch.arange(0, 5)\n            .reshape(1, 1, -1, 1, 1)\n            .expand(1, 3, 5, 5, 5)\n            .to(dtype=torch.float, device=device),\n            \"target\": torch.arange(0, 5)\n            .reshape(1, 1, -1, 1, 1)\n            .expand(1, 3, 5, 5, 5)\n            .to(dtype=torch.float, device=device)\n            ** 2,\n        },\n        -0.918672,\n    ],\n    [\n        {\"ndim\": 3, \"kernel_type\": \"gaussian\"},\n        {\n            \"pred\": torch.arange(0, 3)\n            .reshape(1, 1, -1, 1, 1)\n            .expand(1, 3, 3, 3, 3)\n            .to(dtype=torch.float, device=device),\n            \"target\": torch.arange(0, 3)\n            .reshape(1, 1, -1, 1, 1)\n            .expand(1, 3, 3, 3, 3)\n            .to(dtype=torch.float, device=device)\n            ** 2,\n        },\n        -0.95406944,\n    ],\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_local_normalized_cross_correlation_loss.py_TestLocalNormalizedCrossCorrelationLoss_TestLocalNormalizedCrossCorrelationLoss.test_ill_shape.None_1.loss_forward_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_local_normalized_cross_correlation_loss.py_TestLocalNormalizedCrossCorrelationLoss_TestLocalNormalizedCrossCorrelationLoss.test_ill_shape.None_1.loss_forward_", "embedding": null, "metadata": {"file_path": "tests/test_local_normalized_cross_correlation_loss.py", "file_name": "test_local_normalized_cross_correlation_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 109, "end_line": 128, "span_ids": ["TestLocalNormalizedCrossCorrelationLoss", "TestLocalNormalizedCrossCorrelationLoss.test_shape", "TestLocalNormalizedCrossCorrelationLoss.test_ill_shape"], "tokens": 239}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestLocalNormalizedCrossCorrelationLoss(unittest.TestCase):\n    @parameterized.expand(TEST_CASES)\n    def test_shape(self, input_param, input_data, expected_val):\n        result = LocalNormalizedCrossCorrelationLoss(**input_param).forward(**input_data)\n        np.testing.assert_allclose(result.detach().cpu().numpy(), expected_val, rtol=1e-5)\n\n    def test_ill_shape(self):\n        loss = LocalNormalizedCrossCorrelationLoss(ndim=3)\n        # ndim unmatch\n        with self.assertRaisesRegex(ValueError, \"\"):\n            loss.forward(\n                torch.ones((1, 3, 3, 3), dtype=torch.float, device=device),\n                torch.ones((1, 3, 3, 3), dtype=torch.float, device=device),\n            )\n        # pred, target shape unmatch\n        with self.assertRaisesRegex(ValueError, \"\"):\n            loss.forward(\n                torch.ones((1, 3, 3, 3, 3), dtype=torch.float, device=device),\n                torch.ones((1, 3, 4, 4, 4), dtype=torch.float, device=device),\n            )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_local_normalized_cross_correlation_loss.py_TestLocalNormalizedCrossCorrelationLoss.test_ill_opts_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_local_normalized_cross_correlation_loss.py_TestLocalNormalizedCrossCorrelationLoss.test_ill_opts_", "embedding": null, "metadata": {"file_path": "tests/test_local_normalized_cross_correlation_loss.py", "file_name": "test_local_normalized_cross_correlation_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 130, "end_line": 153, "span_ids": ["impl:5", "TestLocalNormalizedCrossCorrelationLoss.test_ill_opts"], "tokens": 257}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestLocalNormalizedCrossCorrelationLoss(unittest.TestCase):\n\n    def test_ill_opts(self):\n        pred = torch.ones((1, 3, 3, 3, 3), dtype=torch.float)\n        target = torch.ones((1, 3, 3, 3, 3), dtype=torch.float)\n        with self.assertRaisesRegex(ValueError, \"\"):\n            LocalNormalizedCrossCorrelationLoss(kernel_type=\"unknown\")(pred, target)\n        with self.assertRaisesRegex(ValueError, \"\"):\n            LocalNormalizedCrossCorrelationLoss(kernel_type=None)(pred, target)\n        with self.assertRaisesRegex(ValueError, \"\"):\n            LocalNormalizedCrossCorrelationLoss(kernel_size=4)(pred, target)\n        with self.assertRaisesRegex(ValueError, \"\"):\n            LocalNormalizedCrossCorrelationLoss(reduction=\"unknown\")(pred, target)\n        with self.assertRaisesRegex(ValueError, \"\"):\n            LocalNormalizedCrossCorrelationLoss(reduction=None)(pred, target)\n\n\n#     def test_script(self):\n#         input_param, input_data, _ = TEST_CASES[0]\n#         loss = LocalNormalizedCrossCorrelationLoss(**input_param)\n#         test_script_save(loss, input_data[\"pred\"], input_data[\"target\"])\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_localnet.py_unittest_TEST_CASE_LOCALNET_3D._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_localnet.py_unittest_TEST_CASE_LOCALNET_3D._", "embedding": null, "metadata": {"file_path": "tests/test_localnet.py", "file_name": "test_localnet.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 58, "span_ids": ["docstring"], "tokens": 313}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks import eval_mode\nfrom monai.networks.nets.regunet import LocalNet\nfrom tests.utils import test_script_save\n\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\n\nTEST_CASE_LOCALNET_2D = [\n    [\n        {\n            \"spatial_dims\": 2,\n            \"in_channels\": 2,\n            \"num_channel_initial\": 16,\n            \"out_kernel_initializer\": \"kaiming_uniform\",\n            \"out_activation\": None,\n            \"out_channels\": 2,\n            \"extract_levels\": (0, 1),\n            \"pooling\": False,\n            \"concat_skip\": True,\n        },\n        (1, 2, 16, 16),\n        (1, 2, 16, 16),\n    ]\n]\n\nTEST_CASE_LOCALNET_3D = [\n    [\n        {\n            \"spatial_dims\": 3,\n            \"in_channels\": 2,\n            \"num_channel_initial\": 16,\n            \"out_kernel_initializer\": \"zeros\",\n            \"out_activation\": \"sigmoid\",\n            \"out_channels\": 2,\n            \"extract_levels\": (0, 1, 2, 3),\n            \"pooling\": True,\n            \"concat_skip\": False,\n        },\n        (1, 2, 16, 16, 16),\n        (1, 2, 16, 16, 16),\n    ]\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_localnet.py_TestLocalNet_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_localnet.py_TestLocalNet_", "embedding": null, "metadata": {"file_path": "tests/test_localnet.py", "file_name": "test_localnet.py", "file_type": "text/x-python", "category": "test", "start_line": 61, "end_line": 78, "span_ids": ["TestLocalNet.test_shape", "TestLocalNet.test_script", "TestLocalNet", "impl:7"], "tokens": 146}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestLocalNet(unittest.TestCase):\n    @parameterized.expand(TEST_CASE_LOCALNET_2D + TEST_CASE_LOCALNET_3D)\n    def test_shape(self, input_param, input_shape, expected_shape):\n        net = LocalNet(**input_param).to(device)\n        with eval_mode(net):\n            result = net(torch.randn(input_shape).to(device))\n            self.assertEqual(result.shape, expected_shape)\n\n    def test_script(self):\n        input_param, input_shape, _ = TEST_CASE_LOCALNET_2D[0]\n        net = LocalNet(**input_param)\n        test_data = torch.randn(input_shape)\n        test_script_save(net, test_data)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_localnet_block.py_unittest_in_size.4": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_localnet_block.py_unittest_in_size.4", "embedding": null, "metadata": {"file_path": "tests/test_localnet_block.py", "file_name": "test_localnet_block.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 35, "span_ids": ["docstring"], "tokens": 215}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks import eval_mode\nfrom monai.networks.blocks.localnet_block import (\n    LocalNetDownSampleBlock,\n    LocalNetFeatureExtractorBlock,\n    LocalNetUpSampleBlock,\n)\n\nTEST_CASE_DOWN_SAMPLE = [\n    [{\"spatial_dims\": spatial_dims, \"in_channels\": 2, \"out_channels\": 4, \"kernel_size\": 3}] for spatial_dims in [2, 3]\n]\n\nTEST_CASE_UP_SAMPLE = [[{\"spatial_dims\": spatial_dims, \"in_channels\": 4, \"out_channels\": 2}] for spatial_dims in [2, 3]]\n\nTEST_CASE_EXTRACT = [\n    [{\"spatial_dims\": spatial_dims, \"in_channels\": 2, \"out_channels\": 3, \"act\": act, \"initializer\": initializer}]\n    for spatial_dims, act, initializer in zip([2, 3], [\"sigmoid\", None], [\"kaiming_uniform\", \"zeros\"])\n]\n\nin_size = 4", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_localnet_block.py_TestLocalNetDownSampleBlock_TestLocalNetDownSampleBlock.test_shape.with_eval_mode_net_.self_assertEqual_mid_shap": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_localnet_block.py_TestLocalNetDownSampleBlock_TestLocalNetDownSampleBlock.test_shape.with_eval_mode_net_.self_assertEqual_mid_shap", "embedding": null, "metadata": {"file_path": "tests/test_localnet_block.py", "file_name": "test_localnet_block.py", "file_type": "text/x-python", "category": "test", "start_line": 38, "end_line": 48, "span_ids": ["TestLocalNetDownSampleBlock", "TestLocalNetDownSampleBlock.test_shape"], "tokens": 161}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestLocalNetDownSampleBlock(unittest.TestCase):\n    @parameterized.expand(TEST_CASE_DOWN_SAMPLE)\n    def test_shape(self, input_param):\n        net = LocalNetDownSampleBlock(**input_param)\n        input_shape = (1, input_param[\"in_channels\"], *([in_size] * input_param[\"spatial_dims\"]))\n        expect_mid_shape = (1, input_param[\"out_channels\"], *([in_size] * input_param[\"spatial_dims\"]))\n        expect_x_shape = (1, input_param[\"out_channels\"], *([in_size / 2] * input_param[\"spatial_dims\"]))\n        with eval_mode(net):\n            x, mid = net(torch.randn(input_shape))\n            self.assertEqual(x.shape, expect_x_shape)\n            self.assertEqual(mid.shape, expect_mid_shape)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_localnet_block.py_TestLocalNetDownSampleBlock.test_ill_arg_TestLocalNetDownSampleBlock.test_ill_shape.with_self_assertRaises_Va.with_eval_mode_net_.net_torch_randn_input_sha": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_localnet_block.py_TestLocalNetDownSampleBlock.test_ill_arg_TestLocalNetDownSampleBlock.test_ill_shape.with_self_assertRaises_Va.with_eval_mode_net_.net_torch_randn_input_sha", "embedding": null, "metadata": {"file_path": "tests/test_localnet_block.py", "file_name": "test_localnet_block.py", "file_type": "text/x-python", "category": "test", "start_line": 50, "end_line": 61, "span_ids": ["TestLocalNetDownSampleBlock.test_ill_arg", "TestLocalNetDownSampleBlock.test_ill_shape"], "tokens": 137}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestLocalNetDownSampleBlock(unittest.TestCase):\n\n    def test_ill_arg(self):\n        # even kernel_size\n        with self.assertRaises(NotImplementedError):\n            LocalNetDownSampleBlock(spatial_dims=2, in_channels=2, out_channels=4, kernel_size=4)\n\n    @parameterized.expand(TEST_CASE_DOWN_SAMPLE)\n    def test_ill_shape(self, input_param):\n        net = LocalNetDownSampleBlock(**input_param)\n        input_shape = (1, input_param[\"in_channels\"], *([5] * input_param[\"spatial_dims\"]))\n        with self.assertRaises(ValueError):\n            with eval_mode(net):\n                net(torch.randn(input_shape))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_localnet_block.py_TestLocalNetUpSampleBlock_TestLocalNetUpSampleBlock.test_shape.with_eval_mode_net_.self_assertEqual_result_s": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_localnet_block.py_TestLocalNetUpSampleBlock_TestLocalNetUpSampleBlock.test_shape.with_eval_mode_net_.self_assertEqual_result_s", "embedding": null, "metadata": {"file_path": "tests/test_localnet_block.py", "file_name": "test_localnet_block.py", "file_type": "text/x-python", "category": "test", "start_line": 64, "end_line": 73, "span_ids": ["TestLocalNetUpSampleBlock.test_shape", "TestLocalNetUpSampleBlock"], "tokens": 132}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestLocalNetUpSampleBlock(unittest.TestCase):\n    @parameterized.expand(TEST_CASE_UP_SAMPLE)\n    def test_shape(self, input_param):\n        net = LocalNetUpSampleBlock(**input_param)\n        input_shape = (1, input_param[\"in_channels\"], *([in_size] * input_param[\"spatial_dims\"]))\n        mid_shape = (1, input_param[\"out_channels\"], *([in_size * 2] * input_param[\"spatial_dims\"]))\n        expected_shape = mid_shape\n        with eval_mode(net):\n            result = net(torch.randn(input_shape), torch.randn(mid_shape))\n            self.assertEqual(result.shape, expected_shape)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_localnet_block.py_TestLocalNetUpSampleBlock.test_ill_arg_TestLocalNetUpSampleBlock.test_ill_shape.with_self_assertRaises_Va.with_eval_mode_net_.net_torch_randn_input_sha": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_localnet_block.py_TestLocalNetUpSampleBlock.test_ill_arg_TestLocalNetUpSampleBlock.test_ill_shape.with_self_assertRaises_Va.with_eval_mode_net_.net_torch_randn_input_sha", "embedding": null, "metadata": {"file_path": "tests/test_localnet_block.py", "file_name": "test_localnet_block.py", "file_type": "text/x-python", "category": "test", "start_line": 75, "end_line": 87, "span_ids": ["TestLocalNetUpSampleBlock.test_ill_shape", "TestLocalNetUpSampleBlock.test_ill_arg"], "tokens": 161}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestLocalNetUpSampleBlock(unittest.TestCase):\n\n    def test_ill_arg(self):\n        # channel unmatch\n        with self.assertRaises(ValueError):\n            LocalNetUpSampleBlock(spatial_dims=2, in_channels=2, out_channels=2)\n\n    @parameterized.expand(TEST_CASE_UP_SAMPLE)\n    def test_ill_shape(self, input_param):\n        net = LocalNetUpSampleBlock(**input_param)\n        input_shape = (1, input_param[\"in_channels\"], *([in_size] * input_param[\"spatial_dims\"]))\n        mid_shape = (1, input_param[\"out_channels\"], *([in_size] * input_param[\"spatial_dims\"]))\n        with self.assertRaises(ValueError):\n            with eval_mode(net):\n                net(torch.randn(input_shape), torch.randn(mid_shape))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_localnet_block.py_TestExtractBlock_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_localnet_block.py_TestExtractBlock_", "embedding": null, "metadata": {"file_path": "tests/test_localnet_block.py", "file_name": "test_localnet_block.py", "file_type": "text/x-python", "category": "test", "start_line": 90, "end_line": 107, "span_ids": ["TestExtractBlock.test_ill_arg", "impl:9", "TestExtractBlock.test_shape", "TestExtractBlock"], "tokens": 166}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestExtractBlock(unittest.TestCase):\n    @parameterized.expand(TEST_CASE_EXTRACT)\n    def test_shape(self, input_param):\n        net = LocalNetFeatureExtractorBlock(**input_param)\n        input_shape = (1, input_param[\"in_channels\"], *([in_size] * input_param[\"spatial_dims\"]))\n        expected_shape = (1, input_param[\"out_channels\"], *([in_size] * input_param[\"spatial_dims\"]))\n        with eval_mode(net):\n            result = net(torch.randn(input_shape))\n            self.assertEqual(result.shape, expected_shape)\n\n    def test_ill_arg(self):\n        with self.assertRaises(ValueError):\n            LocalNetFeatureExtractorBlock(spatial_dims=2, in_channels=2, out_channels=2, initializer=\"none\")\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_lr_finder.py_os_device._cuda_if_torch_cuda_is_a": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_lr_finder.py_os_device._cuda_if_torch_cuda_is_a", "embedding": null, "metadata": {"file_path": "tests/test_lr_finder.py", "file_name": "test_lr_finder.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 41, "span_ids": ["docstring"], "tokens": 195}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport pickle\nimport random\nimport sys\nimport unittest\nfrom typing import TYPE_CHECKING\n\nimport torch\nfrom torch.utils.data import DataLoader\n\nfrom monai.apps import MedNISTDataset\nfrom monai.networks.nets import DenseNet\nfrom monai.optimizers import LearningRateFinder\nfrom monai.transforms import AddChanneld, Compose, LoadImaged, ScaleIntensityd, ToTensord\nfrom monai.utils import optional_import, set_determinism\n\nif TYPE_CHECKING:\n    import matplotlib.pyplot as plt\n\n    has_matplotlib = True\n    has_pil = True\nelse:\n    plt, has_matplotlib = optional_import(\"matplotlib.pyplot\")\n    _, has_pil = optional_import(\"PIL.Image\")\n\nRAND_SEED = 42\nrandom.seed(RAND_SEED)\nset_determinism(seed=RAND_SEED)\n\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_lr_finder.py_TestLRFinder_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_lr_finder.py_TestLRFinder_", "embedding": null, "metadata": {"file_path": "tests/test_lr_finder.py", "file_name": "test_lr_finder.py", "file_type": "text/x-python", "category": "test", "start_line": 44, "end_line": 105, "span_ids": ["TestLRFinder", "impl:15", "TestLRFinder.setUp", "TestLRFinder.test_lr_finder"], "tokens": 457}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@unittest.skipUnless(sys.platform == \"linux\", \"requires linux\")\n@unittest.skipUnless(has_pil, \"requires PIL\")\nclass TestLRFinder(unittest.TestCase):\n    def setUp(self):\n\n        self.root_dir = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n        if not self.root_dir:\n            self.root_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"testing_data\")\n\n        self.transforms = Compose(\n            [\n                LoadImaged(keys=\"image\"),\n                AddChanneld(keys=\"image\"),\n                ScaleIntensityd(keys=\"image\"),\n                ToTensord(keys=\"image\"),\n            ]\n        )\n\n    def test_lr_finder(self):\n        # 0.001 gives 54 examples\n        train_ds = MedNISTDataset(\n            root_dir=self.root_dir,\n            transform=self.transforms,\n            section=\"validation\",\n            val_frac=0.001,\n            download=True,\n            num_workers=10,\n        )\n        train_loader = DataLoader(train_ds, batch_size=300, shuffle=True, num_workers=10)\n        num_classes = train_ds.get_num_classes()\n\n        model = DenseNet(\n            spatial_dims=2, in_channels=1, out_channels=num_classes, init_features=2, growth_rate=2, block_config=(2,)\n        )\n        loss_function = torch.nn.CrossEntropyLoss()\n        learning_rate = 1e-5\n        optimizer = torch.optim.Adam(model.parameters(), learning_rate)\n\n        lr_finder = LearningRateFinder(\n            model=model,\n            optimizer=optimizer,\n            criterion=loss_function,\n            device=device,\n            pickle_module=pickle,\n            pickle_protocol=4,\n        )\n        lr_finder.range_test(train_loader, val_loader=train_loader, end_lr=10, num_iter=5)\n        print(lr_finder.get_steepest_gradient(0, 0)[0])\n\n        if has_matplotlib:\n            ax = plt.subplot()\n            plt.show(block=False)\n            lr_finder.plot(0, 0, ax=ax)  # to inspect the loss-learning rate graph\n            plt.pause(3)\n            plt.close()\n\n        lr_finder.reset()  # to reset the model and optimizer to their initial state\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_map_label_valued.py_unittest_TEST_CASE_7._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_map_label_valued.py_unittest_TEST_CASE_7._", "embedding": null, "metadata": {"file_path": "tests/test_map_label_valued.py", "file_name": "test_map_label_valued.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 59, "span_ids": ["impl:11", "docstring"], "tokens": 622}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import MapLabelValued\n\nTEST_CASE_1 = [\n    {\"keys\": \"seg\", \"orig_labels\": [3, 2, 1], \"target_labels\": [0, 1, 2]},\n    {\"seg\": np.array([[3, 1], [1, 2]])},\n    np.array([[0, 2], [2, 1]]),\n]\n\nTEST_CASE_2 = [\n    {\"keys\": \"seg\", \"orig_labels\": [3, 5, 8], \"target_labels\": [0, 1, 2]},\n    {\"seg\": np.array([[[3], [5], [5], [8]]])},\n    np.array([[[0], [1], [1], [2]]]),\n]\n\nTEST_CASE_3 = [\n    {\"keys\": \"seg\", \"orig_labels\": [1, 2, 3], \"target_labels\": [0, 1, 2]},\n    {\"seg\": np.array([3, 1, 1, 2])},\n    np.array([2, 0, 0, 1]),\n]\n\nTEST_CASE_4 = [\n    {\"keys\": \"seg\", \"orig_labels\": [1, 2, 3], \"target_labels\": [0.5, 1.5, 2.5]},\n    {\"seg\": np.array([3, 1, 1, 2])},\n    np.array([2.5, 0.5, 0.5, 1.5]),\n]\n\nTEST_CASE_5 = [\n    {\"keys\": \"seg\", \"orig_labels\": [1.5, 2.5, 3.5], \"target_labels\": [0, 1, 2], \"dtype\": np.int8},\n    {\"seg\": np.array([3.5, 1.5, 1.5, 2.5])},\n    np.array([2, 0, 0, 1]),\n]\n\nTEST_CASE_6 = [\n    {\"keys\": \"seg\", \"orig_labels\": [\"label3\", \"label2\", \"label1\"], \"target_labels\": [0, 1, 2]},\n    {\"seg\": np.array([[\"label3\", \"label1\"], [\"label1\", \"label2\"]])},\n    np.array([[0, 2], [2, 1]]),\n]\n\nTEST_CASE_7 = [\n    {\"keys\": \"seg\", \"orig_labels\": [3.5, 2.5, 1.5], \"target_labels\": [\"label0\", \"label1\", \"label2\"], \"dtype\": \"str\"},\n    {\"seg\": np.array([[3.5, 1.5], [1.5, 2.5]])},\n    np.array([[\"label0\", \"label2\"], [\"label2\", \"label1\"]]),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_map_label_valued.py_TestMapLabelValued_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_map_label_valued.py_TestMapLabelValued_", "embedding": null, "metadata": {"file_path": "tests/test_map_label_valued.py", "file_name": "test_map_label_valued.py", "file_type": "text/x-python", "category": "test", "start_line": 62, "end_line": 72, "span_ids": ["TestMapLabelValued.test_shape", "impl:15", "TestMapLabelValued"], "tokens": 118}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestMapLabelValued(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5, TEST_CASE_6, TEST_CASE_7])\n    def test_shape(self, input_param, input_data, expected_value):\n        result = MapLabelValued(**input_param)(input_data)\n        np.testing.assert_equal(result[\"seg\"], expected_value)\n        self.assertTupleEqual(result[\"seg\"].shape, expected_value.shape)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_mask_intensityd.py_unittest_TEST_CASE_3._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_mask_intensityd.py_unittest_TEST_CASE_3._", "embedding": null, "metadata": {"file_path": "tests/test_mask_intensityd.py", "file_name": "test_mask_intensityd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 35, "span_ids": ["docstring"], "tokens": 567}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import MaskIntensityd\n\nTEST_CASE_1 = [\n    {\"keys\": \"img\", \"mask_data\": np.array([[[0, 0, 0], [0, 1, 0], [0, 0, 0]]])},\n    {\"img\": np.array([[[1, 1, 1], [2, 2, 2], [3, 3, 3]], [[4, 4, 4], [5, 5, 5], [6, 6, 6]]])},\n    np.array([[[0, 0, 0], [0, 2, 0], [0, 0, 0]], [[0, 0, 0], [0, 5, 0], [0, 0, 0]]]),\n]\n\nTEST_CASE_2 = [\n    {\"keys\": \"img\", \"mask_data\": np.array([[[0, 0, 0], [0, 5, 0], [0, 0, 0]]])},\n    {\"img\": np.array([[[1, 1, 1], [2, 2, 2], [3, 3, 3]], [[4, 4, 4], [5, 5, 5], [6, 6, 6]]])},\n    np.array([[[0, 0, 0], [0, 2, 0], [0, 0, 0]], [[0, 0, 0], [0, 5, 0], [0, 0, 0]]]),\n]\n\nTEST_CASE_3 = [\n    {\"keys\": \"img\", \"mask_data\": np.array([[[0, 0, 0], [0, 1, 0], [0, 0, 0]], [[0, 1, 0], [0, 1, 0], [0, 1, 0]]])},\n    {\"img\": np.array([[[1, 1, 1], [2, 2, 2], [3, 3, 3]], [[4, 4, 4], [5, 5, 5], [6, 6, 6]]])},\n    np.array([[[0, 0, 0], [0, 2, 0], [0, 0, 0]], [[0, 4, 0], [0, 5, 0], [0, 6, 0]]]),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_mask_intensityd.py_TEST_CASE_4_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_mask_intensityd.py_TEST_CASE_4_", "embedding": null, "metadata": {"file_path": "tests/test_mask_intensityd.py", "file_name": "test_mask_intensityd.py", "file_type": "text/x-python", "category": "test", "start_line": 37, "end_line": 66, "span_ids": ["TestMaskIntensityd.test_value", "impl:11", "TestMaskIntensityd", "impl:7"], "tokens": 501}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "TEST_CASE_4 = [\n    {\"keys\": \"img\", \"mask_key\": \"mask\"},\n    {\n        \"img\": np.array([[[1, 1, 1], [2, 2, 2], [3, 3, 3]], [[4, 4, 4], [5, 5, 5], [6, 6, 6]]]),\n        \"mask\": np.array([[[0, 0, 0], [0, 1, 0], [0, 0, 0]], [[0, 1, 0], [0, 1, 0], [0, 1, 0]]]),\n    },\n    np.array([[[0, 0, 0], [0, 2, 0], [0, 0, 0]], [[0, 4, 0], [0, 5, 0], [0, 6, 0]]]),\n]\n\nTEST_CASE_5 = [\n    {\n        \"keys\": \"img\",\n        \"mask_data\": np.array([[[1, 2, 3], [4, 5, 6], [7, 8, 9]]]),\n        \"select_fn\": lambda x: np.where((x > 3) & (x < 7), True, False),\n    },\n    {\"img\": np.array([[[1, 1, 1], [2, 2, 2], [3, 3, 3]], [[4, 4, 4], [5, 5, 5], [6, 6, 6]]])},\n    np.array([[[0, 0, 0], [2, 2, 2], [0, 0, 0]], [[0, 0, 0], [5, 5, 5], [0, 0, 0]]]),\n]\n\n\nclass TestMaskIntensityd(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5])\n    def test_value(self, argments, image, expected_data):\n        result = MaskIntensityd(**argments)(image)\n        np.testing.assert_allclose(result[\"img\"], expected_data)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_masked_inference_wsi_dataset.py_os_TEST_CASE_1._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_masked_inference_wsi_dataset.py_os_TEST_CASE_1._", "embedding": null, "metadata": {"file_path": "tests/test_masked_inference_wsi_dataset.py", "file_name": "test_masked_inference_wsi_dataset.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 71, "span_ids": ["prepare_data", "impl:20", "docstring"], "tokens": 569}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport unittest\nfrom unittest import skipUnless\n\nimport numpy as np\nfrom numpy.testing import assert_array_equal\nfrom parameterized import parameterized\n\nfrom monai.apps.pathology.data import MaskedInferenceWSIDataset\nfrom monai.apps.utils import download_url\nfrom monai.utils import optional_import\nfrom tests.utils import skip_if_quick\n\n_, has_cim = optional_import(\"cucim\", name=\"CuImage\")\n_, has_osl = optional_import(\"openslide\")\n\nFILE_URL = \"https://drive.google.com/uc?id=1sGTKZlJBIz53pfqTxoTqiIQzIoEzHLAe\"\nbase_name, extension = FILE_URL.split(\"id=\")[1], \".tiff\"\nFILE_NAME = \"temp_\" + base_name\nFILE_PATH = os.path.join(os.path.dirname(__file__), \"testing_data\", FILE_NAME + extension)\n\nMASK1 = os.path.join(os.path.dirname(__file__), \"testing_data\", \"temp_tissue_mask1.npy\")\nMASK2 = os.path.join(os.path.dirname(__file__), \"testing_data\", \"temp_tissue_mask2.npy\")\nMASK4 = os.path.join(os.path.dirname(__file__), \"testing_data\", \"temp_tissue_mask4.npy\")\n\nHEIGHT = 32914\nWIDTH = 46000\n\n\ndef prepare_data():\n\n    mask = np.zeros((HEIGHT // 2, WIDTH // 2))\n    mask[100, 100] = 1\n    np.save(MASK1, mask)\n    mask[100, 101] = 1\n    np.save(MASK2, mask)\n    mask[100:102, 100:102] = 1\n    np.save(MASK4, mask)\n\n\nTEST_CASE_0 = [\n    {\"data\": [{\"image\": FILE_PATH, \"mask\": MASK1}], \"patch_size\": 1, \"image_reader_name\": \"cuCIM\"},\n    [{\"image\": np.array([[[243]], [[243]], [[243]]], dtype=np.uint8), \"name\": FILE_NAME, \"mask_location\": [100, 100]}],\n]\n\nTEST_CASE_1 = [\n    {\"data\": [{\"image\": FILE_PATH, \"mask\": MASK2}], \"patch_size\": 1, \"image_reader_name\": \"cuCIM\"},\n    [\n        {\n            \"image\": np.array([[[243]], [[243]], [[243]]], dtype=np.uint8),\n            \"name\": FILE_NAME,\n            \"mask_location\": [100, 100],\n        },\n        {\n            \"image\": np.array([[[243]], [[243]], [[243]]], dtype=np.uint8),\n            \"name\": FILE_NAME,\n            \"mask_location\": [100, 101],\n        },\n    ],\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_masked_inference_wsi_dataset.py_TestMaskedInferenceWSIDataset_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_masked_inference_wsi_dataset.py_TestMaskedInferenceWSIDataset_", "embedding": null, "metadata": {"file_path": "tests/test_masked_inference_wsi_dataset.py", "file_name": "test_masked_inference_wsi_dataset.py", "file_type": "text/x-python", "category": "test", "start_line": 160, "end_line": 189, "span_ids": ["TestMaskedInferenceWSIDataset", "TestMaskedInferenceWSIDataset.compare_samples_expected", "impl:34", "TestMaskedInferenceWSIDataset.test_read_patches_openslide", "TestMaskedInferenceWSIDataset.setUp", "TestMaskedInferenceWSIDataset.test_read_patches_cucim"], "tokens": 324}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestMaskedInferenceWSIDataset(unittest.TestCase):\n    def setUp(self):\n        prepare_data()\n        download_url(FILE_URL, FILE_PATH, \"5a3cfd4fd725c50578ddb80b517b759f\")\n\n    @parameterized.expand([TEST_CASE_0, TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4])\n    @skipUnless(has_cim, \"Requires CuCIM\")\n    @skip_if_quick\n    def test_read_patches_cucim(self, input_parameters, expected):\n        dataset = MaskedInferenceWSIDataset(**input_parameters)\n        self.compare_samples_expected(dataset, expected)\n\n    @parameterized.expand([TEST_CASE_OPENSLIDE_0, TEST_CASE_OPENSLIDE_1])\n    @skipUnless(has_osl, \"Requires OpenSlide\")\n    @skip_if_quick\n    def test_read_patches_openslide(self, input_parameters, expected):\n        dataset = MaskedInferenceWSIDataset(**input_parameters)\n        self.compare_samples_expected(dataset, expected)\n\n    def compare_samples_expected(self, dataset, expected):\n        for i in range(len(dataset)):\n            self.assertTupleEqual(dataset[i][0][\"image\"].shape, expected[i][\"image\"].shape)\n            self.assertIsNone(assert_array_equal(dataset[i][0][\"image\"], expected[i][\"image\"]))\n            self.assertEqual(dataset[i][0][\"name\"], expected[i][\"name\"])\n            self.assertListEqual(dataset[i][0][\"mask_location\"], expected[i][\"mask_location\"])\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_module_list.py_glob_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_module_list.py_glob_", "embedding": null, "metadata": {"file_path": "tests/test_module_list.py", "file_name": "test_module_list.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 39, "span_ids": ["TestAllImport", "TestAllImport.test_public_api", "impl", "docstring"], "tokens": 188}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import glob\nimport os\nimport unittest\n\nimport monai\n\n\nclass TestAllImport(unittest.TestCase):\n    def test_public_api(self):\n        \"\"\"\n        This is to check \"monai.__all__\" should be consistent with\n        the top-level folders except for \"__pycache__\", \"_extensions\" and \"csrc\" (cpp/cuda src)\n        \"\"\"\n        base_folder = os.path.dirname(monai.__file__)\n        to_search = os.path.join(base_folder, \"*\", \"\")\n        subfolders = [os.path.basename(x[:-1]) for x in glob.glob(to_search)]\n        to_exclude = (\"__pycache__\", \"_extensions\", \"csrc\")\n        mod = []\n        for code_folder in subfolders:\n            if code_folder in to_exclude:\n                continue\n            mod.append(code_folder)\n        self.assertEqual(sorted(monai.__all__), sorted(mod))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_multi_scale.py_unittest_TEST_CASES._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_multi_scale.py_unittest_TEST_CASES._", "embedding": null, "metadata": {"file_path": "tests/test_multi_scale.py", "file_name": "test_multi_scale.py", "file_type": "text/x-python", "category": "test", "start_line": 11, "end_line": 49, "span_ids": ["docstring"], "tokens": 421}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.losses import DiceLoss\nfrom monai.losses.multi_scale import MultiScaleLoss\nfrom tests.utils import SkipIfBeforePyTorchVersion, test_script_save\n\ndice_loss = DiceLoss(include_background=True, sigmoid=True, smooth_nr=1e-5, smooth_dr=1e-5)\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\nTEST_CASES = [\n    [\n        {\"loss\": dice_loss, \"scales\": None, \"kernel\": \"gaussian\"},\n        {\n            \"y_pred\": torch.tensor([[[[1.0, -1.0], [-1.0, 1.0]]]], device=device),\n            \"y_true\": torch.tensor([[[[1.0, 0.0], [1.0, 1.0]]]], device=device),\n        },\n        0.307576,\n    ],\n    [\n        {\"loss\": dice_loss, \"scales\": [0, 1], \"kernel\": \"gaussian\"},\n        {\n            \"y_pred\": torch.tensor([[[[1.0, -1.0], [-1.0, 1.0]]]], device=device),\n            \"y_true\": torch.tensor([[[[1.0, 0.0], [1.0, 1.0]]]], device=device),\n        },\n        0.463116,\n    ],\n    [\n        {\"loss\": dice_loss, \"scales\": [0, 1, 2], \"kernel\": \"cauchy\"},\n        {\n            \"y_pred\": torch.tensor([[[[[1.0, -1.0], [-1.0, 1.0]]]]], device=device),\n            \"y_true\": torch.tensor([[[[[1.0, 0.0], [1.0, 1.0]]]]], device=device),\n        },\n        0.715228,\n    ],\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_multi_scale.py_TestMultiScale_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_multi_scale.py_TestMultiScale_", "embedding": null, "metadata": {"file_path": "tests/test_multi_scale.py", "file_name": "test_multi_scale.py", "file_type": "text/x-python", "category": "test", "start_line": 52, "end_line": 79, "span_ids": ["impl:7", "TestMultiScale.test_script", "TestMultiScale.test_shape", "TestMultiScale.test_ill_opts", "TestMultiScale"], "tokens": 295}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestMultiScale(unittest.TestCase):\n    @parameterized.expand(TEST_CASES)\n    def test_shape(self, input_param, input_data, expected_val):\n        result = MultiScaleLoss(**input_param).forward(**input_data)\n        np.testing.assert_allclose(result.detach().cpu().numpy(), expected_val, rtol=1e-5)\n\n    def test_ill_opts(self):\n        with self.assertRaisesRegex(ValueError, \"\"):\n            MultiScaleLoss(loss=dice_loss, kernel=\"none\")\n        with self.assertRaisesRegex(ValueError, \"\"):\n            MultiScaleLoss(loss=dice_loss, scales=[-1])(\n                torch.ones((1, 1, 3), device=device), torch.ones((1, 1, 3), device=device)\n            )\n        with self.assertRaisesRegex(ValueError, \"\"):\n            MultiScaleLoss(loss=dice_loss, scales=[-1], reduction=\"none\")(\n                torch.ones((1, 1, 3), device=device), torch.ones((1, 1, 3), device=device)\n            )\n\n    @SkipIfBeforePyTorchVersion((1, 7, 0))\n    def test_script(self):\n        input_param, input_data, expected_val = TEST_CASES[0]\n        loss = MultiScaleLoss(**input_param)\n        test_script_save(loss, input_data[\"y_pred\"], input_data[\"y_true\"])\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nifti_endianness.py_os_for_endianness_in_.for_use_array_in_True_F.for_image_only_in_True_.TESTS_append_endianness_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nifti_endianness.py_os_for_endianness_in_.for_use_array_in_True_F.for_image_only_in_True_.TESTS_append_endianness_", "embedding": null, "metadata": {"file_path": "tests/test_nifti_endianness.py", "file_name": "test_nifti_endianness.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 42, "span_ids": ["docstring"], "tokens": 222}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport tempfile\nimport unittest\nfrom pathlib import Path\nfrom typing import TYPE_CHECKING, List, Tuple\nfrom unittest.case import skipUnless\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.data import DataLoader, Dataset, create_test_image_2d\nfrom monai.data.image_reader import PILReader\nfrom monai.transforms import LoadImage, LoadImaged\nfrom monai.transforms.io.array import switch_endianness\nfrom monai.utils.module import optional_import\n\nif TYPE_CHECKING:\n    import nibabel as nib\n    from PIL import Image as PILImage\n\n    has_nib = True\n    has_pil = True\nelse:\n    nib, has_nib = optional_import(\"nibabel\")\n    PILImage, has_pil = optional_import(\"PIL.Image\")\n\nTESTS: List[Tuple] = []\nfor endianness in [\"<\", \">\"]:\n    for use_array in [True, False]:\n        for image_only in [True, False]:\n            TESTS.append((endianness, use_array, image_only))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nifti_endianness.py_TestNiftiEndianness.test_switch_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nifti_endianness.py_TestNiftiEndianness.test_switch_", "embedding": null, "metadata": {"file_path": "tests/test_nifti_endianness.py", "file_name": "test_nifti_endianness.py", "file_type": "text/x-python", "category": "test", "start_line": 66, "end_line": 105, "span_ids": ["TestNiftiEndianness.test_pil", "TestNiftiEndianness.test_switch", "impl:15"], "tokens": 376}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestNiftiEndianness(unittest.TestCase):\n\n    def test_switch(self):  # verify data types\n        for data in (np.zeros((2, 1)), (\"test\",), [24, 42], {\"foo\": \"bar\"}, True, 42):\n            output = switch_endianness(data, \"<\")\n            self.assertEqual(type(data), type(output))\n\n        before = np.array((20, 20), dtype=\">i2\")\n        expected_float = before.astype(float)\n        after = switch_endianness(before)\n        np.testing.assert_allclose(after.astype(float), expected_float)\n        self.assertEqual(after.dtype.byteorder, \"<\")\n\n        before = np.array((20, 20), dtype=\"<i2\")\n        expected_float = before.astype(float)\n        after = switch_endianness(before)\n        np.testing.assert_allclose(after.astype(float), expected_float)\n\n        before = np.array([\"1.12\", \"-9.2\", \"42\"], dtype=np.string_)\n        after = switch_endianness(before)\n        np.testing.assert_array_equal(before, after)\n\n        with self.assertRaises(NotImplementedError):\n            switch_endianness(np.zeros((2, 1)), \"=\")\n\n        with self.assertRaises(RuntimeError):\n            switch_endianness(Path(\"test\"), \"<\")\n\n    @skipUnless(has_pil, \"Requires PIL\")\n    def test_pil(self):\n        tempdir = tempfile.mkdtemp()\n        test_image = np.random.randint(0, 256, size=[128, 256])\n        filename = os.path.join(tempdir, \"test_image.png\")\n        PILImage.fromarray(test_image.astype(\"uint8\")).save(filename)\n\n        loader = LoadImage(PILReader(converter=lambda image: image.convert(\"LA\")))\n        _ = loader(filename)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nifti_saver.py_TestNiftiSaver.test_saved_3d_resize_content_TestNiftiSaver.test_saved_3d_resize_content.with_tempfile_TemporaryDi.for_i_in_range_8_.self_assertTrue_os_path_e": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nifti_saver.py_TestNiftiSaver.test_saved_3d_resize_content_TestNiftiSaver.test_saved_3d_resize_content.with_tempfile_TemporaryDi.for_i_in_range_8_.self_assertTrue_os_path_e", "embedding": null, "metadata": {"file_path": "tests/test_nifti_saver.py", "file_name": "test_nifti_saver.py", "file_type": "text/x-python", "category": "test", "start_line": 51, "end_line": 65, "span_ids": ["TestNiftiSaver.test_saved_3d_resize_content"], "tokens": 241}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestNiftiSaver(unittest.TestCase):\n\n    def test_saved_3d_resize_content(self):\n        with tempfile.TemporaryDirectory() as tempdir:\n\n            saver = NiftiSaver(output_dir=tempdir, output_postfix=\"seg\", output_ext=\".nii.gz\", dtype=np.float32)\n\n            meta_data = {\n                \"filename_or_obj\": [\"testfile\" + str(i) + \".nii.gz\" for i in range(8)],\n                \"spatial_shape\": [(10, 10, 2)] * 8,\n                \"affine\": [np.diag(np.ones(4)) * 5] * 8,\n                \"original_affine\": [np.diag(np.ones(4)) * 1.0] * 8,\n            }\n            saver.save_batch(torch.randint(0, 255, (8, 8, 1, 2, 2)), meta_data)\n            for i in range(8):\n                filepath = os.path.join(\"testfile\" + str(i), \"testfile\" + str(i) + \"_seg.nii.gz\")\n                self.assertTrue(os.path.exists(os.path.join(tempdir, filepath)))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nifti_saver.py_TestNiftiSaver.test_squeeze_end_dims_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nifti_saver.py_TestNiftiSaver.test_squeeze_end_dims_", "embedding": null, "metadata": {"file_path": "tests/test_nifti_saver.py", "file_name": "test_nifti_saver.py", "file_type": "text/x-python", "category": "test", "start_line": 76, "end_line": 102, "span_ids": ["TestNiftiSaver.test_squeeze_end_dims", "impl"], "tokens": 203}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestNiftiSaver(unittest.TestCase):\n\n    def test_squeeze_end_dims(self):\n        with tempfile.TemporaryDirectory() as tempdir:\n\n            for squeeze_end_dims in [False, True]:\n\n                saver = NiftiSaver(\n                    output_dir=tempdir,\n                    output_postfix=\"\",\n                    output_ext=\".nii.gz\",\n                    dtype=np.float32,\n                    squeeze_end_dims=squeeze_end_dims,\n                )\n\n                fname = \"testfile_squeeze\"\n                meta_data = {\"filename_or_obj\": fname}\n\n                # 2d image w channel\n                saver.save(torch.randint(0, 255, (1, 2, 2)), meta_data)\n\n                im, meta = LoadImage()(os.path.join(tempdir, fname, fname + \".nii.gz\"))\n                self.assertTrue(im.ndim == 2 if squeeze_end_dims else 4)\n                self.assertTrue(meta[\"dim\"][0] == im.ndim)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_npzdictitemdataset.py_TestNPZDictItemDataset.test_load_file_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_npzdictitemdataset.py_TestNPZDictItemDataset.test_load_file_", "embedding": null, "metadata": {"file_path": "tests/test_npzdictitemdataset.py", "file_name": "test_npzdictitemdataset.py", "file_type": "text/x-python", "category": "test", "start_line": 37, "end_line": 56, "span_ids": ["TestNPZDictItemDataset.test_load_file", "impl"], "tokens": 194}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestNPZDictItemDataset(unittest.TestCase):\n\n    def test_load_file(self):\n        dat0 = np.random.rand(10, 1, 4, 4)\n        dat1 = np.random.rand(10, 1, 4, 4)\n\n        with tempfile.TemporaryDirectory() as tempdir:\n            npzfile = f\"{tempdir}/test.npz\"\n\n            np.savez_compressed(npzfile, dat0=dat0, dat1=dat1)\n\n            npzds = NPZDictItemDataset(npzfile, {\"dat0\": \"images\", \"dat1\": \"seg\"})\n\n            item = npzds[0]\n\n            np.testing.assert_allclose(item[\"images\"].shape, (1, 4, 4))\n            np.testing.assert_allclose(item[\"seg\"].shape, (1, 4, 4))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_occlusion_sensitivity.py_unittest_TEST_CASE_FAIL_1._2D_should_fail_sinc": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_occlusion_sensitivity.py_unittest_TEST_CASE_FAIL_1._2D_should_fail_sinc", "embedding": null, "metadata": {"file_path": "tests/test_occlusion_sensitivity.py", "file_name": "test_occlusion_sensitivity.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 53, "span_ids": ["impl:17", "docstring"], "tokens": 573}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks.nets import DenseNet, DenseNet121\nfrom monai.visualize import OcclusionSensitivity\n\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\nout_channels_2d = 4\nout_channels_3d = 3\nmodel_2d = DenseNet121(spatial_dims=2, in_channels=1, out_channels=out_channels_2d).to(device)\nmodel_3d = DenseNet(\n    spatial_dims=3, in_channels=1, out_channels=out_channels_3d, init_features=2, growth_rate=2, block_config=(6,)\n).to(device)\nmodel_2d.eval()\nmodel_3d.eval()\n\n# 2D w/ bounding box\nTEST_CASE_0 = [\n    {\"nn_module\": model_2d},\n    {\"x\": torch.rand(1, 1, 48, 64).to(device), \"b_box\": [-1, -1, 2, 40, 1, 62]},\n    (1, 1, 39, 62, out_channels_2d),\n    (1, 1, 39, 62),\n]\n# 3D w/ bounding box and stride\nTEST_CASE_1 = [\n    {\"nn_module\": model_3d, \"n_batch\": 10, \"stride\": (2, 1, 2), \"mask_size\": (16, 15, 14)},\n    {\"x\": torch.rand(1, 1, 6, 6, 6).to(device), \"b_box\": [-1, -1, 2, 3, -1, -1, -1, -1]},\n    (1, 1, 2, 6, 6, out_channels_3d),\n    (1, 1, 2, 6, 6),\n]\n\nTEST_CASE_FAIL_0 = [  # 2D should fail, since 3 stride values given\n    {\"nn_module\": model_2d, \"n_batch\": 10, \"stride\": (2, 2, 2)},\n    {\"x\": torch.rand(1, 1, 48, 64).to(device), \"b_box\": [-1, -1, 2, 3, -1, -1]},\n]\n\nTEST_CASE_FAIL_1 = [  # 2D should fail, since stride is not a factor of image size\n    {\"nn_module\": model_2d, \"stride\": 3},\n    {\"x\": torch.rand(1, 1, 48, 64).to(device)},\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_occlusion_sensitivity.py_TestComputeOcclusionSensitivity_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_occlusion_sensitivity.py_TestComputeOcclusionSensitivity_", "embedding": null, "metadata": {"file_path": "tests/test_occlusion_sensitivity.py", "file_name": "test_occlusion_sensitivity.py", "file_type": "text/x-python", "category": "test", "start_line": 76, "end_line": 97, "span_ids": ["impl:21", "TestComputeOcclusionSensitivity", "TestComputeOcclusionSensitivity.test_fail", "TestComputeOcclusionSensitivity.test_shape"], "tokens": 231}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestComputeOcclusionSensitivity(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_0, TEST_CASE_1])\n    def test_shape(self, init_data, call_data, map_expected_shape, most_prob_expected_shape):\n        occ_sens = OcclusionSensitivity(**init_data)\n        m, most_prob = occ_sens(**call_data)\n        self.assertTupleEqual(m.shape, map_expected_shape)\n        self.assertTupleEqual(most_prob.shape, most_prob_expected_shape)\n        # most probable class should be of type int, and should have min>=0, max<num_classes\n        self.assertEqual(most_prob.dtype, torch.int64)\n        self.assertGreaterEqual(most_prob.min(), 0)\n        self.assertLess(most_prob.max(), m.shape[-1])\n\n    @parameterized.expand([TEST_CASE_FAIL_0, TEST_CASE_FAIL_1])\n    def test_fail(self, init_data, call_data):\n        occ_sens = OcclusionSensitivity(**init_data)\n        with self.assertRaises(ValueError):\n            occ_sens(**call_data)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_pad_collation.py_random__Dataset.__getitem__.return.self_transforms_self_imag": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_pad_collation.py_random__Dataset.__getitem__.return.self_transforms_self_imag", "embedding": null, "metadata": {"file_path": "tests/test_pad_collation.py", "file_name": "test_pad_collation.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 65, "span_ids": ["_Dataset.__getitem__", "_Dataset.__len__", "_Dataset", "_Dataset.__init__", "docstring"], "tokens": 513}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import random\nimport unittest\nfrom typing import List, Tuple\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.data import CacheDataset, DataLoader\nfrom monai.data.utils import decollate_batch, pad_list_data_collate\nfrom monai.transforms import (\n    Compose,\n    PadListDataCollate,\n    RandRotate,\n    RandRotate90,\n    RandRotate90d,\n    RandRotated,\n    RandSpatialCrop,\n    RandSpatialCropd,\n    RandZoom,\n    RandZoomd,\n    ToTensor,\n    ToTensord,\n)\nfrom monai.utils import set_determinism\n\nTESTS: List[Tuple] = []\n\nfor pad_collate in [\n    lambda x: pad_list_data_collate(batch=x, method=\"end\", mode=\"constant\"),\n    PadListDataCollate(method=\"end\", mode=\"constant\"),\n]:\n    TESTS.append((dict, pad_collate, RandSpatialCropd(\"image\", roi_size=[8, 7], random_size=True)))\n    TESTS.append((dict, pad_collate, RandRotated(\"image\", prob=1, range_x=np.pi, keep_size=False)))\n    TESTS.append((dict, pad_collate, RandZoomd(\"image\", prob=1, min_zoom=1.1, max_zoom=2.0, keep_size=False)))\n    TESTS.append((dict, pad_collate, Compose([RandRotate90d(\"image\", prob=1, max_k=2), ToTensord(\"image\")])))\n\n    TESTS.append((list, pad_collate, RandSpatialCrop(roi_size=[8, 7], random_size=True)))\n    TESTS.append((list, pad_collate, RandRotate(prob=1, range_x=np.pi, keep_size=False)))\n    TESTS.append((list, pad_collate, RandZoom(prob=1, min_zoom=1.1, max_zoom=2.0, keep_size=False)))\n    TESTS.append((list, pad_collate, Compose([RandRotate90(prob=1, max_k=2), ToTensor()])))\n\n\nclass _Dataset(torch.utils.data.Dataset):\n    def __init__(self, images, labels, transforms):\n        self.images = images\n        self.labels = labels\n        self.transforms = transforms\n\n    def __len__(self):\n        return len(self.images)\n\n    def __getitem__(self, index):\n        return self.transforms(self.images[index]), self.labels[index]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_pad_collation.py_TestPadCollation_TestPadCollation.tearDown.set_determinism_None_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_pad_collation.py_TestPadCollation_TestPadCollation.tearDown.set_determinism_None_", "embedding": null, "metadata": {"file_path": "tests/test_pad_collation.py", "file_name": "test_pad_collation.py", "file_type": "text/x-python", "category": "test", "start_line": 62, "end_line": 73, "span_ids": ["TestPadCollation.tearDown", "TestPadCollation.setUp", "TestPadCollation"], "tokens": 134}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestPadCollation(unittest.TestCase):\n    def setUp(self) -> None:\n        set_determinism(seed=0)\n        # image is non square to throw rotation errors\n        im = np.arange(0, 10 * 9).reshape(1, 10, 9)\n        num_elements = 20\n        self.dict_data = [{\"image\": im} for _ in range(num_elements)]\n        self.list_data = [im for _ in range(num_elements)]\n        self.list_labels = [random.randint(0, 1) for _ in range(num_elements)]\n\n    def tearDown(self) -> None:\n        set_determinism(None)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_pad_collation.py_TestPadCollation.test_pad_collation_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_pad_collation.py_TestPadCollation.test_pad_collation_", "embedding": null, "metadata": {"file_path": "tests/test_pad_collation.py", "file_name": "test_pad_collation.py", "file_type": "text/x-python", "category": "test", "start_line": 75, "end_line": 101, "span_ids": ["TestPadCollation.test_pad_collation", "impl:12"], "tokens": 203}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestPadCollation(unittest.TestCase):\n\n    @parameterized.expand(TESTS)\n    def test_pad_collation(self, t_type, collate_method, transform):\n\n        if t_type == dict:\n            dataset = CacheDataset(self.dict_data, transform, progress=False)\n        else:\n            dataset = _Dataset(self.list_data, self.list_labels, transform)\n\n        # Default collation should raise an error\n        loader_fail = DataLoader(dataset, batch_size=10)\n        with self.assertRaises(RuntimeError):\n            for _ in loader_fail:\n                pass\n\n        # Padded collation shouldn't\n        loader = DataLoader(dataset, batch_size=10, collate_fn=collate_method)\n        # check collation in forward direction\n        for data in loader:\n            if t_type == dict:\n                decollated_data = decollate_batch(data)\n                for d in decollated_data:\n                    PadListDataCollate.inverse(d)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_partition_dataset_classes.py_unittest_TEST_CASE_3._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_partition_dataset_classes.py_unittest_TEST_CASE_3._", "embedding": null, "metadata": {"file_path": "tests/test_partition_dataset_classes.py", "file_name": "test_partition_dataset_classes.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 59, "span_ids": ["impl:5", "docstring"], "tokens": 600}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.data import partition_dataset_classes\n\nTEST_CASE_1 = [\n    {\n        \"data\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14],\n        \"classes\": [2, 0, 2, 1, 3, 2, 2, 0, 2, 0, 3, 3, 1, 3],\n        \"ratios\": [2, 1],\n        \"num_partitions\": None,\n        \"shuffle\": False,\n        \"seed\": 0,\n        \"drop_last\": False,\n        \"even_divisible\": False,\n    },\n    [[2, 8, 4, 1, 3, 6, 5, 11, 12], [10, 13, 7, 9, 14]],\n]\n\nTEST_CASE_2 = [\n    {\n        \"data\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14],\n        \"classes\": [2, 0, 2, 1, 3, 2, 2, 0, 2, 0, 3, 3, 1, 3],\n        \"ratios\": None,\n        \"num_partitions\": 2,\n        \"shuffle\": False,\n        \"seed\": 0,\n        \"drop_last\": False,\n        \"even_divisible\": False,\n    },\n    [[2, 10, 4, 1, 6, 9, 5, 12], [8, 13, 3, 7, 11, 14]],\n]\n\nTEST_CASE_3 = [\n    {\n        \"data\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14],\n        \"classes\": [2, 0, 2, 1, 3, 2, 2, 0, 2, 0, 3, 3, 1, 3],\n        \"ratios\": None,\n        \"num_partitions\": 2,\n        \"shuffle\": False,\n        \"seed\": 0,\n        \"drop_last\": False,\n        \"even_divisible\": True,\n    },\n    [[2, 10, 4, 1, 6, 9, 5, 12], [8, 2, 13, 3, 7, 1, 11, 14]],\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_partition_dataset_classes.py_TEST_CASE_4_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_partition_dataset_classes.py_TEST_CASE_4_", "embedding": null, "metadata": {"file_path": "tests/test_partition_dataset_classes.py", "file_name": "test_partition_dataset_classes.py", "file_type": "text/x-python", "category": "test", "start_line": 61, "end_line": 84, "span_ids": ["impl:9", "TestPartitionDatasetClasses.test_value", "impl:5", "TestPartitionDatasetClasses"], "tokens": 268}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "TEST_CASE_4 = [\n    {\n        \"data\": np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]),\n        \"classes\": np.array([2, 0, 2, 1, 3, 2, 2, 0, 2, 0, 3, 3, 1, 3]),\n        \"ratios\": [1, 2],\n        \"num_partitions\": None,\n        \"shuffle\": True,\n        \"seed\": 123,\n        \"drop_last\": False,\n        \"even_divisible\": False,\n    },\n    [[13, 7, 14, 2, 3], [6, 8, 1, 5, 12, 11, 4, 9, 10]],\n]\n\n\nclass TestPartitionDatasetClasses(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4])\n    def test_value(self, input_param, result):\n        self.assertListEqual(partition_dataset_classes(**input_param), result)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_patch_wsi_dataset.py_os_TEST_CASE_3._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_patch_wsi_dataset.py_os_TEST_CASE_3._", "embedding": null, "metadata": {"file_path": "tests/test_patch_wsi_dataset.py", "file_name": "test_patch_wsi_dataset.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 79, "span_ids": ["impl:14", "docstring"], "tokens": 751}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport unittest\nfrom unittest import skipUnless\n\nimport numpy as np\nfrom numpy.testing import assert_array_equal\nfrom parameterized import parameterized\n\nfrom monai.apps.pathology.data import PatchWSIDataset\nfrom monai.apps.utils import download_url\nfrom monai.utils import optional_import\n\n_cucim, has_cim = optional_import(\"cucim\")\nhas_cim = has_cim and hasattr(_cucim, \"CuImage\")\n_, has_osl = optional_import(\"openslide\")\n\nFILE_URL = \"https://drive.google.com/uc?id=1sGTKZlJBIz53pfqTxoTqiIQzIoEzHLAe\"\nbase_name, extension = FILE_URL.split(\"id=\")[1], \".tiff\"\nFILE_PATH = os.path.join(os.path.dirname(__file__), \"testing_data\", \"temp_\" + base_name + extension)\n\nTEST_CASE_0 = [\n    {\n        \"data\": [{\"image\": FILE_PATH, \"location\": [0, 0], \"label\": [1]}],\n        \"region_size\": (1, 1),\n        \"grid_shape\": (1, 1),\n        \"patch_size\": 1,\n        \"image_reader_name\": \"cuCIM\",\n    },\n    [{\"image\": np.array([[[239]], [[239]], [[239]]], dtype=np.uint8), \"label\": np.array([[[1]]])}],\n]\n\nTEST_CASE_1 = [\n    {\n        \"data\": [{\"image\": FILE_PATH, \"location\": [10004, 20004], \"label\": [0, 0, 0, 1]}],\n        \"region_size\": (8, 8),\n        \"grid_shape\": (2, 2),\n        \"patch_size\": 1,\n        \"image_reader_name\": \"cuCIM\",\n    },\n    [\n        {\"image\": np.array([[[247]], [[245]], [[248]]], dtype=np.uint8), \"label\": np.array([[[0]]])},\n        {\"image\": np.array([[[245]], [[247]], [[244]]], dtype=np.uint8), \"label\": np.array([[[0]]])},\n        {\"image\": np.array([[[246]], [[246]], [[246]]], dtype=np.uint8), \"label\": np.array([[[0]]])},\n        {\"image\": np.array([[[246]], [[246]], [[246]]], dtype=np.uint8), \"label\": np.array([[[1]]])},\n    ],\n]\n\nTEST_CASE_2 = [\n    {\n        \"data\": [{\"image\": FILE_PATH, \"location\": [0, 0], \"label\": [1]}],\n        \"region_size\": 1,\n        \"grid_shape\": 1,\n        \"patch_size\": 1,\n        \"image_reader_name\": \"cuCIM\",\n    },\n    [{\"image\": np.array([[[239]], [[239]], [[239]]], dtype=np.uint8), \"label\": np.array([[[1]]])}],\n]\n\nTEST_CASE_3 = [\n    {\n        \"data\": [{\"image\": FILE_PATH, \"location\": [0, 0], \"label\": [[[0, 1], [1, 0]]]}],\n        \"region_size\": 1,\n        \"grid_shape\": 1,\n        \"patch_size\": 1,\n        \"image_reader_name\": \"cuCIM\",\n    },\n    [{\"image\": np.array([[[239]], [[239]], [[239]]], dtype=np.uint8), \"label\": np.array([[[0, 1], [1, 0]]])}],\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_patch_wsi_dataset.py_TEST_CASE_OPENSLIDE_0_TEST_CASE_OPENSLIDE_1._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_patch_wsi_dataset.py_TEST_CASE_OPENSLIDE_0_TEST_CASE_OPENSLIDE_1._", "embedding": null, "metadata": {"file_path": "tests/test_patch_wsi_dataset.py", "file_name": "test_patch_wsi_dataset.py", "file_type": "text/x-python", "category": "test", "start_line": 81, "end_line": 106, "span_ids": ["impl:14"], "tokens": 340}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "TEST_CASE_OPENSLIDE_0 = [\n    {\n        \"data\": [{\"image\": FILE_PATH, \"location\": [0, 0], \"label\": [1]}],\n        \"region_size\": (1, 1),\n        \"grid_shape\": (1, 1),\n        \"patch_size\": 1,\n        \"image_reader_name\": \"OpenSlide\",\n    },\n    [{\"image\": np.array([[[239]], [[239]], [[239]]], dtype=np.uint8), \"label\": np.array([[[1]]])}],\n]\n\nTEST_CASE_OPENSLIDE_1 = [\n    {\n        \"data\": [{\"image\": FILE_PATH, \"location\": [10004, 20004], \"label\": [0, 0, 0, 1]}],\n        \"region_size\": (8, 8),\n        \"grid_shape\": (2, 2),\n        \"patch_size\": 1,\n        \"image_reader_name\": \"OpenSlide\",\n    },\n    [\n        {\"image\": np.array([[[247]], [[245]], [[248]]], dtype=np.uint8), \"label\": np.array([[[0]]])},\n        {\"image\": np.array([[[245]], [[247]], [[244]]], dtype=np.uint8), \"label\": np.array([[[0]]])},\n        {\"image\": np.array([[[246]], [[246]], [[246]]], dtype=np.uint8), \"label\": np.array([[[0]]])},\n        {\"image\": np.array([[[246]], [[246]], [[246]]], dtype=np.uint8), \"label\": np.array([[[1]]])},\n    ],\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_patch_wsi_dataset.py_TestPatchWSIDataset_TestPatchWSIDataset.test_read_patches_cucim.for_i_in_range_len_sample.None_3": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_patch_wsi_dataset.py_TestPatchWSIDataset_TestPatchWSIDataset.test_read_patches_cucim.for_i_in_range_len_sample.None_3", "embedding": null, "metadata": {"file_path": "tests/test_patch_wsi_dataset.py", "file_name": "test_patch_wsi_dataset.py", "file_type": "text/x-python", "category": "test", "start_line": 108, "end_line": 121, "span_ids": ["TestPatchWSIDataset.setUp", "TestPatchWSIDataset", "TestPatchWSIDataset.test_read_patches_cucim"], "tokens": 196}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestPatchWSIDataset(unittest.TestCase):\n    def setUp(self):\n        download_url(FILE_URL, FILE_PATH, \"5a3cfd4fd725c50578ddb80b517b759f\")\n\n    @parameterized.expand([TEST_CASE_0, TEST_CASE_1, TEST_CASE_2, TEST_CASE_3])\n    @skipUnless(has_cim, \"Requires CuCIM\")\n    def test_read_patches_cucim(self, input_parameters, expected):\n        dataset = PatchWSIDataset(**input_parameters)\n        samples = dataset[0]\n        for i in range(len(samples)):\n            self.assertTupleEqual(samples[i][\"label\"].shape, expected[i][\"label\"].shape)\n            self.assertTupleEqual(samples[i][\"image\"].shape, expected[i][\"image\"].shape)\n            self.assertIsNone(assert_array_equal(samples[i][\"label\"], expected[i][\"label\"]))\n            self.assertIsNone(assert_array_equal(samples[i][\"image\"], expected[i][\"image\"]))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_patch_wsi_dataset.py_TestPatchWSIDataset.test_read_patches_openslide_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_patch_wsi_dataset.py_TestPatchWSIDataset.test_read_patches_openslide_", "embedding": null, "metadata": {"file_path": "tests/test_patch_wsi_dataset.py", "file_name": "test_patch_wsi_dataset.py", "file_type": "text/x-python", "category": "test", "start_line": 124, "end_line": 138, "span_ids": ["impl:22", "TestPatchWSIDataset.test_read_patches_openslide"], "tokens": 170}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestPatchWSIDataset(unittest.TestCase):\n\n    @parameterized.expand([TEST_CASE_OPENSLIDE_0, TEST_CASE_OPENSLIDE_1])\n    @skipUnless(has_osl, \"Requires OpenSlide\")\n    def test_read_patches_openslide(self, input_parameters, expected):\n        dataset = PatchWSIDataset(**input_parameters)\n        samples = dataset[0]\n        for i in range(len(samples)):\n            self.assertTupleEqual(samples[i][\"label\"].shape, expected[i][\"label\"].shape)\n            self.assertTupleEqual(samples[i][\"image\"].shape, expected[i][\"image\"].shape)\n            self.assertIsNone(assert_array_equal(samples[i][\"label\"], expected[i][\"label\"]))\n            self.assertIsNone(assert_array_equal(samples[i][\"image\"], expected[i][\"image\"]))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_pathology_prob_nms.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_pathology_prob_nms.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_pathology_prob_nms.py", "file_name": "test_pathology_prob_nms.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 53, "span_ids": ["TestPathologyProbNMS.test_output", "TestPathologyProbNMS", "impl:17", "docstring"], "tokens": 436}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.apps.pathology.utils import PathologyProbNMS\n\nprobs_map_2d = np.random.rand(100, 100).clip(0, 0.5)\nprobs_map_2d[33, 33] = 0.7\nprobs_map_2d[66, 66] = 0.9\nexpected_2d = [[0.9, 133, 133], [0.7, 67, 67]]\nTEST_CASES_2D = [\n    {\"spatial_dims\": 2, \"prob_threshold\": 0.5, \"box_size\": [10, 10]},\n    {\"resolution_level\": 1},\n    probs_map_2d,\n    expected_2d,\n]\n\nprobs_map_3d = torch.rand([50, 50, 50]).uniform_(0, 0.5)\nprobs_map_3d[25, 25, 25] = 0.7\nprobs_map_3d[45, 45, 45] = 0.9\nexpected_3d = [[0.9, 91, 91, 91], [0.7, 51, 51, 51]]\nTEST_CASES_3D = [\n    {\"spatial_dims\": 3, \"prob_threshold\": 0.5, \"box_size\": (10, 10, 10)},\n    {\"resolution_level\": 1},\n    probs_map_3d,\n    expected_3d,\n]\n\n\nclass TestPathologyProbNMS(unittest.TestCase):\n    @parameterized.expand([TEST_CASES_2D, TEST_CASES_3D])\n    def test_output(self, class_args, call_args, probs_map, expected):\n        nms = PathologyProbNMS(**class_args)\n        output = nms(probs_map, **call_args)\n        np.testing.assert_allclose(output, expected)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_persistentdataset.py_TestDataset_TestDataset.test_cache.with_tempfile_TemporaryDi.None_5": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_persistentdataset.py_TestDataset_TestDataset.test_cache.with_tempfile_TemporaryDi.None_5", "embedding": null, "metadata": {"file_path": "tests/test_persistentdataset.py", "file_name": "test_persistentdataset.py", "file_type": "text/x-python", "category": "test", "start_line": 54, "end_line": 76, "span_ids": ["TestDataset.test_cache", "TestDataset"], "tokens": 339}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestDataset(unittest.TestCase):\n    def test_cache(self):\n        \"\"\"testing no inplace change to the hashed item\"\"\"\n        items = [[list(range(i))] for i in range(5)]\n\n        with tempfile.TemporaryDirectory() as tempdir:\n            ds = PersistentDataset(\n                data=items,\n                transform=_InplaceXform(),\n                cache_dir=tempdir,\n                pickle_module=\"pickle\",\n                pickle_protocol=pickle.HIGHEST_PROTOCOL,\n            )\n            self.assertEqual(items, [[[]], [[0]], [[0, 1]], [[0, 1, 2]], [[0, 1, 2, 3]]])\n            ds1 = PersistentDataset(items, transform=_InplaceXform(), cache_dir=tempdir)\n            self.assertEqual(list(ds1), list(ds))\n            self.assertEqual(items, [[[]], [[0]], [[0, 1]], [[0, 1, 2]], [[0, 1, 2, 3]]])\n\n            ds = PersistentDataset(items, transform=_InplaceXform(), cache_dir=tempdir, hash_func=json_hashing)\n            self.assertEqual(items, [[[]], [[0]], [[0, 1]], [[0, 1, 2]], [[0, 1, 2, 3]]])\n            ds1 = PersistentDataset(items, transform=_InplaceXform(), cache_dir=tempdir, hash_func=json_hashing)\n            self.assertEqual(list(ds1), list(ds))\n            self.assertEqual(items, [[[]], [[0]], [[0, 1]], [[0, 1, 2]], [[0, 1, 2, 3]]])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_persistentdataset.py_TestDataset.test_shape_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_persistentdataset.py_TestDataset.test_shape_", "embedding": null, "metadata": {"file_path": "tests/test_persistentdataset.py", "file_name": "test_persistentdataset.py", "file_type": "text/x-python", "category": "test", "start_line": 71, "end_line": 149, "span_ids": ["TestDataset.test_shape", "impl:7"], "tokens": 1010}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestDataset(unittest.TestCase):\n\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3])\n    def test_shape(self, transform, expected_shape):\n        test_image = nib.Nifti1Image(np.random.randint(0, 2, size=[128, 128, 128]), np.eye(4))\n        with tempfile.TemporaryDirectory() as tempdir:\n            nib.save(test_image, os.path.join(tempdir, \"test_image1.nii.gz\"))\n            nib.save(test_image, os.path.join(tempdir, \"test_label1.nii.gz\"))\n            nib.save(test_image, os.path.join(tempdir, \"test_extra1.nii.gz\"))\n            nib.save(test_image, os.path.join(tempdir, \"test_image2.nii.gz\"))\n            nib.save(test_image, os.path.join(tempdir, \"test_label2.nii.gz\"))\n            nib.save(test_image, os.path.join(tempdir, \"test_extra2.nii.gz\"))\n            test_data = [\n                {\n                    \"image\": os.path.join(tempdir, \"test_image1.nii.gz\"),\n                    \"label\": os.path.join(tempdir, \"test_label1.nii.gz\"),\n                    \"extra\": os.path.join(tempdir, \"test_extra1.nii.gz\"),\n                },\n                {\n                    \"image\": os.path.join(tempdir, \"test_image2.nii.gz\"),\n                    \"label\": os.path.join(tempdir, \"test_label2.nii.gz\"),\n                    \"extra\": os.path.join(tempdir, \"test_extra2.nii.gz\"),\n                },\n            ]\n\n            cache_dir = os.path.join(os.path.join(tempdir, \"cache\"), \"data\")\n            dataset_precached = PersistentDataset(data=test_data, transform=transform, cache_dir=cache_dir)\n            data1_precached = dataset_precached[0]\n            data2_precached = dataset_precached[1]\n\n            dataset_postcached = PersistentDataset(data=test_data, transform=transform, cache_dir=cache_dir)\n            data1_postcached = dataset_postcached[0]\n            data2_postcached = dataset_postcached[1]\n            data3_postcached = dataset_postcached[0:2]\n\n            if transform is None:\n                self.assertEqual(data1_precached[\"image\"], os.path.join(tempdir, \"test_image1.nii.gz\"))\n                self.assertEqual(data2_precached[\"label\"], os.path.join(tempdir, \"test_label2.nii.gz\"))\n                self.assertEqual(data1_postcached[\"image\"], os.path.join(tempdir, \"test_image1.nii.gz\"))\n                self.assertEqual(data2_postcached[\"extra\"], os.path.join(tempdir, \"test_extra2.nii.gz\"))\n            else:\n                self.assertTupleEqual(data1_precached[\"image\"].shape, expected_shape)\n                self.assertTupleEqual(data1_precached[\"label\"].shape, expected_shape)\n                self.assertTupleEqual(data1_precached[\"extra\"].shape, expected_shape)\n                self.assertTupleEqual(data2_precached[\"image\"].shape, expected_shape)\n                self.assertTupleEqual(data2_precached[\"label\"].shape, expected_shape)\n                self.assertTupleEqual(data2_precached[\"extra\"].shape, expected_shape)\n\n                self.assertTupleEqual(data1_postcached[\"image\"].shape, expected_shape)\n                self.assertTupleEqual(data1_postcached[\"label\"].shape, expected_shape)\n                self.assertTupleEqual(data1_postcached[\"extra\"].shape, expected_shape)\n                self.assertTupleEqual(data2_postcached[\"image\"].shape, expected_shape)\n                self.assertTupleEqual(data2_postcached[\"label\"].shape, expected_shape)\n                self.assertTupleEqual(data2_postcached[\"extra\"].shape, expected_shape)\n                for d in data3_postcached:\n                    self.assertTupleEqual(d[\"image\"].shape, expected_shape)\n\n            # update the data to cache\n            test_data_new = [\n                {\n                    \"image\": os.path.join(tempdir, \"test_image1_new.nii.gz\"),\n                    \"label\": os.path.join(tempdir, \"test_label1_new.nii.gz\"),\n                    \"extra\": os.path.join(tempdir, \"test_extra1_new.nii.gz\"),\n                },\n                {\n                    \"image\": os.path.join(tempdir, \"test_image2_new.nii.gz\"),\n                    \"label\": os.path.join(tempdir, \"test_label2_new.nii.gz\"),\n                    \"extra\": os.path.join(tempdir, \"test_extra2_new.nii.gz\"),\n                },\n            ]\n            dataset_postcached.set_data(data=test_data_new)\n            # test new exchanged cache content\n            if transform is None:\n                self.assertEqual(dataset_postcached[0][\"image\"], os.path.join(tempdir, \"test_image1_new.nii.gz\"))\n                self.assertEqual(dataset_postcached[0][\"label\"], os.path.join(tempdir, \"test_label1_new.nii.gz\"))\n                self.assertEqual(dataset_postcached[1][\"extra\"], os.path.join(tempdir, \"test_extra2_new.nii.gz\"))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_phl_cpu.py_unittest_TEST_CASES": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_phl_cpu.py_unittest_TEST_CASES", "embedding": null, "metadata": {"file_path": "tests/test_phl_cpu.py", "file_name": "test_phl_cpu.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 238, "span_ids": ["docstring"], "tokens": 49}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks.layers.filtering import PHLFilter\nfrom tests.utils import skip_if_no_cpp_extension\n\nTEST_CASES =\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_phl_cpu.py_PHLFilterTestCaseCpu_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_phl_cpu.py_PHLFilterTestCaseCpu_", "embedding": null, "metadata": {"file_path": "tests/test_phl_cpu.py", "file_name": "test_phl_cpu.py", "file_type": "text/x-python", "category": "test", "start_line": 241, "end_line": 259, "span_ids": ["impl:3", "PHLFilterTestCaseCpu.test_cpu", "PHLFilterTestCaseCpu"], "tokens": 157}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_no_cpp_extension\nclass PHLFilterTestCaseCpu(unittest.TestCase):\n    @parameterized.expand(TEST_CASES)\n    def test_cpu(self, test_case_description, sigmas, input, features, expected):\n\n        # Create input tensors\n        input_tensor = torch.from_numpy(np.array(input)).to(dtype=torch.float, device=torch.device(\"cpu\"))\n        feature_tensor = torch.from_numpy(np.array(features)).to(dtype=torch.float, device=torch.device(\"cpu\"))\n\n        # apply filter\n        output = PHLFilter.apply(input_tensor, feature_tensor, sigmas).cpu().numpy()\n\n        # Ensure result are as expected\n        np.testing.assert_allclose(output, expected, atol=1e-4)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_phl_cuda.py_unittest_TEST_CASES._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_phl_cuda.py_unittest_TEST_CASES._", "embedding": null, "metadata": {"file_path": "tests/test_phl_cuda.py", "file_name": "test_phl_cuda.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 145, "span_ids": ["docstring"], "tokens": 1226}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks.layers.filtering import PHLFilter\nfrom tests.utils import skip_if_no_cpp_extension, skip_if_no_cuda\n\nTEST_CASES = [\n    [\n        # Case Description\n        \"2 batches, 1 dimensions, 1 channels, 1 features\",\n        # Sigmas\n        [1, 0.2],\n        # Input\n        [\n            # Batch 0\n            [\n                # Channel 0\n                [1, 0, 0, 0, 1]\n            ],\n            # Batch 1\n            [\n                # Channel 0\n                [0, 0, 1, 0, 0]\n            ],\n        ],\n        # Features\n        [\n            # Batch 0\n            [\n                # Channel 0\n                [1, 0.2, 0.5, 0, 1]\n            ],\n            # Batch 1\n            [\n                # Channel 0\n                [0.5, 0, 1, 1, 1]\n            ],\n        ],\n        # Expected\n        [\n            # Batch 0\n            [\n                # Channel 0\n                [0.468968, 0.364596, 0.408200, 0.332579, 0.468968]\n            ],\n            # Batch 1\n            [\n                # Channel 0\n                [0.202473, 0.176527, 0.220995, 0.220995, 0.220995]\n            ],\n        ],\n    ],\n    [\n        # Case Description\n        \"1 batches, 1 dimensions, 3 channels, 1 features\",\n        # Sigmas\n        [1],\n        # Input\n        [\n            # Batch 0\n            [\n                # Channel 0\n                [1, 0, 0, 0, 0],\n                # Channel 1\n                [0, 0, 0, 0, 1],\n                # Channel 2\n                [0, 0, 1, 0, 0],\n            ]\n        ],\n        # Features\n        [\n            # Batch 0\n            [\n                # Channel 0\n                [1, 0.2, 0.5, 0.2, 1]\n            ]\n        ],\n        # Expected\n        [\n            # Batch 0\n            [\n                # Channel 0\n                [0.229572, 0.182884, 0.202637, 0.182884, 0.229572],\n                # Channel 1\n                [0.229572, 0.182884, 0.202637, 0.182884, 0.229572],\n                # Channel 2\n                [0.201235, 0.208194, 0.205409, 0.208194, 0.201235],\n            ]\n        ],\n    ],\n    [\n        # Case Description\n        \"1 batches, 2 dimensions, 1 channels, 3 features\",\n        # Sigmas\n        [5, 3, 3],\n        # Input\n        [\n            # Batch 0\n            [\n                # Channel 0\n                [[9, 9, 0, 0, 0], [9, 9, 0, 0, 0], [9, 9, 0, 0, 0], [9, 9, 6, 6, 6], [9, 9, 6, 6, 6]]\n            ]\n        ],\n        # Features\n        [\n            # Batch 0\n            [\n                # Channel 0\n                [[9, 9, 0, 0, 0], [9, 9, 0, 0, 0], [9, 9, 0, 0, 0], [9, 9, 6, 6, 6], [9, 9, 6, 6, 6]],\n                # Channel 1\n                [[0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4]],\n                # Channel 2\n                [[0, 0, 0, 0, 0], [1, 1, 1, 1, 1], [2, 2, 2, 2, 2], [3, 3, 3, 3, 3], [4, 4, 4, 4, 4]],\n            ]\n        ],\n        # Expected\n        [\n            # Batch 0\n            [\n                # Channel 0\n                [\n                    [7.792655, 7.511395, 0.953769, 0.860538, 0.912978],\n                    [7.758870, 7.426762, 1.164386, 1.050956, 1.121830],\n                    [7.733974, 7.429964, 1.405752, 1.244949, 1.320862],\n                    [7.712976, 7.429060, 5.789552, 5.594258, 5.371737],\n                    [7.701185, 7.492719, 5.860026, 5.538241, 5.281656],\n                ]\n            ]\n        ],\n    ],\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_phl_cuda.py_PHLFilterTestCaseCuda_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_phl_cuda.py_PHLFilterTestCaseCuda_", "embedding": null, "metadata": {"file_path": "tests/test_phl_cuda.py", "file_name": "test_phl_cuda.py", "file_type": "text/x-python", "category": "test", "start_line": 148, "end_line": 167, "span_ids": ["PHLFilterTestCaseCuda.test_cuda", "impl:3", "PHLFilterTestCaseCuda"], "tokens": 163}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_no_cuda\n@skip_if_no_cpp_extension\nclass PHLFilterTestCaseCuda(unittest.TestCase):\n    @parameterized.expand(TEST_CASES)\n    def test_cuda(self, test_case_description, sigmas, input, features, expected):\n\n        # Create input tensors\n        input_tensor = torch.from_numpy(np.array(input)).to(dtype=torch.float, device=torch.device(\"cuda\"))\n        feature_tensor = torch.from_numpy(np.array(features)).to(dtype=torch.float, device=torch.device(\"cuda\"))\n\n        # apply filter\n        output = PHLFilter.apply(input_tensor, feature_tensor, sigmas).cpu().numpy()\n\n        # Ensure result are as expected\n        np.testing.assert_allclose(output, expected, atol=1e-4)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_png_saver.py_TestPNGSaver.test_saved_content_spatial_size_TestPNGSaver.test_saved_content_spatial_size.with_tempfile_TemporaryDi.for_i_in_range_8_.self_assertTrue_os_path_e": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_png_saver.py_TestPNGSaver.test_saved_content_spatial_size_TestPNGSaver.test_saved_content_spatial_size.with_tempfile_TemporaryDi.for_i_in_range_8_.self_assertTrue_os_path_e", "embedding": null, "metadata": {"file_path": "tests/test_png_saver.py", "file_name": "test_png_saver.py", "file_type": "text/x-python", "category": "test", "start_line": 44, "end_line": 56, "span_ids": ["TestPNGSaver.test_saved_content_spatial_size"], "tokens": 182}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestPNGSaver(unittest.TestCase):\n\n    def test_saved_content_spatial_size(self):\n        with tempfile.TemporaryDirectory() as tempdir:\n\n            saver = PNGSaver(output_dir=tempdir, output_postfix=\"seg\", output_ext=\".png\", scale=255)\n\n            meta_data = {\n                \"filename_or_obj\": [\"testfile\" + str(i) + \".jpg\" for i in range(8)],\n                \"spatial_shape\": [(4, 4) for i in range(8)],\n            }\n            saver.save_batch(torch.randint(1, 200, (8, 1, 2, 2)), meta_data)\n            for i in range(8):\n                filepath = os.path.join(\"testfile\" + str(i), \"testfile\" + str(i) + \"_seg.png\")\n                self.assertTrue(os.path.exists(os.path.join(tempdir, filepath)))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_png_saver.py_TestPNGSaver.test_saved_specified_root_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_png_saver.py_TestPNGSaver.test_saved_specified_root_", "embedding": null, "metadata": {"file_path": "tests/test_png_saver.py", "file_name": "test_png_saver.py", "file_type": "text/x-python", "category": "test", "start_line": 59, "end_line": 77, "span_ids": ["TestPNGSaver.test_saved_specified_root", "impl"], "tokens": 192}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestPNGSaver(unittest.TestCase):\n\n    def test_saved_specified_root(self):\n        with tempfile.TemporaryDirectory() as tempdir:\n\n            saver = PNGSaver(\n                output_dir=tempdir, output_postfix=\"seg\", output_ext=\".png\", scale=255, data_root_dir=\"test\"\n            )\n\n            meta_data = {\n                \"filename_or_obj\": [os.path.join(\"test\", \"testfile\" + str(i), \"image\" + \".jpg\") for i in range(8)]\n            }\n            saver.save_batch(torch.randint(1, 200, (8, 1, 2, 2)), meta_data)\n            for i in range(8):\n                filepath = os.path.join(\"testfile\" + str(i), \"image\", \"image\" + \"_seg.png\")\n                self.assertTrue(os.path.exists(os.path.join(tempdir, filepath)))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_print_info.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_print_info.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_print_info.py", "file_name": "test_print_info.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 24, "span_ids": ["TestPrintInfo", "TestPrintInfo.test_print_info", "impl", "docstring"], "tokens": 43}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nfrom monai.config import print_debug_info\n\n\nclass TestPrintInfo(unittest.TestCase):\n    def test_print_info(self):\n        print_debug_info()\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_axis_flip.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_axis_flip.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_rand_axis_flip.py", "file_name": "test_rand_axis_flip.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 31, "span_ids": ["impl", "TestRandAxisFlip.test_correct_results", "TestRandAxisFlip", "docstring"], "tokens": 134}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\n\nfrom monai.transforms import RandAxisFlip\nfrom tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, assert_allclose\n\n\nclass TestRandAxisFlip(NumpyImageTestCase2D):\n    def test_correct_results(self):\n        for p in TEST_NDARRAYS:\n            flip = RandAxisFlip(prob=1.0)\n            result = flip(p(self.imt[0]))\n            expected = [np.flip(channel, flip._axis) for channel in self.imt[0]]\n            assert_allclose(result, p(np.stack(expected)))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_axis_flipd.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_axis_flipd.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_rand_axis_flipd.py", "file_name": "test_rand_axis_flipd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 32, "span_ids": ["impl", "TestRandAxisFlip.test_correct_results", "TestRandAxisFlip", "docstring"], "tokens": 149}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\n\nfrom monai.transforms import RandAxisFlipd\nfrom tests.utils import TEST_NDARRAYS, NumpyImageTestCase3D, assert_allclose\n\n\nclass TestRandAxisFlip(NumpyImageTestCase3D):\n    def test_correct_results(self):\n        for p in TEST_NDARRAYS:\n            flip = RandAxisFlipd(keys=\"img\", prob=1.0)\n            result = flip({\"img\": p(self.imt[0])})[\"img\"]\n\n            expected = [np.flip(channel, flip.flipper._axis) for channel in self.imt[0]]\n            assert_allclose(result, p(np.stack(expected)))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_gaussian_noised.py_unittest_seed.0": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_gaussian_noised.py_unittest_seed.0", "embedding": null, "metadata": {"file_path": "tests/test_rand_gaussian_noised.py", "file_name": "test_rand_gaussian_noised.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 26, "span_ids": ["docstring"], "tokens": 118}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import RandGaussianNoised\nfrom tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    TESTS.append([\"test_zero_mean\", p, [\"img1\", \"img2\"], 0, 0.1])\n    TESTS.append([\"test_non_zero_mean\", p, [\"img1\", \"img2\"], 1, 0.5])\n\nseed = 0", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_lambdad.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_lambdad.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_rand_lambdad.py", "file_name": "test_rand_lambdad.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 58, "span_ids": ["impl", "RandTest.randomize", "TestRandLambdad.test_rand_lambdad_identity", "docstring", "RandTest", "RandTest.__call__", "TestRandLambdad"], "tokens": 367}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\n\nfrom monai.transforms.transform import Randomizable\nfrom monai.transforms.utility.dictionary import RandLambdad\n\n\nclass RandTest(Randomizable):\n    \"\"\"\n    randomisable transform for testing.\n    \"\"\"\n\n    def randomize(self, data=None):\n        self._a = self.R.random()\n\n    def __call__(self, data):\n        self.randomize()\n        return data + self._a\n\n\nclass TestRandLambdad(unittest.TestCase):\n    def test_rand_lambdad_identity(self):\n        img = np.zeros((10, 10))\n        data = {\"img\": img, \"prop\": 1.0}\n\n        test_func = RandTest()\n        test_func.set_random_state(seed=134)\n        expected = {\"img\": test_func(data[\"img\"]), \"prop\": 1.0}\n        test_func.set_random_state(seed=134)\n        ret = RandLambdad(keys=[\"img\", \"prop\"], func=test_func, overwrite=[True, False])(data)\n        np.testing.assert_allclose(expected[\"img\"], ret[\"img\"])\n        np.testing.assert_allclose(expected[\"prop\"], ret[\"prop\"])\n        ret = RandLambdad(keys=[\"img\", \"prop\"], func=test_func, prob=0.0)(data)\n        np.testing.assert_allclose(data[\"img\"], ret[\"img\"])\n        np.testing.assert_allclose(data[\"prop\"], ret[\"prop\"])\n\n        trans = RandLambdad(keys=[\"img\", \"prop\"], func=test_func, prob=0.5)\n        trans.set_random_state(seed=123)\n        ret = trans(data)\n        np.testing.assert_allclose(data[\"img\"], ret[\"img\"])\n        np.testing.assert_allclose(data[\"prop\"], ret[\"prop\"])\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_std_shift_intensity.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_std_shift_intensity.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_rand_std_shift_intensity.py", "file_name": "test_rand_std_shift_intensity.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 38, "span_ids": ["TestRandStdShiftIntensity", "TestRandStdShiftIntensity.test_value", "impl", "docstring"], "tokens": 203}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\n\nfrom monai.transforms import RandStdShiftIntensity\nfrom tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D\n\n\nclass TestRandStdShiftIntensity(NumpyImageTestCase2D):\n    def test_value(self):\n        for p in TEST_NDARRAYS:\n            np.random.seed(0)\n            # simulate the randomize() of transform\n            np.random.random()\n            factor = np.random.uniform(low=-1.0, high=1.0)\n            offset = factor * np.std(self.imt)\n            expected = p(self.imt + offset)\n            shifter = RandStdShiftIntensity(factors=1.0, prob=1.0)\n            shifter.set_random_state(seed=0)\n            result = shifter(p(self.imt))\n            torch.testing.assert_allclose(result, expected, atol=0, rtol=1e-5)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_std_shift_intensityd.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_std_shift_intensityd.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_rand_std_shift_intensityd.py", "file_name": "test_rand_std_shift_intensityd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 40, "span_ids": ["TestRandStdShiftIntensityd", "TestRandStdShiftIntensityd.test_value", "impl", "docstring"], "tokens": 225}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\n\nfrom monai.transforms import RandStdShiftIntensityd\nfrom tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D\n\n\nclass TestRandStdShiftIntensityd(NumpyImageTestCase2D):\n    def test_value(self):\n        for p in TEST_NDARRAYS:\n            key = \"img\"\n            np.random.seed(0)\n            # simulate the randomize() of transform\n            np.random.random()\n            factor = np.random.uniform(low=-1.0, high=1.0)\n            expected = self.imt + factor * np.std(self.imt)\n            shifter = RandStdShiftIntensityd(keys=[key], factors=1.0, prob=1.0)\n            shifter.set_random_state(seed=0)\n            result = shifter({key: p(self.imt)})[key]\n            if isinstance(result, torch.Tensor):\n                result = result.cpu()\n            np.testing.assert_allclose(result, expected, rtol=1e-5)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_zoom.py_TestRandZoom.test_auto_expand_3d_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_zoom.py_TestRandZoom.test_auto_expand_3d_", "embedding": null, "metadata": {"file_path": "tests/test_rand_zoom.py", "file_name": "test_rand_zoom.py", "file_type": "text/x-python", "category": "test", "start_line": 64, "end_line": 76, "span_ids": ["impl:3", "TestRandZoom.test_auto_expand_3d"], "tokens": 188}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandZoom(NumpyImageTestCase2D):\n\n    def test_auto_expand_3d(self):\n        for p in TEST_NDARRAYS:\n            random_zoom = RandZoom(prob=1.0, min_zoom=[0.8, 0.7], max_zoom=[1.2, 1.3], mode=\"nearest\", keep_size=False)\n            random_zoom.set_random_state(1234)\n            test_data = p(np.random.randint(0, 2, size=[2, 2, 3, 4]))\n            zoomed = random_zoom(test_data)\n            assert_allclose(random_zoom._zoom, (1.048844, 1.048844, 0.962637), atol=1e-2)\n            assert_allclose(zoomed.shape, (2, 2, 3, 3))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_zoomd.py_TestRandZoomd.test_auto_expand_3d_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_zoomd.py_TestRandZoomd.test_auto_expand_3d_", "embedding": null, "metadata": {"file_path": "tests/test_rand_zoomd.py", "file_name": "test_rand_zoomd.py", "file_type": "text/x-python", "category": "test", "start_line": 68, "end_line": 82, "span_ids": ["impl:3", "TestRandZoomd.test_auto_expand_3d"], "tokens": 207}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandZoomd(NumpyImageTestCase2D):\n\n    def test_auto_expand_3d(self):\n        random_zoom = RandZoomd(\n            keys=\"img\", prob=1.0, min_zoom=[0.8, 0.7], max_zoom=[1.2, 1.3], mode=\"nearest\", keep_size=False\n        )\n        for p in TEST_NDARRAYS:\n            random_zoom.set_random_state(1234)\n            test_data = {\"img\": p(np.random.randint(0, 2, size=[2, 2, 3, 4]))}\n            zoomed = random_zoom(test_data)\n            assert_allclose(random_zoom.rand_zoom._zoom, (1.048844, 1.048844, 0.962637), atol=1e-2)\n            assert_allclose(zoomed[\"img\"].shape, (2, 2, 3, 3))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_randtorchvisiond.py_unittest_TEST_CASE_2._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_randtorchvisiond.py_unittest_TEST_CASE_2._", "embedding": null, "metadata": {"file_path": "tests/test_randtorchvisiond.py", "file_name": "test_randtorchvisiond.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 37, "span_ids": ["docstring"], "tokens": 419}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import Randomizable, RandTorchVisiond\nfrom monai.utils import set_determinism\nfrom tests.utils import SkipIfBeforePyTorchVersion\n\nTEST_CASE_1 = [\n    {\"keys\": \"img\", \"name\": \"ColorJitter\"},\n    {\"img\": torch.tensor([[[0.0, 1.0], [1.0, 2.0]], [[0.0, 1.0], [1.0, 2.0]], [[0.0, 1.0], [1.0, 2.0]]])},\n    torch.tensor([[[0.0, 1.0], [1.0, 2.0]], [[0.0, 1.0], [1.0, 2.0]], [[0.0, 1.0], [1.0, 2.0]]]),\n]\n\nTEST_CASE_2 = [\n    {\"keys\": \"img\", \"name\": \"ColorJitter\", \"brightness\": 0.5, \"contrast\": 0.5, \"saturation\": [0.1, 0.8], \"hue\": 0.5},\n    {\"img\": torch.tensor([[[0.0, 1.0], [1.0, 2.0]], [[0.0, 1.0], [1.0, 2.0]], [[0.0, 1.0], [1.0, 2.0]]])},\n    torch.tensor(\n        [\n            [[0.1090, 0.6193], [0.6193, 0.9164]],\n            [[0.1090, 0.6193], [0.6193, 0.9164]],\n            [[0.1090, 0.6193], [0.6193, 0.9164]],\n        ]\n    ),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_randtorchvisiond.py_TEST_CASE_3_TEST_CASE_3._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_randtorchvisiond.py_TEST_CASE_3_TEST_CASE_3._", "embedding": null, "metadata": {"file_path": "tests/test_randtorchvisiond.py", "file_name": "test_randtorchvisiond.py", "file_type": "text/x-python", "category": "test", "start_line": 39, "end_line": 49, "span_ids": ["impl:5"], "tokens": 357}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "TEST_CASE_3 = [\n    {\"keys\": \"img\", \"name\": \"Pad\", \"padding\": [1, 1, 1, 1]},\n    {\"img\": torch.tensor([[[0.0, 1.0], [1.0, 2.0]], [[0.0, 1.0], [1.0, 2.0]], [[0.0, 1.0], [1.0, 2.0]]])},\n    torch.tensor(\n        [\n            [[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 2.0, 0.0], [0.0, 0.0, 0.0, 0.0]],\n            [[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 2.0, 0.0], [0.0, 0.0, 0.0, 0.0]],\n            [[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 2.0, 0.0], [0.0, 0.0, 0.0, 0.0]],\n        ]\n    ),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_randtorchvisiond.py_TestRandTorchVisiond_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_randtorchvisiond.py_TestRandTorchVisiond_", "embedding": null, "metadata": {"file_path": "tests/test_randtorchvisiond.py", "file_name": "test_randtorchvisiond.py", "file_type": "text/x-python", "category": "test", "start_line": 76, "end_line": 89, "span_ids": ["TestRandTorchVisiond.test_value", "TestRandTorchVisiond", "impl:7"], "tokens": 122}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@SkipIfBeforePyTorchVersion((1, 7))\nclass TestRandTorchVisiond(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3])\n    def test_value(self, input_param, input_data, expected_value):\n        set_determinism(seed=0)\n        transform = RandTorchVisiond(**input_param)\n        result = transform(input_data)\n        self.assertTrue(isinstance(transform, Randomizable))\n        torch.testing.assert_allclose(result[\"img\"], expected_value)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_reg_loss_integration.py_unittest_TEST_CASES._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_reg_loss_integration.py_unittest_TEST_CASES._", "embedding": null, "metadata": {"file_path": "tests/test_reg_loss_integration.py", "file_name": "test_reg_loss_integration.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 29, "span_ids": ["docstring"], "tokens": 222}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom parameterized import parameterized\n\nfrom monai.losses import BendingEnergyLoss, GlobalMutualInformationLoss, LocalNormalizedCrossCorrelationLoss\nfrom tests.utils import SkipIfBeforePyTorchVersion\n\nTEST_CASES = [\n    [BendingEnergyLoss, {}, [\"pred\"], 3],\n    [LocalNormalizedCrossCorrelationLoss, {\"kernel_size\": 7, \"kernel_type\": \"rectangular\"}, [\"pred\", \"target\"]],\n    [LocalNormalizedCrossCorrelationLoss, {\"kernel_size\": 5, \"kernel_type\": \"triangular\"}, [\"pred\", \"target\"]],\n    [LocalNormalizedCrossCorrelationLoss, {\"kernel_size\": 3, \"kernel_type\": \"gaussian\"}, [\"pred\", \"target\"]],\n    [GlobalMutualInformationLoss, {\"num_bins\": 10}, [\"pred\", \"target\"]],\n    [GlobalMutualInformationLoss, {\"kernel_type\": \"b-spline\", \"num_bins\": 10}, [\"pred\", \"target\"]],\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_reg_loss_integration.py_TestRegLossIntegration_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_reg_loss_integration.py_TestRegLossIntegration_", "embedding": null, "metadata": {"file_path": "tests/test_reg_loss_integration.py", "file_name": "test_reg_loss_integration.py", "file_type": "text/x-python", "category": "test", "start_line": 32, "end_line": 103, "span_ids": ["impl:3", "TestRegLossIntegration.test_convergence", "TestRegLossIntegration.tearDown", "TestRegLossIntegration", "TestRegLossIntegration.setUp"], "tokens": 552}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRegLossIntegration(unittest.TestCase):\n    def setUp(self):\n        torch.backends.cudnn.deterministic = True\n        torch.backends.cudnn.benchmark = False\n        torch.manual_seed(0)\n        self.device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu:0\")\n\n    def tearDown(self):\n        torch.backends.cudnn.deterministic = False\n        torch.backends.cudnn.benchmark = True\n\n    @parameterized.expand(TEST_CASES)\n    @SkipIfBeforePyTorchVersion((1, 9))\n    def test_convergence(self, loss_type, loss_args, forward_args, pred_channels=1):\n        \"\"\"\n        The goal of this test is to assess if the gradient of the loss function\n        is correct by testing if we can train a one layer neural network\n        to segment one image.\n        We verify that the loss is decreasing in almost all SGD steps.\n        \"\"\"\n        learning_rate = 0.001\n        max_iter = 100\n\n        # define a simple 3d example\n        target = torch.rand((1, 1, 5, 5, 5), device=self.device)\n        image = 12 * target + 27\n        image = image.to(device=self.device)\n\n        # define a one layer model\n        class OnelayerNet(nn.Module):\n            def __init__(self):\n                super().__init__()\n                self.layer = nn.Sequential(\n                    nn.Conv3d(in_channels=1, out_channels=1, kernel_size=3, padding=1),\n                    nn.ReLU(),\n                    nn.Conv3d(in_channels=1, out_channels=pred_channels, kernel_size=3, padding=1),\n                )\n\n            def forward(self, x):\n                return self.layer(x)\n\n        # initialise the network\n        net = OnelayerNet().to(self.device)\n\n        # initialize the loss\n        loss = loss_type(**loss_args).to(self.device)\n\n        # initialize a SGD optimizer\n        optimizer = optim.Adam(net.parameters(), lr=learning_rate)\n\n        # train the network\n        for it in range(max_iter):\n            # set the gradient to zero\n            optimizer.zero_grad()\n\n            # forward pass\n            output = net(image)\n            loss_input = {\"pred\": output, \"target\": target}\n\n            loss_val = loss(**{k: loss_input[k] for k in forward_args})\n            if it == 0:\n                init_loss = loss_val\n\n            # backward pass\n            loss_val.backward()\n            optimizer.step()\n        self.assertTrue(init_loss > loss_val, \"loss did not decrease\")\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_regunet.py_unittest_TEST_CASE_REGUNET_3D._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_regunet.py_unittest_TEST_CASE_REGUNET_3D._", "embedding": null, "metadata": {"file_path": "tests/test_regunet.py", "file_name": "test_regunet.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 61, "span_ids": ["docstring"], "tokens": 347}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks import eval_mode\nfrom monai.networks.nets.regunet import RegUNet\nfrom tests.utils import test_script_save\n\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\n\nTEST_CASE_REGUNET_2D = [\n    [\n        {\n            \"spatial_dims\": 2,\n            \"in_channels\": 2,\n            \"num_channel_initial\": 16,\n            \"depth\": 3,\n            \"out_kernel_initializer\": \"kaiming_uniform\",\n            \"out_activation\": None,\n            \"out_channels\": 2,\n            \"pooling\": False,\n            \"concat_skip\": True,\n            \"encode_kernel_sizes\": 3,\n        },\n        (1, 2, 16, 16),\n        (1, 2, 16, 16),\n    ]\n]\n\nTEST_CASE_REGUNET_3D = [\n    [\n        {\n            \"spatial_dims\": 3,\n            \"in_channels\": 2,\n            \"num_channel_initial\": 16,\n            \"depth\": 3,\n            \"out_kernel_initializer\": \"kaiming_uniform\",\n            \"out_activation\": \"sigmoid\",\n            \"out_channels\": 2,\n            \"extract_levels\": (0, 1, 2, 3),\n            \"pooling\": True,\n            \"concat_skip\": False,\n            \"encode_kernel_sizes\": (3, 3, 3, 7),\n        },\n        (1, 2, 16, 16, 16),\n        (1, 2, 16, 16, 16),\n    ]\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_regunet.py_TestREGUNET_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_regunet.py_TestREGUNET_", "embedding": null, "metadata": {"file_path": "tests/test_regunet.py", "file_name": "test_regunet.py", "file_type": "text/x-python", "category": "test", "start_line": 64, "end_line": 88, "span_ids": ["TestREGUNET.test_shape", "TestREGUNET.test_script", "TestREGUNET.test_ill_shape", "impl:7", "TestREGUNET"], "tokens": 225}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestREGUNET(unittest.TestCase):\n    @parameterized.expand(TEST_CASE_REGUNET_2D + TEST_CASE_REGUNET_3D)\n    def test_shape(self, input_param, input_shape, expected_shape):\n        net = RegUNet(**input_param).to(device)\n        with eval_mode(net):\n            result = net(torch.randn(input_shape).to(device))\n            self.assertEqual(result.shape, expected_shape)\n\n    def test_ill_shape(self):\n        with self.assertRaisesRegex(ValueError, \"\"):\n            input_param, _, _ = TEST_CASE_REGUNET_2D[0]\n            input_shape = (1, input_param[\"in_channels\"], 17, 17)\n            net = RegUNet(**input_param).to(device)\n            net.forward(torch.randn(input_shape).to(device))\n\n    def test_script(self):\n        input_param, input_shape, _ = TEST_CASE_REGUNET_2D[0]\n        net = RegUNet(**input_param)\n        test_data = torch.randn(input_shape)\n        test_script_save(net, test_data)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_regunet_block.py_unittest_TestRegistrationResidualConvBlock.test_shape.with_eval_mode_net_.self_assertEqual_x_shape_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_regunet_block.py_unittest_TestRegistrationResidualConvBlock.test_shape.with_eval_mode_net_.self_assertEqual_x_shape_", "embedding": null, "metadata": {"file_path": "tests/test_regunet_block.py", "file_name": "test_regunet_block.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 70, "span_ids": ["TestRegistrationResidualConvBlock.test_shape", "TestRegistrationResidualConvBlock", "docstring"], "tokens": 586}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks import eval_mode\nfrom monai.networks.blocks.regunet_block import (\n    RegistrationDownSampleBlock,\n    RegistrationExtractionBlock,\n    RegistrationResidualConvBlock,\n)\n\nTEST_CASE_RESIDUAL = [\n    [{\"spatial_dims\": 2, \"in_channels\": 1, \"out_channels\": 2, \"num_layers\": 1}, (1, 1, 5, 5), (1, 2, 5, 5)],\n    [{\"spatial_dims\": 3, \"in_channels\": 2, \"out_channels\": 2, \"num_layers\": 2}, (1, 2, 5, 5, 5), (1, 2, 5, 5, 5)],\n]\n\nTEST_CASE_DOWN_SAMPLE = [\n    [{\"spatial_dims\": 2, \"channels\": 1, \"pooling\": False}, (1, 1, 4, 4), (1, 1, 2, 2)],\n    [{\"spatial_dims\": 3, \"channels\": 2, \"pooling\": True}, (1, 2, 4, 4, 4), (1, 2, 2, 2, 2)],\n]\n\nTEST_CASE_EXTRACTION = [\n    [\n        {\n            \"spatial_dims\": 2,\n            \"extract_levels\": (0,),\n            \"num_channels\": [1],\n            \"out_channels\": 1,\n            \"kernel_initializer\": \"kaiming_uniform\",\n            \"activation\": None,\n        },\n        [(1, 1, 2, 2)],\n        (3, 3),\n        (1, 1, 3, 3),\n    ],\n    [\n        {\n            \"spatial_dims\": 3,\n            \"extract_levels\": (1, 2),\n            \"num_channels\": [1, 2, 3],\n            \"out_channels\": 1,\n            \"kernel_initializer\": \"zeros\",\n            \"activation\": \"sigmoid\",\n        },\n        [(1, 3, 2, 2, 2), (1, 2, 4, 4, 4), (1, 1, 8, 8, 8)],\n        (3, 3, 3),\n        (1, 1, 3, 3, 3),\n    ],\n]\n\n\nclass TestRegistrationResidualConvBlock(unittest.TestCase):\n    @parameterized.expand(TEST_CASE_RESIDUAL)\n    def test_shape(self, input_param, input_shape, expected_shape):\n        net = RegistrationResidualConvBlock(**input_param)\n        with eval_mode(net):\n            x = net(torch.randn(input_shape))\n            self.assertEqual(x.shape, expected_shape)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_regunet_block.py_TestRegistrationDownSampleBlock_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_regunet_block.py_TestRegistrationDownSampleBlock_", "embedding": null, "metadata": {"file_path": "tests/test_regunet_block.py", "file_name": "test_regunet_block.py", "file_type": "text/x-python", "category": "test", "start_line": 73, "end_line": 98, "span_ids": ["impl:7", "TestRegistrationExtractionBlock", "TestRegistrationExtractionBlock.test_shape", "TestRegistrationDownSampleBlock", "TestRegistrationDownSampleBlock.test_shape", "TestRegistrationDownSampleBlock.test_ill_shape"], "tokens": 214}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRegistrationDownSampleBlock(unittest.TestCase):\n    @parameterized.expand(TEST_CASE_DOWN_SAMPLE)\n    def test_shape(self, input_param, input_shape, expected_shape):\n        net = RegistrationDownSampleBlock(**input_param)\n        with eval_mode(net):\n            x = net(torch.rand(input_shape))\n            self.assertEqual(x.shape, expected_shape)\n\n    def test_ill_shape(self):\n        net = RegistrationDownSampleBlock(spatial_dims=2, channels=2, pooling=True)\n        with self.assertRaises(ValueError):\n            net(torch.rand((1, 2, 3, 3)))\n\n\nclass TestRegistrationExtractionBlock(unittest.TestCase):\n    @parameterized.expand(TEST_CASE_EXTRACTION)\n    def test_shape(self, input_param, input_shapes, image_size, expected_shape):\n        net = RegistrationExtractionBlock(**input_param)\n        with eval_mode(net):\n            x = net([torch.rand(input_shape) for input_shape in input_shapes], image_size)\n            self.assertEqual(x.shape, expected_shape)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_remove_repeated_channel.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_remove_repeated_channel.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_remove_repeated_channel.py", "file_name": "test_remove_repeated_channel.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 34, "span_ids": ["impl:5", "TestRemoveRepeatedChannel", "TestRemoveRepeatedChannel.test_shape", "docstring"], "tokens": 159}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import RemoveRepeatedChannel\n\nTEST_CASES = []\nfor q in (torch.Tensor, np.array):\n    TEST_CASES.append([{\"repeats\": 2}, q([[1, 2], [1, 2], [3, 4], [3, 4]]), (2, 2)])  # type: ignore\n\n\nclass TestRemoveRepeatedChannel(unittest.TestCase):\n    @parameterized.expand(TEST_CASES)\n    def test_shape(self, input_param, input_data, expected_shape):\n        result = RemoveRepeatedChannel(**input_param)(input_data)\n        self.assertEqual(result.shape, expected_shape)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_remove_repeated_channeld.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_remove_repeated_channeld.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_remove_repeated_channeld.py", "file_name": "test_remove_repeated_channeld.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 43, "span_ids": ["impl:5", "TestRemoveRepeatedChanneld", "TestRemoveRepeatedChanneld.test_shape", "docstring"], "tokens": 221}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import RemoveRepeatedChanneld\nfrom tests.utils import TEST_NDARRAYS\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    TESTS.append(\n        [\n            {\"keys\": [\"img\"], \"repeats\": 2},\n            {\n                \"img\": p(np.array([[1, 2], [1, 2], [3, 4], [3, 4]])),\n                \"seg\": p(np.array([[1, 2], [1, 2], [3, 4], [3, 4]])),\n            },\n            (2, 2),\n        ]\n    )\n\n\nclass TestRemoveRepeatedChanneld(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_shape(self, input_param, input_data, expected_shape):\n        result = RemoveRepeatedChanneld(**input_param)(input_data)\n        self.assertEqual(result[\"img\"].shape, expected_shape)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rotate.py_TestRotate2D_TestRotate2D.test_correct_results.self_assertLessEqual_np_a": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rotate.py_TestRotate2D_TestRotate2D.test_correct_results.self_assertLessEqual_np_a", "embedding": null, "metadata": {"file_path": "tests/test_rotate.py", "file_name": "test_rotate.py", "file_type": "text/x-python", "category": "test", "start_line": 46, "end_line": 71, "span_ids": ["TestRotate2D.test_correct_results", "TestRotate2D"], "tokens": 288}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRotate2D(NumpyImageTestCase2D):\n    @parameterized.expand(TEST_CASES_2D)\n    def test_correct_results(self, im_type, angle, keep_size, mode, padding_mode, align_corners):\n        rotate_fn = Rotate(angle, keep_size, mode, padding_mode, align_corners)\n        rotated = rotate_fn(im_type(self.imt[0]))\n        if keep_size:\n            np.testing.assert_allclose(self.imt[0].shape, rotated.shape)\n        _order = 0 if mode == \"nearest\" else 1\n        if padding_mode == \"border\":\n            _mode = \"nearest\"\n        elif padding_mode == \"reflection\":\n            _mode = \"reflect\"\n        else:\n            _mode = \"constant\"\n\n        expected = []\n        for channel in self.imt[0]:\n            expected.append(\n                scipy.ndimage.rotate(\n                    channel, -np.rad2deg(angle), (0, 1), not keep_size, order=_order, mode=_mode, prefilter=False\n                )\n            )\n        expected = np.stack(expected).astype(np.float32)\n        rotated = rotated.cpu() if isinstance(rotated, torch.Tensor) else rotated\n        good = np.sum(np.isclose(expected, rotated, atol=1e-3))\n        self.assertLessEqual(np.abs(good - expected.size), 5, \"diff at most 5 pixels\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rotate.py_TestRotate3D_TestRotate3D.test_correct_results.self_assertLessEqual_expe": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rotate.py_TestRotate3D_TestRotate3D.test_correct_results.self_assertLessEqual_expe", "embedding": null, "metadata": {"file_path": "tests/test_rotate.py", "file_name": "test_rotate.py", "file_type": "text/x-python", "category": "test", "start_line": 74, "end_line": 99, "span_ids": ["TestRotate3D", "TestRotate3D.test_correct_results"], "tokens": 294}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRotate3D(NumpyImageTestCase3D):\n    @parameterized.expand(TEST_CASES_3D)\n    def test_correct_results(self, im_type, angle, keep_size, mode, padding_mode, align_corners):\n        rotate_fn = Rotate([angle, 0, 0], keep_size, mode, padding_mode, align_corners)\n        rotated = rotate_fn(im_type(self.imt[0]))\n        if keep_size:\n            np.testing.assert_allclose(self.imt[0].shape, rotated.shape)\n        _order = 0 if mode == \"nearest\" else 1\n        if padding_mode == \"border\":\n            _mode = \"nearest\"\n        elif padding_mode == \"reflection\":\n            _mode = \"reflect\"\n        else:\n            _mode = \"constant\"\n\n        expected = []\n        for channel in self.imt[0]:\n            expected.append(\n                scipy.ndimage.rotate(\n                    channel, -np.rad2deg(angle), (1, 2), not keep_size, order=_order, mode=_mode, prefilter=False\n                )\n            )\n        expected = np.stack(expected).astype(np.float32)\n        rotated = rotated.cpu() if isinstance(rotated, torch.Tensor) else rotated\n        n_good = np.sum(np.isclose(expected, rotated, atol=1e-3))\n        self.assertLessEqual(expected.size - n_good, 5, \"diff at most 5 pixels\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_saliency_inferer.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_saliency_inferer.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_saliency_inferer.py", "file_name": "test_saliency_inferer.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 53, "span_ids": ["TestSaliencyInferer", "TestSaliencyInferer.test_shape", "impl:7", "docstring"], "tokens": 358}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.inferers import SaliencyInferer\nfrom monai.networks.nets import DenseNet\nfrom monai.visualize.visualizer import default_upsampler\n\nTEST_CASE_1 = [\"CAM\"]\n\nTEST_CASE_2 = [\"GradCAM\"]\n\nTEST_CASE_3 = [\"GradCAMpp\"]\n\n\nclass TestSaliencyInferer(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3])\n    def test_shape(self, cam_name):\n        model = DenseNet(\n            spatial_dims=3, in_channels=1, out_channels=3, init_features=2, growth_rate=2, block_config=(6,)\n        )\n        device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n        model.to(device)\n        model.eval()\n\n        image = torch.rand((2, 1, 6, 6, 6), device=device)\n        target_layer = \"class_layers.relu\"\n        fc_layer = \"class_layers.out\"\n        if cam_name == \"CAM\":\n            inferer = SaliencyInferer(cam_name, target_layer, None, fc_layer, upsampler=default_upsampler)\n            result = inferer(inputs=image, network=model, layer_idx=-1)\n        else:\n            inferer = SaliencyInferer(cam_name, target_layer, None, upsampler=default_upsampler)\n            result = inferer(image, model, -1, retain_graph=False)\n\n        self.assertTupleEqual(result.shape, (2, 1, 6, 6, 6))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_save_image.py_TestSaveImage_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_save_image.py_TestSaveImage_", "embedding": null, "metadata": {"file_path": "tests/test_save_image.py", "file_name": "test_save_image.py", "file_type": "text/x-python", "category": "test", "start_line": 26, "end_line": 45, "span_ids": ["TestSaveImage", "TestSaveImage.test_saved_content", "impl:5"], "tokens": 152}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestSaveImage(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2])\n    def test_saved_content(self, test_data, meta_data, output_ext, resample):\n        with tempfile.TemporaryDirectory() as tempdir:\n            trans = SaveImage(\n                output_dir=tempdir,\n                output_ext=output_ext,\n                resample=resample,\n                # test saving into the same folder\n                separate_folder=False,\n            )\n            trans(test_data, meta_data)\n\n            filepath = \"testfile0\" if meta_data is not None else \"0\"\n            self.assertTrue(os.path.exists(os.path.join(tempdir, filepath + \"_trans\" + output_ext)))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_save_imaged.py_TestSaveImaged_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_save_imaged.py_TestSaveImaged_", "embedding": null, "metadata": {"file_path": "tests/test_save_imaged.py", "file_name": "test_save_imaged.py", "file_type": "text/x-python", "category": "test", "start_line": 38, "end_line": 60, "span_ids": ["impl:5", "TestSaveImaged.test_saved_content", "TestSaveImaged"], "tokens": 194}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestSaveImaged(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2])\n    def test_saved_content(self, test_data, output_ext, resample):\n        with tempfile.TemporaryDirectory() as tempdir:\n            trans = SaveImaged(\n                keys=[\"img\", \"pred\"],\n                meta_keys=\"img_meta_dict\",\n                output_dir=tempdir,\n                output_ext=output_ext,\n                resample=resample,\n                allow_missing_keys=True,\n            )\n            trans(test_data)\n\n            patch_index = test_data[\"img_meta_dict\"].get(\"patch_index\", None)\n            patch_index = f\"_{patch_index}\" if patch_index is not None else \"\"\n            filepath = os.path.join(\"testfile0\", \"testfile0\" + \"_trans\" + patch_index + output_ext)\n            self.assertTrue(os.path.exists(os.path.join(tempdir, filepath)))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_savitzky_golay_filter.py_unittest_TEST_CASE_SINGLE_VALUE_REP._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_savitzky_golay_filter.py_unittest_TEST_CASE_SINGLE_VALUE_REP._", "embedding": null, "metadata": {"file_path": "tests/test_savitzky_golay_filter.py", "file_name": "test_savitzky_golay_filter.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 63, "span_ids": ["impl:7", "docstring"], "tokens": 682}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks.layers import SavitzkyGolayFilter\nfrom tests.utils import skip_if_no_cuda\n\n# Zero-padding trivial tests\n\nTEST_CASE_SINGLE_VALUE = [\n    {\"window_length\": 3, \"order\": 1},\n    torch.Tensor([1.0]).unsqueeze(0).unsqueeze(0),  # Input data: Single value\n    torch.Tensor([1 / 3]).unsqueeze(0).unsqueeze(0),  # Expected output: With a window length of 3 and polyorder 1\n    # output should be equal to mean of 0, 1 and 0 = 1/3 (because input will be zero-padded and a linear fit performed)\n    1e-15,  # absolute tolerance\n]\n\nTEST_CASE_1D = [\n    {\"window_length\": 3, \"order\": 1},\n    torch.Tensor([1.0, 1.0, 1.0]).unsqueeze(0).unsqueeze(0),  # Input data\n    torch.Tensor([2 / 3, 1.0, 2 / 3])\n    .unsqueeze(0)\n    .unsqueeze(0),  # Expected output: zero padded, so linear interpolation\n    # over length-3 windows will result in output of [2/3, 1, 2/3].\n    1e-15,  # absolute tolerance\n]\n\nTEST_CASE_2D_AXIS_2 = [\n    {\"window_length\": 3, \"order\": 1},  # along default axis (2, first spatial dim)\n    torch.ones((3, 2)).unsqueeze(0).unsqueeze(0),\n    torch.Tensor([[2 / 3, 2 / 3], [1.0, 1.0], [2 / 3, 2 / 3]]).unsqueeze(0).unsqueeze(0),\n    1e-15,  # absolute tolerance\n]\n\nTEST_CASE_2D_AXIS_3 = [\n    {\"window_length\": 3, \"order\": 1, \"axis\": 3},  # along axis 3 (second spatial dim)\n    torch.ones((2, 3)).unsqueeze(0).unsqueeze(0),\n    torch.Tensor([[2 / 3, 1.0, 2 / 3], [2 / 3, 1.0, 2 / 3]]).unsqueeze(0).unsqueeze(0),\n    1e-15,  # absolute tolerance\n]\n\n# Replicated-padding trivial tests\n\nTEST_CASE_SINGLE_VALUE_REP = [\n    {\"window_length\": 3, \"order\": 1, \"mode\": \"replicate\"},\n    torch.Tensor([1.0]).unsqueeze(0).unsqueeze(0),  # Input data: Single value\n    torch.Tensor([1.0]).unsqueeze(0).unsqueeze(0),  # Expected output: With a window length of 3 and polyorder 1\n    # output will be equal to mean of [1, 1, 1] = 1 (input will be nearest-neighbour-padded and a linear fit performed)\n    1e-15,  # absolute tolerance\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_savitzky_golay_filter.py_TEST_CASE_1D_REP_TEST_CASE_SINE_SMOOTH._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_savitzky_golay_filter.py_TEST_CASE_1D_REP_TEST_CASE_SINE_SMOOTH._", "embedding": null, "metadata": {"file_path": "tests/test_savitzky_golay_filter.py", "file_name": "test_savitzky_golay_filter.py", "file_type": "text/x-python", "category": "test", "start_line": 65, "end_line": 97, "span_ids": ["impl:15", "impl:7"], "tokens": 520}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "TEST_CASE_1D_REP = [\n    {\"window_length\": 3, \"order\": 1, \"mode\": \"replicate\"},\n    torch.Tensor([1.0, 1.0, 1.0]).unsqueeze(0).unsqueeze(0),  # Input data\n    torch.Tensor([1.0, 1.0, 1.0]).unsqueeze(0).unsqueeze(0),  # Expected output: zero padded, so linear interpolation\n    # over length-3 windows will result in output of [2/3, 1, 2/3].\n    1e-15,  # absolute tolerance\n]\n\nTEST_CASE_2D_AXIS_2_REP = [\n    {\"window_length\": 3, \"order\": 1, \"mode\": \"replicate\"},  # along default axis (2, first spatial dim)\n    torch.ones((3, 2)).unsqueeze(0).unsqueeze(0),\n    torch.Tensor([[1.0, 1.0], [1.0, 1.0], [1.0, 1.0]]).unsqueeze(0).unsqueeze(0),\n    1e-15,  # absolute tolerance\n]\n\nTEST_CASE_2D_AXIS_3_REP = [\n    {\"window_length\": 3, \"order\": 1, \"axis\": 3, \"mode\": \"replicate\"},  # along axis 3 (second spatial dim)\n    torch.ones((2, 3)).unsqueeze(0).unsqueeze(0),\n    torch.Tensor([[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]).unsqueeze(0).unsqueeze(0),\n    1e-15,  # absolute tolerance\n]\n\n# Sine smoothing\n\nTEST_CASE_SINE_SMOOTH = [\n    {\"window_length\": 3, \"order\": 1},\n    # Sine wave with period equal to savgol window length (windowed to reduce edge effects).\n    torch.as_tensor(np.sin(2 * np.pi * 1 / 3 * np.arange(100)) * np.hanning(100)).unsqueeze(0).unsqueeze(0),\n    # Should be smoothed out to zeros\n    torch.zeros(100).unsqueeze(0).unsqueeze(0),\n    # tolerance chosen by examining output of SciPy.signal.savgol_filter when provided the above input\n    2e-2,  # absolute tolerance\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_savitzky_golay_filter.py_TestSavitzkyGolayCPU_TestSavitzkyGolayCPU.test_value.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_savitzky_golay_filter.py_TestSavitzkyGolayCPU_TestSavitzkyGolayCPU.test_value.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_savitzky_golay_filter.py", "file_name": "test_savitzky_golay_filter.py", "file_type": "text/x-python", "category": "test", "start_line": 100, "end_line": 106, "span_ids": ["TestSavitzkyGolayCPU", "TestSavitzkyGolayCPU.test_value"], "tokens": 105}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestSavitzkyGolayCPU(unittest.TestCase):\n    @parameterized.expand(\n        [TEST_CASE_SINGLE_VALUE, TEST_CASE_1D, TEST_CASE_2D_AXIS_2, TEST_CASE_2D_AXIS_3, TEST_CASE_SINE_SMOOTH]\n    )\n    def test_value(self, arguments, image, expected_data, atol):\n        result = SavitzkyGolayFilter(**arguments)(image)\n        np.testing.assert_allclose(result, expected_data, atol=atol)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_savitzky_golay_filter.py_TestSavitzkyGolayCPUREP_TestSavitzkyGolayCPUREP.test_value.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_savitzky_golay_filter.py_TestSavitzkyGolayCPUREP_TestSavitzkyGolayCPUREP.test_value.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_savitzky_golay_filter.py", "file_name": "test_savitzky_golay_filter.py", "file_type": "text/x-python", "category": "test", "start_line": 115, "end_line": 121, "span_ids": ["TestSavitzkyGolayCPUREP.test_value", "TestSavitzkyGolayCPUREP"], "tokens": 108}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestSavitzkyGolayCPUREP(unittest.TestCase):\n    @parameterized.expand(\n        [TEST_CASE_SINGLE_VALUE_REP, TEST_CASE_1D_REP, TEST_CASE_2D_AXIS_2_REP, TEST_CASE_2D_AXIS_3_REP]\n    )\n    def test_value(self, arguments, image, expected_data, atol):\n        result = SavitzkyGolayFilter(**arguments)(image)\n        np.testing.assert_allclose(result, expected_data, atol=atol)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_savitzky_golay_filter.py_TestSavitzkyGolayGPU_TestSavitzkyGolayGPU.test_value.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_savitzky_golay_filter.py_TestSavitzkyGolayGPU_TestSavitzkyGolayGPU.test_value.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_savitzky_golay_filter.py", "file_name": "test_savitzky_golay_filter.py", "file_type": "text/x-python", "category": "test", "start_line": 118, "end_line": 125, "span_ids": ["TestSavitzkyGolayGPU", "TestSavitzkyGolayGPU.test_value"], "tokens": 116}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_no_cuda\nclass TestSavitzkyGolayGPU(unittest.TestCase):\n    @parameterized.expand(\n        [TEST_CASE_SINGLE_VALUE, TEST_CASE_1D, TEST_CASE_2D_AXIS_2, TEST_CASE_2D_AXIS_3, TEST_CASE_SINE_SMOOTH]\n    )\n    def test_value(self, arguments, image, expected_data, atol):\n        result = SavitzkyGolayFilter(**arguments)(image.to(device=\"cuda\"))\n        np.testing.assert_allclose(result.cpu(), expected_data, atol=atol)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_savitzky_golay_filter.py_TestSavitzkyGolayGPUREP_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_savitzky_golay_filter.py_TestSavitzkyGolayGPUREP_", "embedding": null, "metadata": {"file_path": "tests/test_savitzky_golay_filter.py", "file_name": "test_savitzky_golay_filter.py", "file_type": "text/x-python", "category": "test", "start_line": 128, "end_line": 140, "span_ids": ["TestSavitzkyGolayGPUREP", "impl:19", "TestSavitzkyGolayGPUREP.test_value"], "tokens": 131}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_no_cuda\nclass TestSavitzkyGolayGPUREP(unittest.TestCase):\n    @parameterized.expand(\n        [TEST_CASE_SINGLE_VALUE_REP, TEST_CASE_1D_REP, TEST_CASE_2D_AXIS_2_REP, TEST_CASE_2D_AXIS_3_REP]\n    )\n    def test_value(self, arguments, image, expected_data, atol):\n        result = SavitzkyGolayFilter(**arguments)(image.to(device=\"cuda\"))\n        np.testing.assert_allclose(result.cpu(), expected_data, atol=atol)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_se_blocks.py_unittest_for_type_1_in_relu_r.for_type_2_in_prelu_.TEST_CASES_3D_append_test": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_se_blocks.py_unittest_for_type_1_in_relu_r.for_type_2_in_prelu_.TEST_CASES_3D_append_test", "embedding": null, "metadata": {"file_path": "tests/test_se_blocks.py", "file_name": "test_se_blocks.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 38, "span_ids": ["docstring"], "tokens": 329}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks import eval_mode\nfrom monai.networks.blocks import ChannelSELayer, ResidualSELayer\nfrom tests.utils import test_script_save\n\nTEST_CASES = [  # single channel 3D, batch 16\n    [{\"spatial_dims\": 2, \"in_channels\": 4, \"r\": 3}, (7, 4, 64, 48), (7, 4, 64, 48)],  # 4-channel 2D, batch 7\n    [  # 4-channel 1D, batch 16\n        {\"spatial_dims\": 1, \"in_channels\": 4, \"r\": 3, \"acti_type_1\": \"relu\"},\n        (16, 4, 63),\n        (16, 4, 63),\n    ],\n]\n\nTEST_CASES_3D = []\nfor type_1 in {\"relu\", \"relu6\", \"leakyrelu\"}:\n    for type_2 in {\"prelu\", \"sigmoid\", \"relu\"}:\n        test_case = [\n            {\"spatial_dims\": 3, \"in_channels\": 10, \"r\": 3, \"acti_type_1\": type_1, \"acti_type_2\": type_2},\n            (16, 10, 32, 24, 48),\n            (16, 10, 32, 24, 48),\n        ]\n        TEST_CASES_3D.append(test_case)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_se_blocks.py_TestChannelSELayer_TestChannelSELayer.test_ill_arg.with_self_assertRaises_Va.ChannelSELayer_spatial_di": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_se_blocks.py_TestChannelSELayer_TestChannelSELayer.test_ill_arg.with_self_assertRaises_Va.ChannelSELayer_spatial_di", "embedding": null, "metadata": {"file_path": "tests/test_se_blocks.py", "file_name": "test_se_blocks.py", "file_type": "text/x-python", "category": "test", "start_line": 41, "end_line": 57, "span_ids": ["TestChannelSELayer.test_shape", "TestChannelSELayer.test_ill_arg", "TestChannelSELayer", "TestChannelSELayer.test_script"], "tokens": 155}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestChannelSELayer(unittest.TestCase):\n    @parameterized.expand(TEST_CASES + TEST_CASES_3D)\n    def test_shape(self, input_param, input_shape, expected_shape):\n        net = ChannelSELayer(**input_param)\n        with eval_mode(net):\n            result = net(torch.randn(input_shape))\n            self.assertEqual(result.shape, expected_shape)\n\n    def test_script(self):\n        input_param, input_shape, _ = TEST_CASES[0]\n        net = ChannelSELayer(**input_param)\n        test_data = torch.randn(input_shape)\n        test_script_save(net, test_data)\n\n    def test_ill_arg(self):\n        with self.assertRaises(ValueError):\n            ChannelSELayer(spatial_dims=1, in_channels=4, r=100)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_se_blocks.py_TestResidualSELayer_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_se_blocks.py_TestResidualSELayer_", "embedding": null, "metadata": {"file_path": "tests/test_se_blocks.py", "file_name": "test_se_blocks.py", "file_type": "text/x-python", "category": "test", "start_line": 60, "end_line": 77, "span_ids": ["TestResidualSELayer.test_script", "TestResidualSELayer", "impl:10", "TestResidualSELayer.test_shape"], "tokens": 132}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestResidualSELayer(unittest.TestCase):\n    @parameterized.expand(TEST_CASES[:1])\n    def test_shape(self, input_param, input_shape, expected_shape):\n        net = ResidualSELayer(**input_param)\n        with eval_mode(net):\n            result = net(torch.randn(input_shape))\n            self.assertEqual(result.shape, expected_shape)\n\n    def test_script(self):\n        input_param, input_shape, _ = TEST_CASES[0]\n        net = ResidualSELayer(**input_param)\n        test_data = torch.randn(input_shape)\n        test_script_save(net, test_data)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_segresnet.py_unittest_None_2.for_init_filters_in_8_1.for_out_channels_in_range.for_upsample_mode_in_Upsa.TEST_CASE_SEGRESNET_VAE_a": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_segresnet.py_unittest_None_2.for_init_filters_in_8_1.for_out_channels_in_range.for_upsample_mode_in_Upsa.TEST_CASE_SEGRESNET_VAE_a", "embedding": null, "metadata": {"file_path": "tests/test_segresnet.py", "file_name": "test_segresnet.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 80, "span_ids": ["impl:22", "docstring"], "tokens": 593}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks import eval_mode\nfrom monai.networks.nets import SegResNet, SegResNetVAE\nfrom monai.utils import UpsampleMode\nfrom tests.utils import test_script_save\n\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\nTEST_CASE_SEGRESNET = []\nfor spatial_dims in range(2, 4):\n    for init_filters in [8, 16]:\n        for dropout_prob in [None, 0.2]:\n            for norm in [(\"GROUP\", {\"num_groups\": 8}), (\"batch\", {\"track_running_stats\": False}), \"instance\"]:\n                for upsample_mode in UpsampleMode:\n                    test_case = [\n                        {\n                            \"spatial_dims\": spatial_dims,\n                            \"init_filters\": init_filters,\n                            \"dropout_prob\": dropout_prob,\n                            \"norm\": norm,\n                            \"upsample_mode\": upsample_mode,\n                            \"use_conv_final\": False,\n                        },\n                        (2, 1, *([16] * spatial_dims)),\n                        (2, init_filters, *([16] * spatial_dims)),\n                    ]\n                    TEST_CASE_SEGRESNET.append(test_case)\n\nTEST_CASE_SEGRESNET_2 = []\nfor spatial_dims in range(2, 4):\n    for init_filters in [8, 16]:\n        for out_channels in range(1, 3):\n            for upsample_mode in UpsampleMode:\n                test_case = [\n                    {\n                        \"spatial_dims\": spatial_dims,\n                        \"init_filters\": init_filters,\n                        \"out_channels\": out_channels,\n                        \"upsample_mode\": upsample_mode,\n                    },\n                    (2, 1, *([16] * spatial_dims)),\n                    (2, out_channels, *([16] * spatial_dims)),\n                ]\n                TEST_CASE_SEGRESNET_2.append(test_case)\n\nTEST_CASE_SEGRESNET_VAE = []\nfor spatial_dims in range(2, 4):\n    for init_filters in [8, 16]:\n        for out_channels in range(1, 3):\n            for upsample_mode in UpsampleMode:\n                for vae_estimate_std in [True, False]:\n                    test_case = [\n                        {\n                            \"spatial_dims\": spatial_dims,\n                            \"init_filters\": init_filters,\n                            \"out_channels\": out_channels,\n                            \"upsample_mode\": upsample_mode,\n                            \"act\": (\"leakyrelu\", {\"inplace\": True, \"negative_slope\": 0.01}),\n                            \"input_image_size\": ([16] * spatial_dims),\n                            \"vae_estimate_std\": vae_estimate_std,\n                        },\n                        (2, 1, *([16] * spatial_dims)),\n                        (2, out_channels, *([16] * spatial_dims)),\n                    ]\n                TEST_CASE_SEGRESNET_VAE.append(test_case)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_segresnet.py_TestResNet_TestResNet.test_script.test_script_save_net_tes": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_segresnet.py_TestResNet_TestResNet.test_script.test_script_save_net_tes", "embedding": null, "metadata": {"file_path": "tests/test_segresnet.py", "file_name": "test_segresnet.py", "file_type": "text/x-python", "category": "test", "start_line": 83, "end_line": 99, "span_ids": ["TestResNet", "TestResNet.test_script", "TestResNet.test_ill_arg", "TestResNet.test_shape"], "tokens": 157}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestResNet(unittest.TestCase):\n    @parameterized.expand(TEST_CASE_SEGRESNET + TEST_CASE_SEGRESNET_2)\n    def test_shape(self, input_param, input_shape, expected_shape):\n        net = SegResNet(**input_param).to(device)\n        with eval_mode(net):\n            result = net(torch.randn(input_shape).to(device))\n            self.assertEqual(result.shape, expected_shape)\n\n    def test_ill_arg(self):\n        with self.assertRaises(ValueError):\n            SegResNet(spatial_dims=4)\n\n    def test_script(self):\n        input_param, input_shape, expected_shape = TEST_CASE_SEGRESNET[0]\n        net = SegResNet(**input_param)\n        test_data = torch.randn(input_shape)\n        test_script_save(net, test_data)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_segresnet.py_TestResNetVAE_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_segresnet.py_TestResNetVAE_", "embedding": null, "metadata": {"file_path": "tests/test_segresnet.py", "file_name": "test_segresnet.py", "file_type": "text/x-python", "category": "test", "start_line": 101, "end_line": 118, "span_ids": ["TestResNetVAE.test_script", "TestResNetVAE", "TestResNetVAE.test_vae_shape", "impl:32"], "tokens": 151}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestResNetVAE(unittest.TestCase):\n    @parameterized.expand(TEST_CASE_SEGRESNET_VAE)\n    def test_vae_shape(self, input_param, input_shape, expected_shape):\n        net = SegResNetVAE(**input_param).to(device)\n        with eval_mode(net):\n            result, _ = net(torch.randn(input_shape).to(device))\n            self.assertEqual(result.shape, expected_shape)\n\n    def test_script(self):\n        input_param, input_shape, expected_shape = TEST_CASE_SEGRESNET_VAE[0]\n        net = SegResNetVAE(**input_param)\n        test_data = torch.randn(input_shape)\n        test_script_save(net, test_data)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_select_cross_validation_folds.py_unittest_TEST_CASE_2._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_select_cross_validation_folds.py_unittest_TEST_CASE_2._", "embedding": null, "metadata": {"file_path": "tests/test_select_cross_validation_folds.py", "file_name": "test_select_cross_validation_folds.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 40, "span_ids": ["docstring"], "tokens": 218}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nfrom parameterized import parameterized\n\nfrom monai.data import partition_dataset, select_cross_validation_folds\n\nTEST_CASE_1 = [\n    {\n        \"data\": [1, 2, 3, 4, 5],\n        \"num_partitions\": 5,\n        \"shuffle\": False,\n        \"seed\": 0,\n        \"drop_last\": False,\n        \"even_divisible\": False,\n    },\n    [[1, 3, 4, 5], [2]],\n]\n\nTEST_CASE_2 = [\n    {\n        \"data\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n        \"num_partitions\": 10,\n        \"shuffle\": False,\n        \"seed\": 0,\n        \"drop_last\": False,\n        \"even_divisible\": False,\n    },\n    [[1, 3, 4, 5, 6, 7, 8, 9, 10], [2]],\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_select_cross_validation_folds.py_TestSelectCrossValidationFolds_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_select_cross_validation_folds.py_TestSelectCrossValidationFolds_", "embedding": null, "metadata": {"file_path": "tests/test_select_cross_validation_folds.py", "file_name": "test_select_cross_validation_folds.py", "file_type": "text/x-python", "category": "test", "start_line": 43, "end_line": 55, "span_ids": ["TestSelectCrossValidationFolds.test_value", "TestSelectCrossValidationFolds", "impl:5"], "tokens": 126}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestSelectCrossValidationFolds(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2])\n    def test_value(self, input_param, result):\n        partitions = partition_dataset(**input_param)\n        train = select_cross_validation_folds(partitions=partitions, folds=[0] + list(range(2, len(partitions))))\n        self.assertListEqual(train, result[0])\n        val = select_cross_validation_folds(partitions=partitions, folds=1)\n        self.assertListEqual(val, result[1])\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_select_itemsd.py_sys_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_select_itemsd.py_sys_", "embedding": null, "metadata": {"file_path": "tests/test_select_itemsd.py", "file_name": "test_select_itemsd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 39, "span_ids": ["impl:3", "TestSelectItemsd", "TestSelectItemsd.test_memory", "docstring"], "tokens": 191}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import sys\nimport time\nimport unittest\n\nfrom parameterized import parameterized\n\nfrom monai.transforms import SelectItemsd\n\nTEST_CASE_1 = [{\"keys\": [str(i) for i in range(30)]}, 30]\n\n\nclass TestSelectItemsd(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1])\n    def test_memory(self, input_param, expected_key_size):\n        input_data = {}\n        for i in range(50):\n            input_data[str(i)] = [time.time()] * 100000\n        result = SelectItemsd(**input_param)(input_data)\n        self.assertEqual(len(result.keys()), expected_key_size)\n        self.assertSetEqual(set(result.keys()), set(input_param[\"keys\"]))\n        self.assertGreaterEqual(\n            sys.getsizeof(input_data) * float(expected_key_size) / len(input_data), sys.getsizeof(result)\n        )\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_senet.py_TestSENET_TestSENET.test_script.test_script_save_net_inp": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_senet.py_TestSENET_TestSENET.test_script.test_script_save_net_inp", "embedding": null, "metadata": {"file_path": "tests/test_senet.py", "file_name": "test_senet.py", "file_type": "text/x-python", "category": "test", "start_line": 45, "end_line": 59, "span_ids": ["TestSENET", "TestSENET.test_senet_shape", "TestSENET.test_script"], "tokens": 208}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestSENET(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5, TEST_CASE_6])\n    def test_senet_shape(self, net, net_args):\n        input_data = torch.randn(2, 2, 64, 64, 64).to(device)\n        expected_shape = (2, 2)\n        net = net(**net_args).to(device)\n        with eval_mode(net):\n            result = net(input_data)\n            self.assertEqual(result.shape, expected_shape)\n\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5, TEST_CASE_6])\n    def test_script(self, net, net_args):\n        net = net(**net_args)\n        input_data = torch.randn(2, 2, 64, 64, 64)\n        test_script_save(net, input_data)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_skip_connection.py_unittest_for_type_1_in_cat_ad.TEST_CASES_3D_append_test": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_skip_connection.py_unittest_for_type_1_in_cat_ad.TEST_CASES_3D_append_test", "embedding": null, "metadata": {"file_path": "tests/test_skip_connection.py", "file_name": "test_skip_connection.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 28, "span_ids": ["docstring"], "tokens": 146}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks import eval_mode\nfrom monai.networks.layers import SkipConnection\n\nTEST_CASES_3D = []\nfor type_1 in (\"cat\", \"add\", \"mul\"):\n    input_shape = (16, 10, 32, 24, 48)\n    if type_1 == \"cat\":\n        result_shape = (input_shape[0] * 2, *input_shape[1:])\n    else:\n        result_shape = input_shape\n    test_case = [{\"dim\": 0, \"mode\": type_1}, input_shape, result_shape]\n    TEST_CASES_3D.append(test_case)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_skip_connection.py_TestSkipConnection_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_skip_connection.py_TestSkipConnection_", "embedding": null, "metadata": {"file_path": "tests/test_skip_connection.py", "file_name": "test_skip_connection.py", "file_type": "text/x-python", "category": "test", "start_line": 35, "end_line": 50, "span_ids": ["TestSkipConnection.test_shape", "TestSkipConnection.test_wrong_mode", "impl:15", "TestSkipConnection"], "tokens": 127}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestSkipConnection(unittest.TestCase):\n    @parameterized.expand(TEST_CASES_3D)\n    def test_shape(self, input_param, input_shape, expected_shape):\n        net = SkipConnection(submodule=torch.nn.Softmax(dim=1), **input_param)\n        with eval_mode(net):\n            result = net(torch.randn(input_shape))\n            self.assertEqual(result.shape, expected_shape)\n\n    def test_wrong_mode(self):\n        with self.assertRaises(ValueError):\n            SkipConnection(torch.nn.Softmax(dim=1), mode=\"test\")(torch.randn(10, 10))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_sliding_window_inference.py_TestSlidingWindowInference.test_sw_device_TestSlidingWindowInference.test_sw_device.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_sliding_window_inference.py_TestSlidingWindowInference.test_sw_device_TestSlidingWindowInference.test_sw_device.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_sliding_window_inference.py", "file_name": "test_sliding_window_inference.py", "file_type": "text/x-python", "category": "test", "start_line": 89, "end_line": 102, "span_ids": ["TestSlidingWindowInference.test_sw_device"], "tokens": 179}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestSlidingWindowInference(unittest.TestCase):\n\n    @skip_if_no_cuda\n    def test_sw_device(self):\n        inputs = torch.ones((1, 3, 16, 15, 7)).to(device=\"cpu\")\n        roi_shape = (4, 10, 7)\n        sw_batch_size = 10\n\n        def compute(data):\n            self.assertEqual(data.device.type, \"cuda\")\n            return data + torch.tensor(1, device=\"cuda\")\n\n        result = sliding_window_inference(inputs, roi_shape, sw_batch_size, compute, sw_device=\"cuda\")\n        np.testing.assert_string_equal(inputs.device.type, result.device.type)\n        expected_val = np.ones((1, 3, 16, 15, 7), dtype=np.float32) + 1\n        np.testing.assert_allclose(result.cpu().numpy(), expected_val)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_sliding_window_inference.py_TestSlidingWindowInference.test_cval_TestSlidingWindowInference.test_cval.None_1": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_sliding_window_inference.py_TestSlidingWindowInference.test_cval_TestSlidingWindowInference.test_cval.None_1", "embedding": null, "metadata": {"file_path": "tests/test_sliding_window_inference.py", "file_name": "test_sliding_window_inference.py", "file_type": "text/x-python", "category": "test", "start_line": 183, "end_line": 207, "span_ids": ["TestSlidingWindowInference.test_cval"], "tokens": 244}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestSlidingWindowInference(unittest.TestCase):\n\n    def test_cval(self):\n        device = \"cuda\" if torch.cuda.is_available() else \"cpu:0\"\n        inputs = torch.ones((1, 1, 3, 3)).to(device=device)\n        roi_shape = (5, 5)\n        sw_batch_size = 10\n\n        def compute(data):\n            return data + data.sum()\n\n        result = sliding_window_inference(\n            inputs,\n            roi_shape,\n            sw_batch_size,\n            compute,\n            overlap=0.5,\n            padding_mode=\"constant\",\n            cval=-1,\n            mode=\"constant\",\n            sigma_scale=1.0,\n        )\n        expected = np.ones((1, 1, 3, 3)) * -6.0\n        np.testing.assert_allclose(result.cpu().numpy(), expected, rtol=1e-4)\n\n        result = SlidingWindowInferer(roi_shape, sw_batch_size, overlap=0.5, mode=\"constant\", cval=-1)(inputs, compute)\n        np.testing.assert_allclose(result.cpu().numpy(), expected, rtol=1e-4)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_sliding_window_inference.py_TestSlidingWindowInference.test_args_kwargs_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_sliding_window_inference.py_TestSlidingWindowInference.test_args_kwargs_", "embedding": null, "metadata": {"file_path": "tests/test_sliding_window_inference.py", "file_name": "test_sliding_window_inference.py", "file_type": "text/x-python", "category": "test", "start_line": 209, "end_line": 246, "span_ids": ["TestSlidingWindowInference.test_args_kwargs", "impl:3"], "tokens": 312}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestSlidingWindowInference(unittest.TestCase):\n\n    def test_args_kwargs(self):\n        device = \"cuda\" if torch.cuda.is_available() else \"cpu:0\"\n        inputs = torch.ones((1, 1, 3, 3)).to(device=device)\n        t1 = torch.ones(1).to(device=device)\n        t2 = torch.ones(1).to(device=device)\n        roi_shape = (5, 5)\n        sw_batch_size = 10\n\n        def compute(data, test1, test2):\n            return data + test1 + test2\n\n        result = sliding_window_inference(\n            inputs,\n            roi_shape,\n            sw_batch_size,\n            compute,\n            0.5,\n            \"constant\",\n            1.0,\n            \"constant\",\n            0.0,\n            device,\n            device,\n            t1,\n            test2=t2,\n        )\n        expected = np.ones((1, 1, 3, 3)) + 2.0\n        np.testing.assert_allclose(result.cpu().numpy(), expected, rtol=1e-4)\n\n        result = SlidingWindowInferer(roi_shape, sw_batch_size, overlap=0.5, mode=\"constant\", cval=-1)(\n            inputs, compute, t1, test2=t2\n        )\n        np.testing.assert_allclose(result.cpu().numpy(), expected, rtol=1e-4)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_smartcache_patch_wsi_dataset.py_os_TEST_CASE_0._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_smartcache_patch_wsi_dataset.py_os_TEST_CASE_0._", "embedding": null, "metadata": {"file_path": "tests/test_smartcache_patch_wsi_dataset.py", "file_name": "test_smartcache_patch_wsi_dataset.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 60, "span_ids": ["docstring"], "tokens": 644}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport unittest\nfrom unittest import skipUnless\n\nimport numpy as np\nfrom numpy.testing import assert_array_equal\nfrom parameterized import parameterized\n\nfrom monai.apps.pathology.data import SmartCachePatchWSIDataset\nfrom monai.apps.utils import download_url\nfrom monai.utils import optional_import\n\n_cucim, has_cim = optional_import(\"cucim\")\nhas_cim = has_cim and hasattr(_cucim, \"CuImage\")\n\nFILE_URL = \"https://drive.google.com/uc?id=1sGTKZlJBIz53pfqTxoTqiIQzIoEzHLAe\"\nbase_name, extension = FILE_URL.split(\"id=\")[1], \".tiff\"\nFILE_PATH = os.path.join(os.path.dirname(__file__), \"testing_data\", \"temp_\" + base_name + extension)\n\nTEST_CASE_0 = [\n    {\n        \"data\": [\n            {\"image\": FILE_PATH, \"location\": [0, 0], \"label\": [0]},\n            {\"image\": FILE_PATH, \"location\": [0, 0], \"label\": [1]},\n            {\"image\": FILE_PATH, \"location\": [0, 0], \"label\": [2]},\n            {\"image\": FILE_PATH, \"location\": [0, 0], \"label\": [3]},\n        ],\n        \"region_size\": (1, 1),\n        \"grid_shape\": (1, 1),\n        \"patch_size\": 1,\n        \"transform\": lambda x: x,\n        \"image_reader_name\": \"cuCIM\",\n        \"replace_rate\": 0.5,\n        \"cache_num\": 2,\n        \"num_init_workers\": 1,\n        \"num_replace_workers\": 1,\n        \"copy_cache\": False,\n    },\n    [\n        {\"image\": np.array([[[239]], [[239]], [[239]]], dtype=np.uint8), \"label\": np.array([[[0]]])},\n        {\"image\": np.array([[[239]], [[239]], [[239]]], dtype=np.uint8), \"label\": np.array([[[1]]])},\n        {\"image\": np.array([[[239]], [[239]], [[239]]], dtype=np.uint8), \"label\": np.array([[[1]]])},\n        {\"image\": np.array([[[239]], [[239]], [[239]]], dtype=np.uint8), \"label\": np.array([[[2]]])},\n        {\"image\": np.array([[[239]], [[239]], [[239]]], dtype=np.uint8), \"label\": np.array([[[2]]])},\n        {\"image\": np.array([[[239]], [[239]], [[239]]], dtype=np.uint8), \"label\": np.array([[[3]]])},\n        {\"image\": np.array([[[239]], [[239]], [[239]]], dtype=np.uint8), \"label\": np.array([[[3]]])},\n        {\"image\": np.array([[[239]], [[239]], [[239]]], dtype=np.uint8), \"label\": np.array([[[0]]])},\n    ],\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_smartcache_patch_wsi_dataset.py_TEST_CASE_1_TEST_CASE_1._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_smartcache_patch_wsi_dataset.py_TEST_CASE_1_TEST_CASE_1._", "embedding": null, "metadata": {"file_path": "tests/test_smartcache_patch_wsi_dataset.py", "file_name": "test_smartcache_patch_wsi_dataset.py", "file_type": "text/x-python", "category": "test", "start_line": 61, "end_line": 86, "span_ids": ["impl:11"], "tokens": 402}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "TEST_CASE_1 = [\n    {\n        \"data\": [\n            {\"image\": FILE_PATH, \"location\": [0, 0], \"label\": [[0, 0]]},\n            {\"image\": FILE_PATH, \"location\": [0, 0], \"label\": [[1, 1]]},\n            {\"image\": FILE_PATH, \"location\": [0, 0], \"label\": [[2, 2]]},\n        ],\n        \"region_size\": (1, 1),\n        \"grid_shape\": (1, 1),\n        \"patch_size\": 1,\n        \"transform\": lambda x: x,\n        \"image_reader_name\": \"cuCIM\",\n        \"replace_rate\": 0.5,\n        \"cache_num\": 2,\n        \"num_init_workers\": 1,\n        \"num_replace_workers\": 1,\n    },\n    [\n        {\"image\": np.array([[[239]], [[239]], [[239]]], dtype=np.uint8), \"label\": np.array([[[0, 0]]])},\n        {\"image\": np.array([[[239]], [[239]], [[239]]], dtype=np.uint8), \"label\": np.array([[[1, 1]]])},\n        {\"image\": np.array([[[239]], [[239]], [[239]]], dtype=np.uint8), \"label\": np.array([[[1, 1]]])},\n        {\"image\": np.array([[[239]], [[239]], [[239]]], dtype=np.uint8), \"label\": np.array([[[2, 2]]])},\n        {\"image\": np.array([[[239]], [[239]], [[239]]], dtype=np.uint8), \"label\": np.array([[[2, 2]]])},\n        {\"image\": np.array([[[239]], [[239]], [[239]]], dtype=np.uint8), \"label\": np.array([[[0, 0]]])},\n    ],\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_smartcache_patch_wsi_dataset.py_TEST_CASE_2_TEST_CASE_2._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_smartcache_patch_wsi_dataset.py_TEST_CASE_2_TEST_CASE_2._", "embedding": null, "metadata": {"file_path": "tests/test_smartcache_patch_wsi_dataset.py", "file_name": "test_smartcache_patch_wsi_dataset.py", "file_type": "text/x-python", "category": "test", "start_line": 88, "end_line": 131, "span_ids": ["impl:11"], "tokens": 999}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "TEST_CASE_2 = [\n    {\n        \"data\": [\n            {\"image\": FILE_PATH, \"location\": [10004, 20004], \"label\": [0, 0, 0, 0]},\n            {\"image\": FILE_PATH, \"location\": [10004, 20004], \"label\": [1, 1, 1, 1]},\n            {\"image\": FILE_PATH, \"location\": [10004, 20004], \"label\": [2, 2, 2, 2]},\n        ],\n        \"region_size\": (8, 8),\n        \"grid_shape\": (2, 2),\n        \"patch_size\": 1,\n        \"transform\": lambda x: x,\n        \"image_reader_name\": \"cuCIM\",\n        \"replace_rate\": 0.5,\n        \"cache_num\": 2,\n        \"num_init_workers\": 1,\n        \"num_replace_workers\": 1,\n    },\n    [\n        {\"image\": np.array([[[247]], [[245]], [[248]]], dtype=np.uint8), \"label\": np.array([[[0]]])},\n        {\"image\": np.array([[[245]], [[247]], [[244]]], dtype=np.uint8), \"label\": np.array([[[0]]])},\n        {\"image\": np.array([[[246]], [[246]], [[246]]], dtype=np.uint8), \"label\": np.array([[[0]]])},\n        {\"image\": np.array([[[246]], [[246]], [[246]]], dtype=np.uint8), \"label\": np.array([[[0]]])},\n        {\"image\": np.array([[[247]], [[245]], [[248]]], dtype=np.uint8), \"label\": np.array([[[1]]])},\n        {\"image\": np.array([[[245]], [[247]], [[244]]], dtype=np.uint8), \"label\": np.array([[[1]]])},\n        {\"image\": np.array([[[246]], [[246]], [[246]]], dtype=np.uint8), \"label\": np.array([[[1]]])},\n        {\"image\": np.array([[[246]], [[246]], [[246]]], dtype=np.uint8), \"label\": np.array([[[1]]])},\n        {\"image\": np.array([[[247]], [[245]], [[248]]], dtype=np.uint8), \"label\": np.array([[[1]]])},\n        {\"image\": np.array([[[245]], [[247]], [[244]]], dtype=np.uint8), \"label\": np.array([[[1]]])},\n        {\"image\": np.array([[[246]], [[246]], [[246]]], dtype=np.uint8), \"label\": np.array([[[1]]])},\n        {\"image\": np.array([[[246]], [[246]], [[246]]], dtype=np.uint8), \"label\": np.array([[[1]]])},\n        {\"image\": np.array([[[247]], [[245]], [[248]]], dtype=np.uint8), \"label\": np.array([[[2]]])},\n        {\"image\": np.array([[[245]], [[247]], [[244]]], dtype=np.uint8), \"label\": np.array([[[2]]])},\n        {\"image\": np.array([[[246]], [[246]], [[246]]], dtype=np.uint8), \"label\": np.array([[[2]]])},\n        {\"image\": np.array([[[246]], [[246]], [[246]]], dtype=np.uint8), \"label\": np.array([[[2]]])},\n        {\"image\": np.array([[[247]], [[245]], [[248]]], dtype=np.uint8), \"label\": np.array([[[2]]])},\n        {\"image\": np.array([[[245]], [[247]], [[244]]], dtype=np.uint8), \"label\": np.array([[[2]]])},\n        {\"image\": np.array([[[246]], [[246]], [[246]]], dtype=np.uint8), \"label\": np.array([[[2]]])},\n        {\"image\": np.array([[[246]], [[246]], [[246]]], dtype=np.uint8), \"label\": np.array([[[2]]])},\n        {\"image\": np.array([[[247]], [[245]], [[248]]], dtype=np.uint8), \"label\": np.array([[[0]]])},\n        {\"image\": np.array([[[245]], [[247]], [[244]]], dtype=np.uint8), \"label\": np.array([[[0]]])},\n        {\"image\": np.array([[[246]], [[246]], [[246]]], dtype=np.uint8), \"label\": np.array([[[0]]])},\n        {\"image\": np.array([[[246]], [[246]], [[246]]], dtype=np.uint8), \"label\": np.array([[[0]]])},\n    ],\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_smartcache_patch_wsi_dataset.py_TestSmartCachePatchWSIDataset_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_smartcache_patch_wsi_dataset.py_TestSmartCachePatchWSIDataset_", "embedding": null, "metadata": {"file_path": "tests/test_smartcache_patch_wsi_dataset.py", "file_name": "test_smartcache_patch_wsi_dataset.py", "file_type": "text/x-python", "category": "test", "start_line": 134, "end_line": 169, "span_ids": ["TestSmartCachePatchWSIDataset", "TestSmartCachePatchWSIDataset.assert_samples_expected", "TestSmartCachePatchWSIDataset.setUp", "TestSmartCachePatchWSIDataset.test_read_patches", "impl:15"], "tokens": 330}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestSmartCachePatchWSIDataset(unittest.TestCase):\n    def setUp(self):\n        download_url(FILE_URL, FILE_PATH, \"5a3cfd4fd725c50578ddb80b517b759f\")\n\n    @parameterized.expand([TEST_CASE_0, TEST_CASE_1, TEST_CASE_2])\n    @skipUnless(has_cim, \"Requires CuCIM\")\n    def test_read_patches(self, input_parameters, expected):\n        dataset = SmartCachePatchWSIDataset(**input_parameters)\n        self.assertEqual(len(dataset), input_parameters[\"cache_num\"])\n        total_num_samples = len(input_parameters[\"data\"])\n        num_epochs = int(\n            np.ceil(total_num_samples / (input_parameters[\"cache_num\"] * input_parameters[\"replace_rate\"]))\n        )\n\n        dataset.start()\n        i = 0\n        for _ in range(num_epochs):\n            for j in range(len(dataset)):\n                samples = dataset[j]\n                n_patches = len(samples)\n                self.assert_samples_expected(samples, expected[i : i + n_patches])\n                i += n_patches\n            dataset.update_cache()\n        dataset.shutdown()\n\n    def assert_samples_expected(self, samples, expected):\n        for i in range(len(samples)):\n            self.assertTupleEqual(samples[i][\"label\"].shape, expected[i][\"label\"].shape)\n            self.assertTupleEqual(samples[i][\"image\"].shape, expected[i][\"image\"].shape)\n            self.assertIsNone(assert_array_equal(samples[i][\"label\"], expected[i][\"label\"]))\n            self.assertIsNone(assert_array_equal(samples[i][\"image\"], expected[i][\"image\"]))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_smartcachedataset.py_TestSmartCacheDataset_TestSmartCacheDataset.test_shape.with_tempfile_TemporaryDi.for___in_range_2_.dataset_shutdown_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_smartcachedataset.py_TestSmartCacheDataset_TestSmartCacheDataset.test_shape.with_tempfile_TemporaryDi.for___in_range_2_.dataset_shutdown_", "embedding": null, "metadata": {"file_path": "tests/test_smartcachedataset.py", "file_name": "test_smartcachedataset.py", "file_type": "text/x-python", "category": "test", "start_line": 34, "end_line": 71, "span_ids": ["TestSmartCacheDataset.test_shape", "TestSmartCacheDataset"], "tokens": 380}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestSmartCacheDataset(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5])\n    def test_shape(self, replace_rate, num_replace_workers, transform):\n        test_image = nib.Nifti1Image(np.random.randint(0, 2, size=[8, 8, 8]), np.eye(4))\n        with tempfile.TemporaryDirectory() as tempdir:\n            nib.save(test_image, os.path.join(tempdir, \"test_image1.nii.gz\"))\n            nib.save(test_image, os.path.join(tempdir, \"test_label1.nii.gz\"))\n            nib.save(test_image, os.path.join(tempdir, \"test_extra1.nii.gz\"))\n            test_data = [\n                {\n                    \"image\": os.path.join(tempdir, \"test_image1.nii.gz\"),\n                    \"label\": os.path.join(tempdir, \"test_label1.nii.gz\"),\n                    \"extra\": os.path.join(tempdir, \"test_extra1.nii.gz\"),\n                }\n            ] * 20\n            dataset = SmartCacheDataset(\n                data=test_data,\n                transform=transform,\n                replace_rate=replace_rate,\n                cache_num=16,\n                num_init_workers=4,\n                num_replace_workers=num_replace_workers,\n            )\n\n            self.assertEqual(len(dataset._cache), dataset.cache_num)\n            for i in range(dataset.cache_num):\n                self.assertIsNotNone(dataset._cache[i])\n\n            for _ in range(2):\n                dataset.start()\n                for _ in range(3):\n                    dataset.update_cache()\n                    self.assertIsNotNone(dataset[15])\n                    if isinstance(dataset[15][\"image\"], np.ndarray):\n                        np.testing.assert_allclose(dataset[15][\"image\"], dataset[15][\"label\"])\n                    else:\n                        self.assertIsInstance(dataset[15][\"image\"], str)\n                dataset.shutdown()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_spatial_crop.py_TestSpatialCrop_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_spatial_crop.py_TestSpatialCrop_", "embedding": null, "metadata": {"file_path": "tests/test_spatial_crop.py", "file_name": "test_spatial_crop.py", "file_type": "text/x-python", "category": "test", "start_line": 34, "end_line": 62, "span_ids": ["TestSpatialCrop.test_shape", "TestSpatialCrop", "impl:5", "TestSpatialCrop.test_error"], "tokens": 240}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestSpatialCrop(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_shape(self, input_param, input_shape, expected_shape):\n        input_data = np.random.randint(0, 2, size=input_shape)\n        results = []\n        for p in TEST_NDARRAYS:\n            for q in TEST_NDARRAYS + (None,):\n                input_param_mod = {\n                    k: q(v) if k != \"roi_slices\" and q is not None else v for k, v in input_param.items()\n                }\n                im = p(input_data)\n                result = SpatialCrop(**input_param_mod)(im)\n                self.assertEqual(type(im), type(result))\n                if isinstance(result, torch.Tensor):\n                    self.assertEqual(result.device, im.device)\n                self.assertTupleEqual(result.shape, expected_shape)\n                results.append(result)\n                if len(results) > 1:\n                    assert_allclose(results[0], results[-1], type_test=False)\n\n    @parameterized.expand(TEST_ERRORS)\n    def test_error(self, input_param):\n        with self.assertRaises(ValueError):\n            SpatialCrop(**input_param)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_state_cacher.py_TestStateCacher_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_state_cacher.py_TestStateCacher_", "embedding": null, "metadata": {"file_path": "tests/test_state_cacher.py", "file_name": "test_state_cacher.py", "file_type": "text/x-python", "category": "test", "start_line": 36, "end_line": 66, "span_ids": ["TestStateCacher", "impl:13", "TestStateCacher.test_state_cacher"], "tokens": 245}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestStateCacher(unittest.TestCase):\n    @parameterized.expand(TEST_CASES)\n    def test_state_cacher(self, data_obj, params):\n\n        key = \"data_obj\"\n\n        state_cacher = StateCacher(**params)\n        # store it\n        state_cacher.store(key, data_obj, pickle_module=pickle)\n        # create clone then modify original\n        data_obj_orig = data_obj.clone()\n        data_obj += 1\n        # Restore and check nothing has changed\n        data_obj_restored = state_cacher.retrieve(key)\n        self.assertEqual(data_obj_orig, data_obj_restored)\n\n        # If not allow overwrite, check an attempt would raise exception\n        if \"allow_overwrite\" in params and params[\"allow_overwrite\"]:\n            with self.assertRaises(RuntimeError):\n                state_cacher.store(key, data_obj)\n\n        # If using a cache dir, check file has been deleted et end\n        if \"cache_dir\" in params:\n            i = id(state_cacher)\n            del state_cacher\n            self.assertFalse(exists(join(params[\"cache_dir\"], f\"state_{key}_{i}.pt\")))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_std_shift_intensity.py_TestStdShiftIntensity.test_channel_wise_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_std_shift_intensity.py_TestStdShiftIntensity.test_channel_wise_", "embedding": null, "metadata": {"file_path": "tests/test_std_shift_intensity.py", "file_name": "test_std_shift_intensity.py", "file_type": "text/x-python", "category": "test", "start_line": 52, "end_line": 75, "span_ids": ["TestStdShiftIntensity.test_dtype", "TestStdShiftIntensity.test_channel_wise", "impl"], "tokens": 277}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestStdShiftIntensity(NumpyImageTestCase2D):\n\n    def test_channel_wise(self):\n        for p in TEST_NDARRAYS:\n            image = p(np.stack((np.asarray([1.0, 2.0]), np.asarray([1.0, 1.0]))))  # std: 0.5, 0\n            factor = np.random.rand()\n            std_shifter = StdShiftIntensity(factor=factor, channel_wise=True)\n            result = std_shifter(image)\n            expected = p(\n                np.stack((np.asarray([1 + 0.5 * factor, 2 + 0.5 * factor]), np.asarray([1, 1]))).astype(np.float32)\n            )\n            torch.testing.assert_allclose(result, expected, atol=0, rtol=1e-5)\n\n    def test_dtype(self):\n        trans_dtype = np.float32\n        for dtype in [int, np.float32, np.float64]:\n            image = np.random.rand(2, 2, 2).astype(dtype)\n            factor = np.random.rand()\n            std_shifter = StdShiftIntensity(factor=factor, dtype=trans_dtype)\n            result = std_shifter(image)\n            np.testing.assert_equal(result.dtype, trans_dtype)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_std_shift_intensityd.py_unittest_TestStdShiftIntensityd.test_zerostd.for_nonzero_in_True_Fal.for_channel_wise_in_True.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_std_shift_intensityd.py_unittest_TestStdShiftIntensityd.test_zerostd.for_nonzero_in_True_Fal.for_channel_wise_in_True.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_std_shift_intensityd.py", "file_name": "test_std_shift_intensityd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 39, "span_ids": ["TestStdShiftIntensityd.test_zerostd", "TestStdShiftIntensityd", "TestStdShiftIntensityd.test_value", "docstring"], "tokens": 270}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\n\nfrom monai.transforms import ShiftIntensityd, StdShiftIntensityd\nfrom tests.utils import NumpyImageTestCase2D\n\n\nclass TestStdShiftIntensityd(NumpyImageTestCase2D):\n    def test_value(self):\n        key = \"img\"\n        factor = np.random.rand()\n        offset = np.std(self.imt) * factor\n        shifter = ShiftIntensityd(keys=[key], offset=offset)\n        expected = shifter({key: self.imt})\n        std_shifter = StdShiftIntensityd(keys=[key], factor=factor)\n        result = std_shifter({key: self.imt})\n        np.testing.assert_allclose(result[key], expected[key], rtol=1e-5)\n\n    def test_zerostd(self):\n        key = \"img\"\n        image = np.ones([2, 3, 3])\n        for nonzero in [True, False]:\n            for channel_wise in [True, False]:\n                factor = np.random.rand()\n                std_shifter = StdShiftIntensityd(keys=[key], factor=factor, nonzero=nonzero, channel_wise=channel_wise)\n                result = std_shifter({key: image})\n                np.testing.assert_allclose(result[key], image, rtol=1e-5)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_std_shift_intensityd.py_TestStdShiftIntensityd.test_nonzero_TestStdShiftIntensityd.test_nonzero.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_std_shift_intensityd.py_TestStdShiftIntensityd.test_nonzero_TestStdShiftIntensityd.test_nonzero.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_std_shift_intensityd.py", "file_name": "test_std_shift_intensityd.py", "file_type": "text/x-python", "category": "test", "start_line": 41, "end_line": 48, "span_ids": ["TestStdShiftIntensityd.test_nonzero"], "tokens": 150}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestStdShiftIntensityd(NumpyImageTestCase2D):\n\n    def test_nonzero(self):\n        key = \"img\"\n        image = np.asarray([[4.0, 0.0, 2.0], [0, 2, 4]])  # std = 1\n        factor = np.random.rand()\n        std_shifter = StdShiftIntensityd(keys=[key], factor=factor, nonzero=True)\n        result = std_shifter({key: image})\n        expected = np.asarray([[4 + factor, 0, 2 + factor], [0, 2 + factor, 4 + factor]])\n        np.testing.assert_allclose(result[key], expected, rtol=1e-5)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_std_shift_intensityd.py_TestStdShiftIntensityd.test_channel_wise_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_std_shift_intensityd.py_TestStdShiftIntensityd.test_channel_wise_", "embedding": null, "metadata": {"file_path": "tests/test_std_shift_intensityd.py", "file_name": "test_std_shift_intensityd.py", "file_type": "text/x-python", "category": "test", "start_line": 50, "end_line": 72, "span_ids": ["TestStdShiftIntensityd.test_channel_wise", "TestStdShiftIntensityd.test_dtype", "impl"], "tokens": 282}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestStdShiftIntensityd(NumpyImageTestCase2D):\n\n    def test_channel_wise(self):\n        key = \"img\"\n        image = np.stack((np.asarray([1.0, 2.0]), np.asarray([1.0, 1.0])))  # std: 0.5, 0\n        factor = np.random.rand()\n        std_shifter = StdShiftIntensityd(keys=[key], factor=factor, channel_wise=True)\n        result = std_shifter({key: image})\n        expected = np.stack((np.asarray([1 + 0.5 * factor, 2 + 0.5 * factor]), np.asarray([1, 1])))\n        np.testing.assert_allclose(result[key], expected, rtol=1e-5)\n\n    def test_dtype(self):\n        key = \"img\"\n        trans_dtype = np.float32\n        for dtype in [int, np.float32, np.float64]:\n            image = np.random.rand(2, 2, 2).astype(dtype)\n            factor = np.random.rand()\n            std_shifter = StdShiftIntensityd(keys=[key], factor=factor, dtype=trans_dtype)\n            result = std_shifter({key: image})\n            np.testing.assert_equal(result[key].dtype, trans_dtype)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_surface_distance.py_TEST_CASES_TEST_CASES_NANS._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_surface_distance.py_TEST_CASES_TEST_CASES_NANS._", "embedding": null, "metadata": {"file_path": "tests/test_surface_distance.py", "file_name": "test_surface_distance.py", "file_type": "text/x-python", "category": "test", "start_line": 49, "end_line": 101, "span_ids": ["impl"], "tokens": 543}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "TEST_CASES = [\n    [[create_spherical_seg_3d(), create_spherical_seg_3d()], [0, 0]],\n    [\n        [\n            create_spherical_seg_3d(radius=20, centre=(20, 20, 20)),\n            create_spherical_seg_3d(radius=20, centre=(19, 19, 19)),\n            \"taxicab\",\n        ],\n        [1.0380029806259314, 1.0380029806259314],\n    ],\n    [\n        [\n            create_spherical_seg_3d(radius=33, centre=(19, 33, 22)),\n            create_spherical_seg_3d(radius=33, centre=(20, 33, 22)),\n        ],\n        [0.35021200688332677, 0.3483278807706289],\n    ],\n    [\n        [\n            create_spherical_seg_3d(radius=20, centre=(20, 33, 22)),\n            create_spherical_seg_3d(radius=40, centre=(20, 33, 22)),\n        ],\n        [13.975673696300824, 12.040033513150455],\n    ],\n    [\n        [\n            create_spherical_seg_3d(radius=20, centre=(20, 33, 22)),\n            create_spherical_seg_3d(radius=40, centre=(20, 33, 22)),\n            \"chessboard\",\n        ],\n        [10.792254295459173, 9.605067064083457],\n    ],\n    [\n        [\n            create_spherical_seg_3d(radius=20, centre=(20, 33, 22)),\n            create_spherical_seg_3d(radius=40, centre=(20, 33, 22)),\n            \"taxicab\",\n        ],\n        [17.32691760951026, 12.432687531048186],\n    ],\n    [[np.zeros([99, 99, 99]), create_spherical_seg_3d(radius=40, centre=(20, 33, 22))], [np.inf, np.inf]],\n    [[create_spherical_seg_3d(), np.zeros([99, 99, 99]), \"taxicab\"], [np.inf, np.inf]],\n]\n\nTEST_CASES_NANS = [\n    [\n        [\n            # both pred and gt do not have foreground, metric and not_nans should be 0\n            np.zeros([99, 99, 99]),\n            np.zeros([99, 99, 99]),\n        ]\n    ]\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_surface_distance.py_TestAllSurfaceMetrics_TestAllSurfaceMetrics.test_value.for_symmetric_in_True_F.ct_1": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_surface_distance.py_TestAllSurfaceMetrics_TestAllSurfaceMetrics.test_value.for_symmetric_in_True_F.ct_1", "embedding": null, "metadata": {"file_path": "tests/test_surface_distance.py", "file_name": "test_surface_distance.py", "file_type": "text/x-python", "category": "test", "start_line": 122, "end_line": 143, "span_ids": ["TestAllSurfaceMetrics", "TestAllSurfaceMetrics.test_value"], "tokens": 291}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestAllSurfaceMetrics(unittest.TestCase):\n    @parameterized.expand(TEST_CASES)\n    def test_value(self, input_data, expected_value):\n        if len(input_data) == 3:\n            [seg_1, seg_2, metric] = input_data\n        else:\n            [seg_1, seg_2] = input_data\n            metric = \"euclidean\"\n        ct = 0\n        seg_1 = torch.tensor(seg_1)\n        seg_2 = torch.tensor(seg_2)\n        for symmetric in [True, False]:\n            sur_metric = SurfaceDistanceMetric(include_background=False, symmetric=symmetric, distance_metric=metric)\n            # shape of seg_1, seg_2 are: HWD, converts to BNHWD\n            batch, n_class = 2, 3\n            batch_seg_1 = seg_1.unsqueeze(0).unsqueeze(0).repeat([batch, n_class, 1, 1, 1])\n            batch_seg_2 = seg_2.unsqueeze(0).unsqueeze(0).repeat([batch, n_class, 1, 1, 1])\n            sur_metric(batch_seg_1, batch_seg_2)\n            result = sur_metric.aggregate()\n            expected_value_curr = expected_value[ct]\n            np.testing.assert_allclose(expected_value_curr, result, rtol=1e-7)\n            ct += 1", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_surface_distance.py_TestAllSurfaceMetrics.test_nans_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_surface_distance.py_TestAllSurfaceMetrics.test_nans_", "embedding": null, "metadata": {"file_path": "tests/test_surface_distance.py", "file_name": "test_surface_distance.py", "file_type": "text/x-python", "category": "test", "start_line": 145, "end_line": 162, "span_ids": ["TestAllSurfaceMetrics.test_nans", "impl:5"], "tokens": 195}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestAllSurfaceMetrics(unittest.TestCase):\n\n    @parameterized.expand(TEST_CASES_NANS)\n    def test_nans(self, input_data):\n        [seg_1, seg_2] = input_data\n        seg_1 = torch.tensor(seg_1)\n        seg_2 = torch.tensor(seg_2)\n        sur_metric = SurfaceDistanceMetric(include_background=False, get_not_nans=True)\n        # test list of channel-first Tensor\n        batch_seg_1 = [seg_1.unsqueeze(0)]\n        batch_seg_2 = [seg_2.unsqueeze(0)]\n        sur_metric(batch_seg_1, batch_seg_2)\n        result, not_nans = sur_metric.aggregate()\n        np.testing.assert_allclose(0, result, rtol=1e-7)\n        np.testing.assert_allclose(0, not_nans, rtol=1e-7)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_testtimeaugmentation.py_unittest_trange.partial_tqdm_trange_desc": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_testtimeaugmentation.py_unittest_trange.partial_tqdm_trange_desc", "embedding": null, "metadata": {"file_path": "tests/test_testtimeaugmentation.py", "file_name": "test_testtimeaugmentation.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 48, "span_ids": ["docstring"], "tokens": 260}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom functools import partial\nfrom typing import TYPE_CHECKING\n\nimport numpy as np\nimport torch\n\nfrom monai.data import CacheDataset, DataLoader, create_test_image_2d\nfrom monai.data.test_time_augmentation import TestTimeAugmentation\nfrom monai.data.utils import pad_list_data_collate\nfrom monai.losses import DiceLoss\nfrom monai.networks.nets import UNet\nfrom monai.transforms import (\n    Activations,\n    AddChanneld,\n    AsDiscrete,\n    Compose,\n    CropForegroundd,\n    DivisiblePadd,\n    RandAffined,\n    RandScaleIntensityd,\n)\nfrom monai.transforms.croppad.dictionary import SpatialPadd\nfrom monai.transforms.spatial.dictionary import RandFlipd, Spacingd\nfrom monai.utils import optional_import, set_determinism\nfrom tests.utils import TEST_NDARRAYS\n\nif TYPE_CHECKING:\n    import tqdm\n\n    has_tqdm = True\n    has_nib = True\nelse:\n    tqdm, has_tqdm = optional_import(\"tqdm\")\n    _, has_nib = optional_import(\"nibabel\")\n\ntrange = partial(tqdm.trange, desc=\"training\") if has_tqdm else range", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_testtimeaugmentation.py_TestTestTimeAugmentation_TestTestTimeAugmentation.tearDown.set_determinism_None_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_testtimeaugmentation.py_TestTestTimeAugmentation_TestTestTimeAugmentation.tearDown.set_determinism_None_", "embedding": null, "metadata": {"file_path": "tests/test_testtimeaugmentation.py", "file_name": "test_testtimeaugmentation.py", "file_type": "text/x-python", "category": "test", "start_line": 51, "end_line": 73, "span_ids": ["TestTestTimeAugmentation", "TestTestTimeAugmentation.tearDown", "TestTestTimeAugmentation.setUp", "TestTestTimeAugmentation.get_data"], "tokens": 217}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestTestTimeAugmentation(unittest.TestCase):\n    @staticmethod\n    def get_data(num_examples, input_size, data_type=np.asarray, include_label=True):\n        custom_create_test_image_2d = partial(\n            create_test_image_2d, *input_size, rad_max=7, num_seg_classes=1, num_objs=1\n        )\n        data = []\n        for i in range(num_examples):\n            im, label = custom_create_test_image_2d()\n            d = {}\n            d[\"image\"] = data_type(im[:, i:])\n            d[\"image_meta_dict\"] = {\"affine\": np.eye(4)}\n            if include_label:\n                d[\"label\"] = data_type(label[:, i:])\n                d[\"label_meta_dict\"] = {\"affine\": np.eye(4)}\n            data.append(d)\n        return data[0] if num_examples == 1 else data\n\n    def setUp(self) -> None:\n        set_determinism(seed=0)\n\n    def tearDown(self) -> None:\n        set_determinism(None)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_testtimeaugmentation.py_TestTestTimeAugmentation.test_test_time_augmentation_TestTestTimeAugmentation.test_test_time_augmentation.self_assertIsInstance_vvc": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_testtimeaugmentation.py_TestTestTimeAugmentation.test_test_time_augmentation_TestTestTimeAugmentation.test_test_time_augmentation.self_assertIsInstance_vvc", "embedding": null, "metadata": {"file_path": "tests/test_testtimeaugmentation.py", "file_name": "test_testtimeaugmentation.py", "file_type": "text/x-python", "category": "test", "start_line": 75, "end_line": 137, "span_ids": ["TestTestTimeAugmentation.test_test_time_augmentation"], "tokens": 587}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestTestTimeAugmentation(unittest.TestCase):\n\n    def test_test_time_augmentation(self):\n        input_size = (20, 20)\n        device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n        keys = [\"image\", \"label\"]\n        num_training_ims = 10\n        train_data = self.get_data(num_training_ims, input_size)\n        test_data = self.get_data(1, input_size)\n\n        transforms = Compose(\n            [\n                AddChanneld(keys),\n                RandAffined(\n                    keys,\n                    prob=1.0,\n                    spatial_size=(30, 30),\n                    rotate_range=(np.pi / 3, np.pi / 3),\n                    translate_range=(3, 3),\n                    scale_range=((0.8, 1), (0.8, 1)),\n                    padding_mode=\"zeros\",\n                    mode=(\"bilinear\", \"nearest\"),\n                    as_tensor_output=False,\n                ),\n                CropForegroundd(keys, source_key=\"image\"),\n                DivisiblePadd(keys, 4),\n            ]\n        )\n\n        train_ds = CacheDataset(train_data, transforms)\n        # output might be different size, so pad so that they match\n        train_loader = DataLoader(train_ds, batch_size=2, collate_fn=pad_list_data_collate)\n\n        model = UNet(2, 1, 1, channels=(6, 6), strides=(2, 2)).to(device)\n        loss_function = DiceLoss(sigmoid=True)\n        optimizer = torch.optim.Adam(model.parameters(), 1e-3)\n\n        num_epochs = 10\n        for _ in trange(num_epochs):\n            epoch_loss = 0\n\n            for batch_data in train_loader:\n                inputs, labels = batch_data[\"image\"].to(device), batch_data[\"label\"].to(device)\n                optimizer.zero_grad()\n                outputs = model(inputs)\n                loss = loss_function(outputs, labels)\n                loss.backward()\n                optimizer.step()\n                epoch_loss += loss.item()\n\n            epoch_loss /= len(train_loader)\n\n        post_trans = Compose([Activations(sigmoid=True), AsDiscrete(threshold=0.5)])\n\n        def inferrer_fn(x):\n            return post_trans(model(x))\n\n        tt_aug = TestTimeAugmentation(transforms, batch_size=5, num_workers=0, inferrer_fn=inferrer_fn, device=device)\n        mode, mean, std, vvc = tt_aug(test_data)\n        self.assertEqual(mode.shape, (1,) + input_size)\n        self.assertEqual(mean.shape, (1,) + input_size)\n        self.assertTrue(all(np.unique(mode) == (0, 1)))\n        self.assertEqual((mean.min(), mean.max()), (0.0, 1.0))\n        self.assertEqual(std.shape, (1,) + input_size)\n        self.assertIsInstance(vvc, float)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_thread_buffer.py_sys_TestDataLoader.test_values.for_d_in_tbuffer_.None_3": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_thread_buffer.py_sys_TestDataLoader.test_values.for_d_in_tbuffer_.None_3", "embedding": null, "metadata": {"file_path": "tests/test_thread_buffer.py", "file_name": "test_thread_buffer.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 42, "span_ids": ["TestDataLoader.test_values", "TestDataLoader", "TestDataLoader.setUp", "docstring"], "tokens": 278}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import sys\nimport time\nimport unittest\n\nfrom monai.data import DataLoader, Dataset, ThreadBuffer, ThreadDataLoader\nfrom monai.transforms import Compose, SimulateDelayd\nfrom monai.utils import PerfContext\n\n\nclass TestDataLoader(unittest.TestCase):\n    def setUp(self):\n        super().setUp()\n\n        self.datalist = [\n            {\"image\": \"spleen_19.nii.gz\", \"label\": \"spleen_label_19.nii.gz\"},\n            {\"image\": \"spleen_31.nii.gz\", \"label\": \"spleen_label_31.nii.gz\"},\n        ]\n\n        self.transform = Compose([SimulateDelayd(keys=[\"image\", \"label\"], delay_time=0.1)])\n\n    def test_values(self):\n        dataset = Dataset(data=self.datalist, transform=self.transform)\n        dataloader = DataLoader(dataset=dataset, batch_size=2, num_workers=0)\n\n        tbuffer = ThreadBuffer(dataloader)\n\n        for d in tbuffer:\n            self.assertEqual(d[\"image\"][0], \"spleen_19.nii.gz\")\n            self.assertEqual(d[\"image\"][1], \"spleen_31.nii.gz\")\n            self.assertEqual(d[\"label\"][0], \"spleen_label_19.nii.gz\")\n            self.assertEqual(d[\"label\"][1], \"spleen_label_31.nii.gz\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_threadcontainer.py_os_compare_images___optio": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_threadcontainer.py_os_compare_images___optio", "embedding": null, "metadata": {"file_path": "tests/test_threadcontainer.py", "file_name": "test_threadcontainer.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 33, "span_ids": ["docstring"], "tokens": 123}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport tempfile\nimport time\nimport unittest\n\nimport torch\n\nfrom monai.data import DataLoader\nfrom monai.utils import optional_import, set_determinism\nfrom monai.utils.enums import CommonKeys\nfrom tests.utils import SkipIfNoModule\n\ntry:\n    _, has_ignite = optional_import(\"ignite\")\n\n    from monai.engines import SupervisedTrainer\n    from monai.handlers import MetricLogger\n    from monai.utils import ThreadContainer\nexcept ImportError:\n    has_ignite = False\n\ncompare_images, _ = optional_import(\"matplotlib.testing.compare\", name=\"compare_images\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_threadcontainer.py_TestThreadContainer_TestThreadContainer.test_container.con_join_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_threadcontainer.py_TestThreadContainer_TestThreadContainer.test_container.con_join_", "embedding": null, "metadata": {"file_path": "tests/test_threadcontainer.py", "file_name": "test_threadcontainer.py", "file_type": "text/x-python", "category": "test", "start_line": 36, "end_line": 64, "span_ids": ["TestThreadContainer", "TestThreadContainer.test_container"], "tokens": 201}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestThreadContainer(unittest.TestCase):\n    @SkipIfNoModule(\"ignite\")\n    def test_container(self):\n        net = torch.nn.Conv2d(1, 1, 3, padding=1)\n\n        opt = torch.optim.Adam(net.parameters())\n\n        img = torch.rand(1, 16, 16)\n        data = {CommonKeys.IMAGE: img, CommonKeys.LABEL: img}\n        loader = DataLoader([data for _ in range(10)])\n\n        trainer = SupervisedTrainer(\n            device=torch.device(\"cpu\"),\n            max_epochs=1,\n            train_data_loader=loader,\n            network=net,\n            optimizer=opt,\n            loss_function=torch.nn.L1Loss(),\n        )\n\n        con = ThreadContainer(trainer)\n        con.start()\n        time.sleep(1)  # wait for trainer to start\n\n        self.assertTrue(con.is_alive)\n        self.assertIsNotNone(con.status())\n        self.assertTrue(len(con.status_dict) > 0)\n\n        con.join()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_threadcontainer.py_TestThreadContainer.test_plot_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_threadcontainer.py_TestThreadContainer.test_plot_", "embedding": null, "metadata": {"file_path": "tests/test_threadcontainer.py", "file_name": "test_threadcontainer.py", "file_type": "text/x-python", "category": "test", "start_line": 66, "end_line": 112, "span_ids": ["impl:7", "TestThreadContainer.test_plot"], "tokens": 338}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestThreadContainer(unittest.TestCase):\n\n    @SkipIfNoModule(\"ignite\")\n    @SkipIfNoModule(\"matplotlib\")\n    def test_plot(self):\n        set_determinism(0)\n\n        testing_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"testing_data\")\n\n        net = torch.nn.Conv2d(1, 1, 3, padding=1)\n\n        opt = torch.optim.Adam(net.parameters())\n\n        img = torch.rand(1, 16, 16)\n\n        # a third non-image key is added to test that this is correctly ignored when plotting\n        data = {CommonKeys.IMAGE: img, CommonKeys.LABEL: img, \"Not Image Data\": [\"This isn't an image\"]}\n\n        loader = DataLoader([data] * 20, batch_size=2)\n\n        trainer = SupervisedTrainer(\n            device=torch.device(\"cpu\"),\n            max_epochs=1,\n            train_data_loader=loader,\n            network=net,\n            optimizer=opt,\n            loss_function=torch.nn.L1Loss(),\n        )\n\n        logger = MetricLogger()\n        logger.attach(trainer)\n\n        con = ThreadContainer(trainer)\n        con.start()\n        con.join()\n\n        fig = con.plot_status(logger)\n\n        with tempfile.TemporaryDirectory() as tempdir:\n            tempimg = f\"{tempdir}/threadcontainer_plot_test.png\"\n            fig.savefig(tempimg)\n            comp = compare_images(f\"{testing_dir}/threadcontainer_plot_test.png\", tempimg, 5e-2)\n\n            self.assertIsNone(comp, comp)  # None indicates test passed\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_torchvisiond.py_unittest_TEST_CASE_2._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_torchvisiond.py_unittest_TEST_CASE_2._", "embedding": null, "metadata": {"file_path": "tests/test_torchvisiond.py", "file_name": "test_torchvisiond.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 37, "span_ids": ["docstring"], "tokens": 414}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import TorchVisiond\nfrom monai.utils import set_determinism\nfrom tests.utils import SkipIfBeforePyTorchVersion\n\nTEST_CASE_1 = [\n    {\"keys\": \"img\", \"name\": \"ColorJitter\"},\n    {\"img\": torch.tensor([[[0.0, 1.0], [1.0, 2.0]], [[0.0, 1.0], [1.0, 2.0]], [[0.0, 1.0], [1.0, 2.0]]])},\n    torch.tensor([[[0.0, 1.0], [1.0, 2.0]], [[0.0, 1.0], [1.0, 2.0]], [[0.0, 1.0], [1.0, 2.0]]]),\n]\n\nTEST_CASE_2 = [\n    {\"keys\": \"img\", \"name\": \"ColorJitter\", \"brightness\": 0.5, \"contrast\": 0.5, \"saturation\": [0.1, 0.8], \"hue\": 0.5},\n    {\"img\": torch.tensor([[[0.0, 1.0], [1.0, 2.0]], [[0.0, 1.0], [1.0, 2.0]], [[0.0, 1.0], [1.0, 2.0]]])},\n    torch.tensor(\n        [\n            [[0.1090, 0.6193], [0.6193, 0.9164]],\n            [[0.1090, 0.6193], [0.6193, 0.9164]],\n            [[0.1090, 0.6193], [0.6193, 0.9164]],\n        ]\n    ),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_torchvisiond.py_TEST_CASE_3_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_torchvisiond.py_TEST_CASE_3_", "embedding": null, "metadata": {"file_path": "tests/test_torchvisiond.py", "file_name": "test_torchvisiond.py", "file_type": "text/x-python", "category": "test", "start_line": 39, "end_line": 63, "span_ids": ["TestTorchVisiond.test_value", "TestTorchVisiond", "impl:5", "impl:7"], "tokens": 463}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "TEST_CASE_3 = [\n    {\"keys\": \"img\", \"name\": \"Pad\", \"padding\": [1, 1, 1, 1]},\n    {\"img\": torch.tensor([[[0.0, 1.0], [1.0, 2.0]], [[0.0, 1.0], [1.0, 2.0]], [[0.0, 1.0], [1.0, 2.0]]])},\n    torch.tensor(\n        [\n            [[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 2.0, 0.0], [0.0, 0.0, 0.0, 0.0]],\n            [[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 2.0, 0.0], [0.0, 0.0, 0.0, 0.0]],\n            [[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 2.0, 0.0], [0.0, 0.0, 0.0, 0.0]],\n        ]\n    ),\n]\n\n\n@SkipIfBeforePyTorchVersion((1, 7))\nclass TestTorchVisiond(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3])\n    def test_value(self, input_param, input_data, expected_value):\n        set_determinism(seed=0)\n        result = TorchVisiond(**input_param)(input_data)\n        torch.testing.assert_allclose(result[\"img\"], expected_value)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_train_mode.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_train_mode.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_train_mode.py", "file_name": "test_train_mode.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 32, "span_ids": ["TestEvalMode", "TestEvalMode.test_eval_mode", "impl", "docstring"], "tokens": 114}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\n\nfrom monai.networks.utils import train_mode\n\n\nclass TestEvalMode(unittest.TestCase):\n    def test_eval_mode(self):\n        t = torch.rand(1, 1, 4, 4)\n        p = torch.nn.Conv2d(1, 1, 3)\n        p.eval()\n        self.assertFalse(p.training)  # False\n        with train_mode(p):\n            self.assertTrue(p.training)  # True\n            p(t).sum().backward()\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_unet.py_unittest_TEST_CASE_4._4_channel_3D_batch_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_unet.py_unittest_TEST_CASE_4._4_channel_3D_batch_", "embedding": null, "metadata": {"file_path": "tests/test_unet.py", "file_name": "test_unet.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 88, "span_ids": ["impl:9", "docstring"], "tokens": 636}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks import eval_mode\nfrom monai.networks.layers import Act, Norm\nfrom monai.networks.nets import UNet\nfrom tests.utils import test_script_save\n\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\nTEST_CASE_0 = [  # single channel 2D, batch 16, no residual\n    {\n        \"spatial_dims\": 2,\n        \"in_channels\": 1,\n        \"out_channels\": 3,\n        \"channels\": (16, 32, 64),\n        \"strides\": (2, 2),\n        \"num_res_units\": 0,\n    },\n    (16, 1, 32, 32),\n    (16, 3, 32, 32),\n]\n\nTEST_CASE_1 = [  # single channel 2D, batch 16\n    {\n        \"spatial_dims\": 2,\n        \"in_channels\": 1,\n        \"out_channels\": 3,\n        \"channels\": (16, 32, 64),\n        \"strides\": (2, 2),\n        \"num_res_units\": 1,\n    },\n    (16, 1, 32, 32),\n    (16, 3, 32, 32),\n]\n\nTEST_CASE_2 = [  # single channel 3D, batch 16\n    {\n        \"spatial_dims\": 3,\n        \"in_channels\": 1,\n        \"out_channels\": 3,\n        \"channels\": (16, 32, 64),\n        \"strides\": (2, 2),\n        \"num_res_units\": 1,\n    },\n    (16, 1, 32, 24, 48),\n    (16, 3, 32, 24, 48),\n]\n\nTEST_CASE_3 = [  # 4-channel 3D, batch 16\n    {\n        \"spatial_dims\": 3,\n        \"in_channels\": 4,\n        \"out_channels\": 3,\n        \"channels\": (16, 32, 64),\n        \"strides\": (2, 2),\n        \"num_res_units\": 1,\n    },\n    (16, 4, 32, 64, 48),\n    (16, 3, 32, 64, 48),\n]\n\nTEST_CASE_4 = [  # 4-channel 3D, batch 16, batch normalization\n    {\n        \"spatial_dims\": 3,\n        \"in_channels\": 4,\n        \"out_channels\": 3,\n        \"channels\": (16, 32, 64),\n        \"strides\": (2, 2),\n        \"num_res_units\": 1,\n        \"norm\": Norm.BATCH,\n    },\n    (16, 4, 32, 64, 48),\n    (16, 3, 32, 64, 48),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_unet.py_TestUNET_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_unet.py_TestUNET_", "embedding": null, "metadata": {"file_path": "tests/test_unet.py", "file_name": "test_unet.py", "file_type": "text/x-python", "category": "test", "start_line": 164, "end_line": 206, "span_ids": ["TestUNET.test_script", "impl:21", "TestUNET", "TestUNET.test_shape", "TestUNET.test_script_without_running_stats", "TestUNET.test_ill_input_hyper_params", "TestUNET.test_ill_input_shape"], "tokens": 385}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestUNET(unittest.TestCase):\n    @parameterized.expand(CASES)\n    def test_shape(self, input_param, input_shape, expected_shape):\n        net = UNet(**input_param).to(device)\n        with eval_mode(net):\n            result = net.forward(torch.randn(input_shape).to(device))\n            self.assertEqual(result.shape, expected_shape)\n\n    def test_script(self):\n        net = UNet(\n            spatial_dims=2, in_channels=1, out_channels=3, channels=(16, 32, 64), strides=(2, 2), num_res_units=0\n        )\n        test_data = torch.randn(16, 1, 32, 32)\n        test_script_save(net, test_data)\n\n    def test_script_without_running_stats(self):\n        net = UNet(\n            spatial_dims=2,\n            in_channels=1,\n            out_channels=3,\n            channels=(16, 32, 64),\n            strides=(2, 2),\n            num_res_units=0,\n            norm=(\"batch\", {\"track_running_stats\": False}),\n        )\n        test_data = torch.randn(16, 1, 16, 4)\n        test_script_save(net, test_data)\n\n    def test_ill_input_shape(self):\n        net = UNet(spatial_dims=2, in_channels=1, out_channels=3, channels=(16, 32, 64), strides=(2, 2))\n        with eval_mode(net):\n            with self.assertRaisesRegex(RuntimeError, \"Sizes of tensors must match\"):\n                net.forward(torch.randn(2, 1, 16, 5))\n\n    @parameterized.expand(ILL_CASES)\n    def test_ill_input_hyper_params(self, input_param):\n        with self.assertRaises(ValueError):\n            net = UNet(**input_param)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_varautoencoder.py_unittest_CASES._TEST_CASE_0_TEST_CASE_1": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_varautoencoder.py_unittest_CASES._TEST_CASE_0_TEST_CASE_1", "embedding": null, "metadata": {"file_path": "tests/test_varautoencoder.py", "file_name": "test_varautoencoder.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 78, "span_ids": ["impl:9", "docstring"], "tokens": 603}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks import eval_mode\nfrom monai.networks.layers import Act\nfrom monai.networks.nets import VarAutoEncoder\nfrom tests.utils import test_script_save\n\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n\nTEST_CASE_0 = [  # single channel 2D, batch 4, no residual\n    {\n        \"spatial_dims\": 2,\n        \"in_shape\": (1, 128, 128),\n        \"out_channels\": 1,\n        \"latent_size\": 2,\n        \"channels\": (4, 8, 16),\n        \"strides\": (2, 2, 2),\n        \"num_res_units\": 0,\n    },\n    (1, 1, 128, 128),\n    (1, 1, 128, 128),\n]\n\nTEST_CASE_1 = [  # single channel 2D, batch 4\n    {\n        \"spatial_dims\": 2,\n        \"in_shape\": (1, 128, 128),\n        \"out_channels\": 1,\n        \"latent_size\": 2,\n        \"channels\": (4, 8, 16),\n        \"strides\": (2, 2, 2),\n    },\n    (1, 1, 128, 128),\n    (1, 1, 128, 128),\n]\n\nTEST_CASE_2 = [  # 3-channel 2D, batch 4, LeakyReLU activation\n    {\n        \"spatial_dims\": 2,\n        \"in_shape\": (3, 128, 128),\n        \"out_channels\": 3,\n        \"latent_size\": 2,\n        \"channels\": (4, 8, 16),\n        \"strides\": (2, 2, 2),\n        \"act\": (Act.LEAKYRELU, {\"negative_slope\": 0.2}),\n    },\n    (1, 3, 128, 128),\n    (1, 3, 128, 128),\n]\n\nTEST_CASE_3 = [  # 4-channel 3D, batch 4\n    {\n        \"spatial_dims\": 3,\n        \"in_shape\": (4, 128, 128, 128),\n        \"out_channels\": 3,\n        \"latent_size\": 2,\n        \"channels\": (4, 8, 16),\n        \"strides\": (2, 2, 2),\n    },\n    (1, 4, 128, 128, 128),\n    (1, 3, 128, 128, 128),\n]\n\nCASES = [TEST_CASE_0, TEST_CASE_1, TEST_CASE_2, TEST_CASE_3]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_varautoencoder.py_TestVarAutoEncoder_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_varautoencoder.py_TestVarAutoEncoder_", "embedding": null, "metadata": {"file_path": "tests/test_varautoencoder.py", "file_name": "test_varautoencoder.py", "file_type": "text/x-python", "category": "test", "start_line": 81, "end_line": 99, "span_ids": ["TestVarAutoEncoder", "TestVarAutoEncoder.test_shape", "impl:13", "TestVarAutoEncoder.test_script"], "tokens": 170}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestVarAutoEncoder(unittest.TestCase):\n    @parameterized.expand(CASES)\n    def test_shape(self, input_param, input_shape, expected_shape):\n        net = VarAutoEncoder(**input_param).to(device)\n        with eval_mode(net):\n            result = net.forward(torch.randn(input_shape).to(device))[0]\n            self.assertEqual(result.shape, expected_shape)\n\n    def test_script(self):\n        net = VarAutoEncoder(\n            spatial_dims=2, in_shape=(1, 32, 32), out_channels=1, latent_size=2, channels=(4, 8), strides=(2, 2)\n        )\n        test_data = torch.randn(2, 1, 32, 32)\n        test_script_save(net, test_data)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_vis_cam.py_unittest_TEST_CASE_3._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_vis_cam.py_unittest_TEST_CASE_3._", "embedding": null, "metadata": {"file_path": "tests/test_vis_cam.py", "file_name": "test_vis_cam.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 64, "span_ids": ["docstring"], "tokens": 425}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks.nets import DenseNet, DenseNet121, SEResNet50\nfrom monai.visualize import CAM\n\n# 2D\nTEST_CASE_0 = [\n    {\n        \"model\": \"densenet2d\",\n        \"shape\": (2, 1, 48, 64),\n        \"feature_shape\": (2, 1, 1, 2),\n        \"target_layers\": \"class_layers.relu\",\n        \"fc_layers\": \"class_layers.out\",\n    },\n    (2, 1, 48, 64),\n]\n# 3D\nTEST_CASE_1 = [\n    {\n        \"model\": \"densenet3d\",\n        \"shape\": (2, 1, 6, 6, 6),\n        \"feature_shape\": (2, 1, 2, 2, 2),\n        \"target_layers\": \"class_layers.relu\",\n        \"fc_layers\": \"class_layers.out\",\n    },\n    (2, 1, 6, 6, 6),\n]\n# 2D\nTEST_CASE_2 = [\n    {\n        \"model\": \"senet2d\",\n        \"shape\": (2, 3, 64, 64),\n        \"feature_shape\": (2, 1, 2, 2),\n        \"target_layers\": \"layer4\",\n        \"fc_layers\": \"last_linear\",\n    },\n    (2, 1, 64, 64),\n]\n\n# 3D\nTEST_CASE_3 = [\n    {\n        \"model\": \"senet3d\",\n        \"shape\": (2, 3, 8, 8, 48),\n        \"feature_shape\": (2, 1, 1, 1, 2),\n        \"target_layers\": \"layer4\",\n        \"fc_layers\": \"last_linear\",\n    },\n    (2, 1, 8, 8, 48),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_vis_cam.py_TestClassActivationMap_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_vis_cam.py_TestClassActivationMap_", "embedding": null, "metadata": {"file_path": "tests/test_vis_cam.py", "file_name": "test_vis_cam.py", "file_type": "text/x-python", "category": "test", "start_line": 67, "end_line": 93, "span_ids": ["TestClassActivationMap", "impl:9", "TestClassActivationMap.test_shape"], "tokens": 342}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestClassActivationMap(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_0, TEST_CASE_1, TEST_CASE_2, TEST_CASE_3])\n    def test_shape(self, input_data, expected_shape):\n        if input_data[\"model\"] == \"densenet2d\":\n            model = DenseNet121(spatial_dims=2, in_channels=1, out_channels=3)\n        if input_data[\"model\"] == \"densenet3d\":\n            model = DenseNet(\n                spatial_dims=3, in_channels=1, out_channels=3, init_features=2, growth_rate=2, block_config=(6,)\n            )\n        if input_data[\"model\"] == \"senet2d\":\n            model = SEResNet50(spatial_dims=2, in_channels=3, num_classes=4)\n        if input_data[\"model\"] == \"senet3d\":\n            model = SEResNet50(spatial_dims=3, in_channels=3, num_classes=4)\n        device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n        model.to(device)\n        model.eval()\n        cam = CAM(nn_module=model, target_layers=input_data[\"target_layers\"], fc_layers=input_data[\"fc_layers\"])\n        image = torch.rand(input_data[\"shape\"], device=device)\n        result = cam(x=image, layer_idx=-1)\n        fea_shape = cam.feature_map_size(input_data[\"shape\"], device=device)\n        self.assertTupleEqual(fea_shape, input_data[\"feature_shape\"])\n        self.assertTupleEqual(result.shape, expected_shape)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_vis_gradcam.py_unittest_TEST_CASE_3._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_vis_gradcam.py_unittest_TEST_CASE_3._", "embedding": null, "metadata": {"file_path": "tests/test_vis_gradcam.py", "file_name": "test_vis_gradcam.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 51, "span_ids": ["docstring"], "tokens": 379}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks.nets import DenseNet, DenseNet121, SEResNet50\nfrom monai.visualize import GradCAM\n\n# 2D\nTEST_CASE_0 = [\n    {\n        \"model\": \"densenet2d\",\n        \"shape\": (2, 1, 48, 64),\n        \"feature_shape\": (2, 1, 1, 2),\n        \"target_layers\": \"class_layers.relu\",\n    },\n    (2, 1, 48, 64),\n]\n# 3D\nTEST_CASE_1 = [\n    {\n        \"model\": \"densenet3d\",\n        \"shape\": (2, 1, 6, 6, 6),\n        \"feature_shape\": (2, 1, 2, 2, 2),\n        \"target_layers\": \"class_layers.relu\",\n    },\n    (2, 1, 6, 6, 6),\n]\n# 2D\nTEST_CASE_2 = [\n    {\"model\": \"senet2d\", \"shape\": (2, 3, 64, 64), \"feature_shape\": (2, 1, 2, 2), \"target_layers\": \"layer4\"},\n    (2, 1, 64, 64),\n]\n\n# 3D\nTEST_CASE_3 = [\n    {\"model\": \"senet3d\", \"shape\": (2, 3, 8, 8, 48), \"feature_shape\": (2, 1, 1, 1, 2), \"target_layers\": \"layer4\"},\n    (2, 1, 8, 8, 48),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_vis_gradcam.py_TestGradientClassActivationMap_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_vis_gradcam.py_TestGradientClassActivationMap_", "embedding": null, "metadata": {"file_path": "tests/test_vis_gradcam.py", "file_name": "test_vis_gradcam.py", "file_type": "text/x-python", "category": "test", "start_line": 54, "end_line": 94, "span_ids": ["impl:9", "TestGradientClassActivationMap.test_shape", "TestGradientClassActivationMap", "TestGradientClassActivationMap.test_ill"], "tokens": 511}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestGradientClassActivationMap(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_0, TEST_CASE_1, TEST_CASE_2, TEST_CASE_3])\n    def test_shape(self, input_data, expected_shape):\n        if input_data[\"model\"] == \"densenet2d\":\n            model = DenseNet121(spatial_dims=2, in_channels=1, out_channels=3)\n        if input_data[\"model\"] == \"densenet3d\":\n            model = DenseNet(\n                spatial_dims=3, in_channels=1, out_channels=3, init_features=2, growth_rate=2, block_config=(6,)\n            )\n        if input_data[\"model\"] == \"senet2d\":\n            model = SEResNet50(spatial_dims=2, in_channels=3, num_classes=4)\n        if input_data[\"model\"] == \"senet3d\":\n            model = SEResNet50(spatial_dims=3, in_channels=3, num_classes=4)\n        device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n        model.to(device)\n        model.eval()\n        cam = GradCAM(nn_module=model, target_layers=input_data[\"target_layers\"])\n        image = torch.rand(input_data[\"shape\"], device=device)\n        result = cam(x=image, layer_idx=-1)\n        np.testing.assert_array_equal(cam.nn_module.class_idx.cpu(), model(image).max(1)[-1].cpu())\n        fea_shape = cam.feature_map_size(input_data[\"shape\"], device=device)\n        self.assertTupleEqual(fea_shape, input_data[\"feature_shape\"])\n        self.assertTupleEqual(result.shape, expected_shape)\n        # check result is same whether class_idx=None is used or not\n        result2 = cam(x=image, layer_idx=-1, class_idx=model(image).max(1)[-1].cpu())\n        torch.testing.assert_allclose(result, result2)\n\n    def test_ill(self):\n        model = DenseNet121(spatial_dims=2, in_channels=1, out_channels=3)\n        for name, x in model.named_parameters():\n            if \"features\" in name:\n                x.requires_grad = False\n        cam = GradCAM(nn_module=model, target_layers=\"class_layers.relu\")\n        image = torch.rand((2, 1, 48, 64))\n        with self.assertRaises(RuntimeError):\n            cam(x=image)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_vis_gradcampp.py_unittest_TEST_CASE_3._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_vis_gradcampp.py_unittest_TEST_CASE_3._", "embedding": null, "metadata": {"file_path": "tests/test_vis_gradcampp.py", "file_name": "test_vis_gradcampp.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 50, "span_ids": ["docstring"], "tokens": 375}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks.nets import DenseNet, DenseNet121, SEResNet50\nfrom monai.visualize import GradCAMpp\n\n# 2D\nTEST_CASE_0 = [\n    {\n        \"model\": \"densenet2d\",\n        \"shape\": (2, 1, 48, 64),\n        \"feature_shape\": (2, 1, 1, 2),\n        \"target_layers\": \"class_layers.relu\",\n    },\n    (2, 1, 48, 64),\n]\n# 3D\nTEST_CASE_1 = [\n    {\n        \"model\": \"densenet3d\",\n        \"shape\": (2, 1, 6, 6, 6),\n        \"feature_shape\": (2, 1, 2, 2, 2),\n        \"target_layers\": \"class_layers.relu\",\n    },\n    (2, 1, 6, 6, 6),\n]\n# 2D\nTEST_CASE_2 = [\n    {\"model\": \"senet2d\", \"shape\": (2, 3, 64, 64), \"feature_shape\": (2, 1, 2, 2), \"target_layers\": \"layer4\"},\n    (2, 1, 64, 64),\n]\n\n# 3D\nTEST_CASE_3 = [\n    {\"model\": \"senet3d\", \"shape\": (2, 3, 8, 8, 48), \"feature_shape\": (2, 1, 1, 1, 2), \"target_layers\": \"layer4\"},\n    (2, 1, 8, 8, 48),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_vis_gradcampp.py_TestGradientClassActivationMapPP_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_vis_gradcampp.py_TestGradientClassActivationMapPP_", "embedding": null, "metadata": {"file_path": "tests/test_vis_gradcampp.py", "file_name": "test_vis_gradcampp.py", "file_type": "text/x-python", "category": "test", "start_line": 63, "end_line": 89, "span_ids": ["impl:9", "TestGradientClassActivationMapPP.test_shape", "TestGradientClassActivationMapPP"], "tokens": 338}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestGradientClassActivationMapPP(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_0, TEST_CASE_1, TEST_CASE_2, TEST_CASE_3])\n    def test_shape(self, input_data, expected_shape):\n        if input_data[\"model\"] == \"densenet2d\":\n            model = DenseNet121(spatial_dims=2, in_channels=1, out_channels=3)\n        if input_data[\"model\"] == \"densenet3d\":\n            model = DenseNet(\n                spatial_dims=3, in_channels=1, out_channels=3, init_features=2, growth_rate=2, block_config=(6,)\n            )\n        if input_data[\"model\"] == \"senet2d\":\n            model = SEResNet50(spatial_dims=2, in_channels=3, num_classes=4)\n        if input_data[\"model\"] == \"senet3d\":\n            model = SEResNet50(spatial_dims=3, in_channels=3, num_classes=4)\n        device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n        model.to(device)\n        model.eval()\n        cam = GradCAMpp(nn_module=model, target_layers=input_data[\"target_layers\"])\n        image = torch.rand(input_data[\"shape\"], device=device)\n        result = cam(x=image, layer_idx=-1)\n        fea_shape = cam.feature_map_size(input_data[\"shape\"], device=device)\n        self.assertTupleEqual(fea_shape, input_data[\"feature_shape\"])\n        self.assertTupleEqual(result.shape, expected_shape)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_warp.py_unittest_LOW_POWER_TEST_CASES._run_with_BUILD_MONAI": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_warp.py_unittest_LOW_POWER_TEST_CASES._run_with_BUILD_MONAI", "embedding": null, "metadata": {"file_path": "tests/test_warp.py", "file_name": "test_warp.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 51, "span_ids": ["docstring"], "tokens": 507}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\nfrom torch.autograd import gradcheck\n\nfrom monai.config.deviceconfig import USE_COMPILED\nfrom monai.networks.blocks.warp import Warp\nfrom monai.utils import GridSampleMode, GridSamplePadMode\nfrom tests.utils import SkipIfBeforePyTorchVersion\n\nLOW_POWER_TEST_CASES = [  # run with BUILD_MONAI=1 to test csrc/resample, BUILD_MONAI=0 to test native grid_sample\n    [\n        {\"mode\": \"nearest\", \"padding_mode\": \"zeros\"},\n        {\"image\": torch.arange(4).reshape((1, 1, 2, 2)).to(dtype=torch.float), \"ddf\": torch.zeros(1, 2, 2, 2)},\n        torch.arange(4).reshape((1, 1, 2, 2)),\n    ],\n    [\n        {\"mode\": \"bilinear\", \"padding_mode\": \"zeros\"},\n        {\"image\": torch.arange(4).reshape((1, 1, 2, 2)).to(dtype=torch.float), \"ddf\": torch.ones(1, 2, 2, 2)},\n        torch.tensor([[[[3, 0], [0, 0]]]]),\n    ],\n    [\n        {\"mode\": \"bilinear\", \"padding_mode\": \"border\"},\n        {\n            \"image\": torch.arange(8).reshape((1, 1, 2, 2, 2)).to(dtype=torch.float),\n            \"ddf\": torch.ones(1, 3, 2, 2, 2) * -1,\n        },\n        torch.tensor([[[[[0, 0], [0, 0]], [[0, 0], [0, 0]]]]]),\n    ],\n    [\n        {\"mode\": \"bilinear\", \"padding_mode\": \"reflection\"},\n        {\n            \"image\": torch.arange(8).reshape((1, 1, 2, 2, 2)).to(dtype=torch.float),\n            \"ddf\": torch.ones(1, 3, 2, 2, 2) * -1,\n        },\n        torch.tensor([[[[[7.0, 6.0], [5.0, 4.0]], [[3.0, 2.0], [1.0, 0.0]]]]]),\n    ],\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_warp.py_CPP_TEST_CASES_if_USE_COMPILED_.TEST_CASES_CPP_TEST_CA": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_warp.py_CPP_TEST_CASES_if_USE_COMPILED_.TEST_CASES_CPP_TEST_CA", "embedding": null, "metadata": {"file_path": "tests/test_warp.py", "file_name": "test_warp.py", "file_type": "text/x-python", "category": "test", "start_line": 53, "end_line": 95, "span_ids": ["impl:3", "impl:5"], "tokens": 700}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "CPP_TEST_CASES = [  # high order, BUILD_MONAI=1 to test csrc/resample\n    [\n        {\"mode\": 2, \"padding_mode\": \"border\"},\n        {\n            \"image\": torch.arange(8).reshape((1, 1, 2, 2, 2)).to(dtype=torch.float),\n            \"ddf\": torch.ones(1, 3, 2, 2, 2) * -1,\n        },\n        torch.tensor([[[[[0.0000, 0.1250], [0.2500, 0.3750]], [[0.5000, 0.6250], [0.7500, 0.8750]]]]]),\n    ],\n    [\n        {\"mode\": 2, \"padding_mode\": \"reflection\"},\n        {\n            \"image\": torch.arange(8).reshape((1, 1, 2, 2, 2)).to(dtype=torch.float),\n            \"ddf\": torch.ones(1, 3, 2, 2, 2) * -1,\n        },\n        torch.tensor([[[[[5.2500, 4.7500], [4.2500, 3.7500]], [[3.2500, 2.7500], [2.2500, 1.7500]]]]]),\n    ],\n    [\n        {\"mode\": 2, \"padding_mode\": \"zeros\"},\n        {\n            \"image\": torch.arange(8).reshape((1, 1, 2, 2, 2)).to(dtype=torch.float),\n            \"ddf\": torch.ones(1, 3, 2, 2, 2) * -1,\n        },\n        torch.tensor([[[[[0.0000, 0.0020], [0.0039, 0.0410]], [[0.0078, 0.0684], [0.0820, 0.6699]]]]]),\n    ],\n    [\n        {\"mode\": 2, \"padding_mode\": 7},\n        {\n            \"image\": torch.arange(8).reshape((1, 1, 2, 2, 2)).to(dtype=torch.float),\n            \"ddf\": torch.ones(1, 3, 2, 2, 2) * -1,\n        },\n        torch.tensor([[[[[0.0000, 0.0020], [0.0039, 0.0410]], [[0.0078, 0.0684], [0.0820, 0.6699]]]]]),\n    ],\n    [\n        {\"mode\": 3, \"padding_mode\": \"reflection\"},\n        {\"image\": torch.arange(8).reshape((1, 1, 2, 2, 2)).to(dtype=torch.float), \"ddf\": torch.ones(1, 3, 2, 2, 2)},\n        torch.tensor([[[[[4.6667, 4.3333], [4.0000, 3.6667]], [[3.3333, 3.0000], [2.6667, 2.3333]]]]]),\n    ],\n]\n\nTEST_CASES = LOW_POWER_TEST_CASES\nif USE_COMPILED:\n    TEST_CASES += CPP_TEST_CASES", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_warp.py_TestWarp_TestWarp.test_ill_shape.None_2.warp_layer_image_torch_ar": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_warp.py_TestWarp_TestWarp.test_ill_shape.None_2.warp_layer_image_torch_ar", "embedding": null, "metadata": {"file_path": "tests/test_warp.py", "file_name": "test_warp.py", "file_type": "text/x-python", "category": "test", "start_line": 98, "end_line": 116, "span_ids": ["TestWarp", "TestWarp.test_resample", "TestWarp.test_ill_shape"], "tokens": 271}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestWarp(unittest.TestCase):\n    @parameterized.expand(TEST_CASES, skip_on_empty=True)\n    def test_resample(self, input_param, input_data, expected_val):\n        warp_layer = Warp(**input_param)\n        result = warp_layer(**input_data)\n        np.testing.assert_allclose(result.cpu().numpy(), expected_val.cpu().numpy(), rtol=1e-4, atol=1e-4)\n\n    def test_ill_shape(self):\n        warp_layer = Warp()\n        with self.assertRaisesRegex(ValueError, \"\"):\n            warp_layer(\n                image=torch.arange(4).reshape((1, 1, 1, 2, 2)).to(dtype=torch.float), ddf=torch.zeros(1, 2, 2, 2)\n            )\n        with self.assertRaisesRegex(ValueError, \"\"):\n            warp_layer(\n                image=torch.arange(4).reshape((1, 1, 2, 2)).to(dtype=torch.float), ddf=torch.zeros(1, 2, 1, 2, 2)\n            )\n        with self.assertRaisesRegex(ValueError, \"\"):\n            warp_layer(image=torch.arange(4).reshape((1, 1, 2, 2)).to(dtype=torch.float), ddf=torch.zeros(1, 2, 3, 3))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_warp.py_TestWarp.test_grad_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_warp.py_TestWarp.test_grad_", "embedding": null, "metadata": {"file_path": "tests/test_warp.py", "file_name": "test_warp.py", "file_type": "text/x-python", "category": "test", "start_line": 118, "end_line": 132, "span_ids": ["impl:9", "TestWarp.test_grad"], "tokens": 188}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestWarp(unittest.TestCase):\n\n    @SkipIfBeforePyTorchVersion((1, 8))\n    def test_grad(self):\n        for b in GridSampleMode:\n            for p in GridSamplePadMode:\n                warp_layer = Warp(mode=b.value, padding_mode=p.value)\n                input_image = torch.rand((2, 3, 20, 20), dtype=torch.float64) * 10.0\n                ddf = torch.rand((2, 2, 20, 20), dtype=torch.float64) * 2.0\n                input_image.requires_grad = True\n                ddf.requires_grad = False  # Jacobian mismatch for output 0 with respect to input 1\n                gradcheck(warp_layer, (input_image, ddf), atol=1e-2, eps=1e-2)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_with_allow_missing_keys.py_unittest_TestWithAllowMissingKeysMode.test_map_transform.for_amk_in_True_False_.if_not_amk_.with_self_assertRaises_Ke._.t_self_data_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_with_allow_missing_keys.py_unittest_TestWithAllowMissingKeysMode.test_map_transform.for_amk_in_True_False_.if_not_amk_.with_self_assertRaises_Ke._.t_self_data_", "embedding": null, "metadata": {"file_path": "tests/test_with_allow_missing_keys.py", "file_name": "test_with_allow_missing_keys.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 36, "span_ids": ["TestWithAllowMissingKeysMode", "TestWithAllowMissingKeysMode.setUp", "TestWithAllowMissingKeysMode.test_map_transform", "docstring"], "tokens": 204}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\n\nfrom monai.transforms import Compose, SpatialPad, SpatialPadd, allow_missing_keys_mode\n\n\nclass TestWithAllowMissingKeysMode(unittest.TestCase):\n    def setUp(self):\n        self.data = {\"image\": np.arange(16, dtype=float).reshape(1, 4, 4)}\n\n    def test_map_transform(self):\n        for amk in [True, False]:\n            t = SpatialPadd([\"image\", \"label\"], 10, allow_missing_keys=amk)\n            with allow_missing_keys_mode(t):\n                # check state is True\n                self.assertTrue(t.allow_missing_keys)\n                # and that transform works even though key is missing\n                _ = t(self.data)\n            # check it has returned to original state\n            self.assertEqual(t.allow_missing_keys, amk)\n            if not amk:\n                # should fail because amks==False and key is missing\n                with self.assertRaises(KeyError):\n                    _ = t(self.data)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_with_allow_missing_keys.py_TestWithAllowMissingKeysMode.test_compose_TestWithAllowMissingKeysMode.test_compose.with_self_assertRaises_K._.t_self_data_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_with_allow_missing_keys.py_TestWithAllowMissingKeysMode.test_compose_TestWithAllowMissingKeysMode.test_compose.with_self_assertRaises_K._.t_self_data_", "embedding": null, "metadata": {"file_path": "tests/test_with_allow_missing_keys.py", "file_name": "test_with_allow_missing_keys.py", "file_type": "text/x-python", "category": "test", "start_line": 38, "end_line": 52, "span_ids": ["TestWithAllowMissingKeysMode.test_compose"], "tokens": 182}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestWithAllowMissingKeysMode(unittest.TestCase):\n\n    def test_compose(self):\n        amks = [True, False, True]\n        t = Compose([SpatialPadd([\"image\", \"label\"], 10, allow_missing_keys=amk) for amk in amks])\n        with allow_missing_keys_mode(t):\n            # check states are all True\n            for _t in t.transforms:\n                self.assertTrue(_t.allow_missing_keys)\n            # and that transform works even though key is missing\n            _ = t(self.data)\n        # check they've returned to original state\n        for _t, amk in zip(t.transforms, amks):\n            self.assertEqual(_t.allow_missing_keys, amk)\n        # should fail because not all amks==True and key is missing\n        with self.assertRaises((KeyError, RuntimeError)):\n            _ = t(self.data)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_with_allow_missing_keys.py_TestWithAllowMissingKeysMode.test_array_transform_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_with_allow_missing_keys.py_TestWithAllowMissingKeysMode.test_array_transform_", "embedding": null, "metadata": {"file_path": "tests/test_with_allow_missing_keys.py", "file_name": "test_with_allow_missing_keys.py", "file_type": "text/x-python", "category": "test", "start_line": 54, "end_line": 74, "span_ids": ["TestWithAllowMissingKeysMode.test_array_transform", "TestWithAllowMissingKeysMode.test_multiple", "impl"], "tokens": 172}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestWithAllowMissingKeysMode(unittest.TestCase):\n\n    def test_array_transform(self):\n        for t in [SpatialPad(10), Compose([SpatialPad(10)])]:\n            with self.assertRaises(TypeError):\n                with allow_missing_keys_mode(t):\n                    pass\n\n    def test_multiple(self):\n        orig_states = [True, False]\n        ts = [SpatialPadd([\"image\", \"label\"], 10, allow_missing_keys=i) for i in orig_states]\n        with allow_missing_keys_mode(ts):\n            for t in ts:\n                self.assertTrue(t.allow_missing_keys)\n                # and that transform works even though key is missing\n                _ = t(self.data)\n        for t, o_s in zip(ts, orig_states):\n            self.assertEqual(t.allow_missing_keys, o_s)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_write_metrics_reports.py_csv_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_write_metrics_reports.py_csv_", "embedding": null, "metadata": {"file_path": "tests/test_write_metrics_reports.py", "file_name": "test_write_metrics_reports.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 66, "span_ids": ["TestWriteMetricsReports", "TestWriteMetricsReports.test_content", "impl", "docstring"], "tokens": 572}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import csv\nimport os\nimport tempfile\nimport unittest\nfrom pathlib import Path\n\nimport torch\n\nfrom monai.handlers.utils import write_metrics_reports\n\n\nclass TestWriteMetricsReports(unittest.TestCase):\n    def test_content(self):\n        with tempfile.TemporaryDirectory() as tempdir:\n            write_metrics_reports(\n                save_dir=Path(tempdir),\n                images=[\"filepath1\", \"filepath2\"],\n                metrics={\"metric1\": 1, \"metric2\": 2},\n                metric_details={\"metric3\": torch.tensor([[1, 2], [2, 3]]), \"metric4\": torch.tensor([[5, 6], [7, 8]])},\n                summary_ops=[\"mean\", \"median\", \"max\", \"90percentile\"],\n                deli=\"\\t\",\n                output_type=\"csv\",\n            )\n\n            # check the metrics.csv and content\n            self.assertTrue(os.path.exists(os.path.join(tempdir, \"metrics.csv\")))\n            with open(os.path.join(tempdir, \"metrics.csv\")) as f:\n                f_csv = csv.reader(f)\n                for i, row in enumerate(f_csv):\n                    self.assertEqual(row, [f\"metric{i + 1}\\t{i + 1}\"])\n            self.assertTrue(os.path.exists(os.path.join(tempdir, \"metric3_raw.csv\")))\n            # check the metric_raw.csv and content\n            with open(os.path.join(tempdir, \"metric3_raw.csv\")) as f:\n                f_csv = csv.reader(f)\n                for i, row in enumerate(f_csv):\n                    if i > 0:\n                        self.assertEqual(row, [f\"filepath{i}\\t{float(i)}\\t{float(i + 1)}\\t{i + 0.5}\"])\n            self.assertTrue(os.path.exists(os.path.join(tempdir, \"metric3_summary.csv\")))\n            # check the metric_summary.csv and content\n            with open(os.path.join(tempdir, \"metric3_summary.csv\")) as f:\n                f_csv = csv.reader(f)\n                for i, row in enumerate(f_csv):\n                    if i == 1:\n                        self.assertEqual(row, [\"class0\\t1.5000\\t1.5000\\t2.0000\\t1.9000\"])\n                    elif i == 2:\n                        self.assertEqual(row, [\"class1\\t2.5000\\t2.5000\\t3.0000\\t2.9000\"])\n                    elif i == 3:\n                        self.assertEqual(row, [\"mean\\t2.0000\\t2.0000\\t2.5000\\t2.4000\"])\n            self.assertTrue(os.path.exists(os.path.join(tempdir, \"metric4_raw.csv\")))\n            self.assertTrue(os.path.exists(os.path.join(tempdir, \"metric4_summary.csv\")))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_zipdataset.py_unittest_TEST_CASE_4._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_zipdataset.py_unittest_TEST_CASE_4._", "embedding": null, "metadata": {"file_path": "tests/test_zipdataset.py", "file_name": "test_zipdataset.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 45, "span_ids": ["Dataset_.__init__", "Dataset_.__getitem__", "impl", "Dataset_.__len__", "Dataset_", "docstring"], "tokens": 262}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.data import ZipDataset\n\n\nclass Dataset_(torch.utils.data.Dataset):\n    def __init__(self, length, index_only=True):\n        self.len = length\n        self.index_only = index_only\n\n    def __len__(self):\n        return self.len\n\n    def __getitem__(self, index):\n        if self.index_only:\n            return index\n        return 1, 2, index\n\n\nTEST_CASE_1 = [[Dataset_(5), Dataset_(5), Dataset_(5)], None, (0, 0, 0), 5]\n\nTEST_CASE_2 = [[Dataset_(3), Dataset_(4), Dataset_(5)], None, (0, 0, 0), 3]\n\nTEST_CASE_3 = [[Dataset_(3), Dataset_(4, index_only=False), Dataset_(5)], None, (0, 1, 2, 0, 0), 3]\n\nTEST_CASE_4 = [\n    [Dataset_(3), Dataset_(4, index_only=False), Dataset_(5)],\n    lambda x: [i + 1 for i in x],\n    (1, 2, 3, 1, 1),\n    3,\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_zipdataset.py_TestZipDataset_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_zipdataset.py_TestZipDataset_", "embedding": null, "metadata": {"file_path": "tests/test_zipdataset.py", "file_name": "test_zipdataset.py", "file_type": "text/x-python", "category": "test", "start_line": 48, "end_line": 70, "span_ids": ["TestZipDataset.test_sequence", "TestZipDataset.test_slicing", "TestZipDataset", "TestZipDataset.test_value", "impl:9"], "tokens": 236}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestZipDataset(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4])\n    def test_value(self, datasets, transform, expected_output, expected_length):\n        test_dataset = ZipDataset(datasets=datasets, transform=transform)\n        self.assertEqual(test_dataset[0], expected_output)\n        self.assertEqual(len(test_dataset), expected_length)\n\n    def test_slicing(self):\n        test_dataset = ZipDataset(datasets=[Dataset_(5), Dataset_(5), Dataset_(5)], transform=None)\n        subset = test_dataset[0:2]\n        self.assertEqual(subset[-1], (1, 1, 1))\n        self.assertEqual(len(subset), 2)\n\n    def test_sequence(self):\n        test_dataset = ZipDataset(datasets=[Dataset_(5), Dataset_(5), Dataset_(5)], transform=None)\n        subset = test_dataset[[1, 3, 4]]\n        self.assertEqual(subset[-1], (4, 4, 4))\n        self.assertEqual(len(subset), 3)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/testing_data/cpp_resample_answers.py_csv_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/testing_data/cpp_resample_answers.py_csv_", "embedding": null, "metadata": {"file_path": "tests/testing_data/cpp_resample_answers.py", "file_name": "cpp_resample_answers.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 42, "span_ids": ["_read_testing_data_answers", "impl", "docstring"], "tokens": 231}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import csv\nimport os\nimport warnings\nfrom typing import List, Optional\n\n\ndef _read_testing_data_answers(fname: Optional[str] = None, delimiter=\",\") -> List:\n    answers: List = []\n    if not fname:\n        return answers\n    # read answers from directory of the current file\n    pwd = os.path.dirname(os.path.abspath(__file__))\n    filename = os.path.join(pwd, fname)\n    if not os.path.isfile(filename):\n        warnings.warn(f\"test data {filename} not found.\")\n        return answers\n    with open(filename) as f:\n        res_reader = csv.reader(f, delimiter=delimiter)\n        for r in res_reader:\n            res_row = []\n            for item in r:\n                if item.strip().startswith(\"#\"):\n                    continue  # allow for some simple comments in the file\n                res_row.append(float(item))\n            answers.append(res_row)\n    return answers\n\n\nExpected_1D_GP_fwd: List = _read_testing_data_answers(fname=\"1D_BP_fwd.txt\")\nExpected_1D_GP_bwd: List = _read_testing_data_answers(fname=\"1D_BP_bwd.txt\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/utils.py_SkipIfBeforePyTorchVersion_SkipIfBeforePyTorchVersion.__call__.return.unittest_skipIf_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/utils.py_SkipIfBeforePyTorchVersion_SkipIfBeforePyTorchVersion.__call__.return.unittest_skipIf_", "embedding": null, "metadata": {"file_path": "tests/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 189, "end_line": 200, "span_ids": ["SkipIfBeforePyTorchVersion.__call__", "SkipIfBeforePyTorchVersion", "SkipIfBeforePyTorchVersion.__init__"], "tokens": 115}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SkipIfBeforePyTorchVersion:\n    \"\"\"Decorator to be used if test should be skipped\n    with PyTorch versions older than that given.\"\"\"\n\n    def __init__(self, pytorch_version_tuple):\n        self.min_version = pytorch_version_tuple\n        self.version_too_old = not pytorch_after(*pytorch_version_tuple)\n\n    def __call__(self, obj):\n        return unittest.skipIf(\n            self.version_too_old, f\"Skipping tests that fail on PyTorch versions before: {self.min_version}\"\n        )(obj)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/utils.py_SkipIfAtLeastPyTorchVersion_SkipIfAtLeastPyTorchVersion.__call__.return.unittest_skipIf_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/utils.py_SkipIfAtLeastPyTorchVersion_SkipIfAtLeastPyTorchVersion.__call__.return.unittest_skipIf_", "embedding": null, "metadata": {"file_path": "tests/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 203, "end_line": 214, "span_ids": ["SkipIfAtLeastPyTorchVersion.__init__", "SkipIfAtLeastPyTorchVersion", "SkipIfAtLeastPyTorchVersion.__call__"], "tokens": 119}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SkipIfAtLeastPyTorchVersion:\n    \"\"\"Decorator to be used if test should be skipped\n    with PyTorch versions newer than or equal to that given.\"\"\"\n\n    def __init__(self, pytorch_version_tuple):\n        self.max_version = pytorch_version_tuple\n        self.version_too_new = pytorch_after(*pytorch_version_tuple)\n\n    def __call__(self, obj):\n        return unittest.skipIf(\n            self.version_too_new, f\"Skipping tests that fail on PyTorch versions at least: {self.max_version}\"\n        )(obj)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/utils.py_make_rand_affine_DistTestCase.__setstate__.self___dict___update_data": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/utils.py_make_rand_affine_DistTestCase.__setstate__.self___dict___update_data", "embedding": null, "metadata": {"file_path": "tests/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 159, "end_line": 183, "span_ids": ["DistTestCase.__setstate__", "DistTestCase", "make_rand_affine", "DistTestCase.__getstate__"], "tokens": 235}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def make_rand_affine(ndim: int = 3, random_state: Optional[np.random.RandomState] = None):\n    \"\"\"Create random affine transformation (with values == -1, 0 or 1).\"\"\"\n    rs = np.random.random.__self__ if random_state is None else random_state  # type: ignore\n\n    vals = rs.choice([-1, 1], size=ndim)\n    positions = rs.choice(range(ndim), size=ndim, replace=False)\n    af = np.zeros([ndim + 1, ndim + 1])\n    af[ndim, ndim] = 1\n    for i, (v, p) in enumerate(zip(vals, positions)):\n        af[i, p] = v\n    return af\n\n\nclass DistTestCase(unittest.TestCase):\n    \"\"\"\n    testcase without _outcome, so that it's picklable.\n    \"\"\"\n\n    def __getstate__(self):\n        self_dict = self.__dict__.copy()\n        del self_dict[\"_outcome\"]\n        return self_dict\n\n    def __setstate__(self, data_dict):\n        self.__dict__.update(data_dict)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/utils.py_DistCall_DistCall.__init__.self.verbose.verbose": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/utils.py_DistCall_DistCall.__init__.self.verbose.verbose", "embedding": null, "metadata": {"file_path": "tests/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 265, "end_line": 338, "span_ids": ["DistCall", "DistCall.__init__"], "tokens": 742}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DistCall:\n    \"\"\"\n    Wrap a test case so that it will run in multiple processes on a single machine using `torch.distributed`.\n    It is designed to be used with `tests.utils.DistTestCase`.\n\n    Usage:\n\n        decorate a unittest testcase method with a `DistCall` instance::\n\n            class MyTests(unittest.TestCase):\n                @DistCall(nnodes=1, nproc_per_node=3, master_addr=\"localhost\")\n                def test_compute(self):\n                ...\n\n        the `test_compute` method should trigger different worker logic according to `dist.get_rank()`.\n\n    Multi-node tests require a fixed master_addr:master_port, with node_rank set manually in multiple scripts\n    or from environment variable \"NODE_RANK\".\n    \"\"\"\n\n    def __init__(\n        self,\n        nnodes: int = 1,\n        nproc_per_node: int = 1,\n        master_addr: str = \"localhost\",\n        master_port: Optional[int] = None,\n        node_rank: Optional[int] = None,\n        timeout=60,\n        init_method=None,\n        backend: Optional[str] = None,\n        daemon: Optional[bool] = None,\n        method: Optional[str] = \"spawn\",\n        verbose: bool = False,\n    ):\n        \"\"\"\n\n        Args:\n            nnodes: The number of nodes to use for distributed call.\n            nproc_per_node: The number of processes to call on each node.\n            master_addr: Master node (rank 0)'s address, should be either the IP address or the hostname of node 0.\n            master_port: Master node (rank 0)'s free port.\n            node_rank: The rank of the node, this could be set via environment variable \"NODE_RANK\".\n            timeout: Timeout for operations executed against the process group.\n            init_method: URL specifying how to initialize the process group.\n                Default is \"env://\" or \"file:///d:/a_temp\" (windows) if unspecified.\n            backend: The backend to use. Depending on build-time configurations,\n                valid values include ``mpi``, ``gloo``, and ``nccl``.\n            daemon: the process\u2019s daemon flag.\n                When daemon=None, the initial value is inherited from the creating process.\n            method: set the method which should be used to start a child process.\n                method can be 'fork', 'spawn' or 'forkserver'.\n            verbose: whether to print NCCL debug info.\n        \"\"\"\n        self.nnodes = int(nnodes)\n        self.nproc_per_node = int(nproc_per_node)\n        if self.nnodes < 1 or self.nproc_per_node < 1:\n            raise ValueError(\n                f\"number of nodes and processes per node must be >= 1, got {self.nnodes} and {self.nproc_per_node}\"\n            )\n        self.node_rank = int(os.environ.get(\"NODE_RANK\", \"0\")) if node_rank is None else int(node_rank)\n        self.master_addr = master_addr\n        self.master_port = np.random.randint(10000, 20000) if master_port is None else master_port\n\n        if backend is None:\n            self.backend = \"nccl\" if torch.distributed.is_nccl_available() and torch.cuda.is_available() else \"gloo\"\n        else:\n            self.backend = backend\n        self.init_method = init_method\n        if self.init_method is None and sys.platform == \"win32\":\n            self.init_method = \"file:///d:/a_temp\"\n        self.timeout = datetime.timedelta(0, timeout)\n        self.daemon = daemon\n        self.method = method\n        self.verbose = verbose", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/utils.py_DistCall.run_process_DistCall.run_process.try_.finally_.try_.except_RuntimeError_as_e_.warnings_warn_f_While_clo": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/utils.py_DistCall.run_process_DistCall.run_process.try_.finally_.try_.except_RuntimeError_as_e_.warnings_warn_f_While_clo", "embedding": null, "metadata": {"file_path": "tests/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 359, "end_line": 399, "span_ids": ["DistCall.run_process"], "tokens": 379}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DistCall:\n\n    def run_process(self, func, local_rank, args, kwargs, results):\n        _env = os.environ.copy()  # keep the original system env\n        try:\n            os.environ[\"MASTER_ADDR\"] = self.master_addr\n            os.environ[\"MASTER_PORT\"] = str(self.master_port)\n            os.environ[\"LOCAL_RANK\"] = str(local_rank)\n            if self.verbose:\n                os.environ[\"NCCL_DEBUG\"] = \"INFO\"\n                os.environ[\"NCCL_DEBUG_SUBSYS\"] = \"ALL\"\n            os.environ[\"NCCL_BLOCKING_WAIT\"] = str(1)\n            os.environ[\"OMP_NUM_THREADS\"] = str(1)\n            os.environ[\"WORLD_SIZE\"] = str(self.nproc_per_node * self.nnodes)\n            os.environ[\"RANK\"] = str(self.nproc_per_node * self.node_rank + local_rank)\n\n            if torch.cuda.is_available():\n                torch.cuda.set_device(int(local_rank))  # using device ids from CUDA_VISIBILE_DEVICES\n\n            dist.init_process_group(\n                backend=self.backend,\n                init_method=self.init_method,\n                timeout=self.timeout,\n                world_size=int(os.environ[\"WORLD_SIZE\"]),\n                rank=int(os.environ[\"RANK\"]),\n            )\n            func(*args, **kwargs)\n            # the primary node lives longer to\n            # avoid _store_based_barrier, RuntimeError: Broken pipe\n            # as the TCP store daemon is on the rank 0\n            if int(os.environ[\"RANK\"]) == 0:\n                time.sleep(0.1)\n            results.put(True)\n        except Exception as e:\n            results.put(False)\n            raise e\n        finally:\n            os.environ.clear()\n            os.environ.update(_env)\n            try:\n                dist.destroy_process_group()\n            except RuntimeError as e:\n                warnings.warn(f\"While closing process group: {e}.\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/utils.py_DistCall.__call___DistCall.__call__.return._wrapper": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/utils.py_DistCall.__call___DistCall.__call__.return._wrapper", "embedding": null, "metadata": {"file_path": "tests/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 401, "end_line": 431, "span_ids": ["DistCall.__call__"], "tokens": 263}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DistCall:\n\n    def __call__(self, obj):\n        if not torch.distributed.is_available():\n            return unittest.skipIf(True, \"Skipping distributed tests because not torch.distributed.is_available()\")(obj)\n        if torch.cuda.is_available() and torch.cuda.device_count() < self.nproc_per_node:\n            return unittest.skipIf(\n                True,\n                f\"Skipping distributed tests because it requires {self.nnodes} devices \"\n                f\"but got {torch.cuda.device_count()}\",\n            )(obj)\n\n        _cache_original_func(obj)\n\n        @functools.wraps(obj)\n        def _wrapper(*args, **kwargs):\n            tmp = torch.multiprocessing.get_context(self.method)\n            processes = []\n            results = tmp.Queue()\n            func = _call_original_func\n            args = [obj.__name__, obj.__module__] + list(args)\n            for proc_rank in range(self.nproc_per_node):\n                p = tmp.Process(\n                    target=self.run_process, args=(func, proc_rank, args, kwargs, results), daemon=self.daemon\n                )\n                p.start()\n                processes.append(p)\n            for p in processes:\n                p.join()\n                assert results.get(), \"Distributed call failed.\"\n            _del_original_func(obj)\n\n        return _wrapper", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/utils.py_TimedCall_TimedCall.run_process.try_.except_Exception_as_e_.results_put_e_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/utils.py_TimedCall_TimedCall.run_process.try_.except_Exception_as_e_.results_put_e_", "embedding": null, "metadata": {"file_path": "tests/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 415, "end_line": 455, "span_ids": ["TimedCall", "TimedCall.run_process", "TimedCall.__init__"], "tokens": 315}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TimedCall:\n    \"\"\"\n    Wrap a test case so that it will run in a new process, raises a TimeoutError if the decorated method takes\n    more than `seconds` to finish. It is designed to be used with `tests.utils.DistTestCase`.\n    \"\"\"\n\n    def __init__(\n        self,\n        seconds: float = 60.0,\n        daemon: Optional[bool] = None,\n        method: Optional[str] = \"spawn\",\n        force_quit: bool = True,\n        skip_timing=False,\n    ):\n        \"\"\"\n\n        Args:\n            seconds: timeout seconds.\n            daemon: the process\u2019s daemon flag.\n                When daemon=None, the initial value is inherited from the creating process.\n            method: set the method which should be used to start a child process.\n                method can be 'fork', 'spawn' or 'forkserver'.\n            force_quit: whether to terminate the child process when `seconds` elapsed.\n            skip_timing: whether to skip the timing constraint.\n                this is useful to include some system conditions such as\n                `torch.cuda.is_available()`.\n        \"\"\"\n        self.timeout_seconds = seconds\n        self.daemon = daemon\n        self.force_quit = force_quit\n        self.skip_timing = skip_timing\n        self.method = method\n\n    @staticmethod\n    def run_process(func, args, kwargs, results):\n        try:\n            output = func(*args, **kwargs)\n            results.put(output)\n        except Exception as e:\n            e.traceback = traceback.format_exc()\n            results.put(e)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/utils.py_TimedCall.__call___TimedCall.__call__.return._wrapper": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/utils.py_TimedCall.__call___TimedCall.__call__.return._wrapper", "embedding": null, "metadata": {"file_path": "tests/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 476, "end_line": 525, "span_ids": ["TimedCall.__call__"], "tokens": 354}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TimedCall:\n\n    def __call__(self, obj):\n\n        if self.skip_timing:\n            return obj\n\n        _cache_original_func(obj)\n\n        @functools.wraps(obj)\n        def _wrapper(*args, **kwargs):\n            tmp = torch.multiprocessing.get_context(self.method)\n            func = _call_original_func\n            args = [obj.__name__, obj.__module__] + list(args)\n            results = tmp.Queue()\n            p = tmp.Process(target=TimedCall.run_process, args=(func, args, kwargs, results), daemon=self.daemon)\n            p.start()\n\n            p.join(timeout=self.timeout_seconds)\n\n            timeout_error = None\n            try:\n                if p.is_alive():\n                    # create an Exception\n                    timeout_error = torch.multiprocessing.TimeoutError(\n                        f\"'{obj.__name__}' in '{obj.__module__}' did not finish in {self.timeout_seconds}s.\"\n                    )\n                    if self.force_quit:\n                        p.terminate()\n                    else:\n                        warnings.warn(\n                            f\"TimedCall: deadline ({self.timeout_seconds}s) \"\n                            f\"reached but waiting for {obj.__name__} to finish.\"\n                        )\n            finally:\n                p.join()\n\n            _del_original_func(obj)\n            res = None\n            try:\n                res = results.get(block=False)\n            except queue.Empty:  # no result returned, took too long\n                pass\n            if isinstance(res, Exception):  # other errors from obj\n                if hasattr(res, \"traceback\"):\n                    raise RuntimeError(res.traceback) from res\n                raise res\n            if timeout_error:  # no force_quit finished\n                raise timeout_error\n            return res\n\n        return _wrapper", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/utils.py__original_funcs__call_original_func.return.f_args_kwargs_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/utils.py__original_funcs__call_original_func.return.f_args_kwargs_", "embedding": null, "metadata": {"file_path": "tests/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 528, "end_line": 553, "span_ids": ["_call_original_func", "_cache_original_func", "_del_original_func", "impl:8"], "tokens": 212}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "_original_funcs = {}\n\n\ndef _cache_original_func(obj) -> None:\n    \"\"\"cache the original function by name, so that the decorator doesn't shadow it.\"\"\"\n    global _original_funcs\n    _original_funcs[obj.__name__] = obj\n\n\ndef _del_original_func(obj):\n    \"\"\"pop the original function from cache.\"\"\"\n    global _original_funcs\n    _original_funcs.pop(obj.__name__, None)\n    if torch.cuda.is_available():  # clean up the cached function\n        torch.cuda.synchronize()\n        torch.cuda.empty_cache()\n\n\ndef _call_original_func(name, module, *args, **kwargs):\n    if name not in _original_funcs:\n        _original_module = importlib.import_module(module)  # reimport, refresh _original_funcs\n        if not hasattr(_original_module, name):\n            # refresh module doesn't work\n            raise RuntimeError(f\"Could not recover the original {name} from {module}: {_original_funcs}.\")\n    f = _original_funcs[name]\n    return f(*args, **kwargs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/utils.py_NumpyImageTestCase3D_TorchImageTestCase3D.setUp.self.segn.torch_tensor_self_segn_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/utils.py_NumpyImageTestCase3D_TorchImageTestCase3D.setUp.self.segn.torch_tensor_self_segn_", "embedding": null, "metadata": {"file_path": "tests/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 551, "end_line": 578, "span_ids": ["TorchImageTestCase3D", "NumpyImageTestCase3D.setUp", "TorchImageTestCase3D.setUp", "NumpyImageTestCase3D"], "tokens": 222}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class NumpyImageTestCase3D(unittest.TestCase):\n    im_shape = (64, 48, 80)\n    input_channels = 1\n    output_channels = 4\n    num_classes = 3\n\n    def setUp(self):\n        im, msk = create_test_image_3d(\n            self.im_shape[0],\n            self.im_shape[1],\n            self.im_shape[2],\n            num_objs=4,\n            rad_max=20,\n            noise_max=0.0,\n            num_seg_classes=self.num_classes,\n        )\n\n        self.imt = im[None, None]\n        self.seg1 = (msk[None, None] > 0).astype(np.float32)\n        self.segn = msk[None, None]\n\n\nclass TorchImageTestCase3D(NumpyImageTestCase3D):\n    def setUp(self):\n        NumpyImageTestCase3D.setUp(self)\n        self.imt = torch.tensor(self.imt)\n        self.seg1 = torch.tensor(self.seg1)\n        self.segn = torch.tensor(self.segn)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py__Version_0_19_get_root.return.root": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py__Version_0_19_get_root.return.root", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1, "end_line": 325, "span_ids": ["VersioneerConfig", "imports", "get_root", "docstring"], "tokens": 501}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "# Version: 0.19\n\nimport configparser\nimport errno\nimport json\nimport os\nimport re\nimport subprocess\nimport sys\n\n\nclass VersioneerConfig:\n    \"\"\"Container for Versioneer configuration parameters.\"\"\"\n\n\ndef get_root():\n    \"\"\"Get the project root directory.\n\n    We require that all commands are run from the project root, i.e. the\n    directory that contains setup.py, setup.cfg, and versioneer.py .\n    \"\"\"\n    root = os.path.realpath(os.path.abspath(os.getcwd()))\n    setup_py = os.path.join(root, \"setup.py\")\n    versioneer_py = os.path.join(root, \"versioneer.py\")\n    if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)):\n        # allow 'python path/to/setup.py COMMAND'\n        root = os.path.dirname(os.path.realpath(os.path.abspath(sys.argv[0])))\n        setup_py = os.path.join(root, \"setup.py\")\n        versioneer_py = os.path.join(root, \"versioneer.py\")\n    if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)):\n        err = (\n            \"Versioneer was unable to run the project root directory. \"\n            \"Versioneer requires setup.py to be executed from \"\n            \"its immediate directory (like 'python setup.py COMMAND'), \"\n            \"or in a way that lets it use sys.argv[0] to find the root \"\n            \"(like 'python path/to/setup.py COMMAND').\"\n        )\n        raise VersioneerBadRootError(err)\n    try:\n        # Certain runtime workflows (setup.py install/develop in a setuptools\n        # tree) execute all dependencies in a single python process, so\n        # \"versioneer\" may be imported multiple times, and python's shared\n        # module-import table will cache the first one. So we can't use\n        # os.path.dirname(__file__), as that will find whichever\n        # versioneer.py was first imported, even in later projects.\n        me = os.path.realpath(os.path.abspath(__file__))\n        me_dir = os.path.normcase(os.path.splitext(me)[0])\n        vsr_dir = os.path.normcase(os.path.splitext(versioneer_py)[0])\n        if me_dir != vsr_dir:\n            print(\"Warning: build in %s is using versioneer.py from %s\" % (os.path.dirname(me), versioneer_py))\n    except NameError:\n        pass\n    return root", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_render_pep440_render_pep440.return.rendered": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_render_pep440_render_pep440.return.rendered", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1243, "end_line": 1264, "span_ids": ["render_pep440"], "tokens": 212}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def render_pep440(pieces):\n    \"\"\"Build up version string, with post-release \"local version identifier\".\n\n    Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you\n    get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty\n\n    Exceptions:\n    1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]\n    \"\"\"\n    if pieces[\"closest-tag\"]:\n        rendered = pieces[\"closest-tag\"]\n        if pieces[\"distance\"] or pieces[\"dirty\"]:\n            rendered += plus_or_dot(pieces)\n            rendered += \"%d.g%s\" % (pieces[\"distance\"], pieces[\"short\"])\n            if pieces[\"dirty\"]:\n                rendered += \".dirty\"\n    else:\n        # exception #1\n        rendered = \"0+untagged.%d.g%s\" % (pieces[\"distance\"], pieces[\"short\"])\n        if pieces[\"dirty\"]:\n            rendered += \".dirty\"\n    return rendered", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_render_pep440_pre_render_pep440_pre.return.rendered": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_render_pep440_pre_render_pep440_pre.return.rendered", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1267, "end_line": 1280, "span_ids": ["render_pep440_pre"], "tokens": 107}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def render_pep440_pre(pieces):\n    \"\"\"TAG[.post0.devDISTANCE] -- No -dirty.\n\n    Exceptions:\n    1: no tags. 0.post0.devDISTANCE\n    \"\"\"\n    if pieces[\"closest-tag\"]:\n        rendered = pieces[\"closest-tag\"]\n        if pieces[\"distance\"]:\n            rendered += \".post0.dev%d\" % pieces[\"distance\"]\n    else:\n        # exception #1\n        rendered = \"0.post0.dev%d\" % pieces[\"distance\"]\n    return rendered", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_get_cmdclass.cmds_version_cmd_ver_get_cmdclass.if_setuptools_in_sys_mo.else_.from_distutils_command_bu": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_get_cmdclass.cmds_version_cmd_ver_get_cmdclass.if_setuptools_in_sys_mo.else_.from_distutils_command_bu", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1544, "end_line": 1587, "span_ids": ["get_cmdclass"], "tokens": 437}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_cmdclass(cmdclass=None):\n    # ... other code\n\n    cmds[\"version\"] = cmd_version\n\n    # we override \"build_py\" in both distutils and setuptools\n    #\n    # most invocation pathways end up running build_py:\n    #  distutils/build -> build_py\n    #  distutils/install -> distutils/build ->..\n    #  setuptools/bdist_wheel -> distutils/install ->..\n    #  setuptools/bdist_egg -> distutils/install_lib -> build_py\n    #  setuptools/install -> bdist_egg ->..\n    #  setuptools/develop -> ?\n    #  pip install:\n    #   copies source tree to a tempdir before running egg_info/etc\n    #   if .git isn't copied too, 'git describe' will fail\n    #   then does setup.py bdist_wheel, or sometimes setup.py install\n    #  setup.py egg_info -> ?\n\n    # we override different \"build_py\" commands for both environments\n    if \"build_py\" in cmds:\n        _build_py = cmds[\"build_py\"]\n    elif \"setuptools\" in sys.modules:\n        from setuptools.command.build_py import build_py as _build_py\n    else:\n        from distutils.command.build_py import build_py as _build_py\n\n    class cmd_build_py(_build_py):\n        def run(self):\n            root = get_root()\n            cfg = get_config_from_root(root)\n            versions = get_versions()\n            _build_py.run(self)\n            # now locate _version.py in the new build/ directory and replace\n            # it with an updated value\n            if cfg.versionfile_build:\n                target_versionfile = os.path.join(self.build_lib, cfg.versionfile_build)\n                print(\"UPDATING %s\" % target_versionfile)\n                write_to_version_file(target_versionfile, versions)\n\n    cmds[\"build_py\"] = cmd_build_py\n\n    if \"setuptools\" in sys.modules:\n        from setuptools.command.build_ext import build_ext as _build_ext\n    else:\n        from distutils.command.build_ext import build_ext as _build_ext\n    # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_get_cmdclass.cmd_build_ext_get_cmdclass.cmd_build_ext.run.write_to_version_file_tar": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_get_cmdclass.cmd_build_ext_get_cmdclass.cmd_build_ext.run.write_to_version_file_tar", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1589, "end_line": 1605, "span_ids": ["get_cmdclass"], "tokens": 195}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_cmdclass(cmdclass=None):\n    # ... other code\n\n    class cmd_build_ext(_build_ext):\n        def run(self):\n            root = get_root()\n            cfg = get_config_from_root(root)\n            versions = get_versions()\n            _build_ext.run(self)\n            if self.inplace:\n                # build_ext --inplace will only build extensions in\n                # build/lib<..> dir with no _version.py to write to.\n                # As in place builds will already have a _version.py\n                # in the module dir, we do not need to write one.\n                return\n            # now locate _version.py in the new build/ directory and replace\n            # it with an updated value\n            target_versionfile = os.path.join(self.build_lib, cfg.versionfile_source)\n            print(\"UPDATING %s\" % target_versionfile)\n            write_to_version_file(target_versionfile, versions)\n    # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_get_cmdclass.cmds_build_ext_cmd_b_get_cmdclass.if_sdist_in_cmds_.else_.from_distutils_command_sd": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/versioneer.py_get_cmdclass.cmds_build_ext_cmd_b_get_cmdclass.if_sdist_in_cmds_.else_.from_distutils_command_sd", "embedding": null, "metadata": {"file_path": "versioneer.py", "file_name": "versioneer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1607, "end_line": 1681, "span_ids": ["get_cmdclass"], "tokens": 631}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_cmdclass(cmdclass=None):\n    # ... other code\n\n    cmds[\"build_ext\"] = cmd_build_ext\n\n    if \"cx_Freeze\" in sys.modules:  # cx_freeze enabled?\n        from cx_Freeze.dist import build_exe as _build_exe\n\n        # nczeczulin reports that py2exe won't like the pep440-style string\n        # as FILEVERSION, but it can be used for PRODUCTVERSION, e.g.\n        # setup(console=[{\n        #   \"version\": versioneer.get_version().split(\"+\", 1)[0], # FILEVERSION\n        #   \"product_version\": versioneer.get_version(),\n        #   ...\n\n        class cmd_build_exe(_build_exe):\n            def run(self):\n                root = get_root()\n                cfg = get_config_from_root(root)\n                versions = get_versions()\n                target_versionfile = cfg.versionfile_source\n                print(\"UPDATING %s\" % target_versionfile)\n                write_to_version_file(target_versionfile, versions)\n\n                _build_exe.run(self)\n                os.unlink(target_versionfile)\n                with open(cfg.versionfile_source, \"w\") as f:\n                    LONG = LONG_VERSION_PY[cfg.VCS]\n                    f.write(\n                        LONG\n                        % {\n                            \"DOLLAR\": \"$\",\n                            \"STYLE\": cfg.style,\n                            \"TAG_PREFIX\": cfg.tag_prefix,\n                            \"PARENTDIR_PREFIX\": cfg.parentdir_prefix,\n                            \"VERSIONFILE_SOURCE\": cfg.versionfile_source,\n                        }\n                    )\n\n        cmds[\"build_exe\"] = cmd_build_exe\n        del cmds[\"build_py\"]\n\n    if \"py2exe\" in sys.modules:  # py2exe enabled?\n        from py2exe.distutils_buildexe import py2exe as _py2exe\n\n        class cmd_py2exe(_py2exe):\n            def run(self):\n                root = get_root()\n                cfg = get_config_from_root(root)\n                versions = get_versions()\n                target_versionfile = cfg.versionfile_source\n                print(\"UPDATING %s\" % target_versionfile)\n                write_to_version_file(target_versionfile, versions)\n\n                _py2exe.run(self)\n                os.unlink(target_versionfile)\n                with open(cfg.versionfile_source, \"w\") as f:\n                    LONG = LONG_VERSION_PY[cfg.VCS]\n                    f.write(\n                        LONG\n                        % {\n                            \"DOLLAR\": \"$\",\n                            \"STYLE\": cfg.style,\n                            \"TAG_PREFIX\": cfg.tag_prefix,\n                            \"PARENTDIR_PREFIX\": cfg.parentdir_prefix,\n                            \"VERSIONFILE_SOURCE\": cfg.versionfile_source,\n                        }\n                    )\n\n        cmds[\"py2exe\"] = cmd_py2exe\n\n    # we override different \"sdist\" commands for both environments\n    if \"sdist\" in cmds:\n        _sdist = cmds[\"sdist\"]\n    elif \"setuptools\" in sys.modules:\n        from setuptools.command.sdist import sdist as _sdist\n    else:\n        from distutils.command.sdist import sdist as _sdist\n    # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/_extensions/__init__.py_load_module_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/_extensions/__init__.py_load_module_", "embedding": null, "metadata": {"file_path": "monai/_extensions/__init__.py", "file_name": "__init__.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 13, "span_ids": ["docstring"], "tokens": 6}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from .loader import load_module", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/_extensions/loader.py_platform_timeout.try_.finally_.if_timer_is_not_None_.try_.finally_.pass": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/_extensions/loader.py_platform_timeout.try_.finally_.if_timer_is_not_None_.try_.finally_.pass", "embedding": null, "metadata": {"file_path": "monai/_extensions/loader.py", "file_name": "loader.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 43, "span_ids": ["timeout", "docstring"], "tokens": 162}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import platform\nfrom _thread import interrupt_main\nfrom contextlib import contextmanager\nfrom glob import glob\nfrom os import path\nfrom threading import Timer\nfrom typing import Optional\n\nimport torch\n\nfrom monai.utils.module import get_torch_version_tuple, optional_import\n\ndir_path = path.dirname(path.realpath(__file__))\n\n\n@contextmanager\ndef timeout(time, message):\n    timer = None\n    try:\n        timer = Timer(time, interrupt_main)\n        timer.daemon = True\n        yield timer.start()\n    except KeyboardInterrupt as e:\n        if timer is not None and timer.is_alive():\n            raise e  # interrupt from user?\n        raise TimeoutError(message) from e\n    finally:\n        if timer is not None:\n            try:\n                timer.cancel()\n            finally:\n                pass", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/_extensions/loader.py_load_module_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/_extensions/loader.py_load_module_", "embedding": null, "metadata": {"file_path": "monai/_extensions/loader.py", "file_name": "loader.py", "file_type": "text/x-python", "category": "implementation", "start_line": 46, "end_line": 91, "span_ids": ["load_module"], "tokens": 479}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def load_module(\n    module_name: str, defines: Optional[dict] = None, verbose_build: bool = False, build_timeout: int = 300\n):\n    \"\"\"\n    Handles the loading of c++ extension modules.\n\n    Args:\n        module_name: Name of the module to load.\n            Must match the name of the relevant source directory in the `_extensions` directory.\n        defines: Dictionary containing names and values of compilation defines.\n        verbose_build: Set to true to enable build logging.\n        build_timeout: Time in seconds before the build will throw an exception to prevent hanging.\n    \"\"\"\n\n    # Ensuring named module exists in _extensions directory.\n    module_dir = path.join(dir_path, module_name)\n    if not path.exists(module_dir):\n        raise ValueError(f\"No extension module named {module_name}\")\n\n    platform_str = f\"_{platform.system()}_{platform.python_version()}_\"\n    platform_str += \"\".join(f\"{v}\" for v in get_torch_version_tuple()[:2])\n    # Adding configuration to module name.\n    if defines is not None:\n        module_name = \"_\".join([module_name] + [f\"{v}\" for v in defines.values()])\n\n    # Gathering source files.\n    source = glob(path.join(module_dir, \"**\", \"*.cpp\"), recursive=True)\n    if torch.cuda.is_available():\n        source += glob(path.join(module_dir, \"**\", \"*.cu\"), recursive=True)\n        platform_str += f\"_{torch.version.cuda}\"\n\n    # Constructing compilation argument list.\n    define_args = [] if not defines else [f\"-D {key}={defines[key]}\" for key in defines]\n\n    # Ninja may be blocked by something out of our control.\n    # This will error if the build takes longer than expected.\n    with timeout(build_timeout, \"Build appears to be blocked. Is there a stopped process building the same extension?\"):\n        load, _ = optional_import(\"torch.utils.cpp_extension\", name=\"load\")  # main trigger some JIT config in pytorch\n        # This will either run the build or return the existing .so object.\n        name = module_name + platform_str.replace(\".\", \"_\")\n        module = load(\n            name=name, sources=source, extra_cflags=define_args, extra_cuda_cflags=define_args, verbose=verbose_build\n        )\n\n    return module", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/interaction.py_from_typing_import_Callab_Interaction.__init__.self.key_probability.key_probability": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/interaction.py_from_typing_import_Callab_Interaction.__init__.self.key_probability.key_probability", "embedding": null, "metadata": {"file_path": "monai/apps/deepgrow/interaction.py", "file_name": "interaction.py", "file_type": "text/x-python", "category": "implementation", "start_line": 11, "end_line": 53, "span_ids": ["Interaction.__init__", "Interaction", "docstring"], "tokens": 320}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Callable, Dict, Sequence, Union\n\nimport torch\n\nfrom monai.data import decollate_batch, list_data_collate\nfrom monai.engines import SupervisedEvaluator, SupervisedTrainer\nfrom monai.engines.utils import IterationEvents\nfrom monai.transforms import Compose\nfrom monai.utils.enums import CommonKeys\n\n\nclass Interaction:\n    \"\"\"\n    Ignite process_function used to introduce interactions (simulation of clicks) for Deepgrow Training/Evaluation.\n    For more details please refer to: https://pytorch.org/ignite/generated/ignite.engine.engine.Engine.html.\n    This implementation is based on:\n\n        Sakinis et al., Interactive segmentation of medical images through\n        fully convolutional neural networks. (2019) https://arxiv.org/abs/1903.08205\n\n    Args:\n        transforms: execute additional transformation during every iteration (before train).\n            Typically, several Tensor based transforms composed by `Compose`.\n        max_interactions: maximum number of interactions per iteration\n        train: training or evaluation\n        key_probability: field name to fill probability for every interaction\n    \"\"\"\n\n    def __init__(\n        self,\n        transforms: Union[Sequence[Callable], Callable],\n        max_interactions: int,\n        train: bool,\n        key_probability: str = \"probability\",\n    ) -> None:\n\n        if not isinstance(transforms, Compose):\n            transforms = Compose(transforms)\n\n        self.transforms = transforms\n        self.max_interactions = max_interactions\n        self.train = train\n        self.key_probability = key_probability", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_Fetch2DSliced_Fetch2DSliced._apply.return.image_idx_idx": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_Fetch2DSliced_Fetch2DSliced._apply.return.image_idx_idx", "embedding": null, "metadata": {"file_path": "monai/apps/deepgrow/transforms.py", "file_name": "transforms.py", "file_type": "text/x-python", "category": "implementation", "start_line": 934, "end_line": 980, "span_ids": ["Fetch2DSliced", "Fetch2DSliced.__init__", "Fetch2DSliced._apply"], "tokens": 478}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Fetch2DSliced(MapTransform):\n    \"\"\"\n    Fetch one slice in case of a 3D volume.\n\n    The volume only contains spatial coordinates.\n\n    Args:\n        keys: keys of the corresponding items to be transformed.\n        guidance: key that represents guidance.\n        axis: axis that represents slice in 3D volume.\n        meta_keys: explicitly indicate the key of the corresponding meta data dictionary.\n            for example, for data with key `image`, the metadata by default is in `image_meta_dict`.\n            the meta data is a dictionary object which contains: filename, original_shape, etc.\n            it can be a sequence of string, map to the `keys`.\n            if None, will try to construct meta_keys by `key_{meta_key_postfix}`.\n        meta_key_postfix: use `key_{meta_key_postfix}` to to fetch the meta data according to the key data,\n            default is `meta_dict`, the meta data is a dictionary object.\n            For example, to handle key `image`,  read/write affine matrices from the\n            metadata `image_meta_dict` dictionary's `affine` field.\n        allow_missing_keys: don't raise exception if key is missing.\n    \"\"\"\n\n    def __init__(\n        self,\n        keys,\n        guidance=\"guidance\",\n        axis: int = 0,\n        meta_keys: Optional[KeysCollection] = None,\n        meta_key_postfix: str = \"meta_dict\",\n        allow_missing_keys: bool = False,\n    ):\n        super().__init__(keys, allow_missing_keys)\n        self.guidance = guidance\n        self.axis = axis\n        self.meta_keys = ensure_tuple_rep(None, len(self.keys)) if meta_keys is None else ensure_tuple(meta_keys)\n        if len(self.keys) != len(self.meta_keys):\n            raise ValueError(\"meta_keys should have the same length as keys.\")\n        self.meta_key_postfix = ensure_tuple_rep(meta_key_postfix, len(self.keys))\n\n    def _apply(self, image, guidance):\n        slice_idx = guidance[2]  # (pos, neg, slice_idx)\n        idx = []\n        for i, size_i in enumerate(image.shape):\n            idx.append(slice_idx) if i == self.axis else idx.append(slice(0, size_i))\n\n        idx = tuple(idx)\n        return image[idx], idx", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_Fetch2DSliced.__call___": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_Fetch2DSliced.__call___", "embedding": null, "metadata": {"file_path": "monai/apps/deepgrow/transforms.py", "file_name": "transforms.py", "file_type": "text/x-python", "category": "implementation", "start_line": 982, "end_line": 992, "span_ids": ["Fetch2DSliced.__call__"], "tokens": 127}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Fetch2DSliced(MapTransform):\n\n    def __call__(self, data):\n        d = dict(data)\n        guidance = d[self.guidance]\n        if len(guidance) < 3:\n            raise RuntimeError(\"Guidance does not container slice_idx!\")\n        for key, meta_key, meta_key_postfix in self.key_iterator(d, self.meta_keys, self.meta_key_postfix):\n            img_slice, idx = self._apply(d[key], guidance)\n            d[key] = img_slice\n            d[meta_key or f\"{key}_{meta_key_postfix}\"][\"slice_idx\"] = idx\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/mmars/__init__.py_download_mmar_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/mmars/__init__.py_download_mmar_", "embedding": null, "metadata": {"file_path": "monai/apps/mmars/__init__.py", "file_name": "__init__.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 14, "span_ids": ["docstring"], "tokens": 30}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from .mmars import download_mmar, get_model_spec, load_from_mmar\nfrom .model_desc import MODEL_DESC, RemoteMMARKeys", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/mmars/mmars.py_download_mmar_download_mmar.return.model_dir": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/mmars/mmars.py_download_mmar_download_mmar.return.model_dir", "embedding": null, "metadata": {"file_path": "monai/apps/mmars/mmars.py", "file_name": "mmars.py", "file_type": "text/x-python", "category": "implementation", "start_line": 102, "end_line": 179, "span_ids": ["download_mmar"], "tokens": 745}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def download_mmar(\n    item, mmar_dir: Optional[PathLike] = None, progress: bool = True, api: bool = False, version: int = -1\n):\n    \"\"\"\n    Download and extract Medical Model Archive (MMAR) from Nvidia Clara Train.\n\n    See Also:\n        - https://docs.nvidia.com/clara/\n        - Nvidia NGC Registry CLI\n        - https://docs.nvidia.com/clara/clara-train-sdk/pt/mmar.html\n\n    Args:\n        item: the corresponding model item from `MODEL_DESC`.\n          Or when api is True, the substring to query NGC's model name field.\n        mmar_dir: target directory to store the MMAR, default is `mmars` subfolder under `torch.hub get_dir()`.\n        progress: whether to display a progress bar.\n        api: whether to query NGC and download via api\n        version: which version of MMAR to download.  -1 means the latest from ngc.\n\n    Examples::\n        >>> from monai.apps import download_mmar\n        >>> download_mmar(\"clara_pt_prostate_mri_segmentation_1\", mmar_dir=\".\")\n        >>> download_mmar(\"prostate_mri_segmentation\", mmar_dir=\".\", api=True)\n\n\n    Returns:\n        The local directory of the downloaded model.\n        If api is True, a list of local directories of downloaded models.\n    \"\"\"\n    if not mmar_dir:\n        get_dir, has_home = optional_import(\"torch.hub\", name=\"get_dir\")\n        if has_home:\n            mmar_dir = Path(get_dir()) / \"mmars\"\n        else:\n            raise ValueError(\"mmar_dir=None, but no suitable default directory computed. Upgrade Pytorch to 1.6+ ?\")\n    mmar_dir = Path(mmar_dir)\n    if api:\n        model_dict = _get_all_ngc_models(item)\n        if len(model_dict) == 0:\n            raise ValueError(f\"api query returns no item for pattern {item}.  Please change or shorten it.\")\n        model_dir_list = []\n        for k, v in model_dict.items():\n            ver = v[\"latest\"] if version == -1 else str(version)\n            download_url = _get_ngc_url(k, ver)\n            model_dir = mmar_dir / v[\"name\"]\n            download_and_extract(\n                url=download_url,\n                filepath=mmar_dir / f'{v[\"name\"]}_{ver}.zip',\n                output_dir=model_dir,\n                hash_val=None,\n                hash_type=\"md5\",\n                file_type=\"zip\",\n                has_base=False,\n                progress=progress,\n            )\n            model_dir_list.append(model_dir)\n        return model_dir_list\n\n    if not isinstance(item, Mapping):\n        item = get_model_spec(item)\n\n    ver = item.get(Keys.VERSION, 1)\n    if version > 0:\n        ver = str(version)\n    model_fullname = f\"{item[Keys.NAME]}_{ver}\"\n    model_dir = mmar_dir / model_fullname\n    model_url = item.get(Keys.URL) or _get_ngc_url(item[Keys.NAME], version=ver, model_prefix=\"nvidia/med/\")\n    download_and_extract(\n        url=model_url,\n        filepath=mmar_dir / f\"{model_fullname}.{item[Keys.FILE_TYPE]}\",\n        output_dir=model_dir,\n        hash_val=item[Keys.HASH_VAL],\n        hash_type=item[Keys.HASH_TYPE],\n        file_type=item[Keys.FILE_TYPE],\n        has_base=False,\n        progress=progress,\n    )\n    return model_dir", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/mmars/mmars.py_load_from_mmar_load_from_mmar._parse_model_config_fo": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/mmars/mmars.py_load_from_mmar_load_from_mmar._parse_model_config_fo", "embedding": null, "metadata": {"file_path": "monai/apps/mmars/mmars.py", "file_name": "mmars.py", "file_type": "text/x-python", "category": "implementation", "start_line": 182, "end_line": 253, "span_ids": ["load_from_mmar"], "tokens": 783}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def load_from_mmar(\n    item,\n    mmar_dir: Optional[PathLike] = None,\n    progress: bool = True,\n    version: int = -1,\n    map_location=None,\n    pretrained=True,\n    weights_only=False,\n    model_key: str = \"model\",\n):\n    \"\"\"\n    Download and extract Medical Model Archive (MMAR) model weights from Nvidia Clara Train.\n\n    Args:\n        item: the corresponding model item from `MODEL_DESC`.\n        mmar_dir: : target directory to store the MMAR, default is mmars subfolder under `torch.hub get_dir()`.\n        progress: whether to display a progress bar when downloading the content.\n        version: version number of the MMAR. Set it to `-1` to use `item[Keys.VERSION]`.\n        map_location: pytorch API parameter for `torch.load` or `torch.jit.load`.\n        pretrained: whether to load the pretrained weights after initializing a network module.\n        weights_only: whether to load only the weights instead of initializing the network module and assign weights.\n        model_key: a key to search in the model file or config file for the model dictionary.\n            Currently this function assumes that the model dictionary has\n            `{\"[name|path]\": \"test.module\", \"args\": {'kw': 'test'}}`.\n\n    Examples::\n        >>> from monai.apps import load_from_mmar\n        >>> unet_model = load_from_mmar(\"clara_pt_prostate_mri_segmentation_1\", mmar_dir=\".\", map_location=\"cpu\")\n        >>> print(unet_model)\n\n    See Also:\n        https://docs.nvidia.com/clara/\n    \"\"\"\n    if not isinstance(item, Mapping):\n        item = get_model_spec(item)\n    model_dir = download_mmar(item=item, mmar_dir=mmar_dir, progress=progress, version=version)\n    model_file = model_dir / item[Keys.MODEL_FILE]\n    logger.info(f'\\n*** \"{item[Keys.ID]}\" available at {model_dir}.')\n\n    # loading with `torch.jit.load`\n    if model_file.name.endswith(\".ts\"):\n        if not pretrained:\n            warnings.warn(\"Loading a ScriptModule, 'pretrained' option ignored.\")\n        if weights_only:\n            warnings.warn(\"Loading a ScriptModule, 'weights_only' option ignored.\")\n        return torch.jit.load(model_file, map_location=map_location)\n\n    # loading with `torch.load`\n    model_dict = torch.load(model_file, map_location=map_location)\n    if weights_only:\n        return model_dict.get(model_key, model_dict)  # model_dict[model_key] or model_dict directly\n\n    # 1. search `model_dict['train_config]` for model config spec.\n    model_config = _get_val(dict(model_dict).get(\"train_conf\", {}), key=model_key, default={})\n    if not model_config:\n        # 2. search json CONFIG_FILE for model config spec.\n        json_path = model_dir / item.get(Keys.CONFIG_FILE, \"config_train.json\")\n        with open(json_path) as f:\n            conf_dict = json.load(f)\n        conf_dict = dict(conf_dict)\n        model_config = _get_val(conf_dict, key=model_key, default={})\n    if not model_config:\n        # 3. search `model_dict` for model config spec.\n        model_config = _get_val(dict(model_dict), key=model_key, default={})\n\n    if not (model_config and isinstance(model_config, Mapping)):\n        raise ValueError(\n            f\"Could not load model config dictionary from config: {item.get(Keys.CONFIG_FILE)}, \"\n            f\"or from model file: {item.get(Keys.MODEL_FILE)}.\"\n        )\n\n    # parse `model_config` for model class and model parameters\n    # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/mmars/mmars.py_load_from_mmar.if_model_config_get_name_load_from_mmar.return.model_inst": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/mmars/mmars.py_load_from_mmar.if_model_config_get_name_load_from_mmar.return.model_inst", "embedding": null, "metadata": {"file_path": "monai/apps/mmars/mmars.py", "file_name": "mmars.py", "file_type": "text/x-python", "category": "implementation", "start_line": 254, "end_line": 282, "span_ids": ["load_from_mmar"], "tokens": 420}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def load_from_mmar(\n    item,\n    mmar_dir: Optional[PathLike] = None,\n    progress: bool = True,\n    version: int = -1,\n    map_location=None,\n    pretrained=True,\n    weights_only=False,\n    model_key: str = \"model\",\n):\n    # ... other code\n    if model_config.get(\"name\"):  # model config section is a \"name\"\n        model_name = model_config[\"name\"]\n        model_cls = monai_nets.__dict__[model_name]\n    elif model_config.get(\"path\"):  # model config section is a \"path\"\n        # https://docs.nvidia.com/clara/clara-train-sdk/pt/byom.html\n        model_module, model_name = model_config.get(\"path\", \".\").rsplit(\".\", 1)\n        model_cls, has_cls = optional_import(module=model_module, name=model_name)\n        if not has_cls:\n            raise ValueError(\n                f\"Could not load MMAR model config {model_config.get('path', '')}, \"\n                f\"Please make sure MMAR's sub-folders in '{model_dir}' is on the PYTHONPATH.\"\n                \"See also: https://docs.nvidia.com/clara/clara-train-sdk/pt/byom.html\"\n            )\n    else:\n        raise ValueError(f\"Could not load model config {model_config}.\")\n\n    logger.info(f\"*** Model: {model_cls}\")\n    model_kwargs = model_config.get(\"args\", None)\n    if model_kwargs:\n        model_inst = model_cls(**model_kwargs)\n        logger.info(f\"*** Model params: {model_kwargs}\")\n    else:\n        model_inst = model_cls()\n    if pretrained:\n        model_inst.load_state_dict(model_dict.get(model_key, model_dict))\n    logger.info(\"\\n---\")\n    doc_url = item.get(Keys.DOC) or _get_ngc_doc_url(item[Keys.NAME], model_prefix=\"nvidia:med:\")\n    logger.info(f\"For more information, please visit {doc_url}\\n\")\n    return model_inst", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/mmars/mmars.py__get_val_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/mmars/mmars.py__get_val_", "embedding": null, "metadata": {"file_path": "monai/apps/mmars/mmars.py", "file_name": "mmars.py", "file_type": "text/x-python", "category": "implementation", "start_line": 193, "end_line": 207, "span_ids": ["_get_val"], "tokens": 117}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _get_val(input_dict: Mapping, key=\"model\", default=None):\n    \"\"\"\n    Search for the item with `key` in `config_dict`.\n    Returns: the first occurrence of `key` in a breadth first search.\n    \"\"\"\n    if key in input_dict:\n        return input_dict[key]\n    for sub_dict in input_dict:\n        val = input_dict[sub_dict]\n        if isinstance(val, Mapping):\n            found_val = _get_val(val, key=key, default=None)\n            if found_val is not None:\n                return found_val\n    return default", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/utils.py__download_with_progress__download_with_progress.try_.except_URLError_HTTPErr.raise_e": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/utils.py__download_with_progress__download_with_progress.try_.except_URLError_HTTPErr.raise_e", "embedding": null, "metadata": {"file_path": "monai/apps/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 78, "end_line": 110, "span_ids": ["_download_with_progress"], "tokens": 351}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _download_with_progress(url, filepath, progress: bool = True):\n    \"\"\"\n    Retrieve file from `url` to `filepath`, optionally showing a progress bar.\n    \"\"\"\n    try:\n        if has_tqdm and progress:\n\n            class TqdmUpTo(tqdm):\n                \"\"\"\n                Provides `update_to(n)` which uses `tqdm.update(delta_n)`.\n                Inspired by the example in https://github.com/tqdm/tqdm.\n                \"\"\"\n\n                def update_to(self, b: int = 1, bsize: int = 1, tsize: Optional[int] = None):\n                    \"\"\"\n                    Args:\n                        b: number of blocks transferred so far, default: 1.\n                        bsize: size of each block (in tqdm units), default: 1.\n                        tsize: total size (in tqdm units). if None, remains unchanged.\n                    \"\"\"\n                    if tsize is not None:\n                        self.total = tsize\n                    self.update(b * bsize - self.n)  # will also set self.n = b * bsize\n\n            with TqdmUpTo(unit=\"B\", unit_scale=True, unit_divisor=1024, miniters=1, desc=_basename(filepath)) as t:\n                urlretrieve(url, filepath, reporthook=t.update_to)\n        else:\n            if not has_tqdm and progress:\n                warnings.warn(\"tqdm is not installed, will not show the downloading progress bar.\")\n            urlretrieve(url, filepath)\n    except (URLError, HTTPError, ContentTooShortError, OSError) as e:\n        logger.error(f\"Download failed from {url} to {filepath}.\")\n        raise e", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/utils.py_extractall_extractall.raise_NotImplementedError": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/utils.py_extractall_extractall.raise_NotImplementedError", "embedding": null, "metadata": {"file_path": "monai/apps/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 210, "end_line": 266, "span_ids": ["extractall"], "tokens": 601}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def extractall(\n    filepath: PathLike,\n    output_dir: PathLike = \".\",\n    hash_val: Optional[str] = None,\n    hash_type: str = \"md5\",\n    file_type: str = \"\",\n    has_base: bool = True,\n) -> None:\n    \"\"\"\n    Extract file to the output directory.\n    Expected file types are: `zip`, `tar.gz` and `tar`.\n\n    Args:\n        filepath: the file path of compressed file.\n        output_dir: target directory to save extracted files.\n        hash_val: expected hash value to validate the compressed file.\n            if None, skip hash validation.\n        hash_type: 'md5' or 'sha1', defaults to 'md5'.\n        file_type: string of file type for decompressing. Leave it empty to infer the type from the filepath basename.\n        has_base: whether the extracted files have a base folder. This flag is used when checking if the existing\n            folder is a result of `extractall`, if it is, the extraction is skipped. For example, if A.zip is unzipped\n            to folder structure `A/*.png`, this flag should be True; if B.zip is unzipped to `*.png`, this flag should\n            be False.\n\n    Raises:\n        RuntimeError: When the hash validation of the ``filepath`` compressed file fails.\n        NotImplementedError: When the ``filepath`` file extension is not one of [zip\", \"tar.gz\", \"tar\"].\n\n    \"\"\"\n    if has_base:\n        # the extracted files will be in this folder\n        cache_dir = Path(output_dir, _basename(filepath).split(\".\")[0])\n    else:\n        cache_dir = Path(output_dir)\n    if cache_dir.exists() and next(cache_dir.iterdir(), None) is not None:\n        logger.info(f\"Non-empty folder exists in {cache_dir}, skipped extracting.\")\n        return\n    filepath = Path(filepath)\n    if hash_val and not check_hash(filepath, hash_val, hash_type):\n        raise RuntimeError(\n            f\"{hash_type} check of compressed file failed: \" f\"filepath={filepath}, expected {hash_type}={hash_val}.\"\n        )\n    logger.info(f\"Writing into directory: {output_dir}.\")\n    _file_type = file_type.lower().strip()\n    if filepath.name.endswith(\"zip\") or _file_type == \"zip\":\n        zip_file = zipfile.ZipFile(filepath)\n        zip_file.extractall(output_dir)\n        zip_file.close()\n        return\n    if filepath.name.endswith(\"tar\") or filepath.name.endswith(\"tar.gz\") or \"tar\" in _file_type:\n        tar_file = tarfile.open(filepath)\n        tar_file.extractall(output_dir)\n        tar_file.close()\n        return\n    raise NotImplementedError(\n        f'Unsupported file type, available options are: [\"zip\", \"tar.gz\", \"tar\"]. name={filepath} type={file_type}.'\n    )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/csv_saver.py_os_CSVSaver.finalize.self_reset_cache_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/csv_saver.py_os_CSVSaver.finalize.self_reset_cache_", "embedding": null, "metadata": {"file_path": "monai/data/csv_saver.py", "file_name": "csv_saver.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 78, "span_ids": ["CSVSaver.finalize", "CSVSaver.__init__", "CSVSaver", "docstring"], "tokens": 514}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport warnings\nfrom collections import OrderedDict\nfrom pathlib import Path\nfrom typing import Dict, Optional, Union\n\nimport numpy as np\nimport torch\n\nfrom monai.config.type_definitions import PathLike\nfrom monai.utils import ImageMetaKey as Key\n\n\nclass CSVSaver:\n    \"\"\"\n    Save the data in a dictionary format cache, and write to a CSV file finally.\n    Typically, the data can be classification predictions, call `save` for single data\n    or call `save_batch` to save a batch of data together, and call `finalize` to write\n    the cached data into CSV file. If no meta data provided, use index from 0 to save data.\n    Note that this saver can't support multi-processing because it reads / writes single\n    CSV file and can't guarantee the data order in multi-processing situation.\n\n    \"\"\"\n\n    def __init__(\n        self,\n        output_dir: PathLike = \"./\",\n        filename: str = \"predictions.csv\",\n        overwrite: bool = True,\n        flush: bool = False,\n    ) -> None:\n        \"\"\"\n        Args:\n            output_dir: output CSV file directory.\n            filename: name of the saved CSV file name.\n            overwrite: whether to overwriting existing CSV file content, if True, will clear the file before saving.\n                otherwise, will append new content to the CSV file.\n            flush: whether to write the cache data to CSV file immediately when `save_batch` and clear the cache.\n                default to False.\n\n        \"\"\"\n        self.output_dir = Path(output_dir)\n        self._cache_dict: OrderedDict = OrderedDict()\n        if not (isinstance(filename, str) and filename[-4:] == \".csv\"):\n            warnings.warn(\"CSV filename is not a string ends with '.csv'.\")\n        self._filepath = self.output_dir / filename\n        if self._filepath.exists() and overwrite:\n            os.remove(self._filepath)\n\n        self.flush = flush\n        self._data_index = 0\n\n    def finalize(self) -> None:\n        \"\"\"\n        Writes the cached dict to a csv\n\n        \"\"\"\n        if not self.output_dir.exists():\n            self.output_dir.mkdir(parents=True, exist_ok=True)\n        with open(self._filepath, \"a\") as f:\n            for k, v in self._cache_dict.items():\n                f.write(k)\n                for result in v.flatten():\n                    f.write(\",\" + str(result))\n                f.write(\"\\n\")\n        # clear cache content after writing\n        self.reset_cache()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_collections.abc_pd___optional_import_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_collections.abc_pd___optional_import_", "embedding": null, "metadata": {"file_path": "monai/data/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 13, "end_line": 46, "span_ids": ["docstring"], "tokens": 249}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import collections.abc\nimport math\nimport pickle\nimport shutil\nimport sys\nimport tempfile\nimport threading\nimport time\nimport warnings\nfrom copy import copy, deepcopy\nfrom multiprocessing.pool import ThreadPool\nfrom pathlib import Path\nfrom typing import IO, TYPE_CHECKING, Any, Callable, Dict, List, Optional, Sequence, Union\n\nimport numpy as np\nimport torch\nfrom torch.serialization import DEFAULT_PROTOCOL\nfrom torch.utils.data import Dataset as _TorchDataset\nfrom torch.utils.data import Subset\n\nfrom monai.data.utils import SUPPORTED_PICKLE_MOD, convert_tables_to_dicts, pickle_hashing\nfrom monai.transforms import Compose, Randomizable, ThreadUnsafe, Transform, apply_transform\nfrom monai.utils import MAX_SEED, deprecated_arg, get_seed, look_up_option, min_version, optional_import\nfrom monai.utils.misc import first\n\nif TYPE_CHECKING:\n    from tqdm import tqdm\n\n    has_tqdm = True\nelse:\n    tqdm, has_tqdm = optional_import(\"tqdm\", \"4.47.0\", min_version, \"tqdm\")\n\nlmdb, _ = optional_import(\"lmdb\")\npd, _ = optional_import(\"pandas\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_CacheDataset_CacheDataset._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_CacheDataset_CacheDataset._", "embedding": null, "metadata": {"file_path": "monai/data/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 520, "end_line": 568, "span_ids": ["CacheDataset"], "tokens": 584}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CacheDataset(Dataset):\n    \"\"\"\n    Dataset with cache mechanism that can load data and cache deterministic transforms' result during training.\n\n    By caching the results of non-random preprocessing transforms, it accelerates the training data pipeline.\n    If the requested data is not in the cache, all transforms will run normally\n    (see also :py:class:`monai.data.dataset.Dataset`).\n\n    Users can set the cache rate or number of items to cache.\n    It is recommended to experiment with different `cache_num` or `cache_rate` to identify the best training speed.\n\n    The transforms which are supposed to be cached must implement the `monai.transforms.Transform`\n    interface and should not be `Randomizable`. This dataset will cache the outcomes before the first\n    `Randomizable` `Transform` within a `Compose` instance.\n    So to improve the caching efficiency, please always put as many as possible non-random transforms\n    before the randomized ones when composing the chain of transforms.\n    If passing slicing indices, will return a PyTorch Subset, for example: `data: Subset = dataset[1:4]`,\n    for more details, please check: https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset\n\n    For example, if the transform is a `Compose` of::\n\n        transforms = Compose([\n            LoadImaged(),\n            AddChanneld(),\n            Spacingd(),\n            Orientationd(),\n            ScaleIntensityRanged(),\n            RandCropByPosNegLabeld(),\n            ToTensord()\n        ])\n\n    when `transforms` is used in a multi-epoch training pipeline, before the first training epoch,\n    this dataset will cache the results up to ``ScaleIntensityRanged``, as\n    all non-random transforms `LoadImaged`, `AddChanneld`, `Spacingd`, `Orientationd`, `ScaleIntensityRanged`\n    can be cached. During training, the dataset will load the cached results and run\n    ``RandCropByPosNegLabeld`` and ``ToTensord``, as ``RandCropByPosNegLabeld`` is a randomized transform\n    and the outcome not cached.\n\n    During training call `set_data()` to update input data and recompute cache content, note that it requires\n    `persistent_workers=False` in the PyTorch DataLoader.\n\n    Note:\n        `CacheDataset` executes non-random transforms and prepares cache content in the main process before\n        the first epoch, then all the subprocesses of DataLoader will read the same cache content in the main process\n        during training. it may take a long time to prepare cache content according to the size of expected cache data.\n        So to debug or verify the program before real training, users can set `cache_rate=0.0` or `cache_num=0` to\n        temporarily skip caching.\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_NPZDictItemDataset_NPZDictItemDataset._transform.raise_AssertionError_Wit": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_NPZDictItemDataset_NPZDictItemDataset._transform.raise_AssertionError_Wit", "embedding": null, "metadata": {"file_path": "monai/data/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1015, "end_line": 1068, "span_ids": ["NPZDictItemDataset.__len__", "NPZDictItemDataset", "NPZDictItemDataset.__init__", "NPZDictItemDataset._transform"], "tokens": 557}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class NPZDictItemDataset(Dataset):\n    \"\"\"\n    Represents a dataset from a loaded NPZ file. The members of the file to load are named in the keys of `keys` and\n    stored under the keyed name. All loaded arrays must have the same 0-dimension (batch) size. Items are always dicts\n    mapping names to an item extracted from the loaded arrays.\n    If passing slicing indices, will return a PyTorch Subset, for example: `data: Subset = dataset[1:4]`,\n    for more details, please check: https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset\n\n    Args:\n        npzfile: Path to .npz file or stream containing .npz file data\n        keys: Maps keys to load from file to name to store in dataset\n        transform: Transform to apply to batch dict\n        other_keys: secondary data to load from file and store in dict `other_keys`, not returned by __getitem__\n    \"\"\"\n\n    def __init__(\n        self,\n        npzfile: Union[str, IO],\n        keys: Dict[str, str],\n        transform: Optional[Callable[..., Dict[str, Any]]] = None,\n        other_keys: Optional[Sequence[str]] = (),\n    ):\n        self.npzfile: Union[str, IO] = npzfile if isinstance(npzfile, str) else \"STREAM\"\n        self.keys: Dict[str, str] = dict(keys)\n        dat = np.load(npzfile)\n\n        self.arrays = {storedk: dat[datak] for datak, storedk in self.keys.items()}\n        self.length = self.arrays[first(self.keys.values())].shape[0]\n\n        self.other_keys = {} if other_keys is None else {k: dat[k] for k in other_keys}\n\n        for k, v in self.arrays.items():\n            if v.shape[0] != self.length:\n                raise ValueError(\n                    \"All loaded arrays must have the same first dimension \"\n                    f\"size {self.length}, array `{k}` has size {v.shape[0]}\"\n                )\n\n        super().__init__([], transform)\n\n    def __len__(self):\n        return self.length\n\n    def _transform(self, index: int):\n        data = {k: v[index] for k, v in self.arrays.items()}\n\n        if not self.transform:\n            return data\n\n        result = apply_transform(self.transform, data)\n\n        if isinstance(result, dict) or (isinstance(result, list) and isinstance(result[0], dict)):\n            return result\n        raise AssertionError(\"With a dict supplied to apply_transform, should return a dict or a list of dicts.\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_CSVDataset_CSVDataset._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_CSVDataset_CSVDataset._", "embedding": null, "metadata": {"file_path": "monai/data/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1258, "end_line": 1306, "span_ids": ["CSVDataset"], "tokens": 703}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CSVDataset(Dataset):\n    \"\"\"\n    Dataset to load data from CSV files and generate a list of dictionaries,\n    every dictionary maps to a row of the CSV file, and the keys of dictionary\n    map to the column names of the CSV file.\n\n    It can load multiple CSV files and join the tables with additional `kwargs` arg.\n    Support to only load specific rows and columns.\n    And it can also group several loaded columns to generate a new column, for example,\n    set `col_groups={\"meta\": [\"meta_0\", \"meta_1\", \"meta_2\"]}`, output can be::\n\n        [\n            {\"image\": \"./image0.nii\", \"meta_0\": 11, \"meta_1\": 12, \"meta_2\": 13, \"meta\": [11, 12, 13]},\n            {\"image\": \"./image1.nii\", \"meta_0\": 21, \"meta_1\": 22, \"meta_2\": 23, \"meta\": [21, 22, 23]},\n        ]\n\n    Args:\n        src: if provided the filename of CSV file, it can be a str, URL, path object or file-like object to load.\n            also support to provide pandas `DataFrame` directly, will skip loading from filename.\n            if provided a list of filenames or pandas `DataFrame`, it will join the tables.\n        row_indices: indices of the expected rows to load. it should be a list,\n            every item can be a int number or a range `[start, end)` for the indices.\n            for example: `row_indices=[[0, 100], 200, 201, 202, 300]`. if None,\n            load all the rows in the file.\n        col_names: names of the expected columns to load. if None, load all the columns.\n        col_types: `type` and `default value` to convert the loaded columns, if None, use original data.\n            it should be a dictionary, every item maps to an expected column, the `key` is the column\n            name and the `value` is None or a dictionary to define the default value and data type.\n            the supported keys in dictionary are: [\"type\", \"default\"]. for example::\n\n                col_types = {\n                    \"subject_id\": {\"type\": str},\n                    \"label\": {\"type\": int, \"default\": 0},\n                    \"ehr_0\": {\"type\": float, \"default\": 0.0},\n                    \"ehr_1\": {\"type\": float, \"default\": 0.0},\n                    \"image\": {\"type\": str, \"default\": None},\n                }\n\n        col_groups: args to group the loaded columns to generate a new column,\n            it should be a dictionary, every item maps to a group, the `key` will\n            be the new column name, the `value` is the names of columns to combine. for example:\n            `col_groups={\"ehr\": [f\"ehr_{i}\" for i in range(10)], \"meta\": [\"meta_1\", \"meta_2\"]}`\n        transform: transform to apply on the loaded items of a dictionary data.\n        kwargs: additional arguments for `pandas.merge()` API to join tables.\n\n    .. deprecated:: 0.8.0\n        ``filename`` is deprecated, use ``src`` instead.\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_CSVDataset.__init___": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_CSVDataset.__init___", "embedding": null, "metadata": {"file_path": "monai/data/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1308, "end_line": 1336, "span_ids": ["CSVDataset.__init__"], "tokens": 315}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CSVDataset(Dataset):\n\n    @deprecated_arg(name=\"filename\", new_name=\"src\", since=\"0.8\", msg_suffix=\"please use `src` instead.\")\n    def __init__(\n        self,\n        src: Optional[Union[str, Sequence[str]]] = None,  # also can be `DataFrame` or sequense of `DataFrame`\n        row_indices: Optional[Sequence[Union[int, str]]] = None,\n        col_names: Optional[Sequence[str]] = None,\n        col_types: Optional[Dict[str, Optional[Dict[str, Any]]]] = None,\n        col_groups: Optional[Dict[str, Sequence[str]]] = None,\n        transform: Optional[Callable] = None,\n        **kwargs,\n    ):\n        srcs = (src,) if not isinstance(src, (tuple, list)) else src\n        dfs: List = []\n        for i in srcs:\n            if isinstance(i, str):\n                dfs.append(pd.read_csv(i))\n            elif isinstance(i, pd.DataFrame):\n                dfs.append(i)\n            else:\n                raise ValueError(\"`src` must be file path or pandas `DataFrame`.\")\n\n        # in case treating deprecated arg `filename` as kwargs, remove it from `kwargs`\n        kwargs.pop(\"filename\", None)\n\n        data = convert_tables_to_dicts(\n            dfs=dfs, row_indices=row_indices, col_names=col_names, col_types=col_types, col_groups=col_groups, **kwargs\n        )\n        super().__init__(data=data, transform=transform)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_NibabelReader._get_meta_dict_NibabelReader._get_affine.return.np_array_img_affine_copy": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_NibabelReader._get_meta_dict_NibabelReader._get_affine.return.np_array_img_affine_copy", "embedding": null, "metadata": {"file_path": "monai/data/image_reader.py", "file_name": "image_reader.py", "file_type": "text/x-python", "category": "implementation", "start_line": 422, "end_line": 446, "span_ids": ["NibabelReader._get_meta_dict", "NibabelReader._get_affine"], "tokens": 183}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@require_pkg(pkg_name=\"nibabel\")\nclass NibabelReader(ImageReader):\n\n    def _get_meta_dict(self, img) -> Dict:\n        \"\"\"\n        Get the all the meta data of the image and convert to dict type.\n\n        Args:\n            img: a Nibabel image object loaded from an image file.\n\n        \"\"\"\n        # swap to little endian as PyTorch doesn't support big endian\n        try:\n            header = img.header.as_byteswapped(\"<\")\n        except ValueError:\n            header = img.header\n        return dict(header)\n\n    def _get_affine(self, img):\n        \"\"\"\n        Get the affine matrix of the image, it can be used to correct\n        spacing, orientation or execute spatial transforms.\n\n        Args:\n            img: a Nibabel image object loaded from an image file.\n\n        \"\"\"\n        return np.array(img.affine, copy=True)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_NibabelReader._get_spatial_shape_NibabelReader._get_array_data.return._array": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_NibabelReader._get_spatial_shape_NibabelReader._get_array_data.return._array", "embedding": null, "metadata": {"file_path": "monai/data/image_reader.py", "file_name": "image_reader.py", "file_type": "text/x-python", "category": "implementation", "start_line": 448, "end_line": 480, "span_ids": ["NibabelReader._get_spatial_shape", "NibabelReader._get_array_data"], "tokens": 273}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@require_pkg(pkg_name=\"nibabel\")\nclass NibabelReader(ImageReader):\n\n    def _get_spatial_shape(self, img):\n        \"\"\"\n        Get the spatial shape of image data, it doesn't contain the channel dim.\n\n        Args:\n            img: a Nibabel image object loaded from an image file.\n\n        \"\"\"\n        # swap to little endian as PyTorch doesn't support big endian\n        try:\n            header = img.header.as_byteswapped(\"<\")\n        except ValueError:\n            header = img.header\n        dim = header.get(\"dim\", None)\n        if dim is None:\n            dim = header.get(\"dims\")  # mgh format?\n            dim = np.insert(dim, 0, 3)\n        ndim = dim[0]\n        spatial_rank = min(ndim, 3)\n        # the img data should have no channel dim or the last dim is channel\n        return np.asarray(dim[1 : spatial_rank + 1])\n\n    def _get_array_data(self, img):\n        \"\"\"\n        Get the raw array data of the image, converted to Numpy array.\n\n        Args:\n            img: a Nibabel image object loaded from an image file.\n\n        \"\"\"\n        _array = np.array(img.get_fdata(dtype=self.dtype))\n        img.uncache()\n        return _array", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/iterable_dataset.py_CSVIterableDataset_CSVIterableDataset._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/iterable_dataset.py_CSVIterableDataset_CSVIterableDataset._", "embedding": null, "metadata": {"file_path": "monai/data/iterable_dataset.py", "file_name": "iterable_dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 122, "end_line": 184, "span_ids": ["CSVIterableDataset"], "tokens": 950}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CSVIterableDataset(IterableDataset):\n    \"\"\"\n    Iterable dataset to load CSV files and generate dictionary data.\n    It is particularly useful when data come from a stream, inherits from PyTorch IterableDataset:\n    https://pytorch.org/docs/stable/data.html?highlight=iterabledataset#torch.utils.data.IterableDataset.\n\n    It also can be helpful when loading extremely big CSV files that can't read into memory directly,\n    just treat the big CSV file as stream input, call `reset()` of `CSVIterableDataset` for every epoch.\n    Note that as a stream input, it can't get the length of dataset.\n\n    To effectively shuffle the data in the big dataset, users can set a big buffer to continuously store\n    the loaded data, then randomly pick data from the buffer for following tasks.\n\n    To accelerate the loading process, it can support multi-processing based on PyTorch DataLoader workers,\n    every process executes transforms on part of every loaded data.\n    Note: the order of output data may not match data source in multi-processing mode.\n\n    It can load data from multiple CSV files and join the tables with additional `kwargs` arg.\n    Support to only load specific columns.\n    And it can also group several loaded columns to generate a new column, for example,\n    set `col_groups={\"meta\": [\"meta_0\", \"meta_1\", \"meta_2\"]}`, output can be::\n\n        [\n            {\"image\": \"./image0.nii\", \"meta_0\": 11, \"meta_1\": 12, \"meta_2\": 13, \"meta\": [11, 12, 13]},\n            {\"image\": \"./image1.nii\", \"meta_0\": 21, \"meta_1\": 22, \"meta_2\": 23, \"meta\": [21, 22, 23]},\n        ]\n\n    Args:\n        src: if provided the filename of CSV file, it can be a str, URL, path object or file-like object to load.\n            also support to provide iter for stream input directly, will skip loading from filename.\n            if provided a list of filenames or iters, it will join the tables.\n        chunksize: rows of a chunk when loading iterable data from CSV files, default to 1000. more details:\n            https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html.\n        buffer_size: size of the buffer to store the loaded chunks, if None, set to `2 x chunksize`.\n        col_names: names of the expected columns to load. if None, load all the columns.\n        col_types: `type` and `default value` to convert the loaded columns, if None, use original data.\n            it should be a dictionary, every item maps to an expected column, the `key` is the column\n            name and the `value` is None or a dictionary to define the default value and data type.\n            the supported keys in dictionary are: [\"type\", \"default\"]. for example::\n\n                col_types = {\n                    \"subject_id\": {\"type\": str},\n                    \"label\": {\"type\": int, \"default\": 0},\n                    \"ehr_0\": {\"type\": float, \"default\": 0.0},\n                    \"ehr_1\": {\"type\": float, \"default\": 0.0},\n                    \"image\": {\"type\": str, \"default\": None},\n                }\n\n        col_groups: args to group the loaded columns to generate a new column,\n            it should be a dictionary, every item maps to a group, the `key` will\n            be the new column name, the `value` is the names of columns to combine. for example:\n            `col_groups={\"ehr\": [f\"ehr_{i}\" for i in range(10)], \"meta\": [\"meta_1\", \"meta_2\"]}`\n        transform: transform to apply on the loaded items of a dictionary data.\n        shuffle: whether to shuffle all the data in the buffer every time a new chunk loaded.\n        seed: random seed to initialize the random state for all the workers if `shuffle` is True,\n            set `seed += 1` in every iter() call, refer to the PyTorch idea:\n            https://github.com/pytorch/pytorch/blob/v1.10.0/torch/utils/data/distributed.py#L98.\n        kwargs: additional arguments for `pandas.merge()` API to join tables.\n\n    .. deprecated:: 0.8.0\n        ``filename`` is deprecated, use ``src`` instead.\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/nifti_saver.py_NiftiSaver.__init___NiftiSaver.__init__.self.print_log.print_log": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/nifti_saver.py_NiftiSaver.__init___NiftiSaver.__init__.self.print_log.print_log", "embedding": null, "metadata": {"file_path": "monai/data/nifti_saver.py", "file_name": "nifti_saver.py", "file_type": "text/x-python", "category": "implementation", "start_line": 37, "end_line": 106, "span_ids": ["NiftiSaver.__init__"], "tokens": 915}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class NiftiSaver:\n\n    def __init__(\n        self,\n        output_dir: PathLike = \"./\",\n        output_postfix: str = \"seg\",\n        output_ext: str = \".nii.gz\",\n        resample: bool = True,\n        mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,\n        padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER,\n        align_corners: bool = False,\n        dtype: DtypeLike = np.float64,\n        output_dtype: DtypeLike = np.float32,\n        squeeze_end_dims: bool = True,\n        data_root_dir: PathLike = \"\",\n        separate_folder: bool = True,\n        print_log: bool = True,\n    ) -> None:\n        \"\"\"\n        Args:\n            output_dir: output image directory.\n            output_postfix: a string appended to all output file names.\n            output_ext: output file extension name.\n            resample: whether to convert the data array to it's original coordinate system\n                based on `original_affine` in the `meta_data`.\n            mode: {``\"bilinear\"``, ``\"nearest\"``}\n                This option is used when ``resample = True``.\n                Interpolation mode to calculate output values. Defaults to ``\"bilinear\"``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            padding_mode: {``\"zeros\"``, ``\"border\"``, ``\"reflection\"``}\n                This option is used when ``resample = True``.\n                Padding mode for outside grid values. Defaults to ``\"border\"``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            align_corners: Geometrically, we consider the pixels of the input as squares rather than points.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            dtype: data type for resampling computation. Defaults to ``np.float64`` for best precision.\n                If None, use the data type of input data.\n            output_dtype: data type for saving data. Defaults to ``np.float32``.\n            squeeze_end_dims: if True, any trailing singleton dimensions will be removed (after the channel\n                has been moved to the end). So if input is (C,H,W,D), this will be altered to (H,W,D,C), and\n                then if C==1, it will be saved as (H,W,D). If D also ==1, it will be saved as (H,W). If false,\n                image will always be saved as (H,W,D,C).\n            data_root_dir: if not empty, it specifies the beginning parts of the input file's\n                absolute path. it's used to compute `input_file_rel_path`, the relative path to the file from\n                `data_root_dir` to preserve folder structure when saving in case there are files in different\n                folders with the same file names. for example:\n                input_file_name: /foo/bar/test1/image.nii,\n                postfix: seg\n                output_ext: nii.gz\n                output_dir: /output,\n                data_root_dir: /foo/bar,\n                output will be: /output/test1/image/image_seg.nii.gz\n            separate_folder: whether to save every file in a separate folder, for example: if input filename is\n                `image.nii`, postfix is `seg` and folder_path is `output`, if `True`, save as:\n                `output/image/image_seg.nii`, if `False`, save as `output/image_seg.nii`. default to `True`.\n            print_log: whether to print log about the saved NIfTI file path, etc. default to `True`.\n\n        \"\"\"\n        self.output_dir = output_dir\n        self.output_postfix = output_postfix\n        self.output_ext = output_ext\n        self.resample = resample\n        self.mode: GridSampleMode = GridSampleMode(mode)\n        self.padding_mode: GridSamplePadMode = GridSamplePadMode(padding_mode)\n        self.align_corners = align_corners\n        self.dtype = dtype\n        self.output_dtype = output_dtype\n        self._data_index = 0\n        self.squeeze_end_dims = squeeze_end_dims\n        self.data_root_dir = data_root_dir\n        self.separate_folder = separate_folder\n        self.print_log = print_log", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/nifti_saver.py_NiftiSaver.save_NiftiSaver.save.if_self_print_log_.print_f_file_written_pa": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/nifti_saver.py_NiftiSaver.save_NiftiSaver.save.if_self_print_log_.print_f_file_written_pa", "embedding": null, "metadata": {"file_path": "monai/data/nifti_saver.py", "file_name": "nifti_saver.py", "file_type": "text/x-python", "category": "implementation", "start_line": 109, "end_line": 178, "span_ids": ["NiftiSaver.save"], "tokens": 702}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class NiftiSaver:\n\n    def save(self, data: Union[torch.Tensor, np.ndarray], meta_data: Optional[Dict] = None) -> None:\n        \"\"\"\n        Save data into a NIfTI file.\n        The meta_data could optionally have the following keys:\n\n            - ``'filename_or_obj'`` -- for output file name creation, corresponding to filename or object.\n            - ``'original_affine'`` -- for data orientation handling, defaulting to an identity matrix.\n            - ``'affine'`` -- for data output affine, defaulting to an identity matrix.\n            - ``'spatial_shape'`` -- for data output shape.\n            - ``'patch_index'`` -- if the data is a patch of big image, append the patch index to filename.\n\n        When meta_data is specified and `resample=True`, the saver will try to resample batch data from the space\n        defined by \"affine\" to the space defined by \"original_affine\".\n\n        If meta_data is None, use the default index (starting from 0) as the filename.\n\n        Args:\n            data: target data content that to be saved as a NIfTI format file.\n                Assuming the data shape starts with a channel dimension and followed by spatial dimensions.\n            meta_data: the meta data information corresponding to the data.\n\n        See Also\n            :py:meth:`monai.data.nifti_writer.write_nifti`\n        \"\"\"\n        filename = meta_data[Key.FILENAME_OR_OBJ] if meta_data else str(self._data_index)\n        self._data_index += 1\n        original_affine = meta_data.get(\"original_affine\", None) if meta_data and self.resample else None\n        affine = meta_data.get(\"affine\", None) if meta_data else None\n        spatial_shape = meta_data.get(\"spatial_shape\", None) if meta_data else None\n        patch_index = meta_data.get(Key.PATCH_INDEX, None) if meta_data else None\n\n        if isinstance(data, torch.Tensor):\n            data = data.detach().cpu().numpy()\n\n        path = create_file_basename(\n            postfix=self.output_postfix,\n            input_file_name=filename,\n            folder_path=self.output_dir,\n            data_root_dir=self.data_root_dir,\n            separate_folder=self.separate_folder,\n            patch_index=patch_index,\n        )\n        path = f\"{path}{self.output_ext}\"\n        # change data shape to be (channel, h, w, d)\n        while len(data.shape) < 4:\n            data = np.expand_dims(data, -1)\n        # change data to \"channel last\" format and write to NIfTI format file\n        data = np.moveaxis(np.asarray(data), 0, -1)\n\n        # if desired, remove trailing singleton dimensions\n        if self.squeeze_end_dims:\n            while data.shape[-1] == 1:\n                data = np.squeeze(data, -1)\n\n        write_nifti(\n            data,\n            file_name=path,\n            affine=affine,\n            target_affine=original_affine,\n            resample=True,\n            output_spatial_shape=spatial_shape,\n            mode=self.mode,\n            padding_mode=self.padding_mode,\n            align_corners=self.align_corners,\n            dtype=self.dtype,\n            output_dtype=self.output_dtype,\n        )\n\n        if self.print_log:\n            print(f\"file written: {path}.\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/png_saver.py_PNGSaver.save_PNGSaver.save.if_self_print_log_.print_f_file_written_pa": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/png_saver.py_PNGSaver.save_PNGSaver.save.if_self_print_log_.print_f_file_written_pa", "embedding": null, "metadata": {"file_path": "monai/data/png_saver.py", "file_name": "png_saver.py", "file_type": "text/x-python", "category": "implementation", "start_line": 86, "end_line": 141, "span_ids": ["PNGSaver.save"], "tokens": 535}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class PNGSaver:\n\n    def save(self, data: Union[torch.Tensor, np.ndarray], meta_data: Optional[Dict] = None) -> None:\n        \"\"\"\n        Save data into a png file.\n        The meta_data could optionally have the following keys:\n\n            - ``'filename_or_obj'`` -- for output file name creation, corresponding to filename or object.\n            - ``'spatial_shape'`` -- for data output shape.\n            - ``'patch_index'`` -- if the data is a patch of big image, append the patch index to filename.\n\n        If meta_data is None, use the default index (starting from 0) as the filename.\n\n        Args:\n            data: target data content that to be saved as a png format file.\n                Assuming the data shape are spatial dimensions.\n                Shape of the spatial dimensions (C,H,W).\n                C should be 1, 3 or 4\n            meta_data: the meta data information corresponding to the data.\n\n        Raises:\n            ValueError: When ``data`` channels is not one of [1, 3, 4].\n\n        See Also\n            :py:meth:`monai.data.png_writer.write_png`\n\n        \"\"\"\n        filename = meta_data[Key.FILENAME_OR_OBJ] if meta_data else str(self._data_index)\n        self._data_index += 1\n        spatial_shape = meta_data.get(\"spatial_shape\", None) if meta_data and self.resample else None\n        patch_index = meta_data.get(Key.PATCH_INDEX, None) if meta_data else None\n\n        if isinstance(data, torch.Tensor):\n            data = data.detach().cpu().numpy()\n\n        path = create_file_basename(\n            postfix=self.output_postfix,\n            input_file_name=filename,\n            folder_path=self.output_dir,\n            data_root_dir=self.data_root_dir,\n            separate_folder=self.separate_folder,\n            patch_index=patch_index,\n        )\n        path = f\"{path}{self.output_ext}\"\n\n        if data.shape[0] == 1:\n            data = data.squeeze(0)\n        elif 2 < data.shape[0] < 5:\n            data = np.moveaxis(np.asarray(data), 0, -1)\n        else:\n            raise ValueError(f\"Unsupported number of channels: {data.shape[0]}, available options are [1, 3, 4]\")\n\n        write_png(\n            np.asarray(data), file_name=path, output_spatial_shape=spatial_shape, mode=self.mode, scale=self.scale\n        )\n\n        if self.print_log:\n            print(f\"file written: {path}.\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_partition_dataset_partition_dataset.if_shuffle_.rs_shuffle_indices_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_partition_dataset_partition_dataset.if_shuffle_.rs_shuffle_indices_", "embedding": null, "metadata": {"file_path": "monai/data/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 817, "end_line": 886, "span_ids": ["partition_dataset"], "tokens": 714}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def partition_dataset(\n    data: Sequence,\n    ratios: Optional[Sequence[float]] = None,\n    num_partitions: Optional[int] = None,\n    shuffle: bool = False,\n    seed: int = 0,\n    drop_last: bool = False,\n    even_divisible: bool = False,\n):\n    \"\"\"\n    Split the dataset into N partitions. It can support shuffle based on specified random seed.\n    Will return a set of datasets, every dataset contains 1 partition of original dataset.\n    And it can split the dataset based on specified ratios or evenly split into `num_partitions`.\n    Refer to: https://pytorch.org/docs/stable/distributed.html#module-torch.distributed.launch.\n\n    Note:\n        It also can be used to partition dataset for ranks in distributed training.\n        For example, partition dataset before training and use `CacheDataset`, every rank trains with its own data.\n        It can avoid duplicated caching content in each rank, but will not do global shuffle before every epoch:\n\n        .. code-block:: python\n\n            data_partition = partition_dataset(\n                data=train_files,\n                num_partitions=dist.get_world_size(),\n                shuffle=True,\n                even_divisible=True,\n            )[dist.get_rank()]\n\n            train_ds = SmartCacheDataset(\n                data=data_partition,\n                transform=train_transforms,\n                replace_rate=0.2,\n                cache_num=15,\n            )\n\n    Args:\n        data: input dataset to split, expect a list of data.\n        ratios: a list of ratio number to split the dataset, like [8, 1, 1].\n        num_partitions: expected number of the partitions to evenly split, only works when `ratios` not specified.\n        shuffle: whether to shuffle the original dataset before splitting.\n        seed: random seed to shuffle the dataset, only works when `shuffle` is True.\n        drop_last: only works when `even_divisible` is False and no ratios specified.\n            if True, will drop the tail of the data to make it evenly divisible across partitions.\n            if False, will add extra indices to make the data evenly divisible across partitions.\n        even_divisible: if True, guarantee every partition has same length.\n\n    Examples::\n\n        >>> data = [1, 2, 3, 4, 5]\n        >>> partition_dataset(data, ratios=[0.6, 0.2, 0.2], shuffle=False)\n        [[1, 2, 3], [4], [5]]\n        >>> partition_dataset(data, num_partitions=2, shuffle=False)\n        [[1, 3, 5], [2, 4]]\n        >>> partition_dataset(data, num_partitions=2, shuffle=False, even_divisible=True, drop_last=True)\n        [[1, 3], [2, 4]]\n        >>> partition_dataset(data, num_partitions=2, shuffle=False, even_divisible=True, drop_last=False)\n        [[1, 3, 5], [2, 4, 1]]\n        >>> partition_dataset(data, num_partitions=2, shuffle=False, even_divisible=False, drop_last=False)\n        [[1, 3, 5], [2, 4]]\n\n    \"\"\"\n    data_len = len(data)\n    datasets = []\n\n    indices = list(range(data_len))\n    if shuffle:\n        # deterministically shuffle based on fixed seed for every process\n        rs = np.random.RandomState(seed)\n        rs.shuffle(indices)\n    # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_partition_dataset.if_ratios__partition_dataset.return.datasets": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_partition_dataset.if_ratios__partition_dataset.return.datasets", "embedding": null, "metadata": {"file_path": "monai/data/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 829, "end_line": 865, "span_ids": ["partition_dataset"], "tokens": 411}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def partition_dataset(\n    data: Sequence,\n    ratios: Optional[Sequence[float]] = None,\n    num_partitions: Optional[int] = None,\n    shuffle: bool = False,\n    seed: int = 0,\n    drop_last: bool = False,\n    even_divisible: bool = False,\n):\n    # ... other code\n\n    if ratios:\n        next_idx = 0\n        rsum = sum(ratios)\n        for r in ratios:\n            start_idx = next_idx\n            next_idx = min(start_idx + int(r / rsum * data_len + 0.5), data_len)\n            datasets.append([data[i] for i in indices[start_idx:next_idx]])\n        return datasets\n\n    if not num_partitions:\n        raise ValueError(\"must specify number of partitions or ratios.\")\n    # evenly split the data without ratios\n    if not even_divisible and drop_last:\n        raise RuntimeError(\"drop_last only works when even_divisible is True.\")\n    if data_len < num_partitions:\n        raise RuntimeError(f\"there is no enough data to be split into {num_partitions} partitions.\")\n\n    if drop_last and data_len % num_partitions != 0:\n        # split to nearest available length that is evenly divisible\n        num_samples = math.ceil((data_len - num_partitions) / num_partitions)\n    else:\n        num_samples = math.ceil(data_len / num_partitions)\n    # use original data length if not even divisible\n    total_size = num_samples * num_partitions if even_divisible else data_len\n\n    if not drop_last and total_size - data_len > 0:\n        # add extra samples to make it evenly divisible\n        indices += indices[: (total_size - data_len)]\n    else:\n        # remove tail of data to make it evenly divisible\n        indices = indices[:total_size]\n\n    for i in range(num_partitions):\n        _indices = indices[i:total_size:num_partitions]\n        datasets.append([data[j] for j in _indices])\n\n    return datasets", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_json_hashing_sorted_dict.return._k_sorted_dict_v_if_isi": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_json_hashing_sorted_dict.return._k_sorted_dict_v_if_isi", "embedding": null, "metadata": {"file_path": "monai/data/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1045, "end_line": 1078, "span_ids": ["json_hashing", "sorted_dict", "pickle_hashing"], "tokens": 240}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def json_hashing(item) -> bytes:\n    \"\"\"\n\n    Args:\n        item: data item to be hashed\n\n    Returns: the corresponding hash key\n\n    \"\"\"\n    # TODO: Find way to hash transforms content as part of the cache\n    cache_key = hashlib.md5(json.dumps(item, sort_keys=True).encode(\"utf-8\")).hexdigest()\n    return f\"{cache_key}\".encode()\n\n\ndef pickle_hashing(item, protocol=pickle.HIGHEST_PROTOCOL) -> bytes:\n    \"\"\"\n\n    Args:\n        item: data item to be hashed\n        protocol: protocol version used for pickling,\n            defaults to `pickle.HIGHEST_PROTOCOL`.\n\n    Returns: the corresponding hash key\n\n    \"\"\"\n    cache_key = hashlib.md5(pickle.dumps(sorted_dict(item), protocol=protocol)).hexdigest()\n    return f\"{cache_key}\".encode()\n\n\ndef sorted_dict(item, key=None, reverse=False):\n    \"\"\"Return a new sorted dictionary from the `item`.\"\"\"\n    if not isinstance(item, dict):\n        return item\n    return {k: sorted_dict(v) if isinstance(v, dict) else v for k, v in sorted(item.items(), key=key, reverse=reverse)}", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_convert_tables_to_dicts_convert_tables_to_dicts.data_.df_loc_rows_if_col_names": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_convert_tables_to_dicts_convert_tables_to_dicts.data_.df_loc_rows_if_col_names", "embedding": null, "metadata": {"file_path": "monai/data/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 997, "end_line": 1051, "span_ids": ["convert_tables_to_dicts"], "tokens": 699}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def convert_tables_to_dicts(\n    dfs,\n    row_indices: Optional[Sequence[Union[int, str]]] = None,\n    col_names: Optional[Sequence[str]] = None,\n    col_types: Optional[Dict[str, Optional[Dict[str, Any]]]] = None,\n    col_groups: Optional[Dict[str, Sequence[str]]] = None,\n    **kwargs,\n) -> List[Dict[str, Any]]:\n    \"\"\"\n    Utility to join pandas tables, select rows, columns and generate groups.\n    Will return a list of dictionaries, every dictionary maps to a row of data in tables.\n\n    Args:\n        dfs: data table in pandas Dataframe format. if providing a list of tables, will join them.\n        row_indices: indices of the expected rows to load. it should be a list,\n            every item can be a int number or a range `[start, end)` for the indices.\n            for example: `row_indices=[[0, 100], 200, 201, 202, 300]`. if None,\n            load all the rows in the file.\n        col_names: names of the expected columns to load. if None, load all the columns.\n        col_types: `type` and `default value` to convert the loaded columns, if None, use original data.\n            it should be a dictionary, every item maps to an expected column, the `key` is the column\n            name and the `value` is None or a dictionary to define the default value and data type.\n            the supported keys in dictionary are: [\"type\", \"default\"], and note that the value of `default`\n            should not be `None`. for example::\n\n                col_types = {\n                    \"subject_id\": {\"type\": str},\n                    \"label\": {\"type\": int, \"default\": 0},\n                    \"ehr_0\": {\"type\": float, \"default\": 0.0},\n                    \"ehr_1\": {\"type\": float, \"default\": 0.0},\n                }\n\n        col_groups: args to group the loaded columns to generate a new column,\n            it should be a dictionary, every item maps to a group, the `key` will\n            be the new column name, the `value` is the names of columns to combine. for example:\n            `col_groups={\"ehr\": [f\"ehr_{i}\" for i in range(10)], \"meta\": [\"meta_1\", \"meta_2\"]}`\n        kwargs: additional arguments for `pandas.merge()` API to join tables.\n\n    \"\"\"\n    df = reduce(lambda l, r: pd.merge(l, r, **kwargs), ensure_tuple(dfs))\n    # parse row indices\n    rows: List[Union[int, str]] = []\n    if row_indices is None:\n        rows = slice(df.shape[0])  # type: ignore\n    else:\n        for i in row_indices:\n            if isinstance(i, (tuple, list)):\n                if len(i) != 2:\n                    raise ValueError(\"range of row indices must contain 2 values: start and end.\")\n                rows.extend(list(range(i[0], i[1])))\n            else:\n                rows.append(i)\n\n    # convert to a list of dictionaries corresponding to every row\n    data_ = df.loc[rows] if col_names is None else df.loc[rows, col_names]\n    # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_convert_tables_to_dicts.if_isinstance_col_types__": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_convert_tables_to_dicts.if_isinstance_col_types__", "embedding": null, "metadata": {"file_path": "monai/data/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1127, "end_line": 1147, "span_ids": ["convert_tables_to_dicts"], "tokens": 327}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def convert_tables_to_dicts(\n    dfs,\n    row_indices: Optional[Sequence[Union[int, str]]] = None,\n    col_names: Optional[Sequence[str]] = None,\n    col_types: Optional[Dict[str, Optional[Dict[str, Any]]]] = None,\n    col_groups: Optional[Dict[str, Sequence[str]]] = None,\n    **kwargs,\n) -> List[Dict[str, Any]]:\n    # ... other code\n    if isinstance(col_types, dict):\n        # fill default values for NaN\n        defaults = {k: v[\"default\"] for k, v in col_types.items() if v is not None and v.get(\"default\") is not None}\n        if defaults:\n            data_ = data_.fillna(value=defaults)\n        # convert data types\n        types = {k: v[\"type\"] for k, v in col_types.items() if v is not None and \"type\" in v}\n        if types:\n            data_ = data_.astype(dtype=types, copy=False)\n    data: List[Dict] = data_.to_dict(orient=\"records\")\n\n    # group columns to generate new column\n    if col_groups is not None:\n        groups: Dict[str, List] = {}\n        for name, cols in col_groups.items():\n            groups[name] = df.loc[rows, cols].values\n        # invert items of groups to every row of data\n        data = [dict(d, **{k: v[i] for k, v in groups.items()}) for i, d in enumerate(data)]\n\n    return data", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/utils.py_IterationEvents_IterationEvents.INNER_ITERATION_COMPLETED._inner_iteration_complete": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/utils.py_IterationEvents_IterationEvents.INNER_ITERATION_COMPLETED._inner_iteration_complete", "embedding": null, "metadata": {"file_path": "monai/engines/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 41, "end_line": 59, "span_ids": ["IterationEvents"], "tokens": 226}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class IterationEvents(EventEnum):\n    \"\"\"\n    Additional Events engine can register and trigger in the iteration process.\n    Refer to the example in ignite: https://pytorch.org/ignite/generated/ignite.engine.events.EventEnum.html.\n    These Events can be triggered during training iteration:\n    `FORWARD_COMPLETED` is the Event when `network(image, label)` completed.\n    `LOSS_COMPLETED` is the Event when `loss(pred, label)` completed.\n    `BACKWARD_COMPLETED` is the Event when `loss.backward()` completed.\n    `MODEL_COMPLETED` is the Event when all the model related operations completed.\n    `INNER_ITERATION_STARTED` is the Event when the iteration has an inner loop and the loop is started.\n    `INNER_ITERATION_COMPLETED` is the Event when the iteration has an inner loop and the loop is completed.\n    \"\"\"\n\n    FORWARD_COMPLETED = \"forward_completed\"\n    LOSS_COMPLETED = \"loss_completed\"\n    BACKWARD_COMPLETED = \"backward_completed\"\n    MODEL_COMPLETED = \"model_completed\"\n    INNER_ITERATION_STARTED = \"inner_iteration_started\"\n    INNER_ITERATION_COMPLETED = \"inner_iteration_completed\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/utils.py_GanKeys_get_devices_spec.return.devices": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/utils.py_GanKeys_get_devices_spec.return.devices", "embedding": null, "metadata": {"file_path": "monai/engines/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 62, "end_line": 103, "span_ids": ["get_devices_spec", "GanKeys"], "tokens": 278}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GanKeys:\n    \"\"\"\n    A set of common keys for generative adversarial networks.\n\n    \"\"\"\n\n    REALS = \"reals\"\n    FAKES = \"fakes\"\n    LATENTS = \"latents\"\n    GLOSS = \"g_loss\"\n    DLOSS = \"d_loss\"\n\n\ndef get_devices_spec(devices: Optional[Sequence[torch.device]] = None) -> List[torch.device]:\n    \"\"\"\n    Get a valid specification for one or more devices. If `devices` is None get devices for all CUDA devices available.\n    If `devices` is and zero-length structure a single CPU compute device is returned. In any other cases `devices` is\n    returned unchanged.\n\n    Args:\n        devices: list of devices to request, None for all GPU devices, [] for CPU.\n\n    Raises:\n        RuntimeError: When all GPUs are selected (``devices=None``) but no GPUs are available.\n\n    Returns:\n        list of torch.device: list of devices.\n\n    \"\"\"\n    if devices is None:\n        devices = [torch.device(f\"cuda:{d:d}\") for d in range(torch.cuda.device_count())]\n\n        if not devices:\n            raise RuntimeError(\"No GPU devices available.\")\n\n    elif len(devices) == 0:\n        devices = [torch.device(\"cpu\")]\n\n    else:\n        devices = list(devices)\n\n    return devices", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/utils.py_default_prepare_batch_default_prepare_batch.return.batchdata_CommonKeys_IMAG": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/utils.py_default_prepare_batch_default_prepare_batch.return.batchdata_CommonKeys_IMAG", "embedding": null, "metadata": {"file_path": "monai/engines/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 106, "end_line": 127, "span_ids": ["default_prepare_batch"], "tokens": 242}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def default_prepare_batch(\n    batchdata: Dict[str, torch.Tensor], device: Optional[Union[str, torch.device]] = None, non_blocking: bool = False\n) -> Union[Tuple[torch.Tensor, Optional[torch.Tensor]], torch.Tensor]:\n    \"\"\"\n    Default function to prepare the data for current iteration.\n    Refer to ignite: https://pytorch.org/ignite/v0.4.5/generated/ignite.engine.create_supervised_trainer.html\n    #ignite.engine.create_supervised_trainer.\n\n    Returns:\n        image, label(optional).\n\n    \"\"\"\n    if not isinstance(batchdata, dict):\n        raise AssertionError(\"default prepare_batch expects dictionary input data.\")\n    if isinstance(batchdata.get(CommonKeys.LABEL), torch.Tensor):\n        return (\n            batchdata[CommonKeys.IMAGE].to(device=device, non_blocking=non_blocking),\n            batchdata[CommonKeys.LABEL].to(device=device, non_blocking=non_blocking),\n        )\n    if GanKeys.REALS in batchdata:\n        return batchdata[GanKeys.REALS].to(device=device, non_blocking=non_blocking)\n    return batchdata[CommonKeys.IMAGE].to(device=device, non_blocking=non_blocking), None", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/classification_saver.py_ClassificationSaver_ClassificationSaver.__init__.self._filenames._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/classification_saver.py_ClassificationSaver_ClassificationSaver.__init__.self._filenames._", "embedding": null, "metadata": {"file_path": "monai/handlers/classification_saver.py", "file_name": "classification_saver.py", "file_type": "text/x-python", "category": "implementation", "start_line": 31, "end_line": 85, "span_ids": ["ClassificationSaver", "ClassificationSaver.__init__"], "tokens": 611}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ClassificationSaver:\n    \"\"\"\n    Event handler triggered on completing every iteration to save the classification predictions as CSV file.\n    If running in distributed data parallel, only saves CSV file in the specified rank.\n\n    \"\"\"\n\n    def __init__(\n        self,\n        output_dir: str = \"./\",\n        filename: str = \"predictions.csv\",\n        overwrite: bool = True,\n        batch_transform: Callable = lambda x: x,\n        output_transform: Callable = lambda x: x,\n        name: Optional[str] = None,\n        save_rank: int = 0,\n        saver: Optional[CSVSaver] = None,\n    ) -> None:\n        \"\"\"\n        Args:\n            output_dir: if `saver=None`, output CSV file directory.\n            filename: if `saver=None`, name of the saved CSV file name.\n            overwrite: if `saver=None`, whether to overwriting existing file content, if True,\n                will clear the file before saving. otherwise, will append new content to the file.\n            batch_transform: a callable that is used to extract the `meta_data` dictionary of\n                the input images from `ignite.engine.state.batch`. the purpose is to get the input\n                filenames from the `meta_data` and store with classification results together.\n                `engine.state` and `batch_transform` inherit from the ignite concept:\n                https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial:\n                https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb.\n            output_transform: a callable that is used to extract the model prediction data from\n                `ignite.engine.state.output`. the first dimension of its output will be treated as\n                the batch dimension. each item in the batch will be saved individually.\n                `engine.state` and `output_transform` inherit from the ignite concept:\n                https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial:\n                https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb.\n            name: identifier of logging.logger to use, defaulting to `engine.logger`.\n            save_rank: only the handler on specified rank will save to CSV file in multi-gpus validation,\n                default to 0.\n            saver: the saver instance to save classification results, if None, create a CSVSaver internally.\n                the saver must provide `save_batch(batch_data, meta_data)` and `finalize()` APIs.\n\n        \"\"\"\n        self.save_rank = save_rank\n        self.output_dir = output_dir\n        self.filename = filename\n        self.overwrite = overwrite\n        self.batch_transform = batch_transform\n        self.output_transform = output_transform\n        self.saver = saver\n\n        self.logger = logging.getLogger(name)\n        self._name = name\n        self._outputs: List[torch.Tensor] = []\n        self._filenames: List[str] = []", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/classification_saver.py_ClassificationSaver._finalize_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/classification_saver.py_ClassificationSaver._finalize_", "embedding": null, "metadata": {"file_path": "monai/handlers/classification_saver.py", "file_name": "classification_saver.py", "file_type": "text/x-python", "category": "implementation", "start_line": 130, "end_line": 159, "span_ids": ["ClassificationSaver._finalize"], "tokens": 261}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ClassificationSaver:\n\n    def _finalize(self, _engine: Engine) -> None:\n        \"\"\"\n        All gather classification results from ranks and save to CSV file.\n\n        Args:\n            _engine: Ignite Engine, unused argument.\n        \"\"\"\n        ws = idist.get_world_size()\n        if self.save_rank >= ws:\n            raise ValueError(\"target save rank is greater than the distributed group size.\")\n\n        outputs = torch.stack(self._outputs, dim=0)\n        filenames = self._filenames\n        if ws > 1:\n            outputs = evenly_divisible_all_gather(outputs, concat=True)\n            filenames = string_list_all_gather(filenames)\n\n        if len(filenames) == 0:\n            meta_dict = None\n        else:\n            if len(filenames) != len(outputs):\n                warnings.warn(f\"filenames length: {len(filenames)} doesn't match outputs length: {len(outputs)}.\")\n            meta_dict = {Key.FILENAME_OR_OBJ: filenames}\n\n        # save to CSV file only in the expected rank\n        if idist.get_rank() == self.save_rank:\n            saver = self.saver or CSVSaver(self.output_dir, self.filename, self.overwrite)\n            saver.save_batch(outputs, meta_dict)\n            saver.finalize()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/confusion_matrix.py_from_typing_import_Callab_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/confusion_matrix.py_from_typing_import_Callab_", "embedding": null, "metadata": {"file_path": "monai/handlers/confusion_matrix.py", "file_name": "confusion_matrix.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 70, "span_ids": ["ConfusionMatrix", "ConfusionMatrix.__init__", "docstring"], "tokens": 738}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Callable, Union\n\nfrom monai.handlers.ignite_metric import IgniteMetric\nfrom monai.metrics import ConfusionMatrixMetric\nfrom monai.utils.enums import MetricReduction\n\n\nclass ConfusionMatrix(IgniteMetric):\n    \"\"\"\n    Compute confusion matrix related metrics from full size Tensor and collects average over batch, class-channels, iterations.\n    \"\"\"\n\n    def __init__(\n        self,\n        include_background: bool = True,\n        metric_name: str = \"hit_rate\",\n        compute_sample: bool = False,\n        reduction: Union[MetricReduction, str] = MetricReduction.MEAN,\n        output_transform: Callable = lambda x: x,\n        save_details: bool = True,\n    ) -> None:\n        \"\"\"\n\n        Args:\n            include_background: whether to skip metric computation on the first channel of\n                the predicted output. Defaults to True.\n            metric_name: [``\"sensitivity\"``, ``\"specificity\"``, ``\"precision\"``, ``\"negative predictive value\"``,\n                ``\"miss rate\"``, ``\"fall out\"``, ``\"false discovery rate\"``, ``\"false omission rate\"``,\n                ``\"prevalence threshold\"``, ``\"threat score\"``, ``\"accuracy\"``, ``\"balanced accuracy\"``,\n                ``\"f1 score\"``, ``\"matthews correlation coefficient\"``, ``\"fowlkes mallows index\"``,\n                ``\"informedness\"``, ``\"markedness\"``]\n                Some of the metrics have multiple aliases (as shown in the wikipedia page aforementioned),\n                and you can also input those names instead.\n            compute_sample: when reducing, if ``True``, each sample's metric will be computed based on each confusion matrix first.\n                if ``False``, compute reduction on the confusion matrices first, defaults to ``False``.\n            reduction: define the mode to reduce metrics, will only execute reduction on `not-nan` values,\n                available reduction modes: {``\"none\"``, ``\"mean\"``, ``\"sum\"``, ``\"mean_batch\"``, ``\"sum_batch\"``,\n                ``\"mean_channel\"``, ``\"sum_channel\"``}, default to ``\"mean\"``. if \"none\", will not do reduction.\n            output_transform: callable to extract `y_pred` and `y` from `ignite.engine.state.output` then\n                construct `(y_pred, y)` pair, where `y_pred` and `y` can be `batch-first` Tensors or\n                lists of `channel-first` Tensors. the form of `(y_pred, y)` is required by the `update()`.\n                `engine.state` and `output_transform` inherit from the ignite concept:\n                https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial:\n                https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb.\n            save_details: whether to save metric computation details per image, for example: TP/TN/FP/FN of every image.\n                default to True, will save to `engine.state.metric_details` dict with the metric name as key.\n\n        See also:\n            :py:meth:`monai.metrics.confusion_matrix`\n        \"\"\"\n        metric_fn = ConfusionMatrixMetric(\n            include_background=include_background,\n            metric_name=metric_name,\n            compute_sample=compute_sample,\n            reduction=reduction,\n        )\n        self.metric_name = metric_name\n        super().__init__(metric_fn=metric_fn, output_transform=output_transform, save_details=save_details)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/ignite_metric.py_warnings_if_TYPE_CHECKING_.else_.Engine___optional_impo": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/ignite_metric.py_warnings_if_TYPE_CHECKING_.else_.Engine___optional_impo", "embedding": null, "metadata": {"file_path": "monai/handlers/ignite_metric.py", "file_name": "ignite_metric.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 29, "span_ids": ["docstring"], "tokens": 178}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import warnings\nfrom typing import TYPE_CHECKING, Any, Callable, List, Optional, Sequence\n\nimport torch\n\nfrom monai.config import IgniteInfo\nfrom monai.metrics import CumulativeIterationMetric\nfrom monai.utils import min_version, optional_import\n\nidist, _ = optional_import(\"ignite\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"distributed\")\nMetric, _ = optional_import(\"ignite.metrics\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Metric\")\nreinit__is_reduced, _ = optional_import(\n    \"ignite.metrics.metric\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"reinit__is_reduced\"\n)\nif TYPE_CHECKING:\n    from ignite.engine import Engine\nelse:\n    Engine, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Engine\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/ignite_metric.py_IgniteMetric_IgniteMetric.reset.self_metric_fn_reset_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/ignite_metric.py_IgniteMetric_IgniteMetric.reset.self_metric_fn_reset_", "embedding": null, "metadata": {"file_path": "monai/handlers/ignite_metric.py", "file_name": "ignite_metric.py", "file_type": "text/x-python", "category": "implementation", "start_line": 32, "end_line": 65, "span_ids": ["IgniteMetric", "IgniteMetric.__init__", "IgniteMetric.reset"], "tokens": 439}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class IgniteMetric(Metric):  # type: ignore[valid-type, misc] # due to optional_import\n    \"\"\"\n    Base Metric class based on ignite event handler mechanism.\n    The input `prediction` or `label` data can be a PyTorch Tensor or numpy array with batch dim and channel dim,\n    or a list of PyTorch Tensor or numpy array without batch dim.\n\n    Args:\n        metric_fn: callable function or class to compute raw metric results after every iteration.\n            expect to return a Tensor with shape (batch, channel, ...) or tuple (Tensor, not_nans).\n        output_transform: callable to extract `y_pred` and `y` from `ignite.engine.state.output` then\n            construct `(y_pred, y)` pair, where `y_pred` and `y` can be `batch-first` Tensors or\n            lists of `channel-first` Tensors. the form of `(y_pred, y)` is required by the `update()`.\n            `engine.state` and `output_transform` inherit from the ignite concept:\n            https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial:\n            https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb.\n        save_details: whether to save metric computation details per image, for example: mean_dice of every image.\n            default to True, will save to `engine.state.metric_details` dict with the metric name as key.\n\n    \"\"\"\n\n    def __init__(\n        self, metric_fn: CumulativeIterationMetric, output_transform: Callable = lambda x: x, save_details: bool = True\n    ) -> None:\n        self._is_reduced: bool = False\n        self.metric_fn = metric_fn\n        self.save_details = save_details\n        self._scores: List = []\n        self._engine: Optional[Engine] = None\n        self._name: Optional[str] = None\n        super().__init__(output_transform)\n\n    @reinit__is_reduced\n    def reset(self) -> None:\n        self.metric_fn.reset()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/ignite_metric.py_IgniteMetric.update_IgniteMetric.update.self_metric_fn_y_pred_y_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/ignite_metric.py_IgniteMetric.update_IgniteMetric.update.self_metric_fn_y_pred_y_", "embedding": null, "metadata": {"file_path": "monai/handlers/ignite_metric.py", "file_name": "ignite_metric.py", "file_type": "text/x-python", "category": "implementation", "start_line": 66, "end_line": 81, "span_ids": ["IgniteMetric.update"], "tokens": 120}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class IgniteMetric(Metric):\n\n    @reinit__is_reduced\n    def update(self, output: Sequence[torch.Tensor]) -> None:\n        \"\"\"\n        Args:\n            output: sequence with contents [y_pred, y].\n\n        Raises:\n            ValueError: When ``output`` length is not 2. metric_fn can only support y_pred and y.\n\n        \"\"\"\n        if len(output) != 2:\n            raise ValueError(f\"output must have length 2, got {len(output)}.\")\n\n        y_pred, y = output\n\n        self.metric_fn(y_pred, y)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/ignite_metric.py_IgniteMetric.attach_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/ignite_metric.py_IgniteMetric.attach_", "embedding": null, "metadata": {"file_path": "monai/handlers/ignite_metric.py", "file_name": "ignite_metric.py", "file_type": "text/x-python", "category": "implementation", "start_line": 106, "end_line": 122, "span_ids": ["IgniteMetric.attach"], "tokens": 159}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class IgniteMetric(Metric):\n\n    def attach(self, engine: Engine, name: str) -> None:\n        \"\"\"\n        Attaches current metric to provided engine. On the end of engine's run,\n        `engine.state.metrics` dictionary will contain computed metric's value under provided name.\n\n        Args:\n            engine: the engine to which the metric must be attached.\n            name: the name of the metric to attach.\n\n        \"\"\"\n        super().attach(engine=engine, name=name)\n        # FIXME: record engine for communication, ignite will support it in the future version soon\n        self._engine = engine\n        self._name = name\n        if self.save_details and not hasattr(engine.state, \"metric_details\"):\n            engine.state.metric_details = {}  # type: ignore", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/metrics_saver.py_MetricsSaver_MetricsSaver._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/metrics_saver.py_MetricsSaver_MetricsSaver._", "embedding": null, "metadata": {"file_path": "monai/handlers/metrics_saver.py", "file_name": "metrics_saver.py", "file_type": "text/x-python", "category": "implementation", "start_line": 28, "end_line": 76, "span_ids": ["MetricsSaver"], "tokens": 814}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class MetricsSaver:\n    \"\"\"\n    ignite handler to save metrics values and details into expected files.\n\n    Args:\n        save_dir: directory to save the metrics and metric details.\n        metrics: expected final metrics to save into files, can be: None, \"*\" or list of strings.\n            None - don't save any metrics into files.\n            \"*\" - save all the existing metrics in `engine.state.metrics` dict into separate files.\n            list of strings - specify the expected metrics to save.\n            default to \"*\" to save all the metrics into `metrics.csv`.\n        metric_details: expected metric details to save into files, the data comes from\n            `engine.state.metric_details`, which should be provided by different `Metrics`,\n            typically, it's some intermediate values in metric computation.\n            for example: mean dice of every channel of every image in the validation dataset.\n            it must contain at least 2 dims: (batch, classes, ...),\n            if not, will unsqueeze to 2 dims.\n            this arg can be: None, \"*\" or list of strings.\n            None - don't save any metric_details into files.\n            \"*\" - save all the existing metric_details in `engine.state.metric_details` dict into separate files.\n            list of strings - specify the metric_details of expected metrics to save.\n            if not None, every metric_details array will save a separate `{metric name}_raw.csv` file.\n        batch_transform: a callable that is used to extract the `meta_data` dictionary of\n            the input images from `ignite.engine.state.batch` if saving metric details. the purpose is to get the\n            input filenames from the `meta_data` and store with metric details together.\n            `engine.state` and `batch_transform` inherit from the ignite concept:\n            https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial:\n            https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb.\n        summary_ops: expected computation operations to generate the summary report.\n            it can be: None, \"*\" or list of strings, default to None.\n            None - don't generate summary report for every expected metric_details.\n            \"*\" - generate summary report for every metric_details with all the supported operations.\n            list of strings - generate summary report for every metric_details with specified operations, they\n            should be within list: [\"mean\", \"median\", \"max\", \"min\", \"<int>percentile\", \"std\", \"notnans\"].\n            the number in \"<int>percentile\" should be [0, 100], like: \"15percentile\". default: \"90percentile\".\n            for more details, please check: https://numpy.org/doc/stable/reference/generated/numpy.nanpercentile.html.\n            note that: for the overall summary, it computes `nanmean` of all classes for each image first,\n            then compute summary. example of the generated summary report::\n\n                class    mean    median    max    5percentile 95percentile  notnans\n                class0  6.0000   6.0000   7.0000   5.1000      6.9000       2.0000\n                class1  6.0000   6.0000   6.0000   6.0000      6.0000       1.0000\n                mean    6.2500   6.2500   7.0000   5.5750      6.9250       2.0000\n\n        save_rank: only the handler on specified rank will save to files in multi-gpus validation, default to 0.\n        delimiter: the delimiter character in CSV file, default to \"\\t\".\n        output_type: expected output file type, supported types: [\"csv\"], default to \"csv\".\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/regression_metrics.py_PeakSignalToNoiseRatio_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/regression_metrics.py_PeakSignalToNoiseRatio_", "embedding": null, "metadata": {"file_path": "monai/handlers/regression_metrics.py", "file_name": "regression_metrics.py", "file_type": "text/x-python", "category": "implementation", "start_line": 118, "end_line": 153, "span_ids": ["PeakSignalToNoiseRatio.__init__", "PeakSignalToNoiseRatio"], "tokens": 501}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class PeakSignalToNoiseRatio(IgniteMetric):\n    \"\"\"\n    Computes Peak Signal to Noise Ratio from full size Tensor and collects average over batch, iterations.\n    \"\"\"\n\n    def __init__(\n        self,\n        max_val: Union[int, float],\n        reduction: Union[MetricReduction, str] = MetricReduction.MEAN,\n        output_transform: Callable = lambda x: x,\n        save_details: bool = True,\n    ) -> None:\n        \"\"\"\n\n        Args:\n            max_val: The dynamic range of the images/volumes (i.e., the difference between the\n                maximum and the minimum allowed values e.g. 255 for a uint8 image).\n            reduction: define the mode to reduce metrics, will only execute reduction on `not-nan` values,\n                available reduction modes: {``\"none\"``, ``\"mean\"``, ``\"sum\"``, ``\"mean_batch\"``, ``\"sum_batch\"``,\n                ``\"mean_channel\"``, ``\"sum_channel\"``}, default to ``\"mean\"``. if \"none\", will not do reduction.\n            output_transform: callable to extract `y_pred` and `y` from `ignite.engine.state.output` then\n                construct `(y_pred, y)` pair, where `y_pred` and `y` can be `batch-first` Tensors or\n                lists of `channel-first` Tensors. the form of `(y_pred, y)` is required by the `update()`.\n                `engine.state` and `output_transform` inherit from the ignite concept:\n                https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial:\n                https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb.\n            save_details: whether to save metric computation details per image, for example: mean squared error of every image.\n                default to True, will save to `engine.state.metric_details` dict with the metric name as key.\n            reduction: {``\"none\"``, ``\"mean\"``, ``\"sum\"``, ``\"mean_batch\"``, ``\"sum_batch\"``,\n\n        See also:\n            :py:class:`monai.metrics.PSNRMetric`\n        \"\"\"\n        metric_fn = PSNRMetric(max_val=max_val, reduction=reduction)\n        super().__init__(metric_fn=metric_fn, output_transform=output_transform, save_details=save_details)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/roc_auc.py_from_typing_import_Callab_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/roc_auc.py_from_typing_import_Callab_", "embedding": null, "metadata": {"file_path": "monai/handlers/roc_auc.py", "file_name": "roc_auc.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 52, "span_ids": ["ROCAUC", "ROCAUC.__init__", "docstring"], "tokens": 497}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Callable, Union\n\nfrom monai.handlers.ignite_metric import IgniteMetric\nfrom monai.metrics import ROCAUCMetric\nfrom monai.utils import Average\n\n\nclass ROCAUC(IgniteMetric):  # type: ignore[valid-type, misc]  # due to optional_import\n    \"\"\"\n    Computes Area Under the Receiver Operating Characteristic Curve (ROC AUC).\n    accumulating predictions and the ground-truth during an epoch and applying `compute_roc_auc`.\n\n    Args:\n        average: {``\"macro\"``, ``\"weighted\"``, ``\"micro\"``, ``\"none\"``}\n            Type of averaging performed if not binary classification. Defaults to ``\"macro\"``.\n\n            - ``\"macro\"``: calculate metrics for each label, and find their unweighted mean.\n                This does not take label imbalance into account.\n            - ``\"weighted\"``: calculate metrics for each label, and find their average,\n                weighted by support (the number of true instances for each label).\n            - ``\"micro\"``: calculate metrics globally by considering each element of the label\n                indicator matrix as a label.\n            - ``\"none\"``: the scores for each class are returned.\n\n        output_transform: callable to extract `y_pred` and `y` from `ignite.engine.state.output` then\n            construct `(y_pred, y)` pair, where `y_pred` and `y` can be `batch-first` Tensors or\n            lists of `channel-first` Tensors. the form of `(y_pred, y)` is required by the `update()`.\n            `engine.state` and `output_transform` inherit from the ignite concept:\n            https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial:\n            https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb.\n\n    Note:\n        ROCAUC expects y to be comprised of 0's and 1's.\n        y_pred must either be probability estimates or confidence values.\n\n    \"\"\"\n\n    def __init__(self, average: Union[Average, str] = Average.MACRO, output_transform: Callable = lambda x: x) -> None:\n        metric_fn = ROCAUCMetric(average=Average(average))\n        super().__init__(metric_fn=metric_fn, output_transform=output_transform, save_details=False)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/segmentation_saver.py_SegmentationSaver_SegmentationSaver._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/segmentation_saver.py_SegmentationSaver_SegmentationSaver._", "embedding": null, "metadata": {"file_path": "monai/handlers/segmentation_saver.py", "file_name": "segmentation_saver.py", "file_type": "text/x-python", "category": "implementation", "start_line": 29, "end_line": 43, "span_ids": ["SegmentationSaver"], "tokens": 209}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@deprecated(since=\"0.6.0\", removed=\"0.9.0\", msg_suffix=\"Please consider using `SaveImage[d]` transform instead.\")\nclass SegmentationSaver:\n    \"\"\"\n    Event handler triggered on completing every iteration to save the segmentation predictions into files.\n    It can extract the input image meta data(filename, affine, original_shape, etc.) and resample the predictions\n    based on the meta data.\n    The name of saved file will be `{input_image_name}_{output_postfix}{output_ext}`,\n    where the input image name is extracted from the meta data dictionary. If no meta data provided,\n    use index from 0 as the filename prefix.\n    The predictions can be PyTorch Tensor with [B, C, H, W, [D]] shape or a list of Tensor without batch dim.\n\n    .. deprecated:: 0.6.0\n        Use :class:`monai.transforms.SaveImage` or :class:`monai.transforms.SaveImaged` instead.\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/segmentation_saver.py_SegmentationSaver.__init___SegmentationSaver.__init__.self._name.name": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/segmentation_saver.py_SegmentationSaver.__init___SegmentationSaver.__init__.self._name.name", "embedding": null, "metadata": {"file_path": "monai/handlers/segmentation_saver.py", "file_name": "segmentation_saver.py", "file_type": "text/x-python", "category": "implementation", "start_line": 45, "end_line": 146, "span_ids": ["SegmentationSaver.__init__"], "tokens": 1360}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@deprecated(since=\"0.6.0\", removed=\"0.9.0\", msg_suffix=\"Please consider using `SaveImage[d]` transform instead.\")\nclass SegmentationSaver:\n\n    def __init__(\n        self,\n        output_dir: str = \"./\",\n        output_postfix: str = \"seg\",\n        output_ext: str = \".nii.gz\",\n        resample: bool = True,\n        mode: Union[GridSampleMode, InterpolateMode, str] = \"nearest\",\n        padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER,\n        scale: Optional[int] = None,\n        dtype: DtypeLike = np.float64,\n        output_dtype: DtypeLike = np.float32,\n        squeeze_end_dims: bool = True,\n        data_root_dir: str = \"\",\n        separate_folder: bool = True,\n        batch_transform: Callable = lambda x: x,\n        output_transform: Callable = lambda x: x,\n        name: Optional[str] = None,\n    ) -> None:\n        \"\"\"\n        Args:\n            output_dir: output image directory.\n            output_postfix: a string appended to all output file names, default to `seg`.\n            output_ext: output file extension name, available extensions: `.nii.gz`, `.nii`, `.png`.\n            resample: whether to resample before saving the data array.\n                if saving PNG format image, based on the `spatial_shape` from metadata.\n                if saving NIfTI format image, based on the `original_affine` from metadata.\n            mode: This option is used when ``resample = True``. Defaults to ``\"nearest\"``.\n\n                - NIfTI files {``\"bilinear\"``, ``\"nearest\"``}\n                    Interpolation mode to calculate output values.\n                    See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n                - PNG files {``\"nearest\"``, ``\"linear\"``, ``\"bilinear\"``, ``\"bicubic\"``, ``\"trilinear\"``, ``\"area\"``}\n                    The interpolation mode.\n                    See also: https://pytorch.org/docs/stable/nn.functional.html#interpolate\n\n            padding_mode: This option is used when ``resample = True``. Defaults to ``\"border\"``.\n\n                - NIfTI files {``\"zeros\"``, ``\"border\"``, ``\"reflection\"``}\n                    Padding mode for outside grid values.\n                    See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n                - PNG files\n                    This option is ignored.\n\n            scale: {``255``, ``65535``} postprocess data by clipping to [0, 1] and scaling\n                [0, 255] (uint8) or [0, 65535] (uint16). Default is None to disable scaling.\n                It's used for PNG format only.\n            dtype: data type for resampling computation. Defaults to ``np.float64`` for best precision.\n                If None, use the data type of input data.\n                It's used for Nifti format only.\n            output_dtype: data type for saving data. Defaults to ``np.float32``, it's used for Nifti format only.\n            squeeze_end_dims: if True, any trailing singleton dimensions will be removed (after the channel\n                has been moved to the end). So if input is (C,H,W,D), this will be altered to (H,W,D,C), and\n                then if C==1, it will be saved as (H,W,D). If D also ==1, it will be saved as (H,W). If false,\n                image will always be saved as (H,W,D,C).\n                it's used for NIfTI format only.\n            data_root_dir: if not empty, it specifies the beginning parts of the input file's\n                absolute path. it's used to compute `input_file_rel_path`, the relative path to the file from\n                `data_root_dir` to preserve folder structure when saving in case there are files in different\n                folders with the same file names. for example:\n                input_file_name: /foo/bar/test1/image.nii,\n                output_postfix: seg\n                output_ext: nii.gz\n                output_dir: /output,\n                data_root_dir: /foo/bar,\n                output will be: /output/test1/image/image_seg.nii.gz\n            separate_folder: whether to save every file in a separate folder, for example: if input filename is\n                `image.nii`, postfix is `seg` and folder_path is `output`, if `True`, save as:\n                `output/image/image_seg.nii`, if `False`, save as `output/image_seg.nii`. default to `True`.\n            batch_transform: a callable that is used to extract the `meta_data` dictionary of the input images\n                from `ignite.engine.state.batch`. the purpose is to extract necessary information from the meta data:\n                filename, affine, original_shape, etc.\n                `engine.state` and `batch_transform` inherit from the ignite concept:\n                https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial:\n                https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb.\n            output_transform: a callable that is used to extract the model prediction data from\n                `ignite.engine.state.output`. the first dimension of its output will be treated as the batch dimension.\n                each item in the batch will be saved individually.\n                `engine.state` and `output_transform` inherit from the ignite concept:\n                https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial:\n                https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb.\n            name: identifier of logging.logger to use, defaulting to `engine.logger`.\n\n        \"\"\"\n        self._saver = SaveImage(\n            output_dir=output_dir,\n            output_postfix=output_postfix,\n            output_ext=output_ext,\n            resample=resample,\n            mode=mode,\n            padding_mode=padding_mode,\n            scale=scale,\n            dtype=dtype,\n            output_dtype=output_dtype,\n            squeeze_end_dims=squeeze_end_dims,\n            data_root_dir=data_root_dir,\n            separate_folder=separate_folder,\n        )\n        self.batch_transform = batch_transform\n        self.output_transform = output_transform\n\n        self.logger = logging.getLogger(name)\n        self._name = name", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/tensorboard_handlers.py_TensorBoardImageHandler_TensorBoardImageHandler._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/tensorboard_handlers.py_TensorBoardImageHandler_TensorBoardImageHandler._", "embedding": null, "metadata": {"file_path": "monai/handlers/tensorboard_handlers.py", "file_name": "tensorboard_handlers.py", "file_type": "text/x-python", "category": "implementation", "start_line": 241, "end_line": 265, "span_ids": ["TensorBoardImageHandler"], "tokens": 378}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TensorBoardImageHandler(TensorBoardHandler):\n    \"\"\"\n    TensorBoardImageHandler is an Ignite Event handler that can visualize images, labels and outputs as 2D/3D images.\n    2D output (shape in Batch, channel, H, W) will be shown as simple image using the first element in the batch,\n    for 3D to ND output (shape in Batch, channel, H, W, D) input, each of ``self.max_channels`` number of images'\n    last three dimensions will be shown as animated GIF along the last axis (typically Depth).\n    And if writer is from TensorBoardX, data has 3 channels and `max_channels=3`, will plot as RGB video.\n\n    It can be used for any Ignite Engine (trainer, validator and evaluator).\n    User can easily add it to engine for any expected Event, for example: ``EPOCH_COMPLETED``,\n    ``ITERATION_COMPLETED``. The expected data source is ignite's ``engine.state.batch`` and ``engine.state.output``.\n\n    Default behavior:\n        - Show y_pred as images (GIF for 3D) on TensorBoard when Event triggered,\n        - Need to use ``batch_transform`` and ``output_transform`` to specify\n          how many images to show and show which channel.\n        - Expects ``batch_transform(engine.state.batch)`` to return data\n          format: (image[N, channel, ...], label[N, channel, ...]).\n        - Expects ``output_transform(engine.state.output)`` to return a torch\n          tensor in format (y_pred[N, channel, ...], loss).\n\n    Usage example is available in the tutorial:\n    https://github.com/Project-MONAI/tutorials/blob/master/3d_segmentation/unet_segmentation_3d_ignite.ipynb.\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/utils.py_write_metrics_reports_write_metrics_reports.if_not_os_path_exists_sav.os_makedirs_save_dir_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/utils.py_write_metrics_reports_write_metrics_reports.if_not_os_path_exists_sav.os_makedirs_save_dir_", "embedding": null, "metadata": {"file_path": "monai/handlers/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 53, "end_line": 99, "span_ids": ["write_metrics_reports"], "tokens": 727}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def write_metrics_reports(\n    save_dir: PathLike,\n    images: Optional[Sequence[str]],\n    metrics: Optional[Dict[str, Union[torch.Tensor, np.ndarray]]],\n    metric_details: Optional[Dict[str, Union[torch.Tensor, np.ndarray]]],\n    summary_ops: Optional[Union[str, Sequence[str]]],\n    deli: str = \"\\t\",\n    output_type: str = \"csv\",\n):\n    \"\"\"\n    Utility function to write the metrics into files, contains 3 parts:\n    1. if `metrics` dict is not None, write overall metrics into file, every line is a metric name and value pair.\n    2. if `metric_details` dict is not None,  write raw metric data of every image into file, every line for 1 image.\n    3. if `summary_ops` is not None, compute summary based on operations on `metric_details` and write to file.\n\n    Args:\n        save_dir: directory to save all the metrics reports.\n        images: name or path of every input image corresponding to the metric_details data.\n            if None, will use index number as the filename of every input image.\n        metrics: a dictionary of (metric name, metric value) pairs.\n        metric_details: a dictionary of (metric name, metric raw values) pairs, usually, it comes from metrics\n            computation, for example, the raw value can be the mean_dice of every channel of every input image.\n        summary_ops: expected computation operations to generate the summary report.\n            it can be: None, \"*\" or list of strings, default to None.\n            None - don't generate summary report for every expected metric_details.\n            \"*\" - generate summary report for every metric_details with all the supported operations.\n            list of strings - generate summary report for every metric_details with specified operations, they\n            should be within list: [\"mean\", \"median\", \"max\", \"min\", \"<int>percentile\", \"std\", \"notnans\"].\n            the number in \"<int>percentile\" should be [0, 100], like: \"15percentile\". default: \"90percentile\".\n            for more details, please check: https://numpy.org/doc/stable/reference/generated/numpy.nanpercentile.html.\n            note that: for the overall summary, it computes `nanmean` of all classes for each image first,\n            then compute summary. example of the generated summary report::\n\n                class    mean    median    max    5percentile 95percentile  notnans\n                class0  6.0000   6.0000   7.0000   5.1000      6.9000       2.0000\n                class1  6.0000   6.0000   6.0000   6.0000      6.0000       1.0000\n                mean    6.2500   6.2500   7.0000   5.5750      6.9250       2.0000\n\n        deli: the delimiter character in the file, default to \"\\t\".\n        output_type: expected output file type, supported types: [\"csv\"], default to \"csv\".\n\n    \"\"\"\n    if output_type.lower() != \"csv\":\n        raise ValueError(f\"unsupported output type: {output_type}.\")\n\n    if not os.path.exists(save_dir):\n        os.makedirs(save_dir)\n    # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/utils.py_write_metrics_reports.if_metrics_is_not_None_an_write_metrics_reports.if_metric_details_is_not_.for_k_v_in_metric_detail.if_summary_ops_is_not_Non.with_open_os_path_join_sa.for_i_c_in_enumerate_np_.f_write_f_class_labels_i": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/utils.py_write_metrics_reports.if_metrics_is_not_None_an_write_metrics_reports.if_metric_details_is_not_.for_k_v_in_metric_detail.if_summary_ops_is_not_Non.with_open_os_path_join_sa.for_i_c_in_enumerate_np_.f_write_f_class_labels_i", "embedding": null, "metadata": {"file_path": "monai/handlers/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 101, "end_line": 152, "span_ids": ["write_metrics_reports"], "tokens": 692}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def write_metrics_reports(\n    save_dir: PathLike,\n    images: Optional[Sequence[str]],\n    metrics: Optional[Dict[str, Union[torch.Tensor, np.ndarray]]],\n    metric_details: Optional[Dict[str, Union[torch.Tensor, np.ndarray]]],\n    summary_ops: Optional[Union[str, Sequence[str]]],\n    deli: str = \"\\t\",\n    output_type: str = \"csv\",\n):\n    # ... other code\n\n    if metrics is not None and len(metrics) > 0:\n        with open(os.path.join(save_dir, \"metrics.csv\"), \"w\") as f:\n            for k, v in metrics.items():\n                f.write(f\"{k}{deli}{str(v)}\\n\")\n    if metric_details is not None and len(metric_details) > 0:\n        for k, v in metric_details.items():\n            if isinstance(v, torch.Tensor):\n                v = v.cpu().numpy()\n            if v.ndim == 0:\n                # reshape to [1, 1] if no batch and class dims\n                v = v.reshape((1, 1))\n            elif v.ndim == 1:\n                # reshape to [N, 1] if no class dim\n                v = v.reshape((-1, 1))\n\n            # add the average value of all classes to v\n            class_labels = [\"class\" + str(i) for i in range(v.shape[1])] + [\"mean\"]\n            v = np.concatenate([v, np.nanmean(v, axis=1, keepdims=True)], axis=1)\n\n            with open(os.path.join(save_dir, f\"{k}_raw.csv\"), \"w\") as f:\n                f.write(f\"filename{deli}{deli.join(class_labels)}\\n\")\n                for i, b in enumerate(v):\n                    f.write(f\"{images[i] if images is not None else str(i)}{deli}{deli.join([str(c) for c in b])}\\n\")\n\n            if summary_ops is not None:\n                supported_ops = OrderedDict(\n                    {\n                        \"mean\": np.nanmean,\n                        \"median\": np.nanmedian,\n                        \"max\": np.nanmax,\n                        \"min\": np.nanmin,\n                        \"90percentile\": lambda x: np.nanpercentile(x[0], x[1]),\n                        \"std\": np.nanstd,\n                        \"notnans\": lambda x: (~np.isnan(x)).sum(),\n                    }\n                )\n                ops = ensure_tuple(summary_ops)\n                if \"*\" in ops:\n                    ops = tuple(supported_ops.keys())\n\n                def _compute_op(op: str, d: np.ndarray):\n                    if not op.endswith(\"percentile\"):\n                        c_op = look_up_option(op, supported_ops)\n                        return c_op(d)\n\n                    threshold = int(op.split(\"percentile\")[0])\n                    return supported_ops[\"90percentile\"]((d, threshold))  # type: ignore\n\n                with open(os.path.join(save_dir, f\"{k}_summary.csv\"), \"w\") as f:\n                    f.write(f\"class{deli}{deli.join(ops)}\\n\")\n                    for i, c in enumerate(np.transpose(v)):\n                        f.write(f\"{class_labels[i]}{deli}{deli.join([f'{_compute_op(k, c):.4f}' for k in ops])}\\n\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/dice.py_MaskedDiceLoss_MaskedDiceLoss.forward.return.self_spatial_weighted_inp": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/dice.py_MaskedDiceLoss_MaskedDiceLoss.forward.return.self_spatial_weighted_inp", "embedding": null, "metadata": {"file_path": "monai/losses/dice.py", "file_name": "dice.py", "file_type": "text/x-python", "category": "implementation", "start_line": 179, "end_line": 203, "span_ids": ["MaskedDiceLoss.forward", "MaskedDiceLoss.__init__", "MaskedDiceLoss"], "tokens": 273}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class MaskedDiceLoss(DiceLoss):\n    \"\"\"\n    Add an additional `masking` process before `DiceLoss`, accept a binary mask ([0, 1]) indicating a region,\n    `input` and `target` will be masked by the region: region with mask `1` will keep the original value,\n    region with `0` mask will be converted to `0`. Then feed `input` and `target` to normal `DiceLoss` computation.\n    This has the effect of ensuring only the masked region contributes to the loss computation and\n    hence gradient calculation.\n\n    \"\"\"\n\n    def __init__(self, *args, **kwargs) -> None:\n        \"\"\"\n        Args follow :py:class:`monai.losses.DiceLoss`.\n        \"\"\"\n        super().__init__(*args, **kwargs)\n        self.spatial_weighted = MaskedLoss(loss=super().forward)\n\n    def forward(self, input: torch.Tensor, target: torch.Tensor, mask: Optional[torch.Tensor] = None):\n        \"\"\"\n        Args:\n            input: the shape should be BNH[WD].\n            target: the shape should be BNH[WD].\n            mask: the shape should B1H[WD] or 11H[WD].\n        \"\"\"\n        return self.spatial_weighted(input=input, target=target, mask=mask)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/spatial_mask.py_inspect_MaskedLoss.__init__.if_not_callable_self_loss.raise_ValueError_The_los": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/spatial_mask.py_inspect_MaskedLoss.__init__.if_not_callable_self_loss.raise_ValueError_The_los", "embedding": null, "metadata": {"file_path": "monai/losses/spatial_mask.py", "file_name": "spatial_mask.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 41, "span_ids": ["MaskedLoss.__init__", "MaskedLoss", "docstring"], "tokens": 237}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import inspect\nimport warnings\nfrom typing import Callable, Optional, Union\n\nimport torch\nfrom torch.nn.modules.loss import _Loss\n\n__all__ = [\"MaskedLoss\"]\n\n\nclass MaskedLoss(_Loss):\n    \"\"\"\n    This is a wrapper class for the loss functions.  It allows for additional\n    weighting masks to be applied to both input and target.\n\n    See Also:\n        - :py:class:`monai.losses.MaskedDiceLoss`\n    \"\"\"\n\n    def __init__(self, loss: Union[Callable, _Loss], *loss_args, **loss_kwargs) -> None:\n        \"\"\"\n        Args:\n            loss: loss function to be wrapped, this could be a loss class or an instance of a loss class.\n            loss_args: arguments to the loss function's constructor if `loss` is a class.\n            loss_kwargs: keyword arguments to the loss function's constructor if `loss` is a class.\n        \"\"\"\n        super().__init__()\n        self.loss = loss(*loss_args, **loss_kwargs) if inspect.isclass(loss) else loss\n        if not callable(self.loss):\n            raise ValueError(\"The loss function is not callable.\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/spatial_mask.py_MaskedLoss.forward_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/spatial_mask.py_MaskedLoss.forward_", "embedding": null, "metadata": {"file_path": "monai/losses/spatial_mask.py", "file_name": "spatial_mask.py", "file_type": "text/x-python", "category": "implementation", "start_line": 43, "end_line": 64, "span_ids": ["MaskedLoss.forward"], "tokens": 272}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class MaskedLoss(_Loss):\n\n    def forward(self, input: torch.Tensor, target: torch.Tensor, mask: Optional[torch.Tensor] = None):\n        \"\"\"\n        Args:\n            input: the shape should be BNH[WD].\n            target: the shape should be BNH[WD].\n            mask: the shape should be B1H[WD] or 11H[WD].\n        \"\"\"\n        if mask is None:\n            warnings.warn(\"No mask value specified for the MaskedLoss.\")\n            return self.loss(input, target)\n\n        if input.dim() != mask.dim():\n            warnings.warn(f\"Dim of input ({input.shape}) is different from mask ({mask.shape}).\")\n        if input.shape[0] != mask.shape[0] and mask.shape[0] != 1:\n            raise ValueError(f\"Batch size of mask ({mask.shape}) must be one or equal to input ({input.shape}).\")\n        if target.dim() > 1:\n            if mask.shape[1] != 1:\n                raise ValueError(f\"Mask ({mask.shape}) must have only one channel.\")\n            if input.shape[2:] != mask.shape[2:]:\n                warnings.warn(f\"Spatial size of input ({input.shape}) is different from mask ({mask.shape}).\")\n        return self.loss(input * mask, target * mask)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/confusion_matrix.py_warnings_ConfusionMatrixMetric._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/confusion_matrix.py_warnings_ConfusionMatrixMetric._", "embedding": null, "metadata": {"file_path": "monai/metrics/confusion_matrix.py", "file_name": "confusion_matrix.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 59, "span_ids": ["ConfusionMatrixMetric", "docstring"], "tokens": 744}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import warnings\nfrom typing import Sequence, Union\n\nimport torch\n\nfrom monai.metrics.utils import do_metric_reduction, ignore_background\nfrom monai.utils import MetricReduction, ensure_tuple\n\nfrom .metric import CumulativeIterationMetric\n\n\nclass ConfusionMatrixMetric(CumulativeIterationMetric):\n    \"\"\"\n    Compute confusion matrix related metrics. This function supports to calculate all metrics mentioned in:\n    `Confusion matrix <https://en.wikipedia.org/wiki/Confusion_matrix>`_.\n    It can support both multi-classes and multi-labels classification and segmentation tasks.\n    `y_preds` is expected to have binarized predictions and `y` should be in one-hot format. You can use suitable transforms\n    in ``monai.transforms.post`` first to achieve binarized values.\n    The `include_background` parameter can be set to ``False`` for an instance to exclude\n    the first category (channel index 0) which is by convention assumed to be background. If the non-background\n    segmentations are small compared to the total image size they can get overwhelmed by the signal from the\n    background so excluding it in such cases helps convergence.\n\n    Args:\n        include_background: whether to skip metric computation on the first channel of\n            the predicted output. Defaults to True.\n        metric_name: [``\"sensitivity\"``, ``\"specificity\"``, ``\"precision\"``, ``\"negative predictive value\"``,\n            ``\"miss rate\"``, ``\"fall out\"``, ``\"false discovery rate\"``, ``\"false omission rate\"``,\n            ``\"prevalence threshold\"``, ``\"threat score\"``, ``\"accuracy\"``, ``\"balanced accuracy\"``,\n            ``\"f1 score\"``, ``\"matthews correlation coefficient\"``, ``\"fowlkes mallows index\"``,\n            ``\"informedness\"``, ``\"markedness\"``]\n            Some of the metrics have multiple aliases (as shown in the wikipedia page aforementioned),\n            and you can also input those names instead.\n            Except for input only one metric, multiple metrics are also supported via input a sequence of metric names, such as\n            (\"sensitivity\", \"precision\", \"recall\"), if ``compute_sample`` is ``True``, multiple ``f`` and ``not_nans`` will be\n            returned with the same order as input names when calling the class.\n        compute_sample: when reducing, if ``True``, each sample's metric will be computed based on each confusion matrix first.\n            if ``False``, compute reduction on the confusion matrices first, defaults to ``False``.\n        reduction: define the mode to reduce metrics, will only execute reduction on `not-nan` values,\n            available reduction modes: {``\"none\"``, ``\"mean\"``, ``\"sum\"``, ``\"mean_batch\"``, ``\"sum_batch\"``,\n            ``\"mean_channel\"``, ``\"sum_channel\"``}, default to ``\"mean\"``. if \"none\", will not do reduction.\n        get_not_nans: whether to return the `not_nans` count, if True, aggregate() returns [(metric, not_nans), ...]. If False,\n            aggregate() returns [metric, ...].\n            Here `not_nans` count the number of not nans for True Positive, False Positive, True Negative and False Negative.\n            Its shape depends on the shape of the metric, and it has one more dimension with size 4. For example, if the shape\n            of the metric is [3, 3], `not_nans` has the shape [3, 3, 4].\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/confusion_matrix.py_ConfusionMatrixMetric.__init___ConfusionMatrixMetric.__init__.self.get_not_nans.get_not_nans": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/confusion_matrix.py_ConfusionMatrixMetric.__init___ConfusionMatrixMetric.__init__.self.get_not_nans.get_not_nans", "embedding": null, "metadata": {"file_path": "monai/metrics/confusion_matrix.py", "file_name": "confusion_matrix.py", "file_type": "text/x-python", "category": "implementation", "start_line": 60, "end_line": 73, "span_ids": ["ConfusionMatrixMetric.__init__"], "tokens": 134}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ConfusionMatrixMetric(CumulativeIterationMetric):\n\n    def __init__(\n        self,\n        include_background: bool = True,\n        metric_name: Union[Sequence[str], str] = \"hit_rate\",\n        compute_sample: bool = False,\n        reduction: Union[MetricReduction, str] = MetricReduction.MEAN,\n        get_not_nans: bool = False,\n    ) -> None:\n        super().__init__()\n        self.include_background = include_background\n        self.metric_name = ensure_tuple(metric_name)\n        self.compute_sample = compute_sample\n        self.reduction = reduction\n        self.get_not_nans = get_not_nans", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/confusion_matrix.py_ConfusionMatrixMetric.aggregate_ConfusionMatrixMetric.aggregate.return.results": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/confusion_matrix.py_ConfusionMatrixMetric.aggregate_ConfusionMatrixMetric.aggregate.return.results", "embedding": null, "metadata": {"file_path": "monai/metrics/confusion_matrix.py", "file_name": "confusion_matrix.py", "file_type": "text/x-python", "category": "implementation", "start_line": 108, "end_line": 129, "span_ids": ["ConfusionMatrixMetric.aggregate"], "tokens": 181}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ConfusionMatrixMetric(CumulativeIterationMetric):\n\n    def aggregate(self):  # type: ignore\n        \"\"\"\n        Execute reduction for the confusion matrix values.\n\n        \"\"\"\n        data = self.get_buffer()\n        if not isinstance(data, torch.Tensor):\n            raise ValueError(\"the data to aggregate must be PyTorch Tensor.\")\n\n        results = []\n        for metric_name in self.metric_name:\n            if self.compute_sample:\n                sub_confusion_matrix = compute_confusion_matrix_metric(metric_name, data)\n                f, not_nans = do_metric_reduction(sub_confusion_matrix, self.reduction)\n            else:\n                f, not_nans = do_metric_reduction(data, self.reduction)\n                f = compute_confusion_matrix_metric(metric_name, f)\n            if self.get_not_nans:\n                results.append((f, not_nans))\n            else:\n                results.append(f)\n        return results", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/hausdorff_distance.py_warnings_HausdorffDistanceMetric.__init__.self.get_not_nans.get_not_nans": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/hausdorff_distance.py_warnings_HausdorffDistanceMetric.__init__.self.get_not_nans.get_not_nans", "embedding": null, "metadata": {"file_path": "monai/metrics/hausdorff_distance.py", "file_name": "hausdorff_distance.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 68, "span_ids": ["HausdorffDistanceMetric", "HausdorffDistanceMetric.__init__", "docstring"], "tokens": 692}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import warnings\nfrom typing import Optional, Union\n\nimport numpy as np\nimport torch\n\nfrom monai.metrics.utils import do_metric_reduction, get_mask_edges, get_surface_distance, ignore_background\nfrom monai.utils import MetricReduction\n\nfrom .metric import CumulativeIterationMetric\n\n__all__ = [\"HausdorffDistanceMetric\", \"compute_hausdorff_distance\", \"compute_percent_hausdorff_distance\"]\n\n\nclass HausdorffDistanceMetric(CumulativeIterationMetric):\n    \"\"\"\n    Compute Hausdorff Distance between two tensors. It can support both multi-classes and multi-labels tasks.\n    It supports both directed and non-directed Hausdorff distance calculation. In addition, specify the `percentile`\n    parameter can get the percentile of the distance. Input `y_pred` is compared with ground truth `y`.\n    `y_preds` is expected to have binarized predictions and `y` should be in one-hot format.\n    You can use suitable transforms in ``monai.transforms.post`` first to achieve binarized values.\n    `y_preds` and `y` can be a list of channel-first Tensor (CHW[D]) or a batch-first Tensor (BCHW[D]).\n    The implementation refers to `DeepMind's implementation <https://github.com/deepmind/surface-distance>`_.\n\n    Args:\n        include_background: whether to include distance computation on the first channel of\n            the predicted output. Defaults to ``False``.\n        distance_metric: : [``\"euclidean\"``, ``\"chessboard\"``, ``\"taxicab\"``]\n            the metric used to compute surface distance. Defaults to ``\"euclidean\"``.\n        percentile: an optional float number between 0 and 100. If specified, the corresponding\n            percentile of the Hausdorff Distance rather than the maximum result will be achieved.\n            Defaults to ``None``.\n        directed: whether to calculate directed Hausdorff distance. Defaults to ``False``.\n        reduction: define the mode to reduce metrics, will only execute reduction on `not-nan` values,\n            available reduction modes: {``\"none\"``, ``\"mean\"``, ``\"sum\"``, ``\"mean_batch\"``, ``\"sum_batch\"``,\n            ``\"mean_channel\"``, ``\"sum_channel\"``}, default to ``\"mean\"``. if \"none\", will not do reduction.\n        get_not_nans: whether to return the `not_nans` count, if True, aggregate() returns (metric, not_nans).\n            Here `not_nans` count the number of not nans for the metric, thus its shape equals to the shape of the metric.\n\n    \"\"\"\n\n    def __init__(\n        self,\n        include_background: bool = False,\n        distance_metric: str = \"euclidean\",\n        percentile: Optional[float] = None,\n        directed: bool = False,\n        reduction: Union[MetricReduction, str] = MetricReduction.MEAN,\n        get_not_nans: bool = False,\n    ) -> None:\n        super().__init__()\n        self.include_background = include_background\n        self.distance_metric = distance_metric\n        self.percentile = percentile\n        self.directed = directed\n        self.reduction = reduction\n        self.get_not_nans = get_not_nans", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/hausdorff_distance.py_HausdorffDistanceMetric._compute_tensor_HausdorffDistanceMetric.aggregate.return._f_not_nans_if_self_get": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/hausdorff_distance.py_HausdorffDistanceMetric._compute_tensor_HausdorffDistanceMetric.aggregate.return._f_not_nans_if_self_get", "embedding": null, "metadata": {"file_path": "monai/metrics/hausdorff_distance.py", "file_name": "hausdorff_distance.py", "file_type": "text/x-python", "category": "implementation", "start_line": 70, "end_line": 113, "span_ids": ["HausdorffDistanceMetric._compute_tensor", "HausdorffDistanceMetric.aggregate"], "tokens": 435}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class HausdorffDistanceMetric(CumulativeIterationMetric):\n\n    def _compute_tensor(self, y_pred: torch.Tensor, y: torch.Tensor):  # type: ignore\n        \"\"\"\n        Args:\n            y_pred: input data to compute, typical segmentation model output.\n                It must be one-hot format and first dim is batch, example shape: [16, 3, 32, 32]. The values\n                should be binarized.\n            y: ground truth to compute the distance. It must be one-hot format and first dim is batch.\n                The values should be binarized.\n\n        Raises:\n            ValueError: when `y` is not a binarized tensor.\n            ValueError: when `y_pred` has less than three dimensions.\n        \"\"\"\n        if not isinstance(y_pred, torch.Tensor) or not isinstance(y, torch.Tensor):\n            raise ValueError(\"y_pred and y must be PyTorch Tensor.\")\n        if not torch.all(y_pred.byte() == y_pred):\n            warnings.warn(\"y_pred should be a binarized tensor.\")\n        if not torch.all(y.byte() == y):\n            raise ValueError(\"y should be a binarized tensor.\")\n        dims = y_pred.ndimension()\n        if dims < 3:\n            raise ValueError(\"y_pred should have at least three dimensions.\")\n        # compute (BxC) for each channel for each batch\n        return compute_hausdorff_distance(\n            y_pred=y_pred,\n            y=y,\n            include_background=self.include_background,\n            distance_metric=self.distance_metric,\n            percentile=self.percentile,\n            directed=self.directed,\n        )\n\n    def aggregate(self):  # type: ignore\n        \"\"\"\n        Execute reduction logic for the output of `compute_hausdorff_distance`.\n\n        \"\"\"\n        data = self.get_buffer()\n        if not isinstance(data, torch.Tensor):\n            raise ValueError(\"the data to aggregate must be PyTorch Tensor.\")\n\n        # do metric reduction\n        f, not_nans = do_metric_reduction(data, self.reduction)\n        return (f, not_nans) if self.get_not_nans else f", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/meandice.py_warnings_DiceMetric.__init__.self.get_not_nans.get_not_nans": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/meandice.py_warnings_DiceMetric.__init__.self.get_not_nans.get_not_nans", "embedding": null, "metadata": {"file_path": "monai/metrics/meandice.py", "file_name": "meandice.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 55, "span_ids": ["DiceMetric.__init__", "DiceMetric", "docstring"], "tokens": 505}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import warnings\nfrom typing import Union\n\nimport torch\n\nfrom monai.metrics.utils import do_metric_reduction, ignore_background\nfrom monai.utils import MetricReduction\n\nfrom .metric import CumulativeIterationMetric\n\n\nclass DiceMetric(CumulativeIterationMetric):\n    \"\"\"\n    Compute average Dice loss between two tensors. It can support both multi-classes and multi-labels tasks.\n    Input `y_pred` is compared with ground truth `y`.\n    `y_preds` is expected to have binarized predictions and `y` should be in one-hot format. You can use suitable transforms\n    in ``monai.transforms.post`` first to achieve binarized values.\n    The `include_background` parameter can be set to ``False`` for an instance of DiceLoss to exclude\n    the first category (channel index 0) which is by convention assumed to be background. If the non-background\n    segmentations are small compared to the total image size they can get overwhelmed by the signal from the\n    background so excluding it in such cases helps convergence.\n    `y_preds` and `y` can be a list of channel-first Tensor (CHW[D]) or a batch-first Tensor (BCHW[D]).\n\n    Args:\n        include_background: whether to skip Dice computation on the first channel of\n            the predicted output. Defaults to ``True``.\n        reduction: define the mode to reduce metrics, will only execute reduction on `not-nan` values,\n            available reduction modes: {``\"none\"``, ``\"mean\"``, ``\"sum\"``, ``\"mean_batch\"``, ``\"sum_batch\"``,\n            ``\"mean_channel\"``, ``\"sum_channel\"``}, default to ``\"mean\"``. if \"none\", will not do reduction.\n        get_not_nans: whether to return the `not_nans` count, if True, aggregate() returns (metric, not_nans).\n            Here `not_nans` count the number of not nans for the metric, thus its shape equals to the shape of the metric.\n\n    \"\"\"\n\n    def __init__(\n        self,\n        include_background: bool = True,\n        reduction: Union[MetricReduction, str] = MetricReduction.MEAN,\n        get_not_nans: bool = False,\n    ) -> None:\n        super().__init__()\n        self.include_background = include_background\n        self.reduction = reduction\n        self.get_not_nans = get_not_nans", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/meandice.py_DiceMetric._compute_tensor_DiceMetric.aggregate.return._f_not_nans_if_self_get": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/meandice.py_DiceMetric._compute_tensor_DiceMetric.aggregate.return._f_not_nans_if_self_get", "embedding": null, "metadata": {"file_path": "monai/metrics/meandice.py", "file_name": "meandice.py", "file_type": "text/x-python", "category": "implementation", "start_line": 57, "end_line": 93, "span_ids": ["DiceMetric.aggregate", "DiceMetric._compute_tensor"], "tokens": 402}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DiceMetric(CumulativeIterationMetric):\n\n    def _compute_tensor(self, y_pred: torch.Tensor, y: torch.Tensor):  # type: ignore\n        \"\"\"\n        Args:\n            y_pred: input data to compute, typical segmentation model output.\n                It must be one-hot format and first dim is batch, example shape: [16, 3, 32, 32]. The values\n                should be binarized.\n            y: ground truth to compute mean dice metric. It must be one-hot format and first dim is batch.\n                The values should be binarized.\n\n        Raises:\n            ValueError: when `y` is not a binarized tensor.\n            ValueError: when `y_pred` has less than three dimensions.\n        \"\"\"\n        if not isinstance(y_pred, torch.Tensor) or not isinstance(y, torch.Tensor):\n            raise ValueError(\"y_pred and y must be PyTorch Tensor.\")\n        if not torch.all(y_pred.byte() == y_pred):\n            warnings.warn(\"y_pred should be a binarized tensor.\")\n        if not torch.all(y.byte() == y):\n            raise ValueError(\"y should be a binarized tensor.\")\n        dims = y_pred.ndimension()\n        if dims < 3:\n            raise ValueError(\"y_pred should have at least three dimensions.\")\n        # compute dice (BxC) for each channel for each batch\n        return compute_meandice(y_pred=y_pred, y=y, include_background=self.include_background)\n\n    def aggregate(self):  # type: ignore\n        \"\"\"\n        Execute reduction logic for the output of `compute_meandice`.\n\n        \"\"\"\n        data = self.get_buffer()\n        if not isinstance(data, torch.Tensor):\n            raise ValueError(\"the data to aggregate must be PyTorch Tensor.\")\n\n        # do metric reduction\n        f, not_nans = do_metric_reduction(data, self.reduction)\n        return (f, not_nans) if self.get_not_nans else f", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/metric.py_from_abc_import_ABC_abst_Metric.__call__.raise_NotImplementedError": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/metric.py_from_abc_import_ABC_abst_Metric.__call__.raise_NotImplementedError", "embedding": null, "metadata": {"file_path": "monai/metrics/metric.py", "file_name": "metric.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 35, "span_ids": ["Metric", "Metric.__call__", "docstring"], "tokens": 169}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from abc import ABC, abstractmethod\nfrom typing import Any, List, Optional\n\nimport torch\n\nfrom monai.config import TensorOrList\nfrom monai.utils import convert_data_type, evenly_divisible_all_gather\n\n__all__ = [\"Metric\", \"IterationMetric\", \"Cumulative\", \"CumulativeIterationMetric\"]\n\n\nclass Metric(ABC):\n    \"\"\"\n    Base class for metric computation for evaluating the performance of a model.\n    `__call__` is designed to execute the computation.\n\n    \"\"\"\n\n    @abstractmethod\n    def __call__(self, *args: Any, **kwargs: Any):\n        \"\"\"\n        This method should take raw model outputs as inputs, and return values that measure the models' quality.\n        \"\"\"\n        raise NotImplementedError(f\"Subclass {self.__class__.__name__} must implement this method.\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/metric.py_Cumulative_Cumulative.reset.self._synced.False": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/metric.py_Cumulative_Cumulative.reset.self._synced.False", "embedding": null, "metadata": {"file_path": "monai/metrics/metric.py", "file_name": "metric.py", "file_type": "text/x-python", "category": "implementation", "start_line": 112, "end_line": 188, "span_ids": ["Cumulative.reset", "Cumulative.__init__", "Cumulative"], "tokens": 672}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Cumulative:\n    \"\"\"\n    Utility class for the typical cumulative computation process based on PyTorch Tensors.\n    It provides interfaces to accumulate values in the local buffers, synchronize buffers across distributed nodes,\n    and aggregate the buffered values.\n\n    In multi-processing, PyTorch programs usually distribute data to multiple nodes. Each node runs with a subset\n    of the data, adds values to its local buffers. Calling `get_buffer` could gather all the results and\n    `aggregate` can further handle the results to generate the final outcomes.\n\n    Users can implement their own `aggregate` method to handle the results,\n    using `get_buffer` to get the buffered contents.\n\n    Note: the data list should have the same length every time calling `add()` in a round,\n    it will automatically create buffers according to the length of data list.\n\n    Typically, this class is expected to execute the following steps:\n\n    .. code-block:: python\n\n        from monai.metrics import Cumulative\n\n        c = Cumulative()\n        c.append(1)  # adds a value\n        c.extend([2, 3])  # adds a batch of values\n        c.extend([4, 5, 6])  # adds a batch of values\n        print(c.get_buffer())  # tensor([1, 2, 3, 4, 5, 6])\n        print(len(c))  # 6\n        c.reset()\n        print(len(c))  # 0\n\n    The following is an example of maintaining two internal buffers:\n\n    .. code-block:: python\n\n        from monai.metrics import Cumulative\n\n        c = Cumulative()\n        c.append(1, 2)  # adds a value to two buffers respectively\n        c.extend([3, 4], [5, 6])  # adds batches of values\n        print(c.get_buffer())  # [tensor([1, 3, 4]), tensor([2, 5, 6])]\n        print(len(c))\n\n    The following is an example of extending with variable length data:\n\n    .. code-block:: python\n\n        import torch\n        from monai.metrics import Cumulative\n\n        c = Cumulative()\n        c.extend(torch.zeros((8, 2)), torch.zeros((6, 2)))  # adds batches\n        c.append(torch.zeros((2, )))  # adds a value\n        print(c.get_buffer())  # [torch.zeros((9, 2)), torch.zeros((6, 2))]\n        print(len(c))\n\n    \"\"\"\n\n    def __init__(self):\n        \"\"\"\n        Initialize the internal buffers.\n        `self._buffers` are local buffers, they are not usually used directly.\n        `self._sync_buffers` are the buffers with all the results across all the nodes.\n        \"\"\"\n        self._buffers: Optional[List[List[torch.Tensor]]] = None\n        self._synced_tensors: Optional[List[Optional[torch.Tensor]]] = None\n        self._synced: bool = False\n        self.reset()\n\n    def reset(self):\n        \"\"\"\n        Reset the buffers for cumulative tensors and the synced results.\n\n        \"\"\"\n        self._buffers = None\n        self._synced_tensors = None\n        self._synced = False", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/regression.py_math_RegressionMetric.aggregate.return._f_not_nans_if_self_get": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/regression.py_math_RegressionMetric.aggregate.return._f_not_nans_if_self_get", "embedding": null, "metadata": {"file_path": "monai/metrics/regression.py", "file_name": "regression.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 54, "span_ids": ["RegressionMetric", "RegressionMetric.aggregate", "RegressionMetric.__init__", "docstring"], "tokens": 437}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import math\nfrom abc import abstractmethod\nfrom functools import partial\nfrom typing import Any, Union\n\nimport torch\n\nfrom monai.metrics.utils import do_metric_reduction\nfrom monai.utils import MetricReduction\n\nfrom .metric import CumulativeIterationMetric\n\n\nclass RegressionMetric(CumulativeIterationMetric):\n    \"\"\"\n    Base class for regression metrics.\n    Input `y_pred` is compared with ground truth `y`.\n    Both `y_pred` and `y` are expected to be real-valued, where `y_pred` is output from a regression model.\n    `y_preds` and `y` can be a list of channel-first Tensor (CHW[D]) or a batch-first Tensor (BCHW[D]).\n\n    Args:\n        reduction: define the mode to reduce metrics, will only execute reduction on `not-nan` values,\n            available reduction modes: {``\"none\"``, ``\"mean\"``, ``\"sum\"``, ``\"mean_batch\"``, ``\"sum_batch\"``,\n            ``\"mean_channel\"``, ``\"sum_channel\"``}, default to ``\"mean\"``. if \"none\", will not do reduction.\n        get_not_nans: whether to return the `not_nans` count, if True, aggregate() returns (metric, not_nans).\n            Here `not_nans` count the number of not nans for the metric, thus its shape equals to the shape of the metric.\n\n    \"\"\"\n\n    def __init__(\n        self, reduction: Union[MetricReduction, str] = MetricReduction.MEAN, get_not_nans: bool = False\n    ) -> None:\n        super().__init__()\n        self.reduction = reduction\n        self.get_not_nans = get_not_nans\n\n    def aggregate(self):  # type: ignore\n        data = self.get_buffer()\n        if not isinstance(data, torch.Tensor):\n            raise ValueError(\"the data to aggregate must be PyTorch Tensor.\")\n\n        f, not_nans = do_metric_reduction(data, self.reduction)\n        return (f, not_nans) if self.get_not_nans else f", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/regression.py_RegressionMetric._check_shape_RegressionMetric._check_shape.if_len_y_pred_shape_2_.raise_ValueError_either_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/regression.py_RegressionMetric._check_shape_RegressionMetric._check_shape.if_len_y_pred_shape_2_.raise_ValueError_either_", "embedding": null, "metadata": {"file_path": "monai/metrics/regression.py", "file_name": "regression.py", "file_type": "text/x-python", "category": "implementation", "start_line": 56, "end_line": 62, "span_ids": ["RegressionMetric._check_shape"], "tokens": 118}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RegressionMetric(CumulativeIterationMetric):\n\n    def _check_shape(self, y_pred: torch.Tensor, y: torch.Tensor) -> None:\n        if y_pred.shape != y.shape:\n            raise ValueError(f\"y_pred and y shapes dont match, received y_pred: [{y_pred.shape}] and y: [{y.shape}]\")\n\n        # also check if there is atleast one non-batch dimension i.e. num_dims >= 2\n        if len(y_pred.shape) < 2:\n            raise ValueError(\"either channel or spatial dimensions required, found only batch dimension\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/regression.py_RegressionMetric._compute_metric_RegressionMetric._compute_tensor.return.self__compute_metric_y_pr": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/regression.py_RegressionMetric._compute_metric_RegressionMetric._compute_tensor.return.self__compute_metric_y_pr", "embedding": null, "metadata": {"file_path": "monai/metrics/regression.py", "file_name": "regression.py", "file_type": "text/x-python", "category": "implementation", "start_line": 68, "end_line": 76, "span_ids": ["RegressionMetric._compute_metric", "RegressionMetric._compute_tensor"], "tokens": 133}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RegressionMetric(CumulativeIterationMetric):\n\n    @abstractmethod\n    def _compute_metric(self, y_pred: torch.Tensor, y: torch.Tensor) -> torch.Tensor:\n        raise NotImplementedError(f\"Subclass {self.__class__.__name__} must implement this method.\")\n\n    def _compute_tensor(self, y_pred: torch.Tensor, y: torch.Tensor):  # type: ignore\n        if not isinstance(y_pred, torch.Tensor) or not isinstance(y, torch.Tensor):\n            raise ValueError(\"y_pred and y must be PyTorch Tensor.\")\n        self._check_shape(y_pred, y)\n        return self._compute_metric(y_pred, y)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/regression.py_PSNRMetric_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/regression.py_PSNRMetric_", "embedding": null, "metadata": {"file_path": "monai/metrics/regression.py", "file_name": "regression.py", "file_type": "text/x-python", "category": "implementation", "start_line": 173, "end_line": 221, "span_ids": ["PSNRMetric._compute_metric", "PSNRMetric.__init__", "compute_mean_error_metrics", "PSNRMetric"], "tokens": 615}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class PSNRMetric(RegressionMetric):\n    r\"\"\"Compute Peak Signal To Noise Ratio between two tensors using function:\n\n    .. math::\n        \\operatorname{PSNR}\\left(Y, \\hat{Y}\\right) = 20 \\cdot \\log_{10} \\left({\\mathit{MAX}}_Y\\right) \\\n        -10 \\cdot \\log_{10}\\left(\\operatorname{MSE\\left(Y, \\hat{Y}\\right)}\\right)\n\n    More info: https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio\n\n    Help taken from:\n    https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/image_ops_impl.py line 4139\n\n    Input `y_pred` is compared with ground truth `y`.\n    Both `y_pred` and `y` are expected to be real-valued, where `y_pred` is output from a regression model.\n\n    Args:\n        max_val: The dynamic range of the images/volumes (i.e., the difference between the\n            maximum and the minimum allowed values e.g. 255 for a uint8 image).\n        reduction: define the mode to reduce metrics, will only execute reduction on `not-nan` values,\n            available reduction modes: {``\"none\"``, ``\"mean\"``, ``\"sum\"``, ``\"mean_batch\"``, ``\"sum_batch\"``,\n            ``\"mean_channel\"``, ``\"sum_channel\"``}, default to ``\"mean\"``. if \"none\", will not do reduction.\n        get_not_nans: whether to return the `not_nans` count, if True, aggregate() returns (metric, not_nans).\n\n    \"\"\"\n\n    def __init__(\n        self,\n        max_val: Union[int, float],\n        reduction: Union[MetricReduction, str] = MetricReduction.MEAN,\n        get_not_nans: bool = False,\n    ) -> None:\n        super().__init__(reduction=reduction, get_not_nans=get_not_nans)\n        self.max_val = max_val\n        self.sq_func = partial(torch.pow, exponent=2.0)\n\n    def _compute_metric(self, y_pred: torch.Tensor, y: torch.Tensor) -> Any:\n        y_pred = y_pred.float()\n        y = y.float()\n\n        mse_out = compute_mean_error_metrics(y_pred, y, func=self.sq_func)\n        return 20 * math.log10(self.max_val) - 10 * torch.log10(mse_out)\n\n\ndef compute_mean_error_metrics(y_pred: torch.Tensor, y: torch.Tensor, func) -> torch.Tensor:\n    # reducing in only channel + spatial dimensions (not batch)\n    # reduction of batch handled inside __call__() using do_metric_reduction() in respective calling class\n    flt = partial(torch.flatten, start_dim=1)\n    return torch.mean(flt(func(y - y_pred)), dim=-1, keepdim=True)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/rocauc.py_from_typing_import_Union__ROCAUCMetric._compute_tensor.return.y_pred_y": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/rocauc.py_from_typing_import_Union__ROCAUCMetric._compute_tensor.return.y_pred_y", "embedding": null, "metadata": {"file_path": "monai/metrics/rocauc.py", "file_name": "rocauc.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 49, "span_ids": ["ROCAUCMetric._compute_tensor", "ROCAUCMetric", "ROCAUCMetric.__init__", "docstring"], "tokens": 357}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Union, cast\n\nimport numpy as np\nimport torch\n\nfrom monai.utils import Average, look_up_option\n\nfrom .metric import CumulativeIterationMetric\n\n\nclass ROCAUCMetric(CumulativeIterationMetric):\n    \"\"\"\n    Computes Area Under the Receiver Operating Characteristic Curve (ROC AUC). Referring to:\n    `sklearn.metrics.roc_auc_score <https://scikit-learn.org/stable/modules/generated/\n    sklearn.metrics.roc_auc_score.html#sklearn.metrics.roc_auc_score>`_.\n    The input `y_pred` and `y` can be a list of `channel-first` Tensor or a `batch-first` Tensor.\n\n    Args:\n        average: {``\"macro\"``, ``\"weighted\"``, ``\"micro\"``, ``\"none\"``}\n            Type of averaging performed if not binary classification.\n            Defaults to ``\"macro\"``.\n\n            - ``\"macro\"``: calculate metrics for each label, and find their unweighted mean.\n                This does not take label imbalance into account.\n            - ``\"weighted\"``: calculate metrics for each label, and find their average,\n                weighted by support (the number of true instances for each label).\n            - ``\"micro\"``: calculate metrics globally by considering each element of the label\n                indicator matrix as a label.\n            - ``\"none\"``: the scores for each class are returned.\n\n    \"\"\"\n\n    def __init__(self, average: Union[Average, str] = Average.MACRO) -> None:\n        super().__init__()\n        self.average = average\n\n    def _compute_tensor(self, y_pred: torch.Tensor, y: torch.Tensor):  # type: ignore\n        return y_pred, y", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/rocauc.py_ROCAUCMetric.aggregate_ROCAUCMetric.aggregate.return.compute_roc_auc_y_pred_y_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/rocauc.py_ROCAUCMetric.aggregate_ROCAUCMetric.aggregate.return.compute_roc_auc_y_pred_y_", "embedding": null, "metadata": {"file_path": "monai/metrics/rocauc.py", "file_name": "rocauc.py", "file_type": "text/x-python", "category": "implementation", "start_line": 51, "end_line": 62, "span_ids": ["ROCAUCMetric.aggregate"], "tokens": 138}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ROCAUCMetric(CumulativeIterationMetric):\n\n    def aggregate(self):  # type: ignore\n        \"\"\"\n        As AUC metric needs to execute on the overall data, so usually users accumulate `y_pred` and `y`\n        of every iteration, then execute real computation and reduction on the accumulated data.\n\n        \"\"\"\n        y_pred, y = self.get_buffer()\n        # compute final value and do metric reduction\n        if not isinstance(y_pred, torch.Tensor) or not isinstance(y, torch.Tensor):\n            raise ValueError(\"y_pred and y must be PyTorch Tensor.\")\n\n        return compute_roc_auc(y_pred=y_pred, y=y, average=self.average)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/rocauc.py__calculate__calculate.return.auc_nneg_n_nneg_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/rocauc.py__calculate__calculate.return.auc_nneg_n_nneg_", "embedding": null, "metadata": {"file_path": "monai/metrics/rocauc.py", "file_name": "rocauc.py", "file_type": "text/x-python", "category": "implementation", "start_line": 65, "end_line": 93, "span_ids": ["_calculate"], "tokens": 323}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _calculate(y_pred: torch.Tensor, y: torch.Tensor) -> float:\n    if not (y.ndimension() == y_pred.ndimension() == 1 and len(y) == len(y_pred)):\n        raise AssertionError(\"y and y_pred must be 1 dimension data with same length.\")\n    if not y.unique().equal(torch.tensor([0, 1], dtype=y.dtype, device=y.device)):\n        raise AssertionError(\"y values must be 0 or 1, can not be all 0 or all 1.\")\n    n = len(y)\n    indices = y_pred.argsort()\n    y = y[indices].cpu().numpy()\n    y_pred = y_pred[indices].cpu().numpy()\n    nneg = auc = tmp_pos = tmp_neg = 0.0\n\n    for i in range(n):\n        y_i = cast(float, y[i])\n        if i + 1 < n and y_pred[i] == y_pred[i + 1]:\n            tmp_pos += y_i\n            tmp_neg += 1 - y_i\n            continue\n        if tmp_pos + tmp_neg > 0:\n            tmp_pos += y_i\n            tmp_neg += 1 - y_i\n            nneg += tmp_neg\n            auc += tmp_pos * (nneg - tmp_neg / 2)\n            tmp_pos = tmp_neg = 0\n            continue\n        if y_i == 1:\n            auc += nneg\n        else:\n            nneg += 1\n    return auc / (nneg * (n - nneg))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/surface_distance.py_warnings_SurfaceDistanceMetric.__init__.self.get_not_nans.get_not_nans": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/surface_distance.py_warnings_SurfaceDistanceMetric.__init__.self.get_not_nans.get_not_nans", "embedding": null, "metadata": {"file_path": "monai/metrics/surface_distance.py", "file_name": "surface_distance.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 61, "span_ids": ["SurfaceDistanceMetric.__init__", "SurfaceDistanceMetric", "docstring"], "tokens": 554}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import warnings\nfrom typing import Union\n\nimport numpy as np\nimport torch\n\nfrom monai.metrics.utils import do_metric_reduction, get_mask_edges, get_surface_distance, ignore_background\nfrom monai.utils import MetricReduction\n\nfrom .metric import CumulativeIterationMetric\n\n\nclass SurfaceDistanceMetric(CumulativeIterationMetric):\n    \"\"\"\n    Compute Surface Distance between two tensors. It can support both multi-classes and multi-labels tasks.\n    It supports both symmetric and asymmetric surface distance calculation.\n    Input `y_pred` is compared with ground truth `y`.\n    `y_preds` is expected to have binarized predictions and `y` should be in one-hot format.\n    You can use suitable transforms in ``monai.transforms.post`` first to achieve binarized values.\n    `y_preds` and `y` can be a list of channel-first Tensor (CHW[D]) or a batch-first Tensor (BCHW[D]).\n\n    Args:\n        include_background: whether to skip distance computation on the first channel of\n            the predicted output. Defaults to ``False``.\n        symmetric: whether to calculate the symmetric average surface distance between\n            `seg_pred` and `seg_gt`. Defaults to ``False``.\n        distance_metric: : [``\"euclidean\"``, ``\"chessboard\"``, ``\"taxicab\"``]\n            the metric used to compute surface distance. Defaults to ``\"euclidean\"``.\n        reduction: define the mode to reduce metrics, will only execute reduction on `not-nan` values,\n            available reduction modes: {``\"none\"``, ``\"mean\"``, ``\"sum\"``, ``\"mean_batch\"``, ``\"sum_batch\"``,\n            ``\"mean_channel\"``, ``\"sum_channel\"``}, default to ``\"mean\"``. if \"none\", will not do reduction.\n        get_not_nans: whether to return the `not_nans` count, if True, aggregate() returns (metric, not_nans).\n            Here `not_nans` count the number of not nans for the metric, thus its shape equals to the shape of the metric.\n\n    \"\"\"\n\n    def __init__(\n        self,\n        include_background: bool = False,\n        symmetric: bool = False,\n        distance_metric: str = \"euclidean\",\n        reduction: Union[MetricReduction, str] = MetricReduction.MEAN,\n        get_not_nans: bool = False,\n    ) -> None:\n        super().__init__()\n        self.include_background = include_background\n        self.distance_metric = distance_metric\n        self.symmetric = symmetric\n        self.reduction = reduction\n        self.get_not_nans = get_not_nans", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/surface_distance.py_SurfaceDistanceMetric._compute_tensor_SurfaceDistanceMetric.aggregate.return._f_not_nans_if_self_get": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/surface_distance.py_SurfaceDistanceMetric._compute_tensor_SurfaceDistanceMetric.aggregate.return._f_not_nans_if_self_get", "embedding": null, "metadata": {"file_path": "monai/metrics/surface_distance.py", "file_name": "surface_distance.py", "file_type": "text/x-python", "category": "implementation", "start_line": 63, "end_line": 105, "span_ids": ["SurfaceDistanceMetric._compute_tensor", "SurfaceDistanceMetric.aggregate"], "tokens": 419}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SurfaceDistanceMetric(CumulativeIterationMetric):\n\n    def _compute_tensor(self, y_pred: torch.Tensor, y: torch.Tensor):  # type: ignore\n        \"\"\"\n        Args:\n            y_pred: input data to compute, typical segmentation model output.\n                It must be one-hot format and first dim is batch, example shape: [16, 3, 32, 32]. The values\n                should be binarized.\n            y: ground truth to compute the distance. It must be one-hot format and first dim is batch.\n                The values should be binarized.\n\n        Raises:\n            ValueError: when `y` is not a binarized tensor.\n            ValueError: when `y_pred` has less than three dimensions.\n        \"\"\"\n        if not isinstance(y_pred, torch.Tensor) or not isinstance(y, torch.Tensor):\n            raise ValueError(\"y_pred and y must be PyTorch Tensor.\")\n        if not torch.all(y_pred.byte() == y_pred):\n            warnings.warn(\"y_pred should be a binarized tensor.\")\n        if not torch.all(y.byte() == y):\n            raise ValueError(\"y should be a binarized tensor.\")\n        dims = y_pred.ndimension()\n        if dims < 3:\n            raise ValueError(\"y_pred should have at least three dimensions.\")\n        # compute (BxC) for each channel for each batch\n        return compute_average_surface_distance(\n            y_pred=y_pred,\n            y=y,\n            include_background=self.include_background,\n            symmetric=self.symmetric,\n            distance_metric=self.distance_metric,\n        )\n\n    def aggregate(self):  # type: ignore\n        \"\"\"\n        Execute reduction logic for the output of `compute_average_surface_distance`.\n\n        \"\"\"\n        data = self.get_buffer()\n        if not isinstance(data, torch.Tensor):\n            raise ValueError(\"the data to aggregate must be PyTorch Tensor.\")\n\n        # do metric reduction\n        f, not_nans = do_metric_reduction(data, self.reduction)\n        return (f, not_nans) if self.get_not_nans else f", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/aspp.py_from_typing_import_Option_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/aspp.py_from_typing_import_Option_", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/aspp.py", "file_name": "aspp.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 106, "span_ids": ["SimpleASPP", "SimpleASPP.forward", "SimpleASPP.__init__", "docstring"], "tokens": 888}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Optional, Sequence, Tuple, Union\n\nimport torch\nimport torch.nn as nn\n\nfrom monai.networks.blocks.convolutions import Convolution\nfrom monai.networks.layers import same_padding\nfrom monai.networks.layers.factories import Conv\n\n\nclass SimpleASPP(nn.Module):\n    \"\"\"\n    A simplified version of the atrous spatial pyramid pooling (ASPP) module.\n\n    Chen et al., Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation.\n    https://arxiv.org/abs/1802.02611\n\n    Wang et al., A Noise-robust Framework for Automatic Segmentation of COVID-19 Pneumonia Lesions\n    from CT Images. https://ieeexplore.ieee.org/document/9109297\n    \"\"\"\n\n    def __init__(\n        self,\n        spatial_dims: int,\n        in_channels: int,\n        conv_out_channels: int,\n        kernel_sizes: Sequence[int] = (1, 3, 3, 3),\n        dilations: Sequence[int] = (1, 2, 4, 6),\n        norm_type: Optional[Union[Tuple, str]] = \"BATCH\",\n        acti_type: Optional[Union[Tuple, str]] = \"LEAKYRELU\",\n        bias: bool = False,\n    ) -> None:\n        \"\"\"\n        Args:\n            spatial_dims: number of spatial dimensions, could be 1, 2, or 3.\n            in_channels: number of input channels.\n            conv_out_channels: number of output channels of each atrous conv.\n                The final number of output channels is conv_out_channels * len(kernel_sizes).\n            kernel_sizes: a sequence of four convolutional kernel sizes.\n                Defaults to (1, 3, 3, 3) for four (dilated) convolutions.\n            dilations: a sequence of four convolutional dilation parameters.\n                Defaults to (1, 2, 4, 6) for four (dilated) convolutions.\n            norm_type: final kernel-size-one convolution normalization type.\n                Defaults to batch norm.\n            acti_type: final kernel-size-one convolution activation type.\n                Defaults to leaky ReLU.\n            bias: whether to have a bias term in convolution blocks. Defaults to False.\n                According to `Performance Tuning Guide <https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html>`_,\n                if a conv layer is directly followed by a batch norm layer, bias should be False.\n\n        Raises:\n            ValueError: When ``kernel_sizes`` length differs from ``dilations``.\n\n        See also:\n\n            :py:class:`monai.networks.layers.Act`\n            :py:class:`monai.networks.layers.Conv`\n            :py:class:`monai.networks.layers.Norm`\n\n        \"\"\"\n        super().__init__()\n        if len(kernel_sizes) != len(dilations):\n            raise ValueError(\n                \"kernel_sizes and dilations length must match, \"\n                f\"got kernel_sizes={len(kernel_sizes)} dilations={len(dilations)}.\"\n            )\n        pads = tuple(same_padding(k, d) for k, d in zip(kernel_sizes, dilations))\n\n        self.convs = nn.ModuleList()\n        for k, d, p in zip(kernel_sizes, dilations, pads):\n            _conv = Conv[Conv.CONV, spatial_dims](\n                in_channels=in_channels, out_channels=conv_out_channels, kernel_size=k, dilation=d, padding=p\n            )\n            self.convs.append(_conv)\n\n        out_channels = conv_out_channels * len(pads)  # final conv. output channels\n        self.conv_k1 = Convolution(\n            spatial_dims=spatial_dims,\n            in_channels=out_channels,\n            out_channels=out_channels,\n            kernel_size=1,\n            act=acti_type,\n            norm=norm_type,\n            bias=bias,\n        )\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        \"\"\"\n        Args:\n            x: in shape (batch, channel, spatial_1[, spatial_2, ...]).\n        \"\"\"\n        x_out = torch.cat([conv(x) for conv in self.convs], dim=1)\n        x_out = self.conv_k1(x_out)\n        return x_out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/crf.py_from_typing_import_Option_CRF.__init__.self.compatibility_matrix.compatibility_matrix": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/crf.py_from_typing_import_Option_CRF.__init__.self.compatibility_matrix.compatibility_matrix", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/crf.py", "file_name": "crf.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 68, "span_ids": ["CRF.__init__", "CRF", "docstring"], "tokens": 453}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Optional\n\nimport torch\nfrom torch.nn.functional import softmax\n\nfrom monai.networks.layers.filtering import PHLFilter\n\n__all__ = [\"CRF\"]\n\n\nclass CRF(torch.nn.Module):\n    \"\"\"\n    Conditional Random Field: Combines message passing with a class\n    compatibility convolution into an iterative process designed\n    to successively minimise the energy of the class labeling.\n\n    In this implementation, the message passing step is a weighted\n    combination of a gaussian filter and a bilateral filter.\n    The bilateral term is included to respect existing structure\n    within the reference tensor.\n\n    See:\n        https://arxiv.org/abs/1502.03240\n    \"\"\"\n\n    def __init__(\n        self,\n        iterations: int = 5,\n        bilateral_weight: float = 1.0,\n        gaussian_weight: float = 1.0,\n        bilateral_spatial_sigma: float = 5.0,\n        bilateral_color_sigma: float = 0.5,\n        gaussian_spatial_sigma: float = 5.0,\n        update_factor: float = 3.0,\n        compatibility_matrix: Optional[torch.Tensor] = None,\n    ):\n        \"\"\"\n        Args:\n            iterations: the number of iterations.\n            bilateral_weight: the weighting of the bilateral term in the message passing step.\n            gaussian_weight: the weighting of the gaussian term in the message passing step.\n            bilateral_spatial_sigma: standard deviation in spatial coordinates for the bilateral term.\n            bilateral_color_sigma: standard deviation in color space for the bilateral term.\n            gaussian_spatial_sigma: standard deviation in spatial coordinates for the gaussian term.\n            update_factor: determines the magnitude of each update.\n            compatibility_matrix: a matrix describing class compatibility,\n                should be NxN where N is the number of classes.\n        \"\"\"\n        super().__init__()\n        self.iterations = iterations\n        self.bilateral_weight = bilateral_weight\n        self.gaussian_weight = gaussian_weight\n        self.bilateral_spatial_sigma = bilateral_spatial_sigma\n        self.bilateral_color_sigma = bilateral_color_sigma\n        self.gaussian_spatial_sigma = gaussian_spatial_sigma\n        self.update_factor = update_factor\n        self.compatibility_matrix = compatibility_matrix", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/crf.py_CRF.forward_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/crf.py_CRF.forward_", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/crf.py", "file_name": "crf.py", "file_type": "text/x-python", "category": "implementation", "start_line": 70, "end_line": 120, "span_ids": ["_create_coordinate_tensor", "CRF.forward"], "tokens": 425}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CRF(torch.nn.Module):\n\n    def forward(self, input_tensor: torch.Tensor, reference_tensor: torch.Tensor):\n        \"\"\"\n        Args:\n            input_tensor: tensor containing initial class logits.\n            reference_tensor: the reference tensor used to guide the message passing.\n\n        Returns:\n            output (torch.Tensor): output tensor.\n        \"\"\"\n\n        # constructing spatial feature tensor\n        spatial_features = _create_coordinate_tensor(reference_tensor)\n\n        # constructing final feature tensors for bilateral and gaussian kernel\n        bilateral_features = torch.cat(\n            [spatial_features / self.bilateral_spatial_sigma, reference_tensor / self.bilateral_color_sigma], dim=1\n        )\n        gaussian_features = spatial_features / self.gaussian_spatial_sigma\n\n        # setting up output tensor\n        output_tensor = softmax(input_tensor, dim=1)\n\n        # mean field loop\n        for _ in range(self.iterations):\n\n            # message passing step for both kernels\n            bilateral_output = PHLFilter.apply(output_tensor, bilateral_features)\n            gaussian_output = PHLFilter.apply(output_tensor, gaussian_features)\n\n            # combining filter outputs\n            combined_output = self.bilateral_weight * bilateral_output + self.gaussian_weight * gaussian_output\n\n            # optionally running a compatibility transform\n            if self.compatibility_matrix is not None:\n                flat = combined_output.flatten(start_dim=2).permute(0, 2, 1)\n                flat = torch.matmul(flat, self.compatibility_matrix)\n                combined_output = flat.permute(0, 2, 1).reshape(combined_output.shape)\n\n            # update and normalize\n            output_tensor = softmax(input_tensor + self.update_factor * combined_output, dim=1)\n\n        return output_tensor\n\n\n# helper methods\ndef _create_coordinate_tensor(tensor):\n    axes = [torch.arange(tensor.size(i)) for i in range(2, tensor.dim())]\n    grids = torch.meshgrid(axes)\n    coords = torch.stack(grids).to(device=tensor.device, dtype=tensor.dtype)\n    return torch.stack(tensor.size(0) * [coords], dim=0)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/dynunet_block.py_from_typing_import_Option_UnetResBlock.forward.return.out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/dynunet_block.py_from_typing_import_Option_UnetResBlock.forward.return.out", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/dynunet_block.py", "file_name": "dynunet_block.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 89, "span_ids": ["UnetResBlock.forward", "UnetResBlock.__init__", "UnetResBlock", "docstring"], "tokens": 691}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Optional, Sequence, Tuple, Union\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\n\nfrom monai.networks.blocks.convolutions import Convolution\nfrom monai.networks.layers.factories import Act, Norm\nfrom monai.networks.layers.utils import get_act_layer, get_norm_layer\n\n\nclass UnetResBlock(nn.Module):\n    \"\"\"\n    A skip-connection based module that can be used for DynUNet, based on:\n    `Automated Design of Deep Learning Methods for Biomedical Image Segmentation <https://arxiv.org/abs/1904.08128>`_.\n    `nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation <https://arxiv.org/abs/1809.10486>`_.\n\n    Args:\n        spatial_dims: number of spatial dimensions.\n        in_channels: number of input channels.\n        out_channels: number of output channels.\n        kernel_size: convolution kernel size.\n        stride: convolution stride.\n        norm_name: feature normalization type and arguments.\n        act_name: activation layer type and arguments.\n        dropout: dropout probability.\n\n    \"\"\"\n\n    def __init__(\n        self,\n        spatial_dims: int,\n        in_channels: int,\n        out_channels: int,\n        kernel_size: Union[Sequence[int], int],\n        stride: Union[Sequence[int], int],\n        norm_name: Union[Tuple, str],\n        act_name: Union[Tuple, str] = (\"leakyrelu\", {\"inplace\": True, \"negative_slope\": 0.01}),\n        dropout: Optional[Union[Tuple, str, float]] = None,\n    ):\n        super().__init__()\n        self.conv1 = get_conv_layer(\n            spatial_dims,\n            in_channels,\n            out_channels,\n            kernel_size=kernel_size,\n            stride=stride,\n            dropout=dropout,\n            conv_only=True,\n        )\n        self.conv2 = get_conv_layer(\n            spatial_dims, out_channels, out_channels, kernel_size=kernel_size, stride=1, dropout=dropout, conv_only=True\n        )\n        self.conv3 = get_conv_layer(\n            spatial_dims, in_channels, out_channels, kernel_size=1, stride=stride, dropout=dropout, conv_only=True\n        )\n        self.lrelu = get_act_layer(name=act_name)\n        self.norm1 = get_norm_layer(name=norm_name, spatial_dims=spatial_dims, channels=out_channels)\n        self.norm2 = get_norm_layer(name=norm_name, spatial_dims=spatial_dims, channels=out_channels)\n        self.norm3 = get_norm_layer(name=norm_name, spatial_dims=spatial_dims, channels=out_channels)\n        self.downsample = in_channels != out_channels\n        stride_np = np.atleast_1d(stride)\n        if not np.all(stride_np == 1):\n            self.downsample = True\n\n    def forward(self, inp):\n        residual = inp\n        out = self.conv1(inp)\n        out = self.norm1(out)\n        out = self.lrelu(out)\n        out = self.conv2(out)\n        out = self.norm2(out)\n        if self.downsample:\n            residual = self.conv3(residual)\n            residual = self.norm3(residual)\n        out += residual\n        out = self.lrelu(out)\n        return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/segresnet_block.py_from_typing_import_Tuple__get_upsample_layer.return.UpSample_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/segresnet_block.py_from_typing_import_Tuple__get_upsample_layer.return.UpSample_", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/segresnet_block.py", "file_name": "segresnet_block.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 42, "span_ids": ["get_upsample_layer", "get_conv_layer", "docstring"], "tokens": 246}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Tuple, Union\n\nimport torch.nn as nn\n\nfrom monai.networks.blocks.convolutions import Convolution\nfrom monai.networks.blocks.upsample import UpSample\nfrom monai.networks.layers.utils import get_act_layer, get_norm_layer\nfrom monai.utils import InterpolateMode, UpsampleMode\n\n\ndef get_conv_layer(\n    spatial_dims: int, in_channels: int, out_channels: int, kernel_size: int = 3, stride: int = 1, bias: bool = False\n):\n\n    return Convolution(\n        spatial_dims, in_channels, out_channels, strides=stride, kernel_size=kernel_size, bias=bias, conv_only=True\n    )\n\n\ndef get_upsample_layer(\n    spatial_dims: int, in_channels: int, upsample_mode: Union[UpsampleMode, str] = \"nontrainable\", scale_factor: int = 2\n):\n    return UpSample(\n        spatial_dims=spatial_dims,\n        in_channels=in_channels,\n        out_channels=in_channels,\n        scale_factor=scale_factor,\n        mode=upsample_mode,\n        interp_mode=InterpolateMode.LINEAR,\n        align_corners=False,\n    )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/gmm.py_torch_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/gmm.py_torch_", "embedding": null, "metadata": {"file_path": "monai/networks/layers/gmm.py", "file_name": "gmm.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 86, "span_ids": ["GaussianMixtureModel.apply", "_ApplyFunc", "docstring", "GaussianMixtureModel", "GaussianMixtureModel.learn", "_ApplyFunc.backward", "_ApplyFunc.forward", "GaussianMixtureModel.reset", "GaussianMixtureModel.__init__"], "tokens": 531}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import torch\n\nfrom monai._extensions.loader import load_module\n\n__all__ = [\"GaussianMixtureModel\"]\n\n\nclass GaussianMixtureModel:\n    \"\"\"\n    Takes an initial labeling and uses a mixture of Gaussians to approximate each classes\n    distribution in the feature space. Each unlabeled element is then assigned a probability\n    of belonging to each class based on it's fit to each classes approximated distribution.\n\n    See:\n        https://en.wikipedia.org/wiki/Mixture_model\n    \"\"\"\n\n    def __init__(self, channel_count: int, mixture_count: int, mixture_size: int, verbose_build: bool = False):\n        \"\"\"\n        Args:\n            channel_count: The number of features per element.\n            mixture_count: The number of class distributions.\n            mixture_size: The number Gaussian components per class distribution.\n            verbose_build: If ``True``, turns on verbose logging of load steps.\n        \"\"\"\n        if not torch.cuda.is_available():\n            raise NotImplementedError(\"GaussianMixtureModel is currently implemented for CUDA.\")\n        self.channel_count = channel_count\n        self.mixture_count = mixture_count\n        self.mixture_size = mixture_size\n        self.compiled_extension = load_module(\n            \"gmm\",\n            {\"CHANNEL_COUNT\": channel_count, \"MIXTURE_COUNT\": mixture_count, \"MIXTURE_SIZE\": mixture_size},\n            verbose_build=verbose_build,\n        )\n        self.params, self.scratch = self.compiled_extension.init()\n\n    def reset(self):\n        \"\"\"\n        Resets the parameters of the model.\n        \"\"\"\n        self.params, self.scratch = self.compiled_extension.init()\n\n    def learn(self, features, labels):\n        \"\"\"\n        Learns, from scratch, the distribution of each class from the provided labels.\n\n        Args:\n            features (torch.Tensor): features for each element.\n            labels (torch.Tensor): initial labeling for each element.\n        \"\"\"\n        self.compiled_extension.learn(self.params, self.scratch, features, labels)\n\n    def apply(self, features):\n        \"\"\"\n        Applies the current model to a set of feature vectors.\n\n        Args:\n            features (torch.Tensor): feature vectors for each element.\n\n        Returns:\n            output (torch.Tensor): class assignment probabilities for each element.\n        \"\"\"\n        return _ApplyFunc.apply(self.params, features, self.compiled_extension)\n\n\nclass _ApplyFunc(torch.autograd.Function):\n    @staticmethod\n    def forward(ctx, params, features, compiled_extension):\n        return compiled_extension.apply(params, features)\n\n    @staticmethod\n    def backward(ctx, grad_output):\n        raise NotImplementedError(\"GMM does not support backpropagation\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/utils.py_from_typing_import_Option_get_norm_layer.return.norm_type_kw_args_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/utils.py_from_typing_import_Option_get_norm_layer.return.norm_type_kw_args_", "embedding": null, "metadata": {"file_path": "monai/networks/layers/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 46, "span_ids": ["get_norm_layer", "docstring"], "tokens": 325}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Optional, Tuple, Union\n\nfrom monai.networks.layers.factories import Act, Dropout, Norm, Pool, split_args\nfrom monai.utils import has_option\n\n__all__ = [\"get_norm_layer\", \"get_act_layer\", \"get_dropout_layer\", \"get_pool_layer\"]\n\n\ndef get_norm_layer(name: Union[Tuple, str], spatial_dims: Optional[int] = 1, channels: Optional[int] = 1):\n    \"\"\"\n    Create a normalization layer instance.\n\n    For example, to create normalization layers:\n\n    .. code-block:: python\n\n        from monai.networks.layers import get_norm_layer\n\n        g_layer = get_norm_layer(name=(\"group\", {\"num_groups\": 1}))\n        n_layer = get_norm_layer(name=\"instance\", spatial_dims=2)\n\n    Args:\n        name: a normalization type string or a tuple of type string and parameters.\n        spatial_dims: number of spatial dimensions of the input.\n        channels: number of features/channels when the normalization layer requires this parameter\n            but it is not specified in the norm parameters.\n    \"\"\"\n    norm_name, norm_args = split_args(name)\n    norm_type = Norm[norm_name, spatial_dims]\n    kw_args = dict(norm_args)\n    if has_option(norm_type, \"num_features\") and \"num_features\" not in kw_args:\n        kw_args[\"num_features\"] = channels\n    if has_option(norm_type, \"num_channels\") and \"num_channels\" not in kw_args:\n        kw_args[\"num_channels\"] = channels\n    return norm_type(**kw_args)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/utils.py_get_act_layer_get_act_layer.return.act_type_act_args_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/utils.py_get_act_layer_get_act_layer.return.act_type_act_args_", "embedding": null, "metadata": {"file_path": "monai/networks/layers/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 49, "end_line": 67, "span_ids": ["get_act_layer"], "tokens": 137}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_act_layer(name: Union[Tuple, str]):\n    \"\"\"\n    Create an activation layer instance.\n\n    For example, to create activation layers:\n\n    .. code-block:: python\n\n        from monai.networks.layers import get_act_layer\n\n        s_layer = get_act_layer(name=\"swish\")\n        p_layer = get_act_layer(name=(\"prelu\", {\"num_parameters\": 1, \"init\": 0.25}))\n\n    Args:\n        name: an activation type string or a tuple of type string and parameters.\n    \"\"\"\n    act_name, act_args = split_args(name)\n    act_type = Act[act_name]\n    return act_type(**act_args)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/utils.py_get_dropout_layer_get_dropout_layer.return.drop_type_drop_args_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/utils.py_get_dropout_layer_get_dropout_layer.return.drop_type_drop_args_", "embedding": null, "metadata": {"file_path": "monai/networks/layers/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 70, "end_line": 94, "span_ids": ["get_dropout_layer"], "tokens": 216}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_dropout_layer(name: Union[Tuple, str, float, int], dropout_dim: Optional[int] = 1):\n    \"\"\"\n    Create a dropout layer instance.\n\n    For example, to create dropout layers:\n\n    .. code-block:: python\n\n        from monai.networks.layers import get_dropout_layer\n\n        d_layer = get_dropout_layer(name=\"dropout\")\n        a_layer = get_dropout_layer(name=(\"alphadropout\", {\"p\": 0.25}))\n\n    Args:\n        name: a dropout ratio or a tuple of dropout type and parameters.\n        dropout_dim: the spatial dimension of the dropout operation.\n    \"\"\"\n    if isinstance(name, (int, float)):\n        # if dropout was specified simply as a p value, use default name and make a keyword map with the value\n        drop_name = Dropout.DROPOUT\n        drop_args = {\"p\": float(name)}\n    else:\n        drop_name, drop_args = split_args(name)\n    drop_type = Dropout[drop_name, dropout_dim]\n    return drop_type(**drop_args)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/utils.py_get_pool_layer_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/utils.py_get_pool_layer_", "embedding": null, "metadata": {"file_path": "monai/networks/layers/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 97, "end_line": 117, "span_ids": ["get_pool_layer"], "tokens": 154}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_pool_layer(name: Union[Tuple, str], spatial_dims: Optional[int] = 1):\n    \"\"\"\n    Create a pooling layer instance.\n\n    For example, to create adaptiveavg layer:\n\n    .. code-block:: python\n\n        from monai.networks.layers import get_pool_layer\n\n        pool_layer = get_pool_layer((\"adaptiveavg\", {\"output_size\": (1, 1, 1)}), spatial_dims=3)\n\n    Args:\n        name: a pooling type string or a tuple of type string and parameters.\n        spatial_dims: number of spatial dimensions of the input.\n\n    \"\"\"\n    pool_name, pool_args = split_args(name)\n    pool_type = Pool[pool_name, spatial_dims]\n    return pool_type(**pool_args)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/densenet.py_re___all__._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/densenet.py_re___all__._", "embedding": null, "metadata": {"file_path": "monai/networks/nets/densenet.py", "file_name": "densenet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 39, "span_ids": ["docstring"], "tokens": 182}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import re\nfrom collections import OrderedDict\nfrom typing import Callable, Sequence, Type, Union\n\nimport torch\nimport torch.nn as nn\nfrom torch.hub import load_state_dict_from_url\n\nfrom monai.networks.layers.factories import Conv, Dropout, Pool\nfrom monai.networks.layers.utils import get_act_layer, get_norm_layer\nfrom monai.utils.module import look_up_option\n\n__all__ = [\n    \"DenseNet\",\n    \"Densenet\",\n    \"DenseNet121\",\n    \"densenet121\",\n    \"Densenet121\",\n    \"DenseNet169\",\n    \"densenet169\",\n    \"Densenet169\",\n    \"DenseNet201\",\n    \"densenet201\",\n    \"Densenet201\",\n    \"DenseNet264\",\n    \"densenet264\",\n    \"Densenet264\",\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/densenet.py__DenseLayer__DenseLayer.forward.return.torch_cat_x_new_feature": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/densenet.py__DenseLayer__DenseLayer.forward.return.torch_cat_x_new_feature", "embedding": null, "metadata": {"file_path": "monai/networks/nets/densenet.py", "file_name": "densenet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 42, "end_line": 85, "span_ids": ["_DenseLayer.__init__", "_DenseLayer.forward", "_DenseLayer"], "tokens": 445}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class _DenseLayer(nn.Module):\n    def __init__(\n        self,\n        spatial_dims: int,\n        in_channels: int,\n        growth_rate: int,\n        bn_size: int,\n        dropout_prob: float,\n        act: Union[str, tuple] = (\"relu\", {\"inplace\": True}),\n        norm: Union[str, tuple] = \"batch\",\n    ) -> None:\n        \"\"\"\n        Args:\n            spatial_dims: number of spatial dimensions of the input image.\n            in_channels: number of the input channel.\n            growth_rate: how many filters to add each layer (k in paper).\n            bn_size: multiplicative factor for number of bottle neck layers.\n                (i.e. bn_size * k features in the bottleneck layer)\n            dropout_prob: dropout rate after each dense layer.\n            act: activation type and arguments. Defaults to relu.\n            norm: feature normalization type and arguments. Defaults to batch norm.\n        \"\"\"\n        super().__init__()\n\n        out_channels = bn_size * growth_rate\n        conv_type: Callable = Conv[Conv.CONV, spatial_dims]\n        dropout_type: Callable = Dropout[Dropout.DROPOUT, spatial_dims]\n\n        self.layers = nn.Sequential()\n\n        self.layers.add_module(\"norm1\", get_norm_layer(name=norm, spatial_dims=spatial_dims, channels=in_channels))\n        self.layers.add_module(\"relu1\", get_act_layer(name=act))\n        self.layers.add_module(\"conv1\", conv_type(in_channels, out_channels, kernel_size=1, bias=False))\n\n        self.layers.add_module(\"norm2\", get_norm_layer(name=norm, spatial_dims=spatial_dims, channels=out_channels))\n        self.layers.add_module(\"relu2\", get_act_layer(name=act))\n        self.layers.add_module(\"conv2\", conv_type(out_channels, growth_rate, kernel_size=3, padding=1, bias=False))\n\n        if dropout_prob > 0:\n            self.layers.add_module(\"dropout\", dropout_type(dropout_prob))\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        new_features = self.layers(x)\n        return torch.cat([x, new_features], 1)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/efficientnet.py_EfficientNet_EfficientNet.__init__": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/efficientnet.py_EfficientNet_EfficientNet.__init__", "embedding": null, "metadata": {"file_path": "monai/networks/nets/efficientnet.py", "file_name": "efficientnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 218, "end_line": 395, "span_ids": ["EfficientNet", "EfficientNet.__init__"], "tokens": 156}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class EfficientNet(nn.Module):\n    def __init__(\n        self,\n        blocks_args_str: List[str],\n        spatial_dims: int = 2,\n        in_channels: int = 3,\n        num_classes: int = 1000,\n        width_coefficient: float = 1.0,\n        depth_coefficient: float = 1.0,\n        dropout_rate: float = 0.2,\n        image_size: int = 224,\n        norm: Union[str, tuple] = (\"batch\", {\"eps\": 1e-3, \"momentum\": 0.01}),\n        drop_connect_rate: float = 0.2,\n        depth_divisor: int = 8,\n    ) -> None:\n        # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/efficientnet.py_EfficientNet.__init__.__EfficientNet.__init__.self__initialize_weights_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/efficientnet.py_EfficientNet.__init__.__EfficientNet.__init__.self__initialize_weights_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/efficientnet.py", "file_name": "efficientnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 233, "end_line": 395, "span_ids": ["EfficientNet.__init__"], "tokens": 1667}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class EfficientNet(nn.Module):\n    def __init__(\n        self,\n        blocks_args_str: List[str],\n        spatial_dims: int = 2,\n        in_channels: int = 3,\n        num_classes: int = 1000,\n        width_coefficient: float = 1.0,\n        depth_coefficient: float = 1.0,\n        dropout_rate: float = 0.2,\n        image_size: int = 224,\n        norm: Union[str, tuple] = (\"batch\", {\"eps\": 1e-3, \"momentum\": 0.01}),\n        drop_connect_rate: float = 0.2,\n        depth_divisor: int = 8,\n    ) -> None:\n        \"\"\"\n        EfficientNet based on `Rethinking Model Scaling for Convolutional Neural Networks <https://arxiv.org/pdf/1905.11946.pdf>`_.\n        Adapted from `EfficientNet-PyTorch <https://github.com/lukemelas/EfficientNet-PyTorch>`_.\n\n        Args:\n            blocks_args_str: block definitions.\n            spatial_dims: number of spatial dimensions.\n            in_channels: number of input channels.\n            num_classes: number of output classes.\n            width_coefficient: width multiplier coefficient (w in paper).\n            depth_coefficient: depth multiplier coefficient (d in paper).\n            dropout_rate: dropout rate for dropout layers.\n            image_size: input image resolution.\n            norm: feature normalization type and arguments.\n            drop_connect_rate: dropconnect rate for drop connection (individual weights) layers.\n            depth_divisor: depth divisor for channel rounding.\n\n        \"\"\"\n        super().__init__()\n\n        if spatial_dims not in (1, 2, 3):\n            raise ValueError(\"spatial_dims can only be 1, 2 or 3.\")\n\n        # select the type of N-Dimensional layers to use\n        # these are based on spatial dims and selected from MONAI factories\n        conv_type: Type[Union[nn.Conv1d, nn.Conv2d, nn.Conv3d]] = Conv[\"conv\", spatial_dims]\n        adaptivepool_type: Type[Union[nn.AdaptiveAvgPool1d, nn.AdaptiveAvgPool2d, nn.AdaptiveAvgPool3d]] = Pool[\n            \"adaptiveavg\", spatial_dims\n        ]\n\n        # decode blocks args into arguments for MBConvBlock\n        blocks_args = [BlockArgs.from_string(s) for s in blocks_args_str]\n\n        # checks for successful decoding of blocks_args_str\n        if not isinstance(blocks_args, list):\n            raise ValueError(\"blocks_args must be a list\")\n\n        if blocks_args == []:\n            raise ValueError(\"block_args must be non-empty\")\n\n        self._blocks_args = blocks_args\n        self.num_classes = num_classes\n        self.in_channels = in_channels\n        self.drop_connect_rate = drop_connect_rate\n\n        # expand input image dimensions to list\n        current_image_size = [image_size] * spatial_dims\n\n        # Stem\n        stride = 2\n        out_channels = _round_filters(32, width_coefficient, depth_divisor)  # number of output channels\n        self._conv_stem = conv_type(self.in_channels, out_channels, kernel_size=3, stride=stride, bias=False)\n        self._conv_stem_padding = _make_same_padder(self._conv_stem, current_image_size)\n        self._bn0 = get_norm_layer(name=norm, spatial_dims=spatial_dims, channels=out_channels)\n        current_image_size = _calculate_output_image_size(current_image_size, stride)\n\n        # build MBConv blocks\n        num_blocks = 0\n        self._blocks = nn.Sequential()\n\n        self.extract_stacks = []\n\n        # update baseline blocks to input/output filters and number of repeats based on width and depth multipliers.\n        for idx, block_args in enumerate(self._blocks_args):\n            block_args = block_args._replace(\n                input_filters=_round_filters(block_args.input_filters, width_coefficient, depth_divisor),\n                output_filters=_round_filters(block_args.output_filters, width_coefficient, depth_divisor),\n                num_repeat=_round_repeats(block_args.num_repeat, depth_coefficient),\n            )\n            self._blocks_args[idx] = block_args\n\n            # calculate the total number of blocks - needed for drop_connect estimation\n            num_blocks += block_args.num_repeat\n\n            if block_args.stride > 1:\n                self.extract_stacks.append(idx)\n\n        self.extract_stacks.append(len(self._blocks_args))\n\n        # create and add MBConvBlocks to self._blocks\n        idx = 0  # block index counter\n        for stack_idx, block_args in enumerate(self._blocks_args):\n            blk_drop_connect_rate = self.drop_connect_rate\n\n            # scale drop connect_rate\n            if blk_drop_connect_rate:\n                blk_drop_connect_rate *= float(idx) / num_blocks\n\n            sub_stack = nn.Sequential()\n            # the first block needs to take care of stride and filter size increase.\n            sub_stack.add_module(\n                str(idx),\n                MBConvBlock(\n                    spatial_dims=spatial_dims,\n                    in_channels=block_args.input_filters,\n                    out_channels=block_args.output_filters,\n                    kernel_size=block_args.kernel_size,\n                    stride=block_args.stride,\n                    image_size=current_image_size,\n                    expand_ratio=block_args.expand_ratio,\n                    se_ratio=block_args.se_ratio,\n                    id_skip=block_args.id_skip,\n                    norm=norm,\n                    drop_connect_rate=blk_drop_connect_rate,\n                ),\n            )\n            idx += 1  # increment blocks index counter\n\n            current_image_size = _calculate_output_image_size(current_image_size, block_args.stride)\n            if block_args.num_repeat > 1:  # modify block_args to keep same output size\n                block_args = block_args._replace(input_filters=block_args.output_filters, stride=1)\n\n            # add remaining block repeated num_repeat times\n            for _ in range(block_args.num_repeat - 1):\n                blk_drop_connect_rate = self.drop_connect_rate\n\n                # scale drop connect_rate\n                if blk_drop_connect_rate:\n                    blk_drop_connect_rate *= float(idx) / num_blocks\n\n                # add blocks\n                sub_stack.add_module(\n                    str(idx),\n                    MBConvBlock(\n                        spatial_dims=spatial_dims,\n                        in_channels=block_args.input_filters,\n                        out_channels=block_args.output_filters,\n                        kernel_size=block_args.kernel_size,\n                        stride=block_args.stride,\n                        image_size=current_image_size,\n                        expand_ratio=block_args.expand_ratio,\n                        se_ratio=block_args.se_ratio,\n                        id_skip=block_args.id_skip,\n                        norm=norm,\n                        drop_connect_rate=blk_drop_connect_rate,\n                    ),\n                )\n                idx += 1  # increment blocks index counter\n\n            self._blocks.add_module(str(stack_idx), sub_stack)\n\n        # sanity check to see if len(self._blocks) equal expected num_blocks\n        if idx != num_blocks:\n            raise ValueError(\"total number of blocks created != num_blocks\")\n\n        # Head\n        head_in_channels = block_args.output_filters\n        out_channels = _round_filters(1280, width_coefficient, depth_divisor)\n        self._conv_head = conv_type(head_in_channels, out_channels, kernel_size=1, bias=False)\n        self._conv_head_padding = _make_same_padder(self._conv_head, current_image_size)\n        self._bn1 = get_norm_layer(name=norm, spatial_dims=spatial_dims, channels=out_channels)\n\n        # final linear layer\n        self._avg_pooling = adaptivepool_type(1)\n        self._dropout = nn.Dropout(dropout_rate)\n        self._fc = nn.Linear(out_channels, self.num_classes)\n\n        # swish activation to use - using memory efficient swish by default\n        # can be switched to normal swish using self.set_swish() function call\n        self._swish = Act[\"memswish\"]()\n\n        # initialize weights using Tensorflow's init method from official impl.\n        self._initialize_weights()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/efficientnet.py__make_same_padder__make_same_padder.return.nn_Identity_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/efficientnet.py__make_same_padder__make_same_padder.return.nn_Identity_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/efficientnet.py", "file_name": "efficientnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 683, "end_line": 704, "span_ids": ["_make_same_padder"], "tokens": 238}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _make_same_padder(conv_op: Union[nn.Conv1d, nn.Conv2d, nn.Conv3d], image_size: List[int]):\n    \"\"\"\n    Helper for initializing ConstantPadNd with SAME padding similar to Tensorflow.\n    Uses output of _get_same_padding_conv_nd() to get the padding size.\n\n    This function is generalized for MONAI's N-Dimensional spatial operations (e.g. Conv1D, Conv2D, Conv3D)\n\n    Args:\n        conv_op: nn.ConvNd operation to extract parameters for op from\n        image_size: input image/feature spatial size\n\n    Returns:\n        If padding required then nn.ConstandNd() padder initialized to paddings otherwise nn.Identity()\n    \"\"\"\n    # calculate padding required\n    padding: List[int] = _get_same_padding_conv_nd(image_size, conv_op.kernel_size, conv_op.dilation, conv_op.stride)\n\n    # initialize and return padder\n    padder = Pad[\"constantpad\", len(padding) // 2]\n    if sum(padding) > 0:\n        return padder(padding=padding, value=0.0)\n    return nn.Identity()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/efficientnet.py_BlockArgs_BlockArgs.from_string.return.BlockArgs_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/efficientnet.py_BlockArgs_BlockArgs.from_string.return.BlockArgs_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/efficientnet.py", "file_name": "efficientnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 777, "end_line": 830, "span_ids": ["BlockArgs.from_string", "BlockArgs"], "tokens": 413}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class BlockArgs(NamedTuple):\n    \"\"\"\n    BlockArgs object to assist in decoding string notation\n        of arguments for MBConvBlock definition.\n    \"\"\"\n\n    num_repeat: int\n    kernel_size: int\n    stride: int\n    expand_ratio: int\n    input_filters: int\n    output_filters: int\n    id_skip: bool\n    se_ratio: Optional[float] = None\n\n    @staticmethod\n    def from_string(block_string: str):\n        \"\"\"\n        Get a BlockArgs object from a string notation of arguments.\n\n        Args:\n            block_string (str): A string notation of arguments.\n                                Examples: \"r1_k3_s11_e1_i32_o16_se0.25\".\n\n        Returns:\n            BlockArgs: namedtuple defined at the top of this function.\n        \"\"\"\n        ops = block_string.split(\"_\")\n        options = {}\n        for op in ops:\n            splits = re.split(r\"(\\d.*)\", op)\n            if len(splits) >= 2:\n                key, value = splits[:2]\n                options[key] = value\n\n        # check stride\n        stride_check = (\n            (\"s\" in options and len(options[\"s\"]) == 1)\n            or (len(options[\"s\"]) == 2 and options[\"s\"][0] == options[\"s\"][1])\n            or (len(options[\"s\"]) == 3 and options[\"s\"][0] == options[\"s\"][1] and options[\"s\"][0] == options[\"s\"][2])\n        )\n        if not stride_check:\n            raise ValueError(\"invalid stride option received\")\n\n        return BlockArgs(\n            num_repeat=int(options[\"r\"]),\n            kernel_size=int(options[\"k\"]),\n            stride=int(options[\"s\"][0]),\n            expand_ratio=int(options[\"e\"]),\n            input_filters=int(options[\"i\"]),\n            output_filters=int(options[\"o\"]),\n            id_skip=(\"noskip\" not in block_string),\n            se_ratio=float(options[\"se\"]) if \"se\" in options else None,\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/efficientnet.py_BlockArgs.to_string_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/efficientnet.py_BlockArgs.to_string_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/efficientnet.py", "file_name": "efficientnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 832, "end_line": 854, "span_ids": ["BlockArgs.to_string"], "tokens": 144}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class BlockArgs(NamedTuple):\n\n    def to_string(self):\n        \"\"\"\n        Return a block string notation for current BlockArgs object\n\n        Returns:\n            A string notation of BlockArgs object arguments.\n                Example: \"r1_k3_s11_e1_i32_o16_se0.25_noskip\".\n        \"\"\"\n        string = \"r{}_k{}_s{}{}_e{}_i{}_o{}_se{}\".format(\n            self.num_repeat,\n            self.kernel_size,\n            self.stride,\n            self.stride,\n            self.expand_ratio,\n            self.input_filters,\n            self.output_filters,\n            self.se_ratio,\n        )\n\n        if not self.id_skip:\n            string += \"_noskip\"\n        return string", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/resnet.py_get_conv1_get_conv1.return._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/resnet.py_get_conv1_get_conv1.return._", "embedding": null, "metadata": {"file_path": "monai/networks/nets/resnet.py", "file_name": "resnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 35, "end_line": 40, "span_ids": ["get_conv1"], "tokens": 123}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_conv1(conv1_t_size: int, conv1_t_stride: int):\n    return (\n        [0, conv1_t_size, (conv1_t_size, 7), (conv1_t_size, 7, 7)],\n        [0, conv1_t_stride, (conv1_t_stride, 2), (conv1_t_stride, 2, 2)],\n        [0, (conv1_t_size // 2), (conv1_t_size // 2, 3), (conv1_t_size // 2, 3, 3)],\n    )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/resnet.py_ResNetBlock_ResNetBlock.forward.return.out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/resnet.py_ResNetBlock_ResNetBlock.forward.return.out", "embedding": null, "metadata": {"file_path": "monai/networks/nets/resnet.py", "file_name": "resnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 43, "end_line": 91, "span_ids": ["ResNetBlock.__init__", "ResNetBlock.forward", "ResNetBlock"], "tokens": 352}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ResNetBlock(nn.Module):\n    expansion = 1\n\n    def __init__(\n        self,\n        in_planes: int,\n        planes: int,\n        spatial_dims: int = 3,\n        stride: int = 1,\n        downsample: Union[nn.Module, partial, None] = None,\n    ) -> None:\n        \"\"\"\n        Args:\n            in_planes: number of input channels.\n            planes: number of output channels.\n            spatial_dims: number of spatial dimensions of the input image.\n            stride: stride to use for first conv layer.\n            downsample: which downsample layer to use.\n        \"\"\"\n        super().__init__()\n\n        conv_type: Callable = Conv[Conv.CONV, spatial_dims]\n        norm_type: Callable = Norm[Norm.BATCH, spatial_dims]\n\n        self.conv1 = conv_type(in_planes, planes, kernel_size=3, padding=1, stride=stride, bias=False)\n        self.bn1 = norm_type(planes)\n        self.relu = nn.ReLU(inplace=True)\n        self.conv2 = conv_type(planes, planes, kernel_size=3, padding=1, bias=False)\n        self.bn2 = norm_type(planes)\n        self.downsample = downsample\n        self.stride = stride\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        residual = x\n\n        out: torch.Tensor = self.conv1(x)\n        out = self.bn1(out)\n        out = self.relu(out)\n\n        out = self.conv2(out)\n        out = self.bn2(out)\n\n        if self.downsample is not None:\n            residual = self.downsample(x)\n\n        out += residual\n        out = self.relu(out)\n\n        return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/resnet.py_ResNetBottleneck_ResNetBottleneck.__init__.self.stride.stride": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/resnet.py_ResNetBottleneck_ResNetBottleneck.__init__.self.stride.stride", "embedding": null, "metadata": {"file_path": "monai/networks/nets/resnet.py", "file_name": "resnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 94, "end_line": 127, "span_ids": ["ResNetBottleneck.__init__", "ResNetBottleneck"], "tokens": 299}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ResNetBottleneck(nn.Module):\n    expansion = 4\n\n    def __init__(\n        self,\n        in_planes: int,\n        planes: int,\n        spatial_dims: int = 3,\n        stride: int = 1,\n        downsample: Union[nn.Module, partial, None] = None,\n    ) -> None:\n        \"\"\"\n        Args:\n            in_planes: number of input channels.\n            planes: number of output channels (taking expansion into account).\n            spatial_dims: number of spatial dimensions of the input image.\n            stride: stride to use for second conv layer.\n            downsample: which downsample layer to use.\n        \"\"\"\n\n        super().__init__()\n\n        conv_type: Callable = Conv[Conv.CONV, spatial_dims]\n        norm_type: Callable = Norm[Norm.BATCH, spatial_dims]\n\n        self.conv1 = conv_type(in_planes, planes, kernel_size=1, bias=False)\n        self.bn1 = norm_type(planes)\n        self.conv2 = conv_type(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)\n        self.bn2 = norm_type(planes)\n        self.conv3 = conv_type(planes, planes * self.expansion, kernel_size=1, bias=False)\n        self.bn3 = norm_type(planes * self.expansion)\n        self.relu = nn.ReLU(inplace=True)\n        self.downsample = downsample\n        self.stride = stride", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/resnet.py_ResNetBottleneck.forward_ResNetBottleneck.forward.return.out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/resnet.py_ResNetBottleneck.forward_ResNetBottleneck.forward.return.out", "embedding": null, "metadata": {"file_path": "monai/networks/nets/resnet.py", "file_name": "resnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 126, "end_line": 146, "span_ids": ["ResNetBottleneck.forward"], "tokens": 125}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ResNetBottleneck(nn.Module):\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        residual = x\n\n        out: torch.Tensor = self.conv1(x)\n        out = self.bn1(out)\n        out = self.relu(out)\n\n        out = self.conv2(out)\n        out = self.bn2(out)\n        out = self.relu(out)\n\n        out = self.conv3(out)\n        out = self.bn3(out)\n\n        if self.downsample is not None:\n            residual = self.downsample(x)\n\n        out += residual\n        out = self.relu(out)\n\n        return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/resnet.py_ResNet_ResNet._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/resnet.py_ResNet_ResNet._", "embedding": null, "metadata": {"file_path": "monai/networks/nets/resnet.py", "file_name": "resnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 152, "end_line": 177, "span_ids": ["ResNet"], "tokens": 355}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ResNet(nn.Module):\n    \"\"\"\n    ResNet based on: `Deep Residual Learning for Image Recognition <https://arxiv.org/pdf/1512.03385.pdf>`_\n    and `Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet? <https://arxiv.org/pdf/1711.09577.pdf>`_.\n    Adapted from `<https://github.com/kenshohara/3D-ResNets-PyTorch/tree/master/models>`_.\n\n    Args:\n        block: which ResNet block to use, either Basic or Bottleneck.\n        layers: how many layers to use.\n        block_inplanes: determine the size of planes at each step. Also tunable with widen_factor.\n        spatial_dims: number of spatial dimensions of the input image.\n        n_input_channels: number of input channels for first convolutional layer.\n        conv1_t_size: size of first convolution layer, determines kernel and padding.\n        conv1_t_stride: stride of first convolution layer.\n        no_max_pool: bool argument to determine if to use maxpool layer.\n        shortcut_type: which downsample block to use. Options are 'A', 'B', default to 'B'.\n            - 'A': using `self._downsample_basic_block`.\n            - 'B': kernel_size 1 conv + norm.\n        widen_factor: widen output for each layer.\n        num_classes: number of output (classifications).\n        feed_forward: whether to add the FC layer for the output, default to `True`.\n\n    .. deprecated:: 0.6.0\n        ``n_classes`` is deprecated, use ``num_classes`` instead.\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/resnet.py_ResNet.__init___ResNet.__init__.for_m_in_self_modules_.if_isinstance_m_conv_typ.elif_isinstance_m_nn_Lin.nn_init_constant__torch_a": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/resnet.py_ResNet.__init___ResNet.__init__.for_m_in_self_modules_.if_isinstance_m_conv_typ.elif_isinstance_m_nn_Lin.nn_init_constant__torch_a", "embedding": null, "metadata": {"file_path": "monai/networks/nets/resnet.py", "file_name": "resnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 178, "end_line": 240, "span_ids": ["ResNet.__init__"], "tokens": 788}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ResNet(nn.Module):\n\n    @deprecated_arg(\"n_classes\", since=\"0.6\")\n    def __init__(\n        self,\n        block: Type[Union[ResNetBlock, ResNetBottleneck]],\n        layers: List[int],\n        block_inplanes: List[int],\n        spatial_dims: int = 3,\n        n_input_channels: int = 3,\n        conv1_t_size: int = 7,\n        conv1_t_stride: int = 1,\n        no_max_pool: bool = False,\n        shortcut_type: str = \"B\",\n        widen_factor: float = 1.0,\n        num_classes: int = 400,\n        feed_forward: bool = True,\n        n_classes: Optional[int] = None,\n    ) -> None:\n\n        super().__init__()\n        # in case the new num_classes is default but you still call deprecated n_classes\n        if n_classes is not None and num_classes == 400:\n            num_classes = n_classes\n\n        conv_type: Type[Union[nn.Conv1d, nn.Conv2d, nn.Conv3d]] = Conv[Conv.CONV, spatial_dims]\n        norm_type: Type[Union[nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d]] = Norm[Norm.BATCH, spatial_dims]\n        pool_type: Type[Union[nn.MaxPool1d, nn.MaxPool2d, nn.MaxPool3d]] = Pool[Pool.MAX, spatial_dims]\n        avgp_type: Type[Union[nn.AdaptiveAvgPool1d, nn.AdaptiveAvgPool2d, nn.AdaptiveAvgPool3d]] = Pool[\n            Pool.ADAPTIVEAVG, spatial_dims\n        ]\n\n        block_avgpool = get_avgpool()\n        conv1_kernel, conv1_stride, conv1_padding = get_conv1(conv1_t_size, conv1_t_stride)\n        block_inplanes = [int(x * widen_factor) for x in block_inplanes]\n\n        self.in_planes = block_inplanes[0]\n        self.no_max_pool = no_max_pool\n\n        self.conv1 = conv_type(\n            n_input_channels,\n            self.in_planes,\n            kernel_size=conv1_kernel[spatial_dims],\n            stride=conv1_stride[spatial_dims],\n            padding=conv1_padding[spatial_dims],\n            bias=False,\n        )\n        self.bn1 = norm_type(self.in_planes)\n        self.relu = nn.ReLU(inplace=True)\n        self.maxpool = pool_type(kernel_size=3, stride=2, padding=1)\n        self.layer1 = self._make_layer(block, block_inplanes[0], layers[0], spatial_dims, shortcut_type)\n        self.layer2 = self._make_layer(block, block_inplanes[1], layers[1], spatial_dims, shortcut_type, stride=2)\n        self.layer3 = self._make_layer(block, block_inplanes[2], layers[2], spatial_dims, shortcut_type, stride=2)\n        self.layer4 = self._make_layer(block, block_inplanes[3], layers[3], spatial_dims, shortcut_type, stride=2)\n        self.avgpool = avgp_type(block_avgpool[spatial_dims])\n        self.fc = nn.Linear(block_inplanes[3] * block.expansion, num_classes) if feed_forward else None\n\n        for m in self.modules():\n            if isinstance(m, conv_type):\n                nn.init.kaiming_normal_(torch.as_tensor(m.weight), mode=\"fan_out\", nonlinearity=\"relu\")\n            elif isinstance(m, norm_type):\n                nn.init.constant_(torch.as_tensor(m.weight), 1)\n                nn.init.constant_(torch.as_tensor(m.bias), 0)\n            elif isinstance(m, nn.Linear):\n                nn.init.constant_(torch.as_tensor(m.bias), 0)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/resnet.py_ResNet._downsample_basic_block_ResNet._downsample_basic_block.return.out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/resnet.py_ResNet._downsample_basic_block_ResNet._downsample_basic_block.return.out", "embedding": null, "metadata": {"file_path": "monai/networks/nets/resnet.py", "file_name": "resnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 243, "end_line": 247, "span_ids": ["ResNet._downsample_basic_block"], "tokens": 126}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ResNet(nn.Module):\n\n    def _downsample_basic_block(self, x: torch.Tensor, planes: int, stride: int, spatial_dims: int = 3) -> torch.Tensor:\n        out: torch.Tensor = get_pool_layer((\"avg\", {\"kernel_size\": 1, \"stride\": stride}), spatial_dims=spatial_dims)(x)\n        zero_pads = torch.zeros(out.size(0), planes - out.size(1), *out.shape[2:], dtype=out.dtype, device=out.device)\n        out = torch.cat([out.data, zero_pads], dim=1)\n        return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/resnet.py_ResNet._make_layer_ResNet._make_layer.return.nn_Sequential_layers_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/resnet.py_ResNet._make_layer_ResNet._make_layer.return.nn_Sequential_layers_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/resnet.py", "file_name": "resnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 249, "end_line": 287, "span_ids": ["ResNet._make_layer"], "tokens": 317}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ResNet(nn.Module):\n\n    def _make_layer(\n        self,\n        block: Type[Union[ResNetBlock, ResNetBottleneck]],\n        planes: int,\n        blocks: int,\n        spatial_dims: int,\n        shortcut_type: str,\n        stride: int = 1,\n    ) -> nn.Sequential:\n\n        conv_type: Callable = Conv[Conv.CONV, spatial_dims]\n        norm_type: Callable = Norm[Norm.BATCH, spatial_dims]\n\n        downsample: Union[nn.Module, partial, None] = None\n        if stride != 1 or self.in_planes != planes * block.expansion:\n            if look_up_option(shortcut_type, {\"A\", \"B\"}) == \"A\":\n                downsample = partial(\n                    self._downsample_basic_block,\n                    planes=planes * block.expansion,\n                    stride=stride,\n                    spatial_dims=spatial_dims,\n                )\n            else:\n                downsample = nn.Sequential(\n                    conv_type(self.in_planes, planes * block.expansion, kernel_size=1, stride=stride),\n                    norm_type(planes * block.expansion),\n                )\n\n        layers = [\n            block(\n                in_planes=self.in_planes, planes=planes, spatial_dims=spatial_dims, stride=stride, downsample=downsample\n            )\n        ]\n\n        self.in_planes = planes * block.expansion\n        for _i in range(1, blocks):\n            layers.append(block(self.in_planes, planes, spatial_dims=spatial_dims))\n\n        return nn.Sequential(*layers)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/resnet.py_ResNet.forward_ResNet.forward.return.x": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/resnet.py_ResNet.forward_ResNet.forward.return.x", "embedding": null, "metadata": {"file_path": "monai/networks/nets/resnet.py", "file_name": "resnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 288, "end_line": 306, "span_ids": ["ResNet.forward"], "tokens": 130}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ResNet(nn.Module):\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        x = self.conv1(x)\n        x = self.bn1(x)\n        x = self.relu(x)\n        if not self.no_max_pool:\n            x = self.maxpool(x)\n\n        x = self.layer1(x)\n        x = self.layer2(x)\n        x = self.layer3(x)\n        x = self.layer4(x)\n\n        x = self.avgpool(x)\n\n        x = x.view(x.size(0), -1)\n        if self.fc is not None:\n            x = self.fc(x)\n\n        return x", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/resnet.py__resnet__resnet.return.model": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/resnet.py__resnet__resnet.return.model", "embedding": null, "metadata": {"file_path": "monai/networks/nets/resnet.py", "file_name": "resnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 309, "end_line": 327, "span_ids": ["_resnet"], "tokens": 196}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _resnet(\n    arch: str,\n    block: Type[Union[ResNetBlock, ResNetBottleneck]],\n    layers: List[int],\n    block_inplanes: List[int],\n    pretrained: bool,\n    progress: bool,\n    **kwargs: Any,\n) -> ResNet:\n    model: ResNet = ResNet(block, layers, block_inplanes, **kwargs)\n    if pretrained:\n        # Author of paper zipped the state_dict on googledrive,\n        # so would need to download, unzip and read (2.8gb file for a ~150mb state dict).\n        # Would like to load dict from url but need somewhere to save the state dicts.\n        raise NotImplementedError(\n            \"Currently not implemented. You need to manually download weights provided by the paper's author\"\n            \" and load then to the model with `state_dict`. See https://github.com/Tencent/MedicalNet\"\n        )\n    return model", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/resnet.py_resnet10_resnet10.return._resnet_resnet10_ResNe": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/resnet.py_resnet10_resnet10.return._resnet_resnet10_ResNe", "embedding": null, "metadata": {"file_path": "monai/networks/nets/resnet.py", "file_name": "resnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 315, "end_line": 324, "span_ids": ["resnet10"], "tokens": 160}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def resnet10(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:\n    \"\"\"ResNet-10 with optional pretrained support when `spatial_dims` is 3.\n\n    Pretraining from `Med3D: Transfer Learning for 3D Medical Image Analysis <https://arxiv.org/pdf/1904.00625.pdf>`_.\n\n    Args:\n        pretrained (bool): If True, returns a model pre-trained on 23 medical datasets\n        progress (bool): If True, displays a progress bar of the download to stderr\n    \"\"\"\n    return _resnet(\"resnet10\", ResNetBlock, [1, 1, 1, 1], get_inplanes(), pretrained, progress, **kwargs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/resnet.py_resnet18_resnet18.return._resnet_resnet18_ResNe": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/resnet.py_resnet18_resnet18.return._resnet_resnet18_ResNe", "embedding": null, "metadata": {"file_path": "monai/networks/nets/resnet.py", "file_name": "resnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 327, "end_line": 336, "span_ids": ["resnet18"], "tokens": 160}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def resnet18(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:\n    \"\"\"ResNet-18 with optional pretrained support when `spatial_dims` is 3.\n\n    Pretraining from `Med3D: Transfer Learning for 3D Medical Image Analysis <https://arxiv.org/pdf/1904.00625.pdf>`_.\n\n    Args:\n        pretrained (bool): If True, returns a model pre-trained on 23 medical datasets\n        progress (bool): If True, displays a progress bar of the download to stderr\n    \"\"\"\n    return _resnet(\"resnet18\", ResNetBlock, [2, 2, 2, 2], get_inplanes(), pretrained, progress, **kwargs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/resnet.py_resnet34_resnet34.return._resnet_resnet34_ResNe": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/resnet.py_resnet34_resnet34.return._resnet_resnet34_ResNe", "embedding": null, "metadata": {"file_path": "monai/networks/nets/resnet.py", "file_name": "resnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 339, "end_line": 348, "span_ids": ["resnet34"], "tokens": 160}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def resnet34(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:\n    \"\"\"ResNet-34 with optional pretrained support when `spatial_dims` is 3.\n\n    Pretraining from `Med3D: Transfer Learning for 3D Medical Image Analysis <https://arxiv.org/pdf/1904.00625.pdf>`_.\n\n    Args:\n        pretrained (bool): If True, returns a model pre-trained on 23 medical datasets\n        progress (bool): If True, displays a progress bar of the download to stderr\n    \"\"\"\n    return _resnet(\"resnet34\", ResNetBlock, [3, 4, 6, 3], get_inplanes(), pretrained, progress, **kwargs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/resnet.py_resnet50_resnet50.return._resnet_resnet50_ResNe": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/resnet.py_resnet50_resnet50.return._resnet_resnet50_ResNe", "embedding": null, "metadata": {"file_path": "monai/networks/nets/resnet.py", "file_name": "resnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 351, "end_line": 360, "span_ids": ["resnet50"], "tokens": 162}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def resnet50(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:\n    \"\"\"ResNet-50 with optional pretrained support when `spatial_dims` is 3.\n\n    Pretraining from `Med3D: Transfer Learning for 3D Medical Image Analysis <https://arxiv.org/pdf/1904.00625.pdf>`_.\n\n    Args:\n        pretrained (bool): If True, returns a model pre-trained on 23 medical datasets\n        progress (bool): If True, displays a progress bar of the download to stderr\n    \"\"\"\n    return _resnet(\"resnet50\", ResNetBottleneck, [3, 4, 6, 3], get_inplanes(), pretrained, progress, **kwargs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/resnet.py_resnet101_resnet101.return._resnet_resnet101_ResN": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/resnet.py_resnet101_resnet101.return._resnet_resnet101_ResN", "embedding": null, "metadata": {"file_path": "monai/networks/nets/resnet.py", "file_name": "resnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 363, "end_line": 372, "span_ids": ["resnet101"], "tokens": 162}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def resnet101(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:\n    \"\"\"ResNet-101 with optional pretrained support when `spatial_dims` is 3.\n\n    Pretraining from `Med3D: Transfer Learning for 3D Medical Image Analysis <https://arxiv.org/pdf/1904.00625.pdf>`_.\n\n    Args:\n        pretrained (bool): If True, returns a model pre-trained on 8 medical datasets\n        progress (bool): If True, displays a progress bar of the download to stderr\n    \"\"\"\n    return _resnet(\"resnet101\", ResNetBottleneck, [3, 4, 23, 3], get_inplanes(), pretrained, progress, **kwargs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/resnet.py_resnet152_resnet152.return._resnet_resnet152_ResN": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/resnet.py_resnet152_resnet152.return._resnet_resnet152_ResN", "embedding": null, "metadata": {"file_path": "monai/networks/nets/resnet.py", "file_name": "resnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 375, "end_line": 384, "span_ids": ["resnet152"], "tokens": 162}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def resnet152(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:\n    \"\"\"ResNet-152 with optional pretrained support when `spatial_dims` is 3.\n\n    Pretraining from `Med3D: Transfer Learning for 3D Medical Image Analysis <https://arxiv.org/pdf/1904.00625.pdf>`_.\n\n    Args:\n        pretrained (bool): If True, returns a model pre-trained on 8 medical datasets\n        progress (bool): If True, displays a progress bar of the download to stderr\n    \"\"\"\n    return _resnet(\"resnet152\", ResNetBottleneck, [3, 8, 36, 3], get_inplanes(), pretrained, progress, **kwargs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/resnet.py_resnet200_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/resnet.py_resnet200_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/resnet.py", "file_name": "resnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 387, "end_line": 397, "span_ids": ["resnet200"], "tokens": 162}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def resnet200(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:\n    \"\"\"ResNet-200 with optional pretrained support when `spatial_dims` is 3.\n\n    Pretraining from `Med3D: Transfer Learning for 3D Medical Image Analysis <https://arxiv.org/pdf/1904.00625.pdf>`_.\n\n    Args:\n        pretrained (bool): If True, returns a model pre-trained on 8 medical datasets\n        progress (bool): If True, displays a progress bar of the download to stderr\n    \"\"\"\n    return _resnet(\"resnet200\", ResNetBottleneck, [3, 24, 36, 3], get_inplanes(), pretrained, progress, **kwargs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/segresnet.py_SegResNet.__init___SegResNet.__init__.if_dropout_prob_is_not_No.self.dropout.Dropout_Dropout_DROPOUT_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/segresnet.py_SegResNet.__init___SegResNet.__init__.if_dropout_prob_is_not_No.self.dropout.Dropout_Dropout_DROPOUT_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/segresnet.py", "file_name": "segresnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 57, "end_line": 99, "span_ids": ["SegResNet.__init__"], "tokens": 451}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SegResNet(nn.Module):\n\n    def __init__(\n        self,\n        spatial_dims: int = 3,\n        init_filters: int = 8,\n        in_channels: int = 1,\n        out_channels: int = 2,\n        dropout_prob: Optional[float] = None,\n        act: Union[Tuple, str] = (\"RELU\", {\"inplace\": True}),\n        norm: Union[Tuple, str] = (\"GROUP\", {\"num_groups\": 8}),\n        norm_name: str = \"\",\n        num_groups: int = 8,\n        use_conv_final: bool = True,\n        blocks_down: tuple = (1, 2, 2, 4),\n        blocks_up: tuple = (1, 1, 1),\n        upsample_mode: Union[UpsampleMode, str] = UpsampleMode.NONTRAINABLE,\n    ):\n        super().__init__()\n\n        if spatial_dims not in (2, 3):\n            raise ValueError(\"`spatial_dims` can only be 2 or 3.\")\n\n        self.spatial_dims = spatial_dims\n        self.init_filters = init_filters\n        self.in_channels = in_channels\n        self.blocks_down = blocks_down\n        self.blocks_up = blocks_up\n        self.dropout_prob = dropout_prob\n        self.act = act  # input options\n        self.act_mod = get_act_layer(act)\n        if norm_name:\n            if norm_name.lower() != \"group\":\n                raise ValueError(f\"Deprecating option 'norm_name={norm_name}', please use 'norm' instead.\")\n            norm = (\"group\", {\"num_groups\": num_groups})\n        self.norm = norm\n        self.upsample_mode = UpsampleMode(upsample_mode)\n        self.use_conv_final = use_conv_final\n        self.convInit = get_conv_layer(spatial_dims, in_channels, init_filters)\n        self.down_layers = self._make_down_layers()\n        self.up_layers, self.up_samples = self._make_up_layers()\n        self.conv_final = self._make_final_conv(out_channels)\n\n        if dropout_prob is not None:\n            self.dropout = Dropout[Dropout.DROPOUT, spatial_dims](dropout_prob)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/torchvision_fc.py_from_typing_import_Any_D_TorchVisionFCModel.__init__.super___init___": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/torchvision_fc.py_from_typing_import_Any_D_TorchVisionFCModel.__init__.super___init___", "embedding": null, "metadata": {"file_path": "monai/networks/nets/torchvision_fc.py", "file_name": "torchvision_fc.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 74, "span_ids": ["TorchVisionFCModel", "TorchVisionFCModel.__init__", "docstring"], "tokens": 698}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Any, Dict, Optional, Tuple, Union\n\nfrom monai.networks.nets import NetAdapter\nfrom monai.utils import deprecated, deprecated_arg, optional_import\n\nmodels, _ = optional_import(\"torchvision.models\")\n\n\n__all__ = [\"TorchVisionFCModel\", \"TorchVisionFullyConvModel\"]\n\n\nclass TorchVisionFCModel(NetAdapter):\n    \"\"\"\n    Customize the fully connected layer of TorchVision model or replace it by convolutional layer.\n\n    Args:\n        model_name: name of any torchvision model with fully connected layer at the end.\n            ``resnet18`` (default), ``resnet34``, ``resnet50``, ``resnet101``, ``resnet152``,\n            ``resnext50_32x4d``, ``resnext101_32x8d``, ``wide_resnet50_2``, ``wide_resnet101_2``.\n            model details: https://pytorch.org/vision/stable/models.html.\n        num_classes: number of classes for the last classification layer. Default to 1.\n        dim: number of supported spatial dimensions in the specified model, depends on the model implementation.\n            default to 2 as most Torchvision models are for 2D image processing.\n        in_channels: number of the input channels of last layer. if None, get it from `in_features` of last layer.\n        use_conv: whether use convolutional layer to replace the last layer, default to False.\n        pool: parameters for the pooling layer, it should be a tuple, the first item is name of the pooling layer,\n            the second item is dictionary of the initialization args. if None, will not replace the `layers[-2]`.\n            default to `(\"avg\", {\"kernel_size\": 7, \"stride\": 1})`.\n        bias: the bias value when replacing the last layer. if False, the layer will not learn an additive bias,\n            default to True.\n        pretrained: whether to use the imagenet pretrained weights. Default to False.\n    \"\"\"\n\n    @deprecated_arg(\"n_classes\", since=\"0.6\")\n    def __init__(\n        self,\n        model_name: str = \"resnet18\",\n        num_classes: int = 1,\n        dim: int = 2,\n        in_channels: Optional[int] = None,\n        use_conv: bool = False,\n        pool: Optional[Tuple[str, Dict[str, Any]]] = (\"avg\", {\"kernel_size\": 7, \"stride\": 1}),\n        bias: bool = True,\n        pretrained: bool = False,\n        n_classes: Optional[int] = None,\n    ):\n        # in case the new num_classes is default but you still call deprecated n_classes\n        if n_classes is not None and num_classes == 1:\n            num_classes = n_classes\n        model = getattr(models, model_name)(pretrained=pretrained)\n        # check if the model is compatible, should have a FC layer at the end\n        if not str(list(model.children())[-1]).startswith(\"Linear\"):\n            raise ValueError(f\"Model ['{model_name}'] does not have a Linear layer at the end.\")\n\n        super().__init__(\n            model=model,\n            num_classes=num_classes,\n            dim=dim,\n            in_channels=in_channels,\n            use_conv=use_conv,\n            pool=pool,\n            bias=bias,\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/torchvision_fc.py_TorchVisionFullyConvModel_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/torchvision_fc.py_TorchVisionFullyConvModel_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/torchvision_fc.py", "file_name": "torchvision_fc.py", "file_type": "text/x-python", "category": "implementation", "start_line": 77, "end_line": 116, "span_ids": ["TorchVisionFullyConvModel", "TorchVisionFullyConvModel.__init__"], "tokens": 463}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@deprecated(since=\"0.6.0\", removed=\"0.9.0\", msg_suffix=\"Please consider using `TorchVisionFCModel` instead.\")\nclass TorchVisionFullyConvModel(TorchVisionFCModel):\n    \"\"\"\n    Customize TorchVision models to replace fully connected layer by convolutional layer.\n\n    Args:\n        model_name: name of any torchvision with adaptive avg pooling and fully connected layer at the end.\n            ``resnet18`` (default), ``resnet34``, ``resnet50``, ``resnet101``, ``resnet152``,\n            ``resnext50_32x4d``, ``resnext101_32x8d``, ``wide_resnet50_2``, ``wide_resnet101_2``.\n        num_classes: number of classes for the last classification layer. Default to 1.\n        pool_size: the kernel size for `AvgPool2d` to replace `AdaptiveAvgPool2d`. Default to (7, 7).\n        pool_stride: the stride for `AvgPool2d` to replace `AdaptiveAvgPool2d`. Default to 1.\n        pretrained: whether to use the imagenet pretrained weights. Default to False.\n\n    .. deprecated:: 0.6.0\n        Use :class:`monai.networks.nets.TorchVisionFCModel` instead.\n\n    \"\"\"\n\n    @deprecated_arg(\"n_classes\", since=\"0.6\")\n    def __init__(\n        self,\n        model_name: str = \"resnet18\",\n        num_classes: int = 1,\n        pool_size: Union[int, Tuple[int, int]] = (7, 7),\n        pool_stride: Union[int, Tuple[int, int]] = 1,\n        pretrained: bool = False,\n        n_classes: Optional[int] = None,\n    ):\n        # in case the new num_classes is default but you still call deprecated n_classes\n        if n_classes is not None and num_classes == 1:\n            num_classes = n_classes\n        super().__init__(\n            model_name=model_name,\n            num_classes=num_classes,\n            use_conv=True,\n            pool=(\"avg\", {\"kernel_size\": pool_size, \"stride\": pool_stride}),\n            pretrained=pretrained,\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/utils.py_re___all__._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/utils.py_re___all__._", "embedding": null, "metadata": {"file_path": "monai/networks/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 14, "end_line": 40, "span_ids": ["docstring"], "tokens": 154}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import re\nimport warnings\nfrom collections import OrderedDict\nfrom contextlib import contextmanager\nfrom typing import Any, Callable, Dict, Mapping, Optional, Sequence, Union\n\nimport torch\nimport torch.nn as nn\n\nfrom monai.utils.deprecate_utils import deprecated_arg\nfrom monai.utils.misc import ensure_tuple, set_determinism\nfrom monai.utils.module import pytorch_after\n\n__all__ = [\n    \"one_hot\",\n    \"slice_channels\",\n    \"predict_segmentation\",\n    \"normalize_transform\",\n    \"to_norm_affine\",\n    \"normal_init\",\n    \"icnr_init\",\n    \"pixelshuffle\",\n    \"eval_mode\",\n    \"train_mode\",\n    \"copy_model_state\",\n    \"convert_to_torchscript\",\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/utils.py_one_hot_one_hot.return.labels": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/utils.py_one_hot_one_hot.return.labels", "embedding": null, "metadata": {"file_path": "monai/networks/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 43, "end_line": 93, "span_ids": ["one_hot"], "tokens": 574}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def one_hot(labels: torch.Tensor, num_classes: int, dtype: torch.dtype = torch.float, dim: int = 1) -> torch.Tensor:\n    \"\"\"\n    For every value v in `labels`, the value in the output will be either 1 or 0. Each vector along the `dim`-th\n    dimension has the \"one-hot\" format, i.e., it has a total length of `num_classes`,\n    with a one and `num_class-1` zeros.\n    Note that this will include the background label, thus a binary mask should be treated as having two classes.\n\n    Args:\n        labels: input tensor of integers to be converted into the 'one-hot' format. Internally `labels` will be\n            converted into integers `labels.long()`.\n        num_classes: number of output channels, the corresponding length of `labels[dim]` will be converted to\n            `num_classes` from `1`.\n        dtype: the data type of the output one_hot label.\n        dim: the dimension to be converted to `num_classes` channels from `1` channel, should be non-negative number.\n\n    Example:\n\n    For a tensor `labels` of dimensions [B]1[spatial_dims], return a tensor of dimensions `[B]N[spatial_dims]`\n    when `num_classes=N` number of classes and `dim=1`.\n\n    .. code-block:: python\n\n        from monai.networks.utils import one_hot\n        import torch\n\n        a = torch.randint(0, 2, size=(1, 2, 2, 2))\n        out = one_hot(a, num_classes=2, dim=0)\n        print(out.shape)  # torch.Size([2, 2, 2, 2])\n\n        a = torch.randint(0, 2, size=(2, 1, 2, 2, 2))\n        out = one_hot(a, num_classes=2, dim=1)\n        print(out.shape)  # torch.Size([2, 2, 2, 2, 2])\n\n    \"\"\"\n\n    # if `dim` is bigger, add singleton dim at the end\n    if labels.ndim < dim + 1:\n        shape = list(labels.shape) + [1] * (dim + 1 - len(labels.shape))\n        labels = torch.reshape(labels, shape)\n\n    sh = list(labels.shape)\n\n    if sh[dim] != 1:\n        raise AssertionError(\"labels should have a channel with length equal to one.\")\n\n    sh[dim] = num_classes\n\n    o = torch.zeros(size=sh, dtype=dtype, device=labels.device)\n    labels = o.scatter_(dim=dim, index=labels.long(), value=1)\n\n    return labels", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/utils.py_train_mode_train_mode.try_.finally_.for_n_in_eval_list_.n_eval_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/utils.py_train_mode_train_mode.try_.finally_.for_n_in_eval_list_.n_eval_", "embedding": null, "metadata": {"file_path": "monai/networks/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 283, "end_line": 314, "span_ids": ["train_mode"], "tokens": 200}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@contextmanager\ndef train_mode(*nets: nn.Module):\n    \"\"\"\n    Set network(s) to train mode and then return to original state at the end.\n\n    Args:\n        nets: Input network(s)\n\n    Examples\n\n    .. code-block:: python\n\n        t=torch.rand(1,1,16,16)\n        p=torch.nn.Conv2d(1,1,3)\n        p.eval()\n        print(p.training)  # False\n        with train_mode(p):\n            print(p.training)  # True\n            print(p(t).sum().backward())  # No exception\n    \"\"\"\n\n    # Get original state of network(s)\n    eval_list = [n for n in nets if not n.training]\n\n    try:\n        # set to train mode\n        with torch.set_grad_enabled(True):\n            yield [n.train() for n in nets]\n    finally:\n        # Return required networks to eval_list\n        for n in eval_list:\n            n.eval()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/utils.py_copy_model_state_copy_model_state.for_s_val_in_src_dict_it.if_dst_key_in_dst_dict_an.updated_keys_append_dst_k": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/utils.py_copy_model_state_copy_model_state.for_s_val_in_src_dict_it.if_dst_key_in_dst_dict_an.updated_keys_append_dst_k", "embedding": null, "metadata": {"file_path": "monai/networks/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 317, "end_line": 379, "span_ids": ["copy_model_state"], "tokens": 746}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def copy_model_state(\n    dst: Union[torch.nn.Module, Mapping],\n    src: Union[torch.nn.Module, Mapping],\n    dst_prefix=\"\",\n    mapping=None,\n    exclude_vars=None,\n    inplace=True,\n):\n    \"\"\"\n    Compute a module state_dict, of which the keys are the same as `dst`. The values of `dst` are overwritten\n    by the ones from `src` whenever their keys match. The method provides additional `dst_prefix` for\n    the `dst` key when matching them. `mapping` can be a `{\"src_key\": \"dst_key\"}` dict, indicating\n    `dst[dst_prefix + dst_key] = src[src_key]`.\n    This function is mainly to return a model state dict\n    for loading the `src` model state into the `dst` model, `src` and `dst` can have different dict keys, but\n    their corresponding values normally have the same shape.\n\n    Args:\n        dst: a pytorch module or state dict to be updated.\n        src: a pytorch module or state dist used to get the values used for the update.\n        dst_prefix: `dst` key prefix, so that `dst[dst_prefix + src_key]`\n            will be assigned to the value of `src[src_key]`.\n        mapping: a `{\"src_key\": \"dst_key\"}` dict, indicating that `dst[dst_prefix + dst_key]`\n            to be assigned to the value of `src[src_key]`.\n        exclude_vars: a regular expression to match the `dst` variable names,\n            so that their values are not overwritten by `src`.\n        inplace: whether to set the `dst` module with the updated `state_dict` via `load_state_dict`.\n            This option is only available when `dst` is a `torch.nn.Module`.\n\n    Examples:\n        .. code-block:: python\n\n            from monai.networks.nets import BasicUNet\n            from monai.networks.utils import copy_model_state\n\n            model_a = BasicUNet(in_channels=1, out_channels=4)\n            model_b = BasicUNet(in_channels=1, out_channels=2)\n            model_a_b, changed, unchanged = copy_model_state(\n                model_a, model_b, exclude_vars=\"conv_0.conv_0\", inplace=False)\n            # dst model updated: 76 of 82 variables.\n            model_a.load_state_dict(model_a_b)\n            # <All keys matched successfully>\n\n    Returns: an OrderedDict of the updated `dst` state, the changed, and unchanged keys.\n    \"\"\"\n\n    if isinstance(src, (nn.DataParallel, nn.parallel.DistributedDataParallel)):\n        src = src.module\n    if isinstance(dst, (nn.DataParallel, nn.parallel.DistributedDataParallel)):\n        dst = dst.module\n    src_dict = src.state_dict() if isinstance(src, torch.nn.Module) else src\n    dst_dict = dst.state_dict() if isinstance(dst, torch.nn.Module) else dst\n    dst_dict = OrderedDict(dst_dict)\n\n    to_skip = {s_key for s_key in src_dict if exclude_vars and re.compile(exclude_vars).search(s_key)}\n\n    # update dst with items from src\n    all_keys, updated_keys = list(dst_dict), list()\n    for s, val in src_dict.items():\n        dst_key = f\"{dst_prefix}{s}\"\n        if dst_key in dst_dict and dst_key not in to_skip and dst_dict[dst_key].shape == val.shape:\n            dst_dict[dst_key] = val\n            updated_keys.append(dst_key)\n    # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/compose.py_Compose_Compose._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/compose.py_Compose_Compose._", "embedding": null, "metadata": {"file_path": "monai/transforms/compose.py", "file_name": "compose.py", "file_type": "text/x-python", "category": "implementation", "start_line": 36, "end_line": 103, "span_ids": ["Compose"], "tokens": 772}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Compose(Randomizable, InvertibleTransform):\n    \"\"\"\n    ``Compose`` provides the ability to chain a series of callables together in\n    a sequential manner. Each transform in the sequence must take a single\n    argument and return a single value.\n\n    ``Compose`` can be used in two ways:\n\n    #. With a series of transforms that accept and return a single\n       ndarray / tensor / tensor-like parameter.\n    #. With a series of transforms that accept and return a dictionary that\n       contains one or more parameters. Such transforms must have pass-through\n       semantics that unused values in the dictionary must be copied to the return\n       dictionary. It is required that the dictionary is copied between input\n       and output of each transform.\n\n    If some transform takes a data item dictionary as input, and returns a\n    sequence of data items in the transform chain, all following transforms\n    will be applied to each item of this list if `map_items` is `True` (the\n    default).  If `map_items` is `False`, the returned sequence is passed whole\n    to the next callable in the chain.\n\n    For example:\n\n    A `Compose([transformA, transformB, transformC],\n    map_items=True)(data_dict)` could achieve the following patch-based\n    transformation on the `data_dict` input:\n\n    #. transformA normalizes the intensity of 'img' field in the `data_dict`.\n    #. transformB crops out image patches from the 'img' and 'seg' of\n       `data_dict`, and return a list of three patch samples::\n\n        {'img': 3x100x100 data, 'seg': 1x100x100 data, 'shape': (100, 100)}\n                             applying transformB\n                                 ---------->\n        [{'img': 3x20x20 data, 'seg': 1x20x20 data, 'shape': (20, 20)},\n         {'img': 3x20x20 data, 'seg': 1x20x20 data, 'shape': (20, 20)},\n         {'img': 3x20x20 data, 'seg': 1x20x20 data, 'shape': (20, 20)},]\n\n    #. transformC then randomly rotates or flips 'img' and 'seg' of\n       each dictionary item in the list returned by transformB.\n\n    The composed transforms will be set the same global random seed if user called\n    `set_determinism()`.\n\n    When using the pass-through dictionary operation, you can make use of\n    :class:`monai.transforms.adaptors.adaptor` to wrap transforms that don't conform\n    to the requirements. This approach allows you to use transforms from\n    otherwise incompatible libraries with minimal additional work.\n\n    Note:\n\n        In many cases, Compose is not the best way to create pre-processing\n        pipelines. Pre-processing is often not a strictly sequential series of\n        operations, and much of the complexity arises when a not-sequential\n        set of functions must be called as if it were a sequence.\n\n        Example: images and labels\n        Images typically require some kind of normalization that labels do not.\n        Both are then typically augmented through the use of random rotations,\n        flips, and deformations.\n        Compose can be used with a series of transforms that take a dictionary\n        that contains 'image' and 'label' entries. This might require wrapping\n        `torchvision` transforms before passing them to compose.\n        Alternatively, one can create a class with a `__call__` function that\n        calls your pre-processing functions taking into account that not all of\n        them are called on the labels.\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/compose.py_Compose.__init___Compose.randomize.for__transform_in_self_tr.try_.except_TypeError_as_type_.warnings_warn_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/compose.py_Compose.__init___Compose.randomize.for__transform_in_self_tr.try_.except_TypeError_as_type_.warnings_warn_", "embedding": null, "metadata": {"file_path": "monai/transforms/compose.py", "file_name": "compose.py", "file_type": "text/x-python", "category": "implementation", "start_line": 104, "end_line": 135, "span_ids": ["Compose.randomize", "Compose.__init__", "Compose.set_random_state"], "tokens": 290}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Compose(Randomizable, InvertibleTransform):\n\n    def __init__(\n        self,\n        transforms: Optional[Union[Sequence[Callable], Callable]] = None,\n        map_items: bool = True,\n        unpack_items: bool = False,\n    ) -> None:\n        if transforms is None:\n            transforms = []\n        self.transforms = ensure_tuple(transforms)\n        self.map_items = map_items\n        self.unpack_items = unpack_items\n        self.set_random_state(seed=get_seed())\n\n    def set_random_state(self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None) -> \"Compose\":\n        super().set_random_state(seed=seed, state=state)\n        for _transform in self.transforms:\n            if not isinstance(_transform, Randomizable):\n                continue\n            _transform.set_random_state(seed=self.R.randint(MAX_SEED, dtype=\"uint32\"))\n        return self\n\n    def randomize(self, data: Optional[Any] = None) -> None:\n        for _transform in self.transforms:\n            if not isinstance(_transform, Randomizable):\n                continue\n            try:\n                _transform.randomize(data)\n            except TypeError as type_error:\n                tfm_name: str = type(_transform).__name__\n                warnings.warn(\n                    f'Transform \"{tfm_name}\" in Compose not randomized\\n{tfm_name}.{type_error}.', RuntimeWarning\n                )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_from_itertools_import_cha___all__._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_from_itertools_import_cha___all__._", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 16, "end_line": 69, "span_ids": ["docstring"], "tokens": 344}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from itertools import chain\nfrom math import ceil\nfrom typing import Any, Callable, List, Optional, Sequence, Tuple, Union\n\nimport numpy as np\nimport torch\nfrom torch.nn.functional import pad as pad_pt\n\nfrom monai.config import IndexSelection\nfrom monai.config.type_definitions import NdarrayOrTensor\nfrom monai.data.utils import get_random_patch, get_valid_patch_size\nfrom monai.transforms.transform import Randomizable, Transform\nfrom monai.transforms.utils import (\n    compute_divisible_spatial_size,\n    convert_pad_mode,\n    generate_label_classes_crop_centers,\n    generate_pos_neg_label_crop_centers,\n    generate_spatial_bounding_box,\n    is_positive,\n    map_binary_to_indices,\n    map_classes_to_indices,\n    weighted_patch_samples,\n)\nfrom monai.transforms.utils_pytorch_numpy_unification import floor_divide, maximum\nfrom monai.utils import (\n    Method,\n    NumpyPadMode,\n    PytorchPadMode,\n    ensure_tuple,\n    ensure_tuple_rep,\n    fall_back_tuple,\n    look_up_option,\n)\nfrom monai.utils.enums import TransformBackends\nfrom monai.utils.type_conversion import convert_data_type, convert_to_dst_type\n\n__all__ = [\n    \"Pad\",\n    \"SpatialPad\",\n    \"BorderPad\",\n    \"DivisiblePad\",\n    \"SpatialCrop\",\n    \"CenterSpatialCrop\",\n    \"CenterScaleCrop\",\n    \"RandSpatialCrop\",\n    \"RandScaleCrop\",\n    \"RandSpatialCropSamples\",\n    \"CropForeground\",\n    \"RandWeightedCrop\",\n    \"RandCropByPosNegLabel\",\n    \"RandCropByLabelClasses\",\n    \"ResizeWithPadOrCrop\",\n    \"BoundingRect\",\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_SpatialPad._determine_data_pad_width_SpatialPad._determine_data_pad_width.return._0_max_sp_i_data_shap": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_SpatialPad._determine_data_pad_width_SpatialPad._determine_data_pad_width.return._0_max_sp_i_data_shap", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 84, "end_line": 92, "span_ids": ["SpatialPad._determine_data_pad_width"], "tokens": 136}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SpatialPad(Transform):\n\n    def _determine_data_pad_width(self, data_shape: Sequence[int]) -> List[Tuple[int, int]]:\n        spatial_size = fall_back_tuple(self.spatial_size, data_shape)\n        if self.method == Method.SYMMETRIC:\n            pad_width = []\n            for i, sp_i in enumerate(spatial_size):\n                width = max(sp_i - data_shape[i], 0)\n                pad_width.append((width // 2, width - (width // 2)))\n            return pad_width\n        return [(0, max(sp_i - data_shape[i], 0)) for i, sp_i in enumerate(spatial_size)]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_CenterScaleCrop_CenterScaleCrop.__call__.return.sp_crop_img_img_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_CenterScaleCrop_CenterScaleCrop.__call__.return.sp_crop_img_img_", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 460, "end_line": 480, "span_ids": ["CenterScaleCrop.__init__", "CenterScaleCrop.__call__", "CenterScaleCrop"], "tokens": 214}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CenterScaleCrop(Transform):\n    \"\"\"\n    Crop at the center of image with specified scale of ROI size.\n\n    Args:\n        roi_scale: specifies the expected scale of image size to crop. e.g. [0.3, 0.4, 0.5] or a number for all dims.\n            If its components have non-positive values, will use `1.0` instead, which means the input image size.\n\n    \"\"\"\n\n    backend = CenterSpatialCrop.backend\n\n    def __init__(self, roi_scale: Union[Sequence[float], float]):\n        self.roi_scale = roi_scale\n\n    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:\n        img_size = img.shape[1:]\n        ndim = len(img_size)\n        roi_size = [ceil(r * s) for r, s in zip(ensure_tuple_rep(self.roi_scale, ndim), img_size)]\n        sp_crop = CenterSpatialCrop(roi_size=roi_size)\n        return sp_crop(img=img)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_RandScaleCrop_RandScaleCrop.__init__.self.max_roi_scale.max_roi_scale": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_RandScaleCrop_RandScaleCrop.__init__.self.max_roi_scale.max_roi_scale", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 393, "end_line": 423, "span_ids": ["RandScaleCrop", "RandScaleCrop.__init__"], "tokens": 428}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandScaleCrop(RandSpatialCrop):\n    \"\"\"\n    Subclass of :py:class:`monai.transforms.RandSpatialCrop`. Crop image with\n    random size or specific size ROI.  It can crop at a random position as\n    center or at the image center.  And allows to set the minimum and maximum\n    scale of image size to limit the randomly generated ROI.\n\n    Args:\n        roi_scale: if `random_size` is True, it specifies the minimum crop size: `roi_scale * image spatial size`.\n            if `random_size` is False, it specifies the expected scale of image size to crop. e.g. [0.3, 0.4, 0.5].\n            If its components have non-positive values, will use `1.0` instead, which means the input image size.\n        max_roi_scale: if `random_size` is True and `roi_scale` specifies the min crop region size, `max_roi_scale`\n            can specify the max crop region size: `max_roi_scale * image spatial size`.\n            if None, defaults to the input image size. if its components have non-positive values,\n            will use `1.0` instead, which means the input image size.\n        random_center: crop at random position as center or the image center.\n        random_size: crop with random size or specified size ROI by `roi_scale * image spatial size`.\n            if True, the actual size is sampled from\n            `randint(roi_scale * image spatial size, max_roi_scale * image spatial size + 1)`.\n    \"\"\"\n\n    def __init__(\n        self,\n        roi_scale: Union[Sequence[float], float],\n        max_roi_scale: Optional[Union[Sequence[float], float]] = None,\n        random_center: bool = True,\n        random_size: bool = True,\n    ) -> None:\n        super().__init__(roi_size=-1, max_roi_size=None, random_center=random_center, random_size=random_size)\n        self.roi_scale = roi_scale\n        self.max_roi_scale = max_roi_scale", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_RandScaleCrop.__call___RandScaleCrop.__call__.return.super___call___img_img_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_RandScaleCrop.__call___RandScaleCrop.__call__.return.super___call___img_img_", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 580, "end_line": 592, "span_ids": ["RandScaleCrop.__call__"], "tokens": 173}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandScaleCrop(RandSpatialCrop):\n\n    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:\n        \"\"\"\n        Apply the transform to `img`, assuming `img` is channel-first and\n        slicing doesn't apply to the channel dim.\n        \"\"\"\n        img_size = img.shape[1:]\n        ndim = len(img_size)\n        self.roi_size = [ceil(r * s) for r, s in zip(ensure_tuple_rep(self.roi_scale, ndim), img_size)]\n        if self.max_roi_scale is not None:\n            self.max_roi_size = [ceil(r * s) for r, s in zip(ensure_tuple_rep(self.max_roi_scale, ndim), img_size)]\n        else:\n            self.max_roi_size = None\n        return super().__call__(img=img)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_CropForeground.compute_bounding_box_CropForeground.compute_bounding_box.return.box_start__box_end_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_CropForeground.compute_bounding_box_CropForeground.compute_bounding_box.return.box_start__box_end_", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 730, "end_line": 745, "span_ids": ["CropForeground.compute_bounding_box"], "tokens": 235}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CropForeground(Transform):\n\n    def compute_bounding_box(self, img: NdarrayOrTensor):\n        \"\"\"\n        Compute the start points and end points of bounding box to crop.\n        And adjust bounding box coords to be divisible by `k`.\n\n        \"\"\"\n        box_start, box_end = generate_spatial_bounding_box(img, self.select_fn, self.channel_indices, self.margin)\n        box_start_, *_ = convert_data_type(box_start, output_type=np.ndarray, dtype=np.int16, wrap_sequence=True)\n        box_end_, *_ = convert_data_type(box_end, output_type=np.ndarray, dtype=np.int16, wrap_sequence=True)\n        orig_spatial_size = box_end_ - box_start_\n        # make the spatial size divisible by `k`\n        spatial_size = np.asarray(compute_divisible_spatial_size(orig_spatial_size.tolist(), k=self.k_divisible))\n        # update box_start and box_end\n        box_start_ = box_start_ - np.floor_divide(np.asarray(spatial_size) - orig_spatial_size, 2)\n        box_end_ = box_start_ + spatial_size\n        return box_start_, box_end_", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_CenterScaleCropd_CenterScaleCropd.__init__.self.roi_scale.roi_scale": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_CenterScaleCropd_CenterScaleCropd.__init__.self.roi_scale.roi_scale", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 456, "end_line": 476, "span_ids": ["CenterScaleCropd", "CenterScaleCropd.__init__"], "tokens": 233}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CenterScaleCropd(MapTransform, InvertibleTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.CenterScaleCrop`.\n    Note: as using the same scaled ROI to crop, all the input data specified by `keys` should have\n    the same spatial shape.\n\n    Args:\n        keys: keys of the corresponding items to be transformed.\n            See also: monai.transforms.MapTransform\n        roi_scale: specifies the expected scale of image size to crop. e.g. [0.3, 0.4, 0.5] or a number for all dims.\n            If its components have non-positive values, will use `1.0` instead, which means the input image size.\n        allow_missing_keys: don't raise exception if key is missing.\n    \"\"\"\n\n    backend = CenterSpatialCrop.backend\n\n    def __init__(\n        self, keys: KeysCollection, roi_scale: Union[Sequence[float], float], allow_missing_keys: bool = False\n    ) -> None:\n        super().__init__(keys, allow_missing_keys=allow_missing_keys)\n        self.roi_scale = roi_scale", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_CenterScaleCropd.__call___CenterScaleCropd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_CenterScaleCropd.__call___CenterScaleCropd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 481, "end_line": 496, "span_ids": ["CenterScaleCropd.__call__"], "tokens": 200}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CenterScaleCropd(MapTransform, InvertibleTransform):\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        first_key: Union[Hashable, List] = self.first_key(d)\n        if first_key == []:\n            return d\n\n        # use the spatial size of first image to scale, expect all images have the same spatial size\n        img_size = d[first_key].shape[1:]  # type: ignore\n        ndim = len(img_size)\n        roi_size = [ceil(r * s) for r, s in zip(ensure_tuple_rep(self.roi_scale, ndim), img_size)]\n        cropper = CenterSpatialCrop(roi_size)\n        for key in self.key_iterator(d):\n            self.push_transform(d, key, orig_size=img_size)\n            d[key] = cropper(d[key])\n\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_CenterScaleCropd.inverse_CenterScaleCropd.inverse.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_CenterScaleCropd.inverse_CenterScaleCropd.inverse.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 494, "end_line": 513, "span_ids": ["CenterScaleCropd.inverse"], "tokens": 247}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CenterScaleCropd(MapTransform, InvertibleTransform):\n\n    def inverse(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = deepcopy(dict(data))\n\n        for key in self.key_iterator(d):\n            transform = self.get_most_recent_transform(d, key)\n            # Create inverse transform\n            orig_size = np.array(transform[TraceKeys.ORIG_SIZE])\n            current_size = np.array(d[key].shape[1:])\n            pad_to_start = np.floor((orig_size - current_size) / 2).astype(int)\n            # in each direction, if original size is even and current size is odd, += 1\n            pad_to_start[np.logical_and(orig_size % 2 == 0, current_size % 2 == 1)] += 1\n            pad_to_end = orig_size - current_size - pad_to_start\n            pad = list(chain(*zip(pad_to_start.tolist(), pad_to_end.tolist())))\n            inverse_transform = BorderPad(pad)\n            # Apply inverse transform\n            d[key] = inverse_transform(d[key])\n            # Remove the applied transform\n            self.pop_transform(d, key)\n\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_RandScaleCropd_RandScaleCropd.__init__.self.max_roi_scale.max_roi_scale": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_RandScaleCropd_RandScaleCropd.__init__.self.max_roi_scale.max_roi_scale", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 632, "end_line": 677, "span_ids": ["RandScaleCropd", "RandScaleCropd.__init__"], "tokens": 520}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandScaleCropd(RandSpatialCropd):\n    \"\"\"\n    Dictionary-based version :py:class:`monai.transforms.RandScaleCrop`.\n    Crop image with random size or specific size ROI.\n    It can crop at a random position as center or at the image center.\n    And allows to set the minimum and maximum scale of image size to limit the randomly generated ROI.\n    Suppose all the expected fields specified by `keys` have same shape.\n\n    Args:\n        keys: keys of the corresponding items to be transformed.\n            See also: monai.transforms.MapTransform\n        roi_scale: if `random_size` is True, it specifies the minimum crop size: `roi_scale * image spatial size`.\n            if `random_size` is False, it specifies the expected scale of image size to crop. e.g. [0.3, 0.4, 0.5].\n            If its components have non-positive values, will use `1.0` instead, which means the input image size.\n        max_roi_scale: if `random_size` is True and `roi_scale` specifies the min crop region size, `max_roi_scale`\n            can specify the max crop region size: `max_roi_scale * image spatial size`.\n            if None, defaults to the input image size. if its components have non-positive values,\n            will use `1.0` instead, which means the input image size.\n        random_center: crop at random position as center or the image center.\n        random_size: crop with random size or specified size ROI by `roi_scale * image spatial size`.\n            if True, the actual size is sampled from:\n            `randint(roi_scale * image spatial size, max_roi_scale * image spatial size + 1)`.\n        allow_missing_keys: don't raise exception if key is missing.\n    \"\"\"\n\n    backend = RandSpatialCropd.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        roi_scale: Union[Sequence[float], float],\n        max_roi_scale: Optional[Union[Sequence[float], float]] = None,\n        random_center: bool = True,\n        random_size: bool = True,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        super().__init__(\n            keys=keys,\n            roi_size=-1,\n            max_roi_size=None,\n            random_center=random_center,\n            random_size=random_size,\n            allow_missing_keys=allow_missing_keys,\n        )\n        self.roi_scale = roi_scale\n        self.max_roi_scale = max_roi_scale", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_RandScaleCropd.__call____nullcontext.yield_x": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_RandScaleCropd.__call____nullcontext.yield_x", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 679, "end_line": 699, "span_ids": ["RandScaleCropd.__call__", "_nullcontext"], "tokens": 239}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandScaleCropd(RandSpatialCropd):\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        first_key: Union[Hashable, List] = self.first_key(data)  # type: ignore\n        if first_key == []:\n            return data  # type: ignore\n\n        img_size = data[first_key].shape[1:]  # type: ignore\n        ndim = len(img_size)\n        self.roi_size = [ceil(r * s) for r, s in zip(ensure_tuple_rep(self.roi_scale, ndim), img_size)]\n        if self.max_roi_scale is not None:\n            self.max_roi_size = [ceil(r * s) for r, s in zip(ensure_tuple_rep(self.max_roi_scale, ndim), img_size)]\n        else:\n            self.max_roi_size = None\n        return super().__call__(data=data)\n\n\n@contextlib.contextmanager\ndef _nullcontext(x):\n    \"\"\"\n    This is just like contextlib.nullcontext but also works in Python 3.6.\n    \"\"\"\n    yield x", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_RandSpatialCropSamplesd.__init___RandSpatialCropSamplesd.randomize.pass": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_RandSpatialCropSamplesd.__init___RandSpatialCropSamplesd.randomize.pass", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 734, "end_line": 764, "span_ids": ["RandSpatialCropSamplesd.set_random_state", "RandSpatialCropSamplesd.randomize", "RandSpatialCropSamplesd.__init__"], "tokens": 348}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandSpatialCropSamplesd(Randomizable, MapTransform, InvertibleTransform):\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        roi_size: Union[Sequence[int], int],\n        num_samples: int,\n        max_roi_size: Optional[Union[Sequence[int], int]] = None,\n        random_center: bool = True,\n        random_size: bool = True,\n        meta_keys: Optional[KeysCollection] = None,\n        meta_key_postfix: str = \"meta_dict\",\n        allow_missing_keys: bool = False,\n    ) -> None:\n        MapTransform.__init__(self, keys, allow_missing_keys)\n        if num_samples < 1:\n            raise ValueError(f\"num_samples must be positive, got {num_samples}.\")\n        self.num_samples = num_samples\n        self.cropper = RandSpatialCropd(keys, roi_size, max_roi_size, random_center, random_size, allow_missing_keys)\n        self.meta_keys = ensure_tuple_rep(None, len(self.keys)) if meta_keys is None else ensure_tuple(meta_keys)\n        if len(self.keys) != len(self.meta_keys):\n            raise ValueError(\"meta_keys should have the same length as keys.\")\n        self.meta_key_postfix = ensure_tuple_rep(meta_key_postfix, len(self.keys))\n\n    def set_random_state(\n        self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None\n    ) -> \"RandSpatialCropSamplesd\":\n        super().set_random_state(seed, state)\n        self.cropper.set_random_state(seed, state)\n        return self\n\n    def randomize(self, data: Optional[Any] = None) -> None:\n        pass", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_RandSpatialCropSamplesd.inverse_RandSpatialCropSamplesd.inverse.with_context_manager_self.return.self_cropper_inverse_d_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_RandSpatialCropSamplesd.inverse_RandSpatialCropSamplesd.inverse.with_context_manager_self.return.self_cropper_inverse_d_", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 790, "end_line": 799, "span_ids": ["RandSpatialCropSamplesd.inverse"], "tokens": 173}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandSpatialCropSamplesd(Randomizable, MapTransform, InvertibleTransform):\n\n    def inverse(self, data: Mapping[Hashable, Any]) -> Dict[Hashable, Any]:\n        d = deepcopy(dict(data))\n        # We changed the transform name from RandSpatialCropd to RandSpatialCropSamplesd\n        # Need to revert that since we're calling RandSpatialCropd's inverse\n        for key in self.key_iterator(d):\n            d[self.trace_key(key)][-1][TraceKeys.CLASS_NAME] = self.cropper.__class__.__name__\n            d[self.trace_key(key)][-1][TraceKeys.ID] = id(self.cropper)\n        context_manager = allow_missing_keys_mode if self.allow_missing_keys else _nullcontext\n        with context_manager(self.cropper):\n            return self.cropper.inverse(d)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_RandWeightedCropd.inverse_RandWeightedCropd.inverse.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_RandWeightedCropd.inverse_RandWeightedCropd.inverse.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 997, "end_line": 1017, "span_ids": ["RandWeightedCropd.inverse"], "tokens": 257}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandWeightedCropd(Randomizable, MapTransform, InvertibleTransform):\n\n    def inverse(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = deepcopy(dict(data))\n        for key in self.key_iterator(d):\n            transform = self.get_most_recent_transform(d, key)\n            # Create inverse transform\n            orig_size = np.asarray(transform[TraceKeys.ORIG_SIZE])\n            current_size = np.asarray(d[key].shape[1:])\n            center = transform[TraceKeys.EXTRA_INFO][\"center\"]\n            cropper = SpatialCrop(roi_center=center, roi_size=self.spatial_size)\n            # get required pad to start and end\n            pad_to_start = np.array([s.indices(o)[0] for s, o in zip(cropper.slices, orig_size)])\n            pad_to_end = orig_size - current_size - pad_to_start\n            # interleave mins and maxes\n            pad = list(chain(*zip(pad_to_start.tolist(), pad_to_end.tolist())))\n            inverse_transform = BorderPad(pad)\n            # Apply inverse transform\n            d[key] = inverse_transform(d[key])\n            # Remove the applied transform\n            self.pop_transform(d, key)\n\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_RandCropByPosNegLabeld.inverse_RandCropByPosNegLabeld.inverse.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_RandCropByPosNegLabeld.inverse_RandCropByPosNegLabeld.inverse.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1177, "end_line": 1197, "span_ids": ["RandCropByPosNegLabeld.inverse"], "tokens": 267}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandCropByPosNegLabeld(Randomizable, MapTransform, InvertibleTransform):\n\n    def inverse(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = deepcopy(dict(data))\n        for key in self.key_iterator(d):\n            transform = self.get_most_recent_transform(d, key)\n            # Create inverse transform\n            orig_size = np.asarray(transform[TraceKeys.ORIG_SIZE])\n            current_size = np.asarray(d[key].shape[1:])\n            center = transform[TraceKeys.EXTRA_INFO][\"center\"]\n            cropper = SpatialCrop(roi_center=tuple(center), roi_size=self.spatial_size)  # type: ignore\n            # get required pad to start and end\n            pad_to_start = np.array([s.indices(o)[0] for s, o in zip(cropper.slices, orig_size)])\n            pad_to_end = orig_size - current_size - pad_to_start\n            # interleave mins and maxes\n            pad = list(chain(*zip(pad_to_start.tolist(), pad_to_end.tolist())))\n            inverse_transform = BorderPad(pad)\n            # Apply inverse transform\n            d[key] = inverse_transform(d[key])\n            # Remove the applied transform\n            self.pop_transform(d, key)\n\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandRicianNoise_RandRicianNoise.__init__.self._noise2": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandRicianNoise_RandRicianNoise.__init__.self._noise2", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 128, "end_line": 174, "span_ids": ["RandRicianNoise", "RandRicianNoise.__init__"], "tokens": 479}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandRicianNoise(RandomizableTransform):\n    \"\"\"\n    Add Rician noise to image.\n    Rician noise in MRI is the result of performing a magnitude operation on complex\n    data with Gaussian noise of the same variance in both channels, as described in\n    `Noise in Magnitude Magnetic Resonance Images <https://doi.org/10.1002/cmr.a.20124>`_.\n    This transform is adapted from `DIPY <https://github.com/dipy/dipy>`_.\n    See also: `The rician distribution of noisy mri data <https://doi.org/10.1002/mrm.1910340618>`_.\n\n    Args:\n        prob: Probability to add Rician noise.\n        mean: Mean or \"centre\" of the Gaussian distributions sampled to make up\n            the Rician noise.\n        std: Standard deviation (spread) of the Gaussian distributions sampled\n            to make up the Rician noise.\n        channel_wise: If True, treats each channel of the image separately.\n        relative: If True, the spread of the sampled Gaussian distributions will\n            be std times the standard deviation of the image or channel's intensity\n            histogram.\n        sample_std: If True, sample the spread of the Gaussian distributions\n            uniformly from 0 to std.\n        dtype: output data type, if None, same as input image. defaults to float32.\n\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __init__(\n        self,\n        prob: float = 0.1,\n        mean: Union[Sequence[float], float] = 0.0,\n        std: Union[Sequence[float], float] = 1.0,\n        channel_wise: bool = False,\n        relative: bool = False,\n        sample_std: bool = True,\n        dtype: DtypeLike = np.float32,\n    ) -> None:\n        RandomizableTransform.__init__(self, prob)\n        self.prob = prob\n        self.mean = mean\n        self.std = std\n        self.channel_wise = channel_wise\n        self.relative = relative\n        self.sample_std = sample_std\n        self.dtype = dtype\n        self._noise1: NdarrayOrTensor\n        self._noise2: NdarrayOrTensor", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandRicianNoise._add_noise_RandRicianNoise._add_noise.return.np_sqrt_img_self__nois": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandRicianNoise._add_noise_RandRicianNoise._add_noise.return.np_sqrt_img_self__nois", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 176, "end_line": 187, "span_ids": ["RandRicianNoise._add_noise"], "tokens": 205}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandRicianNoise(RandomizableTransform):\n\n    def _add_noise(self, img: NdarrayOrTensor, mean: float, std: float):\n        dtype_np = get_equivalent_dtype(img.dtype, np.ndarray)\n        im_shape = img.shape\n        _std = self.R.uniform(0, std) if self.sample_std else std\n        self._noise1 = self.R.normal(mean, _std, size=im_shape).astype(dtype_np, copy=False)\n        self._noise2 = self.R.normal(mean, _std, size=im_shape).astype(dtype_np, copy=False)\n        if isinstance(img, torch.Tensor):\n            n1 = torch.tensor(self._noise1, device=img.device)\n            n2 = torch.tensor(self._noise2, device=img.device)\n            return torch.sqrt((img + n1) ** 2 + n2 ** 2)\n\n        return np.sqrt((img + self._noise1) ** 2 + self._noise2 ** 2)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_GibbsNoise._apply_mask_GibbsNoise._apply_mask.return.k_masked": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_GibbsNoise._apply_mask_GibbsNoise._apply_mask.return.k_masked", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1399, "end_line": 1430, "span_ids": ["GibbsNoise._apply_mask"], "tokens": 306}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GibbsNoise(Transform, Fourier):\n\n    def _apply_mask(self, k: NdarrayOrTensor) -> NdarrayOrTensor:\n        \"\"\"Builds and applies a mask on the spatial dimensions.\n\n        Args:\n            k: k-space version of the image.\n        Returns:\n            masked version of the k-space image.\n        \"\"\"\n        shape = k.shape[1:]\n\n        # compute masking radius and center\n        r = (1 - self.alpha) * np.max(shape) * np.sqrt(2) / 2.0\n        center = (np.array(shape) - 1) / 2\n\n        # gives list w/ len==self.dim. Each dim gives coordinate in that dimension\n        coords = np.ogrid[tuple(slice(0, i) for i in shape)]\n\n        # need to subtract center coord and then square for Euc distance\n        coords_from_center_sq = [(coord - c) ** 2 for coord, c in zip(coords, center)]\n        dist_from_center = np.sqrt(sum(coords_from_center_sq))\n        mask = dist_from_center <= r\n\n        # add channel dimension into mask\n        mask = np.repeat(mask[None], k.shape[0], axis=0)\n\n        if isinstance(k, torch.Tensor):\n            mask, *_ = convert_data_type(mask, torch.Tensor, device=k.device)\n\n        # apply binary mask\n        k_masked: NdarrayOrTensor\n        k_masked = k * mask\n        return k_masked", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandKSpaceSpikeNoise_RandKSpaceSpikeNoise.__init__.super___init___prob_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandKSpaceSpikeNoise_RandKSpaceSpikeNoise.__init__.super___init___prob_", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1660, "end_line": 1714, "span_ids": ["RandKSpaceSpikeNoise.__init__", "RandKSpaceSpikeNoise"], "tokens": 589}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandKSpaceSpikeNoise(RandomizableTransform, Fourier):\n    \"\"\"\n    Naturalistic data augmentation via spike artifacts. The transform applies\n    localized spikes in `k`-space, and it is the random version of\n    :py:class:`monai.transforms.KSpaceSpikeNoise`.\n\n    Spike (Herringbone) artifact is a type of data acquisition artifact which\n    may occur during MRI scans. For general information on spike artifacts,\n    please refer to:\n\n    `AAPM/RSNA physics tutorial for residents: fundamental physics of MR imaging\n    <https://pubmed.ncbi.nlm.nih.gov/16009826>`_.\n\n    `Body MRI artifacts in clinical practice: A physicist's and radiologist's\n    perspective <https://doi.org/10.1002/jmri.24288>`_.\n\n    Args:\n        prob: probability of applying the transform, either on all\n            channels at once, or channel-wise if ``channel_wise = True``.\n        intensity_range: pass a tuple (a, b) to sample the log-intensity from the interval (a, b)\n            uniformly for all channels. Or pass sequence of intervals\n            ((a0, b0), (a1, b1), ...) to sample for each respective channel.\n            In the second case, the number of 2-tuples must match the number of channels.\n            Default ranges is `(0.95x, 1.10x)` where `x` is the mean\n            log-intensity for each channel.\n        channel_wise: treat each channel independently. True by\n            default.\n\n    Example:\n        To apply `k`-space spikes randomly with probability 0.5, and\n        log-intensity sampled from the interval [11, 12] for each channel\n        independently, one uses\n        ``RandKSpaceSpikeNoise(prob=0.5, intensity_range=(11, 12), channel_wise=True)``\n    \"\"\"\n\n    backend = KSpaceSpikeNoise.backend\n\n    @deprecated_arg(name=\"as_tensor_output\", since=\"0.6\")\n    def __init__(\n        self,\n        prob: float = 0.1,\n        intensity_range: Optional[Sequence[Union[Sequence[float], float]]] = None,\n        channel_wise: bool = True,\n        as_tensor_output: bool = True,\n    ):\n\n        self.intensity_range = intensity_range\n        self.channel_wise = channel_wise\n        self.sampled_k_intensity: List = []\n        self.sampled_locs: List[Tuple] = []\n\n        if intensity_range is not None and isinstance(intensity_range[0], Sequence) and not channel_wise:\n            raise ValueError(\"When channel_wise = False, intensity_range should be a 2-tuple (low, high) or None.\")\n\n        super().__init__(prob)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_RandRicianNoised_RandRicianNoised.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_RandRicianNoised_RandRicianNoised.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 202, "end_line": 268, "span_ids": ["RandRicianNoised.__init__", "RandRicianNoised", "RandRicianNoised.set_random_state", "RandRicianNoised.__call__"], "tokens": 623}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandRicianNoised(RandomizableTransform, MapTransform):\n    \"\"\"\n    Dictionary-based version :py:class:`monai.transforms.RandRicianNoise`.\n    Add Rician noise to image. This transform assumes all the expected fields have same shape, if want to add\n    different noise for every field, please use this transform separately.\n\n    Args:\n        keys: Keys of the corresponding items to be transformed.\n            See also: :py:class:`monai.transforms.compose.MapTransform`\n        prob: Probability to add Rician noise to the dictionary.\n        mean: Mean or \"centre\" of the Gaussian distributions sampled to make up\n            the Rician noise.\n        std: Standard deviation (spread) of the Gaussian distributions sampled\n            to make up the Rician noise.\n        channel_wise: If True, treats each channel of the image separately.\n        relative: If True, the spread of the sampled Gaussian distributions will\n            be std times the standard deviation of the image or channel's intensity\n            histogram.\n        sample_std: If True, sample the spread of the Gaussian distributions\n            uniformly from 0 to std.\n        dtype: output data type, if None, same as input image. defaults to float32.\n        allow_missing_keys: Don't raise exception if key is missing.\n    \"\"\"\n\n    backend = RandRicianNoise.backend\n\n    @deprecated_arg(\"global_prob\", since=\"0.7\")\n    def __init__(\n        self,\n        keys: KeysCollection,\n        prob: float = 0.1,\n        mean: Union[Sequence[float], float] = 0.0,\n        std: Union[Sequence[float], float] = 1.0,\n        channel_wise: bool = False,\n        relative: bool = False,\n        sample_std: bool = True,\n        dtype: DtypeLike = np.float32,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        MapTransform.__init__(self, keys, allow_missing_keys)\n        RandomizableTransform.__init__(self, prob)\n        self.rand_rician_noise = RandRicianNoise(\n            prob=1.0,\n            mean=mean,\n            std=std,\n            channel_wise=channel_wise,\n            relative=relative,\n            sample_std=sample_std,\n            dtype=dtype,\n        )\n\n    def set_random_state(\n        self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None\n    ) -> \"RandRicianNoised\":\n        super().set_random_state(seed, state)\n        self.rand_rician_noise.set_random_state(seed, state)\n        return self\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        self.randomize(None)\n        if not self._do_transform:\n            return d\n\n        for key in self.key_iterator(d):\n            d[key] = self.rand_rician_noise(d[key], randomize=True)\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_GibbsNoised_GibbsNoised.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_GibbsNoised_GibbsNoised.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1209, "end_line": 1243, "span_ids": ["GibbsNoised", "GibbsNoised.__call__", "GibbsNoised.__init__"], "tokens": 347}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GibbsNoised(MapTransform):\n    \"\"\"\n    Dictionary-based version of GibbsNoise.\n\n    The transform applies Gibbs noise to 2D/3D MRI images. Gibbs artifacts\n    are one of the common type of type artifacts appearing in MRI scans.\n\n    For general information on Gibbs artifacts, please refer to:\n    https://pubs.rsna.org/doi/full/10.1148/rg.313105115\n    https://pubs.rsna.org/doi/full/10.1148/radiographics.22.4.g02jl14949\n\n    Args:\n        keys: 'image', 'label', or ['image', 'label'] depending on which data\n                you need to transform.\n        alpha (float): Parametrizes the intensity of the Gibbs noise filter applied. Takes\n            values in the interval [0,1] with alpha = 0 acting as the identity mapping.\n        allow_missing_keys: do not raise exception if key is missing.\n    \"\"\"\n\n    backend = GibbsNoise.backend\n\n    @deprecated_arg(name=\"as_tensor_output\", since=\"0.6\")\n    def __init__(\n        self, keys: KeysCollection, alpha: float = 0.5, allow_missing_keys: bool = False, as_tensor_output: bool = True\n    ) -> None:\n\n        MapTransform.__init__(self, keys, allow_missing_keys)\n        self.transform = GibbsNoise(alpha)\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n\n        d = dict(data)\n        for key in self.key_iterator(d):\n            d[key] = self.transform(d[key])\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/inverse_batch_transform.py_warnings___all__._BatchInverseTransform_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/inverse_batch_transform.py_warnings___all__._BatchInverseTransform_", "embedding": null, "metadata": {"file_path": "monai/transforms/inverse_batch_transform.py", "file_name": "inverse_batch_transform.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 26, "span_ids": ["docstring"], "tokens": 136}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import warnings\nfrom typing import Any, Callable, Dict, List, Optional, Sequence, Union\n\nfrom torch.utils.data import Dataset\nfrom torch.utils.data.dataloader import DataLoader as TorchDataLoader\n\nfrom monai.config import KeysCollection\nfrom monai.data.dataloader import DataLoader\nfrom monai.data.utils import decollate_batch, no_collation, pad_list_data_collate\nfrom monai.transforms.croppad.batch import PadListDataCollate\nfrom monai.transforms.inverse import InvertibleTransform\nfrom monai.transforms.transform import MapTransform, Transform\nfrom monai.utils import first\n\n__all__ = [\"BatchInverseTransform\", \"Decollated\"]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/inverse_batch_transform.py__BatchInverseDataset__BatchInverseDataset.__len__.return.len_self_data_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/inverse_batch_transform.py__BatchInverseDataset__BatchInverseDataset.__len__.return.len_self_data_", "embedding": null, "metadata": {"file_path": "monai/transforms/inverse_batch_transform.py", "file_name": "inverse_batch_transform.py", "file_type": "text/x-python", "category": "implementation", "start_line": 29, "end_line": 47, "span_ids": ["_BatchInverseDataset.__init__", "_BatchInverseDataset.__getitem__", "_BatchInverseDataset.__len__", "_BatchInverseDataset"], "tokens": 183}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class _BatchInverseDataset(Dataset):\n    def __init__(self, data: Sequence[Any], transform: InvertibleTransform, pad_collation_used: bool) -> None:\n        self.data = data\n        self.invertible_transform = transform\n        self.pad_collation_used = pad_collation_used\n\n    def __getitem__(self, index: int):\n        data = dict(self.data[index])\n        # If pad collation was used, then we need to undo this first\n        if self.pad_collation_used:\n            data = PadListDataCollate.inverse(data)\n\n        if not isinstance(self.invertible_transform, InvertibleTransform):\n            warnings.warn(\"transform is not invertible, can't invert transform for the input data.\")\n            return data\n        return self.invertible_transform.inverse(data)\n\n    def __len__(self) -> int:\n        return len(self.data)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/inverse_batch_transform.py_BatchInverseTransform_BatchInverseTransform.__init__.self.pad_collation_used.loader_collate_fn___doc__": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/inverse_batch_transform.py_BatchInverseTransform_BatchInverseTransform.__init__.self.pad_collation_used.loader_collate_fn___doc__", "embedding": null, "metadata": {"file_path": "monai/transforms/inverse_batch_transform.py", "file_name": "inverse_batch_transform.py", "file_type": "text/x-python", "category": "implementation", "start_line": 50, "end_line": 91, "span_ids": ["BatchInverseTransform", "BatchInverseTransform.__init__"], "tokens": 421}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class BatchInverseTransform(Transform):\n    \"\"\"\n    Perform inverse on a batch of data. This is useful if you have inferred a batch of images and want to invert\n    them all.\n    \"\"\"\n\n    def __init__(\n        self,\n        transform: InvertibleTransform,\n        loader: TorchDataLoader,\n        collate_fn: Optional[Callable] = no_collation,\n        num_workers: Optional[int] = 0,\n        detach: bool = True,\n        pad_batch: bool = True,\n        fill_value=None,\n    ) -> None:\n        \"\"\"\n        Args:\n            transform: a callable data transform on input data.\n            loader: data loader used to run `transforms` and generate the batch of data.\n            collate_fn: how to collate data after inverse transformations.\n                default won't do any collation, so the output will be a list of size batch size.\n            num_workers: number of workers when run data loader for inverse transforms,\n                default to 0 as only run 1 iteration and multi-processing may be even slower.\n                if the transforms are really slow, set num_workers for multi-processing.\n                if set to `None`, use the `num_workers` of the transform data loader.\n            detach: whether to detach the tensors. Scalars tensors will be detached into number types\n                instead of torch tensors.\n            pad_batch: when the items in a batch indicate different batch size,\n                whether to pad all the sequences to the longest.\n                If False, the batch size will be the length of the shortest sequence.\n            fill_value: the value to fill the padded sequences when `pad_batch=True`.\n\n        \"\"\"\n        self.transform = transform\n        self.batch_size = loader.batch_size\n        self.num_workers = loader.num_workers if num_workers is None else num_workers\n        self.collate_fn = collate_fn\n        self.detach = detach\n        self.pad_batch = pad_batch\n        self.fill_value = fill_value\n        self.pad_collation_used = loader.collate_fn.__doc__ == pad_list_data_collate.__doc__", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/io/dictionary.py_LoadImaged_LoadImaged._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/io/dictionary.py_LoadImaged_LoadImaged._", "embedding": null, "metadata": {"file_path": "monai/transforms/io/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 32, "end_line": 61, "span_ids": ["LoadImaged"], "tokens": 338}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LoadImaged(MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.LoadImage`,\n    It can load both image data and metadata. When loading a list of files in one key,\n    the arrays will be stacked and a new dimension will be added as the first dimension\n    In this case, the meta data of the first image will be used to represent the stacked result.\n    The affine transform of all the stacked images should be same.\n    The output metadata field will be created as ``meta_keys`` or ``key_{meta_key_postfix}``.\n\n    If reader is not specified, this class automatically chooses readers\n    based on the supported suffixes and in the following order:\n\n        - User-specified reader at runtime when calling this loader.\n        - User-specified reader in the constructor of `LoadImage`.\n        - Readers from the last to the first in the registered list.\n        - Current default readers: (nii, nii.gz -> NibabelReader), (png, jpg, bmp -> PILReader),\n          (npz, npy -> NumpyReader), (others -> ITKReader).\n\n    Note:\n\n        - If `reader` is specified, the loader will attempt to use the specified readers and the default supported\n          readers. This might introduce overheads when handling the exceptions of trying the incompatible loaders.\n          In this case, it is therefore recommended to set the most appropriate reader as\n          the last item of the `reader` parameter.\n\n    See also:\n\n        - tutorial: https://github.com/Project-MONAI/tutorials/blob/master/modules/load_medical_images.ipynb\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/io/dictionary.py_LoadImaged.__init___LoadImaged.register.self__loader_register_rea": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/io/dictionary.py_LoadImaged.__init___LoadImaged.register.self__loader_register_rea", "embedding": null, "metadata": {"file_path": "monai/transforms/io/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 55, "end_line": 103, "span_ids": ["LoadImaged.__init__", "LoadImaged.register"], "tokens": 640}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LoadImaged(MapTransform):\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        reader: Optional[Union[ImageReader, str]] = None,\n        dtype: DtypeLike = np.float32,\n        meta_keys: Optional[KeysCollection] = None,\n        meta_key_postfix: str = \"meta_dict\",\n        overwriting: bool = False,\n        image_only: bool = False,\n        allow_missing_keys: bool = False,\n        *args,\n        **kwargs,\n    ) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to be transformed.\n                See also: :py:class:`monai.transforms.compose.MapTransform`\n            reader: register reader to load image file and meta data, if None, still can register readers\n                at runtime or use the default readers. If a string of reader name provided, will construct\n                a reader object with the `*args` and `**kwargs` parameters, supported reader name: \"NibabelReader\",\n                \"PILReader\", \"ITKReader\", \"NumpyReader\".\n            dtype: if not None convert the loaded image data to this data type.\n            meta_keys: explicitly indicate the key to store the corresponding meta data dictionary.\n                the meta data is a dictionary object which contains: filename, original_shape, etc.\n                it can be a sequence of string, map to the `keys`.\n                if None, will try to construct meta_keys by `key_{meta_key_postfix}`.\n            meta_key_postfix: if meta_keys is None, use `key_{postfix}` to store the metadata of the nifti image,\n                default is `meta_dict`. The meta data is a dictionary object.\n                For example, load nifti file for `image`, store the metadata into `image_meta_dict`.\n            overwriting: whether allow to overwrite existing meta data of same key.\n                default is False, which will raise exception if encountering existing key.\n            image_only: if True return dictionary containing just only the image volumes, otherwise return\n                dictionary containing image data array and header dict per input key.\n            allow_missing_keys: don't raise exception if key is missing.\n            args: additional parameters for reader if providing a reader name.\n            kwargs: additional parameters for reader if providing a reader name.\n        \"\"\"\n        super().__init__(keys, allow_missing_keys)\n        self._loader = LoadImage(reader, image_only, dtype, *args, **kwargs)\n        if not isinstance(meta_key_postfix, str):\n            raise TypeError(f\"meta_key_postfix must be a str but is {type(meta_key_postfix).__name__}.\")\n        self.meta_keys = ensure_tuple_rep(None, len(self.keys)) if meta_keys is None else ensure_tuple(meta_keys)\n        if len(self.keys) != len(self.meta_keys):\n            raise ValueError(\"meta_keys should have the same length as keys.\")\n        self.meta_key_postfix = ensure_tuple_rep(meta_key_postfix, len(self.keys))\n        self.overwriting = overwriting\n\n    def register(self, reader: ImageReader):\n        self._loader.register(reader)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/dictionary.py_warnings___all__._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/dictionary.py_warnings___all__._", "embedding": null, "metadata": {"file_path": "monai/transforms/post/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 18, "end_line": 78, "span_ids": ["docstring"], "tokens": 417}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import warnings\nfrom copy import deepcopy\nfrom typing import Any, Callable, Dict, Hashable, Iterable, List, Mapping, Optional, Sequence, Union\n\nimport torch\n\nfrom monai.config.type_definitions import KeysCollection, NdarrayOrTensor, PathLike\nfrom monai.data.csv_saver import CSVSaver\nfrom monai.transforms.inverse import InvertibleTransform\nfrom monai.transforms.post.array import (\n    Activations,\n    AsDiscrete,\n    FillHoles,\n    KeepLargestConnectedComponent,\n    LabelFilter,\n    LabelToContour,\n    MeanEnsemble,\n    ProbNMS,\n    VoteEnsemble,\n)\nfrom monai.transforms.transform import MapTransform\nfrom monai.transforms.utility.array import ToTensor\nfrom monai.transforms.utils import allow_missing_keys_mode, convert_inverse_interp_mode\nfrom monai.utils import deprecated_arg, ensure_tuple, ensure_tuple_rep\n\n__all__ = [\n    \"ActivationsD\",\n    \"ActivationsDict\",\n    \"Activationsd\",\n    \"AsDiscreteD\",\n    \"AsDiscreteDict\",\n    \"AsDiscreted\",\n    \"Ensembled\",\n    \"FillHolesD\",\n    \"FillHolesDict\",\n    \"FillHolesd\",\n    \"InvertD\",\n    \"InvertDict\",\n    \"Invertd\",\n    \"KeepLargestConnectedComponentD\",\n    \"KeepLargestConnectedComponentDict\",\n    \"KeepLargestConnectedComponentd\",\n    \"LabelFilterD\",\n    \"LabelFilterDict\",\n    \"LabelFilterd\",\n    \"LabelToContourD\",\n    \"LabelToContourDict\",\n    \"LabelToContourd\",\n    \"MeanEnsembleD\",\n    \"MeanEnsembleDict\",\n    \"MeanEnsembled\",\n    \"ProbNMSD\",\n    \"ProbNMSDict\",\n    \"ProbNMSd\",\n    \"SaveClassificationD\",\n    \"SaveClassificationDict\",\n    \"SaveClassificationd\",\n    \"VoteEnsembleD\",\n    \"VoteEnsembleDict\",\n    \"VoteEnsembled\",\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/dictionary.py_Invertd_Invertd._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/dictionary.py_Invertd_Invertd._", "embedding": null, "metadata": {"file_path": "monai/transforms/post/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 473, "end_line": 499, "span_ids": ["Invertd"], "tokens": 373}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Invertd(MapTransform):\n    \"\"\"\n    Utility transform to automatically invert the previously applied transforms.\n    When applying preprocessing transforms on a orig_key(like: `image`, `label`, etc.), we record the context\n    information of applied transforms in a dictionary in the input data dictionary with the key\n    \"{orig_key}_transforms\". This transform will extract the transform context information of `orig_keys`\n    then invert the transforms(got from this context information) on the `keys` data.\n    Typical usage is to invert the preprocessing transforms(applied on input `image`) on the model `pred` data.\n\n    The output of the inverted data and metadata will be stored at `keys` and `meta_keys` respectively.\n    To correctly invert the transforms, the information of the previously applied transforms should be\n    available at `orig_keys`, and the original metadata at `orig_meta_keys`.\n    (`meta_key_postfix` is an optional string to conveniently construct \"meta_keys\" and/or \"orig_meta_keys\".)\n\n    A detailed usage example is available in the tutorial:\n    https://github.com/Project-MONAI/tutorials/blob/master/3d_segmentation/torch/unet_inference_dict.py\n\n    Note:\n        According to the `collate_fn`, this transform may return a list of Tensor without batch dim,\n        thus some following transforms may not support a list of Tensor, and users can leverage the\n        `post_func` arg for basic processing logic.\n\n        This transform needs to extract the context information of applied transforms and the meta data\n        dictionary from the input data dictionary, then use some numpy arrays in them to computes the inverse\n        logic, so please don't move `data[\"{orig_key}_transforms\"]` and `data[\"{orig_meta_key}\"]` to GPU device.\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/dictionary.py_Invertd.__init___Invertd.__init__.self._totensor.ToTensor_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/dictionary.py_Invertd.__init___Invertd.__init__.self._totensor.ToTensor_", "embedding": null, "metadata": {"file_path": "monai/transforms/post/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 501, "end_line": 565, "span_ids": ["Invertd.__init__"], "tokens": 993}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Invertd(MapTransform):\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        transform: InvertibleTransform,\n        orig_keys: KeysCollection,\n        meta_keys: Optional[KeysCollection] = None,\n        orig_meta_keys: Optional[KeysCollection] = None,\n        meta_key_postfix: str = \"meta_dict\",\n        nearest_interp: Union[bool, Sequence[bool]] = True,\n        to_tensor: Union[bool, Sequence[bool]] = True,\n        device: Union[Union[str, torch.device], Sequence[Union[str, torch.device]]] = \"cpu\",\n        post_func: Union[Callable, Sequence[Callable]] = lambda x: x,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        \"\"\"\n        Args:\n            keys: the key of expected data in the dict, invert transforms on it, in-place operation.\n                it also can be a list of keys, will invert transform for each of them, like: [\"pred\", \"pred_class2\"].\n            transform: the previous callable transform that applied on input data.\n            orig_keys: the key of the original input data in the dict. will get the applied transform information\n                for this input data, then invert them for the expected data with `keys`.\n                It can also be a list of keys, each matches to the `keys` data.\n            meta_keys: explicitly indicate the key for the inverted meta data dictionary.\n                the meta data is a dictionary object which contains: filename, original_shape, etc.\n                it can be a sequence of string, map to the `keys`.\n                if None, will try to construct meta_keys by `{key}_{meta_key_postfix}`.\n            orig_meta_keys: the key of the meta data of original input data, will get the `affine`, `data_shape`, etc.\n                the meta data is a dictionary object which contains: filename, original_shape, etc.\n                it can be a sequence of string, map to the `keys`.\n                if None, will try to construct meta_keys by `{orig_key}_{meta_key_postfix}`.\n                meta data will also be inverted and stored in `meta_keys`.\n            meta_key_postfix: if `orig_meta_keys` is None, use `{orig_key}_{meta_key_postfix}` to to fetch the\n                meta data from dict, if `meta_keys` is None, use `{key}_{meta_key_postfix}`.\n                default is `meta_dict`, the meta data is a dictionary object.\n                For example, to handle orig_key `image`,  read/write `affine` matrices from the\n                metadata `image_meta_dict` dictionary's `affine` field.\n                the inverted meta dict will be stored with key: \"{key}_{meta_key_postfix}\".\n            nearest_interp: whether to use `nearest` interpolation mode when inverting the spatial transforms,\n                default to `True`. If `False`, use the same interpolation mode as the original transform.\n                it also can be a list of bool, each matches to the `keys` data.\n            to_tensor: whether to convert the inverted data into PyTorch Tensor first, default to `True`.\n                it also can be a list of bool, each matches to the `keys` data.\n            device: if converted to Tensor, move the inverted results to target device before `post_func`,\n                default to \"cpu\", it also can be a list of string or `torch.device`,\n                each matches to the `keys` data.\n            post_func: post processing for the inverted data, should be a callable function.\n                it also can be a list of callable, each matches to the `keys` data.\n            allow_missing_keys: don't raise exception if key is missing.\n\n        \"\"\"\n        super().__init__(keys, allow_missing_keys)\n        if not isinstance(transform, InvertibleTransform):\n            raise ValueError(\"transform is not invertible, can't invert transform for the data.\")\n        self.transform = transform\n        self.orig_keys = ensure_tuple_rep(orig_keys, len(self.keys))\n        self.meta_keys = ensure_tuple_rep(None, len(self.keys)) if meta_keys is None else ensure_tuple(meta_keys)\n        if len(self.keys) != len(self.meta_keys):\n            raise ValueError(\"meta_keys should have the same length as keys.\")\n        self.orig_meta_keys = ensure_tuple_rep(orig_meta_keys, len(self.keys))\n        self.meta_key_postfix = ensure_tuple_rep(meta_key_postfix, len(self.keys))\n        self.nearest_interp = ensure_tuple_rep(nearest_interp, len(self.keys))\n        self.to_tensor = ensure_tuple_rep(to_tensor, len(self.keys))\n        self.device = ensure_tuple_rep(device, len(self.keys))\n        self.post_func = ensure_tuple_rep(post_func, len(self.keys))\n        self._totensor = ToTensor()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/dictionary.py_Invertd.__call___Invertd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/dictionary.py_Invertd.__call___Invertd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/post/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 579, "end_line": 632, "span_ids": ["Invertd.__call__"], "tokens": 419}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Invertd(MapTransform):\n\n    def __call__(self, data: Mapping[Hashable, Any]) -> Dict[Hashable, Any]:\n        d = dict(data)\n        for (\n            key,\n            orig_key,\n            meta_key,\n            orig_meta_key,\n            meta_key_postfix,\n            nearest_interp,\n            to_tensor,\n            device,\n            post_func,\n        ) in self.key_iterator(\n            d,\n            self.orig_keys,\n            self.meta_keys,\n            self.orig_meta_keys,\n            self.meta_key_postfix,\n            self.nearest_interp,\n            self.to_tensor,\n            self.device,\n            self.post_func,\n        ):\n            transform_key = InvertibleTransform.trace_key(orig_key)\n            if transform_key not in d:\n                warnings.warn(f\"transform info of `{orig_key}` is not available or no InvertibleTransform applied.\")\n                continue\n\n            transform_info = d[transform_key]\n            if nearest_interp:\n                transform_info = convert_inverse_interp_mode(\n                    trans_info=deepcopy(transform_info), mode=\"nearest\", align_corners=None\n                )\n\n            input = d[key]\n            if isinstance(input, torch.Tensor):\n                input = input.detach()\n            # construct the input dict data for BatchInverseTransform\n            input_dict = {orig_key: input, transform_key: transform_info}\n            orig_meta_key = orig_meta_key or f\"{orig_key}_{meta_key_postfix}\"\n            meta_key = meta_key or f\"{key}_{meta_key_postfix}\"\n            if orig_meta_key in d:\n                input_dict[orig_meta_key] = d[orig_meta_key]\n\n            with allow_missing_keys_mode(self.transform):  # type: ignore\n                inverted = self.transform.inverse(input_dict)\n\n            # save the inverted data\n            d[key] = post_func(self._totensor(inverted[orig_key]).to(device) if to_tensor else inverted[orig_key])\n            # save the inverted meta dict\n            if orig_meta_key in d:\n                d[meta_key] = inverted.get(orig_meta_key)\n\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_Rotate_Rotate.__init__.self._rotation_matrix.None": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_Rotate_Rotate.__init__.self._rotation_matrix.None", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 452, "end_line": 491, "span_ids": ["Rotate.__init__", "Rotate"], "tokens": 490}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Rotate(Transform, ThreadUnsafe):\n    \"\"\"\n    Rotates an input image by given angle using :py:class:`monai.networks.layers.AffineTransform`.\n\n    Args:\n        angle: Rotation angle(s) in radians. should a float for 2D, three floats for 3D.\n        keep_size: If it is True, the output shape is kept the same as the input.\n            If it is False, the output shape is adapted so that the\n            input array is contained completely in the output. Default is True.\n        mode: {``\"bilinear\"``, ``\"nearest\"``}\n            Interpolation mode to calculate output values. Defaults to ``\"bilinear\"``.\n            See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n        padding_mode: {``\"zeros\"``, ``\"border\"``, ``\"reflection\"``}\n            Padding mode for outside grid values. Defaults to ``\"border\"``.\n            See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n        align_corners: Defaults to False.\n            See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n        dtype: data type for resampling computation. Defaults to ``np.float64`` for best precision.\n            If None, use the data type of input data. To be compatible with other modules,\n            the output data type is always ``np.float32``.\n    \"\"\"\n\n    backend = [TransformBackends.TORCH]\n\n    def __init__(\n        self,\n        angle: Union[Sequence[float], float],\n        keep_size: bool = True,\n        mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,\n        padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER,\n        align_corners: bool = False,\n        dtype: Union[DtypeLike, torch.dtype] = np.float64,\n    ) -> None:\n        self.angle = angle\n        self.keep_size = keep_size\n        self.mode: GridSampleMode = look_up_option(mode, GridSampleMode)\n        self.padding_mode: GridSamplePadMode = look_up_option(padding_mode, GridSamplePadMode)\n        self.align_corners = align_corners\n        self.dtype = dtype\n        self._rotation_matrix: Optional[NdarrayOrTensor] = None", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_Rotate.__call___Rotate.get_rotation_matrix.return.self__rotation_matrix": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_Rotate.__call___Rotate.get_rotation_matrix.return.self__rotation_matrix", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 493, "end_line": 566, "span_ids": ["Rotate.__call__", "Rotate.get_rotation_matrix"], "tokens": 869}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Rotate(Transform, ThreadUnsafe):\n\n    def __call__(\n        self,\n        img: NdarrayOrTensor,\n        mode: Optional[Union[GridSampleMode, str]] = None,\n        padding_mode: Optional[Union[GridSamplePadMode, str]] = None,\n        align_corners: Optional[bool] = None,\n        dtype: Union[DtypeLike, torch.dtype] = None,\n    ) -> NdarrayOrTensor:\n        \"\"\"\n        Args:\n            img: channel first array, must have shape: [chns, H, W] or [chns, H, W, D].\n            mode: {``\"bilinear\"``, ``\"nearest\"``}\n                Interpolation mode to calculate output values. Defaults to ``self.mode``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            padding_mode: {``\"zeros\"``, ``\"border\"``, ``\"reflection\"``}\n                Padding mode for outside grid values. Defaults to ``self.padding_mode``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n                align_corners: Defaults to ``self.align_corners``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            align_corners: Defaults to ``self.align_corners``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            dtype: data type for resampling computation. Defaults to ``self.dtype``.\n                If None, use the data type of input data. To be compatible with other modules,\n                the output data type is always ``np.float32``.\n\n        Raises:\n            ValueError: When ``img`` spatially is not one of [2D, 3D].\n\n        \"\"\"\n        _dtype = dtype or self.dtype or img.dtype\n\n        img_t: torch.Tensor\n        img_t, *_ = convert_data_type(img, torch.Tensor, dtype=_dtype)  # type: ignore\n\n        im_shape = np.asarray(img_t.shape[1:])  # spatial dimensions\n        input_ndim = len(im_shape)\n        if input_ndim not in (2, 3):\n            raise ValueError(f\"Unsupported img dimension: {input_ndim}, available options are [2, 3].\")\n        _angle = ensure_tuple_rep(self.angle, 1 if input_ndim == 2 else 3)\n        transform = create_rotate(input_ndim, _angle)\n        shift = create_translate(input_ndim, ((im_shape - 1) / 2).tolist())\n        if self.keep_size:\n            output_shape = im_shape\n        else:\n            corners = np.asarray(np.meshgrid(*[(0, dim) for dim in im_shape], indexing=\"ij\")).reshape(\n                (len(im_shape), -1)\n            )\n            corners = transform[:-1, :-1] @ corners  # type: ignore\n            output_shape = np.asarray(corners.ptp(axis=1) + 0.5, dtype=int)\n        shift_1 = create_translate(input_ndim, (-(output_shape - 1) / 2).tolist())\n        transform = shift @ transform @ shift_1\n\n        transform_t: torch.Tensor\n        transform_t, *_ = convert_to_dst_type(transform, img_t)  # type: ignore\n\n        xform = AffineTransform(\n            normalized=False,\n            mode=look_up_option(mode or self.mode, GridSampleMode),\n            padding_mode=look_up_option(padding_mode or self.padding_mode, GridSamplePadMode),\n            align_corners=self.align_corners if align_corners is None else align_corners,\n            reverse_indexing=True,\n        )\n        output: torch.Tensor = xform(img_t.unsqueeze(0), transform_t, spatial_size=output_shape).float().squeeze(0)\n        self._rotation_matrix = transform\n        out: NdarrayOrTensor\n        out, *_ = convert_to_dst_type(output, dst=img, dtype=output.dtype)\n        return out\n\n    def get_rotation_matrix(self) -> Optional[NdarrayOrTensor]:\n        \"\"\"\n        Get the most recently applied rotation matrix\n        This is not thread-safe.\n        \"\"\"\n        return self._rotation_matrix", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_Affine.__call___Affine.__call__.return.ret_if_self_image_only_el": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_Affine.__call___Affine.__call__.return.ret_if_self_image_only_el", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1497, "end_line": 1523, "span_ids": ["Affine.__call__"], "tokens": 400}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Affine(Transform):\n\n    def __call__(\n        self,\n        img: NdarrayOrTensor,\n        spatial_size: Optional[Union[Sequence[int], int]] = None,\n        mode: Optional[Union[GridSampleMode, str]] = None,\n        padding_mode: Optional[Union[GridSamplePadMode, str]] = None,\n    ) -> Union[NdarrayOrTensor, Tuple[NdarrayOrTensor, NdarrayOrTensor]]:\n        \"\"\"\n        Args:\n            img: shape must be (num_channels, H, W[, D]),\n            spatial_size: output image spatial size.\n                if `spatial_size` and `self.spatial_size` are not defined, or smaller than 1,\n                the transform will use the spatial size of `img`.\n                if `img` has two spatial dimensions, `spatial_size` should have 2 elements [h, w].\n                if `img` has three spatial dimensions, `spatial_size` should have 3 elements [h, w, d].\n            mode: {``\"bilinear\"``, ``\"nearest\"``}\n                Interpolation mode to calculate output values. Defaults to ``self.mode``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            padding_mode: {``\"zeros\"``, ``\"border\"``, ``\"reflection\"``}\n                Padding mode for outside grid values. Defaults to ``self.padding_mode``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n        \"\"\"\n        sp_size = fall_back_tuple(spatial_size or self.spatial_size, img.shape[1:])\n        grid, affine = self.affine_grid(spatial_size=sp_size)\n        ret = self.resampler(img, grid=grid, mode=mode or self.mode, padding_mode=padding_mode or self.padding_mode)\n\n        return ret if self.image_only else (ret, affine)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_RandAffine.get_identity_grid_RandAffine.randomize.self_rand_affine_grid_ran": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_RandAffine.get_identity_grid_RandAffine.randomize.self_rand_affine_grid_ran", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1640, "end_line": 1669, "span_ids": ["RandAffine.get_identity_grid", "RandAffine.randomize", "RandAffine.set_random_state"], "tokens": 272}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandAffine(RandomizableTransform):\n\n    def get_identity_grid(self, spatial_size: Sequence[int]):\n        \"\"\"\n        Return a cached or new identity grid depends on the availability.\n\n        Args:\n            spatial_size: non-dynamic spatial size\n        \"\"\"\n        ndim = len(spatial_size)\n        if spatial_size != fall_back_tuple(spatial_size, [1] * ndim) or spatial_size != fall_back_tuple(\n            spatial_size, [2] * ndim\n        ):\n            raise RuntimeError(f\"spatial_size should not be dynamic, got {spatial_size}.\")\n        return (\n            create_grid(spatial_size=spatial_size, device=self.rand_affine_grid.device, backend=\"torch\")\n            if self._cached_grid is None\n            else self._cached_grid\n        )\n\n    def set_random_state(\n        self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None\n    ) -> \"RandAffine\":\n        self.rand_affine_grid.set_random_state(seed, state)\n        super().set_random_state(seed, state)\n        return self\n\n    def randomize(self, data: Optional[Any] = None) -> None:\n        super().randomize(None)\n        if not self._do_transform:\n            return None\n        self.rand_affine_grid.randomize()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Spacingd.__init___Spacingd.__init__.self.meta_key_postfix.ensure_tuple_rep_meta_key": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Spacingd.__init___Spacingd.__init__.self.meta_key_postfix.ensure_tuple_rep_meta_key", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 152, "end_line": 225, "span_ids": ["Spacingd.__init__"], "tokens": 1050}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Spacingd(MapTransform, InvertibleTransform):\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        pixdim: Union[Sequence[float], float],\n        diagonal: bool = False,\n        mode: GridSampleModeSequence = GridSampleMode.BILINEAR,\n        padding_mode: GridSamplePadModeSequence = GridSamplePadMode.BORDER,\n        align_corners: Union[Sequence[bool], bool] = False,\n        dtype: Optional[Union[Sequence[DtypeLike], DtypeLike]] = np.float64,\n        meta_keys: Optional[KeysCollection] = None,\n        meta_key_postfix: str = \"meta_dict\",\n        allow_missing_keys: bool = False,\n    ) -> None:\n        \"\"\"\n        Args:\n            pixdim: output voxel spacing. if providing a single number, will use it for the first dimension.\n                items of the pixdim sequence map to the spatial dimensions of input image, if length\n                of pixdim sequence is longer than image spatial dimensions, will ignore the longer part,\n                if shorter, will pad with `1.0`.\n                if the components of the `pixdim` are non-positive values, the transform will use the\n                corresponding components of the original pixdim, which is computed from the `affine`\n                matrix of input image.\n            diagonal: whether to resample the input to have a diagonal affine matrix.\n                If True, the input data is resampled to the following affine::\n\n                    np.diag((pixdim_0, pixdim_1, pixdim_2, 1))\n\n                This effectively resets the volume to the world coordinate system (RAS+ in nibabel).\n                The original orientation, rotation, shearing are not preserved.\n\n                If False, the axes orientation, orthogonal rotation and\n                translations components from the original affine will be\n                preserved in the target affine. This option will not flip/swap\n                axes against the original ones.\n            mode: {``\"bilinear\"``, ``\"nearest\"``}\n                Interpolation mode to calculate output values. Defaults to ``\"bilinear\"``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n                It also can be a sequence of string, each element corresponds to a key in ``keys``.\n            padding_mode: {``\"zeros\"``, ``\"border\"``, ``\"reflection\"``}\n                Padding mode for outside grid values. Defaults to ``\"border\"``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n                It also can be a sequence of string, each element corresponds to a key in ``keys``.\n            align_corners: Geometrically, we consider the pixels of the input as squares rather than points.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n                It also can be a sequence of bool, each element corresponds to a key in ``keys``.\n            dtype: data type for resampling computation. Defaults to ``np.float64`` for best precision.\n                If None, use the data type of input data. To be compatible with other modules,\n                the output data type is always ``np.float32``.\n                It also can be a sequence of dtypes, each element corresponds to a key in ``keys``.\n            meta_keys: explicitly indicate the key of the corresponding meta data dictionary.\n                for example, for data with key `image`, the metadata by default is in `image_meta_dict`.\n                the meta data is a dictionary object which contains: filename, affine, original_shape, etc.\n                it can be a sequence of string, map to the `keys`.\n                if None, will try to construct meta_keys by `key_{meta_key_postfix}`.\n            meta_key_postfix: if meta_keys=None, use `key_{postfix}` to to fetch the meta data according\n                to the key data, default is `meta_dict`, the meta data is a dictionary object.\n                For example, to handle key `image`,  read/write affine matrices from the\n                metadata `image_meta_dict` dictionary's `affine` field.\n            allow_missing_keys: don't raise exception if key is missing.\n\n        Raises:\n            TypeError: When ``meta_key_postfix`` is not a ``str``.\n\n        \"\"\"\n        super().__init__(keys, allow_missing_keys)\n        self.spacing_transform = Spacing(pixdim, diagonal=diagonal)\n        self.mode = ensure_tuple_rep(mode, len(self.keys))\n        self.padding_mode = ensure_tuple_rep(padding_mode, len(self.keys))\n        self.align_corners = ensure_tuple_rep(align_corners, len(self.keys))\n        self.dtype = ensure_tuple_rep(dtype, len(self.keys))\n        self.meta_keys = ensure_tuple_rep(None, len(self.keys)) if meta_keys is None else ensure_tuple(meta_keys)\n        if len(self.keys) != len(self.meta_keys):\n            raise ValueError(\"meta_keys should have the same length as keys.\")\n        self.meta_key_postfix = ensure_tuple_rep(meta_key_postfix, len(self.keys))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Orientationd_Orientationd.__init__.self.meta_key_postfix.ensure_tuple_rep_meta_key": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Orientationd_Orientationd.__init__.self.meta_key_postfix.ensure_tuple_rep_meta_key", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 301, "end_line": 360, "span_ids": ["Orientationd.__init__", "Orientationd"], "tokens": 760}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Orientationd(MapTransform, InvertibleTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.Orientation`.\n\n    This transform assumes the ``data`` dictionary has a key for the input\n    data's metadata and contains `affine` field.  The key is formed by ``key_{meta_key_postfix}``.\n\n    After reorienting the input array, this transform will write the new affine\n    to the `affine` field of metadata which is formed by ``key_{meta_key_postfix}``.\n    \"\"\"\n\n    backend = Orientation.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        axcodes: Optional[str] = None,\n        as_closest_canonical: bool = False,\n        labels: Optional[Sequence[Tuple[str, str]]] = tuple(zip(\"LPI\", \"RAS\")),\n        meta_keys: Optional[KeysCollection] = None,\n        meta_key_postfix: str = \"meta_dict\",\n        allow_missing_keys: bool = False,\n    ) -> None:\n        \"\"\"\n        Args:\n            axcodes: N elements sequence for spatial ND input's orientation.\n                e.g. axcodes='RAS' represents 3D orientation:\n                (Left, Right), (Posterior, Anterior), (Inferior, Superior).\n                default orientation labels options are: 'L' and 'R' for the first dimension,\n                'P' and 'A' for the second, 'I' and 'S' for the third.\n            as_closest_canonical: if True, load the image as closest to canonical axis format.\n            labels: optional, None or sequence of (2,) sequences\n                (2,) sequences are labels for (beginning, end) of output axis.\n                Defaults to ``(('L', 'R'), ('P', 'A'), ('I', 'S'))``.\n            meta_keys: explicitly indicate the key of the corresponding meta data dictionary.\n                for example, for data with key `image`, the metadata by default is in `image_meta_dict`.\n                the meta data is a dictionary object which contains: filename, affine, original_shape, etc.\n                it can be a sequence of string, map to the `keys`.\n                if None, will try to construct meta_keys by `key_{meta_key_postfix}`.\n            meta_key_postfix: if meta_keys is None, use `key_{postfix}` to to fetch the meta data according\n                to the key data, default is `meta_dict`, the meta data is a dictionary object.\n                For example, to handle key `image`,  read/write affine matrices from the\n                metadata `image_meta_dict` dictionary's `affine` field.\n            allow_missing_keys: don't raise exception if key is missing.\n\n        Raises:\n            TypeError: When ``meta_key_postfix`` is not a ``str``.\n\n        See Also:\n            `nibabel.orientations.ornt2axcodes`.\n\n        \"\"\"\n        super().__init__(keys, allow_missing_keys)\n        self.ornt_transform = Orientation(axcodes=axcodes, as_closest_canonical=as_closest_canonical, labels=labels)\n        if not isinstance(meta_key_postfix, str):\n            raise TypeError(f\"meta_key_postfix must be a str but is {type(meta_key_postfix).__name__}.\")\n        self.meta_keys = ensure_tuple_rep(None, len(self.keys)) if meta_keys is None else ensure_tuple(meta_keys)\n        if len(self.keys) != len(self.meta_keys):\n            raise ValueError(\"meta_keys should have the same length as keys.\")\n        self.meta_key_postfix = ensure_tuple_rep(meta_key_postfix, len(self.keys))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/transform.py_apply_transform_apply_transform.try_.except_Exception_as_e_.raise_RuntimeError_f_appl": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/transform.py_apply_transform_apply_transform.try_.except_Exception_as_e_.raise_RuntimeError_f_appl", "embedding": null, "metadata": {"file_path": "monai/transforms/transform.py", "file_name": "transform.py", "file_type": "text/x-python", "category": "implementation", "start_line": 56, "end_line": 106, "span_ids": ["apply_transform"], "tokens": 496}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def apply_transform(\n    transform: Callable[..., ReturnType], data: Any, map_items: bool = True, unpack_items: bool = False\n) -> Union[List[ReturnType], ReturnType]:\n    \"\"\"\n    Transform `data` with `transform`.\n\n    If `data` is a list or tuple and `map_data` is True, each item of `data` will be transformed\n    and this method returns a list of outcomes.\n    otherwise transform will be applied once with `data` as the argument.\n\n    Args:\n        transform: a callable to be used to transform `data`.\n        data: an object to be transformed.\n        map_items: whether to apply transform to each item in `data`,\n            if `data` is a list or tuple. Defaults to True.\n        unpack_items: whether to unpack parameters using `*`. Defaults to False.\n\n    Raises:\n        Exception: When ``transform`` raises an exception.\n\n    Returns:\n        Union[List[ReturnType], ReturnType]: The return type of `transform` or a list thereof.\n    \"\"\"\n    try:\n        if isinstance(data, (list, tuple)) and map_items:\n            return [_apply_transform(transform, item, unpack_items) for item in data]\n        return _apply_transform(transform, data, unpack_items)\n    except Exception as e:\n\n        if not isinstance(transform, transforms.compose.Compose):\n            # log the input data information of exact transform in the transform chain\n            datastats = transforms.utility.array.DataStats(data_shape=False, value_range=False)\n            logger = logging.getLogger(datastats._logger_name)\n            logger.info(f\"\\n=== Transform input info -- {type(transform).__name__} ===\")\n            if isinstance(data, (list, tuple)):\n                data = data[0]\n\n            def _log_stats(data, prefix: Optional[str] = \"Data\"):\n                if isinstance(data, (np.ndarray, torch.Tensor)):\n                    # log data type, shape, range for array\n                    datastats(img=data, data_shape=True, value_range=True, prefix=prefix)  # type: ignore\n                else:\n                    # log data type and value for other meta data\n                    datastats(img=data, data_value=True, prefix=prefix)\n\n            if isinstance(data, dict):\n                for k, v in data.items():\n                    _log_stats(data=v, prefix=k)\n            else:\n                _log_stats(data=data)\n        raise RuntimeError(f\"applying transform {transform}\") from e", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/transform.py_ThreadUnsafe_Randomizable.R.np_random_RandomState_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/transform.py_ThreadUnsafe_Randomizable.R.np_random_RandomState_", "embedding": null, "metadata": {"file_path": "monai/transforms/transform.py", "file_name": "transform.py", "file_type": "text/x-python", "category": "implementation", "start_line": 111, "end_line": 136, "span_ids": ["ThreadUnsafe", "Randomizable"], "tokens": 219}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ThreadUnsafe:\n    \"\"\"\n    A class to denote that the transform will mutate its member variables,\n    when being applied. Transforms inheriting this class should be used\n    cautiously in a multi-thread context.\n\n    This type is typically used by :py:class:`monai.data.CacheDataset` and\n    its extensions, where the transform cache is built with multiple threads.\n    \"\"\"\n\n    pass\n\n\nclass Randomizable(ABC, ThreadUnsafe):\n    \"\"\"\n    An interface for handling random state locally, currently based on a class\n    variable `R`, which is an instance of `np.random.RandomState`.  This\n    provides the flexibility of component-specific determinism without\n    affecting the global states.  It is recommended to use this API with\n    :py:class:`monai.data.DataLoader` for deterministic behaviour of the\n    preprocessing pipelines. This API is not thread-safe. Additionally,\n    deepcopying instance of this class often causes insufficient randomness as\n    the random states will be duplicated.\n    \"\"\"\n\n    R: np.random.RandomState = np.random.RandomState()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/transform.py_Randomizable.set_random_state_Randomizable.set_random_state.return.self": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/transform.py_Randomizable.set_random_state_Randomizable.set_random_state.return.self", "embedding": null, "metadata": {"file_path": "monai/transforms/transform.py", "file_name": "transform.py", "file_type": "text/x-python", "category": "implementation", "start_line": 138, "end_line": 170, "span_ids": ["Randomizable.set_random_state"], "tokens": 270}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Randomizable(ABC, ThreadUnsafe):\n\n    def set_random_state(\n        self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None\n    ) -> \"Randomizable\":\n        \"\"\"\n        Set the random state locally, to control the randomness, the derived\n        classes should use :py:attr:`self.R` instead of `np.random` to introduce random\n        factors.\n\n        Args:\n            seed: set the random state with an integer seed.\n            state: set the random state with a `np.random.RandomState` object.\n\n        Raises:\n            TypeError: When ``state`` is not an ``Optional[np.random.RandomState]``.\n\n        Returns:\n            a Randomizable instance.\n\n        \"\"\"\n        if seed is not None:\n            _seed = id(seed) if not isinstance(seed, (int, np.integer)) else seed\n            _seed = _seed % MAX_SEED\n            self.R = np.random.RandomState(_seed)\n            return self\n\n        if state is not None:\n            if not isinstance(state, np.random.RandomState):\n                raise TypeError(f\"state must be None or a np.random.RandomState but is {type(state).__name__}.\")\n            self.R = state\n            return self\n\n        self.R = np.random.RandomState()\n        return self", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_EnsureChannelFirst_EnsureChannelFirst.__init__.self.strict_check.strict_check": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_EnsureChannelFirst_EnsureChannelFirst.__init__.self.strict_check.strict_check", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 184, "end_line": 199, "span_ids": ["EnsureChannelFirst.__init__", "EnsureChannelFirst"], "tokens": 148}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class EnsureChannelFirst(Transform):\n    \"\"\"\n    Automatically adjust or add the channel dimension of input data to ensure `channel_first` shape.\n    It extracts the `original_channel_dim` info from provided meta_data dictionary.\n    Typical values of `original_channel_dim` can be: \"no_channel\", 0, -1.\n    Convert the data to `channel_first` based on the `original_channel_dim` information.\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __init__(self, strict_check: bool = True):\n        \"\"\"\n        Args:\n            strict_check: whether to raise an error when the meta information is insufficient.\n        \"\"\"\n        self.strict_check = strict_check", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_EnsureChannelFirst.__call___EnsureChannelFirst.__call__.return.AsChannelFirst_channel_di": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_EnsureChannelFirst.__call___EnsureChannelFirst.__call__.return.AsChannelFirst_channel_di", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 201, "end_line": 222, "span_ids": ["EnsureChannelFirst.__call__"], "tokens": 183}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class EnsureChannelFirst(Transform):\n\n    def __call__(self, img: NdarrayOrTensor, meta_dict: Optional[Mapping] = None) -> NdarrayOrTensor:\n        \"\"\"\n        Apply the transform to `img`.\n        \"\"\"\n        if not isinstance(meta_dict, Mapping):\n            msg = \"meta_dict not available, EnsureChannelFirst is not in use.\"\n            if self.strict_check:\n                raise ValueError(msg)\n            warnings.warn(msg)\n            return img\n\n        channel_dim = meta_dict.get(\"original_channel_dim\")\n\n        if channel_dim is None:\n            msg = \"Unknown original_channel_dim in the meta_dict, EnsureChannelFirst is not in use.\"\n            if self.strict_check:\n                raise ValueError(msg)\n            warnings.warn(msg)\n            return img\n        if channel_dim == \"no_channel\":\n            return AddChannel()(img)\n        return AsChannelFirst(channel_dim=channel_dim)(img)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_DataStats_DataStats.__init__.if_logger_handler_is_not_._logger_addHandler_logger": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_DataStats_DataStats.__init__.if_logger_handler_is_not_._logger_addHandler_logger", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 506, "end_line": 560, "span_ids": ["DataStats", "DataStats.__init__"], "tokens": 522}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DataStats(Transform):\n    \"\"\"\n    Utility transform to show the statistics of data for debug or analysis.\n    It can be inserted into any place of a transform chain and check results of previous transforms.\n    It support both `numpy.ndarray` and `torch.tensor` as input data,\n    so it can be used in pre-processing and post-processing.\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __init__(\n        self,\n        prefix: str = \"Data\",\n        data_type: bool = True,\n        data_shape: bool = True,\n        value_range: bool = True,\n        data_value: bool = False,\n        additional_info: Optional[Callable] = None,\n        logger_handler: Optional[logging.Handler] = None,\n    ) -> None:\n        \"\"\"\n        Args:\n            prefix: will be printed in format: \"{prefix} statistics\".\n            data_type: whether to show the type of input data.\n            data_shape: whether to show the shape of input data.\n            value_range: whether to show the value range of input data.\n            data_value: whether to show the raw value of input data.\n                a typical example is to print some properties of Nifti image: affine, pixdim, etc.\n            additional_info: user can define callable function to extract additional info from input data.\n            logger_handler: add additional handler to output data: save to file, etc.\n                add existing python logging handlers: https://docs.python.org/3/library/logging.handlers.html\n                the handler should have a logging level of at least `INFO`.\n\n        Raises:\n            TypeError: When ``additional_info`` is not an ``Optional[Callable]``.\n\n        \"\"\"\n        if not isinstance(prefix, str):\n            raise AssertionError(\"prefix must be a string.\")\n        self.prefix = prefix\n        self.data_type = data_type\n        self.data_shape = data_shape\n        self.value_range = value_range\n        self.data_value = data_value\n        if additional_info is not None and not callable(additional_info):\n            raise TypeError(f\"additional_info must be None or callable but is {type(additional_info).__name__}.\")\n        self.additional_info = additional_info\n        self._logger_name = \"DataStats\"\n        _logger = logging.getLogger(self._logger_name)\n        _logger.setLevel(logging.INFO)\n        console = logging.StreamHandler(sys.stdout)  # always stdout\n        console.setLevel(logging.INFO)\n        _logger.addHandler(console)\n        if logger_handler is not None:\n            _logger.addHandler(logger_handler)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_ToCupyd_ToCupyd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_ToCupyd_ToCupyd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 591, "end_line": 622, "span_ids": ["ToCupyd.__call__", "ToCupyd", "ToCupyd.__init__"], "tokens": 332}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ToCupyd(MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.ToCupy`.\n\n    Args:\n        keys: keys of the corresponding items to be transformed.\n            See also: :py:class:`monai.transforms.compose.MapTransform`\n        dtype: data type specifier. It is inferred from the input by default.\n            if not None, must be an argument of `numpy.dtype`, for more details:\n            https://docs.cupy.dev/en/stable/reference/generated/cupy.array.html.\n        wrap_sequence: if `False`, then lists will recursively call this function, default to `True`.\n            E.g., if `False`, `[1, 2]` -> `[array(1), array(2)]`, if `True`, then `[1, 2]` -> `array([1, 2])`.\n        allow_missing_keys: don't raise exception if key is missing.\n    \"\"\"\n\n    backend = ToCupy.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        dtype: Optional[np.dtype] = None,\n        wrap_sequence: bool = True,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        super().__init__(keys, allow_missing_keys)\n        self.converter = ToCupy(dtype=dtype, wrap_sequence=wrap_sequence)\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key in self.key_iterator(d):\n            d[key] = self.converter(d[key])\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_Transposed_Transposed.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_Transposed_Transposed.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 620, "end_line": 640, "span_ids": ["Transposed", "Transposed.__init__", "Transposed.__call__"], "tokens": 195}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Transposed(MapTransform, InvertibleTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.Transpose`.\n    \"\"\"\n\n    backend = Transpose.backend\n\n    def __init__(\n        self, keys: KeysCollection, indices: Optional[Sequence[int]], allow_missing_keys: bool = False\n    ) -> None:\n        super().__init__(keys, allow_missing_keys)\n        self.transform = Transpose(indices)\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key in self.key_iterator(d):\n            d[key] = self.transform(d[key])\n            # if None was supplied then numpy uses range(a.ndim)[::-1]\n            indices = self.transform.indices or range(d[key].ndim)[::-1]\n            self.push_transform(d, key, extra_info={\"indices\": indices})\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_Transposed.inverse_Transposed.inverse.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_Transposed.inverse_Transposed.inverse.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 643, "end_line": 655, "span_ids": ["Transposed.inverse"], "tokens": 135}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Transposed(MapTransform, InvertibleTransform):\n\n    def inverse(self, data: Mapping[Hashable, Any]) -> Dict[Hashable, Any]:\n        d = deepcopy(dict(data))\n        for key in self.key_iterator(d):\n            transform = self.get_most_recent_transform(d, key)\n            # Create inverse transform\n            fwd_indices = np.array(transform[TraceKeys.EXTRA_INFO][\"indices\"])\n            inv_indices = np.argsort(fwd_indices)\n            inverse_transform = Transpose(inv_indices.tolist())\n            # Apply inverse\n            d[key] = inverse_transform(d[key])\n            # Remove the applied transform\n            self.pop_transform(d, key)\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_convert_inverse_interp_mode_convert_inverse_interp_mode.return.trans_info": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_convert_inverse_interp_mode_convert_inverse_interp_mode.return.trans_info", "embedding": null, "metadata": {"file_path": "monai/transforms/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1194, "end_line": 1225, "span_ids": ["convert_inverse_interp_mode"], "tokens": 368}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def convert_inverse_interp_mode(trans_info: List, mode: str = \"nearest\", align_corners: Optional[bool] = None):\n    \"\"\"\n    Change the interpolation mode when inverting spatial transforms, default to \"nearest\".\n    This function modifies trans_info's `TraceKeys.EXTRA_INFO`.\n\n    See also: :py:class:`monai.transform.inverse.InvertibleTransform`\n\n    Args:\n        trans_info: transforms inverse information list, contains context of every invertible transform.\n        mode: target interpolation mode to convert, default to \"nearest\" as it's usually used to save the mode output.\n        align_corners: target align corner value in PyTorch interpolation API, need to align with the `mode`.\n\n    \"\"\"\n    interp_modes = [i.value for i in InterpolateMode] + [i.value for i in GridSampleMode]\n\n    # set to string for DataLoader collation\n    align_corners_ = TraceKeys.NONE if align_corners is None else align_corners\n\n    for item in ensure_tuple(trans_info):\n        if TraceKeys.EXTRA_INFO in item:\n            orig_mode = item[TraceKeys.EXTRA_INFO].get(\"mode\", None)\n            if orig_mode is not None:\n                if orig_mode[0] in interp_modes:\n                    item[TraceKeys.EXTRA_INFO][\"mode\"] = [mode for _ in range(len(mode))]\n                elif orig_mode in interp_modes:\n                    item[TraceKeys.EXTRA_INFO][\"mode\"] = mode\n            if \"align_corners\" in item[TraceKeys.EXTRA_INFO]:\n                if issequenceiterable(item[TraceKeys.EXTRA_INFO][\"align_corners\"]):\n                    item[TraceKeys.EXTRA_INFO][\"align_corners\"] = [align_corners_ for _ in range(len(mode))]\n                else:\n                    item[TraceKeys.EXTRA_INFO][\"align_corners\"] = align_corners_\n    return trans_info", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/misc.py_copy_to_device_copy_to_device.return.obj": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/misc.py_copy_to_device_copy_to_device.return.obj", "embedding": null, "metadata": {"file_path": "monai/utils/misc.py", "file_name": "misc.py", "file_type": "text/x-python", "category": "implementation", "start_line": 301, "end_line": 332, "span_ids": ["copy_to_device"], "tokens": 352}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def copy_to_device(\n    obj: Any, device: Optional[Union[str, torch.device]], non_blocking: bool = True, verbose: bool = False\n) -> Any:\n    \"\"\"\n    Copy object or tuple/list/dictionary of objects to ``device``.\n\n    Args:\n        obj: object or tuple/list/dictionary of objects to move to ``device``.\n        device: move ``obj`` to this device. Can be a string (e.g., ``cpu``, ``cuda``,\n            ``cuda:0``, etc.) or of type ``torch.device``.\n        non_blocking: when `True`, moves data to device asynchronously if\n            possible, e.g., moving CPU Tensors with pinned memory to CUDA devices.\n        verbose: when `True`, will print a warning for any elements of incompatible type\n            not copied to ``device``.\n    Returns:\n        Same as input, copied to ``device`` where possible. Original input will be\n            unchanged.\n    \"\"\"\n\n    if hasattr(obj, \"to\"):\n        return obj.to(device, non_blocking=non_blocking)\n    if isinstance(obj, tuple):\n        return tuple(copy_to_device(o, device, non_blocking) for o in obj)\n    if isinstance(obj, list):\n        return [copy_to_device(o, device, non_blocking) for o in obj]\n    if isinstance(obj, dict):\n        return {k: copy_to_device(o, device, non_blocking) for k, o in obj.items()}\n    if verbose:\n        fn_name = cast(types.FrameType, inspect.currentframe()).f_code.co_name\n        warnings.warn(f\"{fn_name} called with incompatible type: \" + f\"{type(obj)}. Data will be returned unchanged.\")\n\n    return obj", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/class_activation_maps.py_warnings_default_normalizer.return._compute_x_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/class_activation_maps.py_warnings_default_normalizer.return._compute_x_", "embedding": null, "metadata": {"file_path": "monai/visualize/class_activation_maps.py", "file_name": "class_activation_maps.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 44, "span_ids": ["default_normalizer", "docstring"], "tokens": 277}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import warnings\nfrom typing import Callable, Dict, Sequence, Union\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom monai.config import NdarrayTensor\nfrom monai.transforms import ScaleIntensity\nfrom monai.utils import ensure_tuple, pytorch_after\nfrom monai.visualize.visualizer import default_upsampler\n\n__all__ = [\"CAM\", \"GradCAM\", \"GradCAMpp\", \"ModelWithHooks\", \"default_normalizer\"]\n\n\ndef default_normalizer(x: NdarrayTensor) -> NdarrayTensor:\n    \"\"\"\n    A linear intensity scaling by mapping the (min, max) to (1, 0).\n    If the input data is PyTorch Tensor, the output data will be Tensor on the same device,\n    otherwise, output data will be numpy array.\n\n    Note: This will flip magnitudes (i.e., smallest will become biggest and vice versa).\n    \"\"\"\n\n    def _compute(data: np.ndarray) -> np.ndarray:\n        scaler = ScaleIntensity(minv=1.0, maxv=0.0)\n        return np.stack([scaler(i) for i in data], axis=0)\n\n    if isinstance(x, torch.Tensor):\n        return torch.as_tensor(_compute(x.detach().cpu().numpy()), device=x.device)\n\n    return _compute(x)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/setup.py_get_extensions_get_extensions.return.ext_modules": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/setup.py_get_extensions_get_extensions.return.ext_modules", "embedding": null, "metadata": {"file_path": "setup.py", "file_name": "setup.py", "file_type": "text/x-python", "category": "implementation", "start_line": 85, "end_line": 124, "span_ids": ["get_extensions"], "tokens": 332}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_extensions():\n    this_dir = os.path.dirname(os.path.abspath(__file__))\n    ext_dir = os.path.join(this_dir, \"monai\", \"csrc\")\n    include_dirs = [ext_dir]\n\n    source_cpu = glob.glob(os.path.join(ext_dir, \"**\", \"*.cpp\"), recursive=True)\n    source_cuda = glob.glob(os.path.join(ext_dir, \"**\", \"*.cu\"), recursive=True)\n\n    extension = None\n    define_macros = [(f\"{torch_parallel_backend()}\", 1), (\"MONAI_TORCH_VERSION\", TORCH_VERSION)]\n    extra_compile_args = {}\n    extra_link_args = []\n    sources = source_cpu\n    if BUILD_CPP:\n        extension = CppExtension\n        extra_compile_args.setdefault(\"cxx\", [])\n        if torch_parallel_backend() == \"AT_PARALLEL_OPENMP\":\n            extra_compile_args[\"cxx\"] += omp_flags()\n        extra_link_args = omp_flags()\n    if BUILD_CUDA:\n        extension = CUDAExtension\n        sources += source_cuda\n        define_macros += [(\"WITH_CUDA\", None)]\n        extra_compile_args = {\"cxx\": [], \"nvcc\": []}\n        if torch_parallel_backend() == \"AT_PARALLEL_OPENMP\":\n            extra_compile_args[\"cxx\"] += omp_flags()\n    if extension is None or not sources:\n        return []  # compile nothing\n\n    ext_modules = [\n        extension(\n            name=\"monai._C\",\n            sources=sources,\n            include_dirs=include_dirs,\n            define_macros=define_macros,\n            extra_compile_args=extra_compile_args,\n            extra_link_args=extra_link_args,\n        )\n    ]\n    return ext_modules", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/setup.py_get_cmds_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/setup.py_get_cmds_", "embedding": null, "metadata": {"file_path": "setup.py", "file_name": "setup.py", "file_type": "text/x-python", "category": "implementation", "start_line": 127, "end_line": 154, "span_ids": ["get_cmds", "impl:31"], "tokens": 197}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_cmds():\n    cmds = versioneer.get_cmdclass()\n\n    if not (BUILD_CPP or BUILD_CUDA):\n        return cmds\n\n    cmds.update({\"build_ext\": BuildExtension.with_options(no_python_abi_suffix=True)})\n    return cmds\n\n\n# Gathering source used for JIT extensions to include in package_data.\njit_extension_source = []\n\nfor ext in [\"cpp\", \"cu\", \"h\", \"cuh\"]:\n    glob_path = os.path.join(\"monai\", \"_extensions\", \"**\", f\"*.{ext}\")\n    jit_extension_source += glob.glob(glob_path, recursive=True)\n\njit_extension_source = [os.path.join(\"..\", path) for path in jit_extension_source]\n\nsetup(\n    version=versioneer.get_version(),\n    cmdclass=get_cmds(),\n    packages=find_packages(exclude=(\"docs\", \"examples\", \"tests\")),\n    zip_safe=False,\n    package_data={\"monai\": [\"py.typed\", *jit_extension_source]},\n    ext_modules=get_extensions(),\n)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/min_tests.py_glob_run_testsuit.exclude_cases._these_cases_use_exte": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/min_tests.py_glob_run_testsuit.exclude_cases._these_cases_use_exte", "embedding": null, "metadata": {"file_path": "tests/min_tests.py", "file_name": "min_tests.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 157, "span_ids": ["run_testsuit", "docstring"], "tokens": 1033}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import glob\nimport os\nimport sys\nimport unittest\n\n\ndef run_testsuit():\n    \"\"\"\n    Load test cases by excluding those need external dependencies.\n    The loaded cases should work with \"requirements-min.txt\"::\n\n        # in the monai repo folder:\n        pip install -r requirements-min.txt\n        QUICKTEST=true python -m tests.min_tests\n\n    :return: a test suite\n    \"\"\"\n    exclude_cases = [  # these cases use external dependencies\n        \"test_ahnet\",\n        \"test_arraydataset\",\n        \"test_cachedataset\",\n        \"test_cachedataset_parallel\",\n        \"test_cachedataset_persistent_workers\",\n        \"test_cachentransdataset\",\n        \"test_contrastive_loss\",\n        \"test_check_missing_files\",\n        \"test_csv_dataset\",\n        \"test_csv_iterable_dataset\",\n        \"test_cumulative_average_dist\",\n        \"test_dataset\",\n        \"test_dataset_summary\",\n        \"test_deepedit_transforms\",\n        \"test_deepgrow_dataset\",\n        \"test_deepgrow_interaction\",\n        \"test_deepgrow_transforms\",\n        \"test_detect_envelope\",\n        \"test_dints_network\",\n        \"test_efficientnet\",\n        \"test_ensemble_evaluator\",\n        \"test_ensure_channel_first\",\n        \"test_ensure_channel_firstd\",\n        \"test_fill_holes\",\n        \"test_fill_holesd\",\n        \"test_global_mutual_information_loss\",\n        \"test_handler_checkpoint_loader\",\n        \"test_handler_checkpoint_saver\",\n        \"test_handler_classification_saver\",\n        \"test_handler_classification_saver_dist\",\n        \"test_handler_confusion_matrix\",\n        \"test_handler_confusion_matrix_dist\",\n        \"test_handler_decollate_batch\",\n        \"test_handler_early_stop\",\n        \"test_handler_garbage_collector\",\n        \"test_handler_hausdorff_distance\",\n        \"test_handler_lr_scheduler\",\n        \"test_handler_mean_dice\",\n        \"test_handler_metrics_saver\",\n        \"test_handler_metrics_saver_dist\",\n        \"test_handler_mlflow\",\n        \"test_handler_nvtx\",\n        \"test_handler_parameter_scheduler\",\n        \"test_handler_post_processing\",\n        \"test_handler_prob_map_producer\",\n        \"test_handler_regression_metrics\",\n        \"test_handler_regression_metrics_dist\",\n        \"test_handler_rocauc\",\n        \"test_handler_rocauc_dist\",\n        \"test_handler_segmentation_saver\",\n        \"test_handler_smartcache\",\n        \"test_handler_stats\",\n        \"test_handler_surface_distance\",\n        \"test_handler_tb_image\",\n        \"test_handler_tb_stats\",\n        \"test_handler_validation\",\n        \"test_hausdorff_distance\",\n        \"test_header_correct\",\n        \"test_hilbert_transform\",\n        \"test_image_dataset\",\n        \"test_img2tensorboard\",\n        \"test_integration_fast_train\",\n        \"test_integration_segmentation_3d\",\n        \"test_integration_sliding_window\",\n        \"test_integration_unet_2d\",\n        \"test_integration_workflows\",\n        \"test_integration_workflows_gan\",\n        \"test_invertd\",\n        \"test_iterable_dataset\",\n        \"test_keep_largest_connected_component\",\n        \"test_keep_largest_connected_componentd\",\n        \"test_label_filter\",\n        \"test_lltm\",\n        \"test_lmdbdataset\",\n        \"test_lmdbdataset_dist\",\n        \"test_load_image\",\n        \"test_load_imaged\",\n        \"test_load_spacing_orientation\",\n        \"test_mednistdataset\",\n        \"test_milmodel\",\n        \"test_mlp\",\n        \"test_nifti_header_revise\",\n        \"test_nifti_rw\",\n        \"test_nifti_saver\",\n        \"test_occlusion_sensitivity\",\n        \"test_orientation\",\n        \"test_orientationd\",\n        \"test_parallel_execution\",\n        \"test_patchembedding\",\n        \"test_persistentdataset\",\n        \"test_pil_reader\",\n        \"test_plot_2d_or_3d_image\",\n        \"test_png_rw\",\n        \"test_png_saver\",\n        \"test_prepare_batch_default\",\n        \"test_prepare_batch_extra_input\",\n        \"test_rand_rotate\",\n        \"test_rand_rotated\",\n        \"test_rand_zoom\",\n        \"test_rand_zoomd\",\n        \"test_randtorchvisiond\",\n        \"test_resize\",\n        \"test_resized\",\n        \"test_rotate\",\n        \"test_rotated\",\n        \"test_save_image\",\n        \"test_save_imaged\",\n        \"test_selfattention\",\n        \"test_senet\",\n        \"test_smartcachedataset\",\n        \"test_spacing\",\n        \"test_spacingd\",\n        \"test_surface_distance\",\n        \"test_testtimeaugmentation\",\n        \"test_torchvision\",\n        \"test_torchvisiond\",\n        \"test_transchex\",\n        \"test_transformerblock\",\n        \"test_unetr\",\n        \"test_unetr_block\",\n        \"test_vit\",\n        \"test_vitautoenc\",\n        \"test_write_metrics_reports\",\n        \"test_wsireader\",\n        \"test_zoom\",\n        \"test_zoom_affine\",\n        \"test_zoomd\",\n    ]\n    # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/min_tests.py_run_testsuit.assert_sorted_exclude_cas_run_testsuit.return.test_suite": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/min_tests.py_run_testsuit.assert_sorted_exclude_cas_run_testsuit.return.test_suite", "embedding": null, "metadata": {"file_path": "tests/min_tests.py", "file_name": "min_tests.py", "file_type": "text/x-python", "category": "implementation", "start_line": 131, "end_line": 145, "span_ids": ["run_testsuit"], "tokens": 155}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def run_testsuit():\n    # ... other code\n    assert sorted(exclude_cases) == sorted(set(exclude_cases)), f\"Duplicated items in {exclude_cases}\"\n\n    files = glob.glob(os.path.join(os.path.dirname(__file__), \"test_*.py\"))\n\n    cases = []\n    for case in files:\n        test_module = os.path.basename(case)[:-3]\n        if test_module in exclude_cases:\n            exclude_cases.remove(test_module)\n            print(f\"skipping tests.{test_module}.\")\n        else:\n            cases.append(f\"tests.{test_module}\")\n    assert not exclude_cases, f\"items in exclude_cases not used: {exclude_cases}.\"\n    test_suite = unittest.TestLoader().loadTestsFromNames(cases)\n    return test_suite", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_add_coordinate_channels.py_TestAddCoordinateChannels_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_add_coordinate_channels.py_TestAddCoordinateChannels_", "embedding": null, "metadata": {"file_path": "tests/test_add_coordinate_channels.py", "file_name": "test_add_coordinate_channels.py", "file_type": "text/x-python", "category": "test", "start_line": 29, "end_line": 52, "span_ids": ["TestAddCoordinateChannels", "TestAddCoordinateChannels.test_channel_dim", "impl:7", "TestAddCoordinateChannels.test_shape", "TestAddCoordinateChannels.test_max_channel"], "tokens": 187}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestAddCoordinateChannels(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_shape(self, input_param, input, expected_shape):\n        result = AddCoordinateChannels(**input_param)(input)\n        self.assertEqual(type(result), type(input))\n        if isinstance(result, torch.Tensor):\n            self.assertEqual(result.device, input.device)\n        self.assertEqual(list(result.shape), list(expected_shape))\n        assert_allclose(input[0, ...], result[0, ...])\n\n    @parameterized.expand(TEST_CASES_ERROR_1)\n    def test_max_channel(self, input_param, input):\n        with self.assertRaises(ValueError):\n            AddCoordinateChannels(**input_param)(input)\n\n    @parameterized.expand(TEST_CASES_ERROR_2)\n    def test_channel_dim(self, input_param, input):\n        with self.assertRaises(ValueError):\n            AddCoordinateChannels(**input_param)(input)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_add_coordinate_channelsd.py_TestAddCoordinateChannels_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_add_coordinate_channelsd.py_TestAddCoordinateChannels_", "embedding": null, "metadata": {"file_path": "tests/test_add_coordinate_channelsd.py", "file_name": "test_add_coordinate_channelsd.py", "file_type": "text/x-python", "category": "test", "start_line": 46, "end_line": 70, "span_ids": ["TestAddCoordinateChannels", "TestAddCoordinateChannels.test_channel_dim", "impl:7", "TestAddCoordinateChannels.test_shape", "TestAddCoordinateChannels.test_max_channel"], "tokens": 197}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestAddCoordinateChannels(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_shape(self, input_param, input, expected_shape):\n        result = AddCoordinateChannelsd(**input_param)(input)[\"img\"]\n        input = input[\"img\"]\n        self.assertEqual(type(result), type(input))\n        if isinstance(result, torch.Tensor):\n            self.assertEqual(result.device, input.device)\n        self.assertEqual(result.shape, expected_shape)\n        assert_allclose(input[0, ...], result[0, ...])\n\n    @parameterized.expand(TEST_CASES_ERROR_1)\n    def test_max_channel(self, input_param, input):\n        with self.assertRaises(ValueError):\n            AddCoordinateChannelsd(**input_param)(input)\n\n    @parameterized.expand(TEST_CASES_ERROR_2)\n    def test_channel_dim(self, input_param, input):\n        with self.assertRaises(ValueError):\n            AddCoordinateChannelsd(**input_param)(input)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affine_transform.py_TestAffineTransform.test_ill_affine_transform.with_self_assertRaises_Ru_TestAffineTransform.test_ill_affine_transform.None_6.AffineTransform_1_2_i": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affine_transform.py_TestAffineTransform.test_ill_affine_transform.with_self_assertRaises_Ru_TestAffineTransform.test_ill_affine_transform.None_6.AffineTransform_1_2_i", "embedding": null, "metadata": {"file_path": "tests/test_affine_transform.py", "file_name": "test_affine_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 295, "end_line": 314, "span_ids": ["TestAffineTransform.test_ill_affine_transform"], "tokens": 437}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestAffineTransform(unittest.TestCase):\n\n    def test_ill_affine_transform(self):\n        # ... other code\n\n        with self.assertRaises(RuntimeError):  # input grid dtypes different\n            t = np.pi / 3\n            affine = [[1, 0, 0, 0], [0.0, np.cos(t), -np.sin(t), 0], [0, np.sin(t), np.cos(t), 0], [0, 0, 0, 1]]\n            affine = torch.as_tensor(affine, device=torch.device(\"cpu:0\"), dtype=torch.float32)\n            affine = affine.unsqueeze(0)\n            affine = affine.repeat(2, 1, 1)\n            image = torch.arange(48).view(2, 1, 4, 2, 3).to(device=torch.device(\"cpu:0\"), dtype=torch.int32)\n            xform = AffineTransform((2, 3, 4), padding_mode=\"border\", mode=\"bilinear\", normalized=True)\n            xform(image, affine)\n\n        with self.assertRaises(ValueError):  # wrong affine\n            affine = torch.as_tensor([[1, 0, 0, 0], [0, 0, 0, 1]])\n            image = torch.arange(48).view(2, 1, 4, 2, 3).to(device=torch.device(\"cpu:0\"))\n            xform = AffineTransform((2, 3, 4), padding_mode=\"border\", align_corners=False, mode=\"bilinear\")\n            xform(image, affine)\n\n        with self.assertRaises(RuntimeError):  # dtype doesn't match\n            affine = torch.as_tensor([[2.0, 0.0, 0.0], [0.0, 2.0, 0.0]], dtype=torch.float64)\n            image = torch.arange(1.0, 13.0).view(1, 1, 3, 4).to(device=torch.device(\"cpu:0\"))\n            AffineTransform((1, 2))(image, affine)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cachedataset.py_os_for_c_in_0_1_2_.if_sys_platform_not_in_.for_l_in_1_2_.TEST_DS_append_True_c_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cachedataset.py_os_for_c_in_0_1_2_.if_sys_platform_not_in_.for_l_in_1_2_.TEST_DS_append_True_c_", "embedding": null, "metadata": {"file_path": "tests/test_cachedataset.py", "file_name": "test_cachedataset.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 37, "span_ids": ["docstring"], "tokens": 227}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport sys\nimport tempfile\nimport unittest\n\nimport nibabel as nib\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.data import CacheDataset, DataLoader, PersistentDataset, SmartCacheDataset\nfrom monai.transforms import Compose, Lambda, LoadImaged, RandLambda, ThreadUnsafe, Transform\nfrom monai.utils.module import pytorch_after\n\nTEST_CASE_1 = [Compose([LoadImaged(keys=[\"image\", \"label\", \"extra\"])]), (128, 128, 128)]\n\nTEST_CASE_2 = [None, (128, 128, 128)]\n\n\nTEST_DS = []\nfor c in (0, 1, 2):\n    for l in (0, 1, 2):\n        TEST_DS.append([False, c, 0 if sys.platform in (\"darwin\", \"win32\") else l])\n    if sys.platform not in (\"darwin\", \"win32\"):\n        # persistent_workers need l > 0\n        for l in (1, 2):\n            TEST_DS.append([True, c, l])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cachedataset.py_TestCacheThread_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cachedataset.py_TestCacheThread_", "embedding": null, "metadata": {"file_path": "tests/test_cachedataset.py", "file_name": "test_cachedataset.py", "file_type": "text/x-python", "category": "test", "start_line": 128, "end_line": 201, "span_ids": ["TestCacheThread.test_thread_safe", "impl:13", "TestCacheThread"], "tokens": 585}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestCacheThread(unittest.TestCase):\n    \"\"\"\n    cache dataset and persistent dataset should behave in the same way when used with different loader settings.\n    loader's are tested with two epochs.\n    \"\"\"\n\n    @parameterized.expand(TEST_DS)\n    def test_thread_safe(self, persistent_workers, cache_workers, loader_workers):\n        expected = [102, 202, 302, 402, 502, 602, 702, 802, 902, 1002]\n        _kwg = {\"persistent_workers\": persistent_workers} if pytorch_after(1, 8) else {}\n        data_list = list(range(1, 11))\n        dataset = CacheDataset(\n            data=data_list, transform=_StatefulTransform(), cache_rate=1.0, num_workers=cache_workers, progress=False\n        )\n        self.assertListEqual(expected, list(dataset))\n        loader = DataLoader(\n            CacheDataset(\n                data=data_list,\n                transform=_StatefulTransform(),\n                cache_rate=1.0,\n                num_workers=cache_workers,\n                progress=False,\n            ),\n            batch_size=1,\n            num_workers=loader_workers,\n            **_kwg,\n        )\n        self.assertListEqual(expected, [y.item() for y in loader])\n        self.assertListEqual(expected, [y.item() for y in loader])\n\n        dataset = SmartCacheDataset(\n            data=data_list,\n            transform=_StatefulTransform(),\n            cache_rate=0.7,\n            replace_rate=0.5,\n            num_replace_workers=cache_workers,\n            progress=False,\n            shuffle=False,\n        )\n        self.assertListEqual(expected[:7], list(dataset))\n        loader = DataLoader(\n            SmartCacheDataset(\n                data=data_list,\n                transform=_StatefulTransform(),\n                cache_rate=0.7,\n                replace_rate=0.5,\n                num_replace_workers=cache_workers,\n                progress=False,\n                shuffle=False,\n            ),\n            batch_size=1,\n            num_workers=loader_workers,\n            **_kwg,\n        )\n        self.assertListEqual(expected[:7], [y.item() for y in loader])\n        self.assertListEqual(expected[:7], [y.item() for y in loader])\n\n        with tempfile.TemporaryDirectory() as tempdir:\n            pdata = PersistentDataset(data=data_list, transform=_StatefulTransform(), cache_dir=tempdir)\n            self.assertListEqual(expected, list(pdata))\n            loader = DataLoader(\n                PersistentDataset(data=data_list, transform=_StatefulTransform(), cache_dir=tempdir),\n                batch_size=1,\n                num_workers=loader_workers,\n                shuffle=False,\n                **_kwg,\n            )\n            self.assertListEqual(expected, [y.item() for y in loader])\n            self.assertListEqual(expected, [y.item() for y in loader])\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cachedataset_persistent_workers.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cachedataset_persistent_workers.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_cachedataset_persistent_workers.py", "file_name": "test_cachedataset_persistent_workers.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 41, "span_ids": ["impl", "TestTransformsWCacheDatasetAndPersistentWorkers.test_duplicate_transforms", "TestTransformsWCacheDatasetAndPersistentWorkers", "docstring"], "tokens": 300}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nfrom monai.data import CacheDataset, DataLoader, create_test_image_2d\nfrom monai.transforms import Compose, RandAffined, Spacingd\nfrom tests.utils import SkipIfBeforePyTorchVersion\n\n\n@SkipIfBeforePyTorchVersion((1, 7))\nclass TestTransformsWCacheDatasetAndPersistentWorkers(unittest.TestCase):\n    def test_duplicate_transforms(self):\n        data = [{\"img\": create_test_image_2d(128, 128, num_seg_classes=1, channel_dim=0)[0]} for _ in range(2)]\n\n        # at least 1 deterministic followed by at least 1 random\n        transform = Compose([Spacingd(\"img\", pixdim=(1, 1)), RandAffined(\"img\", prob=1.0)])\n\n        # cachedataset and data loader w persistent_workers\n        train_ds = CacheDataset(data, transform, cache_num=1)\n        # num_workers > 1 may fail randomly with 21.09 on A100 test node\n        # https://github.com/Project-MONAI/MONAI/issues/3283\n        train_loader = DataLoader(train_ds, num_workers=1, persistent_workers=True)\n\n        b1 = next(iter(train_loader))\n        b2 = next(iter(train_loader))\n\n        self.assertEqual(len(b1[\"img_transforms\"]), len(b2[\"img_transforms\"]))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_center_scale_crop.py_unittest_TEST_CASE_3._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_center_scale_crop.py_unittest_TEST_CASE_3._", "embedding": null, "metadata": {"file_path": "tests/test_center_scale_crop.py", "file_name": "test_center_scale_crop.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 34, "span_ids": ["docstring"], "tokens": 316}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import CenterScaleCrop\n\nTEST_CASE_0 = [{\"roi_scale\": [0.6, 0.3, -1]}, np.random.randint(0, 2, size=[3, 3, 3, 3]), (3, 2, 1, 3)]\n\nTEST_CASE_1 = [{\"roi_scale\": 0.6}, np.random.randint(0, 2, size=[3, 3, 3, 3]), (3, 2, 2, 2)]\n\nTEST_CASE_2 = [\n    {\"roi_scale\": [0.4, 0.4]},\n    np.array([[[0, 0, 0, 0, 0], [0, 1, 2, 1, 0], [0, 2, 3, 2, 0], [0, 1, 2, 1, 0], [0, 0, 0, 0, 0]]]),\n    np.array([[[1, 2], [2, 3]]]),\n]\n\nTEST_CASE_3 = [\n    {\"roi_scale\": 0.5},\n    torch.randint(0, 2, size=[3, 3, 3, 3], device=\"cuda\" if torch.cuda.is_available() else \"cpu\"),\n    (3, 2, 2, 2),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_center_scale_crop.py_TestCenterScaleCrop_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_center_scale_crop.py_TestCenterScaleCrop_", "embedding": null, "metadata": {"file_path": "tests/test_center_scale_crop.py", "file_name": "test_center_scale_crop.py", "file_type": "text/x-python", "category": "test", "start_line": 37, "end_line": 53, "span_ids": ["TestCenterScaleCrop.test_value", "impl:9", "TestCenterScaleCrop.test_shape", "TestCenterScaleCrop"], "tokens": 163}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestCenterScaleCrop(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_0, TEST_CASE_1, TEST_CASE_3])\n    def test_shape(self, input_param, input_data, expected_shape):\n        result = CenterScaleCrop(**input_param)(input_data)\n        self.assertEqual(isinstance(result, torch.Tensor), isinstance(input_data, torch.Tensor))\n        np.testing.assert_allclose(result.shape, expected_shape)\n\n    @parameterized.expand([TEST_CASE_2])\n    def test_value(self, input_param, input_data, expected_value):\n        result = CenterScaleCrop(**input_param)(input_data)\n        self.assertEqual(isinstance(result, torch.Tensor), isinstance(input_data, torch.Tensor))\n        np.testing.assert_allclose(result, expected_value)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_center_scale_cropd.py_TestCenterScaleCropd_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_center_scale_cropd.py_TestCenterScaleCropd_", "embedding": null, "metadata": {"file_path": "tests/test_center_scale_cropd.py", "file_name": "test_center_scale_cropd.py", "file_type": "text/x-python", "category": "test", "start_line": 43, "end_line": 57, "span_ids": ["impl:11", "TestCenterScaleCropd", "TestCenterScaleCropd.test_value", "TestCenterScaleCropd.test_shape"], "tokens": 150}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestCenterScaleCropd(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_0, TEST_CASE_1, TEST_CASE_3, TEST_CASE_4])\n    def test_shape(self, input_param, input_data, expected_shape):\n        result = CenterScaleCropd(**input_param)({\"img\": input_data})\n        np.testing.assert_allclose(result[\"img\"].shape, expected_shape)\n\n    @parameterized.expand([TEST_CASE_2])\n    def test_value(self, input_param, input_data, expected_value):\n        result = CenterScaleCropd(**input_param)({\"img\": input_data})\n        np.testing.assert_allclose(result[\"img\"], expected_value)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compose.py_TestCompose.test_non_dict_compose_with_unpack_TestCompose.test_list_non_dict_compose_with_unpack.self_assertEqual_c_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compose.py_TestCompose.test_non_dict_compose_with_unpack_TestCompose.test_list_non_dict_compose_with_unpack.self_assertEqual_c_", "embedding": null, "metadata": {"file_path": "tests/test_compose.py", "file_name": "test_compose.py", "file_type": "text/x-python", "category": "test", "start_line": 82, "end_line": 100, "span_ids": ["TestCompose.test_list_non_dict_compose_with_unpack", "TestCompose.test_non_dict_compose_with_unpack"], "tokens": 222}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestCompose(unittest.TestCase):\n\n    def test_non_dict_compose_with_unpack(self):\n        def a(i, i2):\n            return i + \"a\", i2 + \"a2\"\n\n        def b(i, i2):\n            return i + \"b\", i2 + \"b2\"\n\n        c = Compose([a, b, a, b], map_items=False, unpack_items=True)\n        self.assertEqual(c((\"\", \"\")), (\"abab\", \"a2b2a2b2\"))\n\n    def test_list_non_dict_compose_with_unpack(self):\n        def a(i, i2):\n            return i + \"a\", i2 + \"a2\"\n\n        def b(i, i2):\n            return i + \"b\", i2 + \"b2\"\n\n        c = Compose([a, b, a, b], unpack_items=True)\n        self.assertEqual(c([(\"\", \"\"), (\"t\", \"t\")]), [(\"abab\", \"a2b2a2b2\"), (\"tabab\", \"ta2b2a2b2\")])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compose.py_TestCompose.test_list_dict_compose_no_map_TestCompose.test_list_dict_compose_no_map.for_item_in_value_.self_assertDictEqual_item": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compose.py_TestCompose.test_list_dict_compose_no_map_TestCompose.test_list_dict_compose_no_map.for_item_in_value_.self_assertDictEqual_item", "embedding": null, "metadata": {"file_path": "tests/test_compose.py", "file_name": "test_compose.py", "file_type": "text/x-python", "category": "test", "start_line": 102, "end_line": 123, "span_ids": ["TestCompose.test_list_dict_compose_no_map"], "tokens": 211}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestCompose(unittest.TestCase):\n\n    def test_list_dict_compose_no_map(self):\n        def a(d):  # transform to handle dict data\n            d = dict(d)\n            d[\"a\"] += 1\n            return d\n\n        def b(d):  # transform to generate a batch list of data\n            d = dict(d)\n            d[\"b\"] += 1\n            d = [d] * 5\n            return d\n\n        def c(d):  # transform to handle dict data\n            d = [dict(di) for di in d]\n            for di in d:\n                di[\"c\"] += 1\n            return d\n\n        transforms = Compose([a, a, b, c, c], map_items=False)\n        value = transforms({\"a\": 0, \"b\": 0, \"c\": 0})\n        for item in value:\n            self.assertDictEqual(item, {\"a\": 2, \"b\": 1, \"c\": 2})", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_confusion_matrix.py_data_nan__2_test_metric_with_com": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_confusion_matrix.py_data_nan__2_test_metric_with_com", "embedding": null, "metadata": {"file_path": "tests/test_compute_confusion_matrix.py", "file_name": "test_compute_confusion_matrix.py", "file_type": "text/x-python", "category": "test", "start_line": 42, "end_line": 83, "span_ids": ["impl:5", "docstring"], "tokens": 763}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "data_nan: Dict[Any, Any] = {\n    # confusion matrix:[[[0,1,2,1],[1,1,1,1],[0,1,2,1]],\n    #                   [[0,0,0,4],[0,0,4,0],[0,4,0,0]],\n    #                   [[0,0,2,2],[0,0,2,2],[0,4,0,0]]]\n    \"y_pred\": torch.tensor(\n        [\n            [[[0.0, 1.0], [0.0, 0.0]], [[0.0, 0.0], [1.0, 1.0]], [[1.0, 0.0], [0.0, 0.0]]],\n            [[[0.0, 0.0], [0.0, 0.0]], [[0.0, 0.0], [0.0, 0.0]], [[1.0, 1.0], [1.0, 1.0]]],\n            [[[0.0, 0.0], [0.0, 0.0]], [[0.0, 0.0], [0.0, 0.0]], [[1.0, 1.0], [1.0, 1.0]]],\n        ]\n    ),\n    \"y\": torch.tensor(\n        [\n            [[[0.0, 0.0], [0.0, 1.0]], [[1.0, 0.0], [1.0, 0.0]], [[0.0, 1.0], [0.0, 0.0]]],\n            [[[1.0, 1.0], [1.0, 1.0]], [[0.0, 0.0], [0.0, 0.0]], [[0.0, 0.0], [0.0, 0.0]]],\n            [[[0.0, 1.0], [1.0, 0.0]], [[1.0, 0.0], [0.0, 1.0]], [[0.0, 0.0], [0.0, 0.0]]],\n        ]\n    ),\n}\n\ndata_clf: Dict[Any, Any] = {\n    \"y_pred\": torch.tensor([[1, 0, 0], [0, 0, 1]]),\n    \"y\": torch.tensor([[1, 0, 0], [0, 1, 0]]),\n    \"compute_sample\": False,\n    \"include_background\": True,\n    \"metric_name\": \"tpr\",\n    \"reduction\": \"mean_channel\",\n    \"get_not_nans\": True,\n}\n\n# 1. test confusion matrix\nTEST_CASE_CONFUSION_MATRIX = [\n    data.copy(),\n    torch.tensor(\n        [\n            [[0.0, 1.0, 2.0, 1.0], [1.0, 1.0, 1.0, 1.0], [0.0, 1.0, 2.0, 1.0]],\n            [[1.0, 0.0, 3.0, 0.0], [1.0, 0.0, 2.0, 1.0], [1.0, 1.0, 2.0, 0.0]],\n        ]\n    ),\n]\n\n# 2. test metric with compute_sample", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_confusion_matrix.py_TEST_CASES_COMPUTE_SAMPLE_result.None": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_confusion_matrix.py_TEST_CASES_COMPUTE_SAMPLE_result.None", "embedding": null, "metadata": {"file_path": "tests/test_compute_confusion_matrix.py", "file_name": "test_compute_confusion_matrix.py", "file_type": "text/x-python", "category": "test", "start_line": 84, "end_line": 143, "span_ids": ["impl:15", "impl:5"], "tokens": 626}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "TEST_CASES_COMPUTE_SAMPLE = []\nTEST_CASES_COMPUTE_SAMPLE_MULTI_METRICS = []\nresult_mean_batch = [\n    torch.tensor([0.5000, 0.5000, 0.5000]),\n    torch.tensor([0.1667, 0.2500, 0.3333]),\n    torch.tensor([0.8333, 0.7500, 0.6667]),\n    torch.tensor([0.5000, 0.7500, 0.2500]),\n    torch.tensor([0.8333, 0.5833, 0.8333]),\n    torch.tensor([0.5000, 0.5000, 0.5000]),\n    torch.tensor([0.5000, 0.2500, 0.7500]),\n    torch.tensor([0.1667, 0.4167, 0.1667]),\n    torch.tensor([0.5000, 0.0000, 0.6830]),\n    torch.tensor([0.5000, 0.4167, 0.2500]),\n    torch.tensor([0.7500, 0.6250, 0.6250]),\n    torch.tensor([0.6667, 0.6250, 0.5833]),\n    torch.tensor([0.5000, 0.5833, 0.3333]),\n    torch.tensor([0.3333, 0.2887, 0.1220]),\n    torch.tensor([0.5000, 0.6036, 0.3536]),\n    torch.tensor([0.3333, 0.2500, 0.1667]),\n    torch.tensor([0.3333, 0.3333, 0.0833]),\n]\nresult_mean = [\n    torch.tensor([0.5000]),\n    torch.tensor([0.2500]),\n    torch.tensor([0.7500]),\n    torch.tensor([0.5000]),\n    torch.tensor([0.7500]),\n    torch.tensor([0.5000]),\n    torch.tensor([0.5000]),\n    torch.tensor([0.2500]),\n    torch.tensor([0.5610]),\n    torch.tensor([0.3889]),\n    torch.tensor([0.6667]),\n    torch.tensor([0.6250]),\n    torch.tensor([0.4722]),\n    torch.tensor([0.2480]),\n    torch.tensor([0.4857]),\n    torch.tensor([0.2500]),\n    torch.tensor([0.2500]),\n]\nmetric_names = [\n    \"tpr\",\n    \"fpr\",\n    \"tnr\",\n    \"ppv\",\n    \"npv\",\n    \"fnr\",\n    \"fdr\",\n    \"for\",\n    \"pt\",\n    \"ts\",\n    \"acc\",\n    \"ba\",\n    \"f1\",\n    \"mcc\",\n    \"fm\",\n    \"bm\",\n    \"mk\",\n]\nresult: Any = None", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_confusion_matrix.py_for_idx_in_range_len_metr_TEST_CASES_CLF._data_clf_copy_result_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_confusion_matrix.py_for_idx_in_range_len_metr_TEST_CASES_CLF._data_clf_copy_result_", "embedding": null, "metadata": {"file_path": "tests/test_compute_confusion_matrix.py", "file_name": "test_compute_confusion_matrix.py", "file_type": "text/x-python", "category": "test", "start_line": 144, "end_line": 205, "span_ids": ["impl:15", "impl:54"], "tokens": 772}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "for idx in range(len(metric_names)):\n    for reduction in [\"mean\", \"mean_batch\"]:\n        TEST_CASE: List[Any] = [data.copy()]\n        TEST_CASE[0][\"compute_sample\"] = True\n        TEST_CASE[0][\"include_background\"] = True\n        TEST_CASE[0][\"metric_name\"] = metric_names[idx]\n        TEST_CASE[0][\"reduction\"] = reduction\n        TEST_CASE[0][\"get_not_nans\"] = True\n        if reduction == \"mean_batch\":\n            result = result_mean_batch[idx]\n        elif reduction == \"mean\":\n            result = result_mean[idx]\n        TEST_CASE.append(result)\n        TEST_CASES_COMPUTE_SAMPLE.append(TEST_CASE)\n\n# one input to compute multiple metrics\nfor reduction in [\"mean\", \"mean_batch\"]:\n    TEST_CASE_MULTIPLE: List[Any] = [data.copy()]\n    TEST_CASE_MULTIPLE[0][\"compute_sample\"] = True\n    TEST_CASE_MULTIPLE[0][\"include_background\"] = True\n    TEST_CASE_MULTIPLE[0][\"metric_name\"] = metric_names\n    TEST_CASE_MULTIPLE[0][\"reduction\"] = reduction\n    TEST_CASE_MULTIPLE[0][\"get_not_nans\"] = True\n    if reduction == \"mean_batch\":\n        result = result_mean_batch\n    elif reduction == \"mean\":\n        result = result_mean\n    TEST_CASE_MULTIPLE.append(result)\n    TEST_CASES_COMPUTE_SAMPLE_MULTI_METRICS.append(TEST_CASE_MULTIPLE)\n\n# 3. test metric with compute_sample, denominator may have zeros\nTEST_CASES_COMPUTE_SAMPLE_NAN = []\nmetric_names = [\"tpr\", \"tnr\"]\nresult_sum = [torch.tensor([0.5000]), torch.tensor([4.8333])]\nnot_nans_sum = [torch.tensor([6]), torch.tensor([8])]\nresult_sum_batch = [torch.tensor([0.0000, 0.5000, 0.0000]), torch.tensor([1.6667, 2.5000, 0.6667])]\nnot_nans_sum_batch = [torch.tensor([3.0, 2.0, 1.0]), torch.tensor([2.0, 3.0, 3.0])]\nfor idx in range(2):\n    for reduction in [\"sum\", \"sum_batch\"]:\n        TEST_CASE = [data_nan.copy()]\n        TEST_CASE[0][\"compute_sample\"] = True\n        TEST_CASE[0][\"include_background\"] = True\n        TEST_CASE[0][\"reduction\"] = reduction\n        TEST_CASE[0][\"metric_name\"] = metric_names[idx]\n        TEST_CASE[0][\"get_not_nans\"] = True\n        if reduction == \"sum\":\n            TEST_CASE.append(result_sum[idx])\n            TEST_CASE.append(not_nans_sum[idx])\n        elif reduction == \"sum_batch\":\n            TEST_CASE.append(result_sum_batch[idx])\n            TEST_CASE.append(not_nans_sum_batch[idx])\n        TEST_CASES_COMPUTE_SAMPLE_NAN.append(TEST_CASE)\n\n# 4. test classification task\nresult_clf = torch.tensor(\n    [\n        [[1.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 1.0, 0.0]],\n        [[0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 1.0], [0.0, 1.0, 0.0, 0.0]],\n    ]\n)\n\nTEST_CASES_CLF = [data_clf.copy(), result_clf]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_confusion_matrix.py_TestConfusionMatrix_TestConfusionMatrix.test_value.None_1": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_confusion_matrix.py_TestConfusionMatrix_TestConfusionMatrix.test_value.None_1", "embedding": null, "metadata": {"file_path": "tests/test_compute_confusion_matrix.py", "file_name": "test_compute_confusion_matrix.py", "file_type": "text/x-python", "category": "test", "start_line": 220, "end_line": 229, "span_ids": ["TestConfusionMatrix", "TestConfusionMatrix.test_value"], "tokens": 138}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestConfusionMatrix(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_CONFUSION_MATRIX])\n    def test_value(self, input_data, expected_value):\n        # include or ignore background\n        input_data[\"include_background\"] = True\n        result = get_confusion_matrix(**input_data)\n        np.testing.assert_allclose(result, expected_value, atol=1e-4, rtol=1e-4)\n        input_data[\"include_background\"] = False\n        result = get_confusion_matrix(**input_data)\n        np.testing.assert_allclose(result, expected_value[:, 1:, :], atol=1e-4, rtol=1e-4)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_confusion_matrix.py_TestConfusionMatrix.test_compute_sample_TestConfusionMatrix.test_compute_sample.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_confusion_matrix.py_TestConfusionMatrix.test_compute_sample_TestConfusionMatrix.test_compute_sample.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_compute_confusion_matrix.py", "file_name": "test_compute_confusion_matrix.py", "file_type": "text/x-python", "category": "test", "start_line": 231, "end_line": 240, "span_ids": ["TestConfusionMatrix.test_compute_sample"], "tokens": 119}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestConfusionMatrix(unittest.TestCase):\n\n    @parameterized.expand(TEST_CASES_COMPUTE_SAMPLE)\n    def test_compute_sample(self, input_data, expected_value):\n        params = input_data.copy()\n        vals = {}\n        vals[\"y_pred\"] = params.pop(\"y_pred\")\n        vals[\"y\"] = params.pop(\"y\")\n        metric = ConfusionMatrixMetric(**params)\n        metric(**vals)\n        result, _ = metric.aggregate()[0]\n        np.testing.assert_allclose(result, expected_value, atol=1e-4, rtol=1e-4)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_confusion_matrix.py_TestConfusionMatrix.test_compute_sample_multiple_metrics_TestConfusionMatrix.test_compute_sample_multiple_metrics.for_idx_in_range_len_resu.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_confusion_matrix.py_TestConfusionMatrix.test_compute_sample_multiple_metrics_TestConfusionMatrix.test_compute_sample_multiple_metrics.for_idx_in_range_len_resu.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_compute_confusion_matrix.py", "file_name": "test_compute_confusion_matrix.py", "file_type": "text/x-python", "category": "test", "start_line": 242, "end_line": 254, "span_ids": ["TestConfusionMatrix.test_compute_sample_multiple_metrics"], "tokens": 144}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestConfusionMatrix(unittest.TestCase):\n\n    @parameterized.expand(TEST_CASES_COMPUTE_SAMPLE_MULTI_METRICS)\n    def test_compute_sample_multiple_metrics(self, input_data, expected_values):\n        params = input_data.copy()\n        vals = {}\n        vals[\"y_pred\"] = params.pop(\"y_pred\")\n        vals[\"y\"] = params.pop(\"y\")\n        metric = ConfusionMatrixMetric(**params)\n        metric(**vals)\n        results = metric.aggregate()\n        for idx in range(len(results)):\n            result = results[idx][0]\n            expected_value = expected_values[idx]\n            np.testing.assert_allclose(result, expected_value, atol=1e-4, rtol=1e-4)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_confusion_matrix.py_TestConfusionMatrix.test_compute_sample_with_nan_TestConfusionMatrix.test_compute_sample_with_nan.None_2": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_confusion_matrix.py_TestConfusionMatrix.test_compute_sample_with_nan_TestConfusionMatrix.test_compute_sample_with_nan.None_2", "embedding": null, "metadata": {"file_path": "tests/test_compute_confusion_matrix.py", "file_name": "test_compute_confusion_matrix.py", "file_type": "text/x-python", "category": "test", "start_line": 256, "end_line": 266, "span_ids": ["TestConfusionMatrix.test_compute_sample_with_nan"], "tokens": 160}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestConfusionMatrix(unittest.TestCase):\n\n    @parameterized.expand(TEST_CASES_COMPUTE_SAMPLE_NAN)\n    def test_compute_sample_with_nan(self, input_data, expected_value, expected_not_nans):\n        params = input_data.copy()\n        vals = {}\n        vals[\"y_pred\"] = params.pop(\"y_pred\")\n        vals[\"y\"] = params.pop(\"y\")\n        metric = ConfusionMatrixMetric(**params)\n        metric(**vals)\n        result, not_nans = metric.aggregate()[0]\n        np.testing.assert_allclose(result, expected_value, atol=1e-4, rtol=1e-4)\n        np.testing.assert_allclose(not_nans, expected_not_nans, atol=1e-4, rtol=1e-4)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_confusion_matrix.py_TestConfusionMatrix.test_clf_with_nan_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_confusion_matrix.py_TestConfusionMatrix.test_clf_with_nan_", "embedding": null, "metadata": {"file_path": "tests/test_compute_confusion_matrix.py", "file_name": "test_compute_confusion_matrix.py", "file_type": "text/x-python", "category": "test", "start_line": 268, "end_line": 285, "span_ids": ["impl:86", "TestConfusionMatrix.test_clf_with_nan"], "tokens": 193}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestConfusionMatrix(unittest.TestCase):\n\n    @parameterized.expand([TEST_CASES_CLF])\n    def test_clf_with_nan(self, input_data, expected_value):\n        params = input_data.copy()\n        vals = {}\n        vals[\"y_pred\"] = params.pop(\"y_pred\")\n        vals[\"y\"] = params.pop(\"y\")\n        metric = ConfusionMatrixMetric(**params)\n        result = metric(**vals)\n        np.testing.assert_allclose(result, expected_value, atol=1e-4, rtol=1e-4)\n        result, _ = metric.aggregate()[0]\n        expected_value, _ = do_metric_reduction(expected_value, \"mean_channel\")\n        expected_value = compute_confusion_matrix_metric(\"tpr\", expected_value)\n        np.testing.assert_allclose(result, expected_value, atol=1e-4, rtol=1e-4)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_meandice.py_TEST_CASE_10_TestComputeMeanDice._DiceMetric_class_tests": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_meandice.py_TEST_CASE_10_TestComputeMeanDice._DiceMetric_class_tests", "embedding": null, "metadata": {"file_path": "tests/test_compute_meandice.py", "file_name": "test_compute_meandice.py", "file_type": "text/x-python", "category": "test", "start_line": 170, "end_line": 187, "span_ids": ["TestComputeMeanDice.test_value", "impl:17", "TestComputeMeanDice", "TestComputeMeanDice.test_nans"], "tokens": 218}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "TEST_CASE_10 = [\n    {\"y\": [torch.ones((2, 3, 3)), torch.ones((2, 3, 3))], \"y_pred\": [torch.ones((2, 3, 3)), torch.ones((2, 3, 3))]},\n    [[1.0000, 1.0000], [1.0000, 1.0000]],\n]\n\n\nclass TestComputeMeanDice(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_9])\n    def test_value(self, input_data, expected_value):\n        result = compute_meandice(**input_data)\n        np.testing.assert_allclose(result.cpu().numpy(), expected_value, atol=1e-4)\n\n    @parameterized.expand([TEST_CASE_3])\n    def test_nans(self, input_data, expected_value):\n        result = compute_meandice(**input_data)\n        self.assertTrue(np.allclose(np.isnan(result.cpu().numpy()), expected_value))\n\n    # DiceMetric class tests", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_regression_metrics.py_unittest_psnrmetric_np.return.np_mean_20_np_log10_max": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_regression_metrics.py_unittest_psnrmetric_np.return.np_mean_20_np_log10_max", "embedding": null, "metadata": {"file_path": "tests/test_compute_regression_metrics.py", "file_name": "test_compute_regression_metrics.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 42, "span_ids": ["msemetric_np", "flatten", "maemetric_np", "psnrmetric_np", "docstring", "rmsemetric_np"], "tokens": 236}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom functools import partial\n\nimport numpy as np\nimport torch\n\nfrom monai.metrics import MAEMetric, MSEMetric, PSNRMetric, RMSEMetric\nfrom monai.utils import set_determinism\n\n\n# define a numpy flatten function that only preserves batch dimension\ndef flatten(data):\n    return np.reshape(data, [data.shape[0], -1])\n\n\n# define metrics computation truth functions to check our monai metrics against\ndef msemetric_np(y_pred, y):\n    return np.mean((flatten(y_pred) - flatten(y)) ** 2)\n\n\ndef maemetric_np(y_pred, y):\n    return np.mean(np.abs(flatten(y_pred) - flatten(y)))\n\n\ndef rmsemetric_np(y_pred, y):\n    return np.mean(np.sqrt(np.mean((flatten(y_pred) - flatten(y)) ** 2, axis=1)))\n\n\ndef psnrmetric_np(max_val, y_pred, y):\n    mse = np.mean((flatten(y_pred) - flatten(y)) ** 2, axis=1)\n    return np.mean(20 * np.log10(max_val) - 10 * np.log10(mse))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_regression_metrics.py_TestRegressionMetrics_TestRegressionMetrics.test_shape_reduction.for_batch_in_batch_dims_.for_spatial_in_spatial_di.for_base_in_base_dims_.for_mt_fn_in_metrics_.None_7": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_regression_metrics.py_TestRegressionMetrics_TestRegressionMetrics.test_shape_reduction.for_batch_in_batch_dims_.for_spatial_in_spatial_di.for_base_in_base_dims_.for_mt_fn_in_metrics_.None_7", "embedding": null, "metadata": {"file_path": "tests/test_compute_regression_metrics.py", "file_name": "test_compute_regression_metrics.py", "file_type": "text/x-python", "category": "test", "start_line": 45, "end_line": 86, "span_ids": ["TestRegressionMetrics", "TestRegressionMetrics.test_shape_reduction"], "tokens": 385}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRegressionMetrics(unittest.TestCase):\n    def test_shape_reduction(self):\n        set_determinism(seed=123)\n        device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\n        # regression metrics to check\n        metrics = [MSEMetric, MAEMetric, RMSEMetric, partial(PSNRMetric, max_val=1.0)]\n\n        # define variations in batch/base_dims/spatial_dims\n        batch_dims = [1, 2, 4, 16]\n        base_dims = [16, 32, 64]\n        spatial_dims = [2, 3, 4]\n\n        # iterate over all variations and check shapes for different reduction functions\n        for batch in batch_dims:\n            for spatial in spatial_dims:\n                for base in base_dims:\n\n                    # create random tensors\n                    in_tensor = torch.rand((batch,) + (base,) * (spatial - 1)).to(device)\n\n                    # iterate over regression metrics, check shape for diff. reduction func\n                    for mt_fn in metrics:\n                        mt = mt_fn(reduction=\"mean\")\n                        mt(in_tensor, in_tensor)\n                        out_tensor = mt.aggregate()\n                        self.assertTrue(len(out_tensor.shape) == 1)\n\n                        mt = mt_fn(reduction=\"sum\")\n                        mt(in_tensor, in_tensor)\n                        out_tensor = mt.aggregate()\n                        self.assertTrue(len(out_tensor.shape) == 0)\n\n                        mt = mt_fn(reduction=\"mean_channel\")\n                        mt(in_tensor, in_tensor)\n                        out_tensor = mt.aggregate()\n                        self.assertTrue(len(out_tensor.shape) == 1 and out_tensor.shape[0] == batch)\n\n                        mt = mt_fn(reduction=\"sum_channel\")\n                        mt(in_tensor, in_tensor)\n                        out_tensor = mt.aggregate()\n                        self.assertTrue(len(out_tensor.shape) == 1 and out_tensor.shape[0] == batch)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_regression_metrics.py_TestRegressionMetrics.test_compare_numpy_TestRegressionMetrics.test_compare_numpy.for_batch_in_batch_dims_.for_spatial_in_spatial_di.for_base_in_base_dims_.for_mt_fn_mt_fn_np_in_zi.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_regression_metrics.py_TestRegressionMetrics.test_compare_numpy_TestRegressionMetrics.test_compare_numpy.for_batch_in_batch_dims_.for_spatial_in_spatial_di.for_base_in_base_dims_.for_mt_fn_mt_fn_np_in_zi.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_compute_regression_metrics.py", "file_name": "test_compute_regression_metrics.py", "file_type": "text/x-python", "category": "test", "start_line": 88, "end_line": 117, "span_ids": ["TestRegressionMetrics.test_compare_numpy"], "tokens": 356}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRegressionMetrics(unittest.TestCase):\n\n    def test_compare_numpy(self):\n        set_determinism(seed=123)\n        device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\n        # regression metrics to check + truth metric function in numpy\n        metrics = [MSEMetric, MAEMetric, RMSEMetric, partial(PSNRMetric, max_val=1.0)]\n        metrics_np = [msemetric_np, maemetric_np, rmsemetric_np, partial(psnrmetric_np, max_val=1.0)]\n\n        # define variations in batch/base_dims/spatial_dims\n        batch_dims = [1, 2, 4, 16]\n        base_dims = [16, 32, 64]\n        spatial_dims = [2, 3, 4]\n\n        # iterate over all variations and check shapes for different reduction functions\n        for batch in batch_dims:\n            for spatial in spatial_dims:\n                for base in base_dims:\n\n                    # create random tensors\n                    in_tensor_a = torch.rand((batch,) + (base,) * (spatial - 1)).to(device)\n                    in_tensor_b = torch.rand((batch,) + (base,) * (spatial - 1)).to(device)\n\n                    # check metrics\n                    for mt_fn, mt_fn_np in zip(metrics, metrics_np):\n                        mt = mt_fn(reduction=\"mean\")\n                        mt(y_pred=in_tensor_a, y=in_tensor_b)\n                        out_tensor = mt.aggregate()\n                        out_np = mt_fn_np(y_pred=in_tensor_a.cpu().numpy(), y=in_tensor_b.cpu().numpy())\n\n                        np.testing.assert_allclose(out_tensor.cpu().numpy(), out_np, atol=1e-4)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_regression_metrics.py_TestRegressionMetrics.test_ill_shape_TestRegressionMetrics.test_ill_shape.None_1.for_mt_fn_in_metrics_.mt_fn_y_pred_in_tensor_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_regression_metrics.py_TestRegressionMetrics.test_ill_shape_TestRegressionMetrics.test_ill_shape.None_1.for_mt_fn_in_metrics_.mt_fn_y_pred_in_tensor_", "embedding": null, "metadata": {"file_path": "tests/test_compute_regression_metrics.py", "file_name": "test_compute_regression_metrics.py", "file_type": "text/x-python", "category": "test", "start_line": 119, "end_line": 138, "span_ids": ["TestRegressionMetrics.test_ill_shape"], "tokens": 201}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRegressionMetrics(unittest.TestCase):\n\n    def test_ill_shape(self):\n        set_determinism(seed=123)\n        device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\n        # regression metrics to check + truth metric function in numpy\n        metrics = [MSEMetric, MAEMetric, RMSEMetric, partial(PSNRMetric, max_val=1.0)]\n        basedim = 10\n\n        # too small shape\n        with self.assertRaises(ValueError):\n            in_tensor = torch.rand((basedim,)).to(device)\n            for mt_fn in metrics:\n                mt_fn()(in_tensor, in_tensor)\n\n        # different shape for pred/target\n        with self.assertRaises(ValueError):\n            in_tensor_a = torch.rand((basedim,)).to(device)\n            in_tensor_b = torch.rand((basedim, basedim)).to(device)\n            for mt_fn in metrics:\n                mt_fn()(y_pred=in_tensor_a, y=in_tensor_b)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_regression_metrics.py_TestRegressionMetrics.test_same_input_TestRegressionMetrics.test_same_input.for_batch_in_batch_dims_.for_spatial_in_spatial_di.for_base_in_base_dims_.for_mt_fn_rs_in_zip_metr.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_regression_metrics.py_TestRegressionMetrics.test_same_input_TestRegressionMetrics.test_same_input.for_batch_in_batch_dims_.for_spatial_in_spatial_di.for_base_in_base_dims_.for_mt_fn_rs_in_zip_metr.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_compute_regression_metrics.py", "file_name": "test_compute_regression_metrics.py", "file_type": "text/x-python", "category": "test", "start_line": 140, "end_line": 164, "span_ids": ["TestRegressionMetrics.test_same_input"], "tokens": 271}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRegressionMetrics(unittest.TestCase):\n\n    def test_same_input(self):\n        set_determinism(seed=123)\n        device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n        metrics = [MSEMetric, MAEMetric, RMSEMetric, partial(PSNRMetric, max_val=1.0)]\n        results = [0.0, 0.0, 0.0, float(\"inf\")]\n\n        # define variations in batch/base_dims/spatial_dims\n        batch_dims = [1, 2, 4, 16]\n        base_dims = [16, 32, 64]\n        spatial_dims = [2, 3, 4]\n\n        # iterate over all variations and check shapes for different reduction functions\n        for batch in batch_dims:\n            for spatial in spatial_dims:\n                for base in base_dims:\n\n                    # create random tensors\n                    in_tensor = torch.rand((batch,) + (base,) * (spatial - 1)).to(device)\n\n                    # check metrics\n                    for mt_fn, rs in zip(metrics, results):\n                        mt = mt_fn(reduction=\"mean\")\n                        mt(in_tensor, in_tensor)\n                        out_tensor = mt.aggregate()\n                        np.testing.assert_allclose(out_tensor.cpu(), rs, atol=1e-4)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_regression_metrics.py_TestRegressionMetrics.test_diff_input_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_regression_metrics.py_TestRegressionMetrics.test_diff_input_", "embedding": null, "metadata": {"file_path": "tests/test_compute_regression_metrics.py", "file_name": "test_compute_regression_metrics.py", "file_type": "text/x-python", "category": "test", "start_line": 166, "end_line": 196, "span_ids": ["TestRegressionMetrics.test_diff_input", "impl"], "tokens": 312}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRegressionMetrics(unittest.TestCase):\n\n    def test_diff_input(self):\n        set_determinism(seed=123)\n        device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n        metrics = [MSEMetric, MAEMetric, RMSEMetric, partial(PSNRMetric, max_val=1.0)]\n        results = [1.0, 1.0, 1.0, 0.0]\n\n        # define variations in batch/base_dims/spatial_dims\n        batch_dims = [1, 2, 4, 16]\n        base_dims = [16, 32, 64]\n        spatial_dims = [2, 3, 4]\n\n        # iterate over all variations and check shapes for different reduction functions\n        for batch in batch_dims:\n            for spatial in spatial_dims:\n                for base in base_dims:\n\n                    # create random tensors\n                    in_tensor_a = torch.zeros((batch,) + (base,) * (spatial - 1)).to(device)\n                    in_tensor_b = torch.ones((batch,) + (base,) * (spatial - 1)).to(device)\n\n                    # check metrics\n                    for mt_fn, rs in zip(metrics, results):\n                        mt = mt_fn(reduction=\"mean\")\n                        mt(in_tensor_a, in_tensor_b)\n                        out_tensor = mt.aggregate()\n                        np.testing.assert_allclose(out_tensor.cpu(), rs, atol=1e-4)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_roc_auc.py_TestComputeROCAUC_TestComputeROCAUC.test_value.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_roc_auc.py_TestComputeROCAUC_TestComputeROCAUC.test_value.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_compute_roc_auc.py", "file_name": "test_compute_roc_auc.py", "file_type": "text/x-python", "category": "test", "start_line": 72, "end_line": 80, "span_ids": ["TestComputeROCAUC.test_value", "TestComputeROCAUC"], "tokens": 198}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestComputeROCAUC(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5, TEST_CASE_6, TEST_CASE_7])\n    def test_value(self, y_pred, y, softmax, to_onehot, average, expected_value):\n        y_pred_trans = Compose([ToTensor(), Activations(softmax=softmax)])\n        y_trans = Compose([ToTensor(), AsDiscrete(to_onehot=to_onehot)])\n        y_pred = torch.stack([y_pred_trans(i) for i in decollate_batch(y_pred)], dim=0)\n        y = torch.stack([y_trans(i) for i in decollate_batch(y)], dim=0)\n        result = compute_roc_auc(y_pred=y_pred, y=y, average=average)\n        np.testing.assert_allclose(expected_value, result, rtol=1e-5)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_roc_auc.py_TestComputeROCAUC.test_class_value_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compute_roc_auc.py_TestComputeROCAUC.test_class_value_", "embedding": null, "metadata": {"file_path": "tests/test_compute_roc_auc.py", "file_name": "test_compute_roc_auc.py", "file_type": "text/x-python", "category": "test", "start_line": 82, "end_line": 97, "span_ids": ["TestComputeROCAUC.test_class_value", "impl:15"], "tokens": 212}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestComputeROCAUC(unittest.TestCase):\n\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5, TEST_CASE_6, TEST_CASE_7])\n    def test_class_value(self, y_pred, y, softmax, to_onehot, average, expected_value):\n        y_pred_trans = Compose([ToTensor(), Activations(softmax=softmax)])\n        y_trans = Compose([ToTensor(), AsDiscrete(to_onehot=to_onehot)])\n        y_pred = [y_pred_trans(i) for i in decollate_batch(y_pred)]\n        y = [y_trans(i) for i in decollate_batch(y)]\n        metric = ROCAUCMetric(average=average)\n        metric(y_pred=y_pred, y=y)\n        result = metric.aggregate()\n        metric.reset()\n        np.testing.assert_allclose(expected_value, result, rtol=1e-5)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_copy_itemsd.py_TestCopyItemsd_TestCopyItemsd.test_numpy_values.None_1": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_copy_itemsd.py_TestCopyItemsd_TestCopyItemsd.test_numpy_values.None_1", "embedding": null, "metadata": {"file_path": "tests/test_copy_itemsd.py", "file_name": "test_copy_itemsd.py", "file_type": "text/x-python", "category": "test", "start_line": 31, "end_line": 40, "span_ids": ["TestCopyItemsd", "TestCopyItemsd.test_numpy_values"], "tokens": 186}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestCopyItemsd(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4])\n    def test_numpy_values(self, keys, times, names):\n        input_data = {\"img\": np.array([[0, 1], [1, 2]]), \"seg\": np.array([[3, 4], [4, 5]])}\n        result = CopyItemsd(keys=keys, times=times, names=names)(input_data)\n        for name in ensure_tuple(names):\n            self.assertTrue(name in result)\n        result[\"img_1\"] += 1\n        np.testing.assert_allclose(result[\"img_1\"], np.array([[1, 2], [2, 3]]))\n        np.testing.assert_allclose(result[\"img\"], np.array([[0, 1], [1, 2]]))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_copy_model_state.py_TestModuleState_TestModuleState.test_set_state.self_assertEqual_len_unch": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_copy_model_state.py_TestModuleState_TestModuleState.test_set_state.self_assertEqual_len_unch", "embedding": null, "metadata": {"file_path": "tests/test_copy_model_state.py", "file_name": "test_copy_model_state.py", "file_type": "text/x-python", "category": "test", "start_line": 55, "end_line": 80, "span_ids": ["TestModuleState", "TestModuleState.tearDown", "TestModuleState.test_set_state"], "tokens": 300}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestModuleState(unittest.TestCase):\n    def tearDown(self):\n        set_determinism(None)\n\n    @parameterized.expand(TEST_CASES)\n    def test_set_state(self, device_0, device_1):\n        set_determinism(0)\n        model_one = _TestModelOne(10, 20, 3)\n        model_two = _TestModelTwo(10, 20, 10, 4)\n        model_one.to(device_0)\n        model_two.to(device_1)\n        model_dict, ch, unch = copy_model_state(model_one, model_two)\n        x = np.random.randn(4, 10)\n        x = torch.tensor(x, device=device_0, dtype=torch.float32)\n        output = model_one(x).detach().cpu().numpy()\n        expected = np.array(\n            [\n                [-0.36076584, -0.03177825, -0.7702266],\n                [-0.0526831, -0.15855855, -0.01149344],\n                [-0.3760508, -0.22485238, -0.0634037],\n                [0.5977675, -0.67991066, 0.1919502],\n            ]\n        )\n        np.testing.assert_allclose(output, expected, atol=1e-3)\n        self.assertEqual(len(ch), 2)\n        self.assertEqual(len(unch), 2)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_copy_model_state.py_TestModuleState.test_set_full_state_TestModuleState.test_set_full_state.self_assertEqual_len_unch": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_copy_model_state.py_TestModuleState.test_set_full_state_TestModuleState.test_set_full_state.self_assertEqual_len_unch", "embedding": null, "metadata": {"file_path": "tests/test_copy_model_state.py", "file_name": "test_copy_model_state.py", "file_type": "text/x-python", "category": "test", "start_line": 82, "end_line": 100, "span_ids": ["TestModuleState.test_set_full_state"], "tokens": 241}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestModuleState(unittest.TestCase):\n\n    @parameterized.expand(TEST_CASES)\n    def test_set_full_state(self, device_0, device_1):\n        set_determinism(0)\n        model_one = _TestModelOne(10, 20, 3)\n        model_two = _TestModelOne(10, 20, 3)\n        model_one.to(device_0)\n        model_two.to(device_1)\n        # test module input\n        model_dict, ch, unch = copy_model_state(model_one, model_two)\n        # test dict input\n        model_dict, ch, unch = copy_model_state(model_dict, model_two)\n        x = np.random.randn(4, 10)\n        x = torch.tensor(x, device=device_0, dtype=torch.float32)\n        output = model_one(x).detach().cpu().numpy()\n        model_two.to(device_0)\n        output_1 = model_two(x).detach().cpu().numpy()\n        np.testing.assert_allclose(output, output_1, atol=1e-3)\n        self.assertEqual(len(ch), 4)\n        self.assertEqual(len(unch), 0)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_copy_model_state.py_TestModuleState.test_set_exclude_vars_TestModuleState.test_set_exclude_vars.self_assertEqual_len_unch": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_copy_model_state.py_TestModuleState.test_set_exclude_vars_TestModuleState.test_set_exclude_vars.self_assertEqual_len_unch", "embedding": null, "metadata": {"file_path": "tests/test_copy_model_state.py", "file_name": "test_copy_model_state.py", "file_type": "text/x-python", "category": "test", "start_line": 102, "end_line": 124, "span_ids": ["TestModuleState.test_set_exclude_vars"], "tokens": 302}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestModuleState(unittest.TestCase):\n\n    @parameterized.expand(TEST_CASES)\n    def test_set_exclude_vars(self, device_0, device_1):\n        set_determinism(0)\n        model_one = _TestModelOne(10, 20, 3)\n        model_two = _TestModelTwo(10, 20, 10, 4)\n        model_one.to(device_0)\n        model_two.to(device_1)\n        # test skip layer.bias\n        model_dict, ch, unch = copy_model_state(model_one, model_two, exclude_vars=\"layer.bias\")\n        x = np.random.randn(4, 10)\n        x = torch.tensor(x, device=device_0, dtype=torch.float32)\n        output = model_one(x).detach().cpu().numpy()\n        expected = np.array(\n            [\n                [-0.34172416, 0.0375042, -0.98340976],\n                [-0.03364138, -0.08927619, -0.2246768],\n                [-0.35700908, -0.15556987, -0.27658707],\n                [0.61680925, -0.6106281, -0.02123314],\n            ]\n        )\n        np.testing.assert_allclose(output, expected, atol=1e-3)\n        self.assertEqual(len(ch), 1)\n        self.assertEqual(len(unch), 3)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_copy_model_state.py_TestModuleState.test_set_map_across_TestModuleState.test_set_map_across.self_assertEqual_len_unch": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_copy_model_state.py_TestModuleState.test_set_map_across_TestModuleState.test_set_map_across.self_assertEqual_len_unch", "embedding": null, "metadata": {"file_path": "tests/test_copy_model_state.py", "file_name": "test_copy_model_state.py", "file_type": "text/x-python", "category": "test", "start_line": 126, "end_line": 151, "span_ids": ["TestModuleState.test_set_map_across"], "tokens": 332}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestModuleState(unittest.TestCase):\n\n    @parameterized.expand(TEST_CASES)\n    def test_set_map_across(self, device_0, device_1):\n        set_determinism(0)\n        model_one = _TestModelOne(10, 10, 3)\n        model_two = _TestModelTwo(10, 10, 10, 4)\n        model_one.to(device_0)\n        model_two.to(device_1)\n        # test weight map\n        model_dict, ch, unch = copy_model_state(\n            model_one, model_two, mapping={\"layer_1.weight\": \"layer.weight\", \"layer_1.bias\": \"layer_1.weight\"}\n        )\n        model_one.load_state_dict(model_dict)\n        x = np.random.randn(4, 10)\n        x = torch.tensor(x, device=device_0, dtype=torch.float32)\n        output = model_one(x).detach().cpu().numpy()\n        expected = np.array(\n            [\n                [0.8244487, -0.19650555, 0.65723234],\n                [0.71239626, 0.25617486, 0.5247122],\n                [0.24168758, 1.0301148, 0.39089814],\n                [0.25791705, 0.8653245, 0.14833644],\n            ]\n        )\n        np.testing.assert_allclose(output, expected, atol=1e-3)\n        self.assertEqual(len(ch), 2)\n        self.assertEqual(len(unch), 2)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_copy_model_state.py_TestModuleState.test_set_prefix_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_copy_model_state.py_TestModuleState.test_set_prefix_", "embedding": null, "metadata": {"file_path": "tests/test_copy_model_state.py", "file_name": "test_copy_model_state.py", "file_type": "text/x-python", "category": "test", "start_line": 153, "end_line": 183, "span_ids": ["TestModuleState.test_set_prefix", "impl:8"], "tokens": 325}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestModuleState(unittest.TestCase):\n\n    @parameterized.expand(TEST_CASES)\n    def test_set_prefix(self, device_0, device_1):\n        set_determinism(0)\n        model_one = torch.nn.Sequential(_TestModelOne(10, 20, 3))\n        model_two = _TestModelTwo(10, 20, 10, 4)\n        model_one.to(device_0)\n        model_two.to(device_1)\n        # test skip layer.bias\n        model_dict, ch, unch = copy_model_state(\n            model_one, model_two, dst_prefix=\"0.\", exclude_vars=\"layer.bias\", inplace=False\n        )\n        model_one.load_state_dict(model_dict)\n        x = np.random.randn(4, 10)\n        x = torch.tensor(x, device=device_0, dtype=torch.float32)\n        output = model_one(x).detach().cpu().numpy()\n        expected = np.array(\n            [\n                [-0.360766, -0.031778, -0.770227],\n                [-0.052683, -0.158559, -0.011493],\n                [-0.376051, -0.224852, -0.063404],\n                [0.597767, -0.679911, 0.19195],\n            ]\n        )\n        np.testing.assert_allclose(output, expected, atol=1e-3)\n        self.assertEqual(len(ch), 2)\n        self.assertEqual(len(unch), 2)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_csv_dataset.py_os_TestCSVDataset.test_values.with_tempfile_TemporaryDi._test_group_columns": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_csv_dataset.py_os_TestCSVDataset.test_values.with_tempfile_TemporaryDi._test_group_columns", "embedding": null, "metadata": {"file_path": "tests/test_csv_dataset.py", "file_name": "test_csv_dataset.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 123, "span_ids": ["TestCSVDataset.test_values", "TestCSVDataset", "docstring"], "tokens": 1499}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport tempfile\nimport unittest\n\nimport numpy as np\nimport pandas as pd\n\nfrom monai.data import CSVDataset\nfrom monai.transforms import ToNumpyd\n\n\nclass TestCSVDataset(unittest.TestCase):\n    def test_values(self):\n        with tempfile.TemporaryDirectory() as tempdir:\n            test_data1 = [\n                [\"subject_id\", \"label\", \"image\", \"ehr_0\", \"ehr_1\", \"ehr_2\"],\n                [\"s000000\", 5, \"./imgs/s000000.png\", 2.007843256, 2.29019618, 2.054902077],\n                [\"s000001\", 0, \"./imgs/s000001.png\", 6.839215755, 6.474509716, 5.862744808],\n                [\"s000002\", 4, \"./imgs/s000002.png\", 3.772548914, 4.211764812, 4.635294437],\n                [\"s000003\", 1, \"./imgs/s000003.png\", 3.333333254, 3.235294342, 3.400000095],\n                [\"s000004\", 9, \"./imgs/s000004.png\", 6.427451134, 6.254901886, 5.976470947],\n            ]\n            test_data2 = [\n                [\"subject_id\", \"ehr_3\", \"ehr_4\", \"ehr_5\", \"ehr_6\", \"ehr_7\", \"ehr_8\"],\n                [\"s000000\", 3.019608021, 3.807843208, 3.584313869, 3.141176462, 3.1960783, 4.211764812],\n                [\"s000001\", 5.192157269, 5.274509907, 5.250980377, 4.647058964, 4.886274338, 4.392156601],\n                [\"s000002\", 5.298039436, 9.545097351, 12.57254887, 6.799999714, 2.1960783, 1.882352948],\n                [\"s000003\", 3.164705753, 3.086274624, 3.725490093, 3.698039293, 3.698039055, 3.701960802],\n                [\"s000004\", 6.26274538, 7.717647076, 9.584313393, 6.082352638, 2.662744999, 2.34117651],\n            ]\n            test_data3 = [\n                [\"subject_id\", \"ehr_9\", \"ehr_10\", \"meta_0\", \"meta_1\", \"meta_2\"],\n                [\"s000000\", 6.301961422, 6.470588684, \"TRUE\", \"TRUE\", \"TRUE\"],\n                [\"s000001\", 5.219608307, 7.827450752, \"FALSE\", \"TRUE\", \"FALSE\"],\n                [\"s000002\", 1.882352948, 2.031372547, \"TRUE\", \"FALSE\", \"TRUE\"],\n                [\"s000003\", 3.309803963, 3.729412079, \"FALSE\", \"FALSE\", \"TRUE\"],\n                [\"s000004\", 2.062745094, 2.34117651, \"FALSE\", \"TRUE\", \"TRUE\"],\n                # generate NaN values in the row\n                [\"s000005\", 3.353655643, 1.675674543, \"TRUE\", \"TRUE\", \"FALSE\"],\n            ]\n\n            def prepare_csv_file(data, filepath):\n                with open(filepath, \"a\") as f:\n                    for d in data:\n                        f.write((\",\".join([str(i) for i in d])) + \"\\n\")\n\n            filepath1 = os.path.join(tempdir, \"test_data1.csv\")\n            filepath2 = os.path.join(tempdir, \"test_data2.csv\")\n            filepath3 = os.path.join(tempdir, \"test_data3.csv\")\n            filepaths = [filepath1, filepath2, filepath3]\n            prepare_csv_file(test_data1, filepath1)\n            prepare_csv_file(test_data2, filepath2)\n            prepare_csv_file(test_data3, filepath3)\n\n            # test single CSV file\n            dataset = CSVDataset(filepath1)\n            self.assertDictEqual(\n                {k: round(v, 4) if not isinstance(v, str) else v for k, v in dataset[2].items()},\n                {\n                    \"subject_id\": \"s000002\",\n                    \"label\": 4,\n                    \"image\": \"./imgs/s000002.png\",\n                    \"ehr_0\": 3.7725,\n                    \"ehr_1\": 4.2118,\n                    \"ehr_2\": 4.6353,\n                },\n            )\n\n            # test multiple CSV files, join tables with kwargs\n            dataset = CSVDataset(filepaths, on=\"subject_id\")\n            self.assertDictEqual(\n                {k: round(v, 4) if not isinstance(v, (str, np.bool_)) else v for k, v in dataset[3].items()},\n                {\n                    \"subject_id\": \"s000003\",\n                    \"label\": 1,\n                    \"image\": \"./imgs/s000003.png\",\n                    \"ehr_0\": 3.3333,\n                    \"ehr_1\": 3.2353,\n                    \"ehr_2\": 3.4000,\n                    \"ehr_3\": 3.1647,\n                    \"ehr_4\": 3.0863,\n                    \"ehr_5\": 3.7255,\n                    \"ehr_6\": 3.6980,\n                    \"ehr_7\": 3.6980,\n                    \"ehr_8\": 3.7020,\n                    \"ehr_9\": 3.3098,\n                    \"ehr_10\": 3.7294,\n                    \"meta_0\": False,\n                    \"meta_1\": False,\n                    \"meta_2\": True,\n                },\n            )\n\n            # test selected rows and columns\n            dataset = CSVDataset(\n                src=filepaths,\n                row_indices=[[0, 2], 3],  # load row: 0, 1, 3\n                col_names=[\"subject_id\", \"image\", \"ehr_1\", \"ehr_7\", \"meta_1\"],\n            )\n            self.assertEqual(len(dataset), 3)\n            self.assertDictEqual(\n                {k: round(v, 4) if not isinstance(v, (str, np.bool_)) else v for k, v in dataset[-1].items()},\n                {\n                    \"subject_id\": \"s000003\",\n                    \"image\": \"./imgs/s000003.png\",\n                    \"ehr_1\": 3.2353,\n                    \"ehr_7\": 3.6980,\n                    \"meta_1\": False,\n                },\n            )\n\n            # test group columns\n            # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dataloader.py_TestDataLoader_TestDataLoader.test_exception.with_self_assertRaisesReg.for___in_dataloader_.pass": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dataloader.py_TestDataLoader_TestDataLoader.test_exception.with_self_assertRaisesReg.for___in_dataloader_.pass", "embedding": null, "metadata": {"file_path": "tests/test_dataloader.py", "file_name": "test_dataloader.py", "file_type": "text/x-python", "category": "test", "start_line": 38, "end_line": 65, "span_ids": ["TestDataLoader", "TestDataLoader.test_values", "TestDataLoader.test_exception"], "tokens": 356}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestDataLoader(unittest.TestCase):\n    def test_values(self):\n        datalist = [\n            {\"image\": \"spleen_19.nii.gz\", \"label\": \"spleen_label_19.nii.gz\"},\n            {\"image\": \"spleen_31.nii.gz\", \"label\": \"spleen_label_31.nii.gz\"},\n        ]\n        transform = Compose(\n            [\n                DataStatsd(keys=[\"image\", \"label\"], data_shape=False, value_range=False, data_value=True),\n                SimulateDelayd(keys=[\"image\", \"label\"], delay_time=0.1),\n            ]\n        )\n        dataset = CacheDataset(data=datalist, transform=transform, cache_rate=0.5, cache_num=1)\n        n_workers = 0 if sys.platform == \"win32\" else 2\n        dataloader = DataLoader(dataset=dataset, batch_size=2, num_workers=n_workers)\n        for d in dataloader:\n            self.assertEqual(d[\"image\"][0], \"spleen_19.nii.gz\")\n            self.assertEqual(d[\"image\"][1], \"spleen_31.nii.gz\")\n            self.assertEqual(d[\"label\"][0], \"spleen_label_19.nii.gz\")\n            self.assertEqual(d[\"label\"][1], \"spleen_label_31.nii.gz\")\n\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2])\n    def test_exception(self, datalist):\n        dataset = Dataset(data=datalist, transform=None)\n        dataloader = DataLoader(dataset=dataset, batch_size=2, num_workers=0)\n        with self.assertRaisesRegex((TypeError, RuntimeError), \"Collate error on the key\"):\n            for _ in dataloader:\n                pass", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dataloader.py__RandomDataset_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dataloader.py__RandomDataset_", "embedding": null, "metadata": {"file_path": "tests/test_dataloader.py", "file_name": "test_dataloader.py", "file_type": "text/x-python", "category": "test", "start_line": 68, "end_line": 99, "span_ids": ["_RandomDataset.__len__", "TestLoaderRandom.setUp", "TestLoaderRandom.test_randomize", "TestLoaderRandom", "_RandomDataset.__getitem__", "impl:5", "_RandomDataset", "TestLoaderRandom.tearDown"], "tokens": 226}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class _RandomDataset(torch.utils.data.Dataset, Randomizable):\n    def __getitem__(self, index):\n        return self.R.randint(0, 1000, (1,))\n\n    def __len__(self):\n        return 8\n\n\nclass TestLoaderRandom(unittest.TestCase):\n    \"\"\"\n    Testing data loader working with the randomizable interface\n    \"\"\"\n\n    def setUp(self):\n        set_determinism(0)\n\n    def tearDown(self):\n        set_determinism(None)\n\n    def test_randomize(self):\n        dataset = _RandomDataset()\n        dataloader = DataLoader(dataset, batch_size=2, num_workers=3)\n        output = []\n        for _ in range(2):\n            for batch in dataloader:\n                output.extend(batch.data.numpy().flatten().tolist())\n        self.assertListEqual(output, [594, 170, 524, 778, 370, 906, 292, 589, 762, 763, 156, 886, 42, 405, 221, 166])\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_decollate.py_sys__ListCompose.__call__.return.img_metadata": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_decollate.py_sys__ListCompose.__call__.return.img_metadata", "embedding": null, "metadata": {"file_path": "tests/test_decollate.py", "file_name": "test_decollate.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 87, "span_ids": ["_ListCompose.__call__", "_ListCompose", "docstring"], "tokens": 707}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import sys\nimport unittest\nfrom enum import Enum\nfrom typing import List, Tuple\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.data import CacheDataset, DataLoader, Dataset, create_test_image_2d\nfrom monai.data.utils import decollate_batch\nfrom monai.transforms import (\n    AddChannel,\n    AddChanneld,\n    Compose,\n    LoadImage,\n    LoadImaged,\n    RandAffine,\n    RandFlip,\n    RandFlipd,\n    RandRotate90,\n    SpatialPad,\n    SpatialPadd,\n    ToTensor,\n    ToTensord,\n)\nfrom monai.transforms.inverse_batch_transform import Decollated\nfrom monai.transforms.spatial.dictionary import RandAffined, RandRotate90d\nfrom monai.utils import optional_import, set_determinism\nfrom monai.utils.enums import TraceKeys\nfrom tests.utils import make_nifti_image\n\n_, has_nib = optional_import(\"nibabel\")\n\nKEYS = [\"image\"]\n\nTESTS_DICT: List[Tuple] = []\nTESTS_DICT.append((SpatialPadd(KEYS, 150), RandFlipd(KEYS, prob=1.0, spatial_axis=1)))\nTESTS_DICT.append((RandRotate90d(KEYS, prob=0.0, max_k=1),))\nTESTS_DICT.append((RandAffined(KEYS, prob=0.0, translate_range=10),))\n\nTESTS_LIST: List[Tuple] = []\nTESTS_LIST.append((SpatialPad(150), RandFlip(prob=1.0, spatial_axis=1)))\nTESTS_LIST.append((RandRotate90(prob=0.0, max_k=1),))\nTESTS_LIST.append((RandAffine(prob=0.0, translate_range=10),))\n\n\nTEST_BASIC = [\n    [(\"channel\", \"channel\"), [\"channel\", \"channel\"]],\n    [torch.Tensor([1, 2, 3]), [torch.tensor(1.0), torch.tensor(2.0), torch.tensor(3.0)]],\n    [\n        [[torch.Tensor((1.0, 2.0, 3.0)), torch.Tensor((2.0, 3.0, 1.0))]],\n        [\n            [[torch.tensor(1.0), torch.tensor(2.0)]],\n            [[torch.tensor(2.0), torch.tensor(3.0)]],\n            [[torch.tensor(3.0), torch.tensor(1.0)]],\n        ],\n    ],\n    [torch.Tensor((True, True, False, False)), [1.0, 1.0, 0.0, 0.0]],\n    [\n        [torch.Tensor([1]), torch.Tensor([2]), torch.Tensor([3])],\n        [[torch.tensor(1.0), torch.tensor(2.0), torch.tensor(3.0)]],\n    ],\n    [[None, None], [None, None]],\n    [[\"test\"], [\"test\"]],\n    [[], []],\n    [[(\"ch1\", \"ch2\"), (\"ch3\",)], [[\"ch1\", \"ch3\"], [\"ch2\", None]]],  # default pad None\n]\n\n\nclass _ListCompose(Compose):\n    def __call__(self, input_):\n        img, metadata = self.transforms[0](input_)\n        for t in self.transforms[1:]:\n            img = t(img)\n        return img, metadata", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_decollate.py_TestDeCollate.check_decollate_TestDeCollate.check_decollate.for_b_batch_data_in_enum.for_decollated_in_decoll.for_i_d_in_enumerate_dec.self_check_match_dataset_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_decollate.py_TestDeCollate.check_decollate_TestDeCollate.check_decollate.for_b_batch_data_in_enum.for_decollated_in_decoll.for_i_d_in_enumerate_dec.self_check_match_dataset_", "embedding": null, "metadata": {"file_path": "tests/test_decollate.py", "file_name": "test_decollate.py", "file_type": "text/x-python", "category": "test", "start_line": 121, "end_line": 133, "span_ids": ["TestDeCollate.check_decollate"], "tokens": 146}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestDeCollate(unittest.TestCase):\n\n    def check_decollate(self, dataset):\n        batch_size = 2\n        num_workers = 2 if sys.platform == \"linux\" else 0\n\n        loader = DataLoader(dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers)\n\n        for b, batch_data in enumerate(loader):\n            decollated_1 = decollate_batch(batch_data)\n            decollated_2 = Decollated(detach=True)(batch_data)\n\n            for decollated in [decollated_1, decollated_2]:\n                for i, d in enumerate(decollated):\n                    self.check_match(dataset[b * batch_size + i], d)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_decollate.py_TestDeCollate.test_decollation_dict_TestDeCollate.test_decollation_list.self_check_decollate_data": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_decollate.py_TestDeCollate.test_decollation_dict_TestDeCollate.test_decollation_list.self_check_decollate_data", "embedding": null, "metadata": {"file_path": "tests/test_decollate.py", "file_name": "test_decollate.py", "file_type": "text/x-python", "category": "test", "start_line": 135, "end_line": 163, "span_ids": ["TestDeCollate.test_decollation_tensor", "TestDeCollate.test_decollation_list", "TestDeCollate.test_decollation_dict"], "tokens": 312}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestDeCollate(unittest.TestCase):\n\n    @parameterized.expand(TESTS_DICT)\n    def test_decollation_dict(self, *transforms):\n        t_compose = Compose([AddChanneld(KEYS), Compose(transforms), ToTensord(KEYS)])\n        # If nibabel present, read from disk\n        if has_nib:\n            t_compose = Compose([LoadImaged(\"image\"), t_compose])\n\n        dataset = CacheDataset(self.data_dict, t_compose, progress=False)\n        self.check_decollate(dataset=dataset)\n\n    @parameterized.expand(TESTS_LIST)\n    def test_decollation_tensor(self, *transforms):\n        t_compose = Compose([AddChannel(), Compose(transforms), ToTensor()])\n        # If nibabel present, read from disk\n        if has_nib:\n            t_compose = Compose([LoadImage(image_only=True), t_compose])\n\n        dataset = Dataset(self.data_list, t_compose)\n        self.check_decollate(dataset=dataset)\n\n    @parameterized.expand(TESTS_LIST)\n    def test_decollation_list(self, *transforms):\n        t_compose = Compose([AddChannel(), Compose(transforms), ToTensor()])\n        # If nibabel present, read from disk\n        if has_nib:\n            t_compose = _ListCompose([LoadImage(image_only=False), t_compose])\n\n        dataset = Dataset(self.data_list, t_compose)\n        self.check_decollate(dataset=dataset)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deepgrow_transforms.py_TestSpatialCropForegroundd_TestSpatialCropForegroundd.test_correct_shape.np_testing_assert_equal_r": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deepgrow_transforms.py_TestSpatialCropForegroundd_TestSpatialCropForegroundd.test_correct_shape.np_testing_assert_equal_r", "embedding": null, "metadata": {"file_path": "tests/test_deepgrow_transforms.py", "file_name": "test_deepgrow_transforms.py", "file_type": "text/x-python", "category": "test", "start_line": 370, "end_line": 379, "span_ids": ["TestSpatialCropForegroundd", "TestSpatialCropForegroundd.test_correct_shape", "TestSpatialCropForegroundd.test_correct_results"], "tokens": 118}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestSpatialCropForegroundd(unittest.TestCase):\n    @parameterized.expand([CROP_TEST_CASE_1])\n    def test_correct_results(self, arguments, input_data, expected_result):\n        result = SpatialCropForegroundd(**arguments)(input_data)\n        np.testing.assert_allclose(result[\"image\"], expected_result)\n\n    @parameterized.expand([CROP_TEST_CASE_2])\n    def test_correct_shape(self, arguments, input_data, expected_shape):\n        result = SpatialCropForegroundd(**arguments)(input_data)\n        np.testing.assert_equal(result[\"image\"].shape, expected_shape)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deepgrow_transforms.py_TestSpatialCropForegroundd.test_foreground_position_TestSpatialCropForegroundd.test_foreground_position.None_3": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deepgrow_transforms.py_TestSpatialCropForegroundd.test_foreground_position_TestSpatialCropForegroundd.test_foreground_position.None_3", "embedding": null, "metadata": {"file_path": "tests/test_deepgrow_transforms.py", "file_name": "test_deepgrow_transforms.py", "file_type": "text/x-python", "category": "test", "start_line": 381, "end_line": 391, "span_ids": ["TestSpatialCropForegroundd.test_foreground_position"], "tokens": 197}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestSpatialCropForegroundd(unittest.TestCase):\n\n    @parameterized.expand([CROP_TEST_CASE_1])\n    def test_foreground_position(self, arguments, input_data, _):\n        result = SpatialCropForegroundd(**arguments)(input_data)\n        np.testing.assert_allclose(result[\"image_meta_dict\"][\"foreground_start_coord\"], np.array([0, 1, 1]))\n        np.testing.assert_allclose(result[\"image_meta_dict\"][\"foreground_end_coord\"], np.array([1, 4, 4]))\n\n        arguments[\"start_coord_key\"] = \"test_start_coord\"\n        arguments[\"end_coord_key\"] = \"test_end_coord\"\n        result = SpatialCropForegroundd(**arguments)(input_data)\n        np.testing.assert_allclose(result[\"image_meta_dict\"][\"test_start_coord\"], np.array([0, 1, 1]))\n        np.testing.assert_allclose(result[\"image_meta_dict\"][\"test_end_coord\"], np.array([1, 4, 4]))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deprecated.py_TestDeprecated.test_arg_warn2_TestDeprecated.test_arg_except2.self_assertRaises_Depreca": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deprecated.py_TestDeprecated.test_arg_warn2_TestDeprecated.test_arg_except2.self_assertRaises_Depreca", "embedding": null, "metadata": {"file_path": "tests/test_deprecated.py", "file_name": "test_deprecated.py", "file_type": "text/x-python", "category": "test", "start_line": 171, "end_line": 198, "span_ids": ["TestDeprecated.test_arg_warn2", "TestDeprecated.test_arg_except2", "TestDeprecated.test_arg_except1"], "tokens": 239}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestDeprecated(unittest.TestCase):\n\n    def test_arg_warn2(self):\n        \"\"\"Test deprecated_arg decorator with just `since` set.\"\"\"\n\n        @deprecated_arg(\"b\", since=self.prev_version, version_val=self.test_version)\n        def afoo2(a, **kw):\n            pass\n\n        afoo2(1)  # ok when no b provided\n\n        self.assertWarns(DeprecationWarning, lambda: afoo2(1, b=2))\n\n    def test_arg_except1(self):\n        \"\"\"Test deprecated_arg decorator raises exception with no versions set.\"\"\"\n\n        @deprecated_arg(\"b\", version_val=self.test_version)\n        def afoo3(a, b=None):\n            pass\n\n        self.assertRaises(DeprecatedError, lambda: afoo3(1, b=2))\n\n    def test_arg_except2(self):\n        \"\"\"Test deprecated_arg decorator raises exception with `removed` set in the past.\"\"\"\n\n        @deprecated_arg(\"b\", removed=self.prev_version, version_val=self.test_version)\n        def afoo4(a, b=None):\n            pass\n\n        self.assertRaises(DeprecatedError, lambda: afoo4(1, b=2))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_download_and_extract.py_TestDownloadAndExtract.test_default_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_download_and_extract.py_TestDownloadAndExtract.test_default_", "embedding": null, "metadata": {"file_path": "tests/test_download_and_extract.py", "file_name": "test_download_and_extract.py", "file_type": "text/x-python", "category": "test", "start_line": 55, "end_line": 83, "span_ids": ["TestDownloadAndExtract.test_default", "impl"], "tokens": 354}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestDownloadAndExtract(unittest.TestCase):\n\n    @skip_if_quick\n    def test_default(self):\n        with tempfile.TemporaryDirectory() as tmp_dir:\n            try:\n                # icon.tar.gz https://drive.google.com/file/d/1HrQd-AKPbts9jkTNN4pT8vLZyhM5irVn/view?usp=sharing\n                download_and_extract(\n                    \"https://drive.google.com/uc?id=1HrQd-AKPbts9jkTNN4pT8vLZyhM5irVn\",\n                    output_dir=tmp_dir,\n                    hash_val=\"a55d11ad26ed9eb7277905d796205531\",\n                    file_type=\"tar\",\n                )\n                # favicon.ico.zip https://drive.google.com/file/d/1TqBTJap621NO9arzXRrYi04lr9NTVF8H/view?usp=sharing\n                download_and_extract(\n                    \"https://drive.google.com/uc?id=1TqBTJap621NO9arzXRrYi04lr9NTVF8H\",\n                    output_dir=tmp_dir,\n                    hash_val=\"ac6e167ee40803577d98237f2b0241e5\",\n                    file_type=\"zip\",\n                )\n            except (ContentTooShortError, HTTPError, RuntimeError) as e:\n                print(str(e))\n                if isinstance(e, RuntimeError):\n                    # FIXME: skip MD5 check as current downloading method may fail\n                    self.assertTrue(str(e).startswith(\"md5 check\"))\n                return  # skipping this test due the network connection errors\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_efficientnet.py_get_block_args_get_block_args.return._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_efficientnet.py_get_block_args_get_block_args.return._", "embedding": null, "metadata": {"file_path": "tests/test_efficientnet.py", "file_name": "test_efficientnet.py", "file_type": "text/x-python", "category": "test", "start_line": 58, "end_line": 75, "span_ids": ["get_block_args"], "tokens": 303}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_block_args():\n    # test string list\n    return [\n        \"r1_k3_s11_e1_i32_o16_se0.25\",\n        \"r2_k3_s22_e6_i16_o24_se0.25\",\n        \"r2_k5_s22_e6_i24_o40_se0.25\",\n        \"r3_k3_s22_e6_i40_o80_se0.25\",\n        \"r3_k5_s11_e6_i80_o112_se0.25\",\n        \"r4_k5_s22_e6_i112_o192_se0.25\",\n        \"r1_k3_s11_e6_i192_o320_se0.25\",\n        \"r1_k3_s11_e1_i32_o16_se0.25_noskip\",\n        \"r2_k3_s22_e6_i16_o24_se0.25_noskip\",\n        \"r2_k5_s22_e6_i24_o40_se0.25_noskip\",\n        \"r3_k3_s22_e6_i40_o80_se0.25_noskip\",\n        \"r3_k5_s11_e6_i80_o112_se0.25_noskip\",\n        \"r4_k5_s22_e6_i112_o192_se0.25_noskip\",\n        \"r1_k3_s11_e6_i192_o320_se0.25_noskip\",\n    ]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ensure_channel_first.py_TestEnsureChannelFirst.test_itk_dicom_series_reader_TestEnsureChannelFirst.test_load_png.with_tempfile_TemporaryDi.self_assertEqual_result_s": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ensure_channel_first.py_TestEnsureChannelFirst.test_itk_dicom_series_reader_TestEnsureChannelFirst.test_load_png.with_tempfile_TemporaryDi.self_assertEqual_result_s", "embedding": null, "metadata": {"file_path": "tests/test_ensure_channel_first.py", "file_name": "test_ensure_channel_first.py", "file_type": "text/x-python", "category": "test", "start_line": 68, "end_line": 82, "span_ids": ["TestEnsureChannelFirst.test_itk_dicom_series_reader", "TestEnsureChannelFirst.test_load_png"], "tokens": 188}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestEnsureChannelFirst(unittest.TestCase):\n\n    @parameterized.expand([TEST_CASE_7])\n    def test_itk_dicom_series_reader(self, input_param, filenames, original_channel_dim):\n        result, header = LoadImage(**input_param)(filenames)\n        result = EnsureChannelFirst()(result, header)\n        self.assertEqual(result.shape[0], 1)\n\n    def test_load_png(self):\n        spatial_size = (256, 256, 3)\n        test_image = np.random.randint(0, 256, size=spatial_size)\n        with tempfile.TemporaryDirectory() as tempdir:\n            filename = os.path.join(tempdir, \"test_image.png\")\n            Image.fromarray(test_image.astype(\"uint8\")).save(filename)\n            result, header = LoadImage(image_only=False)(filename)\n            result = EnsureChannelFirst()(result, header)\n            self.assertEqual(result.shape[0], 3)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ensure_channel_first.py_TestEnsureChannelFirst.test_check_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ensure_channel_first.py_TestEnsureChannelFirst.test_check_", "embedding": null, "metadata": {"file_path": "tests/test_ensure_channel_first.py", "file_name": "test_ensure_channel_first.py", "file_type": "text/x-python", "category": "test", "start_line": 84, "end_line": 95, "span_ids": ["TestEnsureChannelFirst.test_check", "impl:15"], "tokens": 137}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestEnsureChannelFirst(unittest.TestCase):\n\n    def test_check(self):\n        with self.assertRaises(ValueError):  # no meta\n            EnsureChannelFirst()(np.zeros((1, 2, 3)), None)\n        with self.assertRaises(ValueError):  # no meta channel\n            EnsureChannelFirst()(np.zeros((1, 2, 3)), {\"original_channel_dim\": None})\n        EnsureChannelFirst(strict_check=False)(np.zeros((1, 2, 3)), None)\n        EnsureChannelFirst(strict_check=False)(np.zeros((1, 2, 3)), {\"original_channel_dim\": None})\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ensure_channel_firstd.py_TestEnsureChannelFirstd.test_load_png_TestEnsureChannelFirstd.test_load_png.with_tempfile_TemporaryDi.self_assertEqual_result_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ensure_channel_firstd.py_TestEnsureChannelFirstd.test_load_png_TestEnsureChannelFirstd.test_load_png.with_tempfile_TemporaryDi.self_assertEqual_result_", "embedding": null, "metadata": {"file_path": "tests/test_ensure_channel_firstd.py", "file_name": "test_ensure_channel_firstd.py", "file_type": "text/x-python", "category": "test", "start_line": 50, "end_line": 58, "span_ids": ["TestEnsureChannelFirstd.test_load_png"], "tokens": 129}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestEnsureChannelFirstd(unittest.TestCase):\n\n    def test_load_png(self):\n        spatial_size = (256, 256, 3)\n        test_image = np.random.randint(0, 256, size=spatial_size)\n        with tempfile.TemporaryDirectory() as tempdir:\n            filename = os.path.join(tempdir, \"test_image.png\")\n            Image.fromarray(test_image.astype(\"uint8\")).save(filename)\n            result = LoadImaged(keys=\"img\")({\"img\": filename})\n            result = EnsureChannelFirstd(keys=\"img\")(result)\n            self.assertEqual(result[\"img\"].shape[0], 3)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ensure_channel_firstd.py_TestEnsureChannelFirstd.test_exceptions_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ensure_channel_firstd.py_TestEnsureChannelFirstd.test_exceptions_", "embedding": null, "metadata": {"file_path": "tests/test_ensure_channel_firstd.py", "file_name": "test_ensure_channel_firstd.py", "file_type": "text/x-python", "category": "test", "start_line": 60, "end_line": 73, "span_ids": ["TestEnsureChannelFirstd.test_exceptions", "impl:7"], "tokens": 187}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestEnsureChannelFirstd(unittest.TestCase):\n\n    def test_exceptions(self):\n        with self.assertRaises(ValueError):  # no meta\n            EnsureChannelFirstd(\"img\")({\"img\": np.zeros((1, 2, 3)), \"img_meta_dict\": None})\n        with self.assertRaises(ValueError):  # no meta channel\n            EnsureChannelFirstd(\"img\")({\"img\": np.zeros((1, 2, 3)), \"img_meta_dict\": {\"original_channel_dim\": None}})\n        EnsureChannelFirstd(\"img\", strict_check=False)({\"img\": np.zeros((1, 2, 3)), \"img_meta_dict\": None})\n        EnsureChannelFirstd(\"img\", strict_check=False)(\n            {\"img\": np.zeros((1, 2, 3)), \"img_meta_dict\": {\"original_channel_dim\": None}}\n        )\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_generate_param_groups.py_unittest_TEST_CASE_6._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_generate_param_groups.py_unittest_TEST_CASE_6._", "embedding": null, "metadata": {"file_path": "tests/test_generate_param_groups.py", "file_name": "test_generate_param_groups.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 64, "span_ids": ["docstring"], "tokens": 426}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks.nets import Unet\nfrom monai.optimizers import generate_param_groups\nfrom monai.utils import ensure_tuple\n\nTEST_CASE_1 = [{\"layer_matches\": [lambda x: x.model[-1]], \"match_types\": \"select\", \"lr_values\": [1]}, (1, 100), [5, 21]]\n\nTEST_CASE_2 = [\n    {\n        \"layer_matches\": [lambda x: x.model[-1], lambda x: x.model[-2], lambda x: x.model[-3]],\n        \"match_types\": \"select\",\n        \"lr_values\": [1, 2, 3],\n    },\n    (1, 2, 3, 100),\n    [5, 16, 5, 0],\n]\n\nTEST_CASE_3 = [\n    {\"layer_matches\": [lambda x: x.model[2][1].conv[0].conv], \"match_types\": [\"select\"], \"lr_values\": [1]},\n    (1, 100),\n    [2, 24],\n]\n\nTEST_CASE_4 = [\n    {\n        \"layer_matches\": [lambda x: x.model[0], lambda x: \"2.0.conv\" in x[0]],\n        \"match_types\": [\"select\", \"filter\"],\n        \"lr_values\": [1, 2],\n    },\n    (1, 2, 100),\n    [5, 4, 17],\n]\n\nTEST_CASE_5 = [\n    {\"layer_matches\": [lambda x: x.model[-1]], \"match_types\": [\"select\"], \"lr_values\": [1], \"include_others\": False},\n    (1),\n    [5],\n]\n\nTEST_CASE_6 = [\n    {\n        \"layer_matches\": [lambda x: \"weight\" in x[0]],\n        \"match_types\": [\"filter\"],\n        \"lr_values\": [1],\n        \"include_others\": True,\n    },\n    (1),\n    [16, 10],\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_generate_param_groups.py_TestGenerateParamGroups_TestGenerateParamGroups.test_lr_values.self_assertListEqual_n_e": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_generate_param_groups.py_TestGenerateParamGroups_TestGenerateParamGroups.test_lr_values.self_assertListEqual_n_e", "embedding": null, "metadata": {"file_path": "tests/test_generate_param_groups.py", "file_name": "test_generate_param_groups.py", "file_type": "text/x-python", "category": "test", "start_line": 67, "end_line": 82, "span_ids": ["TestGenerateParamGroups.test_lr_values", "TestGenerateParamGroups"], "tokens": 205}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestGenerateParamGroups(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5, TEST_CASE_6])\n    def test_lr_values(self, input_param, expected_values, expected_groups):\n        device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n        net = Unet(\n            spatial_dims=3, in_channels=1, out_channels=3, channels=(16, 32, 64), strides=(2, 2), num_res_units=1\n        ).to(device)\n\n        params = generate_param_groups(network=net, **input_param)\n        optimizer = torch.optim.Adam(params, 100)\n\n        for param_group, value in zip(optimizer.param_groups, ensure_tuple(expected_values)):\n            torch.testing.assert_allclose(param_group[\"lr\"], value)\n\n        n = [len(p[\"params\"]) for p in params]\n        self.assertListEqual(n, expected_groups)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_get_layers.py_unittest_TEST_CASE_DROPOUT._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_get_layers.py_unittest_TEST_CASE_DROPOUT._", "embedding": null, "metadata": {"file_path": "tests/test_get_layers.py", "file_name": "test_get_layers.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 34, "span_ids": ["docstring"], "tokens": 234}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nfrom parameterized import parameterized\n\nfrom monai.networks.layers import get_act_layer, get_dropout_layer, get_norm_layer\n\nTEST_CASE_NORM = [\n    [{\"name\": (\"group\", {\"num_groups\": 1})}, \"GroupNorm(1, 1, eps=1e-05, affine=True)\"],\n    [\n        {\"name\": \"instance\", \"spatial_dims\": 2},\n        \"InstanceNorm2d(1, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\",\n    ],\n]\n\nTEST_CASE_ACT = [\n    [{\"name\": \"swish\"}, \"Swish()\"],\n    [{\"name\": (\"prelu\", {\"num_parameters\": 1, \"init\": 0.25})}, \"PReLU(num_parameters=1)\"],\n]\n\nTEST_CASE_DROPOUT = [\n    [{\"name\": \"dropout\"}, \"Dropout(p=0.5, inplace=False)\"],\n    [{\"name\": (\"alphadropout\", {\"p\": 0.25})}, \"AlphaDropout(p=0.25, inplace=False)\"],\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_get_layers.py_TestGetLayers_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_get_layers.py_TestGetLayers_", "embedding": null, "metadata": {"file_path": "tests/test_get_layers.py", "file_name": "test_get_layers.py", "file_type": "text/x-python", "category": "test", "start_line": 37, "end_line": 62, "span_ids": ["TestSuggestion", "TestGetLayers", "TestSuggestion.test_suggested", "TestGetLayers.test_act_layer", "impl:7", "TestGetLayers.test_norm_layer", "TestGetLayers.test_dropout_layer"], "tokens": 186}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestGetLayers(unittest.TestCase):\n    @parameterized.expand(TEST_CASE_NORM)\n    def test_norm_layer(self, input_param, expected):\n        layer = get_norm_layer(**input_param)\n        self.assertEqual(f\"{layer}\", expected)\n\n    @parameterized.expand(TEST_CASE_ACT)\n    def test_act_layer(self, input_param, expected):\n        layer = get_act_layer(**input_param)\n        self.assertEqual(f\"{layer}\", expected)\n\n    @parameterized.expand(TEST_CASE_DROPOUT)\n    def test_dropout_layer(self, input_param, expected):\n        layer = get_dropout_layer(**input_param)\n        self.assertEqual(f\"{layer}\", expected)\n\n\nclass TestSuggestion(unittest.TestCase):\n    def test_suggested(self):\n        with self.assertRaisesRegex(ValueError, \"did you mean 'GROUP'?\"):\n            get_norm_layer(name=\"grop\", spatial_dims=2)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gibbs_noised.py_unittest_KEYS._im_label_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gibbs_noised.py_unittest_KEYS._im_label_", "embedding": null, "metadata": {"file_path": "tests/test_gibbs_noised.py", "file_name": "test_gibbs_noised.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 31, "span_ids": ["docstring"], "tokens": 162}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom copy import deepcopy\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.data.synthetic import create_test_image_2d, create_test_image_3d\nfrom monai.transforms import GibbsNoised\nfrom monai.utils.misc import set_determinism\nfrom monai.utils.module import optional_import\nfrom tests.utils import TEST_NDARRAYS\n\n_, has_torch_fft = optional_import(\"torch.fft\", name=\"fftshift\")\n\nTEST_CASES = []\nfor shape in ((128, 64), (64, 48, 80)):\n    for input_type in TEST_NDARRAYS if has_torch_fft else [np.array]:\n        TEST_CASES.append((shape, input_type))\nKEYS = [\"im\", \"label\"]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gibbs_noised.py_TestGibbsNoised.test_same_result_TestGibbsNoised.test_same_result.for_k_in_KEYS_.self_assertIsInstance_out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gibbs_noised.py_TestGibbsNoised.test_same_result_TestGibbsNoised.test_same_result.for_k_in_KEYS_.self_assertIsInstance_out", "embedding": null, "metadata": {"file_path": "tests/test_gibbs_noised.py", "file_name": "test_gibbs_noised.py", "file_type": "text/x-python", "category": "test", "start_line": 48, "end_line": 57, "span_ids": ["TestGibbsNoised.test_same_result"], "tokens": 127}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestGibbsNoised(unittest.TestCase):\n\n    @parameterized.expand(TEST_CASES)\n    def test_same_result(self, im_shape, input_type):\n        data = self.get_data(im_shape, input_type)\n        alpha = 0.8\n        t = GibbsNoised(KEYS, alpha)\n        out1 = t(deepcopy(data))\n        out2 = t(deepcopy(data))\n        for k in KEYS:\n            torch.testing.assert_allclose(out1[k], out2[k], rtol=1e-7, atol=0)\n            self.assertIsInstance(out1[k], type(data[k]))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gibbs_noised.py_TestGibbsNoised.test_dict_matches_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gibbs_noised.py_TestGibbsNoised.test_dict_matches_", "embedding": null, "metadata": {"file_path": "tests/test_gibbs_noised.py", "file_name": "test_gibbs_noised.py", "file_type": "text/x-python", "category": "test", "start_line": 85, "end_line": 97, "span_ids": ["impl:9", "TestGibbsNoised.test_dict_matches"], "tokens": 148}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestGibbsNoised(unittest.TestCase):\n\n    @parameterized.expand(TEST_CASES)\n    def test_dict_matches(self, im_shape, input_type):\n        data = self.get_data(im_shape, input_type)\n        data = {KEYS[0]: deepcopy(data[KEYS[0]]), KEYS[1]: deepcopy(data[KEYS[0]])}\n        alpha = 1.0\n        t = GibbsNoised(KEYS, alpha)\n        out = t(deepcopy(data))\n        torch.testing.assert_allclose(out[KEYS[0]], out[KEYS[1]], rtol=1e-7, atol=0)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gmm.py_os_TEST_CASES": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gmm.py_os_TEST_CASES", "embedding": null, "metadata": {"file_path": "tests/test_gmm.py", "file_name": "test_gmm.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 310, "span_ids": ["docstring"], "tokens": 56}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport shutil\nimport tempfile\nimport unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks.layers import GaussianMixtureModel\nfrom tests.utils import skip_if_no_cuda\n\nTEST_CASES =\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gmm.py_GMMTestCase_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gmm.py_GMMTestCase_", "embedding": null, "metadata": {"file_path": "tests/test_gmm.py", "file_name": "test_gmm.py", "file_type": "text/x-python", "category": "test", "start_line": 313, "end_line": 357, "span_ids": ["GMMTestCase", "impl:3", "GMMTestCase.test_cuda", "GMMTestCase.tearDown", "GMMTestCase.setUp"], "tokens": 364}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_no_cuda\nclass GMMTestCase(unittest.TestCase):\n    def setUp(self):\n        self._var = os.environ.get(\"TORCH_EXTENSIONS_DIR\", None)\n        self.tempdir = tempfile.mkdtemp()\n        os.environ[\"TORCH_EXTENSIONS_DIR\"] = self.tempdir\n\n    def tearDown(self) -> None:\n        if self._var is None:\n            os.environ.pop(\"TORCH_EXTENSIONS_DIR\", None)\n        else:\n            os.environ[\"TORCH_EXTENSIONS_DIR\"] = f\"{self._var}\"\n        shutil.rmtree(self.tempdir)\n\n    @parameterized.expand(TEST_CASES)\n    def test_cuda(self, test_case_description, mixture_count, class_count, features, labels, expected):\n\n        # Device to run on\n        device = torch.device(\"cuda\")\n\n        # Create tensors\n        features_tensor = torch.tensor(features, dtype=torch.float32, device=device)\n        labels_tensor = torch.tensor(labels, dtype=torch.int32, device=device)\n\n        # Create GMM\n        gmm = GaussianMixtureModel(features_tensor.size(1), mixture_count, class_count, verbose_build=True)\n        # reload GMM to confirm the build\n        _ = GaussianMixtureModel(features_tensor.size(1), mixture_count, class_count, verbose_build=False)\n        # reload quietly\n        _ = GaussianMixtureModel(features_tensor.size(1), mixture_count, class_count, verbose_build=True)\n\n        # Apply GMM\n        gmm.learn(features_tensor, labels_tensor)\n        results_tensor = gmm.apply(features_tensor)\n\n        # Read back results\n        results = results_tensor.cpu().numpy()\n\n        # Ensure result are as expected\n        np.testing.assert_allclose(results, expected, atol=1e-3)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_mean_dice.py_TestHandlerMeanDice_TestHandlerMeanDice.test_compute.self_assertTupleEqual_tup": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_mean_dice.py_TestHandlerMeanDice_TestHandlerMeanDice.test_compute.self_assertTupleEqual_tup", "embedding": null, "metadata": {"file_path": "tests/test_handler_mean_dice.py", "file_name": "test_handler_mean_dice.py", "file_type": "text/x-python", "category": "test", "start_line": 29, "end_line": 55, "span_ids": ["TestHandlerMeanDice", "TestHandlerMeanDice.test_compute"], "tokens": 296}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestHandlerMeanDice(unittest.TestCase):\n    # TODO test multi node averaged dice\n\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3])\n    def test_compute(self, input_params, expected_avg, details_shape):\n        dice_metric = MeanDice(**input_params)\n        # set up engine\n\n        def _val_func(engine, batch):\n            pass\n\n        engine = Engine(_val_func)\n        dice_metric.attach(engine=engine, name=\"mean_dice\")\n        # test input a list of channel-first tensor\n        y_pred = [torch.Tensor([[0], [1]]), torch.Tensor([[1], [0]])]\n        y = torch.Tensor([[[0], [1]], [[0], [1]]])\n        engine.state.output = {\"pred\": y_pred, \"label\": y}\n        engine.fire_event(Events.ITERATION_COMPLETED)\n\n        y_pred = [torch.Tensor([[0], [1]]), torch.Tensor([[1], [0]])]\n        y = torch.Tensor([[[0], [1]], [[1], [0]]])\n        engine.state.output = {\"pred\": y_pred, \"label\": y}\n        engine.fire_event(Events.ITERATION_COMPLETED)\n\n        engine.fire_event(Events.EPOCH_COMPLETED)\n        torch.testing.assert_allclose(engine.state.metrics[\"mean_dice\"], expected_avg)\n        self.assertTupleEqual(tuple(engine.state.metric_details[\"mean_dice\"].shape), details_shape)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_post_processing.py_unittest_TEST_CASE_2._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_post_processing.py_unittest_TEST_CASE_2._", "embedding": null, "metadata": {"file_path": "tests/test_handler_post_processing.py", "file_name": "test_handler_post_processing.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 36, "span_ids": ["docstring"], "tokens": 214}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.engines import SupervisedEvaluator\nfrom monai.handlers import PostProcessing\nfrom monai.transforms import Activationsd, AsDiscreted, Compose, CopyItemsd\n\n# test lambda function as `transform`\nTEST_CASE_1 = [{\"transform\": lambda x: dict(pred=x[\"pred\"] + 1.0)}, False, torch.tensor([[[[1.9975], [1.9997]]]])]\n# test composed postprocessing transforms as `transform`\nTEST_CASE_2 = [\n    {\n        \"transform\": Compose(\n            [\n                CopyItemsd(keys=\"filename\", times=1, names=\"filename_bak\"),\n                AsDiscreted(keys=\"pred\", threshold=0.5, to_onehot=2),\n            ]\n        ),\n        \"event\": \"iteration_completed\",\n    },\n    True,\n    torch.tensor([[[[1.0], [1.0]], [[0.0], [0.0]]]]),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_post_processing.py_TestHandlerPostProcessing_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_post_processing.py_TestHandlerPostProcessing_", "embedding": null, "metadata": {"file_path": "tests/test_handler_post_processing.py", "file_name": "test_handler_post_processing.py", "file_type": "text/x-python", "category": "test", "start_line": 39, "end_line": 72, "span_ids": ["TestHandlerPostProcessing.test_compute", "TestHandlerPostProcessing", "impl:5"], "tokens": 300}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestHandlerPostProcessing(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2])\n    def test_compute(self, input_params, decollate, expected):\n        data = [\n            {\"image\": torch.tensor([[[[2.0], [3.0]]]]), \"filename\": [\"test1\"]},\n            {\"image\": torch.tensor([[[[6.0], [8.0]]]]), \"filename\": [\"test2\"]},\n        ]\n        # set up engine, PostProcessing handler works together with postprocessing transforms of engine\n        engine = SupervisedEvaluator(\n            device=torch.device(\"cpu:0\"),\n            val_data_loader=data,\n            epoch_length=2,\n            network=torch.nn.PReLU(),\n            postprocessing=Compose([Activationsd(keys=\"pred\", sigmoid=True)]),\n            val_handlers=[PostProcessing(**input_params)],\n            decollate=decollate,\n        )\n        engine.run()\n\n        if isinstance(engine.state.output, list):\n            # test decollated list items\n            for o, e in zip(engine.state.output, expected):\n                torch.testing.assert_allclose(o[\"pred\"], e)\n                filename = o.get(\"filename_bak\")\n                if filename is not None:\n                    self.assertEqual(filename, \"test2\")\n        else:\n            # test batch data\n            torch.testing.assert_allclose(engine.state.output[\"pred\"], expected)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_regression_metrics.py_unittest_psnrmetric_np.return.np_mean_20_np_log10_max": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_regression_metrics.py_unittest_psnrmetric_np.return.np_mean_20_np_log10_max", "embedding": null, "metadata": {"file_path": "tests/test_handler_regression_metrics.py", "file_name": "test_handler_regression_metrics.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 43, "span_ids": ["msemetric_np", "flatten", "maemetric_np", "psnrmetric_np", "docstring", "rmsemetric_np"], "tokens": 245}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom functools import partial\n\nimport numpy as np\nimport torch\nfrom ignite.engine import Engine\n\nfrom monai.handlers import MeanAbsoluteError, MeanSquaredError, PeakSignalToNoiseRatio, RootMeanSquaredError\nfrom monai.utils import set_determinism\n\n\n# define a numpy flatten function that only preserves batch dimension\ndef flatten(data):\n    return np.reshape(data, [data.shape[0], -1])\n\n\n# define metrics computation truth functions to check our monai metrics against\ndef msemetric_np(y_pred, y):\n    return np.mean((flatten(y_pred) - flatten(y)) ** 2)\n\n\ndef maemetric_np(y_pred, y):\n    return np.mean(np.abs(flatten(y_pred) - flatten(y)))\n\n\ndef rmsemetric_np(y_pred, y):\n    return np.mean(np.sqrt(np.mean((flatten(y_pred) - flatten(y)) ** 2, axis=1)))\n\n\ndef psnrmetric_np(max_val, y_pred, y):\n    mse = np.mean((flatten(y_pred) - flatten(y)) ** 2, axis=1)\n    return np.mean(20 * np.log10(max_val) - 10 * np.log10(mse))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_regression_metrics.py_TestHandlerRegressionMetrics_TestHandlerRegressionMetrics.test_compute.for_mt_fn_mt_fn_np_in_zi.for_batch_in_batch_dims_.for_spatial_in_spatial_di.for_base_in_base_dims_.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_regression_metrics.py_TestHandlerRegressionMetrics_TestHandlerRegressionMetrics.test_compute.for_mt_fn_mt_fn_np_in_zi.for_batch_in_batch_dims_.for_spatial_in_spatial_di.for_base_in_base_dims_.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_handler_regression_metrics.py", "file_name": "test_handler_regression_metrics.py", "file_type": "text/x-python", "category": "test", "start_line": 46, "end_line": 87, "span_ids": ["TestHandlerRegressionMetrics.test_compute", "TestHandlerRegressionMetrics"], "tokens": 491}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestHandlerRegressionMetrics(unittest.TestCase):\n    def test_compute(self):\n        set_determinism(seed=123)\n        device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\n        # regression metrics to check + truth metric function in numpy\n        metrics = [\n            MeanSquaredError,\n            MeanAbsoluteError,\n            RootMeanSquaredError,\n            partial(PeakSignalToNoiseRatio, max_val=1.0),\n        ]\n        metrics_np = [msemetric_np, maemetric_np, rmsemetric_np, partial(psnrmetric_np, max_val=1.0)]\n\n        # define variations in batch/base_dims/spatial_dims\n        batch_dims = [1, 2, 4, 16]\n        base_dims = [16, 32, 64]\n        spatial_dims = [2, 3, 4]\n\n        # iterate over all variations and check shapes for different reduction functions\n        for mt_fn, mt_fn_np in zip(metrics, metrics_np):\n\n            for batch in batch_dims:\n                for spatial in spatial_dims:\n                    for base in base_dims:\n                        mt_fn_obj = mt_fn(**{\"save_details\": False})\n\n                        # create random tensor\n                        in_tensor_a1 = torch.rand((batch,) + (base,) * (spatial - 1)).to(device)\n                        in_tensor_b1 = torch.rand((batch,) + (base,) * (spatial - 1)).to(device)\n                        mt_fn_obj.update([in_tensor_a1, in_tensor_b1])\n                        out_tensor_np1 = mt_fn_np(y_pred=in_tensor_a1.cpu().numpy(), y=in_tensor_b1.cpu().numpy())\n\n                        in_tensor_a2 = torch.rand((batch,) + (base,) * (spatial - 1)).to(device)\n                        in_tensor_b2 = torch.rand((batch,) + (base,) * (spatial - 1)).to(device)\n                        mt_fn_obj.update([in_tensor_a2, in_tensor_b2])\n                        out_tensor_np2 = mt_fn_np(y_pred=in_tensor_a2.cpu().numpy(), y=in_tensor_b2.cpu().numpy())\n\n                        out_tensor = mt_fn_obj.compute()\n                        out_tensor_np = (out_tensor_np1 + out_tensor_np2) / 2.0\n\n                        np.testing.assert_allclose(out_tensor, out_tensor_np, atol=1e-4)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_regression_metrics.py_TestHandlerRegressionMetrics.test_compute_engine_TestHandlerRegressionMetrics.test_compute_engine.for_mt_fn_name_mt_fn_mt.for_batch_in_batch_dims_.for_spatial_in_spatial_di.for_base_in_base_dims_.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_regression_metrics.py_TestHandlerRegressionMetrics.test_compute_engine_TestHandlerRegressionMetrics.test_compute_engine.for_mt_fn_name_mt_fn_mt.for_batch_in_batch_dims_.for_spatial_in_spatial_di.for_base_in_base_dims_.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_handler_regression_metrics.py", "file_name": "test_handler_regression_metrics.py", "file_type": "text/x-python", "category": "test", "start_line": 89, "end_line": 134, "span_ids": ["TestHandlerRegressionMetrics.test_compute_engine"], "tokens": 553}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestHandlerRegressionMetrics(unittest.TestCase):\n\n    def test_compute_engine(self):\n        set_determinism(seed=123)\n        device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\n        # regression metrics to check + truth metric function in numpy\n        metrics_names = [\"MSE\", \"MAE\", \"RMSE\", \"PSNR\"]\n        metrics = [\n            MeanSquaredError,\n            MeanAbsoluteError,\n            RootMeanSquaredError,\n            partial(PeakSignalToNoiseRatio, max_val=1.0),\n        ]\n        metrics_np = [msemetric_np, maemetric_np, rmsemetric_np, partial(psnrmetric_np, max_val=1.0)]\n\n        def _val_func(engine, batch):\n            pass\n\n        # define variations in batch/base_dims/spatial_dims\n        batch_dims = [1, 2, 4, 16]\n        base_dims = [16, 32, 64]\n        spatial_dims = [2, 3, 4]\n\n        # iterate over all variations and check shapes for different reduction functions\n        for mt_fn_name, mt_fn, mt_fn_np in zip(metrics_names, metrics, metrics_np):\n            for batch in batch_dims:\n                for spatial in spatial_dims:\n                    for base in base_dims:\n                        mt_fn_obj = mt_fn()  # 'save_details' == True\n                        engine = Engine(_val_func)\n                        mt_fn_obj.attach(engine, mt_fn_name)\n\n                        # create random tensor\n                        in_tensor_a1 = torch.rand((batch,) + (base,) * (spatial - 1)).to(device)\n                        in_tensor_b1 = torch.rand((batch,) + (base,) * (spatial - 1)).to(device)\n                        mt_fn_obj.update([in_tensor_a1, in_tensor_b1])\n                        out_tensor_np1 = mt_fn_np(y_pred=in_tensor_a1.cpu().numpy(), y=in_tensor_b1.cpu().numpy())\n\n                        in_tensor_a2 = torch.rand((batch,) + (base,) * (spatial - 1)).to(device)\n                        in_tensor_b2 = torch.rand((batch,) + (base,) * (spatial - 1)).to(device)\n                        mt_fn_obj.update([in_tensor_a2, in_tensor_b2])\n                        out_tensor_np2 = mt_fn_np(y_pred=in_tensor_a2.cpu().numpy(), y=in_tensor_b2.cpu().numpy())\n\n                        out_tensor = mt_fn_obj.compute()\n                        out_tensor_np = (out_tensor_np1 + out_tensor_np2) / 2.0\n\n                        np.testing.assert_allclose(out_tensor, out_tensor_np, atol=1e-4)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_regression_metrics.py_TestHandlerRegressionMetrics.test_ill_shape_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_regression_metrics.py_TestHandlerRegressionMetrics.test_ill_shape_", "embedding": null, "metadata": {"file_path": "tests/test_handler_regression_metrics.py", "file_name": "test_handler_regression_metrics.py", "file_type": "text/x-python", "category": "test", "start_line": 136, "end_line": 159, "span_ids": ["TestHandlerRegressionMetrics.test_ill_shape", "impl"], "tokens": 183}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestHandlerRegressionMetrics(unittest.TestCase):\n\n    def test_ill_shape(self):\n        set_determinism(seed=123)\n        device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\n        # regression metrics to check + truth metric function in numpy\n        metrics = [\n            MeanSquaredError,\n            MeanAbsoluteError,\n            RootMeanSquaredError,\n            partial(PeakSignalToNoiseRatio, max_val=1.0),\n        ]\n        basedim = 10\n\n        # different shape for pred/target\n        with self.assertRaises((AssertionError, ValueError)):\n            in_tensor_a = torch.rand((basedim,)).to(device)\n            in_tensor_b = torch.rand((basedim, basedim)).to(device)\n            for mt_fn in metrics:\n                mt_fn().update([in_tensor_a, in_tensor_b])\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_regression_metrics_dist.py_unittest_SPATIAL_DIM.3": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_regression_metrics_dist.py_unittest_SPATIAL_DIM.3", "embedding": null, "metadata": {"file_path": "tests/test_handler_regression_metrics_dist.py", "file_name": "test_handler_regression_metrics_dist.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 54, "span_ids": ["msemetric_np", "impl", "flatten", "maemetric_np", "psnrmetric_np", "docstring", "rmsemetric_np"], "tokens": 395}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nimport torch.distributed as dist\nfrom ignite.engine import Engine\n\nfrom monai.handlers import MeanAbsoluteError, MeanSquaredError, PeakSignalToNoiseRatio, RootMeanSquaredError\nfrom monai.utils import set_determinism\nfrom tests.utils import DistCall, DistTestCase\n\n\n# define a numpy flatten function that only preserves batch dimension\ndef flatten(data):\n    return np.reshape(data, [data.shape[0], -1])\n\n\n# define metrics computation truth functions to check our monai metrics against\ndef msemetric_np(y_pred, y):\n    return np.mean((flatten(y_pred) - flatten(y)) ** 2)\n\n\ndef maemetric_np(y_pred, y):\n    return np.mean(np.abs(flatten(y_pred) - flatten(y)))\n\n\ndef rmsemetric_np(y_pred, y):\n    return np.mean(np.sqrt(np.mean((flatten(y_pred) - flatten(y)) ** 2, axis=1)))\n\n\ndef psnrmetric_np(max_val, y_pred, y):\n    mse = np.mean((flatten(y_pred) - flatten(y)) ** 2, axis=1)\n    return np.mean(20 * np.log10(max_val) - 10 * np.log10(mse))\n\n\n# define tensor size as (BATCH_SIZE, (BASE_DIM_SIZE,) * SPATIAL_DIM)\n# One tensor with following shape takes 4*32*32*32*32/(8*1000) = 512 MB on a single GPU\n# We have total of 2 tensors each on one GPU for following tests, so required GPU memory is 1024 MB on each GPU\n# The required GPU memory can be lowered by changing BASE_DIM_SIZE to another value e.g. BASE_DIM_SIZE=16 will\n# require 128 MB on each GPU\nBATCH_SIZE = 4\nBASE_DIM_SIZE = 32\nSPATIAL_DIM = 3", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_regression_metrics_dist.py_DistributedMeanSquaredError_DistributedMeanSquaredError._compute.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_regression_metrics_dist.py_DistributedMeanSquaredError_DistributedMeanSquaredError._compute.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_handler_regression_metrics_dist.py", "file_name": "test_handler_regression_metrics_dist.py", "file_type": "text/x-python", "category": "test", "start_line": 57, "end_line": 100, "span_ids": ["DistributedMeanSquaredError", "DistributedMeanSquaredError.test_compute", "DistributedMeanSquaredError._compute"], "tokens": 432}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DistributedMeanSquaredError(DistTestCase):\n    @DistCall(nnodes=1, nproc_per_node=2)\n    def test_compute(self):\n        set_determinism(123)\n        self._compute()\n\n    def _compute(self):\n        device = f\"cuda:{dist.get_rank()}\" if torch.cuda.is_available() else \"cpu\"\n        metric = MeanSquaredError()\n\n        def _val_func(engine, batch):\n            pass\n\n        engine = Engine(_val_func)\n        metric.attach(engine, \"MSE\")\n\n        # get testing data\n        batch = BATCH_SIZE\n        base = BASE_DIM_SIZE\n        spatial = SPATIAL_DIM\n        in_tensor_a1 = torch.rand((batch,) + (base,) * (spatial - 1))\n        in_tensor_b1 = torch.rand((batch,) + (base,) * (spatial - 1))\n\n        in_tensor_a2 = torch.rand((batch,) + (base,) * (spatial - 1))\n        in_tensor_b2 = torch.rand((batch,) + (base,) * (spatial - 1))\n\n        if dist.get_rank() == 0:\n            y_pred = in_tensor_a1.to(device)\n            y = in_tensor_b1.to(device)\n            metric.update([y_pred, y])\n\n        if dist.get_rank() == 1:\n            y_pred = in_tensor_a2.to(device)\n            y = in_tensor_b2.to(device)\n            metric.update([y_pred, y])\n\n        out_tensor = metric.compute()\n\n        # do numpy functions to get ground truth referece\n        out_tensor_np1 = msemetric_np(y_pred=in_tensor_a1.cpu().numpy(), y=in_tensor_b1.cpu().numpy())\n        out_tensor_np2 = msemetric_np(y_pred=in_tensor_a2.cpu().numpy(), y=in_tensor_b2.cpu().numpy())\n        out_tensor_np = (out_tensor_np1 + out_tensor_np2) / 2.0\n\n        np.testing.assert_allclose(out_tensor, out_tensor_np, rtol=1e-04, atol=1e-04)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_regression_metrics_dist.py_DistributedMeanAbsoluteError_DistributedMeanAbsoluteError._compute.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_regression_metrics_dist.py_DistributedMeanAbsoluteError_DistributedMeanAbsoluteError._compute.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_handler_regression_metrics_dist.py", "file_name": "test_handler_regression_metrics_dist.py", "file_type": "text/x-python", "category": "test", "start_line": 103, "end_line": 146, "span_ids": ["DistributedMeanAbsoluteError.test_compute", "DistributedMeanAbsoluteError", "DistributedMeanAbsoluteError._compute"], "tokens": 434}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DistributedMeanAbsoluteError(DistTestCase):\n    @DistCall(nnodes=1, nproc_per_node=2)\n    def test_compute(self):\n        set_determinism(123)\n        self._compute()\n\n    def _compute(self):\n        device = f\"cuda:{dist.get_rank()}\" if torch.cuda.is_available() else \"cpu\"\n        metric = MeanAbsoluteError()\n\n        def _val_func(engine, batch):\n            pass\n\n        engine = Engine(_val_func)\n        metric.attach(engine, \"MAE\")\n\n        # get testing data\n        batch = BATCH_SIZE\n        base = BASE_DIM_SIZE\n        spatial = SPATIAL_DIM\n        in_tensor_a1 = torch.rand((batch,) + (base,) * (spatial - 1))\n        in_tensor_b1 = torch.rand((batch,) + (base,) * (spatial - 1))\n\n        in_tensor_a2 = torch.rand((batch,) + (base,) * (spatial - 1))\n        in_tensor_b2 = torch.rand((batch,) + (base,) * (spatial - 1))\n\n        if dist.get_rank() == 0:\n            y_pred = in_tensor_a1.to(device)\n            y = in_tensor_b1.to(device)\n            metric.update([y_pred, y])\n\n        if dist.get_rank() == 1:\n            y_pred = in_tensor_a2.to(device)\n            y = in_tensor_b2.to(device)\n            metric.update([y_pred, y])\n\n        out_tensor = metric.compute()\n\n        # do numpy functions to get ground truth referece\n        out_tensor_np1 = maemetric_np(y_pred=in_tensor_a1.cpu().numpy(), y=in_tensor_b1.cpu().numpy())\n        out_tensor_np2 = maemetric_np(y_pred=in_tensor_a2.cpu().numpy(), y=in_tensor_b2.cpu().numpy())\n        out_tensor_np = (out_tensor_np1 + out_tensor_np2) / 2.0\n\n        np.testing.assert_allclose(out_tensor, out_tensor_np, rtol=1e-04, atol=1e-04)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_regression_metrics_dist.py_DistributedRootMeanSquaredError_DistributedRootMeanSquaredError._compute.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_regression_metrics_dist.py_DistributedRootMeanSquaredError_DistributedRootMeanSquaredError._compute.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_handler_regression_metrics_dist.py", "file_name": "test_handler_regression_metrics_dist.py", "file_type": "text/x-python", "category": "test", "start_line": 149, "end_line": 192, "span_ids": ["DistributedRootMeanSquaredError.test_compute", "DistributedRootMeanSquaredError._compute", "DistributedRootMeanSquaredError"], "tokens": 436}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DistributedRootMeanSquaredError(DistTestCase):\n    @DistCall(nnodes=1, nproc_per_node=2)\n    def test_compute(self):\n        set_determinism(123)\n        self._compute()\n\n    def _compute(self):\n        device = f\"cuda:{dist.get_rank()}\" if torch.cuda.is_available() else \"cpu\"\n        metric = RootMeanSquaredError()\n\n        def _val_func(engine, batch):\n            pass\n\n        engine = Engine(_val_func)\n        metric.attach(engine, \"RMSE\")\n\n        # get testing data\n        batch = BATCH_SIZE\n        base = BASE_DIM_SIZE\n        spatial = SPATIAL_DIM\n        in_tensor_a1 = torch.rand((batch,) + (base,) * (spatial - 1))\n        in_tensor_b1 = torch.rand((batch,) + (base,) * (spatial - 1))\n\n        in_tensor_a2 = torch.rand((batch,) + (base,) * (spatial - 1))\n        in_tensor_b2 = torch.rand((batch,) + (base,) * (spatial - 1))\n\n        if dist.get_rank() == 0:\n            y_pred = in_tensor_a1.to(device)\n            y = in_tensor_b1.to(device)\n            metric.update([y_pred, y])\n\n        if dist.get_rank() == 1:\n            y_pred = in_tensor_a2.to(device)\n            y = in_tensor_b2.to(device)\n            metric.update([y_pred, y])\n\n        out_tensor = metric.compute()\n\n        # do numpy functions to get ground truth referece\n        out_tensor_np1 = rmsemetric_np(y_pred=in_tensor_a1.cpu().numpy(), y=in_tensor_b1.cpu().numpy())\n        out_tensor_np2 = rmsemetric_np(y_pred=in_tensor_a2.cpu().numpy(), y=in_tensor_b2.cpu().numpy())\n        out_tensor_np = (out_tensor_np1 + out_tensor_np2) / 2.0\n\n        np.testing.assert_allclose(out_tensor, out_tensor_np, rtol=1e-04, atol=1e-04)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_regression_metrics_dist.py_DistributedPeakSignalToNoiseRatio_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_regression_metrics_dist.py_DistributedPeakSignalToNoiseRatio_", "embedding": null, "metadata": {"file_path": "tests/test_handler_regression_metrics_dist.py", "file_name": "test_handler_regression_metrics_dist.py", "file_type": "text/x-python", "category": "test", "start_line": 195, "end_line": 244, "span_ids": ["DistributedPeakSignalToNoiseRatio._compute", "DistributedPeakSignalToNoiseRatio", "impl:7", "DistributedPeakSignalToNoiseRatio.test_compute"], "tokens": 473}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DistributedPeakSignalToNoiseRatio(DistTestCase):\n    @DistCall(nnodes=1, nproc_per_node=2)\n    def test_compute(self):\n        set_determinism(123)\n        self._compute()\n\n    def _compute(self):\n        device = f\"cuda:{dist.get_rank()}\" if torch.cuda.is_available() else \"cpu\"\n        max_val = 1.0\n        metric = PeakSignalToNoiseRatio(max_val=max_val)\n\n        def _val_func(engine, batch):\n            pass\n\n        engine = Engine(_val_func)\n        metric.attach(engine, \"PSNR\")\n\n        # get testing data\n        batch = BATCH_SIZE\n        base = BASE_DIM_SIZE\n        spatial = SPATIAL_DIM\n        in_tensor_a1 = torch.rand((batch,) + (base,) * (spatial - 1))\n        in_tensor_b1 = torch.rand((batch,) + (base,) * (spatial - 1))\n\n        in_tensor_a2 = torch.rand((batch,) + (base,) * (spatial - 1))\n        in_tensor_b2 = torch.rand((batch,) + (base,) * (spatial - 1))\n\n        if dist.get_rank() == 0:\n            y_pred = in_tensor_a1.to(device)\n            y = in_tensor_b1.to(device)\n            metric.update([y_pred, y])\n\n        if dist.get_rank() == 1:\n            y_pred = in_tensor_a2.to(device)\n            y = in_tensor_b2.to(device)\n            metric.update([y_pred, y])\n\n        out_tensor = metric.compute()\n\n        # do numpy functions to get ground truth referece\n        out_tensor_np1 = psnrmetric_np(max_val=max_val, y_pred=in_tensor_a1.cpu().numpy(), y=in_tensor_b1.cpu().numpy())\n        out_tensor_np2 = psnrmetric_np(max_val=max_val, y_pred=in_tensor_a2.cpu().numpy(), y=in_tensor_b2.cpu().numpy())\n        out_tensor_np = (out_tensor_np1 + out_tensor_np2) / 2.0\n\n        np.testing.assert_allclose(out_tensor, out_tensor_np, rtol=1e-04, atol=1e-04)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_smartcache.py_sys_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_smartcache.py_sys_", "embedding": null, "metadata": {"file_path": "tests/test_handler_smartcache.py", "file_name": "test_handler_smartcache.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 46, "span_ids": ["TestHandlerSmartCache.test_content", "TestHandlerSmartCache", "impl", "docstring"], "tokens": 327}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import sys\nimport unittest\n\nimport torch\nfrom ignite.engine import Engine\n\nfrom monai.data import SmartCacheDataset\nfrom monai.handlers import SmartCacheHandler\nfrom tests.utils import SkipIfBeforePyTorchVersion\n\n\n@SkipIfBeforePyTorchVersion((1, 7))\nclass TestHandlerSmartCache(unittest.TestCase):\n    def test_content(self):\n        data = [0, 1, 2, 3, 4, 5, 6, 7, 8]\n        expected = [[0, 1, 2, 3, 4], [1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7], [4, 5, 6, 7, 8]]\n\n        # set up engine\n        def _train_func(engine, batch):\n            self.assertListEqual(batch.tolist(), expected[engine.state.epoch - 1])\n\n        engine = Engine(_train_func)\n\n        # set up testing handler\n        dataset = SmartCacheDataset(data, transform=None, replace_rate=0.2, cache_num=5, shuffle=False)\n        workers = 2 if sys.platform == \"linux\" else 0\n        data_loader = torch.utils.data.DataLoader(dataset, batch_size=5, num_workers=workers, persistent_workers=False)\n        SmartCacheHandler(dataset).attach(engine)\n\n        engine.run(data_loader, max_epochs=5)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_image_dataset.py_os__TestCompose.__call__.return.data_meta": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_image_dataset.py_os__TestCompose.__call__.return.data_meta", "embedding": null, "metadata": {"file_path": "tests/test_image_dataset.py", "file_name": "test_image_dataset.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 44, "span_ids": ["_TestCompose", "RandTest.randomize", "_TestCompose.__call__", "docstring", "RandTest", "RandTest.__call__"], "tokens": 224}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport tempfile\nimport unittest\n\nimport nibabel as nib\nimport numpy as np\n\nfrom monai.data import ImageDataset\nfrom monai.transforms import Compose, EnsureChannelFirst, RandAdjustContrast, RandomizableTransform, Spacing\n\nFILENAMES = [\"test1.nii.gz\", \"test2.nii\", \"test3.nii.gz\"]\n\n\nclass RandTest(RandomizableTransform):\n    \"\"\"\n    randomisable transform for testing.\n    \"\"\"\n\n    def randomize(self, data=None):\n        self._a = self.R.random()\n\n    def __call__(self, data):\n        self.randomize()\n        return data + self._a\n\n\nclass _TestCompose(Compose):\n    def __call__(self, data, meta):\n        data = self.transforms[0](data, meta)  # ensure channel first\n        data, _, meta[\"affine\"] = self.transforms[1](data, meta[\"affine\"])  # spacing\n        if len(self.transforms) == 3:\n            return self.transforms[2](data), meta  # image contrast\n        return data, meta", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_image_dataset.py_TestImageDataset_TestImageDataset.test_use_case.with_tempfile_TemporaryDi.None_3": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_image_dataset.py_TestImageDataset_TestImageDataset.test_use_case.with_tempfile_TemporaryDi.None_3", "embedding": null, "metadata": {"file_path": "tests/test_image_dataset.py", "file_name": "test_image_dataset.py", "file_type": "text/x-python", "category": "test", "start_line": 47, "end_line": 68, "span_ids": ["TestImageDataset", "TestImageDataset.test_use_case"], "tokens": 328}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestImageDataset(unittest.TestCase):\n    def test_use_case(self):\n        with tempfile.TemporaryDirectory() as tempdir:\n            img_ = nib.Nifti1Image(np.random.randint(0, 2, size=(20, 20, 20)), np.eye(4))\n            seg_ = nib.Nifti1Image(np.random.randint(0, 2, size=(20, 20, 20)), np.eye(4))\n            img_name, seg_name = os.path.join(tempdir, \"img.nii.gz\"), os.path.join(tempdir, \"seg.nii.gz\")\n            nib.save(img_, img_name)\n            nib.save(seg_, seg_name)\n            img_list, seg_list = [img_name], [seg_name]\n\n            img_xform = _TestCompose([EnsureChannelFirst(), Spacing(pixdim=(1.5, 1.5, 3.0)), RandAdjustContrast()])\n            seg_xform = _TestCompose([EnsureChannelFirst(), Spacing(pixdim=(1.5, 1.5, 3.0), mode=\"nearest\")])\n            img_dataset = ImageDataset(\n                image_files=img_list,\n                seg_files=seg_list,\n                transform=img_xform,\n                seg_transform=seg_xform,\n                image_only=False,\n                transform_with_metadata=True,\n            )\n            self.assertTupleEqual(img_dataset[0][0].shape, (1, 14, 14, 7))\n            self.assertTupleEqual(img_dataset[0][1].shape, (1, 14, 14, 7))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_image_dataset.py_TestImageDataset.test_dataset_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_image_dataset.py_TestImageDataset.test_dataset_", "embedding": null, "metadata": {"file_path": "tests/test_image_dataset.py", "file_name": "test_image_dataset.py", "file_type": "text/x-python", "category": "test", "start_line": 70, "end_line": 159, "span_ids": ["TestImageDataset.test_dataset", "impl:3"], "tokens": 1002}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestImageDataset(unittest.TestCase):\n\n    def test_dataset(self):\n        with tempfile.TemporaryDirectory() as tempdir:\n            full_names, ref_data = [], []\n            for filename in FILENAMES:\n                test_image = np.random.randint(0, 2, size=(4, 4, 4))\n                ref_data.append(test_image)\n                save_path = os.path.join(tempdir, filename)\n                full_names.append(save_path)\n                nib.save(nib.Nifti1Image(test_image, np.eye(4)), save_path)\n\n            # default loading no meta\n            dataset = ImageDataset(full_names)\n            for d, ref in zip(dataset, ref_data):\n                np.testing.assert_allclose(d, ref, atol=1e-3)\n\n            # loading no meta, int\n            dataset = ImageDataset(full_names, dtype=np.float16)\n            for d, _ in zip(dataset, ref_data):\n                self.assertEqual(d.dtype, np.float16)\n\n            # loading with meta, no transform\n            dataset = ImageDataset(full_names, image_only=False)\n            for d_tuple, ref in zip(dataset, ref_data):\n                d, meta = d_tuple\n                np.testing.assert_allclose(d, ref, atol=1e-3)\n                np.testing.assert_allclose(meta[\"original_affine\"], np.eye(4))\n\n            # loading image/label, no meta\n            dataset = ImageDataset(full_names, seg_files=full_names, image_only=True)\n            for d_tuple, ref in zip(dataset, ref_data):\n                img, seg = d_tuple\n                np.testing.assert_allclose(img, ref, atol=1e-3)\n                np.testing.assert_allclose(seg, ref, atol=1e-3)\n\n            # loading image/label, no meta\n            dataset = ImageDataset(full_names, transform=lambda x: x + 1, image_only=True)\n            for d, ref in zip(dataset, ref_data):\n                np.testing.assert_allclose(d, ref + 1, atol=1e-3)\n\n            # set seg transform, but no seg_files\n            with self.assertRaises(RuntimeError):\n                dataset = ImageDataset(full_names, seg_transform=lambda x: x + 1, image_only=True)\n                _ = dataset[0]\n\n            # set seg transform, but no seg_files\n            with self.assertRaises(RuntimeError):\n                dataset = ImageDataset(full_names, seg_transform=lambda x: x + 1, image_only=True)\n                _ = dataset[0]\n\n            # loading image/label, with meta\n            dataset = ImageDataset(\n                full_names,\n                transform=lambda x: x + 1,\n                seg_files=full_names,\n                seg_transform=lambda x: x + 2,\n                image_only=False,\n            )\n            for d_tuple, ref in zip(dataset, ref_data):\n                img, seg, meta, seg_meta = d_tuple\n                np.testing.assert_allclose(img, ref + 1, atol=1e-3)\n                np.testing.assert_allclose(seg, ref + 2, atol=1e-3)\n                np.testing.assert_allclose(meta[\"original_affine\"], np.eye(4), atol=1e-3)\n                np.testing.assert_allclose(seg_meta[\"original_affine\"], np.eye(4), atol=1e-3)\n\n            # loading image/label, with meta\n            dataset = ImageDataset(\n                full_names, transform=lambda x: x + 1, seg_files=full_names, labels=[1, 2, 3], image_only=False\n            )\n            for idx, (d_tuple, ref) in enumerate(zip(dataset, ref_data)):\n                img, seg, label, meta, seg_meta = d_tuple\n                np.testing.assert_allclose(img, ref + 1, atol=1e-3)\n                np.testing.assert_allclose(seg, ref, atol=1e-3)\n                np.testing.assert_allclose(idx + 1, label)\n                np.testing.assert_allclose(meta[\"original_affine\"], np.eye(4), atol=1e-3)\n                np.testing.assert_allclose(seg_meta[\"original_affine\"], np.eye(4), atol=1e-3)\n\n            # loading image/label, with sync. transform\n            dataset = ImageDataset(\n                full_names, transform=RandTest(), seg_files=full_names, seg_transform=RandTest(), image_only=False\n            )\n            for d_tuple, ref in zip(dataset, ref_data):\n                img, seg, meta, seg_meta = d_tuple\n                np.testing.assert_allclose(img, seg, atol=1e-3)\n                self.assertTrue(not np.allclose(img, ref))\n                np.testing.assert_allclose(meta[\"original_affine\"], np.eye(4), atol=1e-3)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_segmentation_3d.py_run_inference_test_run_inference_test.return.dice_metric_aggregate_i": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_segmentation_3d.py_run_inference_test_run_inference_test.return.dice_metric_aggregate_i", "embedding": null, "metadata": {"file_path": "tests/test_integration_segmentation_3d.py", "file_name": "test_integration_segmentation_3d.py", "file_type": "text/x-python", "category": "test", "start_line": 178, "end_line": 227, "span_ids": ["run_inference_test"], "tokens": 637}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def run_inference_test(root_dir, device=\"cuda:0\"):\n    images = sorted(glob(os.path.join(root_dir, \"im*.nii.gz\")))\n    segs = sorted(glob(os.path.join(root_dir, \"seg*.nii.gz\")))\n    val_files = [{\"img\": img, \"seg\": seg} for img, seg in zip(images, segs)]\n\n    # define transforms for image and segmentation\n    val_transforms = Compose(\n        [\n            LoadImaged(keys=[\"img\", \"seg\"]),\n            EnsureChannelFirstd(keys=[\"img\", \"seg\"]),\n            # resampling with align_corners=True or dtype=float64 will generate\n            # slight different results between PyTorch 1.5 an 1.6\n            Spacingd(keys=[\"img\", \"seg\"], pixdim=[1.2, 0.8, 0.7], mode=[\"bilinear\", \"nearest\"], dtype=np.float32),\n            ScaleIntensityd(keys=\"img\"),\n            ToTensord(keys=[\"img\", \"seg\"]),\n        ]\n    )\n    val_ds = monai.data.Dataset(data=val_files, transform=val_transforms)\n    # sliding window inference need to input 1 image in every iteration\n    val_loader = monai.data.DataLoader(val_ds, batch_size=1, num_workers=4)\n    val_post_tran = Compose([ToTensor(), Activations(sigmoid=True), AsDiscrete(threshold=0.5)])\n    dice_metric = DiceMetric(include_background=True, reduction=\"mean\", get_not_nans=False)\n\n    model = UNet(\n        spatial_dims=3,\n        in_channels=1,\n        out_channels=1,\n        channels=(16, 32, 64, 128, 256),\n        strides=(2, 2, 2, 2),\n        num_res_units=2,\n    ).to(device)\n\n    model_filename = os.path.join(root_dir, \"best_metric_model.pth\")\n    model.load_state_dict(torch.load(model_filename))\n    with eval_mode(model):\n        # resampling with align_corners=True or dtype=float64 will generate\n        # slight different results between PyTorch 1.5 an 1.6\n        saver = NiftiSaver(output_dir=os.path.join(root_dir, \"output\"), dtype=np.float32)\n        for val_data in val_loader:\n            val_images, val_labels = val_data[\"img\"].to(device), val_data[\"seg\"].to(device)\n            # define sliding window size and batch size for windows inference\n            sw_batch_size, roi_size = 4, (96, 96, 96)\n            val_outputs = sliding_window_inference(val_images, roi_size, sw_batch_size, model)\n            # decollate prediction into a list and execute post processing for every item\n            val_outputs = [val_post_tran(i) for i in decollate_batch(val_outputs)]\n            # compute metrics\n            dice_metric(y_pred=val_outputs, y=val_labels)\n            saver.save_batch(val_outputs, val_data[\"img_meta_dict\"])\n\n    return dice_metric.aggregate().item()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_workflows.py_IntegrationWorkflows.train_and_infer_IntegrationWorkflows.train_and_infer.results_append_infer_metr": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_workflows.py_IntegrationWorkflows.train_and_infer_IntegrationWorkflows.train_and_infer.results_append_infer_metr", "embedding": null, "metadata": {"file_path": "tests/test_integration_workflows.py", "file_name": "test_integration_workflows.py", "file_type": "text/x-python", "category": "test", "start_line": 301, "end_line": 320, "span_ids": ["IntegrationWorkflows.train_and_infer"], "tokens": 284}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_quick\nclass IntegrationWorkflows(DistTestCase):\n\n    def train_and_infer(self, idx=0):\n        results = []\n        set_determinism(seed=0)\n        best_metric = run_training_test(self.data_dir, device=self.device, amp=(idx == 2))\n        model_file = sorted(glob(os.path.join(self.data_dir, \"net_key_metric*.pt\")))[-1]\n        infer_metric = run_inference_test(self.data_dir, model_file, device=self.device, amp=(idx == 2))\n\n        print(\"best metric\", best_metric)\n        print(\"infer metric\", infer_metric)\n        if idx == 2:\n            self.assertTrue(test_integration_value(TASK, key=\"best_metric_2\", data=best_metric, rtol=1e-2))\n        else:\n            self.assertTrue(test_integration_value(TASK, key=\"best_metric\", data=best_metric, rtol=1e-2))\n        # check inference properties\n        if idx == 2:\n            self.assertTrue(test_integration_value(TASK, key=\"infer_metric_2\", data=infer_metric, rtol=1e-2))\n        else:\n            self.assertTrue(test_integration_value(TASK, key=\"infer_metric\", data=infer_metric, rtol=1e-2))\n        results.append(best_metric)\n        results.append(infer_metric)\n        # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_inverse.py_TestInverse.test_inverse_TestInverse.test_inverse.for_i_t_in_enumerate_rev.if_isinstance_t_Invertib.if_isinstance_fwd_bck_li.else_.self_check_inverse_name_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_inverse.py_TestInverse.test_inverse_TestInverse.test_inverse.for_i_t_in_enumerate_rev.if_isinstance_t_Invertib.if_isinstance_fwd_bck_li.else_.self_check_inverse_name_", "embedding": null, "metadata": {"file_path": "tests/test_inverse.py", "file_name": "test_inverse.py", "file_type": "text/x-python", "category": "test", "start_line": 586, "end_line": 612, "span_ids": ["TestInverse.test_inverse"], "tokens": 240}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestInverse(unittest.TestCase):\n\n    @parameterized.expand(TESTS)\n    def test_inverse(self, _, data_name, acceptable_diff, *transforms):\n        name = _\n\n        data = self.all_data[data_name]\n\n        forwards = [data.copy()]\n\n        # Apply forwards\n        for t in transforms:\n            if isinstance(t, Randomizable):\n                t.set_random_state(seed=get_seed())\n            forwards.append(t(forwards[-1]))\n\n        # Apply inverses\n        fwd_bck = forwards[-1].copy()\n        for i, t in enumerate(reversed(transforms)):\n            if isinstance(t, InvertibleTransform):\n                if isinstance(fwd_bck, list):\n                    for j, _fwd_bck in enumerate(fwd_bck):\n                        fwd_bck = t.inverse(_fwd_bck)\n                        self.check_inverse(\n                            name, data.keys(), forwards[-i - 2], fwd_bck, forwards[-1][j], acceptable_diff\n                        )\n                else:\n                    fwd_bck = t.inverse(fwd_bck)\n                    self.check_inverse(name, data.keys(), forwards[-i - 2], fwd_bck, forwards[-1], acceptable_diff)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_invertd.py_sys_KEYS._image_label_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_invertd.py_sys_KEYS._image_label_", "embedding": null, "metadata": {"file_path": "tests/test_invertd.py", "file_name": "test_invertd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 42, "span_ids": ["docstring"], "tokens": 166}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import sys\nimport unittest\n\nimport numpy as np\nimport torch\n\nfrom monai.data import CacheDataset, DataLoader, create_test_image_3d, decollate_batch\nfrom monai.transforms import (\n    AddChanneld,\n    CastToTyped,\n    Compose,\n    CopyItemsd,\n    EnsureTyped,\n    Invertd,\n    LoadImaged,\n    Orientationd,\n    RandAffined,\n    RandAxisFlipd,\n    RandFlipd,\n    RandRotate90d,\n    RandRotated,\n    RandZoomd,\n    ResizeWithPadOrCropd,\n    ScaleIntensityd,\n    Spacingd,\n    ToTensord,\n)\nfrom monai.utils import set_determinism\nfrom tests.utils import make_nifti_image\n\nKEYS = [\"image\", \"label\"]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_k_space_spike_noise.py_TestKSpaceSpikeNoise.test_highlighted_kspace_pixel_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_k_space_spike_noise.py_TestKSpaceSpikeNoise.test_highlighted_kspace_pixel_", "embedding": null, "metadata": {"file_path": "tests/test_k_space_spike_noise.py", "file_name": "test_k_space_spike_noise.py", "file_type": "text/x-python", "category": "test", "start_line": 64, "end_line": 86, "span_ids": ["TestKSpaceSpikeNoise.test_highlighted_kspace_pixel", "impl:6"], "tokens": 238}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestKSpaceSpikeNoise(unittest.TestCase):\n\n    @parameterized.expand(TESTS)\n    def test_highlighted_kspace_pixel(self, im_shape, as_tensor_input):\n\n        im = self.get_data(im_shape, as_tensor_input)\n        loc = [0, int(im.shape[1] / 2), 0] if len(im_shape) == 2 else [0, int(im.shape[1] / 2), 0, 0]\n        k_intensity = 10\n        t = KSpaceSpikeNoise(loc, k_intensity)\n        out = t(im)\n\n        self.assertEqual(type(im), type(out))\n        if isinstance(out, torch.Tensor):\n            self.assertEqual(im.device, out.device)\n            out = out.cpu()\n\n        n_dims = len(im_shape)\n        out_k = fftshift(fftn(out, axes=tuple(range(-n_dims, 0))), axes=tuple(range(-n_dims, 0)))\n        log_mag = np.log(np.absolute(out_k))\n        np.testing.assert_allclose(k_intensity, log_mag[tuple(loc)], 1e-4)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_k_space_spike_noised.py_unittest_KEYS._image_label_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_k_space_spike_noised.py_unittest_KEYS._image_label_", "embedding": null, "metadata": {"file_path": "tests/test_k_space_spike_noised.py", "file_name": "test_k_space_spike_noised.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 30, "span_ids": ["docstring"], "tokens": 136}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom copy import deepcopy\n\nimport numpy as np\nimport torch\nfrom numpy.fft import fftn, fftshift\nfrom parameterized import parameterized\n\nfrom monai.data.synthetic import create_test_image_2d, create_test_image_3d\nfrom monai.transforms import KSpaceSpikeNoised\nfrom monai.utils.misc import set_determinism\nfrom tests.utils import TEST_NDARRAYS\n\nTESTS = []\nfor shape in ((128, 64), (64, 48, 80)):\n    for p in TEST_NDARRAYS:\n        TESTS.append((shape, p))\n\nKEYS = [\"image\", \"label\"]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_k_space_spike_noised.py_TestKSpaceSpikeNoised_TestKSpaceSpikeNoised.get_data.return._k_v_for_k_v_in_zip_KEY": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_k_space_spike_noised.py_TestKSpaceSpikeNoised_TestKSpaceSpikeNoised.get_data.return._k_v_for_k_v_in_zip_KEY", "embedding": null, "metadata": {"file_path": "tests/test_k_space_spike_noised.py", "file_name": "test_k_space_spike_noised.py", "file_type": "text/x-python", "category": "test", "start_line": 33, "end_line": 46, "span_ids": ["TestKSpaceSpikeNoised.tearDown", "TestKSpaceSpikeNoised", "TestKSpaceSpikeNoised.setUp", "TestKSpaceSpikeNoised.get_data"], "tokens": 142}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestKSpaceSpikeNoised(unittest.TestCase):\n    def setUp(self):\n        set_determinism(0)\n        super().setUp()\n\n    def tearDown(self):\n        set_determinism(None)\n\n    @staticmethod\n    def get_data(im_shape, im_type):\n        create_test_image = create_test_image_2d if len(im_shape) == 2 else create_test_image_3d\n        ims = create_test_image(*im_shape, rad_max=20, noise_max=0.0, num_seg_classes=5)\n        ims = [im_type(im[None]) for im in ims]\n        return {k: v for k, v in zip(KEYS, ims)}", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_k_space_spike_noised.py_TestKSpaceSpikeNoised.test_highlighted_kspace_pixel_TestKSpaceSpikeNoised.test_highlighted_kspace_pixel.for_k_in_KEYS_.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_k_space_spike_noised.py_TestKSpaceSpikeNoised.test_highlighted_kspace_pixel_TestKSpaceSpikeNoised.test_highlighted_kspace_pixel.for_k_in_KEYS_.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_k_space_spike_noised.py", "file_name": "test_k_space_spike_noised.py", "file_type": "text/x-python", "category": "test", "start_line": 67, "end_line": 86, "span_ids": ["TestKSpaceSpikeNoised.test_highlighted_kspace_pixel"], "tokens": 226}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestKSpaceSpikeNoised(unittest.TestCase):\n\n    @parameterized.expand(TESTS)\n    def test_highlighted_kspace_pixel(self, im_shape, im_type):\n\n        data = self.get_data(im_shape, im_type)\n        loc = [0] + [int(im_shape[i] / 2) for i in range(len(im_shape))]\n        k_intensity = 10\n\n        t = KSpaceSpikeNoised(KEYS, loc, k_intensity)\n        out = t(data)\n\n        for k in KEYS:\n            self.assertEqual(type(out[k]), type(data[k]))\n            if isinstance(out[k], torch.Tensor):\n                self.assertEqual(out[k].device, data[k].device)\n                out[k] = out[k].cpu()\n\n            n_dims = len(im_shape)\n            out_k = fftshift(fftn(out[k], axes=tuple(range(-n_dims, 0))), axes=tuple(range(-n_dims, 0)))\n            log_mag = np.log(np.absolute(out_k))\n            np.testing.assert_allclose(k_intensity, log_mag[tuple(loc)], 1e-1)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_k_space_spike_noised.py_TestKSpaceSpikeNoised.test_dict_matches_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_k_space_spike_noised.py_TestKSpaceSpikeNoised.test_dict_matches_", "embedding": null, "metadata": {"file_path": "tests/test_k_space_spike_noised.py", "file_name": "test_k_space_spike_noised.py", "file_type": "text/x-python", "category": "test", "start_line": 88, "end_line": 110, "span_ids": ["TestKSpaceSpikeNoised.test_dict_matches", "impl:8"], "tokens": 235}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestKSpaceSpikeNoised(unittest.TestCase):\n\n    @parameterized.expand(TESTS)\n    def test_dict_matches(self, im_shape, im_type):\n        data = self.get_data(im_shape, im_type)\n        # use same image for both dictionary entries to check same trans is applied to them\n        data = {KEYS[0]: deepcopy(data[KEYS[0]]), KEYS[1]: deepcopy(data[KEYS[0]])}\n        loc = [0] + [int(im_shape[i] / 2) for i in range(len(im_shape))]\n        k_intensity = 10\n\n        t = KSpaceSpikeNoised(KEYS, loc, k_intensity)\n        out = t(deepcopy(data))\n\n        for k in KEYS:\n            self.assertEqual(type(out[k]), type(data[k]))\n            if isinstance(out[k], torch.Tensor):\n                self.assertEqual(out[k].device, data[k].device)\n                out[k] = out[k].cpu()\n\n        np.testing.assert_allclose(out[KEYS[0]], out[KEYS[1]])\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_lmdbdataset.py_os__InplaceXform.__call__.return.data": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_lmdbdataset.py_os__InplaceXform.__call__.return.data", "embedding": null, "metadata": {"file_path": "tests/test_lmdbdataset.py", "file_name": "test_lmdbdataset.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 88, "span_ids": ["impl:11", "_InplaceXform.__call__", "_InplaceXform", "docstring"], "tokens": 660}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport shutil\nimport tempfile\nimport unittest\n\nimport nibabel as nib\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.data import LMDBDataset, json_hashing\nfrom monai.transforms import Compose, LoadImaged, SimulateDelayd, Transform\nfrom tests.utils import DistCall, DistTestCase, skip_if_windows\n\nTEST_CASE_1 = [\n    Compose(\n        [\n            LoadImaged(keys=[\"image\", \"label\", \"extra\"]),\n            SimulateDelayd(keys=[\"image\", \"label\", \"extra\"], delay_time=[1e-7, 1e-6, 1e-5]),\n        ]\n    ),\n    (128, 128, 128),\n]\n\nTEST_CASE_2 = [\n    [\n        LoadImaged(keys=[\"image\", \"label\", \"extra\"]),\n        SimulateDelayd(keys=[\"image\", \"label\", \"extra\"], delay_time=[1e-7, 1e-6, 1e-5]),\n    ],\n    (128, 128, 128),\n]\n\nTEST_CASE_3 = [None, (128, 128, 128), None]\n\nTEST_CASE_4 = [\n    [\n        LoadImaged(keys=[\"image\", \"label\", \"extra\"]),\n        SimulateDelayd(keys=[\"image\", \"label\", \"extra\"], delay_time=[1e-7, 1e-6, 1e-5]),\n    ],\n    (128, 128, 128),\n    {\"db_name\": \"test42\"},\n]\n\nTEST_CASE_5 = [\n    [\n        LoadImaged(keys=[\"image\", \"label\", \"extra\"]),\n        SimulateDelayd(keys=[\"image\", \"label\", \"extra\"], delay_time=[1e-7, 1e-6, 1e-5]),\n    ],\n    (128, 128, 128),\n    {\"pickle_protocol\": 2, \"lmdb_kwargs\": {\"map_size\": 100 * 1024 ** 2}},\n]\n\nTEST_CASE_6 = [\n    [\n        LoadImaged(keys=[\"image\", \"label\", \"extra\"]),\n        SimulateDelayd(keys=[\"image\", \"label\", \"extra\"], delay_time=[1e-7, 1e-6, 1e-5]),\n    ],\n    (128, 128, 128),\n    {\"db_name\": \"testdb\", \"lmdb_kwargs\": {\"map_size\": 100 * 1024 ** 2}},\n]\n\nTEST_CASE_7 = [\n    [\n        LoadImaged(keys=[\"image\", \"label\", \"extra\"]),\n        SimulateDelayd(keys=[\"image\", \"label\", \"extra\"], delay_time=[1e-7, 1e-6, 1e-5]),\n    ],\n    (128, 128, 128),\n    {\"db_name\": \"testdb\", \"lmdb_kwargs\": {\"map_size\": 2 * 1024 ** 2}},\n]\n\n\nclass _InplaceXform(Transform):\n    def __call__(self, data):\n        if data:\n            data[0] = data[0] + np.pi\n        else:\n            data.append(1)\n        return data", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_lmdbdataset.py_TestMPLMDBDataset_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_lmdbdataset.py_TestMPLMDBDataset_", "embedding": null, "metadata": {"file_path": "tests/test_lmdbdataset.py", "file_name": "test_lmdbdataset.py", "file_type": "text/x-python", "category": "test", "start_line": 182, "end_line": 223, "span_ids": ["TestMPLMDBDataset.test_mp_cache", "TestMPLMDBDataset", "TestMPLMDBDataset.setUp", "TestMPLMDBDataset.tearDown", "impl:15"], "tokens": 448}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_windows\nclass TestMPLMDBDataset(DistTestCase):\n    def setUp(self):\n        self.tempdir = tempfile.mkdtemp()\n\n    def tearDown(self):\n        shutil.rmtree(self.tempdir)\n\n    @DistCall(nnodes=1, nproc_per_node=1)\n    def test_mp_cache(self):\n        items = [[list(range(i))] for i in range(5)]\n\n        ds = LMDBDataset(items, transform=_InplaceXform(), cache_dir=self.tempdir, lmdb_kwargs={\"map_size\": 10 * 1024})\n        self.assertEqual(items, [[[]], [[0]], [[0, 1]], [[0, 1, 2]], [[0, 1, 2, 3]]])\n        ds1 = LMDBDataset(items, transform=_InplaceXform(), cache_dir=self.tempdir, lmdb_kwargs={\"map_size\": 10 * 1024})\n        self.assertEqual(list(ds1), list(ds))\n        self.assertEqual(items, [[[]], [[0]], [[0, 1]], [[0, 1, 2]], [[0, 1, 2, 3]]])\n\n        ds = LMDBDataset(\n            items,\n            transform=_InplaceXform(),\n            cache_dir=self.tempdir,\n            lmdb_kwargs={\"map_size\": 10 * 1024},\n            hash_func=json_hashing,\n        )\n        self.assertEqual(items, [[[]], [[0]], [[0, 1]], [[0, 1, 2]], [[0, 1, 2, 3]]])\n        ds1 = LMDBDataset(\n            items,\n            transform=_InplaceXform(),\n            cache_dir=self.tempdir,\n            lmdb_kwargs={\"map_size\": 10 * 1024},\n            hash_func=json_hashing,\n        )\n        self.assertEqual(list(ds1), list(ds))\n        self.assertEqual(items, [[[]], [[0]], [[0, 1]], [[0, 1, 2]], [[0, 1, 2, 3]]])\n\n        self.assertTrue(isinstance(ds1.info(), dict))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_masked_loss.py_unittest_TEST_CASES._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_masked_loss.py_unittest_TEST_CASES._", "embedding": null, "metadata": {"file_path": "tests/test_masked_loss.py", "file_name": "test_masked_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 11, "end_line": 37, "span_ids": ["docstring"], "tokens": 202}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.losses.dice import DiceFocalLoss, DiceLoss\nfrom monai.losses.spatial_mask import MaskedLoss\nfrom monai.utils import set_determinism\nfrom tests.utils import SkipIfBeforePyTorchVersion, test_script_save\n\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\nTEST_CASES = [\n    [\n        {\n            \"loss\": DiceFocalLoss,\n            \"focal_weight\": torch.tensor([1.0, 1.0, 2.0]),\n            \"gamma\": 0.1,\n            \"lambda_focal\": 0.5,\n            \"include_background\": True,\n            \"to_onehot_y\": True,\n            \"reduction\": \"sum\",\n        },\n        [(14.538666, 20.191753), (13.17672, 8.251623)],\n    ]\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_masked_loss.py_TestMaskedLoss.test_ill_opts_TestMaskedLoss.test_ill_opts.None_2.masked_input_torch_zeros_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_masked_loss.py_TestMaskedLoss.test_ill_opts_TestMaskedLoss.test_ill_opts.None_2.masked_input_torch_zeros_", "embedding": null, "metadata": {"file_path": "tests/test_masked_loss.py", "file_name": "test_masked_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 65, "end_line": 75, "span_ids": ["TestMaskedLoss.test_ill_opts"], "tokens": 195}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestMaskedLoss(unittest.TestCase):\n\n    def test_ill_opts(self):\n        with self.assertRaisesRegex(ValueError, \"\"):\n            MaskedLoss(loss=[])\n\n        dice_loss = DiceLoss(include_background=True, sigmoid=True, smooth_nr=1e-5, smooth_dr=1e-5)\n        with self.assertRaisesRegex(ValueError, \"\"):\n            masked = MaskedLoss(loss=dice_loss)\n            masked(input=torch.zeros((3, 1, 2, 2)), target=torch.zeros((3, 1, 2, 2)), mask=torch.zeros((3, 3, 2, 2)))\n        with self.assertRaisesRegex(ValueError, \"\"):\n            masked = MaskedLoss(loss=dice_loss)\n            masked(input=torch.zeros((3, 3, 2, 2)), target=torch.zeros((3, 2, 2, 2)), mask=torch.zeros((3, 3, 2, 2)))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_masked_loss.py_TestMaskedLoss.test_script_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_masked_loss.py_TestMaskedLoss.test_script_", "embedding": null, "metadata": {"file_path": "tests/test_masked_loss.py", "file_name": "test_masked_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 77, "end_line": 90, "span_ids": ["TestMaskedLoss.test_script", "impl:5"], "tokens": 130}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestMaskedLoss(unittest.TestCase):\n\n    @SkipIfBeforePyTorchVersion((1, 7, 0))\n    def test_script(self):\n        input_param, expected_val = TEST_CASES[0]\n        size = [3, 3, 5, 5]\n        label = torch.randint(low=0, high=2, size=size)\n        label = torch.argmax(label, dim=1, keepdim=True)\n        pred = torch.randn(size)\n        loss = MaskedLoss(**input_param)\n        test_script_save(loss, pred, label)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_mmar_download.py_os_TEST_CASES._clara_pt_prostate_mri_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_mmar_download.py_os_TEST_CASES._clara_pt_prostate_mri_", "embedding": null, "metadata": {"file_path": "tests/test_mmar_download.py", "file_name": "test_mmar_download.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 27, "span_ids": ["docstring"], "tokens": 128}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport tempfile\nimport unittest\nfrom pathlib import Path\nfrom urllib.error import ContentTooShortError, HTTPError\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.apps import RemoteMMARKeys, download_mmar, get_model_spec, load_from_mmar\nfrom monai.apps.mmars import MODEL_DESC\nfrom monai.apps.mmars.mmars import _get_val\nfrom tests.utils import skip_if_quick\n\nTEST_CASES = [[\"clara_pt_prostate_mri_segmentation_1\"], [\"clara_pt_covid19_ct_lesion_segmentation_1\"]]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_mmar_download.py_TEST_EXTRACT_CASES_TEST_EXTRACT_CASES._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_mmar_download.py_TEST_EXTRACT_CASES_TEST_EXTRACT_CASES._", "embedding": null, "metadata": {"file_path": "tests/test_mmar_download.py", "file_name": "test_mmar_download.py", "file_type": "text/x-python", "category": "test", "start_line": 27, "end_line": 100, "span_ids": ["docstring"], "tokens": 1146}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "TEST_EXTRACT_CASES = [\n    (\n        {\n            \"item\": \"clara_pt_prostate_mri_segmentation_1\",\n            \"map_location\": \"cuda\" if torch.cuda.is_available() else \"cpu\",\n        },\n        \"UNet\",\n        np.array(\n            [\n                [[-0.0838, 0.0116, -0.0861], [-0.0792, 0.2216, -0.0301], [-0.0379, 0.0006, -0.0399]],\n                [[-0.0347, 0.0979, 0.0754], [0.1689, 0.3759, 0.2584], [-0.0698, 0.2740, 0.1414]],\n                [[-0.0772, 0.1046, -0.0103], [0.0917, 0.1942, 0.0284], [-0.0165, -0.0181, 0.0247]],\n            ]\n        ),\n    ),\n    (\n        {\n            \"item\": \"clara_pt_covid19_ct_lesion_segmentation_1\",\n            \"map_location\": \"cuda\" if torch.cuda.is_available() else \"cpu\",\n        },\n        \"SegResNet\",\n        np.array(\n            [\n                [\n                    [-0.21147135, 0.10815059, -0.04733997],\n                    [-0.3425553, 0.03304602, 0.113512],\n                    [0.1278807, 0.26298857, -0.0583012],\n                ],\n                [\n                    [-0.3658006, -0.14725913, 0.01149207],\n                    [-0.5453718, -0.12894264, -0.05492746],\n                    [0.16887102, 0.17586298, 0.03977356],\n                ],\n                [\n                    [-0.12767333, -0.07876065, 0.03136465],\n                    [0.26057404, -0.03538669, 0.07552322],\n                    [0.23879515, 0.04919613, 0.01725162],\n                ],\n            ]\n        ),\n    ),\n    (\n        {\n            \"item\": \"clara_pt_fed_learning_brain_tumor_mri_segmentation_1\",\n            \"map_location\": \"cuda\" if torch.cuda.is_available() else \"cpu\",\n        },\n        \"SegResNet\",\n        np.array(\n            [\n                [[-0.0839, 0.0715, -0.0760], [0.0645, 0.1186, 0.0218], [0.0303, 0.0631, -0.0648]],\n                [[0.0128, 0.1440, 0.0213], [0.1658, 0.1813, 0.0541], [-0.0627, 0.0839, 0.0660]],\n                [[-0.1207, 0.0138, -0.0808], [0.0277, 0.0416, 0.0597], [0.0455, -0.0134, -0.0949]],\n            ]\n        ),\n    ),\n    (\n        {\n            \"item\": \"clara_pt_pathology_metastasis_detection_1\",\n            \"map_location\": \"cuda\" if torch.cuda.is_available() else \"cpu\",\n        },\n        \"TorchVisionFullyConvModel\",\n        np.array(\n            [\n                [-0.00693138, -0.00441378, -0.01057985, 0.05604396, 0.03526996, -0.00399302, -0.0267504],\n                [0.00805358, 0.01016939, -0.10749951, -0.28787708, -0.27905375, -0.13328083, -0.00882593],\n                [-0.01909848, 0.04871106, 0.2957697, 0.60376877, 0.53552634, 0.24821444, 0.03773781],\n                [0.02449462, -0.07471243, -0.30943492, -0.43987238, -0.26549947, -0.00698426, 0.04395606],\n                [-0.03124012, 0.00807883, 0.06797771, -0.04612541, -0.30266526, -0.39722857, -0.25109962],\n                [0.02480375, 0.03378576, 0.06519791, 0.24546203, 0.41867673, 0.393786, 0.16055048],\n                [-0.01529332, -0.00062494, -0.016658, -0.06313603, -0.1508078, -0.09107386, -0.01239121],\n            ]\n        ),\n    ),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_mmar_download.py_TestMMMARDownload_TestMMMARDownload.test_download.try_.except_ContentTooShortEr._skipping_this_test_due_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_mmar_download.py_TestMMMARDownload_TestMMMARDownload.test_download.try_.except_ContentTooShortEr._skipping_this_test_due_", "embedding": null, "metadata": {"file_path": "tests/test_mmar_download.py", "file_name": "test_mmar_download.py", "file_type": "text/x-python", "category": "test", "start_line": 104, "end_line": 123, "span_ids": ["TestMMMARDownload.test_download", "TestMMMARDownload"], "tokens": 228}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestMMMARDownload(unittest.TestCase):\n    @parameterized.expand(TEST_CASES)\n    @skip_if_quick\n    def test_download(self, idx):\n        try:\n            # test model specification\n            cand = get_model_spec(idx)\n            self.assertEqual(cand[RemoteMMARKeys.ID], idx)\n            with self.assertLogs(level=\"INFO\", logger=\"monai.apps\"):\n                download_mmar(idx)\n            download_mmar(idx, progress=False)  # repeated to check caching\n            with tempfile.TemporaryDirectory() as tmp_dir:\n                download_mmar(idx, mmar_dir=tmp_dir, progress=False)\n                download_mmar(idx, mmar_dir=Path(tmp_dir), progress=False, version=1)  # repeated to check caching\n                self.assertTrue(os.path.exists(os.path.join(tmp_dir, idx)))\n        except (ContentTooShortError, HTTPError, RuntimeError) as e:\n            print(str(e))\n            if isinstance(e, HTTPError):\n                self.assertTrue(\"500\" in str(e))  # http error has the code 500\n            return  # skipping this test due the network connection errors", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_mmar_download.py_TestMMMARDownload.test_load_ckpt_TestMMMARDownload.test_load_ckpt.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_mmar_download.py_TestMMMARDownload.test_load_ckpt_TestMMMARDownload.test_load_ckpt.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_mmar_download.py", "file_name": "test_mmar_download.py", "file_type": "text/x-python", "category": "test", "start_line": 126, "end_line": 138, "span_ids": ["TestMMMARDownload.test_load_ckpt"], "tokens": 170}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestMMMARDownload(unittest.TestCase):\n\n    @parameterized.expand(TEST_EXTRACT_CASES)\n    @skip_if_quick\n    def test_load_ckpt(self, input_args, expected_name, expected_val):\n        try:\n            output = load_from_mmar(**input_args)\n        except (ContentTooShortError, HTTPError, RuntimeError) as e:\n            print(str(e))\n            if isinstance(e, HTTPError):\n                self.assertTrue(\"500\" in str(e))  # http error has the code 500\n            return\n        self.assertEqual(output.__class__.__name__, expected_name)\n        x = next(output.parameters())  # verify the first element\n        np.testing.assert_allclose(x[0][0].detach().cpu().numpy(), expected_val, rtol=1e-3, atol=1e-3)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_mmar_download.py_TestMMMARDownload.test_unique_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_mmar_download.py_TestMMMARDownload.test_unique_", "embedding": null, "metadata": {"file_path": "tests/test_mmar_download.py", "file_name": "test_mmar_download.py", "file_type": "text/x-python", "category": "test", "start_line": 140, "end_line": 154, "span_ids": ["TestMMMARDownload.test_search", "impl:5", "TestMMMARDownload.test_unique"], "tokens": 185}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestMMMARDownload(unittest.TestCase):\n\n    def test_unique(self):\n        # model ids are unique\n        keys = sorted(m[\"id\"] for m in MODEL_DESC)\n        self.assertTrue(keys == sorted(set(keys)))\n\n    def test_search(self):\n        self.assertEqual(_get_val({\"a\": 1, \"b\": 2}, key=\"b\"), 2)\n        self.assertEqual(_get_val({\"a\": {\"c\": {\"c\": 4}}, \"b\": {\"c\": 2}}, key=\"b\"), {\"c\": 2})\n        self.assertEqual(_get_val({\"a\": {\"c\": 4}, \"b\": {\"c\": 2}}, key=\"c\"), 4)\n        self.assertEqual(_get_val({\"a\": {\"c\": None}, \"b\": {\"c\": 2}}, key=\"c\"), 2)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_net_adapter.py_unittest_TEST_CASE_4._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_net_adapter.py_unittest_TEST_CASE_4._", "embedding": null, "metadata": {"file_path": "tests/test_net_adapter.py", "file_name": "test_net_adapter.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 38, "span_ids": ["docstring"], "tokens": 367}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks import eval_mode\nfrom monai.networks.nets import NetAdapter, resnet18\n\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\nTEST_CASE_0 = [{\"num_classes\": 1, \"use_conv\": True, \"dim\": 2}, (2, 3, 224, 224), (2, 1, 8, 1)]\n\nTEST_CASE_1 = [{\"num_classes\": 1, \"use_conv\": True, \"dim\": 3, \"pool\": None}, (2, 3, 32, 32, 32), (2, 1, 1, 1, 1)]\n\nTEST_CASE_2 = [{\"num_classes\": 5, \"use_conv\": True, \"dim\": 3, \"pool\": None}, (2, 3, 32, 32, 32), (2, 5, 1, 1, 1)]\n\nTEST_CASE_3 = [\n    {\"num_classes\": 5, \"use_conv\": True, \"pool\": (\"avg\", {\"kernel_size\": 4, \"stride\": 1}), \"dim\": 3},\n    (2, 3, 128, 128, 128),\n    (2, 5, 5, 1, 1),\n]\n\nTEST_CASE_4 = [\n    {\"num_classes\": 5, \"use_conv\": False, \"pool\": (\"adaptiveavg\", {\"output_size\": (1, 1, 1)}), \"dim\": 3},\n    (2, 3, 32, 32, 32),\n    (2, 5),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_net_adapter.py_TestNetAdapter_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_net_adapter.py_TestNetAdapter_", "embedding": null, "metadata": {"file_path": "tests/test_net_adapter.py", "file_name": "test_net_adapter.py", "file_type": "text/x-python", "category": "test", "start_line": 53, "end_line": 66, "span_ids": ["TestNetAdapter", "TestNetAdapter.test_shape", "impl:13"], "tokens": 128}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestNetAdapter(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_0, TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4])\n    def test_shape(self, input_param, input_shape, expected_shape):\n        model = resnet18(spatial_dims=input_param[\"dim\"])\n        input_param[\"model\"] = model\n        net = NetAdapter(**input_param).to(device)\n        with eval_mode(net):\n            result = net.forward(torch.randn(input_shape).to(device))\n            self.assertEqual(result.shape, expected_shape)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_network_consistency.py_json_if_extra_test_data_dir_is.for_data_path_in_glob_os_.TESTS_append_net_name_d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_network_consistency.py_json_if_extra_test_data_dir_is.for_data_path_in_glob_os_.TESTS_append_net_name_d", "embedding": null, "metadata": {"file_path": "tests/test_network_consistency.py", "file_name": "test_network_consistency.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 33, "span_ids": ["docstring"], "tokens": 170}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import json\nimport os\nimport unittest\nfrom glob import glob\nfrom typing import Sequence\nfrom unittest.case import skipIf\n\nimport torch\nfrom parameterized.parameterized import parameterized\n\nimport monai.networks.nets as nets\nfrom monai.utils import set_determinism\n\nextra_test_data_dir = os.environ.get(\"MONAI_EXTRA_TEST_DATA\")\n\nTESTS = []\nif extra_test_data_dir is not None:\n    for data_path in glob(os.path.join(extra_test_data_dir, \"**\", \"*.pt\")):\n        json_path = data_path[:-3] + \".json\"\n        # net_name is filename until first underscore (e.g., unet_0.pt is unet)\n        net_name = os.path.basename(data_path).split(\"_\")[0]\n        TESTS.append((net_name, data_path, json_path))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_network_consistency.py_TestNetworkConsistency_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_network_consistency.py_TestNetworkConsistency_", "embedding": null, "metadata": {"file_path": "tests/test_network_consistency.py", "file_name": "test_network_consistency.py", "file_type": "text/x-python", "category": "test", "start_line": 36, "end_line": 84, "span_ids": ["TestNetworkConsistency.check_output_consistency", "TestNetworkConsistency.tearDown", "impl:12", "TestNetworkConsistency", "TestNetworkConsistency.test_network_consistency", "TestNetworkConsistency.setUp"], "tokens": 332}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestNetworkConsistency(unittest.TestCase):\n    def setUp(self):\n        set_determinism(0)\n\n    def tearDown(self):\n        set_determinism(None)\n\n    @skipIf(\n        len(TESTS) == 0,\n        \"To run these tests, clone https://github.com/Project-MONAI/MONAI-extra-test-data and set MONAI_EXTRA_TEST_DATA\",\n    )\n    @parameterized.expand(TESTS, skip_on_empty=True)\n    def test_network_consistency(self, net_name, data_path, json_path):\n\n        print(\"Net name: \" + net_name)\n        print(\"Data path: \" + data_path)\n        print(\"JSON path: \" + json_path)\n\n        # Load data\n        loaded_data = torch.load(data_path)\n\n        # Load json from file\n        json_file = open(json_path)\n        model_params = json.load(json_file)\n        json_file.close()\n\n        # Create model\n        model = getattr(nets, net_name)(**model_params)\n        model.load_state_dict(loaded_data[\"model\"], strict=False)\n        model.eval()\n\n        in_data = loaded_data[\"in_data\"]\n        expected_out_data = loaded_data[\"out_data\"]\n\n        actual_out_data = model(in_data)\n\n        self.check_output_consistency(actual_out_data, expected_out_data)\n\n    def check_output_consistency(self, actual, expected):\n        if isinstance(actual, Sequence):\n            for a, e in zip(actual, expected):\n                self.check_output_consistency(a, e)\n        else:\n            torch.testing.assert_allclose(actual, expected)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nifti_endianness.py_TestNiftiEndianness_TestNiftiEndianness.test_endianness.if_isinstance_ret_dict_.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nifti_endianness.py_TestNiftiEndianness_TestNiftiEndianness.test_endianness.if_isinstance_ret_dict_.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_nifti_endianness.py", "file_name": "test_nifti_endianness.py", "file_type": "text/x-python", "category": "test", "start_line": 44, "end_line": 63, "span_ids": ["TestNiftiEndianness.test_endianness", "TestNiftiEndianness.setUp", "TestNiftiEndianness"], "tokens": 247}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestNiftiEndianness(unittest.TestCase):\n    def setUp(self):\n        self.im, _ = create_test_image_2d(100, 100)\n        self.fname = tempfile.NamedTemporaryFile(suffix=\".nii.gz\").name\n\n    @parameterized.expand(TESTS)\n    @skipUnless(has_nib, \"Requires NiBabel\")\n    def test_endianness(self, endianness, use_array, image_only):\n\n        hdr = nib.Nifti1Header(endianness=endianness)\n        nii = nib.Nifti1Image(self.im, np.eye(4), header=hdr)\n        nib.save(nii, self.fname)\n\n        data = [self.fname] if use_array else [{\"image\": self.fname}]\n        tr = LoadImage(image_only=image_only) if use_array else LoadImaged(\"image\", image_only=image_only)\n        check_ds = Dataset(data, tr)\n        check_loader = DataLoader(check_ds, batch_size=1)\n        ret = next(iter(check_loader))\n        if isinstance(ret, dict) and \"image_meta_dict\" in ret:\n            np.testing.assert_allclose(ret[\"image_meta_dict\"][\"spatial_shape\"], [[100, 100]])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nifti_rw.py_TestNiftiLoadRead.test_consistency_TestNiftiLoadRead.test_consistency.None_3.os_remove_test_image_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nifti_rw.py_TestNiftiLoadRead.test_consistency_TestNiftiLoadRead.test_consistency.None_3.os_remove_test_image_", "embedding": null, "metadata": {"file_path": "tests/test_nifti_rw.py", "file_name": "test_nifti_rw.py", "file_type": "text/x-python", "category": "test", "start_line": 99, "end_line": 138, "span_ids": ["TestNiftiLoadRead.test_consistency"], "tokens": 543}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestNiftiLoadRead(unittest.TestCase):\n\n    def test_consistency(self):\n        np.set_printoptions(suppress=True, precision=3)\n        test_image = make_nifti_image(np.arange(64).reshape(1, 8, 8), np.diag([1.5, 1.5, 1.5, 1]))\n        data, header = LoadImage(reader=\"NibabelReader\", as_closest_canonical=False)(test_image)\n        data, original_affine, new_affine = Spacing([0.8, 0.8, 0.8])(data[None], header[\"affine\"], mode=\"nearest\")\n        data, _, new_affine = Orientation(\"ILP\")(data, new_affine)\n        if os.path.exists(test_image):\n            os.remove(test_image)\n        write_nifti(data[0], test_image, new_affine, original_affine, mode=\"nearest\", padding_mode=\"border\")\n        saved = nib.load(test_image)\n        saved_data = saved.get_fdata()\n        np.testing.assert_allclose(saved_data, np.arange(64).reshape(1, 8, 8), atol=1e-7)\n        if os.path.exists(test_image):\n            os.remove(test_image)\n        write_nifti(\n            data[0],\n            test_image,\n            new_affine,\n            original_affine,\n            mode=\"nearest\",\n            padding_mode=\"border\",\n            output_spatial_shape=(1, 8, 8),\n        )\n        saved = nib.load(test_image)\n        saved_data = saved.get_fdata()\n        np.testing.assert_allclose(saved_data, np.arange(64).reshape(1, 8, 8), atol=1e-7)\n        if os.path.exists(test_image):\n            os.remove(test_image)\n        # test the case that only correct orientation but don't resample\n        write_nifti(data[0], test_image, new_affine, original_affine, resample=False)\n        saved = nib.load(test_image)\n        # compute expected affine\n        start_ornt = nib.orientations.io_orientation(new_affine)\n        target_ornt = nib.orientations.io_orientation(original_affine)\n        ornt_transform = nib.orientations.ornt_transform(start_ornt, target_ornt)\n        data_shape = data[0].shape\n        expected_affine = new_affine @ nib.orientations.inv_ornt_aff(ornt_transform, data_shape)\n        np.testing.assert_allclose(saved.affine, expected_affine)\n        if os.path.exists(test_image):\n            os.remove(test_image)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_npzdictitemdataset.py_tempfile_TestNPZDictItemDataset.test_load_stream.None_3": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_npzdictitemdataset.py_tempfile_TestNPZDictItemDataset.test_load_stream.None_3", "embedding": null, "metadata": {"file_path": "tests/test_npzdictitemdataset.py", "file_name": "test_npzdictitemdataset.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 35, "span_ids": ["TestNPZDictItemDataset.test_load_stream", "TestNPZDictItemDataset", "docstring"], "tokens": 201}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import tempfile\nimport unittest\nfrom io import BytesIO\n\nimport numpy as np\n\nfrom monai.data import NPZDictItemDataset\n\n\nclass TestNPZDictItemDataset(unittest.TestCase):\n    def test_load_stream(self):\n        dat0 = np.random.rand(10, 1, 4, 4)\n        dat1 = np.random.rand(10, 1, 4, 4)\n\n        npzfile = BytesIO()\n        np.savez_compressed(npzfile, dat0=dat0, dat1=dat1)\n        npzfile.seek(0)\n\n        npzds = NPZDictItemDataset(npzfile, {\"dat0\": \"images\", \"dat1\": \"seg\"})\n\n        item = npzds[0]\n\n        np.testing.assert_allclose(item[\"images\"].shape, (1, 4, 4))\n        np.testing.assert_allclose(item[\"seg\"].shape, (1, 4, 4))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_orientationd.py_TestOrientationdCase.test_orntd_canonical_TestOrientationdCase.test_orntd_canonical.None_3": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_orientationd.py_TestOrientationdCase.test_orntd_canonical_TestOrientationdCase.test_orntd_canonical.None_3", "embedding": null, "metadata": {"file_path": "tests/test_orientationd.py", "file_name": "test_orientationd.py", "file_type": "text/x-python", "category": "test", "start_line": 75, "end_line": 89, "span_ids": ["TestOrientationdCase.test_orntd_canonical"], "tokens": 243}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestOrientationdCase(unittest.TestCase):\n\n    def test_orntd_canonical(self):\n        data = {\n            \"seg\": np.ones((2, 1, 2, 3)),\n            \"img\": np.ones((2, 1, 2, 3)),\n            \"seg_meta_dict\": {\"affine\": np.eye(4)},\n            \"img_meta_dict\": {\"affine\": np.eye(4)},\n        }\n        ornt = Orientationd(keys=(\"img\", \"seg\"), as_closest_canonical=True)\n        res = ornt(data)\n        np.testing.assert_allclose(res[\"img\"].shape, (2, 1, 2, 3))\n        np.testing.assert_allclose(res[\"seg\"].shape, (2, 1, 2, 3))\n        code = nib.aff2axcodes(res[\"seg_meta_dict\"][\"affine\"], ornt.ornt_transform.labels)\n        self.assertEqual(code, (\"R\", \"A\", \"S\"))\n        code = nib.aff2axcodes(res[\"img_meta_dict\"][\"affine\"], ornt.ornt_transform.labels)\n        self.assertEqual(code, (\"R\", \"A\", \"S\"))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_orientationd.py_TestOrientationdCase.test_orntd_no_metadata_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_orientationd.py_TestOrientationdCase.test_orntd_no_metadata_", "embedding": null, "metadata": {"file_path": "tests/test_orientationd.py", "file_name": "test_orientationd.py", "file_type": "text/x-python", "category": "test", "start_line": 91, "end_line": 102, "span_ids": ["TestOrientationdCase.test_orntd_no_metadata", "impl"], "tokens": 137}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestOrientationdCase(unittest.TestCase):\n\n    def test_orntd_no_metadata(self):\n        data = {\"seg\": np.ones((2, 1, 2, 3))}\n        ornt = Orientationd(keys=\"seg\", axcodes=\"RAS\")\n        res = ornt(data)\n        np.testing.assert_allclose(res[\"seg\"].shape, (2, 1, 2, 3))\n        code = nib.aff2axcodes(res[\"seg_meta_dict\"][\"affine\"], ornt.ornt_transform.labels)\n        self.assertEqual(code, (\"R\", \"A\", \"S\"))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_persistentdataset.py_os__InplaceXform.__call__.return.data": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_persistentdataset.py_os__InplaceXform.__call__.return.data", "embedding": null, "metadata": {"file_path": "tests/test_persistentdataset.py", "file_name": "test_persistentdataset.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 51, "span_ids": ["_InplaceXform", "_InplaceXform.__call__", "docstring"], "tokens": 272}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport pickle\nimport tempfile\nimport unittest\n\nimport nibabel as nib\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.data import PersistentDataset, json_hashing\nfrom monai.transforms import Compose, LoadImaged, SimulateDelayd, Transform\n\nTEST_CASE_1 = [\n    Compose(\n        [\n            LoadImaged(keys=[\"image\", \"label\", \"extra\"]),\n            SimulateDelayd(keys=[\"image\", \"label\", \"extra\"], delay_time=[1e-7, 1e-6, 1e-5]),\n        ]\n    ),\n    (128, 128, 128),\n]\n\nTEST_CASE_2 = [\n    [\n        LoadImaged(keys=[\"image\", \"label\", \"extra\"]),\n        SimulateDelayd(keys=[\"image\", \"label\", \"extra\"], delay_time=[1e-7, 1e-6, 1e-5]),\n    ],\n    (128, 128, 128),\n]\n\nTEST_CASE_3 = [None, (128, 128, 128)]\n\n\nclass _InplaceXform(Transform):\n    def __call__(self, data):\n        if data:\n            data[0] = data[0] + np.pi\n        else:\n            data.append(1)\n        return data", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_persistentdataset_dist.py_os_TestDistDataset.test_mp_dataset.None_6": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_persistentdataset_dist.py_os_TestDistDataset.test_mp_dataset.None_6", "embedding": null, "metadata": {"file_path": "tests/test_persistentdataset_dist.py", "file_name": "test_persistentdataset_dist.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 55, "span_ids": ["_InplaceXform.__call__", "TestDistDataset.setUp", "docstring", "_InplaceXform", "TestDistDataset.test_mp_dataset", "TestDistDataset", "TestDistDataset.tearDown"], "tokens": 453}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport shutil\nimport tempfile\nimport unittest\n\nimport numpy as np\nimport torch.distributed as dist\n\nfrom monai.data import PersistentDataset, json_hashing\nfrom monai.transforms import Transform\nfrom tests.utils import DistCall, DistTestCase\n\n\nclass _InplaceXform(Transform):\n    def __call__(self, data):\n        if data:\n            data[0] = data[0] + np.pi\n        else:\n            data.append(1)\n        return data\n\n\nclass TestDistDataset(DistTestCase):\n    def setUp(self):\n        self.tempdir = tempfile.mkdtemp()\n\n    def tearDown(self):\n        shutil.rmtree(self.tempdir)\n\n    @DistCall(nnodes=1, nproc_per_node=2)\n    def test_mp_dataset(self):\n        print(\"persistent\", dist.get_rank())\n        items = [[list(range(i))] for i in range(5)]\n        ds = PersistentDataset(items, transform=_InplaceXform(), cache_dir=self.tempdir)\n        self.assertEqual(items, [[[]], [[0]], [[0, 1]], [[0, 1, 2]], [[0, 1, 2, 3]]])\n        ds1 = PersistentDataset(items, transform=_InplaceXform(), cache_dir=self.tempdir)\n        self.assertEqual(list(ds1), list(ds))\n        self.assertEqual(items, [[[]], [[0]], [[0, 1]], [[0, 1, 2]], [[0, 1, 2, 3]]])\n\n        ds = PersistentDataset(items, transform=_InplaceXform(), cache_dir=self.tempdir, hash_func=json_hashing)\n        self.assertEqual(items, [[[]], [[0]], [[0, 1]], [[0, 1, 2]], [[0, 1, 2, 3]]])\n        ds1 = PersistentDataset(items, transform=_InplaceXform(), cache_dir=self.tempdir, hash_func=json_hashing)\n        self.assertEqual(list(ds1), list(ds))\n        self.assertEqual(items, [[[]], [[0]], [[0, 1]], [[0, 1, 2]], [[0, 1, 2, 3]]])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_persistentdataset_dist.py_TestDistCreateDataset_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_persistentdataset_dist.py_TestDistCreateDataset_", "embedding": null, "metadata": {"file_path": "tests/test_persistentdataset_dist.py", "file_name": "test_persistentdataset_dist.py", "file_type": "text/x-python", "category": "test", "start_line": 58, "end_line": 79, "span_ids": ["TestDistCreateDataset.test_mp_dataset", "impl", "TestDistCreateDataset", "TestDistCreateDataset.tearDown", "TestDistCreateDataset.setUp"], "tokens": 238}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestDistCreateDataset(DistTestCase):\n    def setUp(self):\n        self.tempdir = tempfile.mkdtemp()\n\n    def tearDown(self):\n        shutil.rmtree(self.tempdir)\n\n    @DistCall(nnodes=1, nproc_per_node=2)\n    def test_mp_dataset(self):\n        print(\"persistent\", dist.get_rank())\n        items = [[list(range(i))] for i in range(5)]\n        cache_dir = os.path.join(self.tempdir, \"test\")\n        ds = PersistentDataset(items, transform=_InplaceXform(), cache_dir=cache_dir)\n        self.assertEqual(items, [[[]], [[0]], [[0, 1]], [[0, 1, 2]], [[0, 1, 2, 3]]])\n        ds1 = PersistentDataset(items, transform=_InplaceXform(), cache_dir=cache_dir)\n        self.assertEqual(list(ds1), list(ds))\n        self.assertEqual(items, [[[]], [[0]], [[0, 1]], [[0, 1, 2]], [[0, 1, 2, 3]]])\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_affine.py_TestRandAffine.test_skipped_transform_consistency_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_affine.py_TestRandAffine.test_skipped_transform_consistency_", "embedding": null, "metadata": {"file_path": "tests/test_rand_affine.py", "file_name": "test_rand_affine.py", "file_type": "text/x-python", "category": "test", "start_line": 150, "end_line": 168, "span_ids": ["TestRandAffine.test_skipped_transform_consistency", "impl:21"], "tokens": 145}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandAffine(unittest.TestCase):\n\n    @parameterized.expand(TEST_CASES_SKIPPED_CONSISTENCY)\n    def test_skipped_transform_consistency(self, im, in_dtype):\n        t1 = RandAffine(prob=0)\n        t2 = RandAffine(prob=1, spatial_size=(10, 11))\n\n        im, *_ = convert_data_type(im, dtype=in_dtype)\n\n        out1 = t1(im)\n        out2 = t2(im)\n\n        # check same type\n        self.assertEqual(type(out1), type(out2))\n        # check matching dtype\n        self.assertEqual(out1.dtype, out2.dtype)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_gibbs_noise.py_TestRandGibbsNoise.test_same_result_TestRandGibbsNoise.test_same_result.self_assertIsInstance_out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_gibbs_noise.py_TestRandGibbsNoise.test_same_result_TestRandGibbsNoise.test_same_result.self_assertIsInstance_out", "embedding": null, "metadata": {"file_path": "tests/test_rand_gibbs_noise.py", "file_name": "test_rand_gibbs_noise.py", "file_type": "text/x-python", "category": "test", "start_line": 55, "end_line": 65, "span_ids": ["TestRandGibbsNoise.test_same_result"], "tokens": 141}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandGibbsNoise(unittest.TestCase):\n\n    @parameterized.expand(TEST_CASES)\n    def test_same_result(self, im_shape, input_type):\n        im = self.get_data(im_shape, input_type)\n        alpha = [0.5, 0.8]\n        t = RandGibbsNoise(1.0, alpha)\n        t.set_random_state(42)\n        out1 = t(deepcopy(im))\n        t.set_random_state(42)\n        out2 = t(deepcopy(im))\n        torch.testing.assert_allclose(out1, out2, rtol=1e-7, atol=0)\n        self.assertIsInstance(out1, type(im))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_gibbs_noise.py_TestRandGibbsNoise.test_identity_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_gibbs_noise.py_TestRandGibbsNoise.test_identity_", "embedding": null, "metadata": {"file_path": "tests/test_rand_gibbs_noise.py", "file_name": "test_rand_gibbs_noise.py", "file_type": "text/x-python", "category": "test", "start_line": 67, "end_line": 95, "span_ids": ["TestRandGibbsNoise.test_alpha_1", "TestRandGibbsNoise.test_alpha", "impl:7", "TestRandGibbsNoise.test_identity"], "tokens": 314}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandGibbsNoise(unittest.TestCase):\n\n    @parameterized.expand(TEST_CASES)\n    def test_identity(self, im_shape, input_type):\n        im = self.get_data(im_shape, input_type)\n        alpha = [0.0, 0.0]\n        t = RandGibbsNoise(1.0, alpha)\n        out = t(deepcopy(im))\n        torch.testing.assert_allclose(im, out, atol=1e-2, rtol=1e-7)\n\n    @parameterized.expand(TEST_CASES)\n    def test_alpha_1(self, im_shape, input_type):\n        im = self.get_data(im_shape, input_type)\n        alpha = [1.0, 1.0]\n        t = RandGibbsNoise(1.0, alpha)\n        out = t(deepcopy(im))\n        torch.testing.assert_allclose(0 * im, out, rtol=1e-7, atol=0)\n\n    @parameterized.expand(TEST_CASES)\n    def test_alpha(self, im_shape, input_type):\n        im = self.get_data(im_shape, input_type)\n        alpha = [0.5, 0.51]\n        t = RandGibbsNoise(1.0, alpha)\n        _ = t(deepcopy(im))\n        self.assertGreaterEqual(t.sampled_alpha, 0.5)\n        self.assertLessEqual(t.sampled_alpha, 0.51)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_gibbs_noised.py_unittest_KEYS._im_label_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_gibbs_noised.py_unittest_KEYS._im_label_", "embedding": null, "metadata": {"file_path": "tests/test_rand_gibbs_noised.py", "file_name": "test_rand_gibbs_noised.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 32, "span_ids": ["docstring"], "tokens": 165}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom copy import deepcopy\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.data.synthetic import create_test_image_2d, create_test_image_3d\nfrom monai.transforms import RandGibbsNoised\nfrom monai.utils.misc import set_determinism\nfrom monai.utils.module import optional_import\nfrom tests.utils import TEST_NDARRAYS\n\n_, has_torch_fft = optional_import(\"torch.fft\", name=\"fftshift\")\n\nTEST_CASES = []\nfor shape in ((128, 64), (64, 48, 80)):\n    for input_type in TEST_NDARRAYS if has_torch_fft else [np.array]:\n        TEST_CASES.append((shape, input_type))\n\nKEYS = [\"im\", \"label\"]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_gibbs_noised.py_TestRandGibbsNoised.test_same_result_TestRandGibbsNoised.test_same_result.for_k_in_KEYS_.self_assertIsInstance_out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_gibbs_noised.py_TestRandGibbsNoised.test_same_result_TestRandGibbsNoised.test_same_result.for_k_in_KEYS_.self_assertIsInstance_out", "embedding": null, "metadata": {"file_path": "tests/test_rand_gibbs_noised.py", "file_name": "test_rand_gibbs_noised.py", "file_type": "text/x-python", "category": "test", "start_line": 58, "end_line": 69, "span_ids": ["TestRandGibbsNoised.test_same_result"], "tokens": 157}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandGibbsNoised(unittest.TestCase):\n\n    @parameterized.expand(TEST_CASES)\n    def test_same_result(self, im_shape, input_type):\n        data = self.get_data(im_shape, input_type)\n        alpha = [0.5, 0.8]\n        t = RandGibbsNoised(KEYS, 1.0, alpha)\n        t.set_random_state(42)\n        out1 = t(deepcopy(data))\n        t.set_random_state(42)\n        out2 = t(deepcopy(data))\n        for k in KEYS:\n            torch.testing.assert_allclose(out1[k], out2[k], rtol=1e-7, atol=0)\n            self.assertIsInstance(out1[k], type(data[k]))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_k_space_spike_noise.py_TestRandKSpaceSpikeNoise_TestRandKSpaceSpikeNoise.test_0_prob.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_k_space_spike_noise.py_TestRandKSpaceSpikeNoise_TestRandKSpaceSpikeNoise.test_0_prob.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_rand_k_space_spike_noise.py", "file_name": "test_rand_k_space_spike_noise.py", "file_type": "text/x-python", "category": "test", "start_line": 31, "end_line": 55, "span_ids": ["TestRandKSpaceSpikeNoise.get_data", "TestRandKSpaceSpikeNoise", "TestRandKSpaceSpikeNoise.setUp", "TestRandKSpaceSpikeNoise.test_0_prob", "TestRandKSpaceSpikeNoise.tearDown"], "tokens": 242}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandKSpaceSpikeNoise(unittest.TestCase):\n    def setUp(self):\n        set_determinism(0)\n        super().setUp()\n\n    def tearDown(self):\n        set_determinism(None)\n\n    @staticmethod\n    def get_data(im_shape, im_type):\n        create_test_image = create_test_image_2d if len(im_shape) == 2 else create_test_image_3d\n        im = create_test_image(*im_shape, rad_max=20, noise_max=0.0, num_seg_classes=5)[0][None]\n        return im_type(im)\n\n    @parameterized.expand(TESTS)\n    def test_0_prob(self, im_shape, im_type, channel_wise):\n        im = self.get_data(im_shape, im_type)\n        intensity_range = [14, 15]\n        t = RandKSpaceSpikeNoise(0.0, intensity_range, channel_wise)\n        out = t(im)\n        self.assertEqual(type(im), type(out))\n        if isinstance(out, torch.Tensor):\n            self.assertEqual(out.device, im.device)\n            im, out = im.cpu(), out.cpu()\n        np.testing.assert_allclose(im, out)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_k_space_spike_noise.py_TestRandKSpaceSpikeNoise.test_1_prob_TestRandKSpaceSpikeNoise.test_1_prob.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_k_space_spike_noise.py_TestRandKSpaceSpikeNoise.test_1_prob_TestRandKSpaceSpikeNoise.test_1_prob.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_rand_k_space_spike_noise.py", "file_name": "test_rand_k_space_spike_noise.py", "file_type": "text/x-python", "category": "test", "start_line": 57, "end_line": 69, "span_ids": ["TestRandKSpaceSpikeNoise.test_1_prob"], "tokens": 162}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandKSpaceSpikeNoise(unittest.TestCase):\n\n    @parameterized.expand(TESTS)\n    def test_1_prob(self, im_shape, im_type, channel_wise):\n        im = self.get_data(im_shape, im_type)\n        intensity_range = [14, 14]\n        t = RandKSpaceSpikeNoise(1.0, intensity_range, channel_wise)\n        out = t(im)\n        base_t = KSpaceSpikeNoise(t.sampled_locs, [14])\n        out = out - base_t(im)\n        self.assertEqual(type(im), type(out))\n        if isinstance(out, torch.Tensor):\n            self.assertEqual(out.device, im.device)\n            im, out = im.cpu(), out.cpu()\n        np.testing.assert_allclose(out, im * 0)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_k_space_spike_noise.py_TestRandKSpaceSpikeNoise.test_intensity_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_k_space_spike_noise.py_TestRandKSpaceSpikeNoise.test_intensity_", "embedding": null, "metadata": {"file_path": "tests/test_rand_k_space_spike_noise.py", "file_name": "test_rand_k_space_spike_noise.py", "file_type": "text/x-python", "category": "test", "start_line": 86, "end_line": 98, "span_ids": ["TestRandKSpaceSpikeNoise.test_intensity", "impl:7"], "tokens": 135}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandKSpaceSpikeNoise(unittest.TestCase):\n\n    @parameterized.expand(TESTS)\n    def test_intensity(self, im_shape, im_type, channel_wise):\n        im = self.get_data(im_shape, im_type)\n        intensity_range = [14, 14.1]\n        t = RandKSpaceSpikeNoise(1.0, intensity_range, channel_wise)\n        _ = t(deepcopy(im))\n        self.assertGreaterEqual(t.sampled_k_intensity[0], 14)\n        self.assertLessEqual(t.sampled_k_intensity[0], 14.1)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_k_space_spike_noised.py_unittest_KEYS._image_label_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_k_space_spike_noised.py_unittest_KEYS._image_label_", "embedding": null, "metadata": {"file_path": "tests/test_rand_k_space_spike_noised.py", "file_name": "test_rand_k_space_spike_noised.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 29, "span_ids": ["docstring"], "tokens": 127}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom copy import deepcopy\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.data.synthetic import create_test_image_2d, create_test_image_3d\nfrom monai.transforms import RandKSpaceSpikeNoised\nfrom monai.utils.misc import set_determinism\nfrom tests.utils import TEST_NDARRAYS\n\nTESTS = []\nfor shape in ((128, 64), (64, 48, 80)):\n    for p in TEST_NDARRAYS:\n        TESTS.append((shape, p))\n\nKEYS = [\"image\", \"label\"]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_k_space_spike_noised.py_TestKSpaceSpikeNoised_TestKSpaceSpikeNoised.get_data.return._k_v_for_k_v_in_zip_KEY": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_k_space_spike_noised.py_TestKSpaceSpikeNoised_TestKSpaceSpikeNoised.get_data.return._k_v_for_k_v_in_zip_KEY", "embedding": null, "metadata": {"file_path": "tests/test_rand_k_space_spike_noised.py", "file_name": "test_rand_k_space_spike_noised.py", "file_type": "text/x-python", "category": "test", "start_line": 32, "end_line": 45, "span_ids": ["TestKSpaceSpikeNoised.tearDown", "TestKSpaceSpikeNoised", "TestKSpaceSpikeNoised.setUp", "TestKSpaceSpikeNoised.get_data"], "tokens": 142}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestKSpaceSpikeNoised(unittest.TestCase):\n    def setUp(self):\n        set_determinism(0)\n        super().setUp()\n\n    def tearDown(self):\n        set_determinism(None)\n\n    @staticmethod\n    def get_data(im_shape, im_type):\n        create_test_image = create_test_image_2d if len(im_shape) == 2 else create_test_image_3d\n        ims = create_test_image(*im_shape, rad_max=20, noise_max=0.0, num_seg_classes=5)\n        ims = [im_type(im[None]) for im in ims]\n        return {k: v for k, v in zip(KEYS, ims)}", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_rician_noised.py_unittest_seed.0": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_rician_noised.py_unittest_seed.0", "embedding": null, "metadata": {"file_path": "tests/test_rand_rician_noised.py", "file_name": "test_rand_rician_noised.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 26, "span_ids": ["docstring"], "tokens": 118}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import RandRicianNoised\nfrom tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    TESTS.append([\"test_zero_mean\", p, [\"img1\", \"img2\"], 0, 0.1])\n    TESTS.append([\"test_non_zero_mean\", p, [\"img1\", \"img2\"], 1, 0.5])\n\nseed = 0", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_scale_crop.py_unittest_TEST_CASE_6._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_scale_crop.py_unittest_TEST_CASE_6._", "embedding": null, "metadata": {"file_path": "tests/test_rand_scale_crop.py", "file_name": "test_rand_scale_crop.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 53, "span_ids": ["impl:11", "docstring"], "tokens": 529}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import RandScaleCrop\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTEST_CASE_1 = [\n    {\"roi_scale\": [1.0, 1.0, -1.0], \"random_center\": True},\n    np.random.randint(0, 2, size=[3, 3, 3, 4]),\n    (3, 3, 3, 4),\n]\n\nTEST_CASE_2 = [\n    {\"roi_scale\": [1.0, 1.0, 1.0], \"random_center\": False},\n    np.random.randint(0, 2, size=[3, 3, 3, 3]),\n    (3, 3, 3, 3),\n]\n\nTEST_CASE_3 = [\n    {\"roi_scale\": [0.6, 0.6], \"random_center\": False},\n    np.array([[[0, 0, 0, 0, 0], [0, 1, 2, 1, 0], [0, 2, 3, 2, 0], [0, 1, 2, 1, 0], [0, 0, 0, 0, 0]]]),\n]\n\nTEST_CASE_4 = [\n    {\"roi_scale\": [0.75, 0.6, 0.5], \"max_roi_scale\": [1.0, -1.0, 0.6], \"random_center\": True, \"random_size\": True},\n    np.random.randint(0, 2, size=[1, 4, 5, 6]),\n    (1, 3, 4, 3),\n]\n\nTEST_CASE_5 = [\n    {\"roi_scale\": 0.6, \"max_roi_scale\": 0.8, \"random_center\": True, \"random_size\": True},\n    np.random.randint(0, 2, size=[1, 4, 5, 6]),\n    (1, 3, 4, 4),\n]\n\nTEST_CASE_6 = [\n    {\"roi_scale\": 0.2, \"max_roi_scale\": 0.8, \"random_center\": True, \"random_size\": True},\n    np.random.randint(0, 2, size=[1, 4, 5, 6]),\n    (1, 3, 2, 4),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_scale_crop.py_TestRandScaleCrop_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_scale_crop.py_TestRandScaleCrop_", "embedding": null, "metadata": {"file_path": "tests/test_rand_scale_crop.py", "file_name": "test_rand_scale_crop.py", "file_type": "text/x-python", "category": "test", "start_line": 56, "end_line": 82, "span_ids": ["TestRandScaleCrop.test_shape", "TestRandScaleCrop.test_value", "TestRandScaleCrop", "impl:13", "TestRandScaleCrop.test_random_shape"], "tokens": 295}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandScaleCrop(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2])\n    def test_shape(self, input_param, input_data, expected_shape):\n        for p in TEST_NDARRAYS:\n            result = RandScaleCrop(**input_param)(p(input_data))\n            self.assertTupleEqual(result.shape, expected_shape)\n\n    @parameterized.expand([TEST_CASE_3])\n    def test_value(self, input_param, input_data):\n        for p in TEST_NDARRAYS:\n            cropper = RandScaleCrop(**input_param)\n            result = cropper(p(input_data))\n            roi = [(2 - i // 2, 2 + i - i // 2) for i in cropper._size]\n            assert_allclose(result, input_data[:, roi[0][0] : roi[0][1], roi[1][0] : roi[1][1]], type_test=False)\n\n    @parameterized.expand([TEST_CASE_4, TEST_CASE_5, TEST_CASE_6])\n    def test_random_shape(self, input_param, input_data, expected_shape):\n        for p in TEST_NDARRAYS:\n            cropper = RandScaleCrop(**input_param)\n            cropper.set_random_state(seed=123)\n            result = cropper(p(input_data))\n            self.assertTupleEqual(result.shape, expected_shape)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_scale_cropd.py_unittest_TEST_CASE_6._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_scale_cropd.py_unittest_TEST_CASE_6._", "embedding": null, "metadata": {"file_path": "tests/test_rand_scale_cropd.py", "file_name": "test_rand_scale_cropd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 60, "span_ids": ["impl:9", "docstring"], "tokens": 621}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import RandScaleCropd\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTEST_CASE_1 = [\n    {\"keys\": \"img\", \"roi_scale\": [1.0, 1.0, -1.0], \"random_center\": True},\n    {\"img\": np.random.randint(0, 2, size=[3, 3, 3, 4])},\n    (3, 3, 3, 4),\n]\n\nTEST_CASE_2 = [\n    # test `allow_missing_keys` with key \"label\"\n    {\"keys\": [\"label\", \"img\"], \"roi_scale\": [1.0, 1.0, 1.0], \"random_center\": False, \"allow_missing_keys\": True},\n    {\"img\": np.random.randint(0, 2, size=[3, 3, 3, 3])},\n    (3, 3, 3, 3),\n]\n\nTEST_CASE_3 = [\n    {\"keys\": \"img\", \"roi_scale\": [0.6, 0.6], \"random_center\": False},\n    {\"img\": np.array([[[0, 0, 0, 0, 0], [0, 1, 2, 1, 0], [0, 2, 3, 2, 0], [0, 1, 2, 1, 0], [0, 0, 0, 0, 0]]])},\n]\n\nTEST_CASE_4 = [\n    {\n        \"keys\": \"img\",\n        \"roi_scale\": [0.75, 0.6, 0.5],\n        \"max_roi_scale\": [1.0, -1.0, 0.6],\n        \"random_center\": True,\n        \"random_size\": True,\n    },\n    {\"img\": np.random.randint(0, 2, size=[1, 4, 5, 6])},\n    (1, 3, 4, 3),\n]\n\nTEST_CASE_5 = [\n    {\"keys\": \"img\", \"roi_scale\": 0.6, \"max_roi_scale\": 0.8, \"random_center\": True, \"random_size\": True},\n    {\"img\": np.random.randint(0, 2, size=[1, 4, 5, 6])},\n    (1, 3, 4, 4),\n]\n\nTEST_CASE_6 = [\n    {\"keys\": \"img\", \"roi_scale\": 0.2, \"max_roi_scale\": 0.8, \"random_center\": True, \"random_size\": True},\n    {\"img\": np.random.randint(0, 2, size=[1, 4, 5, 6])},\n    (1, 3, 2, 4),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_scale_cropd.py_TestRandScaleCropd_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_scale_cropd.py_TestRandScaleCropd_", "embedding": null, "metadata": {"file_path": "tests/test_rand_scale_cropd.py", "file_name": "test_rand_scale_cropd.py", "file_type": "text/x-python", "category": "test", "start_line": 62, "end_line": 89, "span_ids": ["TestRandScaleCropd.test_value", "impl:13", "TestRandScaleCropd.test_random_shape", "TestRandScaleCropd", "TestRandScaleCropd.test_shape"], "tokens": 304}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandScaleCropd(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2])\n    def test_shape(self, input_param, input_data, expected_shape):\n        result = RandScaleCropd(**input_param)(input_data)\n        self.assertTupleEqual(result[\"img\"].shape, expected_shape)\n\n    @parameterized.expand([TEST_CASE_3])\n    def test_value(self, input_param, input_data):\n        for p in TEST_NDARRAYS:\n            cropper = RandScaleCropd(**input_param)\n            input_data[\"img\"] = p(input_data[\"img\"])\n            result = cropper(input_data)\n            roi = [(2 - i // 2, 2 + i - i // 2) for i in cropper._size]\n            assert_allclose(\n                result[\"img\"], input_data[\"img\"][:, roi[0][0] : roi[0][1], roi[1][0] : roi[1][1]], type_test=False\n            )\n\n    @parameterized.expand([TEST_CASE_4, TEST_CASE_5, TEST_CASE_6])\n    def test_random_shape(self, input_param, input_data, expected_shape):\n        cropper = RandScaleCropd(**input_param)\n        cropper.set_random_state(seed=123)\n        result = cropper(input_data)\n        self.assertTupleEqual(result[\"img\"].shape, expected_shape)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_spatial_crop.py_unittest_TEST_CASE_5._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_spatial_crop.py_unittest_TEST_CASE_5._", "embedding": null, "metadata": {"file_path": "tests/test_rand_spatial_crop.py", "file_name": "test_rand_spatial_crop.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 49, "span_ids": ["impl:11", "docstring"], "tokens": 480}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import RandSpatialCrop\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTEST_CASE_0 = [\n    {\"roi_size\": [3, 3, -1], \"random_center\": True},\n    np.random.randint(0, 2, size=[3, 3, 3, 4]),\n    (3, 3, 3, 4),\n]\n\nTEST_CASE_1 = [{\"roi_size\": [3, 3, 3], \"random_center\": True}, np.random.randint(0, 2, size=[3, 3, 3, 3]), (3, 3, 3, 3)]\n\nTEST_CASE_2 = [\n    {\"roi_size\": [3, 3, 3], \"random_center\": False},\n    np.random.randint(0, 2, size=[3, 3, 3, 3]),\n    (3, 3, 3, 3),\n]\n\nTEST_CASE_3 = [\n    {\"roi_size\": [3, 3], \"random_center\": False},\n    np.array([[[0, 0, 0, 0, 0], [0, 1, 2, 1, 0], [0, 2, 3, 2, 0], [0, 1, 2, 1, 0], [0, 0, 0, 0, 0]]]),\n]\n\nTEST_CASE_4 = [\n    {\"roi_size\": [3, 3, 3], \"max_roi_size\": [5, -1, 4], \"random_center\": True, \"random_size\": True},\n    np.random.randint(0, 2, size=[1, 4, 5, 6]),\n    (1, 4, 4, 3),\n]\n\nTEST_CASE_5 = [\n    {\"roi_size\": 3, \"max_roi_size\": 4, \"random_center\": True, \"random_size\": True},\n    np.random.randint(0, 2, size=[1, 4, 5, 6]),\n    (1, 3, 4, 3),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_spatial_crop_samplesd.py_TestRandSpatialCropSamplesd.test_deep_copy_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_spatial_crop_samplesd.py_TestRandSpatialCropSamplesd.test_deep_copy_", "embedding": null, "metadata": {"file_path": "tests/test_rand_spatial_crop_samplesd.py", "file_name": "test_rand_spatial_crop_samplesd.py", "file_type": "text/x-python", "category": "test", "start_line": 97, "end_line": 112, "span_ids": ["impl:7", "TestRandSpatialCropSamplesd.test_deep_copy"], "tokens": 146}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandSpatialCropSamplesd(unittest.TestCase):\n\n    def test_deep_copy(self):\n        data = {\"img\": np.ones((1, 10, 11, 12))}\n        num_samples = 3\n        sampler = RandSpatialCropSamplesd(\n            keys=[\"img\"], roi_size=(3, 3, 3), num_samples=num_samples, random_center=True, random_size=False\n        )\n        transform = Compose([ToTensord(keys=\"img\"), sampler])\n        samples = transform(data)\n        self.assertEqual(len(samples), num_samples)\n        for sample in samples:\n            self.assertEqual(len(sample[\"img_transforms\"]), len(transform))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_spatial_cropd.py_unittest_TEST_CASE_5._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_spatial_cropd.py_unittest_TEST_CASE_5._", "embedding": null, "metadata": {"file_path": "tests/test_rand_spatial_cropd.py", "file_name": "test_rand_spatial_cropd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 53, "span_ids": ["impl:11", "docstring"], "tokens": 542}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import RandSpatialCropd\nfrom tests.utils import TEST_NDARRAYS\n\nTEST_CASE_0 = [\n    {\"keys\": \"img\", \"roi_size\": [3, 3, -1], \"random_center\": True},\n    {\"img\": np.random.randint(0, 2, size=[3, 3, 3, 5])},\n    (3, 3, 3, 5),\n]\n\nTEST_CASE_1 = [\n    {\"keys\": \"img\", \"roi_size\": [3, 3, 3], \"random_center\": True},\n    {\"img\": np.random.randint(0, 2, size=[3, 3, 3, 3])},\n    (3, 3, 3, 3),\n]\n\nTEST_CASE_2 = [\n    {\"keys\": \"img\", \"roi_size\": [3, 3, 3], \"random_center\": False},\n    {\"img\": np.random.randint(0, 2, size=[3, 3, 3, 3])},\n    (3, 3, 3, 3),\n]\n\nTEST_CASE_3 = [\n    {\"keys\": \"img\", \"roi_size\": [3, 3], \"random_center\": False},\n    {\"img\": np.array([[[0, 0, 0, 0, 0], [0, 1, 2, 1, 0], [0, 2, 3, 2, 0], [0, 1, 2, 1, 0], [0, 0, 0, 0, 0]]])},\n]\n\nTEST_CASE_4 = [\n    {\"keys\": \"img\", \"roi_size\": [3, 3, 3], \"max_roi_size\": [5, -1, 4], \"random_center\": True, \"random_size\": True},\n    {\"img\": np.random.randint(0, 2, size=[1, 4, 5, 6])},\n    (1, 4, 4, 3),\n]\n\nTEST_CASE_5 = [\n    {\"keys\": \"img\", \"roi_size\": 3, \"max_roi_size\": 4, \"random_center\": True, \"random_size\": True},\n    {\"img\": np.random.randint(0, 2, size=[1, 4, 5, 6])},\n    (1, 3, 4, 3),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_weighted_cropd.py_TestRandWeightedCrop3D.test_rand_weighted_crop_patch_index_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_weighted_cropd.py_TestRandWeightedCrop3D.test_rand_weighted_crop_patch_index_", "embedding": null, "metadata": {"file_path": "tests/test_rand_weighted_cropd.py", "file_name": "test_rand_weighted_cropd.py", "file_type": "text/x-python", "category": "test", "start_line": 166, "end_line": 192, "span_ids": ["TestRandWeightedCrop3D.test_rand_weighted_crop_patch_index", "impl"], "tokens": 365}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandWeightedCrop3D(NumpyImageTestCase3D):\n\n    def test_rand_weighted_crop_patch_index(self):\n        for p in TEST_NDARRAYS:\n            for q in TEST_NDARRAYS:\n                img = self.imt[0]\n                n_samples = 3\n                crop = RandWeightedCropd((\"img\", \"seg\"), \"w\", (10, -1, -1), n_samples)\n                weight = np.zeros_like(img)\n                weight[0, 7, 17] = 1.1\n                weight[0, 13, 31] = 1.1\n                weight[0, 24, 21] = 1\n                crop.set_random_state(10)\n                result = crop(\n                    {\"img\": p(img), \"seg\": p(self.segn[0]), \"w\": q(weight), \"img_meta_dict\": {\"affine\": None}}\n                )\n                self.assertTrue(len(result) == n_samples)\n                for c, e in zip(crop.centers, [[14, 32, 40], [41, 32, 40], [20, 32, 40]]):\n                    assert_allclose(c, e, type_test=False)\n                for i in range(n_samples):\n                    np.testing.assert_allclose(result[i][\"img\"].shape, (1, 10, 64, 80))\n                    np.testing.assert_allclose(result[i][\"seg\"].shape, (1, 10, 64, 80))\n                    np.testing.assert_allclose(result[i][\"img_meta_dict\"][\"patch_index\"], i)\n                    np.testing.assert_allclose(result[i][\"seg_meta_dict\"][\"patch_index\"], i)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_resnet.py_unittest_TEST_SCRIPT_CASES._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_resnet.py_unittest_TEST_SCRIPT_CASES._", "embedding": null, "metadata": {"file_path": "tests/test_resnet.py", "file_name": "test_resnet.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 76, "span_ids": ["impl:16", "docstring"], "tokens": 720}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom typing import TYPE_CHECKING\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks import eval_mode\nfrom monai.networks.nets import resnet10, resnet18, resnet34, resnet50, resnet101, resnet152, resnet200\nfrom monai.utils import optional_import\nfrom tests.utils import test_script_save\n\nif TYPE_CHECKING:\n    import torchvision\n\n    has_torchvision = True\nelse:\n    torchvision, has_torchvision = optional_import(\"torchvision\")\n\n\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\nTEST_CASE_1 = [  # 3D, batch 3, 2 input channel\n    {\"pretrained\": False, \"spatial_dims\": 3, \"n_input_channels\": 2, \"num_classes\": 3},\n    (3, 2, 32, 64, 48),\n    (3, 3),\n]\n\nTEST_CASE_2 = [  # 2D, batch 2, 1 input channel\n    {\"pretrained\": False, \"spatial_dims\": 2, \"n_input_channels\": 1, \"num_classes\": 3},\n    (2, 1, 32, 64),\n    (2, 3),\n]\n\nTEST_CASE_2_A = [  # 2D, batch 2, 1 input channel, shortcut type A\n    {\"pretrained\": False, \"spatial_dims\": 2, \"n_input_channels\": 1, \"num_classes\": 3, \"shortcut_type\": \"A\"},\n    (2, 1, 32, 64),\n    (2, 3),\n]\n\nTEST_CASE_3 = [  # 1D, batch 1, 2 input channels\n    {\"pretrained\": False, \"spatial_dims\": 1, \"n_input_channels\": 2, \"num_classes\": 3},\n    (1, 2, 32),\n    (1, 3),\n]\n\nTEST_CASE_3_A = [  # 1D, batch 1, 2 input channels\n    {\"pretrained\": False, \"spatial_dims\": 1, \"n_input_channels\": 2, \"num_classes\": 3, \"shortcut_type\": \"A\"},\n    (1, 2, 32),\n    (1, 3),\n]\n\nTEST_CASE_4 = [  # 2D, batch 2, 1 input channel\n    {\"pretrained\": False, \"spatial_dims\": 2, \"n_input_channels\": 1, \"num_classes\": 3, \"feed_forward\": False},\n    (2, 1, 32, 64),\n    ((2, 512), (2, 2048)),\n]\n\nTEST_CASES = []\nfor case in [TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_2_A, TEST_CASE_3_A]:\n    for model in [resnet10, resnet18, resnet34, resnet50, resnet101, resnet152, resnet200]:\n        TEST_CASES.append([model, *case])\n\nTEST_SCRIPT_CASES = [\n    [model, *TEST_CASE_1] for model in [resnet10, resnet18, resnet34, resnet50, resnet101, resnet152, resnet200]\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_resnet.py_TestResNet_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_resnet.py_TestResNet_", "embedding": null, "metadata": {"file_path": "tests/test_resnet.py", "file_name": "test_resnet.py", "file_type": "text/x-python", "category": "test", "start_line": 79, "end_line": 99, "span_ids": ["TestResNet", "TestResNet.test_script", "impl:27", "TestResNet.test_resnet_shape"], "tokens": 162}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestResNet(unittest.TestCase):\n    @parameterized.expand(TEST_CASES)\n    def test_resnet_shape(self, model, input_param, input_shape, expected_shape):\n        net = model(**input_param).to(device)\n        with eval_mode(net):\n            result = net.forward(torch.randn(input_shape).to(device))\n            if input_param.get(\"feed_forward\", True):\n                self.assertEqual(result.shape, expected_shape)\n            else:\n                self.assertTrue(result.shape in expected_shape)\n\n    @parameterized.expand(TEST_SCRIPT_CASES)\n    def test_script(self, model, input_param, input_shape, expected_shape):\n        net = model(**input_param)\n        test_data = torch.randn(input_shape)\n        test_script_save(net, test_data)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_save_classificationd.py_csv_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_save_classificationd.py_csv_", "embedding": null, "metadata": {"file_path": "tests/test_save_classificationd.py", "file_name": "test_save_classificationd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 102, "span_ids": ["impl", "TestSaveClassificationd.test_saved_content", "TestSaveClassificationd", "docstring"], "tokens": 666}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import csv\nimport os\nimport tempfile\nimport unittest\nfrom pathlib import Path\n\nimport numpy as np\nimport torch\n\nfrom monai.data import CSVSaver, decollate_batch\nfrom monai.transforms import Compose, CopyItemsd, SaveClassificationd\n\n\nclass TestSaveClassificationd(unittest.TestCase):\n    def test_saved_content(self):\n        with tempfile.TemporaryDirectory() as tempdir:\n            data = [\n                {\n                    \"pred\": torch.zeros(8),\n                    \"image_meta_dict\": {\"filename_or_obj\": [\"testfile\" + str(i) for i in range(8)]},\n                },\n                {\n                    \"pred\": torch.zeros(8),\n                    \"image_meta_dict\": {\"filename_or_obj\": [\"testfile\" + str(i) for i in range(8, 16)]},\n                },\n                {\n                    \"pred\": torch.zeros(8),\n                    \"image_meta_dict\": {\"filename_or_obj\": [\"testfile\" + str(i) for i in range(16, 24)]},\n                },\n            ]\n\n            saver = CSVSaver(output_dir=Path(tempdir), filename=\"predictions2.csv\", overwrite=False, flush=False)\n            # set up test transforms\n            post_trans = Compose(\n                [\n                    CopyItemsd(keys=\"image_meta_dict\", times=1, names=\"pred_meta_dict\"),\n                    # 1st saver saves data into CSV file\n                    SaveClassificationd(\n                        keys=\"pred\",\n                        saver=None,\n                        meta_keys=None,\n                        output_dir=Path(tempdir),\n                        filename=\"predictions1.csv\",\n                        overwrite=True,\n                    ),\n                    # 2rd saver only saves data into the cache, manually finalize later\n                    SaveClassificationd(keys=\"pred\", saver=saver, meta_key_postfix=\"meta_dict\"),\n                ]\n            )\n            # simulate inference 2 iterations\n            d = decollate_batch(data[0])\n            for i in d:\n                post_trans(i)\n            d = decollate_batch(data[1])\n            for i in d:\n                post_trans(i)\n            # write into CSV file\n            saver.finalize()\n\n            # 3rd saver will not delete previous data due to `overwrite=False`\n            trans2 = SaveClassificationd(\n                keys=\"pred\",\n                saver=None,\n                meta_keys=\"image_meta_dict\",  # specify meta key, so no need to copy anymore\n                output_dir=tempdir,\n                filename=\"predictions1.csv\",\n                overwrite=False,\n            )\n            d = decollate_batch(data[2])\n            for i in d:\n                trans2(i)\n\n            def _test_file(filename, count):\n                filepath = os.path.join(tempdir, filename)\n                self.assertTrue(os.path.exists(filepath))\n                with open(filepath) as f:\n                    reader = csv.reader(f)\n                    i = 0\n                    for row in reader:\n                        self.assertEqual(row[0], \"testfile\" + str(i))\n                        self.assertEqual(np.array(row[1:]).astype(np.float32), 0.0)\n                        i += 1\n                    self.assertEqual(i, count)\n\n            _test_file(\"predictions1.csv\", 24)\n            _test_file(\"predictions2.csv\", 16)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_set_visible_devices.py_os_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_set_visible_devices.py_os_", "embedding": null, "metadata": {"file_path": "tests/test_set_visible_devices.py", "file_name": "test_set_visible_devices.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 39, "span_ids": ["impl", "TestVisibleDevices.run_process_and_get_exit_code", "TestVisibleDevices.test_visible_devices", "TestVisibleDevices", "docstring"], "tokens": 214}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport unittest\n\nfrom tests.utils import skip_if_no_cuda\n\n\nclass TestVisibleDevices(unittest.TestCase):\n    @staticmethod\n    def run_process_and_get_exit_code(code_to_execute):\n        value = os.system(code_to_execute)\n        return int(bin(value).replace(\"0b\", \"\").rjust(16, \"0\")[:8], 2)\n\n    @skip_if_no_cuda\n    def test_visible_devices(self):\n        num_gpus_before = self.run_process_and_get_exit_code(\n            'python -c \"import os; import torch; '\n            + \"os.environ['CUDA_VISIBLE_DEVICES'] = ''; exit(torch.cuda.device_count())\\\"\"\n        )\n        num_gpus_after = self.run_process_and_get_exit_code(\n            'python -c \"import os; import monai; import torch; '\n            + \"os.environ['CUDA_VISIBLE_DEVICES'] = ''; exit(torch.cuda.device_count())\\\"\"\n        )\n        self.assertEqual(num_gpus_before, num_gpus_after)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_smartcachedataset.py_copy_TEST_CASE_5._0_5_2_Compose_LoadIma": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_smartcachedataset.py_copy_TEST_CASE_5._0_5_2_Compose_LoadIma", "embedding": null, "metadata": {"file_path": "tests/test_smartcachedataset.py", "file_name": "test_smartcachedataset.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 33, "span_ids": ["docstring"], "tokens": 195}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import copy\nimport os\nimport sys\nimport tempfile\nimport unittest\n\nimport nibabel as nib\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.data import DataLoader, SmartCacheDataset\nfrom monai.transforms import Compose, Lambda, LoadImaged\n\nTEST_CASE_1 = [0.1, 0, Compose([LoadImaged(keys=[\"image\", \"label\", \"extra\"])])]\n\nTEST_CASE_2 = [0.1, 4, Compose([LoadImaged(keys=[\"image\", \"label\", \"extra\"])])]\n\nTEST_CASE_3 = [0.1, None, Compose([LoadImaged(keys=[\"image\", \"label\", \"extra\"])])]\n\nTEST_CASE_4 = [0.1, 4, None]\n\nTEST_CASE_5 = [0.5, 2, Compose([LoadImaged(keys=[\"image\", \"label\", \"extra\"])])]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_smartcachedataset.py_TestSmartCacheDataset.test_update_cache_TestSmartCacheDataset.test_update_cache.for_string_new_string_re.assert_string_new_stri": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_smartcachedataset.py_TestSmartCacheDataset.test_update_cache_TestSmartCacheDataset.test_update_cache.for_string_new_string_re.assert_string_new_stri", "embedding": null, "metadata": {"file_path": "tests/test_smartcachedataset.py", "file_name": "test_smartcachedataset.py", "file_type": "text/x-python", "category": "test", "start_line": 74, "end_line": 101, "span_ids": ["TestSmartCacheDataset.test_update_cache"], "tokens": 254}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestSmartCacheDataset(unittest.TestCase):\n\n    def test_update_cache(self):\n        # Given\n        test_data = [{\"image\": f\"test_image{i}.nii.gz\", \"label\": f\"test_image{i}.nii.gz\"} for i in range(40)]\n        dataset = SmartCacheDataset(\n            data=test_data,\n            transform=None,\n            replace_rate=0.2,\n            cache_num=10,\n            num_init_workers=4,\n            num_replace_workers=4,\n            shuffle=False,\n        )\n        dataset.start()\n        start_num = int(0.2 * 10)\n        remain_num = int((1 - 0.2) * 10)\n\n        old_cache = copy.deepcopy(dataset._cache)\n        # When\n        with dataset._update_lock:\n            replacements = copy.deepcopy(dataset._replacements)\n        dataset.update_cache()\n        new_cache = dataset._cache\n        kept_cache = old_cache[start_num:]\n        # Then\n        for string1, string2 in zip(kept_cache, new_cache[0:remain_num]):\n            assert string1 == string2\n        for string_new, string_replacement in zip(replacements, new_cache[remain_num:]):\n            assert string_new == string_replacement", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_to_cupyd.py_TestToCupyd.test_list_tuple_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_to_cupyd.py_TestToCupyd.test_list_tuple_", "embedding": null, "metadata": {"file_path": "tests/test_to_cupyd.py", "file_name": "test_to_cupyd.py", "file_type": "text/x-python", "category": "test", "start_line": 64, "end_line": 75, "span_ids": ["TestToCupyd.test_list_tuple", "impl:2"], "tokens": 149}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skipUnless(HAS_CUPY, \"CuPy is required.\")\nclass TestToCupyd(unittest.TestCase):\n\n    def test_list_tuple(self):\n        test_data = [[1, 2], [3, 4]]\n        result = ToCupyd(keys=\"img\", wrap_sequence=True)({\"img\": test_data})[\"img\"]\n        cp.testing.assert_allclose(result, cp.asarray(test_data))\n        test_data = ((1, 2), (3, 4))\n        result = ToCupyd(keys=\"img\", wrap_sequence=True)({\"img\": test_data})[\"img\"]\n        cp.testing.assert_allclose(result, cp.asarray(test_data))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_to_numpyd.py_TestToNumpyd.test_numpy_input_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_to_numpyd.py_TestToNumpyd.test_numpy_input_", "embedding": null, "metadata": {"file_path": "tests/test_to_numpyd.py", "file_name": "test_to_numpyd.py", "file_type": "text/x-python", "category": "test", "start_line": 36, "end_line": 67, "span_ids": ["impl:2", "TestToNumpyd.test_tensor_input", "TestToNumpyd.test_numpy_input", "TestToNumpyd.test_tensor_cuda_input"], "tokens": 328}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestToNumpyd(unittest.TestCase):\n\n    def test_numpy_input(self):\n        test_data = np.array([[1, 2], [3, 4]])\n        test_data = np.rot90(test_data)\n        self.assertFalse(test_data.flags[\"C_CONTIGUOUS\"])\n        result = ToNumpyd(keys=\"img\")({\"img\": test_data})[\"img\"]\n        self.assertTrue(isinstance(result, np.ndarray))\n        self.assertTrue(result.flags[\"C_CONTIGUOUS\"])\n        assert_allclose(result, test_data, type_test=False)\n\n    def test_tensor_input(self):\n        test_data = torch.tensor([[1, 2], [3, 4]])\n        test_data = test_data.rot90()\n        self.assertFalse(test_data.is_contiguous())\n        result = ToNumpyd(keys=\"img\")({\"img\": test_data})[\"img\"]\n        self.assertTrue(isinstance(result, np.ndarray))\n        self.assertTrue(result.flags[\"C_CONTIGUOUS\"])\n        assert_allclose(result, test_data, type_test=False)\n\n    @skip_if_no_cuda\n    def test_tensor_cuda_input(self):\n        test_data = torch.tensor([[1, 2], [3, 4]]).cuda()\n        test_data = test_data.rot90()\n        self.assertFalse(test_data.is_contiguous())\n        result = ToNumpyd(keys=\"img\")({\"img\": test_data})[\"img\"]\n        self.assertTrue(isinstance(result, np.ndarray))\n        self.assertTrue(result.flags[\"C_CONTIGUOUS\"])\n        assert_allclose(result, test_data, type_test=False)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_torchvision_fc_model.py_unittest_TEST_CASE_PRETRAINED_1._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_torchvision_fc_model.py_unittest_TEST_CASE_PRETRAINED_1._", "embedding": null, "metadata": {"file_path": "tests/test_torchvision_fc_model.py", "file_name": "test_torchvision_fc_model.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 86, "span_ids": ["impl:14", "docstring"], "tokens": 684}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom unittest import skipUnless\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks import eval_mode\nfrom monai.networks.nets import TorchVisionFCModel\nfrom monai.utils import optional_import\n\n_, has_tv = optional_import(\"torchvision\")\n\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\nTEST_CASE_0 = [\n    {\"model_name\": \"resnet18\", \"num_classes\": 1, \"use_conv\": True, \"pretrained\": False},\n    (2, 3, 224, 224),\n    (2, 1, 1, 1),\n]\n\nTEST_CASE_1 = [\n    {\"model_name\": \"resnet18\", \"num_classes\": 1, \"use_conv\": True, \"pretrained\": False},\n    (2, 3, 256, 256),\n    (2, 1, 2, 2),\n]\n\nTEST_CASE_2 = [\n    {\"model_name\": \"resnet101\", \"num_classes\": 5, \"use_conv\": True, \"pretrained\": False},\n    (2, 3, 256, 256),\n    (2, 5, 2, 2),\n]\n\nTEST_CASE_3 = [\n    {\n        \"model_name\": \"resnet101\",\n        \"num_classes\": 5,\n        \"use_conv\": True,\n        \"pool\": (\"avg\", {\"kernel_size\": 6, \"stride\": 1}),\n        \"pretrained\": False,\n    },\n    (2, 3, 224, 224),\n    (2, 5, 2, 2),\n]\n\nTEST_CASE_4 = [\n    {\"model_name\": \"resnet18\", \"num_classes\": 1, \"use_conv\": False, \"pool\": None, \"pretrained\": False},\n    (2, 3, 224, 224),\n    (2, 1),\n]\n\nTEST_CASE_5 = [\n    {\"model_name\": \"resnet18\", \"num_classes\": 1, \"use_conv\": False, \"pool\": None, \"pretrained\": False},\n    (2, 3, 256, 256),\n    (2, 1),\n]\n\nTEST_CASE_6 = [\n    {\"model_name\": \"resnet101\", \"num_classes\": 5, \"use_conv\": False, \"pool\": None, \"pretrained\": False},\n    (2, 3, 256, 256),\n    (2, 5),\n]\n\nTEST_CASE_PRETRAINED_0 = [\n    {\"model_name\": \"resnet18\", \"num_classes\": 1, \"use_conv\": True, \"pretrained\": True},\n    (2, 3, 224, 224),\n    (2, 1, 1, 1),\n    -0.010419349186122417,\n]\n\nTEST_CASE_PRETRAINED_1 = [\n    {\"model_name\": \"resnet18\", \"num_classes\": 1, \"use_conv\": True, \"pretrained\": True},\n    (2, 3, 256, 256),\n    (2, 1, 2, 2),\n    -0.010419349186122417,\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_torchvision_fc_model.py_TEST_CASE_PRETRAINED_2_TEST_CASE_PRETRAINED_5._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_torchvision_fc_model.py_TEST_CASE_PRETRAINED_2_TEST_CASE_PRETRAINED_5._", "embedding": null, "metadata": {"file_path": "tests/test_torchvision_fc_model.py", "file_name": "test_torchvision_fc_model.py", "file_type": "text/x-python", "category": "test", "start_line": 88, "end_line": 114, "span_ids": ["impl:28", "impl:14"], "tokens": 301}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "TEST_CASE_PRETRAINED_2 = [\n    {\"model_name\": \"resnet18\", \"num_classes\": 5, \"use_conv\": True, \"pretrained\": True},\n    (2, 3, 256, 256),\n    (2, 5, 2, 2),\n    -0.010419349186122417,\n]\n\nTEST_CASE_PRETRAINED_3 = [\n    {\"model_name\": \"resnet18\", \"num_classes\": 1, \"use_conv\": False, \"pool\": None, \"pretrained\": True},\n    (2, 3, 224, 224),\n    (2, 1),\n    -0.010419349186122417,\n]\n\nTEST_CASE_PRETRAINED_4 = [\n    {\"model_name\": \"resnet18\", \"num_classes\": 1, \"use_conv\": False, \"pool\": None, \"pretrained\": True},\n    (2, 3, 256, 256),\n    (2, 1),\n    -0.010419349186122417,\n]\n\nTEST_CASE_PRETRAINED_5 = [\n    {\"model_name\": \"resnet18\", \"num_classes\": 5, \"use_conv\": False, \"pool\": None, \"pretrained\": True},\n    (2, 3, 256, 256),\n    (2, 5),\n    -0.010419349186122417,\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_torchvision_fc_model.py_TestTorchVisionFCModel_TestTorchVisionFCModel.test_without_pretrained.with_eval_mode_net_.self_assertEqual_result_s": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_torchvision_fc_model.py_TestTorchVisionFCModel_TestTorchVisionFCModel.test_without_pretrained.with_eval_mode_net_.self_assertEqual_result_s", "embedding": null, "metadata": {"file_path": "tests/test_torchvision_fc_model.py", "file_name": "test_torchvision_fc_model.py", "file_type": "text/x-python", "category": "test", "start_line": 117, "end_line": 124, "span_ids": ["TestTorchVisionFCModel", "TestTorchVisionFCModel.test_without_pretrained"], "tokens": 122}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestTorchVisionFCModel(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_0, TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5, TEST_CASE_6])\n    @skipUnless(has_tv, \"Requires TorchVision.\")\n    def test_without_pretrained(self, input_param, input_shape, expected_shape):\n        net = TorchVisionFCModel(**input_param).to(device)\n        with eval_mode(net):\n            result = net.forward(torch.randn(input_shape).to(device))\n            self.assertEqual(result.shape, expected_shape)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_torchvision_fc_model.py_TestTorchVisionFCModel.test_with_pretrained_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_torchvision_fc_model.py_TestTorchVisionFCModel.test_with_pretrained_", "embedding": null, "metadata": {"file_path": "tests/test_torchvision_fc_model.py", "file_name": "test_torchvision_fc_model.py", "file_type": "text/x-python", "category": "test", "start_line": 136, "end_line": 158, "span_ids": ["impl:30", "TestTorchVisionFCModel.test_with_pretrained"], "tokens": 190}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestTorchVisionFCModel(unittest.TestCase):\n\n    @parameterized.expand(\n        [\n            TEST_CASE_PRETRAINED_0,\n            TEST_CASE_PRETRAINED_1,\n            TEST_CASE_PRETRAINED_2,\n            TEST_CASE_PRETRAINED_3,\n            TEST_CASE_PRETRAINED_4,\n            TEST_CASE_PRETRAINED_5,\n        ]\n    )\n    @skipUnless(has_tv, \"Requires TorchVision.\")\n    def test_with_pretrained(self, input_param, input_shape, expected_shape, expected_value):\n        net = TorchVisionFCModel(**input_param).to(device)\n        with eval_mode(net):\n            result = net.forward(torch.randn(input_shape).to(device))\n            value = next(net.parameters())[0, 0, 0, 0].item()\n            self.assertEqual(value, expected_value)\n            self.assertEqual(result.shape, expected_shape)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_torchvision_fully_conv_model.py_unittest_TEST_CASE_PRETRAINED_2._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_torchvision_fully_conv_model.py_unittest_TEST_CASE_PRETRAINED_2._", "embedding": null, "metadata": {"file_path": "tests/test_torchvision_fully_conv_model.py", "file_name": "test_torchvision_fully_conv_model.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 57, "span_ids": ["impl:16", "docstring"], "tokens": 501}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom unittest import skipUnless\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks import eval_mode\nfrom monai.networks.nets import TorchVisionFullyConvModel\nfrom monai.utils import optional_import\n\n_, has_tv = optional_import(\"torchvision\")\n\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\nTEST_CASE_0 = [{\"model_name\": \"resnet18\", \"num_classes\": 1, \"pretrained\": False}, (2, 3, 224, 224), (2, 1, 1, 1)]\n\nTEST_CASE_1 = [{\"model_name\": \"resnet18\", \"num_classes\": 1, \"pretrained\": False}, (2, 3, 256, 256), (2, 1, 2, 2)]\n\nTEST_CASE_2 = [{\"model_name\": \"resnet101\", \"num_classes\": 5, \"pretrained\": False}, (2, 3, 256, 256), (2, 5, 2, 2)]\n\nTEST_CASE_3 = [\n    {\"model_name\": \"resnet101\", \"num_classes\": 5, \"pool_size\": 6, \"pretrained\": False},\n    (2, 3, 224, 224),\n    (2, 5, 2, 2),\n]\n\nTEST_CASE_PRETRAINED_0 = [\n    {\"model_name\": \"resnet18\", \"num_classes\": 1, \"pretrained\": True},\n    (2, 3, 224, 224),\n    (2, 1, 1, 1),\n    -0.010419349186122417,\n]\n\nTEST_CASE_PRETRAINED_1 = [\n    {\"model_name\": \"resnet18\", \"num_classes\": 1, \"pretrained\": True},\n    (2, 3, 256, 256),\n    (2, 1, 2, 2),\n    -0.010419349186122417,\n]\n\nTEST_CASE_PRETRAINED_2 = [\n    {\"model_name\": \"resnet18\", \"num_classes\": 5, \"pretrained\": True},\n    (2, 3, 256, 256),\n    (2, 5, 2, 2),\n    -0.010419349186122417,\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_transpose.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_transpose.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_transpose.py", "file_name": "test_transpose.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 40, "span_ids": ["TestTranspose.test_transpose", "impl:6", "TestTranspose", "docstring"], "tokens": 204}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import Transpose\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    TESTS.append([p(np.arange(5 * 4).reshape(5, 4)), None])\n    TESTS.append([p(np.arange(5 * 4 * 3).reshape(5, 4, 3)), [2, 0, 1]])\n\n\nclass TestTranspose(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_transpose(self, im, indices):\n        tr = Transpose(indices)\n        out1 = tr(im)\n        if isinstance(im, torch.Tensor):\n            im = im.cpu().numpy()\n        out2 = np.transpose(im, indices)\n        assert_allclose(out1, out2, type_test=False)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_transposed.py_TestTranspose_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_transposed.py_TestTranspose_", "embedding": null, "metadata": {"file_path": "tests/test_transposed.py", "file_name": "test_transposed.py", "file_type": "text/x-python", "category": "test", "start_line": 30, "end_line": 51, "span_ids": ["TestTranspose.test_transpose", "TestTranspose", "impl:8"], "tokens": 189}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestTranspose(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_transpose(self, im, indices):\n        data = {\"i\": deepcopy(im), \"j\": deepcopy(im)}\n        tr = Transposed([\"i\", \"j\"], indices)\n        out_data = tr(data)\n        out_im1, out_im2 = out_data[\"i\"], out_data[\"j\"]\n        if isinstance(im, torch.Tensor):\n            im = im.cpu().numpy()\n        out_gt = np.transpose(im, indices)\n        assert_allclose(out_im1, out_gt, type_test=False)\n        assert_allclose(out_im2, out_gt, type_test=False)\n\n        # test inverse\n        fwd_inv_data = tr.inverse(out_data)\n        for i, j in zip(data.values(), fwd_inv_data.values()):\n            assert_allclose(i, j, type_test=False)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_upsample_block.py_unittest_TEST_CASES._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_upsample_block.py_unittest_TEST_CASES._", "embedding": null, "metadata": {"file_path": "tests/test_upsample_block.py", "file_name": "test_upsample_block.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 70, "span_ids": ["docstring"], "tokens": 808}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks import eval_mode\nfrom monai.networks.blocks import UpSample\nfrom monai.utils import UpsampleMode\n\nTEST_CASES = [\n    [{\"dimensions\": 2, \"in_channels\": 4}, (7, 4, 32, 48), (7, 4, 64, 96)],  # 4-channel 2D, batch 7\n    [{\"dimensions\": 1, \"in_channels\": 4, \"out_channels\": 3}, (16, 4, 63), (16, 3, 126)],  # 4-channel 1D, batch 16\n    [\n        {\"dimensions\": 1, \"in_channels\": 4, \"out_channels\": 8, \"mode\": \"deconv\", \"align_corners\": False},\n        (16, 4, 20),\n        (16, 8, 40),\n    ],  # 4-channel 1D, batch 16\n    [\n        {\"dimensions\": 3, \"in_channels\": 4, \"mode\": \"nontrainable\"},\n        (16, 4, 32, 24, 48),\n        (16, 4, 64, 48, 96),\n    ],  # 4-channel 3D, batch 16\n    [\n        {\"dimensions\": 3, \"in_channels\": 4, \"mode\": \"nontrainable\", \"size\": 64},\n        (16, 4, 32, 24, 48),\n        (16, 4, 64, 64, 64),\n    ],  # 4-channel 3D, batch 16\n    [\n        {\"dimensions\": 3, \"in_channels\": 4, \"mode\": \"nontrainable\", \"size\": (64, 24, 48)},\n        (16, 4, 32, 24, 48),\n        (16, 4, 64, 24, 48),\n    ],  # 4-channel 3D, batch 16\n    [\n        {\"dimensions\": 3, \"in_channels\": 1, \"mode\": \"deconv\", \"scale_factor\": 3, \"align_corners\": False},\n        (16, 1, 10, 15, 20),\n        (16, 1, 30, 45, 60),\n    ],  # 1-channel 3D, batch 16\n    [\n        {\"dimensions\": 3, \"in_channels\": 1, \"mode\": \"pixelshuffle\", \"scale_factor\": 2, \"align_corners\": False},\n        (16, 1, 10, 15, 20),\n        (16, 1, 20, 30, 40),\n    ],  # 1-channel 3D, batch 16\n    [\n        {\"dimensions\": 2, \"in_channels\": 4, \"mode\": \"pixelshuffle\", \"scale_factor\": 2},\n        (16, 4, 10, 15),\n        (16, 4, 20, 30),\n    ],  # 4-channel 2D, batch 16\n    [\n        {\n            \"dimensions\": 3,\n            \"mode\": \"pixelshuffle\",\n            \"scale_factor\": 2,\n            \"align_corners\": False,\n            \"pre_conv\": torch.nn.Conv3d(in_channels=1, out_channels=24, kernel_size=3, stride=1, padding=1),\n        },\n        (16, 1, 10, 15, 20),\n        (16, 3, 20, 30, 40),\n    ],  # 1-channel 3D, batch 16, pre_conv\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_upsample_block.py_TEST_CASES_EQ_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_upsample_block.py_TEST_CASES_EQ_", "embedding": null, "metadata": {"file_path": "tests/test_upsample_block.py", "file_name": "test_upsample_block.py", "file_type": "text/x-python", "category": "test", "start_line": 72, "end_line": 95, "span_ids": ["impl:3", "TestUpsample", "impl:13", "TestUpsample.test_shape"], "tokens": 207}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "TEST_CASES_EQ = []\nfor s in range(1, 5):\n    expected_shape = (16, 5, 4 * s, 5 * s, 6 * s)\n    for t in UpsampleMode:\n        test_case = [\n            {\"dimensions\": 3, \"in_channels\": 3, \"out_channels\": 5, \"mode\": t, \"scale_factor\": s, \"align_corners\": True},\n            (16, 3, 4, 5, 6),\n        ]\n        test_case.append(expected_shape)\n        TEST_CASES_EQ.append(test_case)\n\n\nclass TestUpsample(unittest.TestCase):\n    @parameterized.expand(TEST_CASES + TEST_CASES_EQ)\n    def test_shape(self, input_param, input_shape, expected_shape):\n        net = UpSample(**input_param)\n        with eval_mode(net):\n            result = net(torch.randn(input_shape))\n            self.assertEqual(result.shape, expected_shape)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/mmars/model_desc.py_os_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/mmars/model_desc.py_os_", "embedding": null, "metadata": {"file_path": "monai/apps/mmars/model_desc.py", "file_name": "model_desc.py", "file_type": "text/x-python", "category": "implementation", "start_line": 19, "end_line": 198, "span_ids": ["impl:3", "RemoteMMARKeys", "docstring"], "tokens": 263}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\n\n__all__ = [\"MODEL_DESC\", \"RemoteMMARKeys\"]\n\n\nclass RemoteMMARKeys:\n    \"\"\"\n    Data keys used for loading MMAR.\n    ID must uniquely define an MMAR.\n    \"\"\"\n\n    ID = \"id\"  # unique MMAR\n    NAME = \"name\"  # MMAR name for readability\n    URL = \"url\"  # remote location of the MMAR, see also: `monai.apps.mmars.mmars._get_ngc_url`\n    DOC = \"doc\"  # documentation page of the remote model, see also: `monai.apps.mmars.mmars._get_ngc_doc_url`\n    FILE_TYPE = \"file_type\"  # type of the compressed MMAR\n    HASH_TYPE = \"hash_type\"  # hashing method for the compressed MMAR\n    HASH_VAL = \"hash_val\"  # hashing value for the compressed MMAR\n    MODEL_FILE = \"model_file\"  # within an MMAR folder, the relative path to the model file\n    CONFIG_FILE = \"config_file\"  # within an MMAR folder, the relative path to the config file (for model config)\n    VERSION = \"version\"  # version of the MMAR\n\n\nMODEL_DESC =\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/docs/source/conf.py__General_configuratio_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/docs/source/conf.py__General_configuratio_", "embedding": null, "metadata": {"file_path": "docs/source/conf.py", "file_name": "conf.py", "file_type": "text/x-python", "category": "implementation", "start_line": 71, "end_line": 148, "span_ids": ["generate_apidocs", "impl:56", "impl:18", "setup"], "tokens": 664}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "# -- General configuration ---------------------------------------------------\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom\n# ones.\nsource_suffix = {\".rst\": \"restructuredtext\", \".txt\": \"restructuredtext\", \".md\": \"markdown\"}\n\nextensions = [\n    \"recommonmark\",\n    \"sphinx.ext.intersphinx\",\n    \"sphinx.ext.mathjax\",\n    \"sphinx.ext.napoleon\",\n    \"sphinx.ext.autodoc\",\n    \"sphinx.ext.viewcode\",\n    \"sphinx.ext.autosectionlabel\",\n    \"sphinx_autodoc_typehints\",\n]\n\nautoclass_content = \"both\"\nadd_module_names = True\nsource_encoding = \"utf-8\"\nautosectionlabel_prefix_document = True\nnapoleon_use_param = True\nnapoleon_include_init_with_doc = True\nset_type_checking_flag = True\n\n# Add any paths that contain templates here, relative to this directory.\ntemplates_path = [\"_templates\"]\n\n# -- Options for HTML output -------------------------------------------------\n\n# The theme to use for HTML and HTML Help pages.  See the documentation for\n# a list of builtin themes.\n#\nhtml_theme = \"pydata_sphinx_theme\"\n# html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]\nhtml_theme_options = {\n    \"external_links\": [{\"url\": \"https://github.com/Project-MONAI/tutorials\", \"name\": \"Tutorials\"}],\n    \"icon_links\": [\n        {\"name\": \"GitHub\", \"url\": \"https://github.com/project-monai/monai\", \"icon\": \"fab fa-github-square\"},\n        {\"name\": \"Twitter\", \"url\": \"https://twitter.com/projectmonai\", \"icon\": \"fab fa-twitter-square\"},\n    ],\n    \"collapse_navigation\": True,\n    \"navigation_depth\": 3,\n    \"show_toc_level\": 1,\n    \"footer_items\": [\"copyright\"],\n    \"navbar_align\": \"content\",\n}\nhtml_context = {\n    \"github_user\": \"Project-MONAI\",\n    \"github_repo\": \"MONAI\",\n    \"github_version\": \"dev\",\n    \"doc_path\": \"docs/\",\n    \"conf_py_path\": \"/docs/\",\n    \"VERSION\": version,\n}\nhtml_scaled_image_link = False\nhtml_show_sourcelink = True\nhtml_favicon = \"../images/favicon.ico\"\nhtml_logo = \"../images/MONAI-logo-color.png\"\nhtml_sidebars = {\"**\": [\"search-field\", \"sidebar-nav-bs\"]}\npygments_style = \"sphinx\"\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\nhtml_static_path = [\"../_static\"]\nhtml_css_files = [\"custom.css\"]\nhtml_title = f\"{project} {version} Documentation\"\n\n# -- Auto-convert markdown pages to demo --------------------------------------\n\n\ndef setup(app):\n    # Hook to allow for automatic generation of API docs\n    # before doc deployment begins.\n    app.connect(\"builder-inited\", generate_apidocs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepedit/__init__.py__": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepedit/__init__.py__", "embedding": null, "metadata": {"file_path": "monai/apps/deepedit/__init__.py", "file_name": "__init__.py", "file_type": "text/x-python", "category": "implementation", "start_line": 11, "end_line": 11, "span_ids": [], "tokens": 0}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepedit/transforms.py_json_DiscardAddGuidanced.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepedit/transforms.py_json_DiscardAddGuidanced.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/apps/deepedit/transforms.py", "file_name": "transforms.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 55, "span_ids": ["DiscardAddGuidanced._apply", "DiscardAddGuidanced", "docstring", "DiscardAddGuidanced.__init__", "DiscardAddGuidanced.__call__"], "tokens": 378}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import json\nimport logging\nfrom typing import Dict, Hashable, Mapping, Tuple\n\nimport numpy as np\n\nfrom monai.config import KeysCollection\nfrom monai.transforms.transform import MapTransform, Randomizable, Transform\nfrom monai.utils import optional_import\n\nlogger = logging.getLogger(__name__)\n\n\ndistance_transform_cdt, _ = optional_import(\"scipy.ndimage.morphology\", name=\"distance_transform_cdt\")\n\n\nclass DiscardAddGuidanced(MapTransform):\n    def __init__(self, keys: KeysCollection, probability: float = 1.0, allow_missing_keys: bool = False):\n        \"\"\"\n        Discard positive and negative points randomly or Add the two channels for inference time\n\n        :param probability: Discard probability; For inference it will be always 1.0\n        \"\"\"\n        super().__init__(keys, allow_missing_keys)\n        self.probability = probability\n\n    def _apply(self, image):\n        if self.probability >= 1.0 or np.random.choice([True, False], p=[self.probability, 1 - self.probability]):\n            signal = np.zeros((1, image.shape[-3], image.shape[-2], image.shape[-1]), dtype=np.float32)\n            if image.shape[0] == 3:\n                image[1] = signal\n                image[2] = signal\n            else:\n                image = np.concatenate((image, signal, signal), axis=0)\n        return image\n\n    def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]:\n        d: Dict = dict(data)\n        for key in self.key_iterator(d):\n            if key == \"image\":\n                d[key] = self._apply(d[key])\n            else:\n                print(\"This transform only applies to the image\")\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepedit/transforms.py_ResizeGuidanceCustomd_ResizeGuidanceCustomd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepedit/transforms.py_ResizeGuidanceCustomd_ResizeGuidanceCustomd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/apps/deepedit/transforms.py", "file_name": "transforms.py", "file_type": "text/x-python", "category": "implementation", "start_line": 58, "end_line": 78, "span_ids": ["ResizeGuidanceCustomd.__init__", "ResizeGuidanceCustomd.__call__", "ResizeGuidanceCustomd"], "tokens": 193}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ResizeGuidanceCustomd(Transform):\n    \"\"\"\n    Resize the guidance based on cropped vs resized image.\n    \"\"\"\n\n    def __init__(self, guidance: str, ref_image: str) -> None:\n        self.guidance = guidance\n        self.ref_image = ref_image\n\n    def __call__(self, data):\n        d = dict(data)\n        current_shape = d[self.ref_image].shape[1:]\n\n        factor = np.divide(current_shape, d[\"image_meta_dict\"][\"dim\"][1:4])\n        pos_clicks, neg_clicks = d[\"foreground\"], d[\"background\"]\n\n        pos = np.multiply(pos_clicks, factor).astype(int, copy=False).tolist() if len(pos_clicks) else []\n        neg = np.multiply(neg_clicks, factor).astype(int, copy=False).tolist() if len(neg_clicks) else []\n\n        d[self.guidance] = [pos, neg]\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepedit/transforms.py_ClickRatioAddRandomGuidanced_ClickRatioAddRandomGuidanced.randomize.self._will_interact.self_R_choice_True_Fals": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepedit/transforms.py_ClickRatioAddRandomGuidanced_ClickRatioAddRandomGuidanced.randomize.self._will_interact.self_R_choice_True_Fals", "embedding": null, "metadata": {"file_path": "monai/apps/deepedit/transforms.py", "file_name": "transforms.py", "file_type": "text/x-python", "category": "implementation", "start_line": 70, "end_line": 95, "span_ids": ["ClickRatioAddRandomGuidanced", "ClickRatioAddRandomGuidanced.randomize", "ClickRatioAddRandomGuidanced.__init__"], "tokens": 263}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ClickRatioAddRandomGuidanced(Randomizable, Transform):\n    \"\"\"\n    Add random guidance based on discrepancies that were found between label and prediction.\n    Args:\n        guidance: key to guidance source, shape (2, N, # of dim)\n        discrepancy: key that represents discrepancies found between label and prediction, shape (2, C, D, H, W) or (2, C, H, W)\n        probability: key that represents click/interaction probability, shape (1)\n        fn_fp_click_ratio: ratio of clicks between FN and FP\n    \"\"\"\n\n    def __init__(\n        self,\n        guidance: str = \"guidance\",\n        discrepancy: str = \"discrepancy\",\n        probability: str = \"probability\",\n        fn_fp_click_ratio: Tuple[float, float] = (1.0, 1.0),\n    ):\n        self.guidance = guidance\n        self.discrepancy = discrepancy\n        self.probability = probability\n        self.fn_fp_click_ratio = fn_fp_click_ratio\n        self._will_interact = None\n\n    def randomize(self, data=None):\n        probability = data[self.probability]\n        self._will_interact = self.R.choice([True, False], p=[probability, 1.0 - probability])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepedit/transforms.py_ClickRatioAddRandomGuidanced.find_guidance_ClickRatioAddRandomGuidanced.find_guidance.return.None": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepedit/transforms.py_ClickRatioAddRandomGuidanced.find_guidance_ClickRatioAddRandomGuidanced.find_guidance.return.None", "embedding": null, "metadata": {"file_path": "monai/apps/deepedit/transforms.py", "file_name": "transforms.py", "file_type": "text/x-python", "category": "implementation", "start_line": 97, "end_line": 109, "span_ids": ["ClickRatioAddRandomGuidanced.find_guidance"], "tokens": 150}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ClickRatioAddRandomGuidanced(Randomizable, Transform):\n\n    def find_guidance(self, discrepancy):\n        distance = distance_transform_cdt(discrepancy).flatten()\n        probability = np.exp(distance) - 1.0\n        idx = np.where(discrepancy.flatten() > 0)[0]\n\n        if np.sum(discrepancy > 0) > 0:\n            seed = self.R.choice(idx, size=1, p=probability[idx] / np.sum(probability[idx]))\n            dst = distance[seed]\n\n            g = np.asarray(np.unravel_index(seed, discrepancy.shape)).transpose().tolist()[0]\n            g[0] = dst[0]\n            return g\n        return None", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepedit/transforms.py_ClickRatioAddRandomGuidanced.add_guidance_ClickRatioAddRandomGuidanced.add_guidance.return.None_None": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepedit/transforms.py_ClickRatioAddRandomGuidanced.add_guidance_ClickRatioAddRandomGuidanced.add_guidance.return.None_None", "embedding": null, "metadata": {"file_path": "monai/apps/deepedit/transforms.py", "file_name": "transforms.py", "file_type": "text/x-python", "category": "implementation", "start_line": 111, "end_line": 137, "span_ids": ["ClickRatioAddRandomGuidanced.add_guidance"], "tokens": 265}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ClickRatioAddRandomGuidanced(Randomizable, Transform):\n\n    def add_guidance(self, discrepancy, will_interact):\n        if not will_interact:\n            return None, None\n\n        pos_discr = discrepancy[0]\n        neg_discr = discrepancy[1]\n\n        can_be_positive = np.sum(pos_discr) > 0\n        can_be_negative = np.sum(neg_discr) > 0\n\n        pos_prob = self.fn_fp_click_ratio[0] / (self.fn_fp_click_ratio[0] + self.fn_fp_click_ratio[1])\n        neg_prob = self.fn_fp_click_ratio[1] / (self.fn_fp_click_ratio[0] + self.fn_fp_click_ratio[1])\n\n        correct_pos = self.R.choice([True, False], p=[pos_prob, neg_prob])\n\n        if can_be_positive and not can_be_negative:\n            return self.find_guidance(pos_discr), None\n\n        if not can_be_positive and can_be_negative:\n            return None, self.find_guidance(neg_discr)\n\n        if correct_pos and can_be_positive:\n            return self.find_guidance(pos_discr), None\n\n        if not correct_pos and can_be_negative:\n            return None, self.find_guidance(neg_discr)\n        return None, None", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepedit/transforms.py_ClickRatioAddRandomGuidanced._apply_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepedit/transforms.py_ClickRatioAddRandomGuidanced._apply_", "embedding": null, "metadata": {"file_path": "monai/apps/deepedit/transforms.py", "file_name": "transforms.py", "file_type": "text/x-python", "category": "implementation", "start_line": 150, "end_line": 170, "span_ids": ["ClickRatioAddRandomGuidanced._apply", "ClickRatioAddRandomGuidanced.__call__"], "tokens": 200}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ClickRatioAddRandomGuidanced(Randomizable, Transform):\n\n    def _apply(self, guidance, discrepancy):\n        guidance = guidance.tolist() if isinstance(guidance, np.ndarray) else guidance\n        guidance = json.loads(guidance) if isinstance(guidance, str) else guidance\n        pos, neg = self.add_guidance(discrepancy, self._will_interact)\n        if pos:\n            guidance[0].append(pos)\n            guidance[1].append([-1] * len(pos))\n        if neg:\n            guidance[0].append([-1] * len(neg))\n            guidance[1].append(neg)\n\n        return json.dumps(np.asarray(guidance, dtype=int).tolist())\n\n    def __call__(self, data):\n        d = dict(data)\n        guidance = d[self.guidance]\n        discrepancy = d[self.discrepancy]\n        self.randomize(data)\n        d[self.guidance] = self._apply(guidance, discrepancy)\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_json_distance_transform_cdt__": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_json_distance_transform_cdt__", "embedding": null, "metadata": {"file_path": "monai/apps/deepgrow/transforms.py", "file_name": "transforms.py", "file_type": "text/x-python", "category": "implementation", "start_line": 11, "end_line": 25, "span_ids": ["docstring"], "tokens": 161}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import json\nfrom typing import Callable, Dict, Hashable, List, Optional, Sequence, Union\n\nimport numpy as np\nimport torch\n\nfrom monai.config import IndexSelection, KeysCollection\nfrom monai.networks.layers import GaussianFilter\nfrom monai.transforms import Resize, SpatialCrop\nfrom monai.transforms.transform import MapTransform, Randomizable, Transform\nfrom monai.transforms.utils import generate_spatial_bounding_box, is_positive\nfrom monai.utils import InterpolateMode, deprecated_arg, ensure_tuple, ensure_tuple_rep, min_version, optional_import\n\nmeasure, _ = optional_import(\"skimage.measure\", \"0.14.2\", min_version)\ndistance_transform_cdt, _ = optional_import(\"scipy.ndimage.morphology\", name=\"distance_transform_cdt\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_AddGuidanceSignald_AddGuidanceSignald.__init__.self.number_intensity_ch.number_intensity_ch": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_AddGuidanceSignald_AddGuidanceSignald.__init__.self.number_intensity_ch.number_intensity_ch", "embedding": null, "metadata": {"file_path": "monai/apps/deepgrow/transforms.py", "file_name": "transforms.py", "file_type": "text/x-python", "category": "implementation", "start_line": 152, "end_line": 170, "span_ids": ["AddGuidanceSignald", "AddGuidanceSignald.__init__"], "tokens": 147}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AddGuidanceSignald(Transform):\n    \"\"\"\n    Add Guidance signal for input image.\n\n    Based on the \"guidance\" points, apply gaussian to them and add them as new channel for input image.\n\n    Args:\n        image: key to the image source.\n        guidance: key to store guidance.\n        sigma: standard deviation for Gaussian kernel.\n        number_intensity_ch: channel index.\n\n    \"\"\"\n\n    def __init__(self, image: str = \"image\", guidance: str = \"guidance\", sigma: int = 2, number_intensity_ch: int = 1):\n        self.image = image\n        self.guidance = guidance\n        self.sigma = sigma\n        self.number_intensity_ch = number_intensity_ch", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_AddGuidanceSignald._get_signal_AddGuidanceSignald.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_AddGuidanceSignald._get_signal_AddGuidanceSignald.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/apps/deepgrow/transforms.py", "file_name": "transforms.py", "file_type": "text/x-python", "category": "implementation", "start_line": 172, "end_line": 217, "span_ids": ["AddGuidanceSignald.__call__", "AddGuidanceSignald._apply", "AddGuidanceSignald._get_signal"], "tokens": 545}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AddGuidanceSignald(Transform):\n\n    def _get_signal(self, image, guidance):\n        dimensions = 3 if len(image.shape) > 3 else 2\n        guidance = guidance.tolist() if isinstance(guidance, np.ndarray) else guidance\n        guidance = json.loads(guidance) if isinstance(guidance, str) else guidance\n        if dimensions == 3:\n            signal = np.zeros((len(guidance), image.shape[-3], image.shape[-2], image.shape[-1]), dtype=np.float32)\n        else:\n            signal = np.zeros((len(guidance), image.shape[-2], image.shape[-1]), dtype=np.float32)\n\n        sshape = signal.shape\n        for i, g_i in enumerate(guidance):\n            for point in g_i:\n                if np.any(np.asarray(point) < 0):\n                    continue\n\n                if dimensions == 3:\n                    p1 = max(0, min(int(point[-3]), sshape[-3] - 1))\n                    p2 = max(0, min(int(point[-2]), sshape[-2] - 1))\n                    p3 = max(0, min(int(point[-1]), sshape[-1] - 1))\n                    signal[i, p1, p2, p3] = 1.0\n                else:\n                    p1 = max(0, min(int(point[-2]), sshape[-2] - 1))\n                    p2 = max(0, min(int(point[-1]), sshape[-1] - 1))\n                    signal[i, p1, p2] = 1.0\n\n            if np.max(signal[i]) > 0:\n                signal_tensor = torch.tensor(signal[i])\n                pt_gaussian = GaussianFilter(len(signal_tensor.shape), sigma=self.sigma)\n                signal_tensor = pt_gaussian(signal_tensor.unsqueeze(0).unsqueeze(0))\n                signal_tensor = signal_tensor.squeeze(0).squeeze(0)\n                signal[i] = signal_tensor.detach().cpu().numpy()\n                signal[i] = (signal[i] - np.min(signal[i])) / (np.max(signal[i]) - np.min(signal[i]))\n        return signal\n\n    def _apply(self, image, guidance):\n        signal = self._get_signal(image, guidance)\n        image = image[0 : 0 + self.number_intensity_ch, ...]\n        return np.concatenate([image, signal], axis=0)\n\n    def __call__(self, data):\n        d = dict(data)\n        image = d[self.image]\n        guidance = d[self.guidance]\n\n        d[self.image] = self._apply(image, guidance)\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_AddRandomGuidanced_AddRandomGuidanced.randomize.self._will_interact.self_R_choice_True_Fals": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/deepgrow/transforms.py_AddRandomGuidanced_AddRandomGuidanced.randomize.self._will_interact.self_R_choice_True_Fals", "embedding": null, "metadata": {"file_path": "monai/apps/deepgrow/transforms.py", "file_name": "transforms.py", "file_type": "text/x-python", "category": "implementation", "start_line": 258, "end_line": 281, "span_ids": ["AddRandomGuidanced.randomize", "AddRandomGuidanced", "AddRandomGuidanced.__init__"], "tokens": 229}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AddRandomGuidanced(Randomizable, Transform):\n    \"\"\"\n    Add random guidance based on discrepancies that were found between label and prediction.\n    input shape is as below:\n    Guidance is of shape (2, N, # of dim)\n    Discrepancy is of shape (2, C, D, H, W) or (2, C, H, W)\n    Probability is of shape (1)\n\n    Args:\n        guidance: key to guidance source.\n        discrepancy: key that represents discrepancies found between label and prediction.\n        probability: key that represents click/interaction probability.\n\n    \"\"\"\n\n    def __init__(self, guidance: str = \"guidance\", discrepancy: str = \"discrepancy\", probability: str = \"probability\"):\n        self.guidance = guidance\n        self.discrepancy = discrepancy\n        self.probability = probability\n        self._will_interact = None\n\n    def randomize(self, data=None):\n        probability = data[self.probability]\n        self._will_interact = self.R.choice([True, False], p=[probability, 1.0 - probability])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/mmars/mmars.py_json_get_model_spec.raise_ValueError_f_Unknow": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/mmars/mmars.py_json_get_model_spec.raise_ValueError_f_Unknow", "embedding": null, "metadata": {"file_path": "monai/apps/mmars/mmars.py", "file_name": "mmars.py", "file_type": "text/x-python", "category": "implementation", "start_line": 19, "end_line": 47, "span_ids": ["get_model_spec", "docstring"], "tokens": 228}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import json\nimport warnings\nfrom pathlib import Path\nfrom typing import Mapping, Optional, Union\n\nimport torch\n\nimport monai.networks.nets as monai_nets\nfrom monai.apps.utils import download_and_extract, logger\nfrom monai.config.type_definitions import PathLike\nfrom monai.utils.module import optional_import\n\nfrom .model_desc import MODEL_DESC\nfrom .model_desc import RemoteMMARKeys as Keys\n\n__all__ = [\"get_model_spec\", \"download_mmar\", \"load_from_mmar\"]\n\n\ndef get_model_spec(idx: Union[int, str]):\n    \"\"\"get model specification by `idx`. `idx` could be index of the constant tuple of dict or the actual model ID.\"\"\"\n    if isinstance(idx, int):\n        return MODEL_DESC[idx]\n    if isinstance(idx, str):\n        key = idx.strip().lower()\n        for cand in MODEL_DESC:\n            if str(cand[Keys.ID]).strip().lower() == key:\n                return cand\n    logger.info(f\"Available specs are: {MODEL_DESC}.\")\n    raise ValueError(f\"Unknown MODEL_DESC request: {idx}\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/mmars/mmars.py__get_all_ngc_models__get_ngc_doc_url.return.f_https_ngc_nvidia_com_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/mmars/mmars.py__get_all_ngc_models__get_ngc_doc_url.return.f_https_ngc_nvidia_com_", "embedding": null, "metadata": {"file_path": "monai/apps/mmars/mmars.py", "file_name": "mmars.py", "file_type": "text/x-python", "category": "implementation", "start_line": 49, "end_line": 98, "span_ids": ["_get_all_ngc_models", "_get_ngc_url", "_get_ngc_doc_url"], "tokens": 441}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _get_all_ngc_models(pattern, page_index=0, page_size=50):\n    url = \"https://api.ngc.nvidia.com/v2/search/catalog/resources/MODEL\"\n    query_dict = {\n        \"query\": \"\",\n        \"orderBy\": [{\"field\": \"score\", \"value\": \"DESC\"}],\n        \"queryFields\": [\"all\", \"description\", \"displayName\", \"name\", \"resourceId\"],\n        \"fields\": [\n            \"isPublic\",\n            \"attributes\",\n            \"guestAccess\",\n            \"name\",\n            \"orgName\",\n            \"teamName\",\n            \"displayName\",\n            \"dateModified\",\n            \"labels\",\n            \"description\",\n        ],\n        \"page\": 0,\n    }\n\n    filter = [dict(field=\"name\", value=f\"*{pattern}*\")]\n    query_dict[\"page\"] = page_index\n    query_dict[\"pageSize\"] = page_size\n    query_dict[\"filters\"] = filter\n    query_str = json.dumps(query_dict)\n    full_url = f\"{url}?q={query_str}\"\n    requests_get, has_requests = optional_import(\"requests\", name=\"get\")\n    if has_requests:\n        resp = requests_get(full_url)\n    else:\n        raise ValueError(\"NGC API requires requests package.  Please install it.\")\n    model_list = json.loads(resp.text)\n    model_dict = {}\n    for result in model_list[\"results\"]:\n        for model in result[\"resources\"]:\n            current_res_id = model[\"resourceId\"]\n            model_dict[current_res_id] = {\"name\": model[\"name\"]}\n            for attribute in model[\"attributes\"]:\n                if attribute[\"key\"] == \"latestVersionIdStr\":\n                    model_dict[current_res_id][\"latest\"] = attribute[\"value\"]\n    return model_dict\n\n\ndef _get_ngc_url(model_name: str, version: str, model_prefix=\"\"):\n    return f\"https://api.ngc.nvidia.com/v2/models/{model_prefix}{model_name}/versions/{version}/zip\"\n\n\ndef _get_ngc_doc_url(model_name: str, model_prefix=\"\"):\n    return f\"https://ngc.nvidia.com/catalog/models/{model_prefix}{model_name}\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/data/__init__.py_MaskedInferenceWSIDataset_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/data/__init__.py_MaskedInferenceWSIDataset_", "embedding": null, "metadata": {"file_path": "monai/apps/pathology/data/__init__.py", "file_name": "__init__.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 13, "span_ids": ["docstring"], "tokens": 23}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from .datasets import MaskedInferenceWSIDataset, PatchWSIDataset, SmartCachePatchWSIDataset", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/data/datasets.py_os_PatchWSIDataset._fetch_wsi_objects.for_image_path_in_self_im.self_wsi_object_dict_imag": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/data/datasets.py_os_PatchWSIDataset._fetch_wsi_objects.for_image_path_in_self_im.self_wsi_object_dict_imag", "embedding": null, "metadata": {"file_path": "monai/apps/pathology/data/datasets.py", "file_name": "datasets.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 78, "span_ids": ["PatchWSIDataset._fetch_wsi_objects", "PatchWSIDataset.__init__", "PatchWSIDataset", "docstring"], "tokens": 663}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport sys\nfrom typing import Callable, Dict, List, Optional, Sequence, Tuple, Union\n\nimport numpy as np\n\nfrom monai.data import Dataset, SmartCacheDataset\nfrom monai.data.image_reader import WSIReader\nfrom monai.utils import ensure_tuple_rep\n\n__all__ = [\"PatchWSIDataset\", \"SmartCachePatchWSIDataset\", \"MaskedInferenceWSIDataset\"]\n\n\nclass PatchWSIDataset(Dataset):\n    \"\"\"\n    This dataset reads whole slide images, extracts regions, and creates patches.\n    It also reads labels for each patch and provides each patch with its associated class labels.\n\n    Args:\n        data: the list of input samples including image, location, and label (see the note below for more details).\n        region_size: the size of regions to be extracted from the whole slide image.\n        grid_shape: the grid shape on which the patches should be extracted.\n        patch_size: the size of patches extracted from the region on the grid.\n        transform: transforms to be executed on input data.\n        image_reader_name: the name of library to be used for loading whole slide imaging, either CuCIM or OpenSlide.\n            Defaults to CuCIM.\n\n    Note:\n        The input data has the following form as an example:\n        `[{\"image\": \"path/to/image1.tiff\", \"location\": [200, 500], \"label\": [0,0,0,1]}]`.\n\n        This means from \"image1.tiff\" extract a region centered at the given location `location`\n        with the size of `region_size`, and then extract patches with the size of `patch_size`\n        from a grid with the shape of `grid_shape`.\n        Be aware the the `grid_shape` should construct a grid with the same number of element as `labels`,\n        so for this example the `grid_shape` should be (2, 2).\n\n    \"\"\"\n\n    def __init__(\n        self,\n        data: List,\n        region_size: Union[int, Tuple[int, int]],\n        grid_shape: Union[int, Tuple[int, int]],\n        patch_size: Union[int, Tuple[int, int]],\n        transform: Optional[Callable] = None,\n        image_reader_name: str = \"cuCIM\",\n    ):\n        super().__init__(data, transform)\n\n        self.region_size = ensure_tuple_rep(region_size, 2)\n        self.grid_shape = ensure_tuple_rep(grid_shape, 2)\n        self.patch_size = ensure_tuple_rep(patch_size, 2)\n\n        self.image_path_list = list({x[\"image\"] for x in self.data})\n        self.image_reader_name = image_reader_name.lower()\n        self.image_reader = WSIReader(image_reader_name)\n        self.wsi_object_dict = None\n        if self.image_reader_name != \"openslide\":\n            # OpenSlide causes memory issue if we prefetch image objects\n            self._fetch_wsi_objects()\n\n    def _fetch_wsi_objects(self):\n        \"\"\"Load all the image objects and reuse them when asked for an item.\"\"\"\n        self.wsi_object_dict = {}\n        for image_path in self.image_path_list:\n            self.wsi_object_dict[image_path] = self.image_reader.read(image_path)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/data/datasets.py_PatchWSIDataset.__getitem___PatchWSIDataset.__getitem__.return.patches": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/data/datasets.py_PatchWSIDataset.__getitem___PatchWSIDataset.__getitem__.return.patches", "embedding": null, "metadata": {"file_path": "monai/apps/pathology/data/datasets.py", "file_name": "datasets.py", "file_type": "text/x-python", "category": "implementation", "start_line": 80, "end_line": 101, "span_ids": ["PatchWSIDataset.__getitem__"], "tokens": 231}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class PatchWSIDataset(Dataset):\n\n    def __getitem__(self, index):\n        sample = self.data[index]\n        if self.image_reader_name == \"openslide\":\n            img_obj = self.image_reader.read(sample[\"image\"])\n        else:\n            img_obj = self.wsi_object_dict[sample[\"image\"]]\n        location = [sample[\"location\"][i] - self.region_size[i] // 2 for i in range(len(self.region_size))]\n        images, _ = self.image_reader.get_data(\n            img=img_obj,\n            location=location,\n            size=self.region_size,\n            grid_shape=self.grid_shape,\n            patch_size=self.patch_size,\n        )\n        labels = np.array(sample[\"label\"], dtype=np.float32)\n        # expand dimensions to have 4 dimension as batch, class, height, and width.\n        for _ in range(4 - labels.ndim):\n            labels = np.expand_dims(labels, 1)\n        patches = [{\"image\": images[i], \"label\": labels[i]} for i in range(len(sample[\"label\"]))]\n        if self.transform:\n            patches = self.transform(patches)\n        return patches", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/data/datasets.py_SmartCachePatchWSIDataset_SmartCachePatchWSIDataset.__init__.super___init___": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/data/datasets.py_SmartCachePatchWSIDataset_SmartCachePatchWSIDataset.__init__.super___init___", "embedding": null, "metadata": {"file_path": "monai/apps/pathology/data/datasets.py", "file_name": "datasets.py", "file_type": "text/x-python", "category": "implementation", "start_line": 104, "end_line": 166, "span_ids": ["SmartCachePatchWSIDataset.__init__", "SmartCachePatchWSIDataset"], "tokens": 663}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SmartCachePatchWSIDataset(SmartCacheDataset):\n    \"\"\"Add SmartCache functionality to `PatchWSIDataset`.\n\n    Args:\n        data: the list of input samples including image, location, and label (see `PatchWSIDataset` for more details)\n        region_size: the region to be extracted from the whole slide image.\n        grid_shape: the grid shape on which the patches should be extracted.\n        patch_size: the size of patches extracted from the region on the grid.\n        image_reader_name: the name of library to be used for loading whole slide imaging, either CuCIM or OpenSlide.\n            Defaults to CuCIM.\n        transform: transforms to be executed on input data.\n        replace_rate: percentage of the cached items to be replaced in every epoch.\n        cache_num: number of items to be cached. Default is `sys.maxsize`.\n            will take the minimum of (cache_num, data_length x cache_rate, data_length).\n        cache_rate: percentage of cached data in total, default is 1.0 (cache all).\n            will take the minimum of (cache_num, data_length x cache_rate, data_length).\n        num_init_workers: the number of worker threads to initialize the cache for first epoch.\n            If num_init_workers is None then the number returned by os.cpu_count() is used.\n        num_replace_workers: the number of worker threads to prepare the replacement cache for every epoch.\n            If num_replace_workers is None then the number returned by os.cpu_count() is used.\n        progress: whether to display a progress bar when caching for the first epoch.\n        copy_cache: whether to `deepcopy` the cache content before applying the random transforms,\n            default to `True`. if the random transforms don't modify the cache content\n            or every cache item is only used once in a `multi-processing` environment,\n            may set `copy=False` for better performance.\n\n    \"\"\"\n\n    def __init__(\n        self,\n        data: List,\n        region_size: Union[int, Tuple[int, int]],\n        grid_shape: Union[int, Tuple[int, int]],\n        patch_size: Union[int, Tuple[int, int]],\n        transform: Union[Sequence[Callable], Callable],\n        image_reader_name: str = \"cuCIM\",\n        replace_rate: float = 0.5,\n        cache_num: int = sys.maxsize,\n        cache_rate: float = 1.0,\n        num_init_workers: Optional[int] = None,\n        num_replace_workers: Optional[int] = None,\n        progress: bool = True,\n        copy_cache: bool = True,\n    ):\n        patch_wsi_dataset = PatchWSIDataset(\n            data=data,\n            region_size=region_size,\n            grid_shape=grid_shape,\n            patch_size=patch_size,\n            image_reader_name=image_reader_name,\n        )\n        super().__init__(\n            data=patch_wsi_dataset,  # type: ignore\n            transform=transform,\n            replace_rate=replace_rate,\n            cache_num=cache_num,\n            cache_rate=cache_rate,\n            num_init_workers=num_init_workers,\n            num_replace_workers=num_replace_workers,\n            progress=progress,\n            shuffle=False,\n            copy_cache=copy_cache,\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/data/datasets.py_MaskedInferenceWSIDataset_MaskedInferenceWSIDataset._prepare_data.return.prepared_data": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/data/datasets.py_MaskedInferenceWSIDataset_MaskedInferenceWSIDataset._prepare_data.return.prepared_data", "embedding": null, "metadata": {"file_path": "monai/apps/pathology/data/datasets.py", "file_name": "datasets.py", "file_type": "text/x-python", "category": "implementation", "start_line": 163, "end_line": 209, "span_ids": ["MaskedInferenceWSIDataset", "MaskedInferenceWSIDataset._prepare_data", "MaskedInferenceWSIDataset.__init__"], "tokens": 467}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class MaskedInferenceWSIDataset(Dataset):\n    \"\"\"\n    This dataset load the provided foreground masks at an arbitrary resolution level,\n    and extract patches based on that mask from the associated whole slide image.\n\n    Args:\n        data: a list of sample including the path to the whole slide image and the path to the mask.\n            Like this: `[{\"image\": \"path/to/image1.tiff\", \"mask\": \"path/to/mask1.npy}, ...]\"`.\n        patch_size: the size of patches to be extracted from the whole slide image for inference.\n        transform: transforms to be executed on extracted patches.\n        image_reader_name: the name of library to be used for loading whole slide imaging, either CuCIM or OpenSlide.\n        Defaults to CuCIM.\n\n    Note:\n        The resulting output (probability maps) after performing inference using this dataset is\n            supposed to be the same size as the foreground mask and not the original wsi image size.\n    \"\"\"\n\n    def __init__(\n        self,\n        data: List[Dict[\"str\", \"str\"]],\n        patch_size: Union[int, Tuple[int, int]],\n        transform: Optional[Callable] = None,\n        image_reader_name: str = \"cuCIM\",\n    ) -> None:\n        super().__init__(data, transform)\n\n        self.patch_size = ensure_tuple_rep(patch_size, 2)\n\n        # set up whole slide image reader\n        self.image_reader_name = image_reader_name.lower()\n        self.image_reader = WSIReader(image_reader_name)\n\n        # process data and create a list of dictionaries containing all required data and metadata\n        self.data = self._prepare_data(data)\n\n        # calculate cumulative number of patches for all the samples\n        self.num_patches_per_sample = [len(d[\"image_locations\"]) for d in self.data]\n        self.num_patches = sum(self.num_patches_per_sample)\n        self.cum_num_patches = np.cumsum([0] + self.num_patches_per_sample[:-1])\n\n    def _prepare_data(self, input_data: List[Dict[\"str\", \"str\"]]) -> List[Dict]:\n        prepared_data = []\n        for sample in input_data:\n            prepared_sample = self._prepare_a_sample(sample)\n            prepared_data.append(prepared_sample)\n        return prepared_data", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/data/datasets.py_MaskedInferenceWSIDataset._prepare_a_sample_MaskedInferenceWSIDataset._prepare_a_sample.return._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/data/datasets.py_MaskedInferenceWSIDataset._prepare_a_sample_MaskedInferenceWSIDataset._prepare_a_sample.return._", "embedding": null, "metadata": {"file_path": "monai/apps/pathology/data/datasets.py", "file_name": "datasets.py", "file_type": "text/x-python", "category": "implementation", "start_line": 211, "end_line": 251, "span_ids": ["MaskedInferenceWSIDataset._prepare_a_sample"], "tokens": 417}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class MaskedInferenceWSIDataset(Dataset):\n\n    def _prepare_a_sample(self, sample: Dict[\"str\", \"str\"]) -> Dict:\n        \"\"\"\n        Preprocess input data to load WSIReader object and the foreground mask,\n        and define the locations where patches need to be extracted.\n\n        Args:\n            sample: one sample, a dictionary containing path to the whole slide image and the foreground mask.\n                For example: `{\"image\": \"path/to/image1.tiff\", \"mask\": \"path/to/mask1.npy}`\n\n        Return:\n            A dictionary containing:\n                \"name\": the base name of the whole slide image,\n                \"image\": the WSIReader image object,\n                \"mask_shape\": the size of the foreground mask,\n                \"mask_locations\": the list of non-zero pixel locations (x, y) on the foreground mask,\n                \"image_locations\": the list of pixel locations (x, y) on the whole slide image where patches are extracted, and\n                \"level\": the resolution level of the mask with respect to the whole slide image.\n        }\n        \"\"\"\n        image = self.image_reader.read(sample[\"image\"])\n        mask = np.load(sample[\"mask\"])\n        try:\n            level, ratio = self._calculate_mask_level(image, mask)\n        except ValueError as err:\n            err.args = (sample[\"mask\"],) + err.args\n            raise\n\n        # get all indices for non-zero pixels of the foreground mask\n        mask_locations = np.vstack(mask.nonzero()).T\n\n        # convert mask locations to image locations to extract patches\n        image_locations = (mask_locations + 0.5) * ratio - np.array(self.patch_size) // 2\n\n        return {\n            \"name\": os.path.splitext(os.path.basename(sample[\"image\"]))[0],\n            \"image\": image,\n            \"mask_shape\": mask.shape,\n            \"mask_locations\": mask_locations.astype(int).tolist(),\n            \"image_locations\": image_locations.astype(int).tolist(),\n            \"level\": level,\n        }", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/data/datasets.py_MaskedInferenceWSIDataset._calculate_mask_level_MaskedInferenceWSIDataset._calculate_mask_level.return.int_level_ratios_0_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/data/datasets.py_MaskedInferenceWSIDataset._calculate_mask_level_MaskedInferenceWSIDataset._calculate_mask_level.return.int_level_ratios_0_", "embedding": null, "metadata": {"file_path": "monai/apps/pathology/data/datasets.py", "file_name": "datasets.py", "file_type": "text/x-python", "category": "implementation", "start_line": 253, "end_line": 280, "span_ids": ["MaskedInferenceWSIDataset._calculate_mask_level"], "tokens": 302}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class MaskedInferenceWSIDataset(Dataset):\n\n    def _calculate_mask_level(self, image: np.ndarray, mask: np.ndarray) -> Tuple[int, float]:\n        \"\"\"\n        Calculate level of the mask and its ratio with respect to the whole slide image\n\n        Args:\n            image: the original whole slide image\n            mask: a mask, that can be down-sampled at an arbitrary level.\n                Note that down-sampling ratio should be 2^N and equal in all dimension.\n\n        Return:\n            tuple: (level, ratio) where ratio is 2^level\n\n        \"\"\"\n        image_shape = image.shape\n        mask_shape = mask.shape\n        ratios = [image_shape[i] / mask_shape[i] for i in range(2)]\n        level = np.log2(ratios[0])\n\n        if ratios[0] != ratios[1]:\n            raise ValueError(\n                \"Image/Mask ratio across dimensions does not match!\"\n                f\"ratio 0: {ratios[0]} ({image_shape[0]} / {mask_shape[0]}),\"\n                f\"ratio 1: {ratios[1]} ({image_shape[1]} / {mask_shape[1]}),\"\n            )\n        if not level.is_integer():\n            raise ValueError(f\"Mask is not at a regular level (ratio not power of 2), image / mask ratio: {ratios[0]}\")\n\n        return int(level), ratios[0]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/data/datasets.py_MaskedInferenceWSIDataset._load_a_patch_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/data/datasets.py_MaskedInferenceWSIDataset._load_a_patch_", "embedding": null, "metadata": {"file_path": "monai/apps/pathology/data/datasets.py", "file_name": "datasets.py", "file_type": "text/x-python", "category": "implementation", "start_line": 282, "end_line": 308, "span_ids": ["MaskedInferenceWSIDataset.__getitem__", "MaskedInferenceWSIDataset._load_a_patch", "MaskedInferenceWSIDataset.__len__"], "tokens": 257}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class MaskedInferenceWSIDataset(Dataset):\n\n    def _load_a_patch(self, index):\n        \"\"\"\n        Load sample given the index\n\n        Since index is sequential and the patches are coming in an stream from different images,\n        this method, first, finds the whole slide image and the patch that should be extracted,\n        then it loads the patch and provide it with its image name and the corresponding mask location.\n        \"\"\"\n        sample_num = np.argmax(self.cum_num_patches > index) - 1\n        sample = self.data[sample_num]\n        patch_num = index - self.cum_num_patches[sample_num]\n        location_on_image = sample[\"image_locations\"][patch_num]\n        location_on_mask = sample[\"mask_locations\"][patch_num]\n\n        image, _ = self.image_reader.get_data(img=sample[\"image\"], location=location_on_image, size=self.patch_size)\n        processed_sample = {\"image\": image, \"name\": sample[\"name\"], \"mask_location\": location_on_mask}\n        return processed_sample\n\n    def __len__(self):\n        return self.num_patches\n\n    def __getitem__(self, index):\n        patch = [self._load_a_patch(index)]\n        if self.transform:\n            patch = self.transform(patch)\n        return patch", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/handlers/__init__.py_ProbMapProducer_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/handlers/__init__.py_ProbMapProducer_", "embedding": null, "metadata": {"file_path": "monai/apps/pathology/handlers/__init__.py", "file_name": "__init__.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 13, "span_ids": ["docstring"], "tokens": 10}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from .prob_map_producer import ProbMapProducer", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/handlers/prob_map_producer.py_logging_ProbMapProducer.__init__.self.num_images.0": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/handlers/prob_map_producer.py_logging_ProbMapProducer.__init__.self.num_images.0", "embedding": null, "metadata": {"file_path": "monai/apps/pathology/handlers/prob_map_producer.py", "file_name": "prob_map_producer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 57, "span_ids": ["ProbMapProducer", "ProbMapProducer.__init__", "docstring"], "tokens": 341}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import logging\nimport os\nfrom typing import TYPE_CHECKING, Dict, Optional\n\nimport numpy as np\n\nfrom monai.config import DtypeLike, IgniteInfo\nfrom monai.utils import min_version, optional_import\n\nEvents, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Events\")\nif TYPE_CHECKING:\n    from ignite.engine import Engine\nelse:\n    Engine, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Engine\")\n\n\nclass ProbMapProducer:\n    \"\"\"\n    Event handler triggered on completing every iteration to save the probability map\n    \"\"\"\n\n    def __init__(\n        self,\n        output_dir: str = \"./\",\n        output_postfix: str = \"\",\n        dtype: DtypeLike = np.float64,\n        name: Optional[str] = None,\n    ) -> None:\n        \"\"\"\n        Args:\n            output_dir: output directory to save probability maps.\n            output_postfix: a string appended to all output file names.\n            dtype: the data type in which the probability map is stored. Default np.float64.\n            name: identifier of logging.logger to use, defaulting to `engine.logger`.\n\n        \"\"\"\n        self.logger = logging.getLogger(name)\n        self._name = name\n        self.output_dir = output_dir\n        self.output_postfix = output_postfix\n        self.dtype = dtype\n        self.prob_map: Dict[str, np.ndarray] = {}\n        self.level: Dict[str, int] = {}\n        self.counter: Dict[str, int] = {}\n        self.num_done_images: int = 0\n        self.num_images: int = 0", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/handlers/prob_map_producer.py_ProbMapProducer.attach_ProbMapProducer.attach.None_3.engine_add_event_handler_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/handlers/prob_map_producer.py_ProbMapProducer.attach_ProbMapProducer.attach.None_3.engine_add_event_handler_", "embedding": null, "metadata": {"file_path": "monai/apps/pathology/handlers/prob_map_producer.py", "file_name": "prob_map_producer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 59, "end_line": 79, "span_ids": ["ProbMapProducer.attach"], "tokens": 194}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ProbMapProducer:\n\n    def attach(self, engine: Engine) -> None:\n        \"\"\"\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n        \"\"\"\n\n        data_loader = engine.data_loader  # type: ignore\n        self.num_images = len(data_loader.dataset.data)\n\n        for sample in data_loader.dataset.data:\n            name = sample[\"name\"]\n            self.prob_map[name] = np.zeros(sample[\"mask_shape\"], dtype=self.dtype)\n            self.counter[name] = len(sample[\"mask_locations\"])\n            self.level[name] = sample[\"level\"]\n\n        if self._name is None:\n            self.logger = engine.logger\n        if not engine.has_event_handler(self, Events.ITERATION_COMPLETED):\n            engine.add_event_handler(Events.ITERATION_COMPLETED, self)\n        if not engine.has_event_handler(self.finalize, Events.COMPLETED):\n            engine.add_event_handler(Events.COMPLETED, self.finalize)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/handlers/prob_map_producer.py_ProbMapProducer.__call___ProbMapProducer.__call__.for_i_name_in_enumerate_.if_self_counter_name_.self_save_prob_map_name_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/handlers/prob_map_producer.py_ProbMapProducer.__call___ProbMapProducer.__call__.for_i_name_in_enumerate_.if_self_counter_name_.self_save_prob_map_name_", "embedding": null, "metadata": {"file_path": "monai/apps/pathology/handlers/prob_map_producer.py", "file_name": "prob_map_producer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 81, "end_line": 97, "span_ids": ["ProbMapProducer.__call__"], "tokens": 179}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ProbMapProducer:\n\n    def __call__(self, engine: Engine) -> None:\n        \"\"\"\n        This method assumes self.batch_transform will extract metadata from the input batch.\n\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n        \"\"\"\n        if not isinstance(engine.state.batch, dict) or not isinstance(engine.state.output, dict):\n            raise ValueError(\"engine.state.batch and engine.state.output must be dictionaries.\")\n        names = engine.state.batch[\"name\"]\n        locs = engine.state.batch[\"mask_location\"]\n        pred = engine.state.output[\"pred\"]\n        for i, name in enumerate(names):\n            self.prob_map[name][locs[0][i], locs[1][i]] = pred[i]\n            self.counter[name] -= 1\n            if self.counter[name] == 0:\n                self.save_prob_map(name)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/handlers/prob_map_producer.py_ProbMapProducer.save_prob_map_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/handlers/prob_map_producer.py_ProbMapProducer.save_prob_map_", "embedding": null, "metadata": {"file_path": "monai/apps/pathology/handlers/prob_map_producer.py", "file_name": "prob_map_producer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 99, "end_line": 118, "span_ids": ["ProbMapProducer.save_prob_map", "ProbMapProducer.finalize"], "tokens": 180}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ProbMapProducer:\n\n    def save_prob_map(self, name: str) -> None:\n        \"\"\"\n        This method save the probability map for an image, when its inference is finished,\n        and delete that probability map from memory.\n\n        Args:\n            name: the name of image to be saved.\n        \"\"\"\n        file_path = os.path.join(self.output_dir, name)\n        np.save(file_path + self.output_postfix + \".npy\", self.prob_map[name])\n\n        self.num_done_images += 1\n        self.logger.info(f\"Inference of '{name}' is done [{self.num_done_images}/{self.num_images}]!\")\n        del self.prob_map[name]\n        del self.counter[name]\n        del self.level[name]\n\n    def finalize(self, engine: Engine):\n        self.logger.info(f\"Probability map is created for {self.num_done_images}/{self.num_images} images!\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/metrics/__init__.py_LesionFROC_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/metrics/__init__.py_LesionFROC_", "embedding": null, "metadata": {"file_path": "monai/apps/pathology/metrics/__init__.py", "file_name": "__init__.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 13, "span_ids": ["docstring"], "tokens": 11}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from .lesion_froc import LesionFROC", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/metrics/lesion_froc.py_from_typing_import_TYPE_C_if_not_has_tqdm_.tqdm.return.x": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/metrics/lesion_froc.py_from_typing_import_TYPE_C_if_not_has_tqdm_.tqdm.return.x", "embedding": null, "metadata": {"file_path": "monai/apps/pathology/metrics/lesion_froc.py", "file_name": "lesion_froc.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 31, "span_ids": ["docstring"], "tokens": 153}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import TYPE_CHECKING, Dict, List, Tuple\n\nimport numpy as np\n\nfrom monai.apps.pathology.utils import PathologyProbNMS, compute_isolated_tumor_cells, compute_multi_instance_mask\nfrom monai.data.image_reader import WSIReader\nfrom monai.metrics import compute_fp_tp_probs, compute_froc_curve_data, compute_froc_score\nfrom monai.utils import min_version, optional_import\n\nif TYPE_CHECKING:\n    from tqdm import tqdm\n\n    has_tqdm = True\nelse:\n    tqdm, has_tqdm = optional_import(\"tqdm\", \"4.47.0\", min_version, \"tqdm\")\n\nif not has_tqdm:\n\n    def tqdm(x):\n        return x", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/metrics/lesion_froc.py_LesionFROC_LesionFROC.__init__.self.nms.PathologyProbNMS_sigma_nm": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/metrics/lesion_froc.py_LesionFROC_LesionFROC.__init__.self.nms.PathologyProbNMS_sigma_nm", "embedding": null, "metadata": {"file_path": "monai/apps/pathology/metrics/lesion_froc.py", "file_name": "lesion_froc.py", "file_type": "text/x-python", "category": "implementation", "start_line": 34, "end_line": 81, "span_ids": ["LesionFROC.__init__", "LesionFROC"], "tokens": 575}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LesionFROC:\n    \"\"\"\n    Evaluate with Free Response Operating Characteristic (FROC) score.\n\n    Args:\n        data: either the list of dictionaries containing probability maps (inference result) and\n            tumor mask (ground truth), as below, or the path to a json file containing such list.\n            `{\n            \"prob_map\": \"path/to/prob_map_1.npy\",\n            \"tumor_mask\": \"path/to/ground_truth_1.tiff\",\n            \"level\": 6,\n            \"pixel_spacing\": 0.243\n            }`\n        grow_distance: Euclidean distance (in micrometer) by which to grow the label the ground truth's tumors.\n            Defaults to 75, which is the equivalent size of 5 tumor cells.\n        itc_diameter: the maximum diameter of a region (in micrometer) to be considered as an isolated tumor cell.\n            Defaults to 200.\n        eval_thresholds: the false positive rates for calculating the average sensitivity.\n            Defaults to (0.25, 0.5, 1, 2, 4, 8) which is the same as the CAMELYON 16 Challenge.\n        nms_sigma: the standard deviation for gaussian filter of non-maximal suppression. Defaults to 0.0.\n        nms_prob_threshold: the probability threshold of non-maximal suppression. Defaults to 0.5.\n        nms_box_size: the box size (in pixel) to be removed around the the pixel for non-maximal suppression.\n        image_reader_name: the name of library to be used for loading whole slide imaging, either CuCIM or OpenSlide.\n            Defaults to CuCIM.\n\n    Note:\n        For more info on `nms_*` parameters look at monai.utils.prob_nms.ProbNMS`.\n\n    \"\"\"\n\n    def __init__(\n        self,\n        data: List[Dict],\n        grow_distance: int = 75,\n        itc_diameter: int = 200,\n        eval_thresholds: Tuple = (0.25, 0.5, 1, 2, 4, 8),\n        nms_sigma: float = 0.0,\n        nms_prob_threshold: float = 0.5,\n        nms_box_size: int = 48,\n        image_reader_name: str = \"cuCIM\",\n    ) -> None:\n\n        self.data = data\n        self.grow_distance = grow_distance\n        self.itc_diameter = itc_diameter\n        self.eval_thresholds = eval_thresholds\n        self.image_reader = WSIReader(image_reader_name)\n        self.nms = PathologyProbNMS(sigma=nms_sigma, prob_threshold=nms_prob_threshold, box_size=nms_box_size)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/metrics/lesion_froc.py_LesionFROC.prepare_inference_result_LesionFROC.prepare_inference_result.return.np_array_probs_np_array": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/metrics/lesion_froc.py_LesionFROC.prepare_inference_result_LesionFROC.prepare_inference_result.return.np_array_probs_np_array", "embedding": null, "metadata": {"file_path": "monai/apps/pathology/metrics/lesion_froc.py", "file_name": "lesion_froc.py", "file_type": "text/x-python", "category": "implementation", "start_line": 83, "end_line": 100, "span_ids": ["LesionFROC.prepare_inference_result"], "tokens": 145}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LesionFROC:\n\n    def prepare_inference_result(self, sample: Dict):\n        \"\"\"\n        Prepare the probability map for detection evaluation.\n\n        \"\"\"\n        # load the probability map (the result of model inference)\n        prob_map = np.load(sample[\"prob_map\"])\n\n        # apply non-maximal suppression\n        nms_outputs = self.nms(probs_map=prob_map, resolution_level=sample[\"level\"])\n\n        # separate nms outputs\n        if nms_outputs:\n            probs, x_coord, y_coord = zip(*nms_outputs)\n        else:\n            probs, x_coord, y_coord = [], [], []\n\n        return np.array(probs), np.array(x_coord), np.array(y_coord)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/metrics/lesion_froc.py_LesionFROC.prepare_ground_truth_LesionFROC.prepare_ground_truth.return.tumor_mask_itc_labels": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/metrics/lesion_froc.py_LesionFROC.prepare_ground_truth_LesionFROC.prepare_ground_truth.return.tumor_mask_itc_labels", "embedding": null, "metadata": {"file_path": "monai/apps/pathology/metrics/lesion_froc.py", "file_name": "lesion_froc.py", "file_type": "text/x-python", "category": "implementation", "start_line": 102, "end_line": 122, "span_ids": ["LesionFROC.prepare_ground_truth"], "tokens": 215}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LesionFROC:\n\n    def prepare_ground_truth(self, sample):\n        \"\"\"\n        Prepare the ground truth for evaluation based on the binary tumor mask\n\n        \"\"\"\n        # load binary tumor masks\n        img_obj = self.image_reader.read(sample[\"tumor_mask\"])\n        tumor_mask = self.image_reader.get_data(img_obj, level=sample[\"level\"])[0][0]\n\n        # calculate pixel spacing at the mask level\n        mask_pixel_spacing = sample[\"pixel_spacing\"] * pow(2, sample[\"level\"])\n\n        # compute multi-instance mask from a binary mask\n        grow_pixel_threshold = self.grow_distance / (mask_pixel_spacing * 2)\n        tumor_mask = compute_multi_instance_mask(mask=tumor_mask, threshold=grow_pixel_threshold)\n\n        # identify isolated tumor cells\n        itc_threshold = (self.itc_diameter + self.grow_distance) / mask_pixel_spacing\n        itc_labels = compute_isolated_tumor_cells(tumor_mask=tumor_mask, threshold=itc_threshold)\n\n        return tumor_mask, itc_labels", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/metrics/lesion_froc.py_LesionFROC.evaluate_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/metrics/lesion_froc.py_LesionFROC.evaluate_", "embedding": null, "metadata": {"file_path": "monai/apps/pathology/metrics/lesion_froc.py", "file_name": "lesion_froc.py", "file_type": "text/x-python", "category": "implementation", "start_line": 152, "end_line": 171, "span_ids": ["LesionFROC.evaluate"], "tokens": 194}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LesionFROC:\n\n    def evaluate(self):\n        \"\"\"\n        Evaluate the detection performance of a model based on the model probability map output,\n        the ground truth tumor mask, and their associated metadata (e.g., pixel_spacing, level)\n        \"\"\"\n        # compute false positive (FP) and true positive (TP) probabilities for all images\n        fp_probs, tp_probs, num_targets, num_images = self.compute_fp_tp()\n\n        # compute FROC curve given the evaluation of all images\n        fps_per_image, total_sensitivity = compute_froc_curve_data(\n            fp_probs=fp_probs, tp_probs=tp_probs, num_targets=num_targets, num_images=num_images\n        )\n\n        # compute FROC score give specific evaluation threshold\n        froc_score = compute_froc_score(\n            fps_per_image=fps_per_image, total_sensitivity=total_sensitivity, eval_thresholds=self.eval_thresholds\n        )\n\n        return froc_score", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/transforms/__init__.py_SplitOnGrid_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/transforms/__init__.py_SplitOnGrid_", "embedding": null, "metadata": {"file_path": "monai/apps/pathology/transforms/__init__.py", "file_name": "__init__.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 23, "span_ids": ["docstring"], "tokens": 116}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from .spatial.array import SplitOnGrid, TileOnGrid\nfrom .spatial.dictionary import SplitOnGridd, SplitOnGridD, SplitOnGridDict, TileOnGridd, TileOnGridD, TileOnGridDict\nfrom .stain.array import ExtractHEStains, NormalizeHEStains\nfrom .stain.dictionary import (\n    ExtractHEStainsd,\n    ExtractHEStainsD,\n    ExtractHEStainsDict,\n    NormalizeHEStainsd,\n    NormalizeHEStainsD,\n    NormalizeHEStainsDict,\n)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/transforms/spatial/__init__.py_SplitOnGrid_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/transforms/spatial/__init__.py_SplitOnGrid_", "embedding": null, "metadata": {"file_path": "monai/apps/pathology/transforms/spatial/__init__.py", "file_name": "__init__.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 14, "span_ids": ["docstring"], "tokens": 45}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from .array import SplitOnGrid, TileOnGrid\nfrom .dictionary import SplitOnGridd, SplitOnGridD, SplitOnGridDict, TileOnGridd, TileOnGridD, TileOnGridDict", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/transforms/spatial/array.py_from_typing_import_Option_SplitOnGrid.__init__.if_isinstance_patch_size_.else_.self.patch_size.patch_size": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/transforms/spatial/array.py_from_typing_import_Option_SplitOnGrid.__init__.if_isinstance_patch_size_.else_.self.patch_size.patch_size", "embedding": null, "metadata": {"file_path": "monai/apps/pathology/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 56, "span_ids": ["SplitOnGrid.__init__", "SplitOnGrid", "docstring"], "tokens": 385}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Optional, Sequence, Tuple, Union\n\nimport numpy as np\nimport torch\nfrom numpy.lib.stride_tricks import as_strided\n\nfrom monai.config.type_definitions import NdarrayOrTensor\nfrom monai.transforms.transform import Randomizable, Transform\nfrom monai.utils import convert_data_type, convert_to_dst_type\nfrom monai.utils.enums import TransformBackends\n\n__all__ = [\"SplitOnGrid\", \"TileOnGrid\"]\n\n\nclass SplitOnGrid(Transform):\n    \"\"\"\n    Split the image into patches based on the provided grid shape.\n    This transform works only with torch.Tensor inputs.\n\n    Args:\n        grid_size: a tuple or an integer define the shape of the grid upon which to extract patches.\n            If it's an integer, the value will be repeated for each dimension. Default is 2x2\n        patch_size: a tuple or an integer that defines the output patch sizes.\n            If it's an integer, the value will be repeated for each dimension.\n            The default is (0, 0), where the patch size will be inferred from the grid shape.\n\n    Note: the shape of the input image is inferred based on the first image used.\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __init__(\n        self, grid_size: Union[int, Tuple[int, int]] = (2, 2), patch_size: Optional[Union[int, Tuple[int, int]]] = None\n    ):\n        # Grid size\n        if isinstance(grid_size, int):\n            self.grid_size = (grid_size, grid_size)\n        else:\n            self.grid_size = grid_size\n        # Patch size\n        self.patch_size = None\n        if isinstance(patch_size, int):\n            self.patch_size = (patch_size, patch_size)\n        else:\n            self.patch_size = patch_size", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/transforms/stain/__init__.py_ExtractHEStains_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/transforms/stain/__init__.py_ExtractHEStains_", "embedding": null, "metadata": {"file_path": "monai/apps/pathology/transforms/stain/__init__.py", "file_name": "__init__.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 21, "span_ids": ["docstring"], "tokens": 62}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from .array import ExtractHEStains, NormalizeHEStains\nfrom .dictionary import (\n    ExtractHEStainsd,\n    ExtractHEStainsD,\n    ExtractHEStainsDict,\n    NormalizeHEStainsd,\n    NormalizeHEStainsD,\n    NormalizeHEStainsDict,\n)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/transforms/stain/array.py_from_typing_import_Union_ExtractHEStains.__init__.self.max_cref.np_array_max_cref_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/transforms/stain/array.py_from_typing_import_Union_ExtractHEStains.__init__.self.max_cref.np_array_max_cref_", "embedding": null, "metadata": {"file_path": "monai/apps/pathology/transforms/stain/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 49, "span_ids": ["ExtractHEStains.__init__", "ExtractHEStains", "docstring"], "tokens": 326}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Union\n\nimport numpy as np\n\nfrom monai.transforms.transform import Transform\n\n\nclass ExtractHEStains(Transform):\n    \"\"\"Class to extract a target stain from an image, using stain deconvolution (see Note).\n\n    Args:\n        tli: transmitted light intensity. Defaults to 240.\n        alpha: tolerance in percentile for the pseudo-min (alpha percentile)\n            and pseudo-max (100 - alpha percentile). Defaults to 1.\n        beta: absorbance threshold for transparent pixels. Defaults to 0.15\n        max_cref: reference maximum stain concentrations for Hematoxylin & Eosin (H&E).\n            Defaults to (1.9705, 1.0308).\n\n    Note:\n        For more information refer to:\n        - the original paper: Macenko et al., 2009 http://wwwx.cs.unc.edu/~mn/sites/default/files/macenko2009.pdf\n        - the previous implementations:\n\n          - MATLAB: https://github.com/mitkovetta/staining-normalization\n          - Python: https://github.com/schaugf/HEnorm_python\n    \"\"\"\n\n    def __init__(\n        self,\n        tli: float = 240,\n        alpha: float = 1,\n        beta: float = 0.15,\n        max_cref: Union[tuple, np.ndarray] = (1.9705, 1.0308),\n    ) -> None:\n        self.tli = tli\n        self.alpha = alpha\n        self.beta = beta\n        self.max_cref = np.array(max_cref)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/transforms/stain/array.py_ExtractHEStains._deconvolution_extract_stain_ExtractHEStains._deconvolution_extract_stain.return.he": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/transforms/stain/array.py_ExtractHEStains._deconvolution_extract_stain_ExtractHEStains._deconvolution_extract_stain.return.he", "embedding": null, "metadata": {"file_path": "monai/apps/pathology/transforms/stain/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 51, "end_line": 97, "span_ids": ["ExtractHEStains._deconvolution_extract_stain"], "tokens": 572}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ExtractHEStains(Transform):\n\n    def _deconvolution_extract_stain(self, image: np.ndarray) -> np.ndarray:\n        \"\"\"Perform Stain Deconvolution and return stain matrix for the image.\n\n        Args:\n            image: uint8 RGB image to perform stain deconvolution on\n\n        Return:\n            he: H&E absorbance matrix for the image (first column is H, second column is E, rows are RGB values)\n        \"\"\"\n        # check image type and values\n        if not isinstance(image, np.ndarray):\n            raise TypeError(\"Image must be of type numpy.ndarray.\")\n        if image.min() < 0:\n            raise ValueError(\"Image should not have negative values.\")\n        if image.max() > 255:\n            raise ValueError(\"Image should not have values greater than 255.\")\n\n        # reshape image and calculate absorbance\n        image = image.reshape((-1, 3))\n        image = image.astype(np.float32, copy=False) + 1.0\n        absorbance = -np.log(image.clip(max=self.tli) / self.tli)\n\n        # remove transparent pixels\n        absorbance_hat = absorbance[np.all(absorbance > self.beta, axis=1)]\n        if len(absorbance_hat) == 0:\n            raise ValueError(\"All pixels of the input image are below the absorbance threshold.\")\n\n        # compute eigenvectors\n        _, eigvecs = np.linalg.eigh(np.cov(absorbance_hat.T).astype(np.float32, copy=False))\n\n        # project on the plane spanned by the eigenvectors corresponding to the two largest eigenvalues\n        t_hat = absorbance_hat.dot(eigvecs[:, 1:3])\n\n        # find the min and max vectors and project back to absorbance space\n        phi = np.arctan2(t_hat[:, 1], t_hat[:, 0])\n        min_phi = np.percentile(phi, self.alpha)\n        max_phi = np.percentile(phi, 100 - self.alpha)\n        v_min = eigvecs[:, 1:3].dot(np.array([(np.cos(min_phi), np.sin(min_phi))], dtype=np.float32).T)\n        v_max = eigvecs[:, 1:3].dot(np.array([(np.cos(max_phi), np.sin(max_phi))], dtype=np.float32).T)\n\n        # a heuristic to make the vector corresponding to hematoxylin first and the one corresponding to eosin second\n        if v_min[0] > v_max[0]:\n            he = np.array((v_min[:, 0], v_max[:, 0]), dtype=np.float32).T\n        else:\n            he = np.array((v_max[:, 0], v_min[:, 0]), dtype=np.float32).T\n\n        return he", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/transforms/stain/array.py_ExtractHEStains.__call___ExtractHEStains.__call__.return.target_he": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/transforms/stain/array.py_ExtractHEStains.__call___ExtractHEStains.__call__.return.target_he", "embedding": null, "metadata": {"file_path": "monai/apps/pathology/transforms/stain/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 99, "end_line": 112, "span_ids": ["ExtractHEStains.__call__"], "tokens": 116}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ExtractHEStains(Transform):\n\n    def __call__(self, image: np.ndarray) -> np.ndarray:\n        \"\"\"Perform stain extraction.\n\n        Args:\n            image: uint8 RGB image to extract stain from\n\n        return:\n            target_he: H&E absorbance matrix for the image (first column is H, second column is E, rows are RGB values)\n        \"\"\"\n        if not isinstance(image, np.ndarray):\n            raise TypeError(\"Image must be of type numpy.ndarray.\")\n\n        target_he = self._deconvolution_extract_stain(image)\n        return target_he", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/transforms/stain/array.py_NormalizeHEStains_NormalizeHEStains.__init__.self.stain_extractor.ExtractHEStains_tli_self_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/transforms/stain/array.py_NormalizeHEStains_NormalizeHEStains.__init__.self.stain_extractor.ExtractHEStains_tli_self_", "embedding": null, "metadata": {"file_path": "monai/apps/pathology/transforms/stain/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 115, "end_line": 154, "span_ids": ["NormalizeHEStains", "NormalizeHEStains.__init__"], "tokens": 528}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class NormalizeHEStains(Transform):\n    \"\"\"Class to normalize patches/images to a reference or target image stain (see Note).\n\n    Performs stain deconvolution of the source image using the ExtractHEStains\n    class, to obtain the stain matrix and calculate the stain concentration matrix\n    for the image. Then, performs the inverse Beer-Lambert transform to recreate the\n    patch using the target H&E stain matrix provided. If no target stain provided, a default\n    reference stain is used. Similarly, if no maximum stain concentrations are provided, a\n    reference maximum stain concentrations matrix is used.\n\n    Args:\n        tli: transmitted light intensity. Defaults to 240.\n        alpha: tolerance in percentile for the pseudo-min (alpha percentile) and\n            pseudo-max (100 - alpha percentile). Defaults to 1.\n        beta: absorbance threshold for transparent pixels. Defaults to 0.15.\n        target_he: target stain matrix. Defaults to ((0.5626, 0.2159), (0.7201, 0.8012), (0.4062, 0.5581)).\n        max_cref: reference maximum stain concentrations for Hematoxylin & Eosin (H&E).\n            Defaults to [1.9705, 1.0308].\n\n    Note:\n        For more information refer to:\n        - the original paper: Macenko et al., 2009 http://wwwx.cs.unc.edu/~mn/sites/default/files/macenko2009.pdf\n        - the previous implementations:\n\n            - MATLAB: https://github.com/mitkovetta/staining-normalization\n            - Python: https://github.com/schaugf/HEnorm_python\n    \"\"\"\n\n    def __init__(\n        self,\n        tli: float = 240,\n        alpha: float = 1,\n        beta: float = 0.15,\n        target_he: Union[tuple, np.ndarray] = ((0.5626, 0.2159), (0.7201, 0.8012), (0.4062, 0.5581)),\n        max_cref: Union[tuple, np.ndarray] = (1.9705, 1.0308),\n    ) -> None:\n        self.tli = tli\n        self.target_he = np.array(target_he)\n        self.max_cref = np.array(max_cref)\n        self.stain_extractor = ExtractHEStains(tli=self.tli, alpha=alpha, beta=beta, max_cref=self.max_cref)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/transforms/stain/array.py_NormalizeHEStains.__call___": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/transforms/stain/array.py_NormalizeHEStains.__call___", "embedding": null, "metadata": {"file_path": "monai/apps/pathology/transforms/stain/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 156, "end_line": 197, "span_ids": ["NormalizeHEStains.__call__"], "tokens": 431}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class NormalizeHEStains(Transform):\n\n    def __call__(self, image: np.ndarray) -> np.ndarray:\n        \"\"\"Perform stain normalization.\n\n        Args:\n            image: uint8 RGB image/patch to be stain normalized, pixel values between 0 and 255\n\n        Return:\n            image_norm: stain normalized image/patch\n        \"\"\"\n        # check image type and values\n        if not isinstance(image, np.ndarray):\n            raise TypeError(\"Image must be of type numpy.ndarray.\")\n        if image.min() < 0:\n            raise ValueError(\"Image should not have negative values.\")\n        if image.max() > 255:\n            raise ValueError(\"Image should not have values greater than 255.\")\n\n        # extract stain of the image\n        he = self.stain_extractor(image)\n\n        # reshape image and calculate absorbance\n        h, w, _ = image.shape\n        image = image.reshape((-1, 3))\n        image = image.astype(np.float32) + 1.0\n        absorbance = -np.log(image.clip(max=self.tli) / self.tli)\n\n        # rows correspond to channels (RGB), columns to absorbance values\n        y = np.reshape(absorbance, (-1, 3)).T\n\n        # determine concentrations of the individual stains\n        conc = np.linalg.lstsq(he, y, rcond=None)[0]\n\n        # normalize stain concentrations\n        max_conc = np.asarray([np.percentile(conc[0, :], 99), np.percentile(conc[1, :], 99)], dtype=np.float32)\n        tmp = np.divide(max_conc, self.max_cref, dtype=np.float32)\n        image_c = np.divide(conc, tmp[:, np.newaxis], dtype=np.float32)\n\n        image_norm: np.ndarray = np.multiply(self.tli, np.exp(-self.target_he.dot(image_c)), dtype=np.float32)\n        image_norm[image_norm > 255] = 254\n        image_norm = np.reshape(image_norm.T, (h, w, 3)).astype(np.uint8)\n        return image_norm", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/transforms/stain/dictionary.py_from_typing_import_Dict__ExtractHEStainsd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/transforms/stain/dictionary.py_from_typing_import_Dict__ExtractHEStainsd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/apps/pathology/transforms/stain/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 18, "end_line": 61, "span_ids": ["ExtractHEStainsd.__init__", "ExtractHEStainsd", "ExtractHEStainsd.__call__", "docstring"], "tokens": 413}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Dict, Hashable, Mapping, Union\n\nimport numpy as np\n\nfrom monai.config import KeysCollection\nfrom monai.transforms.transform import MapTransform\n\nfrom .array import ExtractHEStains, NormalizeHEStains\n\n\nclass ExtractHEStainsd(MapTransform):\n    \"\"\"Dictionary-based wrapper of :py:class:`monai.apps.pathology.transforms.ExtractHEStains`.\n    Class to extract a target stain from an image, using stain deconvolution.\n\n    Args:\n        keys: keys of the corresponding items to be transformed.\n            See also: :py:class:`monai.transforms.compose.MapTransform`\n        tli: transmitted light intensity. Defaults to 240.\n        alpha: tolerance in percentile for the pseudo-min (alpha percentile)\n            and pseudo-max (100 - alpha percentile). Defaults to 1.\n        beta: absorbance threshold for transparent pixels. Defaults to 0.15\n        max_cref: reference maximum stain concentrations for Hematoxylin & Eosin (H&E).\n            Defaults to (1.9705, 1.0308).\n        allow_missing_keys: don't raise exception if key is missing.\n\n    \"\"\"\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        tli: float = 240,\n        alpha: float = 1,\n        beta: float = 0.15,\n        max_cref: Union[tuple, np.ndarray] = (1.9705, 1.0308),\n        allow_missing_keys: bool = False,\n    ) -> None:\n        super().__init__(keys, allow_missing_keys)\n        self.extractor = ExtractHEStains(tli=tli, alpha=alpha, beta=beta, max_cref=max_cref)\n\n    def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]:\n        d = dict(data)\n        for key in self.key_iterator(d):\n            d[key] = self.extractor(d[key])\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/transforms/stain/dictionary.py_NormalizeHEStainsd_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/transforms/stain/dictionary.py_NormalizeHEStainsd_", "embedding": null, "metadata": {"file_path": "monai/apps/pathology/transforms/stain/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 64, "end_line": 112, "span_ids": ["NormalizeHEStainsd", "NormalizeHEStainsd.__call__", "NormalizeHEStainsd.__init__", "impl"], "tokens": 551}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class NormalizeHEStainsd(MapTransform):\n    \"\"\"Dictionary-based wrapper of :py:class:`monai.apps.pathology.transforms.NormalizeHEStains`.\n\n    Class to normalize patches/images to a reference or target image stain.\n\n    Performs stain deconvolution of the source image using the ExtractHEStains\n    class, to obtain the stain matrix and calculate the stain concentration matrix\n    for the image. Then, performs the inverse Beer-Lambert transform to recreate the\n    patch using the target H&E stain matrix provided. If no target stain provided, a default\n    reference stain is used. Similarly, if no maximum stain concentrations are provided, a\n    reference maximum stain concentrations matrix is used.\n\n    Args:\n        keys: keys of the corresponding items to be transformed.\n            See also: :py:class:`monai.transforms.compose.MapTransform`\n        tli: transmitted light intensity. Defaults to 240.\n        alpha: tolerance in percentile for the pseudo-min (alpha percentile) and\n            pseudo-max (100 - alpha percentile). Defaults to 1.\n        beta: absorbance threshold for transparent pixels. Defaults to 0.15.\n        target_he: target stain matrix. Defaults to None.\n        max_cref: reference maximum stain concentrations for Hematoxylin & Eosin (H&E).\n            Defaults to None.\n        allow_missing_keys: don't raise exception if key is missing.\n\n    \"\"\"\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        tli: float = 240,\n        alpha: float = 1,\n        beta: float = 0.15,\n        target_he: Union[tuple, np.ndarray] = ((0.5626, 0.2159), (0.7201, 0.8012), (0.4062, 0.5581)),\n        max_cref: Union[tuple, np.ndarray] = (1.9705, 1.0308),\n        allow_missing_keys: bool = False,\n    ) -> None:\n        super().__init__(keys, allow_missing_keys)\n        self.normalizer = NormalizeHEStains(tli=tli, alpha=alpha, beta=beta, target_he=target_he, max_cref=max_cref)\n\n    def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]:\n        d = dict(data)\n        for key in self.key_iterator(d):\n            d[key] = self.normalizer(d[key])\n        return d\n\n\nExtractHEStainsDict = ExtractHEStainsD = ExtractHEStainsd\nNormalizeHEStainsDict = NormalizeHEStainsD = NormalizeHEStainsd", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_PersistentDataset.__init___PersistentDataset.set_data.if_self_cache_dir_is_not_.self_cache_dir_mkdir_pare": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_PersistentDataset.__init___PersistentDataset.set_data.if_self_cache_dir_is_not_.self_cache_dir_mkdir_pare", "embedding": null, "metadata": {"file_path": "monai/data/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 200, "end_line": 258, "span_ids": ["PersistentDataset.set_data", "PersistentDataset.__init__"], "tokens": 655}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class PersistentDataset(Dataset):\n\n    def __init__(\n        self,\n        data: Sequence,\n        transform: Union[Sequence[Callable], Callable],\n        cache_dir: Optional[Union[Path, str]],\n        hash_func: Callable[..., bytes] = pickle_hashing,\n        pickle_module: str = \"pickle\",\n        pickle_protocol: int = DEFAULT_PROTOCOL,\n    ) -> None:\n        \"\"\"\n        Args:\n            data: input data file paths to load and transform to generate dataset for model.\n                `PersistentDataset` expects input data to be a list of serializable\n                and hashes them as cache keys using `hash_func`.\n            transform: transforms to execute operations on input data.\n            cache_dir: If specified, this is the location for persistent storage\n                of pre-computed transformed data tensors. The cache_dir is computed once, and\n                persists on disk until explicitly removed.  Different runs, programs, experiments\n                may share a common cache dir provided that the transforms pre-processing is consistent.\n                If `cache_dir` doesn't exist, will automatically create it.\n                If `cache_dir` is `None`, there is effectively no caching.\n            hash_func: a callable to compute hash from data items to be cached.\n                defaults to `monai.data.utils.pickle_hashing`.\n            pickle_module: string representing the module used for pickling metadata and objects,\n                default to `\"pickle\"`. due to the pickle limitation in multi-processing of Dataloader,\n                we can't use `pickle` as arg directly, so here we use a string name instead.\n                if want to use other pickle module at runtime, just register like:\n                >>> from monai.data import utils\n                >>> utils.SUPPORTED_PICKLE_MOD[\"test\"] = other_pickle\n                this arg is used by `torch.save`, for more details, please check:\n                https://pytorch.org/docs/stable/generated/torch.save.html#torch.save,\n                and ``monai.data.utils.SUPPORTED_PICKLE_MOD``.\n            pickle_protocol: can be specified to override the default protocol, default to `2`.\n                this arg is used by `torch.save`, for more details, please check:\n                https://pytorch.org/docs/stable/generated/torch.save.html#torch.save.\n\n        \"\"\"\n        if not isinstance(transform, Compose):\n            transform = Compose(transform)\n        super().__init__(data=data, transform=transform)\n        self.cache_dir = Path(cache_dir) if cache_dir is not None else None\n        self.hash_func = hash_func\n        self.pickle_module = pickle_module\n        self.pickle_protocol = pickle_protocol\n        if self.cache_dir is not None:\n            if not self.cache_dir.exists():\n                self.cache_dir.mkdir(parents=True, exist_ok=True)\n            if not self.cache_dir.is_dir():\n                raise ValueError(\"cache_dir must be a directory.\")\n\n    def set_data(self, data: Sequence):\n        \"\"\"\n        Set the input data and delete all the out-dated cache content.\n\n        \"\"\"\n        self.data = data\n        if self.cache_dir is not None and self.cache_dir.exists():\n            shutil.rmtree(self.cache_dir, ignore_errors=True)\n            self.cache_dir.mkdir(parents=True, exist_ok=True)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_LMDBDataset_LMDBDataset.set_data.self._read_env.self__fill_cache_start_re": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_LMDBDataset_LMDBDataset.set_data.self._read_env.self__fill_cache_start_re", "embedding": null, "metadata": {"file_path": "monai/data/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 456, "end_line": 526, "span_ids": ["LMDBDataset", "LMDBDataset.set_data", "LMDBDataset.__init__"], "tokens": 752}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LMDBDataset(PersistentDataset):\n    \"\"\"\n    Extension of `PersistentDataset` using LMDB as the backend.\n\n    See Also:\n        :py:class:`monai.data.PersistentDataset`\n\n    Examples:\n\n        >>> items = [{\"data\": i} for i in range(5)]\n        # [{'data': 0}, {'data': 1}, {'data': 2}, {'data': 3}, {'data': 4}]\n        >>> lmdb_ds = monai.data.LMDBDataset(items, transform=monai.transforms.SimulateDelayd(\"data\", delay_time=1))\n        >>> print(list(lmdb_ds))  # using the cached results\n\n    \"\"\"\n\n    def __init__(\n        self,\n        data: Sequence,\n        transform: Union[Sequence[Callable], Callable],\n        cache_dir: Union[Path, str] = \"cache\",\n        hash_func: Callable[..., bytes] = pickle_hashing,\n        db_name: str = \"monai_cache\",\n        progress: bool = True,\n        pickle_protocol=pickle.HIGHEST_PROTOCOL,\n        lmdb_kwargs: Optional[dict] = None,\n    ) -> None:\n        \"\"\"\n        Args:\n            data: input data file paths to load and transform to generate dataset for model.\n                `LMDBDataset` expects input data to be a list of serializable\n                and hashes them as cache keys using `hash_func`.\n            transform: transforms to execute operations on input data.\n            cache_dir: if specified, this is the location for persistent storage\n                of pre-computed transformed data tensors. The cache_dir is computed once, and\n                persists on disk until explicitly removed.  Different runs, programs, experiments\n                may share a common cache dir provided that the transforms pre-processing is consistent.\n                If the cache_dir doesn't exist, will automatically create it. Defaults to \"./cache\".\n            hash_func: a callable to compute hash from data items to be cached.\n                defaults to `monai.data.utils.pickle_hashing`.\n            db_name: lmdb database file name. Defaults to \"monai_cache\".\n            progress: whether to display a progress bar.\n            pickle_protocol: pickle protocol version. Defaults to pickle.HIGHEST_PROTOCOL.\n                https://docs.python.org/3/library/pickle.html#pickle-protocols\n            lmdb_kwargs: additional keyword arguments to the lmdb environment.\n                for more details please visit: https://lmdb.readthedocs.io/en/release/#environment-class\n        \"\"\"\n        super().__init__(\n            data=data, transform=transform, cache_dir=cache_dir, hash_func=hash_func, pickle_protocol=pickle_protocol\n        )\n        self.progress = progress\n        if not self.cache_dir:\n            raise ValueError(\"cache_dir must be specified.\")\n        self.db_file = self.cache_dir / f\"{db_name}.lmdb\"\n        self.lmdb_kwargs = lmdb_kwargs or {}\n        if not self.lmdb_kwargs.get(\"map_size\", 0):\n            self.lmdb_kwargs[\"map_size\"] = 1024 ** 4  # default map_size\n        # lmdb is single-writer multi-reader by default\n        # the cache is created without multi-threading\n        self._read_env = None\n        # this runs on the primary thread/process\n        self._fill_cache_start_reader(show_progress=self.progress)\n        print(f\"Accessing lmdb file: {self.db_file.absolute()}.\")\n\n    def set_data(self, data: Sequence):\n        \"\"\"\n        Set the input data and delete all the out-dated cache content.\n\n        \"\"\"\n        super().set_data(data=data)\n        self._read_env = self._fill_cache_start_reader(show_progress=self.progress)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_CacheDataset.__init___CacheDataset.set_data.self._cache.self__fill_cache_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_CacheDataset.__init___CacheDataset.set_data.self._cache.self__fill_cache_", "embedding": null, "metadata": {"file_path": "monai/data/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 570, "end_line": 618, "span_ids": ["CacheDataset.__init__", "CacheDataset.set_data"], "tokens": 506}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CacheDataset(Dataset):\n\n    def __init__(\n        self,\n        data: Sequence,\n        transform: Union[Sequence[Callable], Callable],\n        cache_num: int = sys.maxsize,\n        cache_rate: float = 1.0,\n        num_workers: Optional[int] = None,\n        progress: bool = True,\n        copy_cache: bool = True,\n    ) -> None:\n        \"\"\"\n        Args:\n            data: input data to load and transform to generate dataset for model.\n            transform: transforms to execute operations on input data.\n            cache_num: number of items to be cached. Default is `sys.maxsize`.\n                will take the minimum of (cache_num, data_length x cache_rate, data_length).\n            cache_rate: percentage of cached data in total, default is 1.0 (cache all).\n                will take the minimum of (cache_num, data_length x cache_rate, data_length).\n            num_workers: the number of worker processes to use.\n                If num_workers is None then the number returned by os.cpu_count() is used.\n            progress: whether to display a progress bar.\n            copy_cache: whether to `deepcopy` the cache content before applying the random transforms,\n                default to `True`. if the random transforms don't modify the cached content\n                (for example, randomly crop from the cached image and deepcopy the crop region)\n                or if every cache item is only used once in a `multi-processing` environment,\n                may set `copy=False` for better performance.\n        \"\"\"\n        if not isinstance(transform, Compose):\n            transform = Compose(transform)\n        super().__init__(data=data, transform=transform)\n        self.progress = progress\n        self.copy_cache = copy_cache\n        self.cache_num = min(int(cache_num), int(len(data) * cache_rate), len(data))\n        self.num_workers = num_workers\n        if self.num_workers is not None:\n            self.num_workers = max(int(self.num_workers), 1)\n        self._cache: List = self._fill_cache()\n\n    def set_data(self, data: Sequence):\n        \"\"\"\n        Set the input data and run deterministic transforms to generate cache content.\n\n        Note: should call this func after an entire epoch and must set `persistent_workers=False`\n        in PyTorch DataLoader, because it needs to create new worker processes based on new\n        generated cache content.\n\n        \"\"\"\n        self.data = data\n        self._cache = self._fill_cache()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset_summary.py_from_itertools_import_cha_DatasetSummary.collect_meta_data.for_data_in_self_data_loa.self_all_meta_data_append": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset_summary.py_from_itertools_import_cha_DatasetSummary.collect_meta_data.for_data_in_self_data_loa.self_all_meta_data_append", "embedding": null, "metadata": {"file_path": "monai/data/dataset_summary.py", "file_name": "dataset_summary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 82, "span_ids": ["DatasetSummary.__init__", "DatasetSummary.collect_meta_data", "DatasetSummary", "docstring"], "tokens": 680}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from itertools import chain\nfrom typing import List, Optional\n\nimport numpy as np\nimport torch\n\nfrom monai.config import KeysCollection\nfrom monai.data.dataloader import DataLoader\nfrom monai.data.dataset import Dataset\nfrom monai.transforms import concatenate\nfrom monai.utils import convert_data_type\n\n\nclass DatasetSummary:\n    \"\"\"\n    This class provides a way to calculate a reasonable output voxel spacing according to\n    the input dataset. The achieved values can used to resample the input in 3d segmentation tasks\n    (like using as the `pixdim` parameter in `monai.transforms.Spacingd`).\n    In addition, it also supports to count the mean, std, min and max intensities of the input,\n    and these statistics are helpful for image normalization\n    (like using in `monai.transforms.ScaleIntensityRanged` and `monai.transforms.NormalizeIntensityd`).\n\n    The algorithm for calculation refers to:\n    `Automated Design of Deep Learning Methods for Biomedical Image Segmentation <https://arxiv.org/abs/1904.08128>`_.\n\n    \"\"\"\n\n    def __init__(\n        self,\n        dataset: Dataset,\n        image_key: Optional[str] = \"image\",\n        label_key: Optional[str] = \"label\",\n        meta_key: Optional[KeysCollection] = None,\n        meta_key_postfix: str = \"meta_dict\",\n        num_workers: int = 0,\n        **kwargs,\n    ):\n        \"\"\"\n        Args:\n            dataset: dataset from which to load the data.\n            image_key: key name of images (default: ``image``).\n            label_key: key name of labels (default: ``label``).\n            meta_key: explicitly indicate the key of the corresponding meta data dictionary.\n                for example, for data with key `image`, the metadata by default is in `image_meta_dict`.\n                the meta data is a dictionary object which contains: filename, affine, original_shape, etc.\n                if None, will try to construct meta_keys by `{image_key}_{meta_key_postfix}`.\n            meta_key_postfix: use `{image_key}_{meta_key_postfix}` to fetch the meta data from dict,\n                the meta data is a dictionary object (default: ``meta_dict``).\n            num_workers: how many subprocesses to use for data loading.\n                ``0`` means that the data will be loaded in the main process (default: ``0``).\n            kwargs: other parameters (except `batch_size` and `num_workers`) for DataLoader,\n                this class forces to use ``batch_size=1``.\n\n        \"\"\"\n\n        self.data_loader = DataLoader(dataset=dataset, batch_size=1, num_workers=num_workers, **kwargs)\n\n        self.image_key = image_key\n        self.label_key = label_key\n        self.meta_key = meta_key or f\"{image_key}_{meta_key_postfix}\"\n        self.all_meta_data: List = []\n\n    def collect_meta_data(self):\n        \"\"\"\n        This function is used to collect the meta data for all images of the dataset.\n        \"\"\"\n\n        for data in self.data_loader:\n            if self.meta_key not in data:\n                raise ValueError(f\"To collect meta data for the dataset, key `{self.meta_key}` must exist in `data`.\")\n            self.all_meta_data.append(data[self.meta_key])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset_summary.py_DatasetSummary.get_target_spacing_DatasetSummary.get_target_spacing.return.tuple_output_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset_summary.py_DatasetSummary.get_target_spacing_DatasetSummary.get_target_spacing.return.tuple_output_", "embedding": null, "metadata": {"file_path": "monai/data/dataset_summary.py", "file_name": "dataset_summary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 84, "end_line": 113, "span_ids": ["DatasetSummary.get_target_spacing"], "tokens": 390}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DatasetSummary:\n\n    def get_target_spacing(self, spacing_key: str = \"pixdim\", anisotropic_threshold: int = 3, percentile: float = 10.0):\n        \"\"\"\n        Calculate the target spacing according to all spacings.\n        If the target spacing is very anisotropic,\n        decrease the spacing value of the maximum axis according to percentile.\n        So far, this function only supports NIFTI images which store spacings in headers with key \"pixdim\".\n        After loading with `monai.DataLoader`, \"pixdim\" is in the form of `torch.Tensor` with size `(batch_size, 8)`.\n\n        Args:\n            spacing_key: key of spacing in meta data (default: ``pixdim``).\n            anisotropic_threshold: threshold to decide if the target spacing is anisotropic (default: ``3``).\n            percentile: for anisotropic target spacing, use the percentile of all spacings of the anisotropic axis to\n                replace that axis.\n\n        \"\"\"\n        if len(self.all_meta_data) == 0:\n            self.collect_meta_data()\n        if spacing_key not in self.all_meta_data[0]:\n            raise ValueError(\"The provided spacing_key is not in self.all_meta_data.\")\n        all_spacings = concatenate(to_cat=[data[spacing_key][:, 1:4] for data in self.all_meta_data], axis=0)\n        all_spacings, *_ = convert_data_type(data=all_spacings, output_type=np.ndarray, wrap_sequence=True)\n\n        target_spacing = np.median(all_spacings, axis=0)\n        if max(target_spacing) / min(target_spacing) >= anisotropic_threshold:\n            largest_axis = np.argmax(target_spacing)\n            target_spacing[largest_axis] = np.percentile(all_spacings[:, largest_axis], percentile)\n\n        output = list(target_spacing)\n\n        return tuple(output)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset_summary.py_DatasetSummary.calculate_statistics_DatasetSummary.calculate_statistics.self.data_std._torch_sqrt_voxel_square_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset_summary.py_DatasetSummary.calculate_statistics_DatasetSummary.calculate_statistics.self.data_std._torch_sqrt_voxel_square_", "embedding": null, "metadata": {"file_path": "monai/data/dataset_summary.py", "file_name": "dataset_summary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 115, "end_line": 150, "span_ids": ["DatasetSummary.calculate_statistics"], "tokens": 371}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DatasetSummary:\n\n    def calculate_statistics(self, foreground_threshold: int = 0):\n        \"\"\"\n        This function is used to calculate the maximum, minimum, mean and standard deviation of intensities of\n        the input dataset.\n\n        Args:\n            foreground_threshold: the threshold to distinguish if a voxel belongs to foreground, this parameter\n                is used to select the foreground of images for calculation. Normally, `label > 0` means the corresponding\n                voxel belongs to foreground, thus if you need to calculate the statistics for whole images, you can set\n                the threshold to ``-1`` (default: ``0``).\n\n        \"\"\"\n        voxel_sum = torch.as_tensor(0.0)\n        voxel_square_sum = torch.as_tensor(0.0)\n        voxel_max, voxel_min = [], []\n        voxel_ct = 0\n\n        for data in self.data_loader:\n            if self.image_key and self.label_key:\n                image, label = data[self.image_key], data[self.label_key]\n            else:\n                image, label = data\n            image, *_ = convert_data_type(data=image, output_type=torch.Tensor)\n            label, *_ = convert_data_type(data=label, output_type=torch.Tensor)\n\n            voxel_max.append(image.max().item())\n            voxel_min.append(image.min().item())\n\n            image_foreground = image[torch.where(label > foreground_threshold)]\n            voxel_ct += len(image_foreground)\n            voxel_sum += image_foreground.sum()\n            voxel_square_sum += torch.square(image_foreground).sum()\n\n        self.data_max, self.data_min = max(voxel_max), min(voxel_min)\n        self.data_mean = (voxel_sum / voxel_ct).item()\n        self.data_std = (torch.sqrt(voxel_square_sum / voxel_ct - self.data_mean ** 2)).item()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset_summary.py_DatasetSummary.calculate_percentiles_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset_summary.py_DatasetSummary.calculate_percentiles_", "embedding": null, "metadata": {"file_path": "monai/data/dataset_summary.py", "file_name": "dataset_summary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 152, "end_line": 195, "span_ids": ["DatasetSummary.calculate_percentiles"], "tokens": 463}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DatasetSummary:\n\n    def calculate_percentiles(\n        self,\n        foreground_threshold: int = 0,\n        sampling_flag: bool = True,\n        interval: int = 10,\n        min_percentile: float = 0.5,\n        max_percentile: float = 99.5,\n    ):\n        \"\"\"\n        This function is used to calculate the percentiles of intensities (and median) of the input dataset. To get\n        the required values, all voxels need to be accumulated. To reduce the memory used, this function can be set\n        to accumulate only a part of the voxels.\n\n        Args:\n            foreground_threshold: the threshold to distinguish if a voxel belongs to foreground, this parameter\n                is used to select the foreground of images for calculation. Normally, `label > 0` means the corresponding\n                voxel belongs to foreground, thus if you need to calculate the statistics for whole images, you can set\n                the threshold to ``-1`` (default: ``0``).\n            sampling_flag: whether to sample only a part of the voxels (default: ``True``).\n            interval: the sampling interval for accumulating voxels (default: ``10``).\n            min_percentile: minimal percentile (default: ``0.5``).\n            max_percentile: maximal percentile (default: ``99.5``).\n\n        \"\"\"\n        all_intensities = []\n        for data in self.data_loader:\n            if self.image_key and self.label_key:\n                image, label = data[self.image_key], data[self.label_key]\n            else:\n                image, label = data\n            image, *_ = convert_data_type(data=image, output_type=torch.Tensor)\n            label, *_ = convert_data_type(data=label, output_type=torch.Tensor)\n\n            intensities = image[torch.where(label > foreground_threshold)].tolist()\n            if sampling_flag:\n                intensities = intensities[::interval]\n            all_intensities.append(intensities)\n\n        all_intensities = list(chain(*all_intensities))\n        self.data_min_percentile, self.data_max_percentile = np.percentile(\n            all_intensities, [min_percentile, max_percentile]\n        )\n        self.data_median = np.median(all_intensities)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_ImageReader_ImageReader._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_ImageReader_ImageReader._", "embedding": null, "metadata": {"file_path": "monai/data/image_reader.py", "file_name": "image_reader.py", "file_type": "text/x-python", "category": "implementation", "start_line": 43, "end_line": 60, "span_ids": ["ImageReader"], "tokens": 131}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ImageReader(ABC):\n    \"\"\"\n    An abstract class defines APIs to load image files.\n\n    Typical usage of an implementation of this class is:\n\n    .. code-block:: python\n\n        image_reader = MyImageReader()\n        img_obj = image_reader.read(path_to_image)\n        img_data, meta_data = image_reader.get_data(img_obj)\n\n    - The `read` call converts image filenames into image objects,\n    - The `get_data` call fetches the image data, as well as meta data.\n    - A reader should implement `verify_suffix` with the logic of checking the input filename\n      by the filename extensions.\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_ImageReader.verify_suffix_ImageReader.verify_suffix.raise_NotImplementedError": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_ImageReader.verify_suffix_ImageReader.verify_suffix.raise_NotImplementedError", "embedding": null, "metadata": {"file_path": "monai/data/image_reader.py", "file_name": "image_reader.py", "file_type": "text/x-python", "category": "implementation", "start_line": 66, "end_line": 78, "span_ids": ["ImageReader.verify_suffix"], "tokens": 123}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ImageReader(ABC):\n\n    @abstractmethod\n    def verify_suffix(self, filename: Union[Sequence[PathLike], PathLike]) -> bool:\n        \"\"\"\n        Verify whether the specified `filename` is supported by the current reader.\n        This method should return True if the reader is able to read the format suggested by the\n        `filename`.\n\n        Args:\n            filename: file name or a list of file names to read.\n                if a list of files, verify all the suffixes.\n\n        \"\"\"\n        raise NotImplementedError(f\"Subclass {self.__class__.__name__} must implement this method.\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_ImageReader.read_ImageReader.read.raise_NotImplementedError": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_ImageReader.read_ImageReader.read.raise_NotImplementedError", "embedding": null, "metadata": {"file_path": "monai/data/image_reader.py", "file_name": "image_reader.py", "file_type": "text/x-python", "category": "implementation", "start_line": 80, "end_line": 91, "span_ids": ["ImageReader.read"], "tokens": 122}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ImageReader(ABC):\n\n    @abstractmethod\n    def read(self, data: Union[Sequence[PathLike], PathLike], **kwargs) -> Union[Sequence[Any], Any]:\n        \"\"\"\n        Read image data from specified file or files.\n        Note that it returns a data object or a sequence of data objects.\n\n        Args:\n            data: file name or a list of file names to read.\n            kwargs: additional args for actual `read` API of 3rd party libs.\n\n        \"\"\"\n        raise NotImplementedError(f\"Subclass {self.__class__.__name__} must implement this method.\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_ImageReader.get_data_ImageReader.get_data.raise_NotImplementedError": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_ImageReader.get_data_ImageReader.get_data.raise_NotImplementedError", "embedding": null, "metadata": {"file_path": "monai/data/image_reader.py", "file_name": "image_reader.py", "file_type": "text/x-python", "category": "implementation", "start_line": 89, "end_line": 100, "span_ids": ["ImageReader.get_data"], "tokens": 112}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ImageReader(ABC):\n\n    @abstractmethod\n    def get_data(self, img) -> Tuple[np.ndarray, Dict]:\n        \"\"\"\n        Extract data array and meta data from loaded image and return them.\n        This function must return two objects, the first is a numpy array of image data,\n        the second is a dictionary of meta data.\n\n        Args:\n            img: an image object loaded from an image file or a list of image objects.\n\n        \"\"\"\n        raise NotImplementedError(f\"Subclass {self.__class__.__name__} must implement this method.\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py__copy_compatible_dict__stack_images.return.np_stack_image_list_axis": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py__copy_compatible_dict__stack_images.return.np_stack_image_list_axis", "embedding": null, "metadata": {"file_path": "monai/data/image_reader.py", "file_name": "image_reader.py", "file_type": "text/x-python", "category": "implementation", "start_line": 103, "end_line": 132, "span_ids": ["_copy_compatible_dict", "_stack_images"], "tokens": 354}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _copy_compatible_dict(from_dict: Dict, to_dict: Dict):\n    if not isinstance(to_dict, dict):\n        raise ValueError(f\"to_dict must be a Dict, got {type(to_dict)}.\")\n    if not to_dict:\n        for key in from_dict:\n            datum = from_dict[key]\n            if isinstance(datum, np.ndarray) and np_str_obj_array_pattern.search(datum.dtype.str) is not None:\n                continue\n            to_dict[key] = datum\n    else:\n        affine_key, shape_key = \"affine\", \"spatial_shape\"\n        if affine_key in from_dict and not np.allclose(from_dict[affine_key], to_dict[affine_key]):\n            raise RuntimeError(\n                \"affine matrix of all images should be the same for channel-wise concatenation. \"\n                f\"Got {from_dict[affine_key]} and {to_dict[affine_key]}.\"\n            )\n        if shape_key in from_dict and not np.allclose(from_dict[shape_key], to_dict[shape_key]):\n            raise RuntimeError(\n                \"spatial_shape of all images should be the same for channel-wise concatenation. \"\n                f\"Got {from_dict[shape_key]} and {to_dict[shape_key]}.\"\n            )\n\n\ndef _stack_images(image_list: List, meta_dict: Dict):\n    if len(image_list) <= 1:\n        return image_list[0]\n    if meta_dict.get(\"original_channel_dim\", None) not in (\"no_channel\", None):\n        raise RuntimeError(\"can not read a list of images which already have channel dimension.\")\n    meta_dict[\"original_channel_dim\"] = 0\n    return np.stack(image_list, axis=0)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_ITKReader._get_affine_ITKReader._get_affine.return.affine": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_ITKReader._get_affine_ITKReader._get_affine.return.affine", "embedding": null, "metadata": {"file_path": "monai/data/image_reader.py", "file_name": "image_reader.py", "file_type": "text/x-python", "category": "implementation", "start_line": 263, "end_line": 283, "span_ids": ["ITKReader._get_affine"], "tokens": 236}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@require_pkg(pkg_name=\"itk\")\nclass ITKReader(ImageReader):\n\n    def _get_affine(self, img):\n        \"\"\"\n        Get or construct the affine matrix of the image, it can be used to correct\n        spacing, orientation or execute spatial transforms.\n\n        Args:\n            img: an ITK image object loaded from an image file.\n\n        \"\"\"\n        direction = itk.array_from_matrix(img.GetDirection())\n        spacing = np.asarray(img.GetSpacing())\n        origin = np.asarray(img.GetOrigin())\n\n        direction = np.asarray(direction)\n        sr = min(max(direction.shape[0], 1), 3)\n        affine: np.ndarray = np.eye(sr + 1)\n        affine[:sr, :sr] = direction[:sr, :sr] @ np.diag(spacing[:sr])\n        affine[:sr, -1] = origin[:sr]\n        flip_diag = [[-1, 1], [-1, -1, 1], [-1, -1, 1, 1]][sr - 1]  # itk to nibabel affine\n        affine = np.diag(flip_diag) @ affine\n        return affine", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_ITKReader._get_spatial_shape_ITKReader._get_spatial_shape.return.np_asarray__size_sr_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_ITKReader._get_spatial_shape_ITKReader._get_spatial_shape.return.np_asarray__size_sr_", "embedding": null, "metadata": {"file_path": "monai/data/image_reader.py", "file_name": "image_reader.py", "file_type": "text/x-python", "category": "implementation", "start_line": 285, "end_line": 302, "span_ids": ["ITKReader._get_spatial_shape"], "tokens": 157}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@require_pkg(pkg_name=\"itk\")\nclass ITKReader(ImageReader):\n\n    def _get_spatial_shape(self, img):\n        \"\"\"\n        Get the spatial shape of `img`.\n\n        Args:\n            img: an ITK image object loaded from an image file.\n\n        \"\"\"\n        # the img data should have no channel dim\n\n        sr = itk.array_from_matrix(img.GetDirection()).shape[0]\n        sr = max(min(sr, 3), 1)\n        _size = list(itk.size(img))\n        if self.channel_dim is not None:\n            # channel_dim is given in the numpy convention, which is different from ITK\n            # size is reversed\n            _size.pop(-self.channel_dim)\n        return np.asarray(_size[:sr])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/nifti_writer.py_write_nifti.if_isinstance_data_torch_write_nifti.output_spatial_shape_.list_output_spatial_shape": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/nifti_writer.py_write_nifti.if_isinstance_data_torch_write_nifti.output_spatial_shape_.list_output_spatial_shape", "embedding": null, "metadata": {"file_path": "monai/data/nifti_writer.py", "file_name": "nifti_writer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 101, "end_line": 142, "span_ids": ["write_nifti"], "tokens": 657}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def write_nifti(\n    data: NdarrayOrTensor,\n    file_name: str,\n    affine: Optional[NdarrayOrTensor] = None,\n    target_affine: Optional[np.ndarray] = None,\n    resample: bool = True,\n    output_spatial_shape: Union[Sequence[int], np.ndarray, None] = None,\n    mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,\n    padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER,\n    align_corners: bool = False,\n    dtype: DtypeLike = np.float64,\n    output_dtype: DtypeLike = np.float32,\n) -> None:\n    if isinstance(data, torch.Tensor):\n        data, *_ = convert_data_type(data, np.ndarray)\n    if isinstance(affine, torch.Tensor):\n        affine, *_ = convert_data_type(affine, np.ndarray)\n    if not isinstance(data, np.ndarray):\n        raise AssertionError(\"input data must be numpy array or torch tensor.\")\n    dtype = dtype or data.dtype\n    sr = min(data.ndim, 3)\n    if affine is None:\n        affine = np.eye(4, dtype=np.float64)\n    affine = to_affine_nd(sr, affine)  # type: ignore\n\n    if target_affine is None:\n        target_affine = affine\n    target_affine = to_affine_nd(sr, target_affine)\n\n    if np.allclose(affine, target_affine, atol=1e-3):\n        # no affine changes, save (data, affine)\n        results_img = nib.Nifti1Image(data.astype(output_dtype, copy=False), to_affine_nd(3, target_affine))\n        nib.save(results_img, file_name)\n        return\n\n    # resolve orientation\n    start_ornt = nib.orientations.io_orientation(affine)\n    target_ornt = nib.orientations.io_orientation(target_affine)\n    ornt_transform = nib.orientations.ornt_transform(start_ornt, target_ornt)\n    data_shape = data.shape\n    data = nib.orientations.apply_orientation(data, ornt_transform)\n    _affine = affine @ nib.orientations.inv_ornt_aff(ornt_transform, data_shape)\n    if np.allclose(_affine, target_affine, atol=1e-3) or not resample:\n        results_img = nib.Nifti1Image(data.astype(output_dtype, copy=False), to_affine_nd(3, _affine))  # type: ignore\n        nib.save(results_img, file_name)\n        return\n\n    # need resampling\n    affine_xform = AffineTransform(\n        normalized=False, mode=mode, padding_mode=padding_mode, align_corners=align_corners, reverse_indexing=True\n    )\n    transform = np.linalg.inv(_affine) @ target_affine\n    if output_spatial_shape is None:\n        output_spatial_shape, _ = compute_shape_offset(data.shape, _affine, target_affine)\n    output_spatial_shape_ = list(output_spatial_shape) if output_spatial_shape is not None else []\n    # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_list_data_collate_list_data_collate.try_.except_TypeError_as_re_.raise_TypeError_re_str_f": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_list_data_collate_list_data_collate.try_.except_TypeError_as_re_.raise_TypeError_re_str_f", "embedding": null, "metadata": {"file_path": "monai/data/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 253, "end_line": 296, "span_ids": ["list_data_collate"], "tokens": 423}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def list_data_collate(batch: Sequence):\n    \"\"\"\n    Enhancement for PyTorch DataLoader default collate.\n    If dataset already returns a list of batch data that generated in transforms, need to merge all data to 1 list.\n    Then it's same as the default collate behavior.\n\n    Note:\n        Need to use this collate if apply some transforms that can generate batch data.\n\n    \"\"\"\n    elem = batch[0]\n    data = [i for k in batch for i in k] if isinstance(elem, list) else batch\n    key = None\n    try:\n        elem = batch[0]\n        if isinstance(elem, Mapping):\n            ret = {}\n            for k in elem:\n                key = k\n                ret[k] = default_collate([d[k] for d in data])\n            return ret\n        return default_collate(data)\n    except RuntimeError as re:\n        re_str = str(re)\n        if \"equal size\" in re_str:\n            if key is not None:\n                re_str += f\"\\nCollate error on the key '{key}' of dictionary data.\"\n            re_str += (\n                \"\\n\\nMONAI hint: if your transforms intentionally create images of different shapes, creating your \"\n                + \"`DataLoader` with `collate_fn=pad_list_data_collate` might solve this problem (check its \"\n                + \"documentation).\"\n            )\n        raise RuntimeError(re_str) from re\n    except TypeError as re:\n        re_str = str(re)\n        if \"numpy\" in re_str and \"Tensor\" in re_str:\n            if key is not None:\n                re_str += f\"\\nCollate error on the key '{key}' of dictionary data.\"\n            re_str += (\n                \"\\n\\nMONAI hint: if your transforms intentionally create mixtures of torch Tensor and numpy ndarray, \"\n                + \"creating your `DataLoader` with `collate_fn=pad_list_data_collate` might solve this problem \"\n                + \"(check its documentation).\"\n            )\n        raise TypeError(re_str) from re", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_resample_datalist_resample_datalist.return.ret": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_resample_datalist_resample_datalist.return.ret", "embedding": null, "metadata": {"file_path": "monai/data/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 995, "end_line": 1017, "span_ids": ["resample_datalist"], "tokens": 247}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def resample_datalist(data: Sequence, factor: float, random_pick: bool = False, seed: int = 0):\n    \"\"\"\n    Utility function to resample the loaded datalist for training, for example:\n    If factor < 1.0, randomly pick part of the datalist and set to Dataset, useful to quickly test the program.\n    If factor > 1.0, repeat the datalist to enhance the Dataset.\n\n    Args:\n        data: original datalist to scale.\n        factor: scale factor for the datalist, for example, factor=4.5, repeat the datalist 4 times and plus\n            50% of the original datalist.\n        random_pick: whether to randomly pick data if scale factor has decimal part.\n        seed: random seed to randomly pick data.\n\n    \"\"\"\n    scale, repeats = math.modf(factor)\n    ret: List = list()\n\n    for _ in range(int(repeats)):\n        ret.extend(list(deepcopy(data)))\n    if scale > 1e-6:\n        ret.extend(partition_dataset(data=data, ratios=[scale, 1 - scale], shuffle=random_pick, seed=seed)[0])\n\n    return ret", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/trainer.py_SupervisedTrainer.__init___SupervisedTrainer.__init__.self.optim_set_to_none.optim_set_to_none": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/trainer.py_SupervisedTrainer.__init___SupervisedTrainer.__init__.self.optim_set_to_none.optim_set_to_none", "embedding": null, "metadata": {"file_path": "monai/engines/trainer.py", "file_name": "trainer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 111, "end_line": 158, "span_ids": ["SupervisedTrainer.__init__"], "tokens": 405}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SupervisedTrainer(Trainer):\n\n    def __init__(\n        self,\n        device: torch.device,\n        max_epochs: int,\n        train_data_loader: Union[Iterable, DataLoader],\n        network: torch.nn.Module,\n        optimizer: Optimizer,\n        loss_function: Callable,\n        epoch_length: Optional[int] = None,\n        non_blocking: bool = False,\n        prepare_batch: Callable = default_prepare_batch,\n        iteration_update: Optional[Callable[[Engine, Any], Any]] = None,\n        inferer: Optional[Inferer] = None,\n        postprocessing: Optional[Transform] = None,\n        key_train_metric: Optional[Dict[str, Metric]] = None,\n        additional_metrics: Optional[Dict[str, Metric]] = None,\n        metric_cmp_fn: Callable = default_metric_cmp_fn,\n        train_handlers: Optional[Sequence] = None,\n        amp: bool = False,\n        event_names: Optional[List[Union[str, EventEnum]]] = None,\n        event_to_attr: Optional[dict] = None,\n        decollate: bool = True,\n        optim_set_to_none: bool = False,\n    ) -> None:\n        super().__init__(\n            device=device,\n            max_epochs=max_epochs,\n            data_loader=train_data_loader,\n            epoch_length=epoch_length,\n            non_blocking=non_blocking,\n            prepare_batch=prepare_batch,\n            iteration_update=iteration_update,\n            postprocessing=postprocessing,\n            key_metric=key_train_metric,\n            additional_metrics=additional_metrics,\n            metric_cmp_fn=metric_cmp_fn,\n            handlers=train_handlers,\n            amp=amp,\n            event_names=event_names,\n            event_to_attr=event_to_attr,\n            decollate=decollate,\n        )\n\n        self.network = network\n        self.optimizer = optimizer\n        self.loss_function = loss_function\n        self.inferer = SimpleInferer() if inferer is None else inferer\n        self.optim_set_to_none = optim_set_to_none", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/trainer.py_GanTrainer.__init___GanTrainer.__init__.self.optim_set_to_none.optim_set_to_none": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/trainer.py_GanTrainer.__init___GanTrainer.__init__.self.optim_set_to_none.optim_set_to_none", "embedding": null, "metadata": {"file_path": "monai/engines/trainer.py", "file_name": "trainer.py", "file_type": "text/x-python", "category": "implementation", "start_line": 277, "end_line": 337, "span_ids": ["GanTrainer.__init__"], "tokens": 563}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GanTrainer(Trainer):\n\n    def __init__(\n        self,\n        device: torch.device,\n        max_epochs: int,\n        train_data_loader: DataLoader,\n        g_network: torch.nn.Module,\n        g_optimizer: Optimizer,\n        g_loss_function: Callable,\n        d_network: torch.nn.Module,\n        d_optimizer: Optimizer,\n        d_loss_function: Callable,\n        epoch_length: Optional[int] = None,\n        g_inferer: Optional[Inferer] = None,\n        d_inferer: Optional[Inferer] = None,\n        d_train_steps: int = 1,\n        latent_shape: int = 64,\n        non_blocking: bool = False,\n        d_prepare_batch: Callable = default_prepare_batch,\n        g_prepare_batch: Callable = default_make_latent,\n        g_update_latents: bool = True,\n        iteration_update: Optional[Callable[[Engine, Any], Any]] = None,\n        postprocessing: Optional[Transform] = None,\n        key_train_metric: Optional[Dict[str, Metric]] = None,\n        additional_metrics: Optional[Dict[str, Metric]] = None,\n        metric_cmp_fn: Callable = default_metric_cmp_fn,\n        train_handlers: Optional[Sequence] = None,\n        decollate: bool = True,\n        optim_set_to_none: bool = False,\n    ):\n        if not isinstance(train_data_loader, DataLoader):\n            raise ValueError(\"train_data_loader must be PyTorch DataLoader.\")\n\n        # set up Ignite engine and environments\n        super().__init__(\n            device=device,\n            max_epochs=max_epochs,\n            data_loader=train_data_loader,\n            epoch_length=epoch_length,\n            non_blocking=non_blocking,\n            prepare_batch=d_prepare_batch,\n            iteration_update=iteration_update,\n            key_metric=key_train_metric,\n            additional_metrics=additional_metrics,\n            metric_cmp_fn=metric_cmp_fn,\n            handlers=train_handlers,\n            postprocessing=postprocessing,\n            decollate=decollate,\n        )\n        self.g_network = g_network\n        self.g_optimizer = g_optimizer\n        self.g_loss_function = g_loss_function\n        self.g_inferer = SimpleInferer() if g_inferer is None else g_inferer\n        self.d_network = d_network\n        self.d_optimizer = d_optimizer\n        self.d_loss_function = d_loss_function\n        self.d_inferer = SimpleInferer() if d_inferer is None else d_inferer\n        self.d_train_steps = d_train_steps\n        self.latent_shape = latent_shape\n        self.g_prepare_batch = g_prepare_batch\n        self.g_update_latents = g_update_latents\n        self.optim_set_to_none = optim_set_to_none", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/utils.py_from_abc_import_ABC_abst___all__._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/utils.py_from_abc_import_ABC_abst___all__._", "embedding": null, "metadata": {"file_path": "monai/engines/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 38, "span_ids": ["docstring"], "tokens": 183}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from abc import ABC, abstractmethod\nfrom typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Sequence, Tuple, Union\n\nimport torch\n\nfrom monai.config import IgniteInfo\nfrom monai.transforms import apply_transform\nfrom monai.utils import ensure_tuple, min_version, optional_import\nfrom monai.utils.enums import CommonKeys\n\nif TYPE_CHECKING:\n    from ignite.engine import EventEnum\nelse:\n    EventEnum, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"EventEnum\")\n\n__all__ = [\n    \"IterationEvents\",\n    \"GanKeys\",\n    \"get_devices_spec\",\n    \"default_prepare_batch\",\n    \"PrepareBatch\",\n    \"PrepareBatchDefault\",\n    \"PrepareBatchExtraInput\",\n    \"default_make_latent\",\n    \"engine_apply_transform\",\n    \"default_metric_cmp_fn\",\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/utils.py_PrepareBatch_PrepareBatchDefault.__call__.return.default_prepare_batch_bat": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/utils.py_PrepareBatch_PrepareBatchDefault.__call__.return.default_prepare_batch_bat", "embedding": null, "metadata": {"file_path": "monai/engines/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 130, "end_line": 159, "span_ids": ["PrepareBatchDefault", "PrepareBatch.__call__", "PrepareBatchDefault.__call__", "PrepareBatch"], "tokens": 205}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class PrepareBatch(ABC):\n    \"\"\"\n    Interface of customized prepare_batch in the trainer or evaluator workflows.\n    It takes the data of current batch, target device and non_blocking flag as input.\n\n    \"\"\"\n\n    @abstractmethod\n    def __call__(\n        self,\n        batchdata: Dict[str, torch.Tensor],\n        device: Optional[Union[str, torch.device]] = None,\n        non_blocking: bool = False,\n    ):\n        raise NotImplementedError(f\"Subclass {self.__class__.__name__} must implement this method.\")\n\n\nclass PrepareBatchDefault(PrepareBatch):\n    \"\"\"\n    Default prepare batch method to return `image` and `label` only,\n    it's to be consistent with `default_prepare_batch` API.\n    \"\"\"\n\n    def __call__(\n        self,\n        batchdata: Dict[str, torch.Tensor],\n        device: Optional[Union[str, torch.device]] = None,\n        non_blocking: bool = False,\n    ):\n        return default_prepare_batch(batchdata, device, non_blocking)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/utils.py_PrepareBatchExtraInput_PrepareBatchExtraInput.__init__.self.extra_keys.extra_keys": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/utils.py_PrepareBatchExtraInput_PrepareBatchExtraInput.__init__.self.extra_keys.extra_keys", "embedding": null, "metadata": {"file_path": "monai/engines/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 162, "end_line": 177, "span_ids": ["PrepareBatchExtraInput.__init__", "PrepareBatchExtraInput"], "tokens": 193}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class PrepareBatchExtraInput(PrepareBatch):\n    \"\"\"\n    Customized prepare_batch for trainer or evaluator that support extra input data for network.\n    Extra items are specified by the `extra_keys` parameter.\n\n    Args:\n        extra_keys: if a string or list provided, every item is the key of extra data in current batch,\n            and will pass the extra data to the `network(*args)` in order.\n            If a dictionary is provided, every `{k, v}` pair is the key of extra data in current batch,\n            `k` is the param name in network, `v` is the key of extra data in current batch,\n            and will pass the `{k1: batch[v1], k2: batch[v2], ...}` as kwargs to the network.\n\n    \"\"\"\n\n    def __init__(self, extra_keys: Union[str, Sequence[str], Dict[str, str]]) -> None:\n        self.extra_keys = extra_keys", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/utils.py_PrepareBatchExtraInput.__call___default_make_latent.return.torch_randn_num_latents_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/utils.py_PrepareBatchExtraInput.__call___default_make_latent.return.torch_randn_num_latents_", "embedding": null, "metadata": {"file_path": "monai/engines/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 180, "end_line": 207, "span_ids": ["default_make_latent", "PrepareBatchExtraInput.__call__"], "tokens": 255}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class PrepareBatchExtraInput(PrepareBatch):\n\n    def __call__(\n        self,\n        batchdata: Dict[str, torch.Tensor],\n        device: Optional[Union[str, torch.device]] = None,\n        non_blocking: bool = False,\n    ):\n        image, label = default_prepare_batch(batchdata, device, non_blocking)\n        args = list()\n        kwargs = dict()\n\n        def _get_data(key: str):\n            data = batchdata[key]\n            return data.to(device=device, non_blocking=non_blocking) if isinstance(data, torch.Tensor) else data\n\n        if isinstance(self.extra_keys, (str, list, tuple)):\n            for k in ensure_tuple(self.extra_keys):\n                args.append(_get_data(k))\n        elif isinstance(self.extra_keys, dict):\n            for k, v in self.extra_keys.items():\n                kwargs.update({k: _get_data(v)})\n\n        return image, label, tuple(args), kwargs\n\n\ndef default_make_latent(\n    num_latents: int, latent_size: int, device: Optional[Union[str, torch.device]] = None, non_blocking: bool = False\n) -> torch.Tensor:\n    return torch.randn(num_latents, latent_size).to(device=device, non_blocking=non_blocking)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/utils.py_engine_apply_transform_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/utils.py_engine_apply_transform_", "embedding": null, "metadata": {"file_path": "monai/engines/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 210, "end_line": 248, "span_ids": ["default_metric_cmp_fn", "engine_apply_transform"], "tokens": 294}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def engine_apply_transform(batch: Any, output: Any, transform: Callable[..., Dict]):\n    \"\"\"\n    Apply transform on `batch` and `output`.\n    If `batch` and `output` are dictionaries, temporarily combine them for the transform,\n    otherwise, apply the transform for `output` data only.\n\n    \"\"\"\n    if isinstance(batch, dict) and isinstance(output, dict):\n        data = dict(batch)\n        data.update(output)\n        transformed_data = apply_transform(transform, data)\n\n        if not isinstance(transformed_data, dict):\n            raise AssertionError(\"With a dict supplied to apply_transform a single dict return is expected.\")\n\n        for k, v in transformed_data.items():\n            # split the output data of post transforms into `output` and `batch`,\n            # `batch` should be read-only, so save the generated key-value into `output`\n            if k in output or k not in batch:\n                output[k] = v\n            else:\n                batch[k] = v\n    else:\n        output = apply_transform(transform, output)\n\n    return batch, output\n\n\ndef default_metric_cmp_fn(current_metric: float, prev_best: float) -> bool:\n    \"\"\"\n    The default function to compare metric values between current metric and previous best metric.\n\n    Args:\n        current_metric: metric value of current round computation.\n        prev_best: the best metric value of previous rounds to compare with.\n\n    \"\"\"\n    return current_metric > prev_best", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/workflow.py_Workflow.__init___Workflow.__init__.if_handlers_is_not_None_.self__register_handlers_h": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/workflow.py_Workflow.__init___Workflow.__init__.if_handlers_is_not_None_.self__register_handlers_h", "embedding": null, "metadata": {"file_path": "monai/engines/workflow.py", "file_name": "workflow.py", "file_type": "text/x-python", "category": "implementation", "start_line": 106, "end_line": 195, "span_ids": ["Workflow.__init__"], "tokens": 795}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Workflow(IgniteEngine):\n\n    def __init__(\n        self,\n        device: torch.device,\n        max_epochs: int,\n        data_loader: Union[Iterable, DataLoader],\n        epoch_length: Optional[int] = None,\n        non_blocking: bool = False,\n        prepare_batch: Callable = default_prepare_batch,\n        iteration_update: Optional[Callable[[Engine, Any], Any]] = None,\n        postprocessing: Optional[Callable] = None,\n        key_metric: Optional[Dict[str, Metric]] = None,\n        additional_metrics: Optional[Dict[str, Metric]] = None,\n        metric_cmp_fn: Callable = default_metric_cmp_fn,\n        handlers: Optional[Sequence] = None,\n        amp: bool = False,\n        event_names: Optional[List[Union[str, EventEnum]]] = None,\n        event_to_attr: Optional[dict] = None,\n        decollate: bool = True,\n    ) -> None:\n        if iteration_update is not None:\n            super().__init__(iteration_update)\n        else:\n            super().__init__(self._iteration)\n        if not isinstance(device, torch.device):\n            raise TypeError(f\"device must be a torch.device but is {type(device).__name__}.\")\n\n        if isinstance(data_loader, DataLoader):\n            sampler = data_loader.__dict__[\"sampler\"]\n            if isinstance(sampler, DistributedSampler):\n\n                @self.on(Events.EPOCH_STARTED)\n                def set_sampler_epoch(engine: Engine):\n                    sampler.set_epoch(engine.state.epoch)\n\n            if epoch_length is None:\n                epoch_length = len(data_loader)\n        else:\n            if epoch_length is None:\n                raise ValueError(\"if data_loader is not PyTorch DataLoader, must specify the epoch_length.\")\n\n        # set all sharable data for the workflow based on Ignite engine.state\n        self.state = State(\n            rank=dist.get_rank() if dist.is_available() and dist.is_initialized() else 0,\n            seed=0,\n            iteration=0,\n            epoch=0,\n            max_epochs=max_epochs,\n            epoch_length=epoch_length,\n            output=None,\n            batch=None,\n            metrics={},\n            metric_details={},\n            dataloader=None,\n            device=device,\n            key_metric_name=None,  # we can set many metrics, only use key_metric to compare and save the best model\n            best_metric=-1,\n            best_metric_epoch=-1,\n        )\n        self.data_loader = data_loader\n        self.non_blocking = non_blocking\n        self.prepare_batch = prepare_batch\n        self.metric_cmp_fn = metric_cmp_fn\n        self.amp = amp\n        self.scaler: Optional[torch.cuda.amp.GradScaler] = None\n\n        if event_names is None:\n            event_names = [IterationEvents]  # type: ignore\n        else:\n            if not isinstance(event_names, list):\n                raise ValueError(\"event_names must be a list or string or EventEnum.\")\n            event_names += [IterationEvents]  # type: ignore\n        for name in event_names:\n            if isinstance(name, str):\n                self.register_events(name, event_to_attr=event_to_attr)\n            elif issubclass(name, EventEnum):  # type: ignore\n                self.register_events(*name, event_to_attr=event_to_attr)\n            else:\n                raise ValueError(\"event_names must be a list or string or EventEnum.\")\n\n        if decollate:\n            self._register_decollate()\n\n        if postprocessing is not None:\n            # tips: if `decollate=False` and `postprocessing` is MONAI transforms, it may not work well\n            # because all the MONAI transforms expect `channel-first` data\n            self._register_postprocessing(postprocessing)\n        if key_metric is not None:\n            self._register_metrics(key_metric, additional_metrics)\n        if handlers is not None:\n            self._register_handlers(handlers)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/workflow.py_Workflow._register_decollate_Workflow._register_decollate._decollate_data.None_1.engine.state.output.transform_engine_state_ou": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/workflow.py_Workflow._register_decollate_Workflow._register_decollate._decollate_data.None_1.engine.state.output.transform_engine_state_ou", "embedding": null, "metadata": {"file_path": "monai/engines/workflow.py", "file_name": "workflow.py", "file_type": "text/x-python", "category": "implementation", "start_line": 180, "end_line": 193, "span_ids": ["Workflow._register_decollate"], "tokens": 141}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Workflow(IgniteEngine):\n\n    def _register_decollate(self):\n        \"\"\"\n        Register the decollate operation for batch data, will execute after model forward and loss forward.\n\n        \"\"\"\n\n        @self.on(IterationEvents.MODEL_COMPLETED)\n        def _decollate_data(engine: Engine) -> None:\n            # replicate the scalar values to make sure all the items have batch dimension, then decollate\n            transform = Decollated(keys=None, detach=True)\n            if isinstance(engine.state.batch, (list, dict)):\n                engine.state.batch = transform(engine.state.batch)\n            if isinstance(engine.state.output, (list, dict)):\n                engine.state.output = transform(engine.state.output)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/workflow.py_Workflow._register_postprocessing_Workflow._register_postprocessing._run_postprocessing.if_not_isinstance_engine_.else_.for_i_b_o_in_enumerat.engine_state_batch_i_en": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/workflow.py_Workflow._register_postprocessing_Workflow._register_postprocessing._run_postprocessing.if_not_isinstance_engine_.else_.for_i_b_o_in_enumerat.engine_state_batch_i_en", "embedding": null, "metadata": {"file_path": "monai/engines/workflow.py", "file_name": "workflow.py", "file_type": "text/x-python", "category": "implementation", "start_line": 195, "end_line": 209, "span_ids": ["Workflow._register_postprocessing"], "tokens": 163}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Workflow(IgniteEngine):\n\n    def _register_postprocessing(self, posttrans: Callable):\n        \"\"\"\n        Register the postprocessing logic to the engine, will execute them as a chain when iteration completed.\n\n        \"\"\"\n\n        @self.on(IterationEvents.MODEL_COMPLETED)\n        def _run_postprocessing(engine: Engine) -> None:\n            if not isinstance(engine.state.batch, list) or not isinstance(engine.state.output, list):\n                engine.state.batch, engine.state.output = engine_apply_transform(\n                    batch=engine.state.batch, output=engine.state.output, transform=posttrans\n                )\n            else:\n                for i, (b, o) in enumerate(zip(engine.state.batch, engine.state.output)):\n                    engine.state.batch[i], engine.state.output[i] = engine_apply_transform(b, o, posttrans)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/workflow.py_Workflow._register_metrics_Workflow._register_metrics.for_name_metric_in_metri.metric_attach_self_name_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/workflow.py_Workflow._register_metrics_Workflow._register_metrics.for_name_metric_in_metri.metric_attach_self_name_", "embedding": null, "metadata": {"file_path": "monai/engines/workflow.py", "file_name": "workflow.py", "file_type": "text/x-python", "category": "implementation", "start_line": 211, "end_line": 225, "span_ids": ["Workflow._register_metrics"], "tokens": 179}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Workflow(IgniteEngine):\n\n    def _register_metrics(self, k_metric: Dict, add_metrics: Optional[Dict] = None):\n        \"\"\"\n        Register the key metric and additional metrics to the engine, supports ignite Metrics.\n\n        \"\"\"\n        if not isinstance(k_metric, dict):\n            raise TypeError(f\"key_metric must be None or a dict but is {type(k_metric).__name__}.\")\n        self.state.key_metric_name = list(k_metric.keys())[0]\n        metrics = dict(k_metric)\n        if add_metrics is not None and len(add_metrics) > 0:\n            if not isinstance(add_metrics, dict):\n                raise TypeError(f\"additional metrics must be None or a dict but is {type(add_metrics).__name__}.\")\n            metrics.update(add_metrics)\n        for name, metric in metrics.items():\n            metric.attach(self, name)\n        # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/classification_saver.py_ClassificationSaver.attach_ClassificationSaver._started.self._filenames._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/classification_saver.py_ClassificationSaver.attach_ClassificationSaver._started.self._filenames._", "embedding": null, "metadata": {"file_path": "monai/handlers/classification_saver.py", "file_name": "classification_saver.py", "file_type": "text/x-python", "category": "implementation", "start_line": 87, "end_line": 110, "span_ids": ["ClassificationSaver._started", "ClassificationSaver.attach"], "tokens": 197}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ClassificationSaver:\n\n    def attach(self, engine: Engine) -> None:\n        \"\"\"\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n        \"\"\"\n        if self._name is None:\n            self.logger = engine.logger\n        if not engine.has_event_handler(self._started, Events.EPOCH_STARTED):\n            engine.add_event_handler(Events.EPOCH_STARTED, self._started)\n        if not engine.has_event_handler(self, Events.ITERATION_COMPLETED):\n            engine.add_event_handler(Events.ITERATION_COMPLETED, self)\n        if not engine.has_event_handler(self._finalize, Events.EPOCH_COMPLETED):\n            engine.add_event_handler(Events.EPOCH_COMPLETED, self._finalize)\n\n    def _started(self, _engine: Engine) -> None:\n        \"\"\"\n        Initialize internal buffers.\n\n        Args:\n            _engine: Ignite Engine, unused argument.\n\n        \"\"\"\n        self._outputs = []\n        self._filenames = []", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/classification_saver.py_ClassificationSaver.__call___ClassificationSaver.__call__.for_m_o_in_zip_meta_data.self__outputs_append_o_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/classification_saver.py_ClassificationSaver.__call___ClassificationSaver.__call__.for_m_o_in_zip_meta_data.self__outputs_append_o_", "embedding": null, "metadata": {"file_path": "monai/handlers/classification_saver.py", "file_name": "classification_saver.py", "file_type": "text/x-python", "category": "implementation", "start_line": 105, "end_line": 121, "span_ids": ["ClassificationSaver.__call__"], "tokens": 167}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ClassificationSaver:\n\n    def __call__(self, engine: Engine) -> None:\n        \"\"\"\n        This method assumes self.batch_transform will extract metadata from the input batch.\n\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n        \"\"\"\n        meta_data = self.batch_transform(engine.state.batch)\n        if isinstance(meta_data, dict):\n            # decollate the `dictionary of list` to `list of dictionaries`\n            meta_data = decollate_batch(meta_data)\n        engine_output = self.output_transform(engine.state.output)\n        for m, o in zip(meta_data, engine_output):\n            self._filenames.append(f\"{m.get(Key.FILENAME_OR_OBJ)}\")\n            if isinstance(o, torch.Tensor):\n                o = o.detach()\n            self._outputs.append(o)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/decollate_batch.py_from_typing_import_TYPE_C_if_TYPE_CHECKING_.else_.Engine___optional_impo": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/decollate_batch.py_from_typing_import_TYPE_C_if_TYPE_CHECKING_.else_.Engine___optional_impo", "embedding": null, "metadata": {"file_path": "monai/handlers/decollate_batch.py", "file_name": "decollate_batch.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 23, "span_ids": ["docstring"], "tokens": 115}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import TYPE_CHECKING, Optional\n\nfrom monai.config import IgniteInfo, KeysCollection\nfrom monai.engines.utils import IterationEvents\nfrom monai.transforms import Decollated\nfrom monai.utils import min_version, optional_import\n\nEvents, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Events\")\nif TYPE_CHECKING:\n    from ignite.engine import Engine\nelse:\n    Engine, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Engine\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/decollate_batch.py_DecollateBatch_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/decollate_batch.py_DecollateBatch_", "embedding": null, "metadata": {"file_path": "monai/handlers/decollate_batch.py", "file_name": "decollate_batch.py", "file_type": "text/x-python", "category": "implementation", "start_line": 26, "end_line": 95, "span_ids": ["DecollateBatch.attach", "DecollateBatch.__call__", "DecollateBatch.__init__", "DecollateBatch"], "tokens": 705}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DecollateBatch:\n    \"\"\"\n    Ignite handler to execute the `decollate batch` logic for `engine.state.batch` and `engine.state.output`.\n    Typical usage is to set `decollate=False` in the engine and execute some postprocessing logic first\n    then decollate the batch, otherwise, engine will decollate batch before the postprocessing.\n\n    Args:\n        event: expected EVENT to attach the handler, should be \"MODEL_COMPLETED\" or \"ITERATION_COMPLETED\".\n            default to \"MODEL_COMPLETED\".\n        detach: whether to detach the tensors. scalars tensors will be detached into number types\n            instead of torch tensors.\n        decollate_batch: whether to decollate `engine.state.batch` of ignite engine.\n        batch_keys: if `decollate_batch=True`, specify the keys of the corresponding items to decollate\n            in `engine.state.batch`, note that it will delete other keys not specified. if None,\n            will decollate all the keys. it replicates the scalar values to every item of the decollated list.\n        decollate_output: whether to decollate `engine.state.output` of ignite engine.\n        output_keys: if `decollate_output=True`, specify the keys of the corresponding items to decollate\n            in `engine.state.output`, note that it will delete other keys not specified. if None,\n            will decollate all the keys. it replicates the scalar values to every item of the decollated list.\n        allow_missing_keys: don't raise exception if key is missing.\n\n    \"\"\"\n\n    def __init__(\n        self,\n        event: str = \"MODEL_COMPLETED\",\n        detach: bool = True,\n        decollate_batch: bool = True,\n        batch_keys: Optional[KeysCollection] = None,\n        decollate_output: bool = True,\n        output_keys: Optional[KeysCollection] = None,\n        allow_missing_keys: bool = False,\n    ):\n        event = event.upper()\n        if event not in (\"MODEL_COMPLETED\", \"ITERATION_COMPLETED\"):\n            raise ValueError(\"event should be `MODEL_COMPLETED` or `ITERATION_COMPLETED`.\")\n        self.event = event\n\n        self.batch_transform = (\n            Decollated(keys=batch_keys, detach=detach, allow_missing_keys=allow_missing_keys)\n            if decollate_batch\n            else None\n        )\n\n        self.output_transform = (\n            Decollated(keys=output_keys, detach=detach, allow_missing_keys=allow_missing_keys)\n            if decollate_output\n            else None\n        )\n\n    def attach(self, engine: Engine) -> None:\n        \"\"\"\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n        \"\"\"\n        if self.event == \"MODEL_COMPLETED\":\n            engine.add_event_handler(IterationEvents.MODEL_COMPLETED, self)\n        else:\n            engine.add_event_handler(Events.ITERATION_COMPLETED, self)\n\n    def __call__(self, engine: Engine) -> None:\n        \"\"\"\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n        \"\"\"\n        if self.batch_transform is not None and isinstance(engine.state.batch, (list, dict)):\n            engine.state.batch = self.batch_transform(engine.state.batch)\n        if self.output_transform is not None and isinstance(engine.state.output, (list, dict)):\n            engine.state.output = self.output_transform(engine.state.output)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/metrics_saver.py_MetricsSaver.__init___MetricsSaver._get_filenames.if_self_metric_details_is.for_m_in_meta_data_.self__filenames_append_f_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/metrics_saver.py_MetricsSaver.__init___MetricsSaver._get_filenames.if_self_metric_details_is.for_m_in_meta_data_.self__filenames_append_f_", "embedding": null, "metadata": {"file_path": "monai/handlers/metrics_saver.py", "file_name": "metrics_saver.py", "file_type": "text/x-python", "category": "implementation", "start_line": 78, "end_line": 125, "span_ids": ["MetricsSaver._started", "MetricsSaver._get_filenames", "MetricsSaver.attach", "MetricsSaver.__init__"], "tokens": 431}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class MetricsSaver:\n\n    def __init__(\n        self,\n        save_dir: str,\n        metrics: Optional[Union[str, Sequence[str]]] = \"*\",\n        metric_details: Optional[Union[str, Sequence[str]]] = None,\n        batch_transform: Callable = lambda x: x,\n        summary_ops: Optional[Union[str, Sequence[str]]] = None,\n        save_rank: int = 0,\n        delimiter: str = \"\\t\",\n        output_type: str = \"csv\",\n    ) -> None:\n        self.save_dir = save_dir\n        self.metrics = ensure_tuple(metrics) if metrics is not None else None\n        self.metric_details = ensure_tuple(metric_details) if metric_details is not None else None\n        self.batch_transform = batch_transform\n        self.summary_ops = ensure_tuple(summary_ops) if summary_ops is not None else None\n        self.save_rank = save_rank\n        self.deli = delimiter\n        self.output_type = output_type\n        self._filenames: List[str] = []\n\n    def attach(self, engine: Engine) -> None:\n        \"\"\"\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n        \"\"\"\n        engine.add_event_handler(Events.EPOCH_STARTED, self._started)\n        engine.add_event_handler(Events.ITERATION_COMPLETED, self._get_filenames)\n        engine.add_event_handler(Events.EPOCH_COMPLETED, self)\n\n    def _started(self, _engine: Engine) -> None:\n        \"\"\"\n        Initialize internal buffers.\n\n        Args:\n            _engine: Ignite Engine, unused argument.\n\n        \"\"\"\n        self._filenames = []\n\n    def _get_filenames(self, engine: Engine) -> None:\n        if self.metric_details is not None:\n            meta_data = self.batch_transform(engine.state.batch)\n            if isinstance(meta_data, dict):\n                # decollate the `dictionary of list` to `list of dictionaries`\n                meta_data = decollate_batch(meta_data)\n            for m in meta_data:\n                self._filenames.append(f\"{m.get(Key.FILENAME_OR_OBJ)}\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/mlflow_handler.py_from_typing_import_TYPE_C_DEFAULT_TAG._Loss_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/mlflow_handler.py_from_typing_import_TYPE_C_DEFAULT_TAG._Loss_", "embedding": null, "metadata": {"file_path": "monai/handlers/mlflow_handler.py", "file_name": "mlflow_handler.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 27, "span_ids": ["docstring"], "tokens": 118}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import TYPE_CHECKING, Any, Callable, Optional, Sequence\n\nimport torch\n\nfrom monai.config import IgniteInfo\nfrom monai.utils import min_version, optional_import\n\nEvents, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Events\")\nmlflow, _ = optional_import(\"mlflow\")\n\nif TYPE_CHECKING:\n    from ignite.engine import Engine\nelse:\n    Engine, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Engine\")\n\nDEFAULT_TAG = \"Loss\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/mlflow_handler.py_MLFlowHandler_MLFlowHandler._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/mlflow_handler.py_MLFlowHandler_MLFlowHandler._", "embedding": null, "metadata": {"file_path": "monai/handlers/mlflow_handler.py", "file_name": "mlflow_handler.py", "file_type": "text/x-python", "category": "implementation", "start_line": 30, "end_line": 74, "span_ids": ["MLFlowHandler"], "tokens": 616}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class MLFlowHandler:\n    \"\"\"\n    MLFlowHandler defines a set of Ignite Event-handlers for the MLFlow tracking logics.\n    It can be used for any Ignite Engine(trainer, validator and evaluator).\n    And it can track both epoch level and iteration level logging, then MLFlow can store\n    the data and visualize.\n    The expected data source is Ignite ``engine.state.output`` and ``engine.state.metrics``.\n\n    Default behaviors:\n        - When EPOCH_COMPLETED, track each dictionary item in\n          ``engine.state.metrics`` in MLFlow.\n        - When ITERATION_COMPLETED, track expected item in\n          ``self.output_transform(engine.state.output)`` in MLFlow, default to `Loss`.\n\n    Usage example is available in the tutorial:\n    https://github.com/Project-MONAI/tutorials/blob/master/3d_segmentation/unet_segmentation_3d_ignite.ipynb.\n\n    Args:\n        tracking_uri: connects to a tracking URI. can also set the `MLFLOW_TRACKING_URI` environment\n            variable to have MLflow find a URI from there. in both cases, the URI can either be\n            a HTTP/HTTPS URI for a remote server, a database connection string, or a local path\n            to log data to a directory. The URI defaults to path `mlruns`.\n            for more details: https://mlflow.org/docs/latest/python_api/mlflow.html#mlflow.set_tracking_uri.\n        epoch_logger: customized callable logger for epoch level logging with MLFlow.\n            Must accept parameter \"engine\", use default logger if None.\n        iteration_logger: customized callable logger for iteration level logging with MLFlow.\n            Must accept parameter \"engine\", use default logger if None.\n        output_transform: a callable that is used to transform the\n            ``ignite.engine.state.output`` into a scalar to track, or a dictionary of {key: scalar}.\n            By default this value logging happens when every iteration completed.\n            The default behavior is to track loss from output[0] as output is a decollated list\n            and we replicated loss value for every item of the decollated list.\n            `engine.state` and `output_transform` inherit from the ignite concept:\n            https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial:\n            https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb.\n        global_epoch_transform: a callable that is used to customize global epoch number.\n            For example, in evaluation, the evaluator engine might want to track synced epoch number\n            with the trainer engine.\n        state_attributes: expected attributes from `engine.state`, if provided, will extract them\n            when epoch completed.\n        tag_name: when iteration output is a scalar, `tag_name` is used to track, defaults to `'Loss'`.\n\n    For more details of MLFlow usage, please refer to: https://mlflow.org/docs/latest/index.html.\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/mlflow_handler.py_MLFlowHandler.__init___MLFlowHandler.__init__.self.tag_name.tag_name": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/mlflow_handler.py_MLFlowHandler.__init___MLFlowHandler.__init__.self.tag_name.tag_name", "embedding": null, "metadata": {"file_path": "monai/handlers/mlflow_handler.py", "file_name": "mlflow_handler.py", "file_type": "text/x-python", "category": "implementation", "start_line": 76, "end_line": 94, "span_ids": ["MLFlowHandler.__init__"], "tokens": 172}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class MLFlowHandler:\n\n    def __init__(\n        self,\n        tracking_uri: Optional[str] = None,\n        epoch_logger: Optional[Callable[[Engine], Any]] = None,\n        iteration_logger: Optional[Callable[[Engine], Any]] = None,\n        output_transform: Callable = lambda x: x[0],\n        global_epoch_transform: Callable = lambda x: x,\n        state_attributes: Optional[Sequence[str]] = None,\n        tag_name: str = DEFAULT_TAG,\n    ) -> None:\n        if tracking_uri is not None:\n            mlflow.set_tracking_uri(tracking_uri)\n\n        self.epoch_logger = epoch_logger\n        self.iteration_logger = iteration_logger\n        self.output_transform = output_transform\n        self.global_epoch_transform = global_epoch_transform\n        self.state_attributes = state_attributes\n        self.tag_name = tag_name", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/mlflow_handler.py_MLFlowHandler.attach_MLFlowHandler.attach.None_2.engine_add_event_handler_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/mlflow_handler.py_MLFlowHandler.attach_MLFlowHandler.attach.None_2.engine_add_event_handler_", "embedding": null, "metadata": {"file_path": "monai/handlers/mlflow_handler.py", "file_name": "mlflow_handler.py", "file_type": "text/x-python", "category": "implementation", "start_line": 96, "end_line": 109, "span_ids": ["MLFlowHandler.attach"], "tokens": 151}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class MLFlowHandler:\n\n    def attach(self, engine: Engine) -> None:\n        \"\"\"\n        Register a set of Ignite Event-Handlers to a specified Ignite engine.\n\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n\n        \"\"\"\n        if not engine.has_event_handler(self.start, Events.STARTED):\n            engine.add_event_handler(Events.STARTED, self.start)\n        if not engine.has_event_handler(self.iteration_completed, Events.ITERATION_COMPLETED):\n            engine.add_event_handler(Events.ITERATION_COMPLETED, self.iteration_completed)\n        if not engine.has_event_handler(self.epoch_completed, Events.EPOCH_COMPLETED):\n            engine.add_event_handler(Events.EPOCH_COMPLETED, self.epoch_completed)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/mlflow_handler.py_MLFlowHandler.start_MLFlowHandler.iteration_completed.if_self_iteration_logger_.else_.self__default_iteration_l": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/mlflow_handler.py_MLFlowHandler.start_MLFlowHandler.iteration_completed.if_self_iteration_logger_.else_.self__default_iteration_l", "embedding": null, "metadata": {"file_path": "monai/handlers/mlflow_handler.py", "file_name": "mlflow_handler.py", "file_type": "text/x-python", "category": "implementation", "start_line": 111, "end_line": 152, "span_ids": ["MLFlowHandler.start", "MLFlowHandler.epoch_completed", "MLFlowHandler.iteration_completed", "MLFlowHandler.close"], "tokens": 248}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class MLFlowHandler:\n\n    def start(self) -> None:\n        \"\"\"\n        Check MLFlow status and start if not active.\n\n        \"\"\"\n        if mlflow.active_run() is None:\n            mlflow.start_run()\n\n    def close(self) -> None:\n        \"\"\"\n        Stop current running logger of MLFlow.\n\n        \"\"\"\n        mlflow.end_run()\n\n    def epoch_completed(self, engine: Engine) -> None:\n        \"\"\"\n        Handler for train or validation/evaluation epoch completed Event.\n        Track epoch level log, default values are from Ignite `engine.state.metrics` dict.\n\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n\n        \"\"\"\n        if self.epoch_logger is not None:\n            self.epoch_logger(engine)\n        else:\n            self._default_epoch_log(engine)\n\n    def iteration_completed(self, engine: Engine) -> None:\n        \"\"\"\n        Handler for train or validation/evaluation iteration completed Event.\n        Track iteration level log.\n\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n\n        \"\"\"\n        if self.iteration_logger is not None:\n            self.iteration_logger(engine)\n        else:\n            self._default_iteration_log(engine)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/mlflow_handler.py_MLFlowHandler._default_epoch_log_MLFlowHandler._default_epoch_log.if_self_state_attributes_.mlflow_log_metrics_attrs_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/mlflow_handler.py_MLFlowHandler._default_epoch_log_MLFlowHandler._default_epoch_log.if_self_state_attributes_.mlflow_log_metrics_attrs_", "embedding": null, "metadata": {"file_path": "monai/handlers/mlflow_handler.py", "file_name": "mlflow_handler.py", "file_type": "text/x-python", "category": "implementation", "start_line": 154, "end_line": 173, "span_ids": ["MLFlowHandler._default_epoch_log"], "tokens": 162}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class MLFlowHandler:\n\n    def _default_epoch_log(self, engine: Engine) -> None:\n        \"\"\"\n        Execute epoch level log operation.\n        Default to track the values from Ignite `engine.state.metrics` dict and\n        track the values of specified attributes of `engine.state`.\n\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n\n        \"\"\"\n        log_dict = engine.state.metrics\n        if not log_dict:\n            return\n\n        current_epoch = self.global_epoch_transform(engine.state.epoch)\n        mlflow.log_metrics(log_dict, step=current_epoch)\n\n        if self.state_attributes is not None:\n            attrs = {attr: getattr(engine.state, attr, None) for attr in self.state_attributes}\n            mlflow.log_metrics(attrs, step=current_epoch)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/mlflow_handler.py_MLFlowHandler._default_iteration_log_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/mlflow_handler.py_MLFlowHandler._default_iteration_log_", "embedding": null, "metadata": {"file_path": "monai/handlers/mlflow_handler.py", "file_name": "mlflow_handler.py", "file_type": "text/x-python", "category": "implementation", "start_line": 175, "end_line": 194, "span_ids": ["MLFlowHandler._default_iteration_log"], "tokens": 182}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class MLFlowHandler:\n\n    def _default_iteration_log(self, engine: Engine) -> None:\n        \"\"\"\n        Execute iteration log operation based on Ignite `engine.state.output` data.\n        Log the values from `self.output_transform(engine.state.output)`.\n        Since `engine.state.output` is a decollated list and we replicated the loss value for every item\n        of the decollated list, the default behavior is to track the loss from `output[0]`.\n\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n\n        \"\"\"\n        loss = self.output_transform(engine.state.output)\n        if loss is None:\n            return\n\n        if not isinstance(loss, dict):\n            loss = {self.tag_name: loss.item() if isinstance(loss, torch.Tensor) else loss}\n\n        mlflow.log_metrics(loss, step=engine.state.iteration)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/nvtx_handlers.py_from_typing_import_TYPE_C___all__._RangeHandler_RangePu": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/nvtx_handlers.py_from_typing_import_TYPE_C___all__._RangeHandler_RangePu", "embedding": null, "metadata": {"file_path": "monai/handlers/nvtx_handlers.py", "file_name": "nvtx_handlers.py", "file_type": "text/x-python", "category": "implementation", "start_line": 15, "end_line": 28, "span_ids": ["docstring"], "tokens": 157}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import TYPE_CHECKING, Optional, Tuple, Union\n\nfrom monai.config import IgniteInfo\nfrom monai.utils import ensure_tuple, min_version, optional_import\n\n_nvtx, _ = optional_import(\"torch._C._nvtx\", descriptor=\"NVTX is not installed. Are you sure you have a CUDA build?\")\nif TYPE_CHECKING:\n    from ignite.engine import Engine, Events\nelse:\n    Engine, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Engine\")\n    Events, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Events\")\n\n\n__all__ = [\"RangeHandler\", \"RangePushHandler\", \"RangePopHandler\", \"MarkHandler\"]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/nvtx_handlers.py_RangeHandler_RangeHandler.__init__.self.depth.None": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/nvtx_handlers.py_RangeHandler_RangeHandler.__init__.self.depth.None", "embedding": null, "metadata": {"file_path": "monai/handlers/nvtx_handlers.py", "file_name": "nvtx_handlers.py", "file_type": "text/x-python", "category": "implementation", "start_line": 31, "end_line": 64, "span_ids": ["RangeHandler", "RangeHandler.__init__"], "tokens": 373}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RangeHandler:\n    \"\"\"\n    Attach a NVTX range to a pair of Ignite events.\n    It pushes an NVTX range at the first event and pops it at the second event.\n    Stores zero-based depth of the range that is started.\n\n    Args:\n        events: a string, pair of Ignite events, pair of Ignite event literals, or pair of Ignite events and literals.\n            If a single string is provided, it should  describe the base name of a pair of default Ignite events\n            with _STARTED and _COMPLETED postfix (like \"EPOCH\" for Events.EPOCH_STARTED and Events.EPOCH_COMPLETED).\n            The accepted events are: BATCH, ITERATION, EPOCH, and ENGINE.\n            If pair of literals, each should be the literal equivalent of an Ignite event, fo instance:\n            (\"EPOCH_STARTED\" and \"EPOCH_COMPLETED\").\n            One can combine events and literals, like (Events.EPOCH_STARTED and \"EPOCH_COMPLETED\").\n            For the complete list of Events,\n            check https://pytorch.org/ignite/generated/ignite.engine.events.Events.html.\n\n        msg: ASCII message to associate with range.\n            If not provided, the name of first event will be assigned to the NVTX range.\n    \"\"\"\n\n    def __init__(\n        self, events: Union[str, Tuple[Union[str, Events], Union[str, Events]]], msg: Optional[str] = None\n    ) -> None:\n        self.events = self.resolve_events(events)\n        if msg is None:\n            if isinstance(events, str):\n                # assign the prefix of the events\n                msg = events\n            else:\n                # combine events' names\n                msg = \"/\".join([e.name for e in self.events])\n        self.msg = msg\n        self.depth = None", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/nvtx_handlers.py_RangeHandler.resolve_events_RangeHandler.resolve_events.raise_ValueError_f_Exactl": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/nvtx_handlers.py_RangeHandler.resolve_events_RangeHandler.resolve_events.raise_ValueError_f_Exactl", "embedding": null, "metadata": {"file_path": "monai/handlers/nvtx_handlers.py", "file_name": "nvtx_handlers.py", "file_type": "text/x-python", "category": "implementation", "start_line": 66, "end_line": 75, "span_ids": ["RangeHandler.resolve_events"], "tokens": 115}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RangeHandler:\n\n    def resolve_events(self, events: Union[str, Tuple]) -> Tuple[Events, Events]:\n        \"\"\"\n        Resolve the input events to create a pair of Ignite events\n        \"\"\"\n        events = ensure_tuple(events)\n        if len(events) == 1:\n            return self.create_paired_events(events[0])\n        if len(events) == 2:\n            return self.get_event(events[0]), self.get_event(events[1])\n        raise ValueError(f\"Exactly two Ignite events should be provided [received {len(events)}].\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/nvtx_handlers.py_RangeHandler.create_paired_events_RangeHandler.range_pop._nvtx_rangePop_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/nvtx_handlers.py_RangeHandler.create_paired_events_RangeHandler.range_pop._nvtx_rangePop_", "embedding": null, "metadata": {"file_path": "monai/handlers/nvtx_handlers.py", "file_name": "nvtx_handlers.py", "file_type": "text/x-python", "category": "implementation", "start_line": 77, "end_line": 101, "span_ids": ["RangeHandler.get_event", "RangeHandler.create_paired_events", "RangeHandler.range_pop", "RangeHandler.range_push", "RangeHandler.attach"], "tokens": 248}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RangeHandler:\n\n    def create_paired_events(self, event: str) -> Tuple[Events, Events]:\n        \"\"\"\n        Create pair of Ignite events from a event prefix name\n        \"\"\"\n        event = event.upper()\n        event_prefix = {\"\": \"\", \"ENGINE\": \"\", \"EPOCH\": \"EPOCH_\", \"ITERATION\": \"ITERATION_\", \"BATCH\": \"GET_BATCH_\"}\n        return self.get_event(event_prefix[event] + \"STARTED\"), self.get_event(event_prefix[event] + \"COMPLETED\")\n\n    def get_event(self, event: Union[str, Events]) -> Events:\n        return Events[event.upper()] if isinstance(event, str) else event\n\n    def attach(self, engine: Engine) -> None:\n        \"\"\"\n        Attach an NVTX Range to specific Ignite events\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n        \"\"\"\n        engine.add_event_handler(self.events[0], self.range_push)\n        engine.add_event_handler(self.events[1], self.range_pop)\n\n    def range_push(self):\n        self.depth = _nvtx.rangePushA(self.msg)\n\n    def range_pop(self):\n        _nvtx.rangePop()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/nvtx_handlers.py_RangePushHandler_RangePushHandler.range_push.self.depth._nvtx_rangePushA_self_msg": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/nvtx_handlers.py_RangePushHandler_RangePushHandler.range_push.self.depth._nvtx_rangePushA_self_msg", "embedding": null, "metadata": {"file_path": "monai/handlers/nvtx_handlers.py", "file_name": "nvtx_handlers.py", "file_type": "text/x-python", "category": "implementation", "start_line": 104, "end_line": 129, "span_ids": ["RangePushHandler.attach", "RangePushHandler.range_push", "RangePushHandler.__init__", "RangePushHandler"], "tokens": 199}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RangePushHandler:\n    \"\"\"\n    At a specific event, pushes a range onto a stack of nested range span.\n    Stores zero-based depth of the range that is started.\n\n    Args:\n        msg: ASCII message to associate with range\n    \"\"\"\n\n    def __init__(self, event: Union[str, Events], msg: Optional[str] = None) -> None:\n        self.event = Events[event.upper()] if isinstance(event, str) else event\n        if msg is None:\n            msg = self.event.name\n        self.msg = msg\n        self.depth = None\n\n    def attach(self, engine: Engine) -> None:\n        \"\"\"\n        Push an NVTX range at a specific Ignite event\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n        \"\"\"\n        engine.add_event_handler(self.event, self.range_push)\n\n    def range_push(self):\n        self.depth = _nvtx.rangePushA(self.msg)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/nvtx_handlers.py_RangePopHandler_RangePopHandler.range_pop._nvtx_rangePop_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/nvtx_handlers.py_RangePopHandler_RangePopHandler.range_pop._nvtx_rangePop_", "embedding": null, "metadata": {"file_path": "monai/handlers/nvtx_handlers.py", "file_name": "nvtx_handlers.py", "file_type": "text/x-python", "category": "implementation", "start_line": 132, "end_line": 153, "span_ids": ["RangePopHandler.__init__", "RangePopHandler.range_pop", "RangePopHandler.attach", "RangePopHandler"], "tokens": 155}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RangePopHandler:\n    \"\"\"\n    At a specific event, pop a previously pushed range.\n    Stores zero-based depth of the range that is started.\n\n    Args:\n        msg: ASCII message to associate with range\n    \"\"\"\n\n    def __init__(self, event: Union[str, Events]) -> None:\n        self.event = Events[event.upper()] if isinstance(event, str) else event\n\n    def attach(self, engine: Engine) -> None:\n        \"\"\"\n        Pop an NVTX range at a specific Ignite event\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n        \"\"\"\n        engine.add_event_handler(self.event, self.range_pop)\n\n    def range_pop(self):\n        _nvtx.rangePop()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/nvtx_handlers.py_MarkHandler_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/nvtx_handlers.py_MarkHandler_", "embedding": null, "metadata": {"file_path": "monai/handlers/nvtx_handlers.py", "file_name": "nvtx_handlers.py", "file_type": "text/x-python", "category": "implementation", "start_line": 156, "end_line": 180, "span_ids": ["MarkHandler.attach", "MarkHandler.mark", "MarkHandler", "MarkHandler.__init__"], "tokens": 168}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class MarkHandler:\n    \"\"\"\n    Mark an instantaneous event that occurred at some point.\n\n    Args:\n        msg: ASCII message to associate with range\n    \"\"\"\n\n    def __init__(self, event: Union[str, Events], msg: Optional[str] = None) -> None:\n        self.event = Events[event.upper()] if isinstance(event, str) else event\n        if msg is None:\n            msg = self.event.name\n        self.msg = msg\n\n    def attach(self, engine: Engine) -> None:\n        \"\"\"\n        Add an NVTX mark to a specific Ignite event\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n        \"\"\"\n        engine.add_event_handler(self.event, self.mark)\n\n    def mark(self):\n        _nvtx.markA(self.msg)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/parameter_scheduler.py_logging_if_TYPE_CHECKING_.else_.Events___optional_impo": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/parameter_scheduler.py_logging_if_TYPE_CHECKING_.else_.Events___optional_impo", "embedding": null, "metadata": {"file_path": "monai/handlers/parameter_scheduler.py", "file_name": "parameter_scheduler.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 23, "span_ids": ["docstring"], "tokens": 114}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import logging\nfrom bisect import bisect_right\nfrom typing import TYPE_CHECKING, Callable, Dict, List, Optional, Union\n\nfrom monai.config import IgniteInfo\nfrom monai.utils import min_version, optional_import\n\nif TYPE_CHECKING:\n    from ignite.engine import Engine, Events\nelse:\n    Engine, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Engine\")\n    Events, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Events\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/parameter_scheduler.py_ParamSchedulerHandler_ParamSchedulerHandler.attach.engine_add_event_handler_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/parameter_scheduler.py_ParamSchedulerHandler_ParamSchedulerHandler.attach.engine_add_event_handler_", "embedding": null, "metadata": {"file_path": "monai/handlers/parameter_scheduler.py", "file_name": "parameter_scheduler.py", "file_type": "text/x-python", "category": "implementation", "start_line": 26, "end_line": 92, "span_ids": ["ParamSchedulerHandler", "ParamSchedulerHandler.__init__", "ParamSchedulerHandler.__call__", "ParamSchedulerHandler._get_value_calculator", "ParamSchedulerHandler.attach"], "tokens": 565}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ParamSchedulerHandler:\n    \"\"\"\n    General purpose scheduler for parameters values. By default it can schedule in a linear, exponential, step or\n    multistep function. One can also pass Callables to have customized scheduling logic.\n\n    Args:\n        parameter_setter (Callable): Function that sets the required parameter\n        value_calculator (Union[str,Callable]): Either a string ('linear', 'exponential', 'step' or 'multistep')\n         or Callable for custom logic.\n        vc_kwargs (Dict): Dictionary that stores the required parameters for the value_calculator.\n        epoch_level (bool): Whether the the step is based on epoch or iteration. Defaults to False.\n        name (Optional[str]): Identifier of logging.logger to use, if None, defaulting to ``engine.logger``.\n        event (Optional[str]): Event to which the handler attaches. Defaults to Events.ITERATION_COMPLETED.\n    \"\"\"\n\n    def __init__(\n        self,\n        parameter_setter: Callable,\n        value_calculator: Union[str, Callable],\n        vc_kwargs: Dict,\n        epoch_level: bool = False,\n        name: Optional[str] = None,\n        event=Events.ITERATION_COMPLETED,\n    ):\n        self.epoch_level = epoch_level\n        self.event = event\n\n        self._calculators = {\n            \"linear\": self._linear,\n            \"exponential\": self._exponential,\n            \"step\": self._step,\n            \"multistep\": self._multistep,\n        }\n\n        self._parameter_setter = parameter_setter\n        self._vc_kwargs = vc_kwargs\n        self._value_calculator = self._get_value_calculator(value_calculator=value_calculator)\n\n        self.logger = logging.getLogger(name)\n        self._name = name\n\n    def _get_value_calculator(self, value_calculator):\n        if isinstance(value_calculator, str):\n            return self._calculators[value_calculator]\n        if callable(value_calculator):\n            return value_calculator\n        raise ValueError(\n            f\"value_calculator must be either a string from {list(self._calculators.keys())} or a Callable.\"\n        )\n\n    def __call__(self, engine: Engine):\n        if self.epoch_level:\n            self._vc_kwargs[\"current_step\"] = engine.state.epoch\n        else:\n            self._vc_kwargs[\"current_step\"] = engine.state.iteration\n\n        new_value = self._value_calculator(**self._vc_kwargs)\n        self._parameter_setter(new_value)\n\n    def attach(self, engine: Engine) -> None:\n        \"\"\"\n        Args:\n            engine: Ignite Engine that is used for training.\n        \"\"\"\n        if self._name is None:\n            self.logger = engine.logger\n        engine.add_event_handler(self.event, self)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/postprocessing.py_from_typing_import_TYPE_C_if_TYPE_CHECKING_.else_.Engine___optional_impo": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/postprocessing.py_from_typing_import_TYPE_C_if_TYPE_CHECKING_.else_.Engine___optional_impo", "embedding": null, "metadata": {"file_path": "monai/handlers/postprocessing.py", "file_name": "postprocessing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 22, "span_ids": ["docstring"], "tokens": 107}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import TYPE_CHECKING, Callable\n\nfrom monai.config import IgniteInfo\nfrom monai.engines.utils import IterationEvents, engine_apply_transform\nfrom monai.utils import min_version, optional_import\n\nEvents, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Events\")\nif TYPE_CHECKING:\n    from ignite.engine import Engine\nelse:\n    Engine, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Engine\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/postprocessing.py_PostProcessing_PostProcessing.attach.if_self_event_MODEL_C.else_.engine_add_event_handler_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/postprocessing.py_PostProcessing_PostProcessing.attach.if_self_event_MODEL_C.else_.engine_add_event_handler_", "embedding": null, "metadata": {"file_path": "monai/handlers/postprocessing.py", "file_name": "postprocessing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 25, "end_line": 57, "span_ids": ["PostProcessing", "PostProcessing.attach", "PostProcessing.__init__"], "tokens": 305}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class PostProcessing:\n    \"\"\"\n    Ignite handler to execute additional post processing after the post processing in engines.\n    So users can insert other handlers between engine postprocessing and this post processing handler.\n    If using components from `monai.transforms` as the `transform`, recommend to decollate `engine.state.batch`\n    and `engine.state.batch` in the engine(set `decollate=True`) or in the `DecollateBatch` handler first.\n\n    \"\"\"\n\n    def __init__(self, transform: Callable, event: str = \"MODEL_COMPLETED\") -> None:\n        \"\"\"\n        Args:\n            transform: callable function to execute on the `engine.state.batch` and `engine.state.output`.\n                can also be composed transforms.\n            event: expected EVENT to attach the handler, should be \"MODEL_COMPLETED\" or \"ITERATION_COMPLETED\".\n                default to \"MODEL_COMPLETED\".\n\n        \"\"\"\n        self.transform = transform\n        event = event.upper()\n        if event not in (\"MODEL_COMPLETED\", \"ITERATION_COMPLETED\"):\n            raise ValueError(\"event should be `MODEL_COMPLETED` or `ITERATION_COMPLETED`.\")\n        self.event = event\n\n    def attach(self, engine: Engine) -> None:\n        \"\"\"\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n        \"\"\"\n        if self.event == \"MODEL_COMPLETED\":\n            engine.add_event_handler(IterationEvents.MODEL_COMPLETED, self)\n        else:\n            engine.add_event_handler(Events.ITERATION_COMPLETED, self)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/postprocessing.py_PostProcessing.__call___": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/postprocessing.py_PostProcessing.__call___", "embedding": null, "metadata": {"file_path": "monai/handlers/postprocessing.py", "file_name": "postprocessing.py", "file_type": "text/x-python", "category": "implementation", "start_line": 59, "end_line": 71, "span_ids": ["PostProcessing.__call__"], "tokens": 138}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class PostProcessing:\n\n    def __call__(self, engine: Engine) -> None:\n        \"\"\"\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n        \"\"\"\n        if not isinstance(engine.state.batch, list) or not isinstance(engine.state.output, list):\n            engine.state.batch, engine.state.output = engine_apply_transform(\n                batch=engine.state.batch, output=engine.state.output, transform=self.transform\n            )\n        else:\n            for i, (b, o) in enumerate(zip(engine.state.batch, engine.state.output)):\n                engine.state.batch[i], engine.state.output[i] = engine_apply_transform(b, o, self.transform)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/regression_metrics.py_from_typing_import_Callab_MeanSquaredError.__init__.super___init___metric_f": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/regression_metrics.py_from_typing_import_Callab_MeanSquaredError.__init__.super___init___metric_f", "embedding": null, "metadata": {"file_path": "monai/handlers/regression_metrics.py", "file_name": "regression_metrics.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 49, "span_ids": ["MeanSquaredError", "MeanSquaredError.__init__", "docstring"], "tokens": 460}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Callable, Union\n\nfrom monai.handlers.ignite_metric import IgniteMetric\nfrom monai.metrics import MAEMetric, MSEMetric, PSNRMetric, RMSEMetric\nfrom monai.utils import MetricReduction\n\n\nclass MeanSquaredError(IgniteMetric):\n    \"\"\"\n    Computes Mean Squared Error from full size Tensor and collects average over batch, iterations.\n    \"\"\"\n\n    def __init__(\n        self,\n        reduction: Union[MetricReduction, str] = MetricReduction.MEAN,\n        output_transform: Callable = lambda x: x,\n        save_details: bool = True,\n    ) -> None:\n        \"\"\"\n\n        Args:\n            reduction: define the mode to reduce metrics, will only execute reduction on `not-nan` values,\n                available reduction modes: {``\"none\"``, ``\"mean\"``, ``\"sum\"``, ``\"mean_batch\"``, ``\"sum_batch\"``,\n                ``\"mean_channel\"``, ``\"sum_channel\"``}, default to ``\"mean\"``. if \"none\", will not do reduction.\n            output_transform: callable to extract `y_pred` and `y` from `ignite.engine.state.output` then\n                construct `(y_pred, y)` pair, where `y_pred` and `y` can be `batch-first` Tensors or\n                lists of `channel-first` Tensors. the form of `(y_pred, y)` is required by the `update()`.\n                `engine.state` and `output_transform` inherit from the ignite concept:\n                https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial:\n                https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb.\n            save_details: whether to save metric computation details per image, for example: mean squared error of every image.\n                default to True, will save to `engine.state.metric_details` dict with the metric name as key.\n\n        See also:\n            :py:class:`monai.metrics.MSEMetric`\n        \"\"\"\n        metric_fn = MSEMetric(reduction=reduction)\n        super().__init__(metric_fn=metric_fn, output_transform=output_transform, save_details=save_details)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/regression_metrics.py_MeanAbsoluteError_MeanAbsoluteError.__init__.super___init___metric_f": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/regression_metrics.py_MeanAbsoluteError_MeanAbsoluteError.__init__.super___init___metric_f", "embedding": null, "metadata": {"file_path": "monai/handlers/regression_metrics.py", "file_name": "regression_metrics.py", "file_type": "text/x-python", "category": "implementation", "start_line": 52, "end_line": 82, "span_ids": ["MeanAbsoluteError", "MeanAbsoluteError.__init__"], "tokens": 412}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class MeanAbsoluteError(IgniteMetric):\n    \"\"\"\n    Computes Mean Absolute Error from full size Tensor and collects average over batch, iterations.\n    \"\"\"\n\n    def __init__(\n        self,\n        reduction: Union[MetricReduction, str] = MetricReduction.MEAN,\n        output_transform: Callable = lambda x: x,\n        save_details: bool = True,\n    ) -> None:\n        \"\"\"\n\n        Args:\n            reduction: define the mode to reduce metrics, will only execute reduction on `not-nan` values,\n                available reduction modes: {``\"none\"``, ``\"mean\"``, ``\"sum\"``, ``\"mean_batch\"``, ``\"sum_batch\"``,\n                ``\"mean_channel\"``, ``\"sum_channel\"``}, default to ``\"mean\"``. if \"none\", will not do reduction.\n            output_transform: callable to extract `y_pred` and `y` from `ignite.engine.state.output` then\n                construct `(y_pred, y)` pair, where `y_pred` and `y` can be `batch-first` Tensors or\n                lists of `channel-first` Tensors. the form of `(y_pred, y)` is required by the `update()`.\n                `engine.state` and `output_transform` inherit from the ignite concept:\n                https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial:\n                https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb.\n            save_details: whether to save metric computation details per image, for example: mean squared error of every image.\n                default to True, will save to `engine.state.metric_details` dict with the metric name as key.\n\n        See also:\n            :py:class:`monai.metrics.MAEMetric`\n        \"\"\"\n        metric_fn = MAEMetric(reduction=reduction)\n        super().__init__(metric_fn=metric_fn, output_transform=output_transform, save_details=save_details)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/regression_metrics.py_RootMeanSquaredError_RootMeanSquaredError.__init__.super___init___metric_f": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/regression_metrics.py_RootMeanSquaredError_RootMeanSquaredError.__init__.super___init___metric_f", "embedding": null, "metadata": {"file_path": "monai/handlers/regression_metrics.py", "file_name": "regression_metrics.py", "file_type": "text/x-python", "category": "implementation", "start_line": 85, "end_line": 115, "span_ids": ["RootMeanSquaredError.__init__", "RootMeanSquaredError"], "tokens": 415}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RootMeanSquaredError(IgniteMetric):\n    \"\"\"\n    Computes Root Mean Squared Error from full size Tensor and collects average over batch, iterations.\n    \"\"\"\n\n    def __init__(\n        self,\n        reduction: Union[MetricReduction, str] = MetricReduction.MEAN,\n        output_transform: Callable = lambda x: x,\n        save_details: bool = True,\n    ) -> None:\n        \"\"\"\n\n        Args:\n            reduction: define the mode to reduce metrics, will only execute reduction on `not-nan` values,\n                available reduction modes: {``\"none\"``, ``\"mean\"``, ``\"sum\"``, ``\"mean_batch\"``, ``\"sum_batch\"``,\n                ``\"mean_channel\"``, ``\"sum_channel\"``}, default to ``\"mean\"``. if \"none\", will not do reduction.\n            output_transform: callable to extract `y_pred` and `y` from `ignite.engine.state.output` then\n                construct `(y_pred, y)` pair, where `y_pred` and `y` can be `batch-first` Tensors or\n                lists of `channel-first` Tensors. the form of `(y_pred, y)` is required by the `update()`.\n                `engine.state` and `output_transform` inherit from the ignite concept:\n                https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial:\n                https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb.\n            save_details: whether to save metric computation details per image, for example: mean squared error of every image.\n                default to True, will save to `engine.state.metric_details` dict with the metric name as key.\n\n        See also:\n            :py:class:`monai.metrics.RMSEMetric`\n        \"\"\"\n        metric_fn = RMSEMetric(reduction=reduction)\n        super().__init__(metric_fn=metric_fn, output_transform=output_transform, save_details=save_details)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/stats_handler.py_StatsHandler._default_epoch_print_StatsHandler._default_epoch_print.if_self_state_attributes_.self_logger_info_out_str_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/stats_handler.py_StatsHandler._default_epoch_print_StatsHandler._default_epoch_print.if_self_state_attributes_.self_logger_info_out_str_", "embedding": null, "metadata": {"file_path": "monai/handlers/stats_handler.py", "file_name": "stats_handler.py", "file_type": "text/x-python", "category": "implementation", "start_line": 160, "end_line": 194, "span_ids": ["StatsHandler._default_epoch_print"], "tokens": 350}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class StatsHandler:\n\n    def _default_epoch_print(self, engine: Engine) -> None:\n        \"\"\"\n        Execute epoch level log operation.\n        Default to print the values from Ignite `engine.state.metrics` dict and\n        print the values of specified attributes of `engine.state`.\n\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n\n        \"\"\"\n        current_epoch = self.global_epoch_transform(engine.state.epoch)\n\n        prints_dict = engine.state.metrics\n        if prints_dict is not None and len(prints_dict) > 0:\n            out_str = f\"Epoch[{current_epoch}] Metrics -- \"\n            for name in sorted(prints_dict):\n                value = prints_dict[name]\n                out_str += self.key_var_format.format(name, value) if is_scalar(value) else f\"{name}: {str(value)}\"\n            self.logger.info(out_str)\n\n        if (\n            hasattr(engine.state, \"key_metric_name\")\n            and hasattr(engine.state, \"best_metric\")\n            and hasattr(engine.state, \"best_metric_epoch\")\n        ):\n            out_str = f\"Key metric: {engine.state.key_metric_name} \"  # type: ignore\n            out_str += f\"best value: {engine.state.best_metric} \"  # type: ignore\n            out_str += f\"at epoch: {engine.state.best_metric_epoch}\"  # type: ignore\n            self.logger.info(out_str)\n\n        if self.state_attributes is not None and len(self.state_attributes) > 0:\n            out_str = \"State values: \"\n            for attr in self.state_attributes:\n                out_str += f\"{attr}: {getattr(engine.state, attr, None)} \"\n            self.logger.info(out_str)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/tensorboard_handlers.py_TensorBoardStatsHandler_TensorBoardStatsHandler._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/tensorboard_handlers.py_TensorBoardStatsHandler_TensorBoardStatsHandler._", "embedding": null, "metadata": {"file_path": "monai/handlers/tensorboard_handlers.py", "file_name": "tensorboard_handlers.py", "file_type": "text/x-python", "category": "implementation", "start_line": 64, "end_line": 80, "span_ids": ["TensorBoardStatsHandler"], "tokens": 187}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TensorBoardStatsHandler(TensorBoardHandler):\n    \"\"\"\n    TensorBoardStatsHandler defines a set of Ignite Event-handlers for all the TensorBoard logics.\n    It can be used for any Ignite Engine(trainer, validator and evaluator).\n    And it can support both epoch level and iteration level with pre-defined TensorBoard event writer.\n    The expected data source is Ignite ``engine.state.output`` and ``engine.state.metrics``.\n\n    Default behaviors:\n        - When EPOCH_COMPLETED, write each dictionary item in\n          ``engine.state.metrics`` to TensorBoard.\n        - When ITERATION_COMPLETED, write each dictionary item in\n          ``self.output_transform(engine.state.output)`` to TensorBoard.\n\n    Usage example is available in the tutorial:\n    https://github.com/Project-MONAI/tutorials/blob/master/3d_segmentation/unet_segmentation_3d_ignite.ipynb.\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/tensorboard_handlers.py_TensorBoardStatsHandler.__init___TensorBoardStatsHandler.__init__.self.tag_name.tag_name": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/tensorboard_handlers.py_TensorBoardStatsHandler.__init___TensorBoardStatsHandler.__init__.self.tag_name.tag_name", "embedding": null, "metadata": {"file_path": "monai/handlers/tensorboard_handlers.py", "file_name": "tensorboard_handlers.py", "file_type": "text/x-python", "category": "implementation", "start_line": 83, "end_line": 131, "span_ids": ["TensorBoardStatsHandler.__init__"], "tokens": 652}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TensorBoardStatsHandler(TensorBoardHandler):\n\n    def __init__(\n        self,\n        summary_writer: Optional[SummaryWriter] = None,\n        log_dir: str = \"./runs\",\n        epoch_event_writer: Optional[Callable[[Engine, SummaryWriter], Any]] = None,\n        epoch_interval: int = 1,\n        iteration_event_writer: Optional[Callable[[Engine, SummaryWriter], Any]] = None,\n        iteration_interval: int = 1,\n        output_transform: Callable = lambda x: x[0],\n        global_epoch_transform: Callable = lambda x: x,\n        state_attributes: Optional[Sequence[str]] = None,\n        tag_name: str = DEFAULT_TAG,\n    ) -> None:\n        \"\"\"\n        Args:\n            summary_writer: user can specify TensorBoard or TensorBoardX SummaryWriter,\n                default to create a new TensorBoard writer.\n            log_dir: if using default SummaryWriter, write logs to this directory, default is `./runs`.\n            epoch_event_writer: customized callable TensorBoard writer for epoch level.\n                Must accept parameter \"engine\" and \"summary_writer\", use default event writer if None.\n            epoch_interval: the epoch interval at which the epoch_event_writer is called. Defaults to 1.\n            iteration_event_writer: customized callable TensorBoard writer for iteration level.\n                Must accept parameter \"engine\" and \"summary_writer\", use default event writer if None.\n            iteration_interval: the iteration interval at which the iteration_event_writer is called. Defaults to 1.\n            output_transform: a callable that is used to transform the\n                ``ignite.engine.state.output`` into a scalar to plot, or a dictionary of {key: scalar}.\n                In the latter case, the output string will be formatted as key: value.\n                By default this value plotting happens when every iteration completed.\n                The default behavior is to print loss from output[0] as output is a decollated list\n                and we replicated loss value for every item of the decollated list.\n                `engine.state` and `output_transform` inherit from the ignite concept:\n                https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial:\n                https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb.\n            global_epoch_transform: a callable that is used to customize global epoch number.\n                For example, in evaluation, the evaluator engine might want to use trainer engines epoch number\n                when plotting epoch vs metric curves.\n            state_attributes: expected attributes from `engine.state`, if provided, will extract them\n                when epoch completed.\n            tag_name: when iteration output is a scalar, tag_name is used to plot, defaults to ``'Loss'``.\n        \"\"\"\n        super().__init__(summary_writer=summary_writer, log_dir=log_dir)\n        self.epoch_event_writer = epoch_event_writer\n        self.epoch_interval = epoch_interval\n        self.iteration_event_writer = iteration_event_writer\n        self.iteration_interval = iteration_interval\n        self.output_transform = output_transform\n        self.global_epoch_transform = global_epoch_transform\n        self.state_attributes = state_attributes\n        self.tag_name = tag_name", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/utils.py_from_engine_from_engine.keys.ensure_tuple_keys_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/utils.py_from_engine_from_engine.keys.ensure_tuple_keys_", "embedding": null, "metadata": {"file_path": "monai/handlers/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 155, "end_line": 184, "span_ids": ["from_engine"], "tokens": 348}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def from_engine(keys: KeysCollection, first: bool = False):\n    \"\"\"\n    Utility function to simplify the `batch_transform` or `output_transform` args of ignite components\n    when handling dictionary or list of dictionaries(for example: `engine.state.batch` or `engine.state.output`).\n    Users only need to set the expected keys, then it will return a callable function to extract data from\n    dictionary and construct a tuple respectively.\n\n    If data is a list of dictionaries after decollating, extract expected keys and construct lists respectively,\n    for example, if data is `[{\"A\": 1, \"B\": 2}, {\"A\": 3, \"B\": 4}]`, from_engine([\"A\", \"B\"]): `([1, 3], [2, 4])`.\n\n    It can help avoid a complicated `lambda` function and make the arg of metrics more straight-forward.\n    For example, set the first key as the prediction and the second key as label to get the expected data\n    from `engine.state.output` for a metric::\n\n        from monai.handlers import MeanDice, from_engine\n\n        metric = MeanDice(\n            include_background=False,\n            output_transform=from_engine([\"pred\", \"label\"])\n        )\n\n    Args:\n        keys: specified keys to extract data from dictionary or decollated list of dictionaries.\n        first: whether only extract specified keys from the first item if input data is a list of dictionaries,\n            it's used to extract the scalar data which doesn't have batch dim and was replicated into every\n            dictionary when decollating, like `loss`, etc.\n\n\n    \"\"\"\n    keys = ensure_tuple(keys)\n    # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/utils.py_from_engine._wrapper_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/utils.py_from_engine._wrapper_", "embedding": null, "metadata": {"file_path": "monai/handlers/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 186, "end_line": 196, "span_ids": ["from_engine"], "tokens": 142}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def from_engine(keys: KeysCollection, first: bool = False):\n    # ... other code\n\n    def _wrapper(data):\n        if isinstance(data, dict):\n            return tuple(data[k] for k in keys)\n        if isinstance(data, list) and isinstance(data[0], dict):\n            # if data is a list of dictionaries, extract expected keys and construct lists,\n            # if `first=True`, only extract keys from the first item of the list\n            ret = [data[0][k] if first else [i[k] for i in data] for k in keys]\n            return tuple(ret) if len(ret) > 1 else ret[0]\n\n    return _wrapper", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/dice.py_GeneralizedWassersteinDiceLoss._compute_generalized_true_positive_GeneralizedWassersteinDiceLoss._compute_generalized_true_positive.return.torch_sum_alpha_extended_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/dice.py_GeneralizedWassersteinDiceLoss._compute_generalized_true_positive_GeneralizedWassersteinDiceLoss._compute_generalized_true_positive.return.torch_sum_alpha_extended_", "embedding": null, "metadata": {"file_path": "monai/losses/dice.py", "file_name": "dice.py", "file_type": "text/x-python", "category": "implementation", "start_line": 524, "end_line": 539, "span_ids": ["GeneralizedWassersteinDiceLoss._compute_generalized_true_positive"], "tokens": 206}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GeneralizedWassersteinDiceLoss(_Loss):\n\n    def _compute_generalized_true_positive(\n        self, alpha: torch.Tensor, flat_target: torch.Tensor, wasserstein_distance_map: torch.Tensor\n    ) -> torch.Tensor:\n        \"\"\"\n        Args:\n            alpha: generalised number of true positives of target class.\n            flat_target: the target tensor.\n            wasserstein_distance_map: the map obtained from the above function.\n        \"\"\"\n        # Extend alpha to a map and select value at each voxel according to flat_target\n        alpha_extended = torch.unsqueeze(alpha, dim=2)\n        alpha_extended = alpha_extended.expand((flat_target.size(0), self.num_classes, flat_target.size(1)))\n        flat_target_extended = torch.unsqueeze(flat_target, dim=1)\n        alpha_extended = torch.gather(alpha_extended, index=flat_target_extended, dim=1)\n\n        return torch.sum(alpha_extended * (1.0 - wasserstein_distance_map), dim=[1, 2])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/dice.py_GeneralizedWassersteinDiceLoss._compute_denominator_GeneralizedWassersteinDiceLoss._compute_denominator.return.torch_sum_alpha_extended_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/dice.py_GeneralizedWassersteinDiceLoss._compute_denominator_GeneralizedWassersteinDiceLoss._compute_denominator.return.torch_sum_alpha_extended_", "embedding": null, "metadata": {"file_path": "monai/losses/dice.py", "file_name": "dice.py", "file_type": "text/x-python", "category": "implementation", "start_line": 541, "end_line": 556, "span_ids": ["GeneralizedWassersteinDiceLoss._compute_denominator"], "tokens": 204}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GeneralizedWassersteinDiceLoss(_Loss):\n\n    def _compute_denominator(\n        self, alpha: torch.Tensor, flat_target: torch.Tensor, wasserstein_distance_map: torch.Tensor\n    ) -> torch.Tensor:\n        \"\"\"\n        Args:\n            alpha: generalised number of true positives of target class.\n            flat_target: the target tensor.\n            wasserstein_distance_map: the map obtained from the above function.\n        \"\"\"\n        # Extend alpha to a map and select value at each voxel according to flat_target\n        alpha_extended = torch.unsqueeze(alpha, dim=2)\n        alpha_extended = alpha_extended.expand((flat_target.size(0), self.num_classes, flat_target.size(1)))\n        flat_target_extended = torch.unsqueeze(flat_target, dim=1)\n        alpha_extended = torch.gather(alpha_extended, index=flat_target_extended, dim=1)\n\n        return torch.sum(alpha_extended * (2.0 - wasserstein_distance_map), dim=[1, 2])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/dice.py_DiceCELoss.ce_DiceCELoss.ce.return.self_cross_entropy_input_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/dice.py_DiceCELoss.ce_DiceCELoss.ce.return.self_cross_entropy_input_", "embedding": null, "metadata": {"file_path": "monai/losses/dice.py", "file_name": "dice.py", "file_type": "text/x-python", "category": "implementation", "start_line": 662, "end_line": 676, "span_ids": ["DiceCELoss.ce"], "tokens": 168}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DiceCELoss(_Loss):\n\n    def ce(self, input: torch.Tensor, target: torch.Tensor):\n        \"\"\"\n        Compute CrossEntropy loss for the input and target.\n        Will remove the channel dim according to PyTorch CrossEntropyLoss:\n        https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html?#torch.nn.CrossEntropyLoss.\n\n        \"\"\"\n        n_pred_ch, n_target_ch = input.shape[1], target.shape[1]\n        if n_pred_ch == n_target_ch:\n            # target is in the one-hot format, convert to BH[WD] format to calculate ce loss\n            target = torch.argmax(target, dim=1)\n        else:\n            target = torch.squeeze(target, dim=1)\n        target = target.long()\n        return self.cross_entropy(input, target)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/image_dissimilarity.py_from_typing_import_Option_kernel_dict._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/image_dissimilarity.py_from_typing_import_Option_kernel_dict._", "embedding": null, "metadata": {"file_path": "monai/losses/image_dissimilarity.py", "file_name": "image_dissimilarity.py", "file_type": "text/x-python", "category": "implementation", "start_line": 11, "end_line": 47, "span_ids": ["make_rectangular_kernel", "impl", "make_gaussian_kernel", "make_triangular_kernel", "docstring"], "tokens": 306}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Optional, Tuple, Union\n\nimport torch\nfrom torch.nn import functional as F\nfrom torch.nn.modules.loss import _Loss\n\nfrom monai.networks.layers import gaussian_1d, separable_filtering\nfrom monai.utils import LossReduction, deprecated_arg\nfrom monai.utils.module import look_up_option\n\n\ndef make_rectangular_kernel(kernel_size: int) -> torch.Tensor:\n    return torch.ones(kernel_size)\n\n\ndef make_triangular_kernel(kernel_size: int) -> torch.Tensor:\n    fsize = (kernel_size + 1) // 2\n    if fsize % 2 == 0:\n        fsize -= 1\n    f = torch.ones((1, 1, fsize), dtype=torch.float).div(fsize)\n    padding = (kernel_size - fsize) // 2 + fsize // 2\n    return F.conv1d(f, f, padding=padding).reshape(-1)\n\n\ndef make_gaussian_kernel(kernel_size: int) -> torch.Tensor:\n    sigma = torch.tensor(kernel_size / 3.0)\n    kernel = gaussian_1d(sigma=sigma, truncated=kernel_size // 2, approx=\"sampled\", normalize=False) * (\n        2.5066282 * sigma\n    )\n    return kernel[:kernel_size]\n\n\nkernel_dict = {\n    \"rectangular\": make_rectangular_kernel,\n    \"triangular\": make_triangular_kernel,\n    \"gaussian\": make_gaussian_kernel,\n}", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/image_dissimilarity.py_GlobalMutualInformationLoss.parzen_windowing_GlobalMutualInformationLoss.parzen_windowing.return.pred_weight_pred_probabi": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/image_dissimilarity.py_GlobalMutualInformationLoss.parzen_windowing_GlobalMutualInformationLoss.parzen_windowing.return.pred_weight_pred_probabi", "embedding": null, "metadata": {"file_path": "monai/losses/image_dissimilarity.py", "file_name": "image_dissimilarity.py", "file_type": "text/x-python", "category": "implementation", "start_line": 225, "end_line": 238, "span_ids": ["GlobalMutualInformationLoss.parzen_windowing"], "tokens": 199}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GlobalMutualInformationLoss(_Loss):\n\n    def parzen_windowing(\n        self, pred: torch.Tensor, target: torch.Tensor\n    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:\n        if self.kernel_type == \"gaussian\":\n            pred_weight, pred_probability = self.parzen_windowing_gaussian(pred)\n            target_weight, target_probability = self.parzen_windowing_gaussian(target)\n        elif self.kernel_type == \"b-spline\":\n            # a third order BSpline kernel is used for the pred image intensity PDF.\n            pred_weight, pred_probability = self.parzen_windowing_b_spline(pred, order=3)\n            # a zero order (box car) BSpline kernel is used for the target image intensity PDF.\n            target_weight, target_probability = self.parzen_windowing_b_spline(target, order=0)\n        else:\n            raise ValueError\n        return pred_weight, pred_probability, target_weight, target_probability", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/image_dissimilarity.py_GlobalMutualInformationLoss.parzen_windowing_b_spline_GlobalMutualInformationLoss.parzen_windowing_b_spline.return.weight_probability": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/image_dissimilarity.py_GlobalMutualInformationLoss.parzen_windowing_b_spline_GlobalMutualInformationLoss.parzen_windowing_b_spline.return.weight_probability", "embedding": null, "metadata": {"file_path": "monai/losses/image_dissimilarity.py", "file_name": "image_dissimilarity.py", "file_type": "text/x-python", "category": "implementation", "start_line": 240, "end_line": 291, "span_ids": ["GlobalMutualInformationLoss.parzen_windowing_b_spline"], "tokens": 758}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GlobalMutualInformationLoss(_Loss):\n\n    def parzen_windowing_b_spline(self, img: torch.Tensor, order: int) -> Tuple[torch.Tensor, torch.Tensor]:\n        \"\"\"\n        Parzen windowing with b-spline kernel (adapted from ITK)\n\n        Args:\n            img: the shape should be B[NDHW].\n            order: int.\n        \"\"\"\n\n        # Compute binsize for the histograms.\n        #\n        # The binsize for the image intensities needs to be adjusted so that\n        # we can avoid dealing with boundary conditions using the cubic\n        # spline as the Parzen window.  We do this by increasing the size\n        # of the bins so that the joint histogram becomes \"padded\" at the\n        # borders. Because we are changing the binsize,\n        # we also need to shift the minimum by the padded amount in order to\n        # avoid minimum values filling in our padded region.\n        #\n        # Note that there can still be non-zero bin values in the padded region,\n        # it's just that these bins will never be a central bin for the Parzen\n        # window.\n        _max, _min = torch.max(img), torch.min(img)\n        padding = 2\n        bin_size = (_max - _min) / (self.num_bins - 2 * padding)\n        norm_min = torch.div(_min, bin_size) - padding\n\n        # assign bin/window index to each voxel\n        window_term = torch.div(img, bin_size) - norm_min  # B[NDHW]\n        # make sure the extreme values are in valid (non-padded) bins\n        window_term = torch.clamp(window_term, padding, self.num_bins - padding - 1)  # B[NDHW]\n        window_term = window_term.reshape(window_term.shape[0], -1, 1)  # (batch, num_sample, 1)\n        bins = torch.arange(self.num_bins, device=window_term.device).reshape(1, 1, -1)  # (1, 1, num_bins)\n        sample_bin_matrix = torch.abs(bins - window_term)  # (batch, num_sample, num_bins)\n\n        # b-spleen kernel\n        # (4 - 6 * abs ** 2 + 3 * abs ** 3) / 6 when 0 <= abs < 1\n        # (2 - abs) ** 3 / 6 when 1 <= abs < 2\n        weight = torch.zeros_like(sample_bin_matrix, dtype=torch.float)  # (batch, num_sample, num_bins)\n        if order == 0:\n            weight = weight + (sample_bin_matrix < 0.5) + (sample_bin_matrix == 0.5) * 0.5\n        elif order == 3:\n            weight = (\n                weight + (4 - 6 * sample_bin_matrix ** 2 + 3 * sample_bin_matrix ** 3) * (sample_bin_matrix < 1) / 6\n            )\n            weight = weight + (2 - sample_bin_matrix) ** 3 * (sample_bin_matrix >= 1) * (sample_bin_matrix < 2) / 6\n        else:\n            raise ValueError(f\"Do not support b-spline {order}-order parzen windowing\")\n\n        weight = weight / torch.sum(weight, dim=-1, keepdim=True)  # (batch, num_sample, num_bins)\n        probability = torch.mean(weight, dim=-2, keepdim=True)  # (batch, 1, num_bins)\n        return weight, probability", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/image_dissimilarity.py_GlobalMutualInformationLoss.parzen_windowing_gaussian_GlobalMutualInformationLoss.parzen_windowing_gaussian.return.weight_probability": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/image_dissimilarity.py_GlobalMutualInformationLoss.parzen_windowing_gaussian_GlobalMutualInformationLoss.parzen_windowing_gaussian.return.weight_probability", "embedding": null, "metadata": {"file_path": "monai/losses/image_dissimilarity.py", "file_name": "image_dissimilarity.py", "file_type": "text/x-python", "category": "implementation", "start_line": 293, "end_line": 307, "span_ids": ["GlobalMutualInformationLoss.parzen_windowing_gaussian"], "tokens": 225}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GlobalMutualInformationLoss(_Loss):\n\n    def parzen_windowing_gaussian(self, img: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:\n        \"\"\"\n        Parzen windowing with gaussian kernel (adapted from DeepReg implementation)\n        Note: the input is expected to range between 0 and 1\n        Args:\n            img: the shape should be B[NDHW].\n        \"\"\"\n        img = torch.clamp(img, 0, 1)\n        img = img.reshape(img.shape[0], -1, 1)  # (batch, num_sample, 1)\n        weight = torch.exp(\n            -self.preterm.to(img) * (img - self.bin_centers.to(img)) ** 2\n        )  # (batch, num_sample, num_bin)\n        weight = weight / torch.sum(weight, dim=-1, keepdim=True)  # (batch, num_sample, num_bin)\n        probability = torch.mean(weight, dim=-2, keepdim=True)  # (batch, 1, num_bin)\n        return weight, probability", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/multi_scale.py_from_typing_import_List__kernel_fn_dict._gaussian_make_gaussia": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/multi_scale.py_from_typing_import_List__kernel_fn_dict._gaussian_make_gaussia", "embedding": null, "metadata": {"file_path": "monai/losses/multi_scale.py", "file_name": "multi_scale.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 37, "span_ids": ["make_gaussian_kernel", "make_cauchy_kernel", "impl", "docstring"], "tokens": 226}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import List, Optional, Union\n\nimport torch\nfrom torch.nn.modules.loss import _Loss\n\nfrom monai.networks.layers import gaussian_1d, separable_filtering\nfrom monai.utils import LossReduction\n\n\ndef make_gaussian_kernel(sigma: int) -> torch.Tensor:\n    if sigma <= 0:\n        raise ValueError(f\"expecting positive sigma, got sigma={sigma}\")\n    return gaussian_1d(sigma=torch.tensor(sigma), truncated=3, approx=\"sampled\", normalize=False)\n\n\ndef make_cauchy_kernel(sigma: int) -> torch.Tensor:\n    if sigma <= 0:\n        raise ValueError(f\"expecting positive sigma, got sigma={sigma}\")\n    tail = int(sigma * 5)\n    k = torch.tensor([((x / sigma) ** 2 + 1) for x in range(-tail, tail + 1)])\n    k = torch.reciprocal(k)\n    k = k / torch.sum(k)\n    return k\n\n\nkernel_fn_dict = {\"gaussian\": make_gaussian_kernel, \"cauchy\": make_cauchy_kernel}", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/confusion_matrix.py_ConfusionMatrixMetric._compute_tensor_ConfusionMatrixMetric._compute_tensor.return.get_confusion_matrix_y_pr": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/confusion_matrix.py_ConfusionMatrixMetric._compute_tensor_ConfusionMatrixMetric._compute_tensor.return.get_confusion_matrix_y_pr", "embedding": null, "metadata": {"file_path": "monai/metrics/confusion_matrix.py", "file_name": "confusion_matrix.py", "file_type": "text/x-python", "category": "implementation", "start_line": 75, "end_line": 102, "span_ids": ["ConfusionMatrixMetric._compute_tensor"], "tokens": 331}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ConfusionMatrixMetric(CumulativeIterationMetric):\n\n    def _compute_tensor(self, y_pred: torch.Tensor, y: torch.Tensor):  # type: ignore\n        \"\"\"\n        Args:\n            y_pred: input data to compute. It must be one-hot format and first dim is batch.\n                The values should be binarized.\n            y: ground truth to compute the metric. It must be one-hot format and first dim is batch.\n                The values should be binarized.\n        Raises:\n            ValueError: when `y` is not a binarized tensor.\n            ValueError: when `y_pred` has less than two dimensions.\n        \"\"\"\n        if not isinstance(y_pred, torch.Tensor) or not isinstance(y, torch.Tensor):\n            raise ValueError(\"y_pred and y must be PyTorch Tensor.\")\n        # check binarized input\n        if not torch.all(y_pred.byte() == y_pred):\n            warnings.warn(\"y_pred should be a binarized tensor.\")\n        if not torch.all(y.byte() == y):\n            raise ValueError(\"y should be a binarized tensor.\")\n        # check dimension\n        dims = y_pred.ndimension()\n        if dims < 2:\n            raise ValueError(\"y_pred should have at least two dimensions.\")\n        if dims == 2 or (dims == 3 and y_pred.shape[-1] == 1):\n            if self.compute_sample:\n                warnings.warn(\"As for classification task, compute_sample should be False.\")\n                self.compute_sample = False\n\n        return get_confusion_matrix(y_pred=y_pred, y=y, include_background=self.include_background)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/metric.py_CumulativeIterationMetric_CumulativeIterationMetric._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/metric.py_CumulativeIterationMetric_CumulativeIterationMetric._", "embedding": null, "metadata": {"file_path": "monai/metrics/metric.py", "file_name": "metric.py", "file_type": "text/x-python", "category": "implementation", "start_line": 282, "end_line": 310, "span_ids": ["CumulativeIterationMetric"], "tokens": 260}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CumulativeIterationMetric(Cumulative, IterationMetric):\n    \"\"\"\n    Base class of cumulative metric which collects metrics on each mini-batch data at the iteration level.\n\n    Typically, it computes some intermediate results for each iteration, adds them to the buffers,\n    then the buffer contents could be gathered and aggregated for the final result when epoch completed.\n\n    For example, `MeanDice` inherits this class and the usage is as follows:\n\n    .. code-block:: python\n\n        dice_metric = DiceMetric(include_background=True, reduction=\"mean\")\n\n        for val_data in val_loader:\n            val_outputs = model(val_data[\"img\"])\n            val_outputs = [postprocessing_transform(i) for i in decollate_batch(val_outputs)]\n            # compute metric for current iteration\n            dice_metric(y_pred=val_outputs, y=val_data[\"seg\"])  # callable to add metric to the buffer\n\n        # aggregate the final mean dice result\n        metric = dice_metric.aggregate().item()\n\n        # reset the status for next computation round\n        dice_metric.reset()\n\n    And to load `predictions` and `labels` from files, then compute metrics with multi-processing, please refer to:\n    https://github.com/Project-MONAI/tutorials/blob/master/modules/compute_metric.py.\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/metric.py_CumulativeIterationMetric.__call___": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/metric.py_CumulativeIterationMetric.__call___", "embedding": null, "metadata": {"file_path": "monai/metrics/metric.py", "file_name": "metric.py", "file_type": "text/x-python", "category": "implementation", "start_line": 312, "end_line": 335, "span_ids": ["CumulativeIterationMetric.__call__"], "tokens": 235}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CumulativeIterationMetric(Cumulative, IterationMetric):\n\n    def __call__(self, y_pred: TensorOrList, y: Optional[TensorOrList] = None):  # type: ignore\n        \"\"\"\n        Execute basic computation for model prediction and ground truth.\n        It can support  both `list of channel-first Tensor` and `batch-first Tensor`.\n        Users call this API to execute computation on every batch of data, then accumulate the results,\n        or accumulate the original `y_pred` and `y`, then execute on the accumulated data.\n\n        Args:\n            y_pred: the model prediction data to compute, must be a list of `channel-first` Tensor\n                or a `batch-first` Tensor.\n            y: the ground truth to compute, must be a list of `channel-first` Tensor\n                or a `batch-first` Tensor.\n\n        Returns:\n            The computed metric values at the iteration level.\n        \"\"\"\n        ret = super().__call__(y_pred=y_pred, y=y)\n        if isinstance(ret, (tuple, list)):\n            self.extend(*ret)\n        else:\n            self.extend(ret)\n\n        return ret", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/regression.py_MSEMetric_MSEMetric._compute_metric.return.compute_mean_error_metric": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/regression.py_MSEMetric_MSEMetric._compute_metric.return.compute_mean_error_metric", "embedding": null, "metadata": {"file_path": "monai/metrics/regression.py", "file_name": "regression.py", "file_type": "text/x-python", "category": "implementation", "start_line": 75, "end_line": 104, "span_ids": ["MSEMetric", "MSEMetric._compute_metric", "MSEMetric.__init__"], "tokens": 385}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class MSEMetric(RegressionMetric):\n    r\"\"\"Compute Mean Squared Error between two tensors using function:\n\n    .. math::\n        \\operatorname {MSE}\\left(Y, \\hat{Y}\\right) =\\frac {1}{n}\\sum _{i=1}^{n}\\left(y_i-\\hat{y_i} \\right)^{2}.\n\n    More info: https://en.wikipedia.org/wiki/Mean_squared_error\n\n    Input `y_pred` is compared with ground truth `y`.\n    Both `y_pred` and `y` are expected to be real-valued, where `y_pred` is output from a regression model.\n\n    Args:\n        reduction: define the mode to reduce metrics, will only execute reduction on `not-nan` values,\n            available reduction modes: {``\"none\"``, ``\"mean\"``, ``\"sum\"``, ``\"mean_batch\"``, ``\"sum_batch\"``,\n            ``\"mean_channel\"``, ``\"sum_channel\"``}, default to ``\"mean\"``. if \"none\", will not do reduction.\n        get_not_nans: whether to return the `not_nans` count, if True, aggregate() returns (metric, not_nans).\n\n    \"\"\"\n\n    def __init__(\n        self, reduction: Union[MetricReduction, str] = MetricReduction.MEAN, get_not_nans: bool = False\n    ) -> None:\n        super().__init__(reduction=reduction, get_not_nans=get_not_nans)\n        self.sq_func = partial(torch.pow, exponent=2.0)\n\n    def _compute_metric(self, y_pred: torch.Tensor, y: torch.Tensor) -> torch.Tensor:\n        y_pred = y_pred.float()\n        y = y.float()\n\n        return compute_mean_error_metrics(y_pred, y, func=self.sq_func)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/regression.py_MAEMetric_MAEMetric._compute_metric.return.compute_mean_error_metric": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/regression.py_MAEMetric_MAEMetric._compute_metric.return.compute_mean_error_metric", "embedding": null, "metadata": {"file_path": "monai/metrics/regression.py", "file_name": "regression.py", "file_type": "text/x-python", "category": "implementation", "start_line": 107, "end_line": 136, "span_ids": ["MAEMetric._compute_metric", "MAEMetric.__init__", "MAEMetric"], "tokens": 373}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class MAEMetric(RegressionMetric):\n    r\"\"\"Compute Mean Absolute Error between two tensors using function:\n\n    .. math::\n        \\operatorname {MAE}\\left(Y, \\hat{Y}\\right) =\\frac {1}{n}\\sum _{i=1}^{n}\\left|y_i-\\hat{y_i}\\right|.\n\n    More info: https://en.wikipedia.org/wiki/Mean_absolute_error\n\n    Input `y_pred` is compared with ground truth `y`.\n    Both `y_pred` and `y` are expected to be real-valued, where `y_pred` is output from a regression model.\n\n    Args:\n        reduction: define the mode to reduce metrics, will only execute reduction on `not-nan` values,\n            available reduction modes: {``\"none\"``, ``\"mean\"``, ``\"sum\"``, ``\"mean_batch\"``, ``\"sum_batch\"``,\n            ``\"mean_channel\"``, ``\"sum_channel\"``}, default to ``\"mean\"``. if \"none\", will not do reduction.\n        get_not_nans: whether to return the `not_nans` count, if True, aggregate() returns (metric, not_nans).\n\n    \"\"\"\n\n    def __init__(\n        self, reduction: Union[MetricReduction, str] = MetricReduction.MEAN, get_not_nans: bool = False\n    ) -> None:\n        super().__init__(reduction=reduction, get_not_nans=get_not_nans)\n        self.abs_func = torch.abs\n\n    def _compute_metric(self, y_pred: torch.Tensor, y: torch.Tensor) -> torch.Tensor:\n        y_pred = y_pred.float()\n        y = y.float()\n\n        return compute_mean_error_metrics(y_pred, y, func=self.abs_func)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/regression.py_RMSEMetric_RMSEMetric._compute_metric.return.torch_sqrt_mse_out_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/regression.py_RMSEMetric_RMSEMetric._compute_metric.return.torch_sqrt_mse_out_", "embedding": null, "metadata": {"file_path": "monai/metrics/regression.py", "file_name": "regression.py", "file_type": "text/x-python", "category": "implementation", "start_line": 139, "end_line": 170, "span_ids": ["RMSEMetric.__init__", "RMSEMetric", "RMSEMetric._compute_metric"], "tokens": 423}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RMSEMetric(RegressionMetric):\n    r\"\"\"Compute Root Mean Squared Error between two tensors using function:\n\n    .. math::\n        \\operatorname {RMSE}\\left(Y, \\hat{Y}\\right) ={ \\sqrt{ \\frac {1}{n}\\sum _{i=1}^{n}\\left(y_i-\\hat{y_i}\\right)^2 } } \\\n        = \\sqrt {\\operatorname{MSE}\\left(Y, \\hat{Y}\\right)}.\n\n    More info: https://en.wikipedia.org/wiki/Root-mean-square_deviation\n\n    Input `y_pred` is compared with ground truth `y`.\n    Both `y_pred` and `y` are expected to be real-valued, where `y_pred` is output from a regression model.\n\n    Args:\n        reduction: define the mode to reduce metrics, will only execute reduction on `not-nan` values,\n            available reduction modes: {``\"none\"``, ``\"mean\"``, ``\"sum\"``, ``\"mean_batch\"``, ``\"sum_batch\"``,\n            ``\"mean_channel\"``, ``\"sum_channel\"``}, default to ``\"mean\"``. if \"none\", will not do reduction.\n        get_not_nans: whether to return the `not_nans` count, if True, aggregate() returns (metric, not_nans).\n\n    \"\"\"\n\n    def __init__(\n        self, reduction: Union[MetricReduction, str] = MetricReduction.MEAN, get_not_nans: bool = False\n    ) -> None:\n        super().__init__(reduction=reduction, get_not_nans=get_not_nans)\n        self.sq_func = partial(torch.pow, exponent=2.0)\n\n    def _compute_metric(self, y_pred: torch.Tensor, y: torch.Tensor) -> torch.Tensor:\n        y_pred = y_pred.float()\n        y = y.float()\n\n        mse_out = compute_mean_error_metrics(y_pred, y, func=self.sq_func)\n        return torch.sqrt(mse_out)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/utils.py_from_typing_import_Tuple____all__._ignore_background_do": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/utils.py_from_typing_import_Tuple____all__._ignore_background_do", "embedding": null, "metadata": {"file_path": "monai/metrics/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 25, "span_ids": ["docstring"], "tokens": 154}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Tuple, Union\n\nimport numpy as np\nimport torch\n\nfrom monai.transforms.croppad.array import SpatialCrop\nfrom monai.transforms.utils import generate_spatial_bounding_box\nfrom monai.utils import MetricReduction, look_up_option, optional_import\n\nbinary_erosion, _ = optional_import(\"scipy.ndimage.morphology\", name=\"binary_erosion\")\ndistance_transform_edt, _ = optional_import(\"scipy.ndimage.morphology\", name=\"distance_transform_edt\")\ndistance_transform_cdt, _ = optional_import(\"scipy.ndimage.morphology\", name=\"distance_transform_cdt\")\n\n__all__ = [\"ignore_background\", \"do_metric_reduction\", \"get_mask_edges\", \"get_surface_distance\"]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/__init__.py_convert_to_torchscript_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/__init__.py_convert_to_torchscript_", "embedding": null, "metadata": {"file_path": "monai/networks/__init__.py", "file_name": "__init__.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 26, "span_ids": ["docstring"], "tokens": 62}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from .utils import (\n    convert_to_torchscript,\n    copy_model_state,\n    eval_mode,\n    icnr_init,\n    normal_init,\n    normalize_transform,\n    one_hot,\n    pixelshuffle,\n    predict_segmentation,\n    slice_channels,\n    to_norm_affine,\n    train_mode,\n)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/activation.py_torch_None_1.else_.monai_swish.return.SwishImplementation_apply": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/activation.py_torch_None_1.else_.monai_swish.return.SwishImplementation_apply", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/activation.py", "file_name": "activation.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 36, "span_ids": ["docstring"], "tokens": 155}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import torch\nfrom torch import nn\n\nfrom monai.utils import optional_import\n\nif optional_import(\"torch.nn.functional\", name=\"mish\")[1]:\n\n    def monai_mish(x, inplace: bool = False):\n        return torch.nn.functional.mish(x, inplace=inplace)\n\nelse:\n\n    def monai_mish(x, inplace: bool = False):\n        return x * torch.tanh(torch.nn.functional.softplus(x))\n\n\nif optional_import(\"torch.nn.functional\", name=\"silu\")[1]:\n\n    def monai_swish(x, inplace: bool = False):\n        return torch.nn.functional.silu(x, inplace=inplace)\n\nelse:\n\n    def monai_swish(x, inplace: bool = False):\n        return SwishImplementation.apply(x)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/activation.py_Swish_Swish.forward.return.input_torch_sigmoid_sel": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/activation.py_Swish_Swish.forward.return.input_torch_sigmoid_sel", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/activation.py", "file_name": "activation.py", "file_type": "text/x-python", "category": "implementation", "start_line": 41, "end_line": 69, "span_ids": ["Swish.forward", "Swish", "Swish.__init__"], "tokens": 233}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Swish(nn.Module):\n    r\"\"\"Applies the element-wise function:\n\n    .. math::\n        \\text{Swish}(x) = x * \\text{Sigmoid}(\\alpha * x) ~~~~\\text{for constant value}~ \\alpha.\n\n    Citation: Searching for Activation Functions, Ramachandran et al., 2017, https://arxiv.org/abs/1710.05941.\n\n\n    Shape:\n        - Input: :math:`(N, *)` where `*` means, any number of additional dimensions\n        - Output: :math:`(N, *)`, same shape as the input\n\n\n    Examples::\n\n        >>> import torch\n        >>> from monai.networks.layers.factories import Act\n        >>> m = Act['swish']()\n        >>> input = torch.randn(2)\n        >>> output = m(input)\n    \"\"\"\n\n    def __init__(self, alpha=1.0):\n        super().__init__()\n        self.alpha = alpha\n\n    def forward(self, input: torch.Tensor) -> torch.Tensor:\n        return input * torch.sigmoid(self.alpha * input)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/activation.py_MemoryEfficientSwish_MemoryEfficientSwish.forward.return.monai_swish_input_self_i": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/activation.py_MemoryEfficientSwish_MemoryEfficientSwish.forward.return.monai_swish_input_self_i", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/activation.py", "file_name": "activation.py", "file_type": "text/x-python", "category": "implementation", "start_line": 93, "end_line": 130, "span_ids": ["MemoryEfficientSwish", "MemoryEfficientSwish.forward", "MemoryEfficientSwish.__init__"], "tokens": 357}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class MemoryEfficientSwish(nn.Module):\n    r\"\"\"Applies the element-wise function:\n\n    .. math::\n        \\text{Swish}(x) = x * \\text{Sigmoid}(\\alpha * x) ~~~~\\text{for constant value}~ \\alpha=1.\n\n    Memory efficient implementation for training following recommendation from:\n    https://github.com/lukemelas/EfficientNet-PyTorch/issues/18#issuecomment-511677853\n\n    Results in ~ 30% memory saving during training as compared to Swish()\n\n    Citation: Searching for Activation Functions, Ramachandran et al., 2017, https://arxiv.org/abs/1710.05941.\n\n    From Pytorch 1.7.0+, the optimized version of `Swish` named `SiLU` is implemented,\n    this class will utilize `torch.nn.functional.silu` to do the calculation if meets the version.\n\n    Shape:\n        - Input: :math:`(N, *)` where `*` means, any number of additional\n          dimensions\n        - Output: :math:`(N, *)`, same shape as the input\n\n\n    Examples::\n\n        >>> import torch\n        >>> from monai.networks.layers.factories import Act\n        >>> m = Act['memswish']()\n        >>> input = torch.randn(2)\n        >>> output = m(input)\n    \"\"\"\n\n    def __init__(self, inplace: bool = False):\n        super().__init__()\n        # inplace only works when using torch.nn.functional.silu\n        self.inplace = inplace\n\n    def forward(self, input: torch.Tensor):\n        return monai_swish(input, self.inplace)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/dynunet_block.py_UnetOutBlock_UnetOutBlock.forward.return.self_conv_inp_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/dynunet_block.py_UnetOutBlock_UnetOutBlock.forward.return.self_conv_inp_", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/dynunet_block.py", "file_name": "dynunet_block.py", "file_type": "text/x-python", "category": "implementation", "start_line": 203, "end_line": 213, "span_ids": ["UnetOutBlock.forward", "UnetOutBlock", "UnetOutBlock.__init__"], "tokens": 106}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class UnetOutBlock(nn.Module):\n    def __init__(\n        self, spatial_dims: int, in_channels: int, out_channels: int, dropout: Optional[Union[Tuple, str, float]] = None\n    ):\n        super().__init__()\n        self.conv = get_conv_layer(\n            spatial_dims, in_channels, out_channels, kernel_size=1, stride=1, dropout=dropout, bias=True, conv_only=True\n        )\n\n    def forward(self, inp):\n        return self.conv(inp)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/dynunet_block.py_get_conv_layer_get_conv_layer.return.Convolution_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/dynunet_block.py_get_conv_layer_get_conv_layer.return.Convolution_", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/dynunet_block.py", "file_name": "dynunet_block.py", "file_type": "text/x-python", "category": "implementation", "start_line": 216, "end_line": 247, "span_ids": ["get_conv_layer"], "tokens": 236}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_conv_layer(\n    spatial_dims: int,\n    in_channels: int,\n    out_channels: int,\n    kernel_size: Union[Sequence[int], int] = 3,\n    stride: Union[Sequence[int], int] = 1,\n    act: Optional[Union[Tuple, str]] = Act.PRELU,\n    norm: Union[Tuple, str] = Norm.INSTANCE,\n    dropout: Optional[Union[Tuple, str, float]] = None,\n    bias: bool = False,\n    conv_only: bool = True,\n    is_transposed: bool = False,\n):\n    padding = get_padding(kernel_size, stride)\n    output_padding = None\n    if is_transposed:\n        output_padding = get_output_padding(kernel_size, stride, padding)\n    return Convolution(\n        spatial_dims,\n        in_channels,\n        out_channels,\n        strides=stride,\n        kernel_size=kernel_size,\n        act=act,\n        norm=norm,\n        dropout=dropout,\n        bias=bias,\n        conv_only=conv_only,\n        is_transposed=is_transposed,\n        padding=padding,\n        output_padding=output_padding,\n    )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/fcn.py_Refine_Refine.forward.return.residual_x": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/fcn.py_Refine_Refine.forward.return.residual_x", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/fcn.py", "file_name": "fcn.py", "file_type": "text/x-python", "category": "implementation", "start_line": 60, "end_line": 94, "span_ids": ["Refine", "Refine.__init__", "Refine.forward"], "tokens": 287}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Refine(nn.Module):\n    \"\"\"\n    Simple residual block to refine the details of the activation maps.\n    \"\"\"\n\n    def __init__(self, planes: int):\n        \"\"\"\n        Args:\n            planes: number of input channels.\n        \"\"\"\n        super().__init__()\n\n        relu_type: Type[nn.ReLU] = Act[Act.RELU]\n        conv2d_type: Type[nn.Conv2d] = Conv[Conv.CONV, 2]\n        norm2d_type: Type[nn.BatchNorm2d] = Norm[Norm.BATCH, 2]\n\n        self.bn = norm2d_type(num_features=planes)\n        self.relu = relu_type(inplace=True)\n        self.conv1 = conv2d_type(in_channels=planes, out_channels=planes, kernel_size=3, padding=1)\n        self.conv2 = conv2d_type(in_channels=planes, out_channels=planes, kernel_size=3, padding=1)\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        \"\"\"\n        Args:\n            x: in shape (batch, planes, spatial_1, spatial_2).\n        \"\"\"\n        residual = x\n        x = self.bn(x)\n        x = self.relu(x)\n        x = self.conv1(x)\n        x = self.bn(x)\n        x = self.relu(x)\n        x = self.conv2(x)\n\n        return residual + x", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/fcn.py_FCN_FCN.__init__.if_self_upsample_mode_.self.up_conv.UpSample_spatial_dims_2_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/fcn.py_FCN_FCN.__init__.if_self_upsample_mode_.self.up_conv.UpSample_spatial_dims_2_", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/fcn.py", "file_name": "fcn.py", "file_type": "text/x-python", "category": "implementation", "start_line": 97, "end_line": 157, "span_ids": ["FCN.__init__", "FCN"], "tokens": 652}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class FCN(nn.Module):\n    \"\"\"\n    2D FCN network with 3 input channels. The small decoder is built\n    with the GCN and Refine modules.\n    The code is adapted from `lsqshr's official 2D code <https://github.com/lsqshr/AH-Net/blob/master/net2d.py>`_.\n\n    Args:\n        out_channels: number of output channels. Defaults to 1.\n        upsample_mode: [``\"transpose\"``, ``\"bilinear\"``]\n            The mode of upsampling manipulations.\n            Using the second mode cannot guarantee the model's reproducibility. Defaults to ``bilinear``.\n\n            - ``transpose``, uses transposed convolution layers.\n            - ``bilinear``, uses bilinear interpolation.\n\n        pretrained: If True, returns a model pre-trained on ImageNet\n        progress: If True, displays a progress bar of the download to stderr.\n    \"\"\"\n\n    def __init__(\n        self, out_channels: int = 1, upsample_mode: str = \"bilinear\", pretrained: bool = True, progress: bool = True\n    ):\n        super().__init__()\n\n        conv2d_type: Type[nn.Conv2d] = Conv[Conv.CONV, 2]\n\n        self.upsample_mode = upsample_mode\n        self.conv2d_type = conv2d_type\n        self.out_channels = out_channels\n        resnet = models.resnet50(pretrained=pretrained, progress=progress)\n\n        self.conv1 = resnet.conv1\n        self.bn0 = resnet.bn1\n        self.relu = resnet.relu\n        self.maxpool = resnet.maxpool\n\n        self.layer1 = resnet.layer1\n        self.layer2 = resnet.layer2\n        self.layer3 = resnet.layer3\n        self.layer4 = resnet.layer4\n\n        self.gcn1 = GCN(2048, self.out_channels)\n        self.gcn2 = GCN(1024, self.out_channels)\n        self.gcn3 = GCN(512, self.out_channels)\n        self.gcn4 = GCN(64, self.out_channels)\n        self.gcn5 = GCN(64, self.out_channels)\n\n        self.refine1 = Refine(self.out_channels)\n        self.refine2 = Refine(self.out_channels)\n        self.refine3 = Refine(self.out_channels)\n        self.refine4 = Refine(self.out_channels)\n        self.refine5 = Refine(self.out_channels)\n        self.refine6 = Refine(self.out_channels)\n        self.refine7 = Refine(self.out_channels)\n        self.refine8 = Refine(self.out_channels)\n        self.refine9 = Refine(self.out_channels)\n        self.refine10 = Refine(self.out_channels)\n        self.transformer = self.conv2d_type(in_channels=256, out_channels=64, kernel_size=1)\n\n        if self.upsample_mode == \"transpose\":\n            self.up_conv = UpSample(spatial_dims=2, in_channels=self.out_channels, scale_factor=2, mode=\"deconv\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/fcn.py_FCN.forward_FCN.forward.return.self_refine10_F_interpola": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/fcn.py_FCN.forward_FCN.forward.return.self_refine10_F_interpola", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/fcn.py", "file_name": "fcn.py", "file_type": "text/x-python", "category": "implementation", "start_line": 159, "end_line": 193, "span_ids": ["FCN.forward"], "tokens": 492}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class FCN(nn.Module):\n\n    def forward(self, x: torch.Tensor):\n        \"\"\"\n        Args:\n            x: in shape (batch, 3, spatial_1, spatial_2).\n        \"\"\"\n        org_input = x\n        x = self.conv1(x)\n        x = self.bn0(x)\n        x = self.relu(x)\n        conv_x = x\n        x = self.maxpool(x)\n        pool_x = x\n\n        fm1 = self.layer1(x)\n        fm2 = self.layer2(fm1)\n        fm3 = self.layer3(fm2)\n        fm4 = self.layer4(fm3)\n\n        gcfm1 = self.refine1(self.gcn1(fm4))\n        gcfm2 = self.refine2(self.gcn2(fm3))\n        gcfm3 = self.refine3(self.gcn3(fm2))\n        gcfm4 = self.refine4(self.gcn4(pool_x))\n        gcfm5 = self.refine5(self.gcn5(conv_x))\n\n        if self.upsample_mode == \"transpose\":\n            fs1 = self.refine6(self.up_conv(gcfm1) + gcfm2)\n            fs2 = self.refine7(self.up_conv(fs1) + gcfm3)\n            fs3 = self.refine8(self.up_conv(fs2) + gcfm4)\n            fs4 = self.refine9(self.up_conv(fs3) + gcfm5)\n            return self.refine10(self.up_conv(fs4))\n        fs1 = self.refine6(F.interpolate(gcfm1, fm3.size()[2:], mode=self.upsample_mode, align_corners=True) + gcfm2)\n        fs2 = self.refine7(F.interpolate(fs1, fm2.size()[2:], mode=self.upsample_mode, align_corners=True) + gcfm3)\n        fs3 = self.refine8(F.interpolate(fs2, pool_x.size()[2:], mode=self.upsample_mode, align_corners=True) + gcfm4)\n        fs4 = self.refine9(F.interpolate(fs3, conv_x.size()[2:], mode=self.upsample_mode, align_corners=True) + gcfm5)\n        return self.refine10(F.interpolate(fs4, org_input.size()[2:], mode=self.upsample_mode, align_corners=True))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/localnet_block.py_from_typing_import_Option_get_conv_block.return.mod": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/localnet_block.py_from_typing_import_Option_get_conv_block.return.mod", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/localnet_block.py", "file_name": "localnet_block.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 43, "span_ids": ["get_conv_block", "docstring"], "tokens": 204}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Optional, Sequence, Tuple, Type, Union\n\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nfrom monai.networks.blocks import Convolution\nfrom monai.networks.layers import same_padding\nfrom monai.networks.layers.factories import Conv, Norm, Pool\n\n\ndef get_conv_block(\n    spatial_dims: int,\n    in_channels: int,\n    out_channels: int,\n    kernel_size: Union[Sequence[int], int] = 3,\n    act: Optional[Union[Tuple, str]] = \"RELU\",\n    norm: Optional[Union[Tuple, str]] = \"BATCH\",\n) -> nn.Module:\n    padding = same_padding(kernel_size)\n    mod: nn.Module = Convolution(\n        spatial_dims,\n        in_channels,\n        out_channels,\n        kernel_size=kernel_size,\n        act=act,\n        norm=norm,\n        bias=False,\n        conv_only=False,\n        padding=padding,\n    )\n    return mod", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/localnet_block.py_get_conv_layer_get_deconv_block.return.mod": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/localnet_block.py_get_conv_layer_get_deconv_block.return.mod", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/localnet_block.py", "file_name": "localnet_block.py", "file_type": "text/x-python", "category": "implementation", "start_line": 46, "end_line": 69, "span_ids": ["get_deconv_block", "get_conv_layer"], "tokens": 187}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_conv_layer(\n    spatial_dims: int, in_channels: int, out_channels: int, kernel_size: Union[Sequence[int], int] = 3\n) -> nn.Module:\n    padding = same_padding(kernel_size)\n    mod: nn.Module = Convolution(\n        spatial_dims, in_channels, out_channels, kernel_size=kernel_size, bias=False, conv_only=True, padding=padding\n    )\n    return mod\n\n\ndef get_deconv_block(spatial_dims: int, in_channels: int, out_channels: int) -> nn.Module:\n    mod: nn.Module = Convolution(\n        spatial_dims=spatial_dims,\n        in_channels=in_channels,\n        out_channels=out_channels,\n        strides=2,\n        act=\"RELU\",\n        norm=\"BATCH\",\n        bias=False,\n        is_transposed=True,\n        padding=1,\n        output_padding=1,\n    )\n    return mod", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/mlp.py_nn_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/mlp.py_nn_", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/mlp.py", "file_name": "mlp.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 47, "span_ids": ["MLPBlock.forward", "MLPBlock.__init__", "MLPBlock", "docstring"], "tokens": 273}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import torch.nn as nn\n\n\nclass MLPBlock(nn.Module):\n    \"\"\"\n    A multi-layer perceptron block, based on: \"Dosovitskiy et al.,\n    An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale <https://arxiv.org/abs/2010.11929>\"\n    \"\"\"\n\n    def __init__(self, hidden_size: int, mlp_dim: int, dropout_rate: float = 0.0) -> None:\n        \"\"\"\n        Args:\n            hidden_size: dimension of hidden layer.\n            mlp_dim: dimension of feedforward layer.\n            dropout_rate: faction of the input units to drop.\n\n        \"\"\"\n\n        super().__init__()\n\n        if not (0 <= dropout_rate <= 1):\n            raise ValueError(\"dropout_rate should be between 0 and 1.\")\n\n        self.linear1 = nn.Linear(hidden_size, mlp_dim)\n        self.linear2 = nn.Linear(mlp_dim, hidden_size)\n        self.fn = nn.GELU()\n        self.drop1 = nn.Dropout(dropout_rate)\n        self.drop2 = nn.Dropout(dropout_rate)\n\n    def forward(self, x):\n        x = self.fn(self.linear1(x))\n        x = self.drop1(x)\n        x = self.linear2(x)\n        x = self.drop2(x)\n        return x", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/patchembedding.py_math_PatchEmbeddingBlock._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/patchembedding.py_math_PatchEmbeddingBlock._", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/patchembedding.py", "file_name": "patchembedding.py", "file_type": "text/x-python", "category": "implementation", "start_line": 13, "end_line": 38, "span_ids": ["PatchEmbeddingBlock", "docstring"], "tokens": 209}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import math\nfrom typing import Sequence, Union\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\n\nfrom monai.networks.layers import Conv\nfrom monai.utils import ensure_tuple_rep, optional_import\nfrom monai.utils.module import look_up_option\n\nRearrange, _ = optional_import(\"einops.layers.torch\", name=\"Rearrange\")\nSUPPORTED_EMBEDDING_TYPES = {\"conv\", \"perceptron\"}\n\n\nclass PatchEmbeddingBlock(nn.Module):\n    \"\"\"\n    A patch embedding block, based on: \"Dosovitskiy et al.,\n    An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale <https://arxiv.org/abs/2010.11929>\"\n\n    Example::\n\n        >>> from monai.networks.blocks import PatchEmbeddingBlock\n        >>> PatchEmbeddingBlock(in_channels=4, img_size=32, patch_size=8, hidden_size=32, num_heads=4, pos_embed=\"conv\")\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/patchembedding.py_PatchEmbeddingBlock.__init___PatchEmbeddingBlock.__init__.self_apply_self__init_wei": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/patchembedding.py_PatchEmbeddingBlock.__init___PatchEmbeddingBlock.__init__.self_apply_self__init_wei", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/patchembedding.py", "file_name": "patchembedding.py", "file_type": "text/x-python", "category": "implementation", "start_line": 40, "end_line": 102, "span_ids": ["PatchEmbeddingBlock.__init__"], "tokens": 710}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class PatchEmbeddingBlock(nn.Module):\n\n    def __init__(\n        self,\n        in_channels: int,\n        img_size: Union[Sequence[int], int],\n        patch_size: Union[Sequence[int], int],\n        hidden_size: int,\n        num_heads: int,\n        pos_embed: str,\n        dropout_rate: float = 0.0,\n        spatial_dims: int = 3,\n    ) -> None:\n        \"\"\"\n        Args:\n            in_channels: dimension of input channels.\n            img_size: dimension of input image.\n            patch_size: dimension of patch size.\n            hidden_size: dimension of hidden layer.\n            num_heads: number of attention heads.\n            pos_embed: position embedding layer type.\n            dropout_rate: faction of the input units to drop.\n            spatial_dims: number of spatial dimensions.\n\n\n        \"\"\"\n\n        super().__init__()\n\n        if not (0 <= dropout_rate <= 1):\n            raise ValueError(\"dropout_rate should be between 0 and 1.\")\n\n        if hidden_size % num_heads != 0:\n            raise ValueError(\"hidden size should be divisible by num_heads.\")\n\n        self.pos_embed = look_up_option(pos_embed, SUPPORTED_EMBEDDING_TYPES)\n\n        img_size = ensure_tuple_rep(img_size, spatial_dims)\n        patch_size = ensure_tuple_rep(patch_size, spatial_dims)\n        for m, p in zip(img_size, patch_size):\n            if m < p:\n                raise ValueError(\"patch_size should be smaller than img_size.\")\n            if self.pos_embed == \"perceptron\" and m % p != 0:\n                raise ValueError(\"patch_size should be divisible by img_size for perceptron.\")\n        self.n_patches = np.prod([im_d // p_d for im_d, p_d in zip(img_size, patch_size)])\n        self.patch_dim = in_channels * np.prod(patch_size)\n\n        self.patch_embeddings: nn.Module\n        if self.pos_embed == \"conv\":\n            self.patch_embeddings = Conv[Conv.CONV, spatial_dims](\n                in_channels=in_channels, out_channels=hidden_size, kernel_size=patch_size, stride=patch_size\n            )\n        elif self.pos_embed == \"perceptron\":\n            # for 3d: \"b c (h p1) (w p2) (d p3)-> b (h w d) (p1 p2 p3 c)\"\n            chars = ((\"h\", \"p1\"), (\"w\", \"p2\"), (\"d\", \"p3\"))[:spatial_dims]\n            from_chars = \"b c \" + \" \".join(f\"({k} {v})\" for k, v in chars)\n            to_chars = f\"b ({' '.join([c[0] for c in chars])}) ({' '.join([c[1] for c in chars])} c)\"\n            axes_len = {f\"p{i+1}\": p for i, p in enumerate(patch_size)}\n            self.patch_embeddings = nn.Sequential(\n                Rearrange(f\"{from_chars} -> {to_chars}\", **axes_len), nn.Linear(self.patch_dim, hidden_size)\n            )\n        self.position_embeddings = nn.Parameter(torch.zeros(1, self.n_patches, hidden_size))\n        self.dropout = nn.Dropout(dropout_rate)\n        self.trunc_normal_(self.position_embeddings, mean=0.0, std=0.02, a=-2.0, b=2.0)\n        self.apply(self._init_weights)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/patchembedding.py_PatchEmbeddingBlock._init_weights_PatchEmbeddingBlock._init_weights.if_isinstance_m_nn_Linea.elif_isinstance_m_nn_Lay.nn_init_constant__m_weigh": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/patchembedding.py_PatchEmbeddingBlock._init_weights_PatchEmbeddingBlock._init_weights.if_isinstance_m_nn_Linea.elif_isinstance_m_nn_Lay.nn_init_constant__m_weigh", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/patchembedding.py", "file_name": "patchembedding.py", "file_type": "text/x-python", "category": "implementation", "start_line": 105, "end_line": 112, "span_ids": ["PatchEmbeddingBlock._init_weights"], "tokens": 117}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class PatchEmbeddingBlock(nn.Module):\n\n    def _init_weights(self, m):\n        if isinstance(m, nn.Linear):\n            self.trunc_normal_(m.weight, mean=0.0, std=0.02, a=-2.0, b=2.0)\n            if isinstance(m, nn.Linear) and m.bias is not None:\n                nn.init.constant_(m.bias, 0)\n        elif isinstance(m, nn.LayerNorm):\n            nn.init.constant_(m.bias, 0)\n            nn.init.constant_(m.weight, 1.0)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/patchembedding.py_PatchEmbeddingBlock.trunc_normal__": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/patchembedding.py_PatchEmbeddingBlock.trunc_normal__", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/patchembedding.py", "file_name": "patchembedding.py", "file_type": "text/x-python", "category": "implementation", "start_line": 114, "end_line": 137, "span_ids": ["PatchEmbeddingBlock.trunc_normal_", "PatchEmbeddingBlock.forward"], "tokens": 254}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class PatchEmbeddingBlock(nn.Module):\n\n    def trunc_normal_(self, tensor, mean, std, a, b):\n        # From PyTorch official master until it's in a few official releases - RW\n        # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf\n        def norm_cdf(x):\n            return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0\n\n        with torch.no_grad():\n            l = norm_cdf((a - mean) / std)\n            u = norm_cdf((b - mean) / std)\n            tensor.uniform_(2 * l - 1, 2 * u - 1)\n            tensor.erfinv_()\n            tensor.mul_(std * math.sqrt(2.0))\n            tensor.add_(mean)\n            tensor.clamp_(min=a, max=b)\n            return tensor\n\n    def forward(self, x):\n        x = self.patch_embeddings(x)\n        if self.pos_embed == \"conv\":\n            x = x.flatten(2).transpose(-1, -2)\n        embeddings = x + self.position_embeddings\n        embeddings = self.dropout(embeddings)\n        return embeddings", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/regunet_block.py_RegistrationDownSampleBlock.forward_get_deconv_block.return.mod": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/regunet_block.py_RegistrationDownSampleBlock.forward_get_deconv_block.return.mod", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/regunet_block.py", "file_name": "regunet_block.py", "file_type": "text/x-python", "category": "implementation", "start_line": 155, "end_line": 186, "span_ids": ["get_deconv_block", "RegistrationDownSampleBlock.forward"], "tokens": 272}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RegistrationDownSampleBlock(nn.Module):\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        \"\"\"\n        Halves the spatial dimensions and keeps the same channel.\n        output in shape (batch, ``channels``, insize_1 / 2, insize_2 / 2, [insize_3 / 2]),\n\n        Args:\n            x: Tensor in shape (batch, ``channels``, insize_1, insize_2, [insize_3])\n\n        Raises:\n            ValueError: when input spatial dimensions are not even.\n        \"\"\"\n        for i in x.shape[2:]:\n            if i % 2 != 0:\n                raise ValueError(\"expecting x spatial dimensions be even, \" f\"got x of shape {x.shape}\")\n        out: torch.Tensor = self.layer(x)\n        return out\n\n\ndef get_deconv_block(spatial_dims: int, in_channels: int, out_channels: int) -> nn.Module:\n    mod: nn.Module = Convolution(\n        spatial_dims=spatial_dims,\n        in_channels=in_channels,\n        out_channels=out_channels,\n        strides=2,\n        act=\"RELU\",\n        norm=\"BATCH\",\n        bias=False,\n        is_transposed=True,\n        padding=1,\n        output_padding=1,\n    )\n    return mod", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/selfattention.py_torch_SABlock.__init__.self.scale.self_head_dim_0_5": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/selfattention.py_torch_SABlock.__init__.self.scale.self_head_dim_0_5", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/selfattention.py", "file_name": "selfattention.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 49, "span_ids": ["SABlock.__init__", "SABlock", "docstring"], "tokens": 298}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import torch\nimport torch.nn as nn\n\nfrom monai.utils import optional_import\n\neinops, _ = optional_import(\"einops\")\n\n\nclass SABlock(nn.Module):\n    \"\"\"\n    A self-attention block, based on: \"Dosovitskiy et al.,\n    An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale <https://arxiv.org/abs/2010.11929>\"\n    \"\"\"\n\n    def __init__(self, hidden_size: int, num_heads: int, dropout_rate: float = 0.0) -> None:\n        \"\"\"\n        Args:\n            hidden_size: dimension of hidden layer.\n            num_heads: number of attention heads.\n            dropout_rate: faction of the input units to drop.\n\n        \"\"\"\n\n        super().__init__()\n\n        if not (0 <= dropout_rate <= 1):\n            raise ValueError(\"dropout_rate should be between 0 and 1.\")\n\n        if hidden_size % num_heads != 0:\n            raise ValueError(\"hidden size should be divisible by num_heads.\")\n\n        self.num_heads = num_heads\n        self.out_proj = nn.Linear(hidden_size, hidden_size)\n        self.qkv = nn.Linear(hidden_size, hidden_size * 3, bias=False)\n        self.drop_output = nn.Dropout(dropout_rate)\n        self.drop_weights = nn.Dropout(dropout_rate)\n        self.head_dim = hidden_size // num_heads\n        self.scale = self.head_dim ** -0.5", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/selfattention.py_SABlock.forward_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/selfattention.py_SABlock.forward_", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/selfattention.py", "file_name": "selfattention.py", "file_type": "text/x-python", "category": "implementation", "start_line": 51, "end_line": 60, "span_ids": ["SABlock.forward"], "tokens": 163}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SABlock(nn.Module):\n\n    def forward(self, x):\n        q, k, v = einops.rearrange(self.qkv(x), \"b h (qkv l d) -> qkv b l h d\", qkv=3, l=self.num_heads)\n        att_mat = (torch.einsum(\"blxd,blyd->blxy\", q, k) * self.scale).softmax(dim=-1)\n        att_mat = self.drop_weights(att_mat)\n        x = torch.einsum(\"bhxy,bhyd->bhxd\", att_mat, v)\n        x = einops.rearrange(x, \"b h l d -> b l (h d)\")\n        x = self.out_proj(x)\n        x = self.drop_output(x)\n        return x", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/squeeze_and_excitation.py_SEBottleneck_SEBottleneck.__init__.super___init___": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/squeeze_and_excitation.py_SEBottleneck_SEBottleneck.__init__.super___init___", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/squeeze_and_excitation.py", "file_name": "squeeze_and_excitation.py", "file_type": "text/x-python", "category": "implementation", "start_line": 232, "end_line": 278, "span_ids": ["SEBottleneck.__init__", "SEBottleneck"], "tokens": 336}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SEBottleneck(SEBlock):\n    \"\"\"\n    Bottleneck for SENet154.\n    \"\"\"\n\n    expansion = 4\n\n    def __init__(\n        self,\n        spatial_dims: int,\n        inplanes: int,\n        planes: int,\n        groups: int,\n        reduction: int,\n        stride: int = 1,\n        downsample: Optional[Convolution] = None,\n    ) -> None:\n\n        conv_param_1 = {\n            \"strides\": 1,\n            \"kernel_size\": 1,\n            \"act\": (\"relu\", {\"inplace\": True}),\n            \"norm\": Norm.BATCH,\n            \"bias\": False,\n        }\n        conv_param_2 = {\n            \"strides\": stride,\n            \"kernel_size\": 3,\n            \"act\": (\"relu\", {\"inplace\": True}),\n            \"norm\": Norm.BATCH,\n            \"groups\": groups,\n            \"bias\": False,\n        }\n        conv_param_3 = {\"strides\": 1, \"kernel_size\": 1, \"act\": None, \"norm\": Norm.BATCH, \"bias\": False}\n\n        super().__init__(\n            spatial_dims=spatial_dims,\n            in_channels=inplanes,\n            n_chns_1=planes * 2,\n            n_chns_2=planes * 4,\n            n_chns_3=planes * 4,\n            conv_param_1=conv_param_1,\n            conv_param_2=conv_param_2,\n            conv_param_3=conv_param_3,\n            project=downsample,\n            r=reduction,\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/squeeze_and_excitation.py_SEResNetBottleneck_SEResNetBottleneck.__init__.super___init___": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/squeeze_and_excitation.py_SEResNetBottleneck_SEResNetBottleneck.__init__.super___init___", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/squeeze_and_excitation.py", "file_name": "squeeze_and_excitation.py", "file_type": "text/x-python", "category": "implementation", "start_line": 281, "end_line": 329, "span_ids": ["SEResNetBottleneck", "SEResNetBottleneck.__init__"], "tokens": 379}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SEResNetBottleneck(SEBlock):\n    \"\"\"\n    ResNet bottleneck with a Squeeze-and-Excitation module. It follows Caffe\n    implementation and uses `strides=stride` in `conv1` and not in `conv2`\n    (the latter is used in the torchvision implementation of ResNet).\n    \"\"\"\n\n    expansion = 4\n\n    def __init__(\n        self,\n        spatial_dims: int,\n        inplanes: int,\n        planes: int,\n        groups: int,\n        reduction: int,\n        stride: int = 1,\n        downsample: Optional[Convolution] = None,\n    ) -> None:\n\n        conv_param_1 = {\n            \"strides\": stride,\n            \"kernel_size\": 1,\n            \"act\": (\"relu\", {\"inplace\": True}),\n            \"norm\": Norm.BATCH,\n            \"bias\": False,\n        }\n        conv_param_2 = {\n            \"strides\": 1,\n            \"kernel_size\": 3,\n            \"act\": (\"relu\", {\"inplace\": True}),\n            \"norm\": Norm.BATCH,\n            \"groups\": groups,\n            \"bias\": False,\n        }\n        conv_param_3 = {\"strides\": 1, \"kernel_size\": 1, \"act\": None, \"norm\": Norm.BATCH, \"bias\": False}\n\n        super().__init__(\n            spatial_dims=spatial_dims,\n            in_channels=inplanes,\n            n_chns_1=planes,\n            n_chns_2=planes,\n            n_chns_3=planes * 4,\n            conv_param_1=conv_param_1,\n            conv_param_2=conv_param_2,\n            conv_param_3=conv_param_3,\n            project=downsample,\n            r=reduction,\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/transformerblock.py_nn_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/transformerblock.py_nn_", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/transformerblock.py", "file_name": "transformerblock.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 51, "span_ids": ["TransformerBlock", "TransformerBlock.forward", "TransformerBlock.__init__", "docstring"], "tokens": 322}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import torch.nn as nn\n\nfrom monai.networks.blocks.mlp import MLPBlock\nfrom monai.networks.blocks.selfattention import SABlock\n\n\nclass TransformerBlock(nn.Module):\n    \"\"\"\n    A transformer block, based on: \"Dosovitskiy et al.,\n    An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale <https://arxiv.org/abs/2010.11929>\"\n    \"\"\"\n\n    def __init__(self, hidden_size: int, mlp_dim: int, num_heads: int, dropout_rate: float = 0.0) -> None:\n        \"\"\"\n        Args:\n            hidden_size: dimension of hidden layer.\n            mlp_dim: dimension of feedforward layer.\n            num_heads: number of attention heads.\n            dropout_rate: faction of the input units to drop.\n\n        \"\"\"\n\n        super().__init__()\n\n        if not (0 <= dropout_rate <= 1):\n            raise ValueError(\"dropout_rate should be between 0 and 1.\")\n\n        if hidden_size % num_heads != 0:\n            raise ValueError(\"hidden_size should be divisible by num_heads.\")\n\n        self.mlp = MLPBlock(hidden_size, mlp_dim, dropout_rate)\n        self.norm1 = nn.LayerNorm(hidden_size)\n        self.attn = SABlock(hidden_size, num_heads, dropout_rate)\n        self.norm2 = nn.LayerNorm(hidden_size)\n\n    def forward(self, x):\n        x = x + self.attn(self.norm1(x))\n        x = x + self.mlp(self.norm2(x))\n        return x", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/unetr_block.py_from_typing_import_Sequen_UnetrUpBlock.forward.return.out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/unetr_block.py_from_typing_import_Sequen_UnetrUpBlock.forward.return.out", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/unetr_block.py", "file_name": "unetr_block.py", "file_type": "text/x-python", "category": "implementation", "start_line": 13, "end_line": 85, "span_ids": ["UnetrUpBlock.__init__", "UnetrUpBlock.forward", "UnetrUpBlock", "docstring"], "tokens": 490}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Sequence, Tuple, Union\n\nimport torch\nimport torch.nn as nn\n\nfrom monai.networks.blocks.dynunet_block import UnetBasicBlock, UnetResBlock, get_conv_layer\n\n\nclass UnetrUpBlock(nn.Module):\n    \"\"\"\n    An upsampling module that can be used for UNETR: \"Hatamizadeh et al.,\n    UNETR: Transformers for 3D Medical Image Segmentation <https://arxiv.org/abs/2103.10504>\"\n    \"\"\"\n\n    def __init__(\n        self,\n        spatial_dims: int,\n        in_channels: int,\n        out_channels: int,\n        kernel_size: Union[Sequence[int], int],\n        upsample_kernel_size: Union[Sequence[int], int],\n        norm_name: Union[Tuple, str],\n        res_block: bool = False,\n    ) -> None:\n        \"\"\"\n        Args:\n            spatial_dims: number of spatial dimensions.\n            in_channels: number of input channels.\n            out_channels: number of output channels.\n            kernel_size: convolution kernel size.\n            upsample_kernel_size: convolution kernel size for transposed convolution layers.\n            norm_name: feature normalization type and arguments.\n            res_block: bool argument to determine if residual block is used.\n\n        \"\"\"\n\n        super().__init__()\n        upsample_stride = upsample_kernel_size\n        self.transp_conv = get_conv_layer(\n            spatial_dims,\n            in_channels,\n            out_channels,\n            kernel_size=upsample_kernel_size,\n            stride=upsample_stride,\n            conv_only=True,\n            is_transposed=True,\n        )\n\n        if res_block:\n            self.conv_block = UnetResBlock(\n                spatial_dims,\n                out_channels + out_channels,\n                out_channels,\n                kernel_size=kernel_size,\n                stride=1,\n                norm_name=norm_name,\n            )\n        else:\n            self.conv_block = UnetBasicBlock(  # type: ignore\n                spatial_dims,\n                out_channels + out_channels,\n                out_channels,\n                kernel_size=kernel_size,\n                stride=1,\n                norm_name=norm_name,\n            )\n\n    def forward(self, inp, skip):\n        # number of channels for skip should equals to out_channels\n        out = self.transp_conv(inp)\n        out = torch.cat((out, skip), dim=1)\n        out = self.conv_block(out)\n        return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/unetr_block.py_UnetrPrUpBlock_UnetrPrUpBlock.forward.return.x": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/unetr_block.py_UnetrPrUpBlock_UnetrPrUpBlock.forward.return.x", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/unetr_block.py", "file_name": "unetr_block.py", "file_type": "text/x-python", "category": "implementation", "start_line": 88, "end_line": 205, "span_ids": ["UnetrPrUpBlock.__init__", "UnetrPrUpBlock.forward", "UnetrPrUpBlock"], "tokens": 701}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class UnetrPrUpBlock(nn.Module):\n    \"\"\"\n    A projection upsampling module that can be used for UNETR: \"Hatamizadeh et al.,\n    UNETR: Transformers for 3D Medical Image Segmentation <https://arxiv.org/abs/2103.10504>\"\n    \"\"\"\n\n    def __init__(\n        self,\n        spatial_dims: int,\n        in_channels: int,\n        out_channels: int,\n        num_layer: int,\n        kernel_size: Union[Sequence[int], int],\n        stride: Union[Sequence[int], int],\n        upsample_kernel_size: Union[Sequence[int], int],\n        norm_name: Union[Tuple, str],\n        conv_block: bool = False,\n        res_block: bool = False,\n    ) -> None:\n        \"\"\"\n        Args:\n            spatial_dims: number of spatial dimensions.\n            in_channels: number of input channels.\n            out_channels: number of output channels.\n            num_layer: number of upsampling blocks.\n            kernel_size: convolution kernel size.\n            stride: convolution stride.\n            upsample_kernel_size: convolution kernel size for transposed convolution layers.\n            norm_name: feature normalization type and arguments.\n            conv_block: bool argument to determine if convolutional block is used.\n            res_block: bool argument to determine if residual block is used.\n\n        \"\"\"\n\n        super().__init__()\n\n        upsample_stride = upsample_kernel_size\n        self.transp_conv_init = get_conv_layer(\n            spatial_dims,\n            in_channels,\n            out_channels,\n            kernel_size=upsample_kernel_size,\n            stride=upsample_stride,\n            conv_only=True,\n            is_transposed=True,\n        )\n        if conv_block:\n            if res_block:\n                self.blocks = nn.ModuleList(\n                    [\n                        nn.Sequential(\n                            get_conv_layer(\n                                spatial_dims,\n                                out_channels,\n                                out_channels,\n                                kernel_size=upsample_kernel_size,\n                                stride=upsample_stride,\n                                conv_only=True,\n                                is_transposed=True,\n                            ),\n                            UnetResBlock(\n                                spatial_dims=spatial_dims,\n                                in_channels=out_channels,\n                                out_channels=out_channels,\n                                kernel_size=kernel_size,\n                                stride=stride,\n                                norm_name=norm_name,\n                            ),\n                        )\n                        for i in range(num_layer)\n                    ]\n                )\n            else:\n                self.blocks = nn.ModuleList(\n                    [\n                        nn.Sequential(\n                            get_conv_layer(\n                                spatial_dims,\n                                out_channels,\n                                out_channels,\n                                kernel_size=upsample_kernel_size,\n                                stride=upsample_stride,\n                                conv_only=True,\n                                is_transposed=True,\n                            ),\n                            UnetBasicBlock(\n                                spatial_dims=spatial_dims,\n                                in_channels=out_channels,\n                                out_channels=out_channels,\n                                kernel_size=kernel_size,\n                                stride=stride,\n                                norm_name=norm_name,\n                            ),\n                        )\n                        for i in range(num_layer)\n                    ]\n                )\n        else:\n            self.blocks = nn.ModuleList(\n                [\n                    get_conv_layer(\n                        spatial_dims,\n                        out_channels,\n                        out_channels,\n                        kernel_size=upsample_kernel_size,\n                        stride=upsample_stride,\n                        conv_only=True,\n                        is_transposed=True,\n                    )\n                    for i in range(num_layer)\n                ]\n            )\n\n    def forward(self, x):\n        x = self.transp_conv_init(x)\n        for blk in self.blocks:\n            x = blk(x)\n        return x", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/unetr_block.py_UnetrBasicBlock_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/unetr_block.py_UnetrBasicBlock_", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/unetr_block.py", "file_name": "unetr_block.py", "file_type": "text/x-python", "category": "implementation", "start_line": 208, "end_line": 259, "span_ids": ["UnetrBasicBlock", "UnetrBasicBlock.__init__", "UnetrBasicBlock.forward"], "tokens": 340}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class UnetrBasicBlock(nn.Module):\n    \"\"\"\n    A CNN module that can be used for UNETR, based on: \"Hatamizadeh et al.,\n    UNETR: Transformers for 3D Medical Image Segmentation <https://arxiv.org/abs/2103.10504>\"\n    \"\"\"\n\n    def __init__(\n        self,\n        spatial_dims: int,\n        in_channels: int,\n        out_channels: int,\n        kernel_size: Union[Sequence[int], int],\n        stride: Union[Sequence[int], int],\n        norm_name: Union[Tuple, str],\n        res_block: bool = False,\n    ) -> None:\n        \"\"\"\n        Args:\n            spatial_dims: number of spatial dimensions.\n            in_channels: number of input channels.\n            out_channels: number of output channels.\n            kernel_size: convolution kernel size.\n            stride: convolution stride.\n            norm_name: feature normalization type and arguments.\n            res_block: bool argument to determine if residual block is used.\n\n        \"\"\"\n\n        super().__init__()\n\n        if res_block:\n            self.layer = UnetResBlock(\n                spatial_dims=spatial_dims,\n                in_channels=in_channels,\n                out_channels=out_channels,\n                kernel_size=kernel_size,\n                stride=stride,\n                norm_name=norm_name,\n            )\n        else:\n            self.layer = UnetBasicBlock(  # type: ignore\n                spatial_dims=spatial_dims,\n                in_channels=in_channels,\n                out_channels=out_channels,\n                kernel_size=kernel_size,\n                stride=stride,\n                norm_name=norm_name,\n            )\n\n    def forward(self, inp):\n        return self.layer(inp)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/warp.py_Warp.forward_Warp.forward.return.grid_pull_image_grid_bo": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/warp.py_Warp.forward_Warp.forward.return.grid_pull_image_grid_bo", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/warp.py", "file_name": "warp.py", "file_type": "text/x-python", "category": "implementation", "start_line": 92, "end_line": 122, "span_ids": ["Warp.forward"], "tokens": 427}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Warp(nn.Module):\n\n    def forward(self, image: torch.Tensor, ddf: torch.Tensor):\n        \"\"\"\n        Args:\n            image: Tensor in shape (batch, num_channels, H, W[, D])\n            ddf: Tensor in the same spatial size as image, in shape (batch, ``spatial_dims``, H, W[, D])\n\n        Returns:\n            warped_image in the same shape as image (batch, num_channels, H, W[, D])\n        \"\"\"\n        spatial_dims = len(image.shape) - 2\n        if spatial_dims not in (2, 3):\n            raise NotImplementedError(f\"got unsupported spatial_dims={spatial_dims}, currently support 2 or 3.\")\n        ddf_shape = (image.shape[0], spatial_dims) + tuple(image.shape[2:])\n        if ddf.shape != ddf_shape:\n            raise ValueError(\n                f\"Given input {spatial_dims}-d image shape {image.shape}, \" f\"the input DDF shape must be {ddf_shape}.\"\n            )\n        grid = self.get_reference_grid(ddf) + ddf\n        grid = grid.permute([0] + list(range(2, 2 + spatial_dims)) + [1])  # (batch, ..., spatial_dims)\n\n        if not USE_COMPILED:  # pytorch native grid_sample\n            for i, dim in enumerate(grid.shape[1:-1]):\n                grid[..., i] = grid[..., i] * 2 / (dim - 1) - 1\n            index_ordering: List[int] = list(range(spatial_dims - 1, -1, -1))\n            grid = grid[..., index_ordering]  # z, y, x -> x, y, z\n            return F.grid_sample(\n                image, grid, mode=self._interp_mode, padding_mode=f\"{self._padding_mode}\", align_corners=True\n            )\n\n        # using csrc resampling\n        return grid_pull(image, grid, bound=self._padding_mode, extrapolate=True, interpolation=self._interp_mode)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/factories.py_LayerFactory.factory_function_LayerFactory.get_constructor.return.func_args_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/factories.py_LayerFactory.factory_function_LayerFactory.get_constructor.return.func_args_", "embedding": null, "metadata": {"file_path": "monai/networks/layers/factories.py", "file_name": "factories.py", "file_type": "text/x-python", "category": "implementation", "start_line": 102, "end_line": 126, "span_ids": ["LayerFactory.get_constructor", "LayerFactory.factory_function"], "tokens": 164}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LayerFactory:\n\n    def factory_function(self, name: str) -> Callable:\n        \"\"\"\n        Decorator for adding a factory function with the given name.\n        \"\"\"\n\n        def _add(func: Callable) -> Callable:\n            self.add_factory_callable(name, func)\n            return func\n\n        return _add\n\n    def get_constructor(self, factory_name: str, *args) -> Any:\n        \"\"\"\n        Get the constructor for the given factory name and arguments.\n\n        Raises:\n            TypeError: When ``factory_name`` is not a ``str``.\n\n        \"\"\"\n\n        if not isinstance(factory_name, str):\n            raise TypeError(f\"factory_name must a str but is {type(factory_name).__name__}.\")\n\n        func = look_up_option(factory_name.upper(), self.factories)\n        return func(*args)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/spatial_transforms.py_grid_pull_grid_pull.return.out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/spatial_transforms.py_grid_pull_grid_pull.return.out", "embedding": null, "metadata": {"file_path": "monai/networks/layers/spatial_transforms.py", "file_name": "spatial_transforms.py", "file_type": "text/x-python", "category": "implementation", "start_line": 49, "end_line": 119, "span_ids": ["grid_pull"], "tokens": 818}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def grid_pull(\n    input: torch.Tensor, grid: torch.Tensor, interpolation=\"linear\", bound=\"zero\", extrapolate: bool = True\n) -> torch.Tensor:\n    \"\"\"\n    Sample an image with respect to a deformation field.\n\n    `interpolation` can be an int, a string or an InterpolationType.\n    Possible values are::\n\n        - 0 or 'nearest'    or InterpolationType.nearest\n        - 1 or 'linear'     or InterpolationType.linear\n        - 2 or 'quadratic'  or InterpolationType.quadratic\n        - 3 or 'cubic'      or InterpolationType.cubic\n        - 4 or 'fourth'     or InterpolationType.fourth\n        - 5 or 'fifth'      or InterpolationType.fifth\n        - 6 or 'sixth'      or InterpolationType.sixth\n        - 7 or 'seventh'    or InterpolationType.seventh\n\n    A list of values can be provided, in the order [W, H, D],\n    to specify dimension-specific interpolation orders.\n\n    `bound` can be an int, a string or a BoundType.\n    Possible values are::\n\n        - 0 or 'replicate' or 'nearest'      or BoundType.replicate\n        - 1 or 'dct1'      or 'mirror'       or BoundType.dct1\n        - 2 or 'dct2'      or 'reflect'      or BoundType.dct2\n        - 3 or 'dst1'      or 'antimirror'   or BoundType.dst1\n        - 4 or 'dst2'      or 'antireflect'  or BoundType.dst2\n        - 5 or 'dft'       or 'wrap'         or BoundType.dft\n        - 7 or 'zero'                        or BoundType.zero\n\n    A list of values can be provided, in the order [W, H, D],\n    to specify dimension-specific boundary conditions.\n    `sliding` is a specific condition than only applies to flow fields\n    (with as many channels as dimensions). It cannot be dimension-specific.\n    Note that:\n\n        - `dft` corresponds to circular padding\n        - `dct2` corresponds to Neumann boundary conditions (symmetric)\n        - `dst2` corresponds to Dirichlet boundary conditions (antisymmetric)\n\n    See Also:\n        - https://en.wikipedia.org/wiki/Discrete_cosine_transform\n        - https://en.wikipedia.org/wiki/Discrete_sine_transform\n        - ``help(monai._C.BoundType)``\n        - ``help(monai._C.InterpolationType)``\n\n    Args:\n        input: Input image. `(B, C, Wi, Hi, Di)`.\n        grid: Deformation field. `(B, Wo, Ho, Do, 1|2|3)`.\n        interpolation (int or list[int] , optional): Interpolation order.\n            Defaults to `'linear'`.\n        bound (BoundType, or list[BoundType], optional): Boundary conditions.\n            Defaults to `'zero'`.\n        extrapolate: Extrapolate out-of-bound data.\n            Defaults to `True`.\n\n    Returns:\n        output (torch.Tensor): Deformed image `(B, C, Wo, Ho, Do)`.\n\n    \"\"\"\n    # Convert parameters\n    bound = [_C.BoundType.__members__[b] if isinstance(b, str) else _C.BoundType(b) for b in ensure_tuple(bound)]\n    interpolation = [\n        _C.InterpolationType.__members__[i] if isinstance(i, str) else _C.InterpolationType(i)\n        for i in ensure_tuple(interpolation)\n    ]\n    out: torch.Tensor\n    out = _GridPull.apply(input, grid, interpolation, bound, extrapolate)  # type: ignore\n    return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/ahnet.py_PSP_PSP.__init__.if_self_upsample_mode_.for_i_in_range_psp_block_.self_up_modules_append_co": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/ahnet.py_PSP_PSP.__init__.if_self_upsample_mode_.for_i_in_range_psp_block_.self_up_modules_append_co", "embedding": null, "metadata": {"file_path": "monai/networks/nets/ahnet.py", "file_name": "ahnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 245, "end_line": 271, "span_ids": ["PSP.__init__", "PSP"], "tokens": 364}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class PSP(nn.Module):\n    def __init__(self, spatial_dims: int, psp_block_num: int, in_ch: int, upsample_mode: str = \"transpose\"):\n        super().__init__()\n        self.up_modules = nn.ModuleList()\n        conv_type = Conv[Conv.CONV, spatial_dims]\n        pool_type: Type[Union[nn.MaxPool2d, nn.MaxPool3d]] = Pool[Pool.MAX, spatial_dims]\n\n        self.pool_modules = nn.ModuleList()\n        self.project_modules = nn.ModuleList()\n\n        for i in range(psp_block_num):\n            size = (2 ** (i + 3), 2 ** (i + 3), 1)[-spatial_dims:]\n            self.pool_modules.append(pool_type(kernel_size=size, stride=size))\n            self.project_modules.append(\n                conv_type(in_ch, 1, kernel_size=(1, 1, 1)[-spatial_dims:], stride=1, padding=(1, 1, 0)[-spatial_dims:])\n            )\n\n        self.spatial_dims = spatial_dims\n        self.psp_block_num = psp_block_num\n        self.upsample_mode = upsample_mode\n\n        if self.upsample_mode == \"transpose\":\n            conv_trans_type = Conv[Conv.CONVTRANS, spatial_dims]\n            for i in range(psp_block_num):\n                size = (2 ** (i + 3), 2 ** (i + 3), 1)[-spatial_dims:]\n                pad_size = (2 ** (i + 3), 2 ** (i + 3), 0)[-spatial_dims:]\n                self.up_modules.append(conv_trans_type(1, 1, kernel_size=size, stride=size, padding=pad_size))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/autoencoder.py_from_typing_import_Any_O_AutoEncoder._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/autoencoder.py_from_typing_import_Any_O_AutoEncoder._", "embedding": null, "metadata": {"file_path": "monai/networks/nets/autoencoder.py", "file_name": "autoencoder.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 89, "span_ids": ["AutoEncoder", "docstring"], "tokens": 860}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Any, Optional, Sequence, Tuple, Union\n\nimport torch\nimport torch.nn as nn\n\nfrom monai.networks.blocks import Convolution, ResidualUnit\nfrom monai.networks.layers.factories import Act, Norm\nfrom monai.utils import deprecated_arg\n\n__all__ = [\"AutoEncoder\"]\n\n\nclass AutoEncoder(nn.Module):\n    \"\"\"\n    Simple definition of an autoencoder and base class for the architecture implementing\n    :py:class:`monai.networks.nets.VarAutoEncoder`. The network is composed of an encode sequence of blocks, followed\n    by an intermediary sequence of blocks, and finally a decode sequence of blocks. The encode and decode blocks are\n    default :py:class:`monai.networks.blocks.Convolution` instances with the encode blocks having the given stride\n    and the decode blocks having transpose convolutions with the same stride. If `num_res_units` is given residual\n    blocks are used instead.\n\n    By default the intermediary sequence is empty but if `inter_channels` is given to specify the output channels of\n    blocks then this will be become a sequence of Convolution blocks or of residual blocks if `num_inter_units` is\n    given. The optional parameter `inter_dilations` can be used to specify the dilation values of the convolutions in\n    these blocks, this allows a network to use dilated kernels in this  middle section. Since the intermediary section\n    isn't meant to change the size of the output the strides for all these kernels is 1.\n\n    Args:\n        spatial_dims: number of spatial dimensions.\n        in_channels: number of input channels.\n        out_channels: number of output channels.\n        channels: sequence of channels. Top block first. The length of `channels` should be no less than 2.\n        strides: sequence of convolution strides. The length of `stride` should equal to `len(channels) - 1`.\n        kernel_size: convolution kernel size, the value(s) should be odd. If sequence,\n            its length should equal to dimensions. Defaults to 3.\n        up_kernel_size: upsampling convolution kernel size, the value(s) should be odd. If sequence,\n            its length should equal to dimensions. Defaults to 3.\n        num_res_units: number of residual units. Defaults to 0.\n        inter_channels: sequence of channels defining the blocks in the intermediate layer between encode and decode.\n        inter_dilations: defines the dilation value for each block of the intermediate layer. Defaults to 1.\n        num_inter_units: number of residual units for each block of the intermediate layer. Defaults to 0.\n        act: activation type and arguments. Defaults to PReLU.\n        norm: feature normalization type and arguments. Defaults to instance norm.\n        dropout: dropout ratio. Defaults to no dropout.\n        bias: whether to have a bias term in convolution blocks. Defaults to True.\n            According to `Performance Tuning Guide <https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html>`_,\n            if a conv layer is directly followed by a batch norm layer, bias should be False.\n\n    .. deprecated:: 0.6.0\n        ``dimensions`` is deprecated, use ``spatial_dims`` instead.\n\n    Examples::\n\n        from monai.networks.nets import AutoEncoder\n\n        # 3 layers each down/up sampling their inputs by a factor 2 with no intermediate layer\n        net = AutoEncoder(\n            spatial_dims=2,\n            in_channels=1,\n            out_channels=1,\n            channels=(2, 4, 8),\n            strides=(2, 2, 2)\n        )\n\n        # 1 layer downsampling by 2, followed by a sequence of residual units with 2 convolutions defined by\n        # progressively increasing dilations, then final upsample layer\n        net = AutoEncoder(\n                spatial_dims=2,\n                in_channels=1,\n                out_channels=1,\n                channels=(4,),\n                strides=(2,),\n                inter_channels=(8, 8, 8),\n                inter_dilations=(1, 2, 4),\n                num_inter_units=2\n            )\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/autoencoder.py_AutoEncoder.__init___AutoEncoder.__init__.self_decode___self__ge": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/autoencoder.py_AutoEncoder.__init___AutoEncoder.__init__.self_decode___self__ge", "embedding": null, "metadata": {"file_path": "monai/networks/nets/autoencoder.py", "file_name": "autoencoder.py", "file_type": "text/x-python", "category": "implementation", "start_line": 91, "end_line": 140, "span_ids": ["AutoEncoder.__init__"], "tokens": 510}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AutoEncoder(nn.Module):\n\n    @deprecated_arg(\n        name=\"dimensions\", new_name=\"spatial_dims\", since=\"0.6\", msg_suffix=\"Please use `spatial_dims` instead.\"\n    )\n    def __init__(\n        self,\n        spatial_dims: int,\n        in_channels: int,\n        out_channels: int,\n        channels: Sequence[int],\n        strides: Sequence[int],\n        kernel_size: Union[Sequence[int], int] = 3,\n        up_kernel_size: Union[Sequence[int], int] = 3,\n        num_res_units: int = 0,\n        inter_channels: Optional[list] = None,\n        inter_dilations: Optional[list] = None,\n        num_inter_units: int = 2,\n        act: Optional[Union[Tuple, str]] = Act.PRELU,\n        norm: Union[Tuple, str] = Norm.INSTANCE,\n        dropout: Optional[Union[Tuple, str, float]] = None,\n        bias: bool = True,\n        dimensions: Optional[int] = None,\n    ) -> None:\n\n        super().__init__()\n        self.dimensions = spatial_dims if dimensions is None else dimensions\n        self.in_channels = in_channels\n        self.out_channels = out_channels\n        self.channels = list(channels)\n        self.strides = list(strides)\n        self.kernel_size = kernel_size\n        self.up_kernel_size = up_kernel_size\n        self.num_res_units = num_res_units\n        self.act = act\n        self.norm = norm\n        self.dropout = dropout\n        self.bias = bias\n        self.num_inter_units = num_inter_units\n        self.inter_channels = inter_channels if inter_channels is not None else []\n        self.inter_dilations = list(inter_dilations or [1] * len(self.inter_channels))\n\n        # The number of channels and strides should match\n        if len(channels) != len(strides):\n            raise ValueError(\"Autoencoder expects matching number of channels and strides\")\n\n        self.encoded_channels = in_channels\n        decode_channel_list = list(channels[-2::-1]) + [out_channels]\n\n        self.encode, self.encoded_channels = self._get_encode_module(self.encoded_channels, channels, strides)\n        self.intermediate, self.encoded_channels = self._get_intermediate_module(self.encoded_channels, num_inter_units)\n        self.decode, _ = self._get_decode_module(self.encoded_channels, decode_channel_list, strides[::-1] or [1])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/autoencoder.py_AutoEncoder._get_encode_module_AutoEncoder._get_encode_module.return.encode_layer_channels": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/autoencoder.py_AutoEncoder._get_encode_module_AutoEncoder._get_encode_module.return.encode_layer_channels", "embedding": null, "metadata": {"file_path": "monai/networks/nets/autoencoder.py", "file_name": "autoencoder.py", "file_type": "text/x-python", "category": "implementation", "start_line": 142, "end_line": 156, "span_ids": ["AutoEncoder._get_encode_module"], "tokens": 140}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AutoEncoder(nn.Module):\n\n    def _get_encode_module(\n        self, in_channels: int, channels: Sequence[int], strides: Sequence[int]\n    ) -> Tuple[nn.Sequential, int]:\n        \"\"\"\n        Returns the encode part of the network by building up a sequence of layers returned by `_get_encode_layer`.\n        \"\"\"\n        encode = nn.Sequential()\n        layer_channels = in_channels\n\n        for i, (c, s) in enumerate(zip(channels, strides)):\n            layer = self._get_encode_layer(layer_channels, c, s, False)\n            encode.add_module(\"encode_%i\" % i, layer)\n            layer_channels = c\n\n        return encode, layer_channels", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/autoencoder.py_AutoEncoder._get_encode_layer_AutoEncoder._get_encode_layer.return.mod": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/autoencoder.py_AutoEncoder._get_encode_layer_AutoEncoder._get_encode_layer.return.mod", "embedding": null, "metadata": {"file_path": "monai/networks/nets/autoencoder.py", "file_name": "autoencoder.py", "file_type": "text/x-python", "category": "implementation", "start_line": 223, "end_line": 254, "span_ids": ["AutoEncoder._get_encode_layer"], "tokens": 211}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AutoEncoder(nn.Module):\n\n    def _get_encode_layer(self, in_channels: int, out_channels: int, strides: int, is_last: bool) -> nn.Module:\n        \"\"\"\n        Returns a single layer of the encoder part of the network.\n        \"\"\"\n        mod: nn.Module\n        if self.num_res_units > 0:\n            mod = ResidualUnit(\n                spatial_dims=self.dimensions,\n                in_channels=in_channels,\n                out_channels=out_channels,\n                strides=strides,\n                kernel_size=self.kernel_size,\n                subunits=self.num_res_units,\n                act=self.act,\n                norm=self.norm,\n                dropout=self.dropout,\n                bias=self.bias,\n                last_conv_only=is_last,\n            )\n        mod = Convolution(\n            spatial_dims=self.dimensions,\n            in_channels=in_channels,\n            out_channels=out_channels,\n            strides=strides,\n            kernel_size=self.kernel_size,\n            act=self.act,\n            norm=self.norm,\n            dropout=self.dropout,\n            bias=self.bias,\n            conv_only=is_last,\n        )\n        return mod", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/basic_unet.py_from_typing_import_Option_TwoConv.__init__.self_add_module_conv_1_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/basic_unet.py_from_typing_import_Option_TwoConv.__init__.self_add_module_conv_1_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/basic_unet.py", "file_name": "basic_unet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 61, "span_ids": ["TwoConv", "TwoConv.__init__", "docstring"], "tokens": 436}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Optional, Sequence, Union\n\nimport torch\nimport torch.nn as nn\n\nfrom monai.networks.blocks import Convolution, UpSample\nfrom monai.networks.layers.factories import Conv, Pool\nfrom monai.utils import deprecated_arg, ensure_tuple_rep\n\n__all__ = [\"BasicUnet\", \"Basicunet\", \"basicunet\", \"BasicUNet\"]\n\n\nclass TwoConv(nn.Sequential):\n    \"\"\"two convolutions.\"\"\"\n\n    @deprecated_arg(name=\"dim\", new_name=\"spatial_dims\", since=\"0.6\", msg_suffix=\"Please use `spatial_dims` instead.\")\n    def __init__(\n        self,\n        spatial_dims: int,\n        in_chns: int,\n        out_chns: int,\n        act: Union[str, tuple],\n        norm: Union[str, tuple],\n        bias: bool,\n        dropout: Union[float, tuple] = 0.0,\n        dim: Optional[int] = None,\n    ):\n        \"\"\"\n        Args:\n            spatial_dims: number of spatial dimensions.\n            in_chns: number of input channels.\n            out_chns: number of output channels.\n            act: activation type and arguments.\n            norm: feature normalization type and arguments.\n            bias: whether to have a bias term in convolution blocks.\n            dropout: dropout ratio. Defaults to no dropout.\n\n        .. deprecated:: 0.6.0\n            ``dim`` is deprecated, use ``spatial_dims`` instead.\n        \"\"\"\n        super().__init__()\n\n        if dim is not None:\n            spatial_dims = dim\n        conv_0 = Convolution(spatial_dims, in_chns, out_chns, act=act, norm=norm, dropout=dropout, bias=bias, padding=1)\n        conv_1 = Convolution(\n            spatial_dims, out_chns, out_chns, act=act, norm=norm, dropout=dropout, bias=bias, padding=1\n        )\n        self.add_module(\"conv_0\", conv_0)\n        self.add_module(\"conv_1\", conv_1)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/basic_unet.py_UpCat_UpCat.__init__.self.convs.TwoConv_spatial_dims_cat": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/basic_unet.py_UpCat_UpCat.__init__.self.convs.TwoConv_spatial_dims_cat", "embedding": null, "metadata": {"file_path": "monai/networks/nets/basic_unet.py", "file_name": "basic_unet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 101, "end_line": 163, "span_ids": ["UpCat.__init__", "UpCat"], "tokens": 652}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class UpCat(nn.Module):\n    \"\"\"upsampling, concatenation with the encoder feature map, two convolutions\"\"\"\n\n    @deprecated_arg(name=\"dim\", new_name=\"spatial_dims\", since=\"0.6\", msg_suffix=\"Please use `spatial_dims` instead.\")\n    def __init__(\n        self,\n        spatial_dims: int,\n        in_chns: int,\n        cat_chns: int,\n        out_chns: int,\n        act: Union[str, tuple],\n        norm: Union[str, tuple],\n        bias: bool,\n        dropout: Union[float, tuple] = 0.0,\n        upsample: str = \"deconv\",\n        pre_conv: Optional[Union[nn.Module, str]] = \"default\",\n        interp_mode: str = \"linear\",\n        align_corners: Optional[bool] = True,\n        halves: bool = True,\n        dim: Optional[int] = None,\n    ):\n        \"\"\"\n        Args:\n            spatial_dims: number of spatial dimensions.\n            in_chns: number of input channels to be upsampled.\n            cat_chns: number of channels from the decoder.\n            out_chns: number of output channels.\n            act: activation type and arguments.\n            norm: feature normalization type and arguments.\n            bias: whether to have a bias term in convolution blocks.\n            dropout: dropout ratio. Defaults to no dropout.\n            upsample: upsampling mode, available options are\n                ``\"deconv\"``, ``\"pixelshuffle\"``, ``\"nontrainable\"``.\n            pre_conv: a conv block applied before upsampling.\n                Only used in the \"nontrainable\" or \"pixelshuffle\" mode.\n            interp_mode: {``\"nearest\"``, ``\"linear\"``, ``\"bilinear\"``, ``\"bicubic\"``, ``\"trilinear\"``}\n                Only used in the \"nontrainable\" mode.\n            align_corners: set the align_corners parameter for upsample. Defaults to True.\n                Only used in the \"nontrainable\" mode.\n            halves: whether to halve the number of channels during upsampling.\n                This parameter does not work on ``nontrainable`` mode if ``pre_conv`` is `None`.\n\n        .. deprecated:: 0.6.0\n            ``dim`` is deprecated, use ``spatial_dims`` instead.\n        \"\"\"\n        super().__init__()\n        if dim is not None:\n            spatial_dims = dim\n        if upsample == \"nontrainable\" and pre_conv is None:\n            up_chns = in_chns\n        else:\n            up_chns = in_chns // 2 if halves else in_chns\n        self.upsample = UpSample(\n            spatial_dims,\n            in_chns,\n            up_chns,\n            2,\n            mode=upsample,\n            pre_conv=pre_conv,\n            interp_mode=interp_mode,\n            align_corners=align_corners,\n        )\n        self.convs = TwoConv(spatial_dims, cat_chns + up_chns, out_chns, act, norm, bias, dropout)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/basic_unet.py_BasicUNet_BasicUNet.__init__.self.final_conv.Conv_conv_spatial_dims": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/basic_unet.py_BasicUNet_BasicUNet.__init__.self.final_conv.Conv_conv_spatial_dims", "embedding": null, "metadata": {"file_path": "monai/networks/nets/basic_unet.py", "file_name": "basic_unet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 189, "end_line": 272, "span_ids": ["BasicUNet.__init__", "BasicUNet"], "tokens": 1101}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class BasicUNet(nn.Module):\n    @deprecated_arg(\n        name=\"dimensions\", new_name=\"spatial_dims\", since=\"0.6\", msg_suffix=\"Please use `spatial_dims` instead.\"\n    )\n    def __init__(\n        self,\n        spatial_dims: int = 3,\n        in_channels: int = 1,\n        out_channels: int = 2,\n        features: Sequence[int] = (32, 32, 64, 128, 256, 32),\n        act: Union[str, tuple] = (\"LeakyReLU\", {\"negative_slope\": 0.1, \"inplace\": True}),\n        norm: Union[str, tuple] = (\"instance\", {\"affine\": True}),\n        bias: bool = True,\n        dropout: Union[float, tuple] = 0.0,\n        upsample: str = \"deconv\",\n        dimensions: Optional[int] = None,\n    ):\n        \"\"\"\n        A UNet implementation with 1D/2D/3D supports.\n\n        Based on:\n\n            Falk et al. \"U-Net \u2013 Deep Learning for Cell Counting, Detection, and\n            Morphometry\". Nature Methods 16, 67\u201370 (2019), DOI:\n            http://dx.doi.org/10.1038/s41592-018-0261-2\n\n        Args:\n            spatial_dims: number of spatial dimensions. Defaults to 3 for spatial 3D inputs.\n            in_channels: number of input channels. Defaults to 1.\n            out_channels: number of output channels. Defaults to 2.\n            features: six integers as numbers of features.\n                Defaults to ``(32, 32, 64, 128, 256, 32)``,\n\n                - the first five values correspond to the five-level encoder feature sizes.\n                - the last value corresponds to the feature size after the last upsampling.\n\n            act: activation type and arguments. Defaults to LeakyReLU.\n            norm: feature normalization type and arguments. Defaults to instance norm.\n            bias: whether to have a bias term in convolution blocks. Defaults to True.\n                According to `Performance Tuning Guide <https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html>`_,\n                if a conv layer is directly followed by a batch norm layer, bias should be False.\n            dropout: dropout ratio. Defaults to no dropout.\n            upsample: upsampling mode, available options are\n                ``\"deconv\"``, ``\"pixelshuffle\"``, ``\"nontrainable\"``.\n\n        .. deprecated:: 0.6.0\n            ``dimensions`` is deprecated, use ``spatial_dims`` instead.\n\n        Examples::\n\n            # for spatial 2D\n            >>> net = BasicUNet(spatial_dims=2, features=(64, 128, 256, 512, 1024, 128))\n\n            # for spatial 2D, with group norm\n            >>> net = BasicUNet(spatial_dims=2, features=(64, 128, 256, 512, 1024, 128), norm=(\"group\", {\"num_groups\": 4}))\n\n            # for spatial 3D\n            >>> net = BasicUNet(spatial_dims=3, features=(32, 32, 64, 128, 256, 32))\n\n        See Also\n\n            - :py:class:`monai.networks.nets.DynUNet`\n            - :py:class:`monai.networks.nets.UNet`\n\n        \"\"\"\n        super().__init__()\n        if dimensions is not None:\n            spatial_dims = dimensions\n\n        fea = ensure_tuple_rep(features, 6)\n        print(f\"BasicUNet features: {fea}.\")\n\n        self.conv_0 = TwoConv(spatial_dims, in_channels, features[0], act, norm, bias, dropout)\n        self.down_1 = Down(spatial_dims, fea[0], fea[1], act, norm, bias, dropout)\n        self.down_2 = Down(spatial_dims, fea[1], fea[2], act, norm, bias, dropout)\n        self.down_3 = Down(spatial_dims, fea[2], fea[3], act, norm, bias, dropout)\n        self.down_4 = Down(spatial_dims, fea[3], fea[4], act, norm, bias, dropout)\n\n        self.upcat_4 = UpCat(spatial_dims, fea[4], fea[3], fea[3], act, norm, bias, dropout, upsample)\n        self.upcat_3 = UpCat(spatial_dims, fea[3], fea[2], fea[2], act, norm, bias, dropout, upsample)\n        self.upcat_2 = UpCat(spatial_dims, fea[2], fea[1], fea[1], act, norm, bias, dropout, upsample)\n        self.upcat_1 = UpCat(spatial_dims, fea[1], fea[0], fea[5], act, norm, bias, dropout, upsample, halves=False)\n\n        self.final_conv = Conv[\"conv\", spatial_dims](fea[5], out_channels, kernel_size=1)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/efficientnet.py_url_map_url_map._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/efficientnet.py_url_map_url_map._", "embedding": null, "metadata": {"file_path": "monai/networks/nets/efficientnet.py", "file_name": "efficientnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 42, "end_line": 61, "span_ids": ["impl:5"], "tokens": 748}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "url_map = {\n    \"efficientnet-b0\": \"https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b0-355c32eb.pth\",\n    \"efficientnet-b1\": \"https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b1-f1951068.pth\",\n    \"efficientnet-b2\": \"https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b2-8bb594d6.pth\",\n    \"efficientnet-b3\": \"https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b3-5fb5a3c3.pth\",\n    \"efficientnet-b4\": \"https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b4-6ed6700e.pth\",\n    \"efficientnet-b5\": \"https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b5-b6417697.pth\",\n    \"efficientnet-b6\": \"https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b6-c76e70fd.pth\",\n    \"efficientnet-b7\": \"https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b7-dcc49843.pth\",\n    # trained with adversarial examples, simplify the name to decrease string length\n    \"b0-ap\": \"https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b0-b64d5a18.pth\",\n    \"b1-ap\": \"https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b1-0f3ce85a.pth\",\n    \"b2-ap\": \"https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b2-6e9d97e5.pth\",\n    \"b3-ap\": \"https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b3-cdd7c0f4.pth\",\n    \"b4-ap\": \"https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b4-44fb3a87.pth\",\n    \"b5-ap\": \"https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b5-86493f6b.pth\",\n    \"b6-ap\": \"https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b6-ac80338e.pth\",\n    \"b7-ap\": \"https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b7-4652b6dd.pth\",\n    \"b8-ap\": \"https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b8-22a8fe65.pth\",\n}", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/efficientnet.py_MBConvBlock_MBConvBlock.__init__.self._swish.Act_memswish_inplace_T": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/efficientnet.py_MBConvBlock_MBConvBlock.__init__.self._swish.Act_memswish_inplace_T", "embedding": null, "metadata": {"file_path": "monai/networks/nets/efficientnet.py", "file_name": "efficientnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 71, "end_line": 174, "span_ids": ["MBConvBlock.__init__", "MBConvBlock"], "tokens": 1090}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class MBConvBlock(nn.Module):\n    def __init__(\n        self,\n        spatial_dims: int,\n        in_channels: int,\n        out_channels: int,\n        kernel_size: int,\n        stride: int,\n        image_size: List[int],\n        expand_ratio: int,\n        se_ratio: Optional[float],\n        id_skip: Optional[bool] = True,\n        norm: Union[str, tuple] = (\"batch\", {\"eps\": 1e-3, \"momentum\": 0.01}),\n        drop_connect_rate: Optional[float] = 0.2,\n    ) -> None:\n        \"\"\"\n        Mobile Inverted Residual Bottleneck Block.\n\n        Args:\n            spatial_dims: number of spatial dimensions.\n            in_channels: number of input channels.\n            out_channels: number of output channels.\n            kernel_size: size of the kernel for conv ops.\n            stride: stride to use for conv ops.\n            image_size: input image resolution.\n            expand_ratio: expansion ratio for inverted bottleneck.\n            se_ratio: squeeze-excitation ratio for se layers.\n            id_skip: whether to use skip connection.\n            norm: feature normalization type and arguments. Defaults to batch norm.\n            drop_connect_rate: dropconnect rate for drop connection (individual weights) layers.\n\n        References:\n            [1] https://arxiv.org/abs/1704.04861 (MobileNet v1)\n            [2] https://arxiv.org/abs/1801.04381 (MobileNet v2)\n            [3] https://arxiv.org/abs/1905.02244 (MobileNet v3)\n        \"\"\"\n        super().__init__()\n\n        # select the type of N-Dimensional layers to use\n        # these are based on spatial dims and selected from MONAI factories\n        conv_type = Conv[\"conv\", spatial_dims]\n        adaptivepool_type = Pool[\"adaptiveavg\", spatial_dims]\n\n        self.in_channels = in_channels\n        self.out_channels = out_channels\n        self.id_skip = id_skip\n        self.stride = stride\n        self.expand_ratio = expand_ratio\n        self.drop_connect_rate = drop_connect_rate\n\n        if (se_ratio is not None) and (0.0 < se_ratio <= 1.0):\n            self.has_se = True\n            self.se_ratio = se_ratio\n        else:\n            self.has_se = False\n\n        # Expansion phase (Inverted Bottleneck)\n        inp = in_channels  # number of input channels\n        oup = in_channels * expand_ratio  # number of output channels\n        if self.expand_ratio != 1:\n            self._expand_conv = conv_type(in_channels=inp, out_channels=oup, kernel_size=1, bias=False)\n            self._expand_conv_padding = _make_same_padder(self._expand_conv, image_size)\n\n            self._bn0 = get_norm_layer(name=norm, spatial_dims=spatial_dims, channels=oup)\n        else:\n            # need to have the following to fix JIT error:\n            #   \"Module 'MBConvBlock' has no attribute '_expand_conv'\"\n\n            # FIXME: find a better way to bypass JIT error\n            self._expand_conv = nn.Identity()\n            self._expand_conv_padding = nn.Identity()\n            self._bn0 = nn.Identity()\n\n        # Depthwise convolution phase\n        self._depthwise_conv = conv_type(\n            in_channels=oup,\n            out_channels=oup,\n            groups=oup,  # groups makes it depthwise\n            kernel_size=kernel_size,\n            stride=self.stride,\n            bias=False,\n        )\n        self._depthwise_conv_padding = _make_same_padder(self._depthwise_conv, image_size)\n        self._bn1 = get_norm_layer(name=norm, spatial_dims=spatial_dims, channels=oup)\n        image_size = _calculate_output_image_size(image_size, self.stride)\n\n        # Squeeze and Excitation layer, if desired\n        if self.has_se:\n            self._se_adaptpool = adaptivepool_type(1)\n            num_squeezed_channels = max(1, int(in_channels * self.se_ratio))\n            self._se_reduce = conv_type(in_channels=oup, out_channels=num_squeezed_channels, kernel_size=1)\n            self._se_reduce_padding = _make_same_padder(self._se_reduce, [1, 1])\n            self._se_expand = conv_type(in_channels=num_squeezed_channels, out_channels=oup, kernel_size=1)\n            self._se_expand_padding = _make_same_padder(self._se_expand, [1, 1])\n\n        # Pointwise convolution phase\n        final_oup = out_channels\n        self._project_conv = conv_type(in_channels=oup, out_channels=final_oup, kernel_size=1, bias=False)\n        self._project_conv_padding = _make_same_padder(self._project_conv, image_size)\n        self._bn2 = get_norm_layer(name=norm, spatial_dims=spatial_dims, channels=final_oup)\n\n        # swish activation to use - using memory efficient swish by default\n        # can be switched to normal swish using self.set_swish() function call\n        self._swish = Act[\"memswish\"](inplace=True)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/efficientnet.py_MBConvBlock.forward_MBConvBlock.set_swish.self._swish.Act_memswish_inplace_T": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/efficientnet.py_MBConvBlock.forward_MBConvBlock.set_swish.self._swish.Act_memswish_inplace_T", "embedding": null, "metadata": {"file_path": "monai/networks/nets/efficientnet.py", "file_name": "efficientnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 169, "end_line": 215, "span_ids": ["MBConvBlock.forward", "MBConvBlock.set_swish"], "tokens": 445}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class MBConvBlock(nn.Module):\n\n    def forward(self, inputs: torch.Tensor):\n        \"\"\"MBConvBlock\"s forward function.\n\n        Args:\n            inputs: Input tensor.\n\n        Returns:\n            Output of this block after processing.\n        \"\"\"\n        # Expansion and Depthwise Convolution\n        x = inputs\n        if self.expand_ratio != 1:\n            x = self._expand_conv(self._expand_conv_padding(x))\n            x = self._bn0(x)\n            x = self._swish(x)\n\n        x = self._depthwise_conv(self._depthwise_conv_padding(x))\n        x = self._bn1(x)\n        x = self._swish(x)\n\n        # Squeeze and Excitation\n        if self.has_se:\n            x_squeezed = self._se_adaptpool(x)\n            x_squeezed = self._se_reduce(self._se_reduce_padding(x_squeezed))\n            x_squeezed = self._swish(x_squeezed)\n            x_squeezed = self._se_expand(self._se_expand_padding(x_squeezed))\n            x = torch.sigmoid(x_squeezed) * x\n\n        # Pointwise Convolution\n        x = self._project_conv(self._project_conv_padding(x))\n        x = self._bn2(x)\n\n        # Skip connection and drop connect\n        if self.id_skip and self.stride == 1 and self.in_channels == self.out_channels:\n            # the combination of skip connection and drop connect brings about stochastic depth.\n            if self.drop_connect_rate:\n                x = drop_connect(x, p=self.drop_connect_rate, training=self.training)\n            x = x + inputs  # skip connection\n        return x\n\n    def set_swish(self, memory_efficient: bool = True) -> None:\n        \"\"\"Sets swish function as memory efficient (for training) or standard (for export).\n\n        Args:\n            memory_efficient (bool): Whether to use memory-efficient version of swish.\n        \"\"\"\n        self._swish = Act[\"memswish\"](inplace=True) if memory_efficient else Act[\"swish\"](alpha=1.0)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/efficientnet.py_EfficientNet.set_swish_EfficientNet.set_swish.for_sub_stack_in_self__bl.for_block_in_sub_stack_.block_set_swish_memory_ef": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/efficientnet.py_EfficientNet.set_swish_EfficientNet.set_swish.for_sub_stack_in_self__bl.for_block_in_sub_stack_.block_set_swish_memory_ef", "embedding": null, "metadata": {"file_path": "monai/networks/nets/efficientnet.py", "file_name": "efficientnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 397, "end_line": 408, "span_ids": ["EfficientNet.set_swish"], "tokens": 119}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class EfficientNet(nn.Module):\n\n    def set_swish(self, memory_efficient: bool = True) -> None:\n        \"\"\"\n        Sets swish function as memory efficient (for training) or standard (for JIT export).\n\n        Args:\n            memory_efficient: whether to use memory-efficient version of swish.\n\n        \"\"\"\n        self._swish = Act[\"memswish\"]() if memory_efficient else Act[\"swish\"](alpha=1.0)\n        for sub_stack in self._blocks:\n            for block in sub_stack:\n                block.set_swish(memory_efficient)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/efficientnet.py_EfficientNetBN_EfficientNetBN.__init__.if_pretrained_and_spatia._load_state_dict_self_mo": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/efficientnet.py_EfficientNetBN_EfficientNetBN.__init__.if_pretrained_and_spatia._load_state_dict_self_mo", "embedding": null, "metadata": {"file_path": "monai/networks/nets/efficientnet.py", "file_name": "efficientnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 463, "end_line": 549, "span_ids": ["EfficientNetBN", "EfficientNetBN.__init__"], "tokens": 846}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class EfficientNetBN(EfficientNet):\n    def __init__(\n        self,\n        model_name: str,\n        pretrained: bool = True,\n        progress: bool = True,\n        spatial_dims: int = 2,\n        in_channels: int = 3,\n        num_classes: int = 1000,\n        norm: Union[str, tuple] = (\"batch\", {\"eps\": 1e-3, \"momentum\": 0.01}),\n        adv_prop: bool = False,\n    ) -> None:\n        \"\"\"\n        Generic wrapper around EfficientNet, used to initialize EfficientNet-B0 to EfficientNet-B7 models\n        model_name is mandatory argument as there is no EfficientNetBN itself,\n        it needs the N in [0, 1, 2, 3, 4, 5, 6, 7, 8] to be a model\n\n        Args:\n            model_name: name of model to initialize, can be from [efficientnet-b0, ..., efficientnet-b8, efficientnet-l2].\n            pretrained: whether to initialize pretrained ImageNet weights, only available for spatial_dims=2 and batch\n                norm is used.\n            progress: whether to show download progress for pretrained weights download.\n            spatial_dims: number of spatial dimensions.\n            in_channels: number of input channels.\n            num_classes: number of output classes.\n            norm: feature normalization type and arguments.\n            adv_prop: whether to use weights trained with adversarial examples.\n                This argument only works when `pretrained` is `True`.\n\n        Examples::\n\n            # for pretrained spatial 2D ImageNet\n            >>> image_size = get_efficientnet_image_size(\"efficientnet-b0\")\n            >>> inputs = torch.rand(1, 3, image_size, image_size)\n            >>> model = EfficientNetBN(\"efficientnet-b0\", pretrained=True)\n            >>> model.eval()\n            >>> outputs = model(inputs)\n\n            # create spatial 2D\n            >>> model = EfficientNetBN(\"efficientnet-b0\", spatial_dims=2)\n\n            # create spatial 3D\n            >>> model = EfficientNetBN(\"efficientnet-b0\", spatial_dims=3)\n\n            # create EfficientNetB7 for spatial 2D\n            >>> model = EfficientNetBN(\"efficientnet-b7\", spatial_dims=2)\n\n        \"\"\"\n        # block args\n        blocks_args_str = [\n            \"r1_k3_s11_e1_i32_o16_se0.25\",\n            \"r2_k3_s22_e6_i16_o24_se0.25\",\n            \"r2_k5_s22_e6_i24_o40_se0.25\",\n            \"r3_k3_s22_e6_i40_o80_se0.25\",\n            \"r3_k5_s11_e6_i80_o112_se0.25\",\n            \"r4_k5_s22_e6_i112_o192_se0.25\",\n            \"r1_k3_s11_e6_i192_o320_se0.25\",\n        ]\n\n        # check if model_name is valid model\n        if model_name not in efficientnet_params.keys():\n            raise ValueError(\n                \"invalid model_name {} found, must be one of {} \".format(\n                    model_name, \", \".join(efficientnet_params.keys())\n                )\n            )\n\n        # get network parameters\n        weight_coeff, depth_coeff, image_size, dropout_rate, dropconnect_rate = efficientnet_params[model_name]\n\n        # create model and initialize random weights\n        super().__init__(\n            blocks_args_str=blocks_args_str,\n            spatial_dims=spatial_dims,\n            in_channels=in_channels,\n            num_classes=num_classes,\n            width_coefficient=weight_coeff,\n            depth_coefficient=depth_coeff,\n            dropout_rate=dropout_rate,\n            image_size=image_size,\n            drop_connect_rate=dropconnect_rate,\n            norm=norm,\n        )\n\n        # only pretrained for when `spatial_dims` is 2\n        if pretrained and (spatial_dims == 2):\n            _load_state_dict(self, model_name, progress, adv_prop)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/efficientnet.py_EfficientNetBNFeatures_EfficientNetBNFeatures.__init__.if_pretrained_and_spatia._load_state_dict_self_mo": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/efficientnet.py_EfficientNetBNFeatures_EfficientNetBNFeatures.__init__.if_pretrained_and_spatia._load_state_dict_self_mo", "embedding": null, "metadata": {"file_path": "monai/networks/nets/efficientnet.py", "file_name": "efficientnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 552, "end_line": 609, "span_ids": ["EfficientNetBNFeatures.__init__", "EfficientNetBNFeatures"], "tokens": 552}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class EfficientNetBNFeatures(EfficientNet):\n    def __init__(\n        self,\n        model_name: str,\n        pretrained: bool = True,\n        progress: bool = True,\n        spatial_dims: int = 2,\n        in_channels: int = 3,\n        num_classes: int = 1000,\n        norm: Union[str, tuple] = (\"batch\", {\"eps\": 1e-3, \"momentum\": 0.01}),\n        adv_prop: bool = False,\n    ) -> None:\n        \"\"\"\n        Initialize EfficientNet-B0 to EfficientNet-B7 models as a backbone, the backbone can\n        be used as an encoder for segmentation and objection models.\n        Compared with the class `EfficientNetBN`, the only different place is the forward function.\n\n        This class refers to `PyTorch image models <https://github.com/rwightman/pytorch-image-models>`_.\n\n        \"\"\"\n        blocks_args_str = [\n            \"r1_k3_s11_e1_i32_o16_se0.25\",\n            \"r2_k3_s22_e6_i16_o24_se0.25\",\n            \"r2_k5_s22_e6_i24_o40_se0.25\",\n            \"r3_k3_s22_e6_i40_o80_se0.25\",\n            \"r3_k5_s11_e6_i80_o112_se0.25\",\n            \"r4_k5_s22_e6_i112_o192_se0.25\",\n            \"r1_k3_s11_e6_i192_o320_se0.25\",\n        ]\n\n        # check if model_name is valid model\n        if model_name not in efficientnet_params.keys():\n            raise ValueError(\n                \"invalid model_name {} found, must be one of {} \".format(\n                    model_name, \", \".join(efficientnet_params.keys())\n                )\n            )\n\n        # get network parameters\n        weight_coeff, depth_coeff, image_size, dropout_rate, dropconnect_rate = efficientnet_params[model_name]\n\n        # create model and initialize random weights\n        super().__init__(\n            blocks_args_str=blocks_args_str,\n            spatial_dims=spatial_dims,\n            in_channels=in_channels,\n            num_classes=num_classes,\n            width_coefficient=weight_coeff,\n            depth_coefficient=depth_coeff,\n            dropout_rate=dropout_rate,\n            image_size=image_size,\n            drop_connect_rate=dropconnect_rate,\n            norm=norm,\n        )\n\n        # only pretrained for when `spatial_dims` is 2\n        if pretrained and (spatial_dims == 2):\n            _load_state_dict(self, model_name, progress, adv_prop)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/efficientnet.py_EfficientNetBNFeatures.forward_EfficientNetBNFeatures.forward.return.features": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/efficientnet.py_EfficientNetBNFeatures.forward_EfficientNetBNFeatures.forward.return.features", "embedding": null, "metadata": {"file_path": "monai/networks/nets/efficientnet.py", "file_name": "efficientnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 611, "end_line": 631, "span_ids": ["EfficientNetBNFeatures.forward"], "tokens": 165}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class EfficientNetBNFeatures(EfficientNet):\n\n    def forward(self, inputs: torch.Tensor):\n        \"\"\"\n        Args:\n            inputs: input should have spatially N dimensions\n            ``(Batch, in_channels, dim_0[, dim_1, ..., dim_N])``, N is defined by `dimensions`.\n\n        Returns:\n            a list of torch Tensors.\n        \"\"\"\n        # Stem\n        x = self._conv_stem(self._conv_stem_padding(inputs))\n        x = self._swish(self._bn0(x))\n\n        features = []\n        if 0 in self.extract_stacks:\n            features.append(x)\n        for i, block in enumerate(self._blocks):\n            x = block(x)\n            if i + 1 in self.extract_stacks:\n                features.append(x)\n        return features", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/efficientnet.py__load_state_dict__load_state_dict.if_model_url_is_None_.else_.model_load_state_dict_mod": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/efficientnet.py__load_state_dict__load_state_dict.if_model_url_is_None_.else_.model_load_state_dict_mod", "embedding": null, "metadata": {"file_path": "monai/networks/nets/efficientnet.py", "file_name": "efficientnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 703, "end_line": 721, "span_ids": ["_load_state_dict"], "tokens": 228}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _load_state_dict(model: nn.Module, arch: str, progress: bool, adv_prop: bool) -> None:\n    if adv_prop:\n        arch = arch.split(\"efficientnet-\")[-1] + \"-ap\"\n    model_url = look_up_option(arch, url_map, None)\n    if model_url is None:\n        print(f\"pretrained weights of {arch} is not provided\")\n    else:\n        # load state dict from url\n        model_url = url_map[arch]\n        pretrain_state_dict = model_zoo.load_url(model_url, progress=progress)\n        model_state_dict = model.state_dict()\n\n        pattern = re.compile(r\"(.+)\\.\\d+(\\.\\d+\\..+)\")\n        for key, value in model_state_dict.items():\n            pretrain_key = re.sub(pattern, r\"\\1\\2\", key)\n            if pretrain_key in pretrain_state_dict and value.shape == pretrain_state_dict[pretrain_key].shape:\n                model_state_dict[key] = pretrain_state_dict[pretrain_key]\n\n        model.load_state_dict(model_state_dict)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/netadapter.py_from_typing_import_Any_D_NetAdapter._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/netadapter.py_from_typing_import_Any_D_NetAdapter._", "embedding": null, "metadata": {"file_path": "monai/networks/nets/netadapter.py", "file_name": "netadapter.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 44, "span_ids": ["NetAdapter", "docstring"], "tokens": 433}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Any, Dict, Optional, Tuple, Union\n\nimport torch\n\nfrom monai.networks.layers import Conv, get_pool_layer\nfrom monai.utils import deprecated_arg\n\n\nclass NetAdapter(torch.nn.Module):\n    \"\"\"\n    Wrapper to replace the last layer of model by convolutional layer or FC layer.\n    This module expects the output of `model layers[0: -2]` is a feature map with shape [B, C, spatial dims],\n    then replace the model's last two layers with an optional `pooling` and a `conv` or `linear` layer.\n\n    Args:\n        model: a PyTorch model, which can be both 2D and 3D models. typically, it can be a pretrained model\n            in Torchvision, like: ``resnet18``, ``resnet34``, ``resnet50``, ``resnet101``, ``resnet152``, etc.\n            more details: https://pytorch.org/vision/stable/models.html.\n        num_classes: number of classes for the last classification layer. Default to 1.\n        dim: number of supported spatial dimensions in the specified model, depends on the model implementation.\n            default to 2 as most Torchvision models are for 2D image processing.\n        in_channels: number of the input channels of last layer. if None, get it from `in_features` of last layer.\n        use_conv: whether use convolutional layer to replace the last layer, default to False.\n        pool: parameters for the pooling layer, it should be a tuple, the first item is name of the pooling layer,\n            the second item is dictionary of the initialization args. if None, will not replace the `layers[-2]`.\n            default to `(\"avg\", {\"kernel_size\": 7, \"stride\": 1})`.\n        bias: the bias value when replacing the last layer. if False, the layer will not learn an additive bias,\n            default to True.\n\n    .. deprecated:: 0.6.0\n        ``n_classes`` is deprecated, use ``num_classes`` instead.\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/netadapter.py_NetAdapter.__init___": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/netadapter.py_NetAdapter.__init___", "embedding": null, "metadata": {"file_path": "monai/networks/nets/netadapter.py", "file_name": "netadapter.py", "file_type": "text/x-python", "category": "implementation", "start_line": 46, "end_line": 104, "span_ids": ["NetAdapter.forward", "NetAdapter.__init__"], "tokens": 515}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class NetAdapter(torch.nn.Module):\n\n    @deprecated_arg(\"n_classes\", since=\"0.6\")\n    def __init__(\n        self,\n        model: torch.nn.Module,\n        num_classes: int = 1,\n        dim: int = 2,\n        in_channels: Optional[int] = None,\n        use_conv: bool = False,\n        pool: Optional[Tuple[str, Dict[str, Any]]] = (\"avg\", {\"kernel_size\": 7, \"stride\": 1}),\n        bias: bool = True,\n        n_classes: Optional[int] = None,\n    ):\n        super().__init__()\n        # in case the new num_classes is default but you still call deprecated n_classes\n        if n_classes is not None and num_classes == 1:\n            num_classes = n_classes\n        layers = list(model.children())\n        orig_fc = layers[-1]\n        in_channels_: int\n\n        if in_channels is None:\n            if not hasattr(orig_fc, \"in_features\"):\n                raise ValueError(\"please specify the input channels of last layer with arg `in_channels`.\")\n            in_channels_ = orig_fc.in_features  # type: ignore\n        else:\n            in_channels_ = in_channels\n\n        if pool is None:\n            # remove the last layer\n            self.features = torch.nn.Sequential(*layers[:-1])\n            self.pool = None\n        else:\n            # remove the last 2 layers\n            self.features = torch.nn.Sequential(*layers[:-2])\n            self.pool = get_pool_layer(name=pool, spatial_dims=dim)\n\n        self.fc: Union[torch.nn.Linear, torch.nn.Conv2d, torch.nn.Conv3d]\n        if use_conv:\n            # add 1x1 conv (it behaves like a FC layer)\n            self.fc = Conv[Conv.CONV, dim](in_channels=in_channels_, out_channels=num_classes, kernel_size=1, bias=bias)\n        else:\n            # remove the last Linear layer (fully connected)\n            self.features = torch.nn.Sequential(*layers[:-1])\n            # replace the out_features of FC layer\n            self.fc = torch.nn.Linear(in_features=in_channels_, out_features=num_classes, bias=bias)\n        self.use_conv = use_conv\n\n    def forward(self, x):\n        x = self.features(x)\n        if self.pool is not None:\n            x = self.pool(x)\n\n        if not self.use_conv:\n            x = torch.flatten(x, 1)\n\n        x = self.fc(x)\n\n        return x", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/regressor.py_from_typing_import_Option_Regressor._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/regressor.py_from_typing_import_Option_Regressor._", "embedding": null, "metadata": {"file_path": "monai/networks/nets/regressor.py", "file_name": "regressor.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 56, "span_ids": ["Regressor", "docstring"], "tokens": 525}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Optional, Sequence, Union\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\n\nfrom monai.networks.blocks import Convolution, ResidualUnit\nfrom monai.networks.layers.convutils import calculate_out_shape, same_padding\nfrom monai.networks.layers.factories import Act, Norm\nfrom monai.networks.layers.simplelayers import Reshape\nfrom monai.utils import ensure_tuple, ensure_tuple_rep\n\n__all__ = [\"Regressor\"]\n\n\nclass Regressor(nn.Module):\n    \"\"\"\n    This defines a network for relating large-sized input tensors to small output tensors, ie. regressing large\n    values to a prediction. An output of a single dimension can be used as value regression or multi-label\n    classification prediction, an output of a single value can be used as a discriminator or critic prediction.\n\n    The network is constructed as a sequence of layers, either :py:class:`monai.networks.blocks.Convolution` or\n    :py:class:`monai.networks.blocks.ResidualUnit`, with a final fully-connected layer resizing the output from the\n    blocks to the final size. Each block is defined with a stride value typically used to downsample the input using\n    strided convolutions. In this way each block progressively condenses information from the input into a deep\n    representation the final fully-connected layer relates to a final result.\n\n    Args:\n        in_shape: tuple of integers stating the dimension of the input tensor (minus batch dimension)\n        out_shape: tuple of integers stating the dimension of the final output tensor (minus batch dimension)\n        channels: tuple of integers stating the output channels of each convolutional layer\n        strides: tuple of integers stating the stride (downscale factor) of each convolutional layer\n        kernel_size: integer or tuple of integers stating size of convolutional kernels\n        num_res_units: integer stating number of convolutions in residual units, 0 means no residual units\n        act: name or type defining activation layers\n        norm: name or type defining normalization layers\n        dropout: optional float value in range [0, 1] stating dropout probability for layers, None for no dropout\n        bias: boolean stating if convolution layers should have a bias component\n\n    Examples::\n\n        # infers a 2-value result (eg. a 2D cartesian coordinate) from a 64x64 image\n        net = Regressor((1, 64, 64), (2,), (2, 4, 8), (2, 2, 2))\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/regressor.py_Regressor.__init___Regressor.__init__.self.final.self__get_final_layer_ec": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/regressor.py_Regressor.__init___Regressor.__init__.self.final.self__get_final_layer_ec", "embedding": null, "metadata": {"file_path": "monai/networks/nets/regressor.py", "file_name": "regressor.py", "file_type": "text/x-python", "category": "implementation", "start_line": 58, "end_line": 100, "span_ids": ["Regressor.__init__"], "tokens": 381}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Regressor(nn.Module):\n\n    def __init__(\n        self,\n        in_shape: Sequence[int],\n        out_shape: Sequence[int],\n        channels: Sequence[int],\n        strides: Sequence[int],\n        kernel_size: Union[Sequence[int], int] = 3,\n        num_res_units: int = 2,\n        act=Act.PRELU,\n        norm=Norm.INSTANCE,\n        dropout: Optional[float] = None,\n        bias: bool = True,\n    ) -> None:\n        super().__init__()\n\n        self.in_channels, *self.in_shape = ensure_tuple(in_shape)\n        self.dimensions = len(self.in_shape)\n        self.channels = ensure_tuple(channels)\n        self.strides = ensure_tuple(strides)\n        self.out_shape = ensure_tuple(out_shape)\n        self.kernel_size = ensure_tuple_rep(kernel_size, self.dimensions)\n        self.num_res_units = num_res_units\n        self.act = act\n        self.norm = norm\n        self.dropout = dropout\n        self.bias = bias\n        self.net = nn.Sequential()\n\n        echannel = self.in_channels\n\n        padding = same_padding(kernel_size)\n\n        self.final_size = np.asarray(self.in_shape, dtype=int)\n        self.reshape = Reshape(*self.out_shape)\n\n        # encode stage\n        for i, (c, s) in enumerate(zip(self.channels, self.strides)):\n            layer = self._get_layer(echannel, c, s, i == len(channels) - 1)\n            echannel = c  # use the output channel number as the input for the next loop\n            self.net.add_module(\"layer_%i\" % i, layer)\n            self.final_size = calculate_out_shape(self.final_size, kernel_size, s, padding)  # type: ignore\n\n        self.final = self._get_final_layer((echannel,) + self.final_size)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/segresnet.py_from_typing_import_List____all__._SegResNet_SegResNetV": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/segresnet.py_from_typing_import_List____all__._SegResNet_SegResNetV", "embedding": null, "metadata": {"file_path": "monai/networks/nets/segresnet.py", "file_name": "segresnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 24, "span_ids": ["docstring"], "tokens": 109}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import List, Optional, Sequence, Tuple, Union\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom monai.networks.blocks.segresnet_block import ResBlock, get_conv_layer, get_upsample_layer\nfrom monai.networks.layers.factories import Dropout\nfrom monai.networks.layers.utils import get_act_layer, get_norm_layer\nfrom monai.utils import UpsampleMode\n\n__all__ = [\"SegResNet\", \"SegResNetVAE\"]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/segresnet.py_SegResNet_SegResNet._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/segresnet.py_SegResNet_SegResNet._", "embedding": null, "metadata": {"file_path": "monai/networks/nets/segresnet.py", "file_name": "segresnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 27, "end_line": 55, "span_ids": ["SegResNet"], "tokens": 405}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SegResNet(nn.Module):\n    \"\"\"\n    SegResNet based on `3D MRI brain tumor segmentation using autoencoder regularization\n    <https://arxiv.org/pdf/1810.11654.pdf>`_.\n    The module does not include the variational autoencoder (VAE).\n    The model supports 2D or 3D inputs.\n\n    Args:\n        spatial_dims: spatial dimension of the input data. Defaults to 3.\n        init_filters: number of output channels for initial convolution layer. Defaults to 8.\n        in_channels: number of input channels for the network. Defaults to 1.\n        out_channels: number of output channels for the network. Defaults to 2.\n        dropout_prob: probability of an element to be zero-ed. Defaults to ``None``.\n        act: activation type and arguments. Defaults to ``RELU``.\n        norm: feature normalization type and arguments. Defaults to ``GROUP``.\n        norm_name: deprecating option for feature normalization type.\n        num_groups: deprecating option for group norm. parameters.\n        use_conv_final: if add a final convolution block to output. Defaults to ``True``.\n        blocks_down: number of down sample blocks in each layer. Defaults to ``[1,2,2,4]``.\n        blocks_up: number of up sample blocks in each layer. Defaults to ``[1,1,1]``.\n        upsample_mode: [``\"deconv\"``, ``\"nontrainable\"``, ``\"pixelshuffle\"``]\n            The mode of upsampling manipulations.\n            Using the ``nontrainable`` modes cannot guarantee the model's reproducibility. Defaults to``nontrainable``.\n\n            - ``deconv``, uses transposed convolution layers.\n            - ``nontrainable``, uses non-trainable `linear` interpolation.\n            - ``pixelshuffle``, uses :py:class:`monai.networks.blocks.SubpixelUpsample`.\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/senet.py_re_SE_NET_MODELS._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/senet.py_re_SE_NET_MODELS._", "embedding": null, "metadata": {"file_path": "monai/networks/nets/senet.py", "file_name": "senet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 45, "span_ids": ["docstring"], "tokens": 413}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import re\nfrom collections import OrderedDict\nfrom typing import Any, List, Optional, Sequence, Tuple, Type, Union\n\nimport torch\nimport torch.nn as nn\nfrom torch.hub import load_state_dict_from_url\n\nfrom monai.apps.utils import download_url\nfrom monai.networks.blocks.convolutions import Convolution\nfrom monai.networks.blocks.squeeze_and_excitation import SEBottleneck, SEResNetBottleneck, SEResNeXtBottleneck\nfrom monai.networks.layers.factories import Act, Conv, Dropout, Norm, Pool\nfrom monai.utils.module import look_up_option\n\n__all__ = [\n    \"SENet\",\n    \"SENet154\",\n    \"SEResNet50\",\n    \"SEResNet101\",\n    \"SEResNet152\",\n    \"SEResNeXt50\",\n    \"SEResNext101\",\n    \"SE_NET_MODELS\",\n]\n\n\nSE_NET_MODELS = {\n    \"senet154\": \"http://data.lip6.fr/cadene/pretrainedmodels/senet154-c7b49a05.pth\",\n    \"se_resnet50\": \"http://data.lip6.fr/cadene/pretrainedmodels/se_resnet50-ce0d4300.pth\",\n    \"se_resnet101\": \"http://data.lip6.fr/cadene/pretrainedmodels/se_resnet101-7e38fcc6.pth\",\n    \"se_resnet152\": \"http://data.lip6.fr/cadene/pretrainedmodels/se_resnet152-d17c99b7.pth\",\n    \"se_resnext50_32x4d\": \"http://data.lip6.fr/cadene/pretrainedmodels/se_resnext50_32x4d-a260b3a4.pth\",\n    \"se_resnext101_32x4d\": \"http://data.lip6.fr/cadene/pretrainedmodels/se_resnext101_32x4d-3b2fe3d8.pth\",\n}", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/transchex.py_math___all__._BertPreTrainedModel_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/transchex.py_math___all__._BertPreTrainedModel_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/transchex.py", "file_name": "transchex.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 30, "span_ids": ["docstring"], "tokens": 183}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import math\nimport os\nimport shutil\nimport tarfile\nimport tempfile\nfrom typing import Sequence, Tuple, Union\n\nimport torch\nfrom torch import nn\n\nfrom monai.utils import optional_import\n\ntransformers = optional_import(\"transformers\")\nload_tf_weights_in_bert = optional_import(\"transformers\", name=\"load_tf_weights_in_bert\")\ncached_path = optional_import(\"transformers.file_utils\", name=\"cached_path\")[0]\nBertEmbeddings = optional_import(\"transformers.models.bert.modeling_bert\", name=\"BertEmbeddings\")[0]\nBertLayer = optional_import(\"transformers.models.bert.modeling_bert\", name=\"BertLayer\")[0]\n\n__all__ = [\"BertPreTrainedModel\", \"BertAttention\", \"BertOutput\", \"BertMixedLayer\", \"Pooler\", \"MultiModal\", \"Transchex\"]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/transchex.py_BertPreTrainedModel_BertPreTrainedModel.init_bert_weights.if_isinstance_module_nn_.module_bias_data_zero__": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/transchex.py_BertPreTrainedModel_BertPreTrainedModel.init_bert_weights.if_isinstance_module_nn_.module_bias_data_zero__", "embedding": null, "metadata": {"file_path": "monai/networks/nets/transchex.py", "file_name": "transchex.py", "file_type": "text/x-python", "category": "implementation", "start_line": 33, "end_line": 52, "span_ids": ["BertPreTrainedModel.init_bert_weights", "BertPreTrainedModel.__init__", "BertPreTrainedModel"], "tokens": 175}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class BertPreTrainedModel(nn.Module):\n    \"\"\"Module to load BERT pre-trained weights.\n    Based on:\n    LXMERT\n    https://github.com/airsplay/lxmert\n    BERT (pytorch-transformer)\n    https://github.com/huggingface/transformers\n    \"\"\"\n\n    def __init__(self, *inputs, **kwargs) -> None:\n        super().__init__()\n\n    def init_bert_weights(self, module):\n        if isinstance(module, (nn.Linear, nn.Embedding)):\n            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)\n        elif isinstance(module, torch.nn.LayerNorm):\n            module.bias.data.zero_()\n            module.weight.data.fill_(1.0)\n        if isinstance(module, nn.Linear) and module.bias is not None:\n            module.bias.data.zero_()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/transchex.py_BertPreTrainedModel.from_pretrained_BertPreTrainedModel.from_pretrained.return.model": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/transchex.py_BertPreTrainedModel.from_pretrained_BertPreTrainedModel.from_pretrained.return.model", "embedding": null, "metadata": {"file_path": "monai/networks/nets/transchex.py", "file_name": "transchex.py", "file_type": "text/x-python", "category": "implementation", "start_line": 54, "end_line": 120, "span_ids": ["BertPreTrainedModel.from_pretrained"], "tokens": 597}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class BertPreTrainedModel(nn.Module):\n\n    @classmethod\n    def from_pretrained(\n        cls,\n        num_language_layers,\n        num_vision_layers,\n        num_mixed_layers,\n        bert_config,\n        state_dict=None,\n        cache_dir=None,\n        from_tf=False,\n        *inputs,\n        **kwargs,\n    ):\n        archive_file = \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased.tar.gz\"\n        resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir)\n        tempdir = None\n        if os.path.isdir(resolved_archive_file) or from_tf:\n            serialization_dir = resolved_archive_file\n        else:\n            tempdir = tempfile.mkdtemp()\n            with tarfile.open(resolved_archive_file, \"r:gz\") as archive:\n                archive.extractall(tempdir)\n            serialization_dir = tempdir\n        model = cls(num_language_layers, num_vision_layers, num_mixed_layers, bert_config, *inputs, **kwargs)\n        if state_dict is None and not from_tf:\n            weights_path = os.path.join(serialization_dir, \"pytorch_model.bin\")\n            state_dict = torch.load(weights_path, map_location=\"cpu\" if not torch.cuda.is_available() else None)\n        if tempdir:\n            shutil.rmtree(tempdir)\n        if from_tf:\n            weights_path = os.path.join(serialization_dir, \"model.ckpt\")\n            return load_tf_weights_in_bert(model, weights_path)\n        old_keys = []\n        new_keys = []\n        for key in state_dict.keys():\n            new_key = None\n            if \"gamma\" in key:\n                new_key = key.replace(\"gamma\", \"weight\")\n            if \"beta\" in key:\n                new_key = key.replace(\"beta\", \"bias\")\n            if new_key:\n                old_keys.append(key)\n                new_keys.append(new_key)\n        for old_key, new_key in zip(old_keys, new_keys):\n            state_dict[new_key] = state_dict.pop(old_key)\n        missing_keys = []\n        unexpected_keys = []\n        error_msgs = []\n        metadata = getattr(state_dict, \"_metadata\", None)\n        state_dict = state_dict.copy()\n        if metadata is not None:\n            state_dict._metadata = metadata\n\n        def load(module, prefix=\"\"):\n            local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})\n            module._load_from_state_dict(\n                state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs\n            )\n            for name, child in module._modules.items():\n                if child is not None:\n                    load(child, prefix + name + \".\")\n\n        start_prefix = \"\"\n        if not hasattr(model, \"bert\") and any(s.startswith(\"bert.\") for s in state_dict.keys()):\n            start_prefix = \"bert.\"\n        load(model, prefix=start_prefix)\n        return model", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/transchex.py_BertAttention_BertAttention.transpose_for_scores.return.x_permute_0_2_1_3_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/transchex.py_BertAttention_BertAttention.transpose_for_scores.return.x_permute_0_2_1_3_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/transchex.py", "file_name": "transchex.py", "file_type": "text/x-python", "category": "implementation", "start_line": 123, "end_line": 142, "span_ids": ["BertAttention", "BertAttention.transpose_for_scores", "BertAttention.__init__"], "tokens": 218}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class BertAttention(nn.Module):\n    \"\"\"BERT attention layer.\n    Based on: BERT (pytorch-transformer)\n    https://github.com/huggingface/transformers\n    \"\"\"\n\n    def __init__(self, config) -> None:\n        super().__init__()\n        self.num_attention_heads = config.num_attention_heads\n        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)\n        self.all_head_size = self.num_attention_heads * self.attention_head_size\n        self.query = nn.Linear(config.hidden_size, self.all_head_size)\n        self.key = nn.Linear(config.hidden_size, self.all_head_size)\n        self.value = nn.Linear(config.hidden_size, self.all_head_size)\n        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)\n\n    def transpose_for_scores(self, x):\n        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)\n        x = x.view(*new_x_shape)\n        return x.permute(0, 2, 1, 3)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/transchex.py_BertAttention.forward_BertAttention.forward.return.context_layer": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/transchex.py_BertAttention.forward_BertAttention.forward.return.context_layer", "embedding": null, "metadata": {"file_path": "monai/networks/nets/transchex.py", "file_name": "transchex.py", "file_type": "text/x-python", "category": "implementation", "start_line": 144, "end_line": 158, "span_ids": ["BertAttention.forward"], "tokens": 206}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class BertAttention(nn.Module):\n\n    def forward(self, hidden_states, context):\n        mixed_query_layer = self.query(hidden_states)\n        mixed_key_layer = self.key(context)\n        mixed_value_layer = self.value(context)\n        query_layer = self.transpose_for_scores(mixed_query_layer)\n        key_layer = self.transpose_for_scores(mixed_key_layer)\n        value_layer = self.transpose_for_scores(mixed_value_layer)\n        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))\n        attention_scores = attention_scores / math.sqrt(self.attention_head_size)\n        attention_probs = self.dropout(nn.Softmax(dim=-1)(attention_scores))\n        context_layer = torch.matmul(attention_probs, value_layer)\n        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()\n        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)\n        context_layer = context_layer.view(*new_context_layer_shape)\n        return context_layer", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/transchex.py_BertOutput_BertOutput.forward.return.hidden_states": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/transchex.py_BertOutput_BertOutput.forward.return.hidden_states", "embedding": null, "metadata": {"file_path": "monai/networks/nets/transchex.py", "file_name": "transchex.py", "file_type": "text/x-python", "category": "implementation", "start_line": 161, "end_line": 177, "span_ids": ["BertOutput.forward", "BertOutput", "BertOutput.__init__"], "tokens": 146}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class BertOutput(nn.Module):\n    \"\"\"BERT output layer.\n    Based on: BERT (pytorch-transformer)\n    https://github.com/huggingface/transformers\n    \"\"\"\n\n    def __init__(self, config) -> None:\n        super().__init__()\n        self.dense = nn.Linear(config.hidden_size, config.hidden_size)\n        self.LayerNorm = torch.nn.LayerNorm(config.hidden_size, eps=1e-12)\n        self.dropout = nn.Dropout(config.hidden_dropout_prob)\n\n    def forward(self, hidden_states, input_tensor):\n        hidden_states = self.dense(hidden_states)\n        hidden_states = self.dropout(hidden_states)\n        hidden_states = self.LayerNorm(hidden_states + input_tensor)\n        return hidden_states", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/transchex.py_BertMixedLayer_Pooler.forward.return.pooled_output": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/transchex.py_BertMixedLayer_Pooler.forward.return.pooled_output", "embedding": null, "metadata": {"file_path": "monai/networks/nets/transchex.py", "file_name": "transchex.py", "file_type": "text/x-python", "category": "implementation", "start_line": 180, "end_line": 211, "span_ids": ["Pooler.__init__", "Pooler", "BertMixedLayer.forward", "BertMixedLayer.__init__", "BertMixedLayer", "Pooler.forward"], "tokens": 219}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class BertMixedLayer(nn.Module):\n    \"\"\"BERT cross attention layer.\n    Based on: BERT (pytorch-transformer)\n    https://github.com/huggingface/transformers\n    \"\"\"\n\n    def __init__(self, config) -> None:\n        super().__init__()\n        self.att = BertAttention(config)\n        self.output = BertOutput(config)\n\n    def forward(self, x, y):\n        output = self.att(x, y)\n        return self.output(output, x)\n\n\nclass Pooler(nn.Module):\n    \"\"\"BERT pooler layer.\n    Based on: BERT (pytorch-transformer)\n    https://github.com/huggingface/transformers\n    \"\"\"\n\n    def __init__(self, hidden_size) -> None:\n        super().__init__()\n        self.dense = nn.Linear(hidden_size, hidden_size)\n        self.activation = nn.Tanh()\n\n    def forward(self, hidden_states):\n        first_token_tensor = hidden_states[:, 0]\n        pooled_output = self.dense(first_token_tensor)\n        pooled_output = self.activation(pooled_output)\n        return pooled_output", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/transchex.py_MultiModal_MultiModal.__init__.self_apply_self_init_bert": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/transchex.py_MultiModal_MultiModal.__init__.self_apply_self_init_bert", "embedding": null, "metadata": {"file_path": "monai/networks/nets/transchex.py", "file_name": "transchex.py", "file_type": "text/x-python", "category": "implementation", "start_line": 214, "end_line": 235, "span_ids": ["MultiModal.__init__", "MultiModal"], "tokens": 224}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class MultiModal(BertPreTrainedModel):\n    \"\"\"\n    Multimodal Transformers From Pretrained BERT Weights\"\n    \"\"\"\n\n    def __init__(\n        self, num_language_layers: int, num_vision_layers: int, num_mixed_layers: int, bert_config: dict  # type: ignore\n    ) -> None:\n        \"\"\"\n        Args:\n            num_language_layers: number of language transformer layers.\n            num_vision_layers: number of vision transformer layers.\n            bert_config: configuration for bert language transformer encoder.\n\n        \"\"\"\n        super().__init__()\n        self.config = type(\"obj\", (object,), bert_config)\n        self.embeddings = BertEmbeddings(self.config)\n        self.language_encoder = nn.ModuleList([BertLayer(self.config) for _ in range(num_language_layers)])\n        self.vision_encoder = nn.ModuleList([BertLayer(self.config) for _ in range(num_vision_layers)])\n        self.mixed_encoder = nn.ModuleList([BertMixedLayer(self.config) for _ in range(num_mixed_layers)])\n        self.apply(self.init_bert_weights)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/transchex.py_MultiModal.forward_MultiModal.forward.return.hidden_state_mixed": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/transchex.py_MultiModal.forward_MultiModal.forward.return.hidden_state_mixed", "embedding": null, "metadata": {"file_path": "monai/networks/nets/transchex.py", "file_name": "transchex.py", "file_type": "text/x-python", "category": "implementation", "start_line": 237, "end_line": 245, "span_ids": ["MultiModal.forward"], "tokens": 122}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class MultiModal(BertPreTrainedModel):\n\n    def forward(self, input_ids, token_type_ids=None, vision_feats=None, attention_mask=None):\n        language_features = self.embeddings(input_ids, token_type_ids)\n        for layer in self.vision_encoder:\n            hidden_state_vision = layer(vision_feats, None)[0]\n        for layer in self.language_encoder:\n            hidden_state_language = layer(language_features, attention_mask)[0]\n        for layer in self.mixed_encoder:\n            hidden_state_mixed = layer(hidden_state_language, hidden_state_vision)\n        return hidden_state_mixed", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/transchex.py_Transchex_Transchex.__init__.self.cls_head.torch_nn_Linear_hidden_si": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/transchex.py_Transchex_Transchex.__init__.self.cls_head.torch_nn_Linear_hidden_si", "embedding": null, "metadata": {"file_path": "monai/networks/nets/transchex.py", "file_name": "transchex.py", "file_type": "text/x-python", "category": "implementation", "start_line": 248, "end_line": 364, "span_ids": ["Transchex", "Transchex.__init__"], "tokens": 1110}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Transchex(torch.nn.Module):\n    \"\"\"\n    TransChex based on: \"Hatamizadeh et al.,TransCheX: Self-Supervised Pretraining of Vision-Language\n    Transformers for Chest X-ray Analysis\"\n    \"\"\"\n\n    def __init__(\n        self,\n        in_channels: int,\n        img_size: Union[Sequence[int], int],  # type: ignore\n        patch_size: Union[int, Tuple[int, int]],  # type: ignore\n        num_classes: int,\n        num_language_layers: int,\n        num_vision_layers: int,\n        num_mixed_layers: int,\n        hidden_size: int = 768,\n        drop_out: float = 0.0,\n        attention_probs_dropout_prob: float = 0.1,\n        gradient_checkpointing: bool = False,\n        hidden_act: str = \"gelu\",\n        hidden_dropout_prob: float = 0.1,\n        initializer_range: float = 0.02,\n        intermediate_size: int = 3072,\n        layer_norm_eps: float = 1e-12,\n        max_position_embeddings: int = 512,\n        model_type: str = \"bert\",\n        num_attention_heads: int = 12,\n        num_hidden_layers: int = 12,\n        pad_token_id: int = 0,\n        position_embedding_type: str = \"absolute\",\n        transformers_version: str = \"4.10.2\",\n        type_vocab_size: int = 2,\n        use_cache: bool = True,\n        vocab_size: int = 30522,\n        chunk_size_feed_forward: int = 0,\n        is_decoder: bool = False,\n        add_cross_attention: bool = False,\n    ) -> None:\n        \"\"\"\n        Args:\n            in_channels: dimension of input channels.\n            img_size: dimension of input image.\n            patch_size: dimension of patch size.\n            num_classes: number of classes if classification is used.\n            num_language_layers: number of language transformer layers.\n            num_vision_layers: number of vision transformer layers.\n            num_mixed_layers: number of mixed transformer layers.\n            drop_out: faction of the input units to drop.\n\n        The other parameters are part of the `bert_config` to `MultiModal.from_pretrained`.\n\n        Examples:\n\n        .. code-block:: python\n\n            # for 3-channel with image size of (224,224), patch size of (32,32), 3 classes, 2 language layers,\n            # 2 vision layers, 2 mixed modality layers and dropout of 0.2 in the classification head\n            net = Transchex(in_channels=3,\n                                 img_size=(224, 224),\n                                 num_classes=3,\n                                 num_language_layers=2,\n                                 num_vision_layers=2,\n                                 num_mixed_layers=2,\n                                 drop_out=0.2)\n\n        \"\"\"\n        super().__init__()\n        bert_config = {\n            \"attention_probs_dropout_prob\": attention_probs_dropout_prob,\n            \"classifier_dropout\": None,\n            \"gradient_checkpointing\": gradient_checkpointing,\n            \"hidden_act\": hidden_act,\n            \"hidden_dropout_prob\": hidden_dropout_prob,\n            \"hidden_size\": hidden_size,\n            \"initializer_range\": initializer_range,\n            \"intermediate_size\": intermediate_size,\n            \"layer_norm_eps\": layer_norm_eps,\n            \"max_position_embeddings\": max_position_embeddings,\n            \"model_type\": model_type,\n            \"num_attention_heads\": num_attention_heads,\n            \"num_hidden_layers\": num_hidden_layers,\n            \"pad_token_id\": pad_token_id,\n            \"position_embedding_type\": position_embedding_type,\n            \"transformers_version\": transformers_version,\n            \"type_vocab_size\": type_vocab_size,\n            \"use_cache\": use_cache,\n            \"vocab_size\": vocab_size,\n            \"chunk_size_feed_forward\": chunk_size_feed_forward,\n            \"is_decoder\": is_decoder,\n            \"add_cross_attention\": add_cross_attention,\n        }\n        if not (0 <= drop_out <= 1):\n            raise ValueError(\"dropout_rate should be between 0 and 1.\")\n\n        if (img_size[0] % patch_size[0] != 0) or (img_size[1] % patch_size[1] != 0):  # type: ignore\n            raise ValueError(\"img_size should be divisible by patch_size.\")\n\n        self.multimodal = MultiModal.from_pretrained(\n            num_language_layers=num_language_layers,\n            num_vision_layers=num_vision_layers,\n            num_mixed_layers=num_mixed_layers,\n            bert_config=bert_config,\n        )\n\n        self.patch_size = patch_size\n        self.num_patches = (img_size[0] // self.patch_size[0]) * (img_size[1] // self.patch_size[1])  # type: ignore\n        self.vision_proj = nn.Conv2d(\n            in_channels=in_channels,\n            out_channels=hidden_size,\n            kernel_size=self.patch_size,  # type: ignore\n            stride=self.patch_size,  # type: ignore\n        )\n        self.norm_vision_pos = nn.LayerNorm(hidden_size)\n        self.pos_embed_vis = nn.Parameter(torch.zeros(1, self.num_patches, hidden_size))\n        self.pooler = Pooler(hidden_size=hidden_size)\n        self.drop = torch.nn.Dropout(drop_out)\n        self.cls_head = torch.nn.Linear(hidden_size, num_classes)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/transchex.py_Transchex.forward_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/transchex.py_Transchex.forward_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/transchex.py", "file_name": "transchex.py", "file_type": "text/x-python", "category": "implementation", "start_line": 365, "end_line": 378, "span_ids": ["Transchex.forward"], "tokens": 191}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Transchex(torch.nn.Module):\n\n    def forward(self, input_ids, token_type_ids=None, vision_feats=None):\n        attention_mask = torch.ones_like(input_ids).unsqueeze(1).unsqueeze(2)\n        attention_mask = attention_mask.to(dtype=next(self.parameters()).dtype)\n        attention_mask = (1.0 - attention_mask) * -10000.0\n        vision_feats = self.vision_proj(vision_feats).flatten(2).transpose(1, 2)\n        vision_feats = self.norm_vision_pos(vision_feats)\n        vision_feats = vision_feats + self.pos_embed_vis\n        hidden_state_mixed = self.multimodal(\n            input_ids=input_ids, token_type_ids=token_type_ids, vision_feats=vision_feats, attention_mask=attention_mask\n        )\n        pooled_features = self.pooler(hidden_state_mixed)\n        logits = self.cls_head(self.drop(pooled_features))\n        return logits", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/unet.py_warnings_UNet._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/unet.py_warnings_UNet._", "embedding": null, "metadata": {"file_path": "monai/networks/nets/unet.py", "file_name": "unet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 106, "span_ids": ["UNet", "docstring"], "tokens": 1216}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import warnings\nfrom typing import Optional, Sequence, Tuple, Union\n\nimport torch\nimport torch.nn as nn\n\nfrom monai.networks.blocks.convolutions import Convolution, ResidualUnit\nfrom monai.networks.layers.factories import Act, Norm\nfrom monai.networks.layers.simplelayers import SkipConnection\nfrom monai.utils import alias, deprecated_arg, export\n\n__all__ = [\"UNet\", \"Unet\"]\n\n\n@export(\"monai.networks.nets\")\n@alias(\"Unet\")\nclass UNet(nn.Module):\n    \"\"\"\n    Enhanced version of UNet which has residual units implemented with the ResidualUnit class.\n    The residual part uses a convolution to change the input dimensions to match the output dimensions\n    if this is necessary but will use nn.Identity if not.\n    Refer to: https://link.springer.com/chapter/10.1007/978-3-030-12029-0_40.\n\n    Each layer of the network has a encode and decode path with a skip connection between them. Data in the encode path\n    is downsampled using strided convolutions (if `strides` is given values greater than 1) and in the decode path\n    upsampled using strided transpose convolutions. These down or up sampling operations occur at the beginning of each\n    block rather than afterwards as is typical in UNet implementations.\n\n    To further explain this consider the first example network given below. This network has 3 layers with strides\n    of 2 for each of the middle layers (the last layer is the bottom connection which does not down/up sample). Input\n    data to this network is immediately reduced in the spatial dimensions by a factor of 2 by the first convolution of\n    the residual unit defining the first layer of the encode part. The last layer of the decode part will upsample its\n    input (data from the previous layer concatenated with data from the skip connection) in the first convolution. this\n    ensures the final output of the network has the same shape as the input.\n\n    Padding values for the convolutions are chosen to ensure output sizes are even divisors/multiples of the input\n    sizes if the `strides` value for a layer is a factor of the input sizes. A typical case is to use `strides` values\n    of 2 and inputs that are multiples of powers of 2. An input can thus be downsampled evenly however many times its\n    dimensions can be divided by 2, so for the example network inputs would have to have dimensions that are multiples\n    of 4. In the second example network given below the input to the bottom layer will have shape (1, 64, 15, 15) for\n    an input of shape (1, 1, 240, 240) demonstrating the input being reduced in size spatially by 2**4.\n\n    Args:\n        spatial_dims: number of spatial dimensions.\n        in_channels: number of input channels.\n        out_channels: number of output channels.\n        channels: sequence of channels. Top block first. The length of `channels` should be no less than 2.\n        strides: sequence of convolution strides. The length of `stride` should equal to `len(channels) - 1`.\n        kernel_size: convolution kernel size, the value(s) should be odd. If sequence,\n            its length should equal to dimensions. Defaults to 3.\n        up_kernel_size: upsampling convolution kernel size, the value(s) should be odd. If sequence,\n            its length should equal to dimensions. Defaults to 3.\n        num_res_units: number of residual units. Defaults to 0.\n        act: activation type and arguments. Defaults to PReLU.\n        norm: feature normalization type and arguments. Defaults to instance norm.\n        dropout: dropout ratio. Defaults to no dropout.\n        bias: whether to have a bias term in convolution blocks. Defaults to True.\n            According to `Performance Tuning Guide <https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html>`_,\n            if a conv layer is directly followed by a batch norm layer, bias should be False.\n\n    Examples::\n\n        from monai.networks.nets import UNet\n\n        # 3 layer network with down/upsampling by a factor of 2 at each layer with 2-convolution residual units\n        net = UNet(\n            spatial_dims=2,\n            in_channels=1,\n            out_channels=1,\n            channels=(4, 8, 16),\n            strides=(2, 2),\n            num_res_units=2\n        )\n\n        # 5 layer network with simple convolution/normalization/dropout/activation blocks defining the layers\n        net=UNet(\n            spatial_dims=2,\n            in_channels=1,\n            out_channels=1,\n            channels=(4, 8, 16, 32, 64),\n            strides=(2, 2, 2, 2),\n        )\n\n    .. deprecated:: 0.6.0\n        ``dimensions`` is deprecated, use ``spatial_dims`` instead.\n\n    Note: The acceptable spatial size of input data depends on the parameters of the network,\n        to set appropriate spatial size, please check the tutorial for more details:\n        https://github.com/Project-MONAI/tutorials/blob/master/modules/UNet_input_size_constrains.ipynb.\n        Typically, when using a stride of 2 in down / up sampling, the output dimensions are either half of the\n        input when downsampling, or twice when upsampling. In this case with N numbers of layers in the network,\n        the inputs must have spatial dimensions that are all multiples of 2^N.\n        Usually, applying `resize`, `pad` or `crop` transforms can help adjust the spatial size of input data.\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/unet.py_UNet.__init___UNet.__init__.self.model._create_block_in_channels": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/unet.py_UNet.__init___UNet.__init__.self.model._create_block_in_channels", "embedding": null, "metadata": {"file_path": "monai/networks/nets/unet.py", "file_name": "unet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 108, "end_line": 191, "span_ids": ["UNet.__init__"], "tokens": 836}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@export(\"monai.networks.nets\")\n@alias(\"Unet\")\nclass UNet(nn.Module):\n\n    @deprecated_arg(\n        name=\"dimensions\", new_name=\"spatial_dims\", since=\"0.6\", msg_suffix=\"Please use `spatial_dims` instead.\"\n    )\n    def __init__(\n        self,\n        spatial_dims: int,\n        in_channels: int,\n        out_channels: int,\n        channels: Sequence[int],\n        strides: Sequence[int],\n        kernel_size: Union[Sequence[int], int] = 3,\n        up_kernel_size: Union[Sequence[int], int] = 3,\n        num_res_units: int = 0,\n        act: Union[Tuple, str] = Act.PRELU,\n        norm: Union[Tuple, str] = Norm.INSTANCE,\n        dropout: float = 0.0,\n        bias: bool = True,\n        dimensions: Optional[int] = None,\n    ) -> None:\n\n        super().__init__()\n\n        if len(channels) < 2:\n            raise ValueError(\"the length of `channels` should be no less than 2.\")\n        delta = len(strides) - (len(channels) - 1)\n        if delta < 0:\n            raise ValueError(\"the length of `strides` should equal to `len(channels) - 1`.\")\n        if delta > 0:\n            warnings.warn(f\"`len(strides) > len(channels) - 1`, the last {delta} values of strides will not be used.\")\n        if dimensions is not None:\n            spatial_dims = dimensions\n        if isinstance(kernel_size, Sequence):\n            if len(kernel_size) != spatial_dims:\n                raise ValueError(\"the length of `kernel_size` should equal to `dimensions`.\")\n        if isinstance(up_kernel_size, Sequence):\n            if len(up_kernel_size) != spatial_dims:\n                raise ValueError(\"the length of `up_kernel_size` should equal to `dimensions`.\")\n\n        self.dimensions = spatial_dims\n        self.in_channels = in_channels\n        self.out_channels = out_channels\n        self.channels = channels\n        self.strides = strides\n        self.kernel_size = kernel_size\n        self.up_kernel_size = up_kernel_size\n        self.num_res_units = num_res_units\n        self.act = act\n        self.norm = norm\n        self.dropout = dropout\n        self.bias = bias\n\n        def _create_block(\n            inc: int, outc: int, channels: Sequence[int], strides: Sequence[int], is_top: bool\n        ) -> nn.Sequential:\n            \"\"\"\n            Builds the UNet structure from the bottom up by recursing down to the bottom block, then creating sequential\n            blocks containing the downsample path, a skip connection around the previous block, and the upsample path.\n\n            Args:\n                inc: number of input channels.\n                outc: number of output channels.\n                channels: sequence of channels. Top block first.\n                strides: convolution stride.\n                is_top: True if this is the top block.\n            \"\"\"\n            c = channels[0]\n            s = strides[0]\n\n            subblock: nn.Module\n\n            if len(channels) > 2:\n                subblock = _create_block(c, c, channels[1:], strides[1:], False)  # continue recursion down\n                upc = c * 2\n            else:\n                # the next layer is the bottom so stop recursion, create the bottom layer as the sublock for this layer\n                subblock = self._get_bottom_layer(c, channels[1])\n                upc = c + channels[1]\n\n            down = self._get_down_layer(inc, c, s, is_top)  # create layer in downsampling path\n            up = self._get_up_layer(upc, outc, s, is_top)  # create layer in upsampling path\n\n            return nn.Sequential(down, SkipConnection(subblock), up)\n\n        self.model = _create_block(in_channels, out_channels, self.channels, self.strides, True)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/unetr.py_from_typing_import_Sequen_UNETR.__init__.self.out.UnetOutBlock_spatial_dims": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/unetr.py_from_typing_import_Sequen_UNETR.__init__.self.out.UnetOutBlock_spatial_dims", "embedding": null, "metadata": {"file_path": "monai/networks/nets/unetr.py", "file_name": "unetr.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 181, "span_ids": ["UNETR", "UNETR.__init__", "docstring"], "tokens": 1423}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Sequence, Tuple, Union\n\nimport torch.nn as nn\n\nfrom monai.networks.blocks.dynunet_block import UnetOutBlock\nfrom monai.networks.blocks.unetr_block import UnetrBasicBlock, UnetrPrUpBlock, UnetrUpBlock\nfrom monai.networks.nets.vit import ViT\nfrom monai.utils import ensure_tuple_rep\n\n\nclass UNETR(nn.Module):\n    \"\"\"\n    UNETR based on: \"Hatamizadeh et al.,\n    UNETR: Transformers for 3D Medical Image Segmentation <https://arxiv.org/abs/2103.10504>\"\n    \"\"\"\n\n    def __init__(\n        self,\n        in_channels: int,\n        out_channels: int,\n        img_size: Union[Sequence[int], int],\n        feature_size: int = 16,\n        hidden_size: int = 768,\n        mlp_dim: int = 3072,\n        num_heads: int = 12,\n        pos_embed: str = \"conv\",\n        norm_name: Union[Tuple, str] = \"instance\",\n        conv_block: bool = True,\n        res_block: bool = True,\n        dropout_rate: float = 0.0,\n        spatial_dims: int = 3,\n    ) -> None:\n        \"\"\"\n        Args:\n            in_channels: dimension of input channels.\n            out_channels: dimension of output channels.\n            img_size: dimension of input image.\n            feature_size: dimension of network feature size.\n            hidden_size: dimension of hidden layer.\n            mlp_dim: dimension of feedforward layer.\n            num_heads: number of attention heads.\n            pos_embed: position embedding layer type.\n            norm_name: feature normalization type and arguments.\n            conv_block: bool argument to determine if convolutional block is used.\n            res_block: bool argument to determine if residual block is used.\n            dropout_rate: faction of the input units to drop.\n            spatial_dims: number of spatial dims.\n\n        Examples::\n\n            # for single channel input 4-channel output with image size of (96,96,96), feature size of 32 and batch norm\n            >>> net = UNETR(in_channels=1, out_channels=4, img_size=(96,96,96), feature_size=32, norm_name='batch')\n\n             # for single channel input 4-channel output with image size of (96,96), feature size of 32 and batch norm\n            >>> net = UNETR(in_channels=1, out_channels=4, img_size=96, feature_size=32, norm_name='batch', spatial_dims=2)\n\n            # for 4-channel input 3-channel output with image size of (128,128,128), conv position embedding and instance norm\n            >>> net = UNETR(in_channels=4, out_channels=3, img_size=(128,128,128), pos_embed='conv', norm_name='instance')\n\n        \"\"\"\n\n        super().__init__()\n\n        if not (0 <= dropout_rate <= 1):\n            raise ValueError(\"dropout_rate should be between 0 and 1.\")\n\n        if hidden_size % num_heads != 0:\n            raise ValueError(\"hidden_size should be divisible by num_heads.\")\n\n        self.num_layers = 12\n        img_size = ensure_tuple_rep(img_size, spatial_dims)\n        self.patch_size = ensure_tuple_rep(16, spatial_dims)\n        self.feat_size = tuple(img_d // p_d for img_d, p_d in zip(img_size, self.patch_size))\n        self.hidden_size = hidden_size\n        self.classification = False\n        self.vit = ViT(\n            in_channels=in_channels,\n            img_size=img_size,\n            patch_size=self.patch_size,\n            hidden_size=hidden_size,\n            mlp_dim=mlp_dim,\n            num_layers=self.num_layers,\n            num_heads=num_heads,\n            pos_embed=pos_embed,\n            classification=self.classification,\n            dropout_rate=dropout_rate,\n            spatial_dims=spatial_dims,\n        )\n        self.encoder1 = UnetrBasicBlock(\n            spatial_dims=spatial_dims,\n            in_channels=in_channels,\n            out_channels=feature_size,\n            kernel_size=3,\n            stride=1,\n            norm_name=norm_name,\n            res_block=res_block,\n        )\n        self.encoder2 = UnetrPrUpBlock(\n            spatial_dims=spatial_dims,\n            in_channels=hidden_size,\n            out_channels=feature_size * 2,\n            num_layer=2,\n            kernel_size=3,\n            stride=1,\n            upsample_kernel_size=2,\n            norm_name=norm_name,\n            conv_block=conv_block,\n            res_block=res_block,\n        )\n        self.encoder3 = UnetrPrUpBlock(\n            spatial_dims=spatial_dims,\n            in_channels=hidden_size,\n            out_channels=feature_size * 4,\n            num_layer=1,\n            kernel_size=3,\n            stride=1,\n            upsample_kernel_size=2,\n            norm_name=norm_name,\n            conv_block=conv_block,\n            res_block=res_block,\n        )\n        self.encoder4 = UnetrPrUpBlock(\n            spatial_dims=spatial_dims,\n            in_channels=hidden_size,\n            out_channels=feature_size * 8,\n            num_layer=0,\n            kernel_size=3,\n            stride=1,\n            upsample_kernel_size=2,\n            norm_name=norm_name,\n            conv_block=conv_block,\n            res_block=res_block,\n        )\n        self.decoder5 = UnetrUpBlock(\n            spatial_dims=spatial_dims,\n            in_channels=hidden_size,\n            out_channels=feature_size * 8,\n            kernel_size=3,\n            upsample_kernel_size=2,\n            norm_name=norm_name,\n            res_block=res_block,\n        )\n        self.decoder4 = UnetrUpBlock(\n            spatial_dims=spatial_dims,\n            in_channels=feature_size * 8,\n            out_channels=feature_size * 4,\n            kernel_size=3,\n            upsample_kernel_size=2,\n            norm_name=norm_name,\n            res_block=res_block,\n        )\n        self.decoder3 = UnetrUpBlock(\n            spatial_dims=spatial_dims,\n            in_channels=feature_size * 4,\n            out_channels=feature_size * 2,\n            kernel_size=3,\n            upsample_kernel_size=2,\n            norm_name=norm_name,\n            res_block=res_block,\n        )\n        self.decoder2 = UnetrUpBlock(\n            spatial_dims=spatial_dims,\n            in_channels=feature_size * 2,\n            out_channels=feature_size,\n            kernel_size=3,\n            upsample_kernel_size=2,\n            norm_name=norm_name,\n            res_block=res_block,\n        )\n        self.out = UnetOutBlock(spatial_dims=spatial_dims, in_channels=feature_size, out_channels=out_channels)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/unetr.py_UNETR.proj_feat_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/unetr.py_UNETR.proj_feat_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/unetr.py", "file_name": "unetr.py", "file_type": "text/x-python", "category": "implementation", "start_line": 183, "end_line": 205, "span_ids": ["UNETR.forward", "UNETR.proj_feat"], "tokens": 294}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class UNETR(nn.Module):\n\n    def proj_feat(self, x, hidden_size, feat_size):\n        new_view = (x.size(0), *feat_size, hidden_size)\n        x = x.view(new_view)\n        new_axes = (0, len(x.shape) - 1) + tuple(d + 1 for d in range(len(feat_size)))\n        x = x.permute(new_axes).contiguous()\n        return x\n\n    def forward(self, x_in):\n        x, hidden_states_out = self.vit(x_in)\n        enc1 = self.encoder1(x_in)\n        x2 = hidden_states_out[3]\n        enc2 = self.encoder2(self.proj_feat(x2, self.hidden_size, self.feat_size))\n        x3 = hidden_states_out[6]\n        enc3 = self.encoder3(self.proj_feat(x3, self.hidden_size, self.feat_size))\n        x4 = hidden_states_out[9]\n        enc4 = self.encoder4(self.proj_feat(x4, self.hidden_size, self.feat_size))\n        dec4 = self.proj_feat(x, self.hidden_size, self.feat_size)\n        dec3 = self.decoder5(dec4, enc4)\n        dec2 = self.decoder4(dec3, enc3)\n        dec1 = self.decoder3(dec2, enc2)\n        out = self.decoder2(dec1, enc1)\n        return self.out(out)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/varautoencoder.py_from_typing_import_Option_VarAutoEncoder._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/varautoencoder.py_from_typing_import_Option_VarAutoEncoder._", "embedding": null, "metadata": {"file_path": "monai/networks/nets/varautoencoder.py", "file_name": "varautoencoder.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 73, "span_ids": ["VarAutoEncoder", "docstring"], "tokens": 643}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Optional, Sequence, Tuple, Union\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom torch.nn import functional as F\n\nfrom monai.networks.layers.convutils import calculate_out_shape, same_padding\nfrom monai.networks.layers.factories import Act, Norm\nfrom monai.networks.nets import AutoEncoder\nfrom monai.utils import deprecated_arg\n\n__all__ = [\"VarAutoEncoder\"]\n\n\nclass VarAutoEncoder(AutoEncoder):\n    \"\"\"\n    Variational Autoencoder based on the paper - https://arxiv.org/abs/1312.6114\n\n    Args:\n        spatial_dims: number of spatial dimensions.\n        in_shape: shape of input data starting with channel dimension.\n        out_channels: number of output channels.\n        latent_size: size of the latent variable.\n        channels: sequence of channels. Top block first. The length of `channels` should be no less than 2.\n        strides: sequence of convolution strides. The length of `stride` should equal to `len(channels) - 1`.\n        kernel_size: convolution kernel size, the value(s) should be odd. If sequence,\n            its length should equal to dimensions. Defaults to 3.\n        up_kernel_size: upsampling convolution kernel size, the value(s) should be odd. If sequence,\n            its length should equal to dimensions. Defaults to 3.\n        num_res_units: number of residual units. Defaults to 0.\n        inter_channels: sequence of channels defining the blocks in the intermediate layer between encode and decode.\n        inter_dilations: defines the dilation value for each block of the intermediate layer. Defaults to 1.\n        num_inter_units: number of residual units for each block of the intermediate layer. Defaults to 0.\n        act: activation type and arguments. Defaults to PReLU.\n        norm: feature normalization type and arguments. Defaults to instance norm.\n        dropout: dropout ratio. Defaults to no dropout.\n        bias: whether to have a bias term in convolution blocks. Defaults to True.\n            According to `Performance Tuning Guide <https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html>`_,\n            if a conv layer is directly followed by a batch norm layer, bias should be False.\n\n    .. deprecated:: 0.6.0\n        ``dimensions`` is deprecated, use ``spatial_dims`` instead.\n\n    Examples::\n\n        from monai.networks.nets import VarAutoEncoder\n\n        # 3 layer network accepting images with dimensions (1, 32, 32) and using a latent vector with 2 values\n        model = VarAutoEncoder(\n            dimensions=2,\n            in_shape=(32, 32),  # image spatial shape\n            out_channels=1,\n            latent_size=2,\n            channels=(16, 32, 64),\n            strides=(1, 2, 2),\n        )\n\n    see also:\n        - Variational autoencoder network with MedNIST Dataset\n          https://github.com/Project-MONAI/tutorials/blob/master/modules/varautoencoder_mednist.ipynb\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/varautoencoder.py_VarAutoEncoder.__init___": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/varautoencoder.py_VarAutoEncoder.__init___", "embedding": null, "metadata": {"file_path": "monai/networks/nets/varautoencoder.py", "file_name": "varautoencoder.py", "file_type": "text/x-python", "category": "implementation", "start_line": 75, "end_line": 162, "span_ids": ["VarAutoEncoder.encode_forward", "VarAutoEncoder.decode_forward", "VarAutoEncoder.forward", "VarAutoEncoder.reparameterize", "VarAutoEncoder.__init__"], "tokens": 728}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class VarAutoEncoder(AutoEncoder):\n\n    @deprecated_arg(\n        name=\"dimensions\", new_name=\"spatial_dims\", since=\"0.6\", msg_suffix=\"Please use `spatial_dims` instead.\"\n    )\n    def __init__(\n        self,\n        spatial_dims: int,\n        in_shape: Sequence[int],\n        out_channels: int,\n        latent_size: int,\n        channels: Sequence[int],\n        strides: Sequence[int],\n        kernel_size: Union[Sequence[int], int] = 3,\n        up_kernel_size: Union[Sequence[int], int] = 3,\n        num_res_units: int = 0,\n        inter_channels: Optional[list] = None,\n        inter_dilations: Optional[list] = None,\n        num_inter_units: int = 2,\n        act: Optional[Union[Tuple, str]] = Act.PRELU,\n        norm: Union[Tuple, str] = Norm.INSTANCE,\n        dropout: Optional[Union[Tuple, str, float]] = None,\n        bias: bool = True,\n        dimensions: Optional[int] = None,\n    ) -> None:\n\n        self.in_channels, *self.in_shape = in_shape\n\n        self.latent_size = latent_size\n        self.final_size = np.asarray(self.in_shape, dtype=int)\n        if dimensions is not None:\n            spatial_dims = dimensions\n\n        super().__init__(\n            spatial_dims,\n            self.in_channels,\n            out_channels,\n            channels,\n            strides,\n            kernel_size,\n            up_kernel_size,\n            num_res_units,\n            inter_channels,\n            inter_dilations,\n            num_inter_units,\n            act,\n            norm,\n            dropout,\n            bias,\n        )\n\n        padding = same_padding(self.kernel_size)\n\n        for s in strides:\n            self.final_size = calculate_out_shape(self.final_size, self.kernel_size, s, padding)  # type: ignore\n\n        linear_size = int(np.product(self.final_size)) * self.encoded_channels\n        self.mu = nn.Linear(linear_size, self.latent_size)\n        self.logvar = nn.Linear(linear_size, self.latent_size)\n        self.decodeL = nn.Linear(self.latent_size, linear_size)\n\n    def encode_forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:\n        x = self.encode(x)\n        x = self.intermediate(x)\n        x = x.view(x.shape[0], -1)\n        mu = self.mu(x)\n        logvar = self.logvar(x)\n        return mu, logvar\n\n    def decode_forward(self, z: torch.Tensor, use_sigmoid: bool = True) -> torch.Tensor:\n        x = F.relu(self.decodeL(z))\n        x = x.view(x.shape[0], self.channels[-1], *self.final_size)\n        x = self.decode(x)\n        if use_sigmoid:\n            x = torch.sigmoid(x)\n        return x\n\n    def reparameterize(self, mu: torch.Tensor, logvar: torch.Tensor) -> torch.Tensor:\n        std = torch.exp(0.5 * logvar)\n\n        if self.training:  # multiply random noise with std only during training\n            std = torch.randn_like(std).mul(std)\n\n        return std.add_(mu)\n\n    def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:\n        mu, logvar = self.encode_forward(x)\n        z = self.reparameterize(mu, logvar)\n        return self.decode_forward(z), mu, logvar, z", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/vit.py_from_typing_import_Sequen_ViT.__init__.if_self_classification_.self.classification_head.nn_Sequential_nn_Linear_h": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/vit.py_from_typing_import_Sequen_ViT.__init__.if_self_classification_.self.classification_head.nn_Sequential_nn_Linear_h", "embedding": null, "metadata": {"file_path": "monai/networks/nets/vit.py", "file_name": "vit.py", "file_type": "text/x-python", "category": "implementation", "start_line": 13, "end_line": 98, "span_ids": ["ViT", "ViT.__init__", "docstring"], "tokens": 736}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Sequence, Union\n\nimport torch\nimport torch.nn as nn\n\nfrom monai.networks.blocks.patchembedding import PatchEmbeddingBlock\nfrom monai.networks.blocks.transformerblock import TransformerBlock\n\n__all__ = [\"ViT\"]\n\n\nclass ViT(nn.Module):\n    \"\"\"\n    Vision Transformer (ViT), based on: \"Dosovitskiy et al.,\n    An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale <https://arxiv.org/abs/2010.11929>\"\n    \"\"\"\n\n    def __init__(\n        self,\n        in_channels: int,\n        img_size: Union[Sequence[int], int],\n        patch_size: Union[Sequence[int], int],\n        hidden_size: int = 768,\n        mlp_dim: int = 3072,\n        num_layers: int = 12,\n        num_heads: int = 12,\n        pos_embed: str = \"conv\",\n        classification: bool = False,\n        num_classes: int = 2,\n        dropout_rate: float = 0.0,\n        spatial_dims: int = 3,\n    ) -> None:\n        \"\"\"\n        Args:\n            in_channels: dimension of input channels.\n            img_size: dimension of input image.\n            patch_size: dimension of patch size.\n            hidden_size: dimension of hidden layer.\n            mlp_dim: dimension of feedforward layer.\n            num_layers: number of transformer blocks.\n            num_heads: number of attention heads.\n            pos_embed: position embedding layer type.\n            classification: bool argument to determine if classification is used.\n            num_classes: number of classes if classification is used.\n            dropout_rate: faction of the input units to drop.\n            spatial_dims: number of spatial dimensions.\n\n        Examples::\n\n            # for single channel input with image size of (96,96,96), conv position embedding and segmentation backbone\n            >>> net = ViT(in_channels=1, img_size=(96,96,96), pos_embed='conv')\n\n            # for 3-channel with image size of (128,128,128), 24 layers and classification backbone\n            >>> net = ViT(in_channels=3, img_size=(128,128,128), pos_embed='conv', classification=True)\n\n            # for 3-channel with image size of (224,224), 12 layers and classification backbone\n            >>> net = ViT(in_channels=3, img_size=(224,224), pos_embed='conv', classification=True, spatial_dims=2)\n\n        \"\"\"\n\n        super().__init__()\n\n        if not (0 <= dropout_rate <= 1):\n            raise ValueError(\"dropout_rate should be between 0 and 1.\")\n\n        if hidden_size % num_heads != 0:\n            raise ValueError(\"hidden_size should be divisible by num_heads.\")\n\n        self.classification = classification\n        self.patch_embedding = PatchEmbeddingBlock(\n            in_channels=in_channels,\n            img_size=img_size,\n            patch_size=patch_size,\n            hidden_size=hidden_size,\n            num_heads=num_heads,\n            pos_embed=pos_embed,\n            dropout_rate=dropout_rate,\n            spatial_dims=spatial_dims,\n        )\n        self.blocks = nn.ModuleList(\n            [TransformerBlock(hidden_size, mlp_dim, num_heads, dropout_rate) for i in range(num_layers)]\n        )\n        self.norm = nn.LayerNorm(hidden_size)\n        if self.classification:\n            self.cls_token = nn.Parameter(torch.zeros(1, 1, hidden_size))\n            self.classification_head = nn.Sequential(nn.Linear(hidden_size, num_classes), nn.Tanh())", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/vit.py_ViT.forward_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/vit.py_ViT.forward_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/vit.py", "file_name": "vit.py", "file_type": "text/x-python", "category": "implementation", "start_line": 100, "end_line": 113, "span_ids": ["ViT.forward"], "tokens": 119}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ViT(nn.Module):\n\n    def forward(self, x):\n        x = self.patch_embedding(x)\n        if self.classification:\n            cls_token = self.cls_token.expand(x.shape[0], -1, -1)\n            x = torch.cat((cls_token, x), dim=1)\n        hidden_states_out = []\n        for blk in self.blocks:\n            x = blk(x)\n            hidden_states_out.append(x)\n        x = self.norm(x)\n        if self.classification:\n            x = self.classification_head(x[:, 0])\n        return x, hidden_states_out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/utils.py_copy_model_state.for_s_in_mapping_if_mappi_copy_model_state.return.dst_dict_updated_keys_u": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/utils.py_copy_model_state.for_s_in_mapping_if_mappi_copy_model_state.return.dst_dict_updated_keys_u", "embedding": null, "metadata": {"file_path": "monai/networks/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 416, "end_line": 429, "span_ids": ["copy_model_state"], "tokens": 225}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def copy_model_state(\n    dst: Union[torch.nn.Module, Mapping],\n    src: Union[torch.nn.Module, Mapping],\n    dst_prefix=\"\",\n    mapping=None,\n    exclude_vars=None,\n    inplace=True,\n):\n    # ... other code\n    for s in mapping if mapping else {}:\n        dst_key = f\"{dst_prefix}{mapping[s]}\"\n        if dst_key in dst_dict and dst_key not in to_skip:\n            if dst_dict[dst_key].shape != src_dict[s].shape:\n                warnings.warn(f\"Param. shape changed from {dst_dict[dst_key].shape} to {src_dict[s].shape}.\")\n            dst_dict[dst_key] = src_dict[s]\n            updated_keys.append(dst_key)\n\n    updated_keys = sorted(set(updated_keys))\n    unchanged_keys = sorted(set(all_keys).difference(updated_keys))\n    print(f\"'dst' model updated: {len(updated_keys)} of {len(dst_dict)} variables.\")\n    if inplace and isinstance(dst, torch.nn.Module):\n        dst.load_state_dict(dst_dict)\n    return dst_dict, updated_keys, unchanged_keys", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/utils.py_convert_to_torchscript_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/utils.py_convert_to_torchscript_", "embedding": null, "metadata": {"file_path": "monai/networks/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 432, "end_line": 497, "span_ids": ["convert_to_torchscript"], "tokens": 706}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def convert_to_torchscript(\n    model: nn.Module,\n    filename_or_obj: Optional[Any] = None,\n    extra_files: Optional[Dict] = None,\n    verify: bool = False,\n    inputs: Optional[Sequence[Any]] = None,\n    device: Optional[torch.device] = None,\n    rtol: float = 1e-4,\n    atol: float = 0.0,\n    **kwargs,\n):\n    \"\"\"\n    Utility to convert a model into TorchScript model and save to file,\n    with optional input / output data verification.\n\n    Args:\n        model: source PyTorch model to save.\n        filename_or_obj: if not None, specify a file-like object (has to implement write and flush)\n            or a string containing a file path name to save the TorchScript model.\n        extra_files: map from filename to contents which will be stored as part of the save model file.\n            works for PyTorch 1.7 or later.\n            for more details: https://pytorch.org/docs/stable/generated/torch.jit.save.html.\n        verify: whether to verify the input and output of TorchScript model.\n            if `filename_or_obj` is not None, load the saved TorchScript model and verify.\n        inputs: input test data to verify model, should be a sequence of data, every item maps to a argument\n            of `model()` function.\n        device: target device to verify the model, if None, use CUDA if available.\n        rtol: the relative tolerance when comparing the outputs of PyTorch model and TorchScript model.\n        atol: the absolute tolerance when comparing the outputs of PyTorch model and TorchScript model.\n        kwargs: other arguments except `obj` for `torch.jit.script()` to convert model, for more details:\n            https://pytorch.org/docs/master/generated/torch.jit.script.html.\n\n    \"\"\"\n    model.eval()\n    with torch.no_grad():\n        script_module = torch.jit.script(model, **kwargs)\n        if filename_or_obj is not None:\n            if not pytorch_after(1, 7):\n                torch.jit.save(m=script_module, f=filename_or_obj)\n            else:\n                torch.jit.save(m=script_module, f=filename_or_obj, _extra_files=extra_files)\n\n    if verify:\n        if device is None:\n            device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n        if inputs is None:\n            raise ValueError(\"missing input data for verification.\")\n\n        inputs = [i.to(device) if isinstance(i, torch.Tensor) else i for i in inputs]\n        ts_model = torch.jit.load(filename_or_obj) if filename_or_obj is not None else script_module\n        ts_model.eval().to(device)\n        model = model.to(device)\n\n        with torch.no_grad():\n            set_determinism(seed=0)\n            torch_out = ensure_tuple(model(*inputs))\n            set_determinism(seed=0)\n            torchscript_out = ensure_tuple(ts_model(*inputs))\n            set_determinism(seed=None)\n        # compare TorchScript and PyTorch results\n        for r1, r2 in zip(torch_out, torchscript_out):\n            if isinstance(r1, torch.Tensor) or isinstance(r2, torch.Tensor):\n                torch.testing.assert_allclose(r1, r2, rtol=rtol, atol=atol)\n\n    return script_module", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/optimizers/lr_scheduler.py_math__LRSchedulerMONAI.__init__.super___init___optimize": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/optimizers/lr_scheduler.py_math__LRSchedulerMONAI.__init__.super___init___optimize", "embedding": null, "metadata": {"file_path": "monai/optimizers/lr_scheduler.py", "file_name": "lr_scheduler.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 36, "span_ids": ["_LRSchedulerMONAI.__init__", "_LRSchedulerMONAI", "docstring"], "tokens": 181}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import math\n\nfrom torch.optim import Optimizer\nfrom torch.optim.lr_scheduler import LambdaLR, _LRScheduler\n\n__all__ = [\"LinearLR\", \"ExponentialLR\"]\n\n\nclass _LRSchedulerMONAI(_LRScheduler):\n    \"\"\"Base class for increasing the learning rate between two boundaries over a number\n    of iterations\"\"\"\n\n    def __init__(self, optimizer: Optimizer, end_lr: float, num_iter: int, last_epoch: int = -1) -> None:\n        \"\"\"\n        Args:\n            optimizer: wrapped optimizer.\n            end_lr: the final learning rate.\n            num_iter: the number of iterations over which the test occurs.\n            last_epoch: the index of last epoch.\n        Returns:\n            None\n        \"\"\"\n        self.end_lr = end_lr\n        self.num_iter = num_iter\n        super().__init__(optimizer, last_epoch)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/optimizers/lr_scheduler.py_LinearLR_ExponentialLR.get_lr.return._base_lr_self_end_lr_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/optimizers/lr_scheduler.py_LinearLR_ExponentialLR.get_lr.return._base_lr_self_end_lr_", "embedding": null, "metadata": {"file_path": "monai/optimizers/lr_scheduler.py", "file_name": "lr_scheduler.py", "file_type": "text/x-python", "category": "implementation", "start_line": 39, "end_line": 56, "span_ids": ["LinearLR", "ExponentialLR", "LinearLR.get_lr", "ExponentialLR.get_lr"], "tokens": 156}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LinearLR(_LRSchedulerMONAI):\n    \"\"\"Linearly increases the learning rate between two boundaries over a number of\n    iterations.\n    \"\"\"\n\n    def get_lr(self):\n        r = self.last_epoch / (self.num_iter - 1)\n        return [base_lr + r * (self.end_lr - base_lr) for base_lr in self.base_lrs]\n\n\nclass ExponentialLR(_LRSchedulerMONAI):\n    \"\"\"Exponentially increases the learning rate between two boundaries over a number of\n    iterations.\n    \"\"\"\n\n    def get_lr(self):\n        r = self.last_epoch / (self.num_iter - 1)\n        return [base_lr * (self.end_lr / base_lr) ** r for base_lr in self.base_lrs]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/optimizers/lr_scheduler.py_WarmupCosineSchedule_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/optimizers/lr_scheduler.py_WarmupCosineSchedule_", "embedding": null, "metadata": {"file_path": "monai/optimizers/lr_scheduler.py", "file_name": "lr_scheduler.py", "file_type": "text/x-python", "category": "implementation", "start_line": 59, "end_line": 87, "span_ids": ["WarmupCosineSchedule", "WarmupCosineSchedule.__init__", "WarmupCosineSchedule.lr_lambda"], "tokens": 278}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class WarmupCosineSchedule(LambdaLR):\n    \"\"\"Linear warmup and then cosine decay.\n    Based on https://huggingface.co/ implementation.\n    \"\"\"\n\n    def __init__(\n        self, optimizer: Optimizer, warmup_steps: int, t_total: int, cycles: float = 0.5, last_epoch: int = -1\n    ) -> None:\n        \"\"\"\n        Args:\n            optimizer: wrapped optimizer.\n            warmup_steps: number of warmup iterations.\n            t_total: total number of training iterations.\n            cycles: cosine cycles parameter.\n            last_epoch: the index of last epoch.\n        Returns:\n            None\n        \"\"\"\n        self.warmup_steps = warmup_steps\n        self.t_total = t_total\n        self.cycles = cycles\n        super().__init__(optimizer, self.lr_lambda, last_epoch)\n\n    def lr_lambda(self, step):\n        if step < self.warmup_steps:\n            return float(step) / float(max(1.0, self.warmup_steps))\n        progress = float(step - self.warmup_steps) / float(max(1, self.t_total - self.warmup_steps))\n        return max(0.0, 0.5 * (1.0 + math.cos(math.pi * float(self.cycles) * 2.0 * progress)))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/__init__.py_AddChanneld_AddChanneld": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/__init__.py_AddChanneld_AddChanneld", "embedding": null, "metadata": {"file_path": "monai/transforms/__init__.py", "file_name": "__init__.py", "file_type": "text/x-python", "category": "implementation", "start_line": 397, "end_line": 509, "span_ids": ["imports:21"], "tokens": 655}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from .utility.dictionary import (\n    AddChanneld,\n    AddChannelD,\n    AddChannelDict,\n    AddCoordinateChannelsd,\n    AddCoordinateChannelsD,\n    AddCoordinateChannelsDict,\n    AddExtremePointsChanneld,\n    AddExtremePointsChannelD,\n    AddExtremePointsChannelDict,\n    AsChannelFirstd,\n    AsChannelFirstD,\n    AsChannelFirstDict,\n    AsChannelLastd,\n    AsChannelLastD,\n    AsChannelLastDict,\n    CastToTyped,\n    CastToTypeD,\n    CastToTypeDict,\n    ClassesToIndicesd,\n    ClassesToIndicesD,\n    ClassesToIndicesDict,\n    ConcatItemsd,\n    ConcatItemsD,\n    ConcatItemsDict,\n    ConvertToMultiChannelBasedOnBratsClassesd,\n    ConvertToMultiChannelBasedOnBratsClassesD,\n    ConvertToMultiChannelBasedOnBratsClassesDict,\n    CopyItemsd,\n    CopyItemsD,\n    CopyItemsDict,\n    CuCIMd,\n    CuCIMD,\n    CuCIMDict,\n    DataStatsd,\n    DataStatsD,\n    DataStatsDict,\n    DeleteItemsd,\n    DeleteItemsD,\n    DeleteItemsDict,\n    EnsureChannelFirstd,\n    EnsureChannelFirstD,\n    EnsureChannelFirstDict,\n    EnsureTyped,\n    EnsureTypeD,\n    EnsureTypeDict,\n    FgBgToIndicesd,\n    FgBgToIndicesD,\n    FgBgToIndicesDict,\n    Identityd,\n    IdentityD,\n    IdentityDict,\n    IntensityStatsd,\n    IntensityStatsD,\n    IntensityStatsDict,\n    LabelToMaskd,\n    LabelToMaskD,\n    LabelToMaskDict,\n    Lambdad,\n    LambdaD,\n    LambdaDict,\n    MapLabelValued,\n    MapLabelValueD,\n    MapLabelValueDict,\n    RandCuCIMd,\n    RandCuCIMD,\n    RandCuCIMDict,\n    RandLambdad,\n    RandLambdaD,\n    RandLambdaDict,\n    RandTorchVisiond,\n    RandTorchVisionD,\n    RandTorchVisionDict,\n    RemoveRepeatedChanneld,\n    RemoveRepeatedChannelD,\n    RemoveRepeatedChannelDict,\n    RepeatChanneld,\n    RepeatChannelD,\n    RepeatChannelDict,\n    SelectItemsd,\n    SelectItemsD,\n    SelectItemsDict,\n    SimulateDelayd,\n    SimulateDelayD,\n    SimulateDelayDict,\n    SplitChanneld,\n    SplitChannelD,\n    SplitChannelDict,\n    SqueezeDimd,\n    SqueezeDimD,\n    SqueezeDimDict,\n    ToCupyd,\n    ToCupyD,\n    ToCupyDict,\n    ToDeviced,\n    ToDeviceD,\n    ToDeviceDict,\n    ToNumpyd,\n    ToNumpyD,\n    ToNumpyDict,\n    ToPILd,\n    ToPILD,\n    ToPILDict,\n    TorchVisiond,\n    TorchVisionD,\n    TorchVisionDict,\n    ToTensord,\n    ToTensorD,\n    ToTensorDict,\n    Transposed,\n    TransposeD,\n    TransposeDict,\n)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/__init__.py_Fourier_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/__init__.py_Fourier_", "embedding": null, "metadata": {"file_path": "monai/transforms/__init__.py", "file_name": "__init__.py", "file_type": "text/x-python", "category": "implementation", "start_line": 507, "end_line": 563, "span_ids": ["imports:22"], "tokens": 275}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from .utils import (\n    Fourier,\n    allow_missing_keys_mode,\n    compute_divisible_spatial_size,\n    convert_inverse_interp_mode,\n    convert_pad_mode,\n    copypaste_arrays,\n    create_control_grid,\n    create_grid,\n    create_rotate,\n    create_scale,\n    create_shear,\n    create_translate,\n    equalize_hist,\n    extreme_points_to_image,\n    generate_label_classes_crop_centers,\n    generate_pos_neg_label_crop_centers,\n    generate_spatial_bounding_box,\n    get_extreme_points,\n    get_largest_connected_component_mask,\n    get_number_image_type_conversions,\n    get_transform_backends,\n    img_bounds,\n    in_bounds,\n    is_empty,\n    is_positive,\n    map_binary_to_indices,\n    map_classes_to_indices,\n    map_spatial_axes,\n    print_transform_backends,\n    rand_choice,\n    rescale_array,\n    rescale_array_int_max,\n    rescale_instance_array,\n    resize_center,\n    weighted_patch_samples,\n    zero_margins,\n)\nfrom .utils_pytorch_numpy_unification import (\n    any_np_pt,\n    clip,\n    concatenate,\n    cumsum,\n    floor_divide,\n    in1d,\n    isfinite,\n    isnan,\n    maximum,\n    moveaxis,\n    nonzero,\n    percentile,\n    ravel,\n    repeat,\n    unravel_index,\n    where,\n)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/compose.py_warnings___all__._Compose_OneOf_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/compose.py_warnings___all__._Compose_OneOf_", "embedding": null, "metadata": {"file_path": "monai/transforms/compose.py", "file_name": "compose.py", "file_type": "text/x-python", "category": "implementation", "start_line": 15, "end_line": 33, "span_ids": ["docstring"], "tokens": 123}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import warnings\nfrom typing import Any, Callable, Mapping, Optional, Sequence, Union\n\nimport numpy as np\n\nfrom monai.transforms.inverse import InvertibleTransform\n\n# For backwards compatibility (so this still works: from monai.transforms.compose import MapTransform)\nfrom monai.transforms.transform import (  # noqa: F401\n    MapTransform,\n    Randomizable,\n    RandomizableTransform,\n    Transform,\n    apply_transform,\n)\nfrom monai.utils import MAX_SEED, ensure_tuple, get_seed\nfrom monai.utils.enums import TraceKeys\n\n__all__ = [\"Compose\", \"OneOf\"]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/compose.py_Compose.__len___Compose.inverse.return.data": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/compose.py_Compose.__len___Compose.inverse.return.data", "embedding": null, "metadata": {"file_path": "monai/transforms/compose.py", "file_name": "compose.py", "file_type": "text/x-python", "category": "implementation", "start_line": 154, "end_line": 171, "span_ids": ["Compose.__call__", "Compose.__len__", "Compose.inverse"], "tokens": 169}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Compose(Randomizable, InvertibleTransform):\n\n    def __len__(self):\n        \"\"\"Return number of transformations.\"\"\"\n        return len(self.flatten().transforms)\n\n    def __call__(self, input_):\n        for _transform in self.transforms:\n            input_ = apply_transform(_transform, input_, self.map_items, self.unpack_items)\n        return input_\n\n    def inverse(self, data):\n        invertible_transforms = [t for t in self.flatten().transforms if isinstance(t, InvertibleTransform)]\n        if not invertible_transforms:\n            warnings.warn(\"inverse has been called but no invertible transforms have been supplied\")\n\n        # loop backwards over transforms\n        for t in reversed(invertible_transforms):\n            data = apply_transform(t.inverse, data, self.map_items, self.unpack_items)\n        return data", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/compose.py_OneOf_OneOf._normalize_probabilities.return.list_weights_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/compose.py_OneOf_OneOf._normalize_probabilities.return.list_weights_", "embedding": null, "metadata": {"file_path": "monai/transforms/compose.py", "file_name": "compose.py", "file_type": "text/x-python", "category": "implementation", "start_line": 174, "end_line": 213, "span_ids": ["OneOf", "OneOf._normalize_probabilities", "OneOf.__init__"], "tokens": 345}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class OneOf(Compose):\n    \"\"\"\n    ``OneOf`` provides the ability to randomly choose one transform out of a\n    list of callables with pre-defined probabilities for each.\n\n    Args:\n        transforms: sequence of callables.\n        weights: probabilities corresponding to each callable in transforms.\n            Probabilities are normalized to sum to one.\n\n    ``OneOf`` inherits from ``Compose`` and uses args ``map_items`` and ``unpack_items`` in\n    the same way.\n    \"\"\"\n\n    def __init__(\n        self,\n        transforms: Optional[Union[Sequence[Callable], Callable]] = None,\n        weights: Optional[Union[Sequence[float], float]] = None,\n        map_items: bool = True,\n        unpack_items: bool = False,\n    ) -> None:\n        super().__init__(transforms, map_items, unpack_items)\n        if len(self.transforms) == 0:\n            weights = []\n        elif weights is None or isinstance(weights, float):\n            weights = [1.0 / len(self.transforms)] * len(self.transforms)\n        if len(weights) != len(self.transforms):\n            raise AssertionError(\"transforms and weights should be same size if both specified as sequences.\")\n        self.weights = ensure_tuple(self._normalize_probabilities(weights))\n\n    def _normalize_probabilities(self, weights):\n        if len(weights) == 0:\n            return weights\n        weights = np.array(weights)\n        if np.any(weights < 0):\n            raise AssertionError(\"Probabilities must be greater than or equal to zero.\")\n        if np.all(weights == 0):\n            raise AssertionError(\"At least one probability must be greater than zero.\")\n        weights = weights / weights.sum()\n        return list(weights)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/compose.py_OneOf.flatten_OneOf.flatten.return.OneOf_transforms_weights": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/compose.py_OneOf.flatten_OneOf.flatten.return.OneOf_transforms_weights", "embedding": null, "metadata": {"file_path": "monai/transforms/compose.py", "file_name": "compose.py", "file_type": "text/x-python", "category": "implementation", "start_line": 215, "end_line": 229, "span_ids": ["OneOf.flatten"], "tokens": 126}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class OneOf(Compose):\n\n    def flatten(self):\n        transforms = []\n        weights = []\n        for t, w in zip(self.transforms, self.weights):\n            # if nested, probability is the current weight multiplied by the nested weights,\n            # and so on recursively\n            if isinstance(t, OneOf):\n                tr = t.flatten()\n                for t_, w_ in zip(tr.transforms, tr.weights):\n                    transforms.append(t_)\n                    weights.append(w_ * w)\n            else:\n                transforms.append(t)\n                weights.append(w)\n        return OneOf(transforms, weights, self.map_items, self.unpack_items)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/compose.py_OneOf.__call___OneOf.__call__.return.data": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/compose.py_OneOf.__call___OneOf.__call__.return.data", "embedding": null, "metadata": {"file_path": "monai/transforms/compose.py", "file_name": "compose.py", "file_type": "text/x-python", "category": "implementation", "start_line": 231, "end_line": 242, "span_ids": ["OneOf.__call__"], "tokens": 134}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class OneOf(Compose):\n\n    def __call__(self, data):\n        if len(self.transforms) == 0:\n            return data\n        index = self.R.multinomial(1, self.weights).argmax()\n        _transform = self.transforms[index]\n        data = apply_transform(_transform, data, self.map_items, self.unpack_items)\n        # if the data is a mapping (dictionary), append the OneOf transform to the end\n        if isinstance(data, Mapping):\n            for key in data.keys():\n                if self.trace_key(key) in data:\n                    self.push_transform(data, key, extra_info={\"index\": index})\n        return data", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/compose.py_OneOf.inverse_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/compose.py_OneOf.inverse_", "embedding": null, "metadata": {"file_path": "monai/transforms/compose.py", "file_name": "compose.py", "file_type": "text/x-python", "category": "implementation", "start_line": 244, "end_line": 265, "span_ids": ["OneOf.inverse"], "tokens": 193}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class OneOf(Compose):\n\n    def inverse(self, data):\n        if len(self.transforms) == 0:\n            return data\n        if not isinstance(data, Mapping):\n            raise RuntimeError(\"Inverse only implemented for Mapping (dictionary) data\")\n\n        # loop until we get an index and then break (since they'll all be the same)\n        index = None\n        for key in data.keys():\n            if self.trace_key(key) in data:\n                # get the index of the applied OneOf transform\n                index = self.get_most_recent_transform(data, key)[TraceKeys.EXTRA_INFO][\"index\"]\n                # and then remove the OneOf transform\n                self.pop_transform(data, key)\n        if index is None:\n            # no invertible transforms have been applied\n            return data\n\n        _transform = self.transforms[index]\n        # apply the inverse\n        return _transform.inverse(data) if isinstance(_transform, InvertibleTransform) else data", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_Pad_Pad._pt_pad.return.pad_pt_img_unsqueeze_0_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_Pad_Pad._pt_pad.return.pad_pt_img_unsqueeze_0_", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 71, "end_line": 108, "span_ids": ["Pad._np_pad", "Pad", "Pad.__init__", "Pad._pt_pad"], "tokens": 512}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Pad(Transform):\n    \"\"\"\n    Perform padding for a given an amount of padding in each dimension.\n    If input is `torch.Tensor`, `torch.nn.functional.pad` will be used, otherwise, `np.pad` will be used.\n\n    Args:\n        to_pad: the amount to be padded in each dimension [(low_H, high_H), (low_W, high_W), ...].\n        mode: available modes for numpy array:{``\"constant\"``, ``\"edge\"``, ``\"linear_ramp\"``, ``\"maximum\"``,\n            ``\"mean\"``, ``\"median\"``, ``\"minimum\"``, ``\"reflect\"``, ``\"symmetric\"``, ``\"wrap\"``, ``\"empty\"``}\n            available modes for PyTorch Tensor: {``\"constant\"``, ``\"reflect\"``, ``\"replicate\"``, ``\"circular\"``}.\n            One of the listed string values or a user supplied function. Defaults to ``\"constant\"``.\n            See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html\n            https://pytorch.org/docs/stable/generated/torch.nn.functional.pad.html\n        kwargs: other arguments for the `np.pad` or `torch.pad` function.\n            note that `np.pad` treats channel dimension as the first dimension.\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __init__(\n        self,\n        to_pad: List[Tuple[int, int]],\n        mode: Union[NumpyPadMode, PytorchPadMode, str] = NumpyPadMode.CONSTANT,\n        **kwargs,\n    ) -> None:\n        self.to_pad = to_pad\n        self.mode = mode\n        self.kwargs = kwargs\n\n    @staticmethod\n    def _np_pad(img: np.ndarray, all_pad_width, mode, **kwargs) -> np.ndarray:\n        return np.pad(img, all_pad_width, mode=mode, **kwargs)  # type: ignore\n\n    @staticmethod\n    def _pt_pad(img: torch.Tensor, all_pad_width, mode, **kwargs) -> torch.Tensor:\n        pt_pad_width = [val for sublist in all_pad_width[1:] for val in sublist[::-1]][::-1]\n        # torch.pad expects `[B, C, H, W, [D]]` shape\n        return pad_pt(img.unsqueeze(0), pt_pad_width, mode=mode, **kwargs).squeeze(0)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_Pad.__call___Pad.__call__._type_ignore": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_Pad.__call___Pad.__call__._type_ignore", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 110, "end_line": 130, "span_ids": ["Pad.__call__"], "tokens": 321}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Pad(Transform):\n\n    def __call__(\n        self, img: NdarrayOrTensor, mode: Optional[Union[NumpyPadMode, PytorchPadMode, str]] = None\n    ) -> NdarrayOrTensor:\n        \"\"\"\n        Args:\n            img: data to be transformed, assuming `img` is channel-first and\n                padding doesn't apply to the channel dim.\n        mode: available modes for numpy array:{``\"constant\"``, ``\"edge\"``, ``\"linear_ramp\"``, ``\"maximum\"``,\n            ``\"mean\"``, ``\"median\"``, ``\"minimum\"``, ``\"reflect\"``, ``\"symmetric\"``, ``\"wrap\"``, ``\"empty\"``}\n            available modes for PyTorch Tensor: {``\"constant\"``, ``\"reflect\"``, ``\"replicate\"`` or ``\"circular\"``}.\n            One of the listed string values or a user supplied function. Defaults to `self.mode`.\n            See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html\n            https://pytorch.org/docs/stable/generated/torch.nn.functional.pad.html\n\n        \"\"\"\n        if not np.asarray(self.to_pad).any():\n            # all zeros, skip padding\n            return img\n        mode = convert_pad_mode(dst=img, mode=mode or self.mode).value\n        pad = self._pt_pad if isinstance(img, torch.Tensor) else self._np_pad\n        return pad(img, self.to_pad, mode, **self.kwargs)  # type: ignore", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_SpatialPad_SpatialPad.__init__.self.kwargs.kwargs": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_SpatialPad_SpatialPad.__init__.self.kwargs.kwargs", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 133, "end_line": 174, "span_ids": ["SpatialPad.__init__", "SpatialPad"], "tokens": 560}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SpatialPad(Transform):\n    \"\"\"\n    Performs padding to the data, symmetric for all sides or all on one side for each dimension.\n\n    If input is `torch.Tensor` and mode is `constant`, `torch.nn.functional.pad` will be used.\n    Otherwise, `np.pad` will be used (input converted to `np.ndarray` if necessary).\n\n    Uses np.pad so in practice, a mode needs to be provided. See numpy.lib.arraypad.pad\n    for additional details.\n\n    Args:\n        spatial_size: the spatial size of output data after padding, if a dimension of the input\n            data size is bigger than the pad size, will not pad that dimension.\n            If its components have non-positive values, the corresponding size of input image will be used\n            (no padding). for example: if the spatial size of input data is [30, 30, 30] and\n            `spatial_size=[32, 25, -1]`, the spatial size of output data will be [32, 30, 30].\n        method: {``\"symmetric\"``, ``\"end\"``}\n            Pad image symmetrically on every side or only pad at the end sides. Defaults to ``\"symmetric\"``.\n        mode: available modes for numpy array:{``\"constant\"``, ``\"edge\"``, ``\"linear_ramp\"``, ``\"maximum\"``,\n            ``\"mean\"``, ``\"median\"``, ``\"minimum\"``, ``\"reflect\"``, ``\"symmetric\"``, ``\"wrap\"``, ``\"empty\"``}\n            available modes for PyTorch Tensor: {``\"constant\"``, ``\"reflect\"``, ``\"replicate\"``, ``\"circular\"``}.\n            One of the listed string values or a user supplied function. Defaults to ``\"constant\"``.\n            See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html\n            https://pytorch.org/docs/stable/generated/torch.nn.functional.pad.html\n        kwargs: other arguments for the `np.pad` or `torch.pad` function.\n            note that `np.pad` treats channel dimension as the first dimension.\n\n    \"\"\"\n\n    backend = Pad.backend\n\n    def __init__(\n        self,\n        spatial_size: Union[Sequence[int], int],\n        method: Union[Method, str] = Method.SYMMETRIC,\n        mode: Union[NumpyPadMode, PytorchPadMode, str] = NumpyPadMode.CONSTANT,\n        **kwargs,\n    ) -> None:\n        self.spatial_size = spatial_size\n        self.method: Method = look_up_option(method, Method)\n        self.mode = mode\n        self.kwargs = kwargs", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_SpatialPad.__call___SpatialPad.__call__.return.padder_img_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_SpatialPad.__call___SpatialPad.__call__.return.padder_img_", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 186, "end_line": 208, "span_ids": ["SpatialPad.__call__"], "tokens": 320}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SpatialPad(Transform):\n\n    def __call__(\n        self, img: NdarrayOrTensor, mode: Optional[Union[NumpyPadMode, PytorchPadMode, str]] = None\n    ) -> NdarrayOrTensor:\n        \"\"\"\n        Args:\n            img: data to be transformed, assuming `img` is channel-first and\n                padding doesn't apply to the channel dim.\n            mode: available modes for numpy array:{``\"constant\"``, ``\"edge\"``, ``\"linear_ramp\"``, ``\"maximum\"``,\n                ``\"mean\"``, ``\"median\"``, ``\"minimum\"``, ``\"reflect\"``, ``\"symmetric\"``, ``\"wrap\"``, ``\"empty\"``}\n                available modes for PyTorch Tensor: {``\"constant\"``, ``\"reflect\"``, ``\"replicate\"``, ``\"circular\"``}.\n                One of the listed string values or a user supplied function. Defaults to `self.mode`.\n                See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html\n                https://pytorch.org/docs/stable/generated/torch.nn.functional.pad.html\n\n        \"\"\"\n        data_pad_width = self._determine_data_pad_width(img.shape[1:])\n        all_pad_width = [(0, 0)] + data_pad_width\n        if not np.asarray(all_pad_width).any():\n            # all zeros, skip padding\n            return img\n\n        padder = Pad(all_pad_width, mode or self.mode, **self.kwargs)\n        return padder(img)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_BorderPad_BorderPad.__init__.self.kwargs.kwargs": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_BorderPad_BorderPad.__init__.self.kwargs.kwargs", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 211, "end_line": 247, "span_ids": ["BorderPad.__init__", "BorderPad"], "tokens": 523}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class BorderPad(Transform):\n    \"\"\"\n    Pad the input data by adding specified borders to every dimension.\n\n    Args:\n        spatial_border: specified size for every spatial border. Any -ve values will be set to 0. It can be 3 shapes:\n\n            - single int number, pad all the borders with the same size.\n            - length equals the length of image shape, pad every spatial dimension separately.\n              for example, image shape(CHW) is [1, 4, 4], spatial_border is [2, 1],\n              pad every border of H dim with 2, pad every border of W dim with 1, result shape is [1, 8, 6].\n            - length equals 2 x (length of image shape), pad every border of every dimension separately.\n              for example, image shape(CHW) is [1, 4, 4], spatial_border is [1, 2, 3, 4], pad top of H dim with 1,\n              pad bottom of H dim with 2, pad left of W dim with 3, pad right of W dim with 4.\n              the result shape is [1, 7, 11].\n        mode: available modes for numpy array:{``\"constant\"``, ``\"edge\"``, ``\"linear_ramp\"``, ``\"maximum\"``,\n            ``\"mean\"``, ``\"median\"``, ``\"minimum\"``, ``\"reflect\"``, ``\"symmetric\"``, ``\"wrap\"``, ``\"empty\"``}\n            available modes for PyTorch Tensor: {``\"constant\"``, ``\"reflect\"``, ``\"replicate\"``, ``\"circular\"``}.\n            One of the listed string values or a user supplied function. Defaults to ``\"constant\"``.\n            See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html\n            https://pytorch.org/docs/stable/generated/torch.nn.functional.pad.html\n        kwargs: other arguments for the `np.pad` or `torch.pad` function.\n            note that `np.pad` treats channel dimension as the first dimension.\n\n    \"\"\"\n\n    backend = Pad.backend\n\n    def __init__(\n        self,\n        spatial_border: Union[Sequence[int], int],\n        mode: Union[NumpyPadMode, PytorchPadMode, str] = NumpyPadMode.CONSTANT,\n        **kwargs,\n    ) -> None:\n        self.spatial_border = spatial_border\n        self.mode = mode\n        self.kwargs = kwargs", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_BorderPad.__call___BorderPad.__call__.return.padder_img_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_BorderPad.__call___BorderPad.__call__.return.padder_img_", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 249, "end_line": 289, "span_ids": ["BorderPad.__call__"], "tokens": 581}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class BorderPad(Transform):\n\n    def __call__(\n        self, img: NdarrayOrTensor, mode: Optional[Union[NumpyPadMode, PytorchPadMode, str]] = None\n    ) -> NdarrayOrTensor:\n        \"\"\"\n        Args:\n            img: data to be transformed, assuming `img` is channel-first and\n                padding doesn't apply to the channel dim.\n            mode: available modes for numpy array:{``\"constant\"``, ``\"edge\"``, ``\"linear_ramp\"``, ``\"maximum\"``,\n                ``\"mean\"``, ``\"median\"``, ``\"minimum\"``, ``\"reflect\"``, ``\"symmetric\"``, ``\"wrap\"``, ``\"empty\"``}\n                available modes for PyTorch Tensor: {``\"constant\"``, ``\"reflect\"``, ``\"replicate\"``, ``\"circular\"``}.\n                One of the listed string values or a user supplied function. Defaults to `self.mode`.\n                See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html\n                https://pytorch.org/docs/stable/generated/torch.nn.functional.pad.html\n\n        Raises:\n            ValueError: When ``self.spatial_border`` does not contain ints.\n            ValueError: When ``self.spatial_border`` length is not one of\n                [1, len(spatial_shape), 2*len(spatial_shape)].\n\n        \"\"\"\n        spatial_shape = img.shape[1:]\n        spatial_border = ensure_tuple(self.spatial_border)\n        if not all(isinstance(b, int) for b in spatial_border):\n            raise ValueError(f\"self.spatial_border must contain only ints, got {spatial_border}.\")\n        spatial_border = tuple(max(0, b) for b in spatial_border)\n\n        if len(spatial_border) == 1:\n            data_pad_width = [(spatial_border[0], spatial_border[0]) for _ in spatial_shape]\n        elif len(spatial_border) == len(spatial_shape):\n            data_pad_width = [(sp, sp) for sp in spatial_border[: len(spatial_shape)]]\n        elif len(spatial_border) == len(spatial_shape) * 2:\n            data_pad_width = [(spatial_border[2 * i], spatial_border[2 * i + 1]) for i in range(len(spatial_shape))]\n        else:\n            raise ValueError(\n                f\"Unsupported spatial_border length: {len(spatial_border)}, available options are \"\n                f\"[1, len(spatial_shape)={len(spatial_shape)}, 2*len(spatial_shape)={2*len(spatial_shape)}].\"\n            )\n\n        all_pad_width = [(0, 0)] + data_pad_width\n        padder = Pad(all_pad_width, mode or self.mode, **self.kwargs)\n        return padder(img)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_DivisiblePad_DivisiblePad.__init__.self.kwargs.kwargs": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_DivisiblePad_DivisiblePad.__init__.self.kwargs.kwargs", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 292, "end_line": 327, "span_ids": ["DivisiblePad.__init__", "DivisiblePad"], "tokens": 444}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DivisiblePad(Transform):\n    \"\"\"\n    Pad the input data, so that the spatial sizes are divisible by `k`.\n    \"\"\"\n\n    backend = SpatialPad.backend\n\n    def __init__(\n        self,\n        k: Union[Sequence[int], int],\n        mode: Union[NumpyPadMode, PytorchPadMode, str] = NumpyPadMode.CONSTANT,\n        method: Union[Method, str] = Method.SYMMETRIC,\n        **kwargs,\n    ) -> None:\n        \"\"\"\n        Args:\n            k: the target k for each spatial dimension.\n                if `k` is negative or 0, the original size is preserved.\n                if `k` is an int, the same `k` be applied to all the input spatial dimensions.\n            mode: available modes for numpy array:{``\"constant\"``, ``\"edge\"``, ``\"linear_ramp\"``, ``\"maximum\"``,\n                ``\"mean\"``, ``\"median\"``, ``\"minimum\"``, ``\"reflect\"``, ``\"symmetric\"``, ``\"wrap\"``, ``\"empty\"``}\n                available modes for PyTorch Tensor: {``\"constant\"``, ``\"reflect\"``, ``\"replicate\"``, ``\"circular\"``}.\n                One of the listed string values or a user supplied function. Defaults to ``\"constant\"``.\n                See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html\n                https://pytorch.org/docs/stable/generated/torch.nn.functional.pad.html\n            method: {``\"symmetric\"``, ``\"end\"``}\n                Pad image symmetrically on every side or only pad at the end sides. Defaults to ``\"symmetric\"``.\n            kwargs: other arguments for the `np.pad` or `torch.pad` function.\n                note that `np.pad` treats channel dimension as the first dimension.\n\n        See also :py:class:`monai.transforms.SpatialPad`\n        \"\"\"\n        self.k = k\n        self.mode: NumpyPadMode = NumpyPadMode(mode)\n        self.method: Method = Method(method)\n        self.kwargs = kwargs", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_SpatialCrop_SpatialCrop.backend._TransformBackends_TORCH_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_SpatialCrop_SpatialCrop.backend._TransformBackends_TORCH_", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 350, "end_line": 364, "span_ids": ["SpatialCrop"], "tokens": 162}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SpatialCrop(Transform):\n    \"\"\"\n    General purpose cropper to produce sub-volume region of interest (ROI).\n    If a dimension of the expected ROI size is bigger than the input image size, will not crop that dimension.\n    So the cropped result may be smaller than the expected ROI, and the cropped results of several images may\n    not have exactly the same shape.\n    It can support to crop ND spatial (channel-first) data.\n\n    The cropped region can be parameterised in various ways:\n        - a list of slices for each spatial dimension (allows for use of -ve indexing and `None`)\n        - a spatial center and size\n        - the start and end coordinates of the ROI\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_SpatialCrop.__init___SpatialCrop.__call__.return.img_tuple_slices_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_SpatialCrop.__init___SpatialCrop.__call__.return.img_tuple_slices_", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 367, "end_line": 422, "span_ids": ["SpatialCrop.__call__", "SpatialCrop.__init__"], "tokens": 744}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SpatialCrop(Transform):\n\n    def __init__(\n        self,\n        roi_center: Union[Sequence[int], NdarrayOrTensor, None] = None,\n        roi_size: Union[Sequence[int], NdarrayOrTensor, None] = None,\n        roi_start: Union[Sequence[int], NdarrayOrTensor, None] = None,\n        roi_end: Union[Sequence[int], NdarrayOrTensor, None] = None,\n        roi_slices: Optional[Sequence[slice]] = None,\n    ) -> None:\n        \"\"\"\n        Args:\n            roi_center: voxel coordinates for center of the crop ROI.\n            roi_size: size of the crop ROI, if a dimension of ROI size is bigger than image size,\n                will not crop that dimension of the image.\n            roi_start: voxel coordinates for start of the crop ROI.\n            roi_end: voxel coordinates for end of the crop ROI, if a coordinate is out of image,\n                use the end coordinate of image.\n            roi_slices: list of slices for each of the spatial dimensions.\n        \"\"\"\n        roi_start_torch: torch.Tensor\n\n        if roi_slices:\n            if not all(s.step is None or s.step == 1 for s in roi_slices):\n                raise ValueError(\"Only slice steps of 1/None are currently supported\")\n            self.slices = list(roi_slices)\n        else:\n            if roi_center is not None and roi_size is not None:\n                roi_center, *_ = convert_data_type(\n                    data=roi_center, output_type=torch.Tensor, dtype=torch.int16, wrap_sequence=True\n                )\n                roi_size, *_ = convert_to_dst_type(src=roi_size, dst=roi_center, wrap_sequence=True)\n                _zeros = torch.zeros_like(roi_center)  # type: ignore\n                roi_start_torch = maximum(roi_center - floor_divide(roi_size, 2), _zeros)  # type: ignore\n                roi_end_torch = maximum(roi_start_torch + roi_size, roi_start_torch)\n            else:\n                if roi_start is None or roi_end is None:\n                    raise ValueError(\"Please specify either roi_center, roi_size or roi_start, roi_end.\")\n                roi_start_torch, *_ = convert_data_type(  # type: ignore\n                    data=roi_start, output_type=torch.Tensor, dtype=torch.int16, wrap_sequence=True\n                )\n                roi_start_torch = maximum(roi_start_torch, torch.zeros_like(roi_start_torch))  # type: ignore\n                roi_end_torch, *_ = convert_to_dst_type(src=roi_end, dst=roi_start_torch, wrap_sequence=True)\n                roi_end_torch = maximum(roi_end_torch, roi_start_torch)\n            # convert to slices (accounting for 1d)\n            if roi_start_torch.numel() == 1:\n                self.slices = [slice(int(roi_start_torch.item()), int(roi_end_torch.item()))]\n            else:\n                self.slices = [slice(int(s), int(e)) for s, e in zip(roi_start_torch.tolist(), roi_end_torch.tolist())]\n\n    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:\n        \"\"\"\n        Apply the transform to `img`, assuming `img` is channel-first and\n        slicing doesn't apply to the channel dim.\n        \"\"\"\n        sd = min(len(self.slices), len(img.shape[1:]))  # spatial dims\n        slices = [slice(None)] + self.slices[:sd]\n        return img[tuple(slices)]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_CenterSpatialCrop_CenterSpatialCrop.__init__.self.roi_size.roi_size": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_CenterSpatialCrop_CenterSpatialCrop.__init__.self.roi_size.roi_size", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 429, "end_line": 447, "span_ids": ["CenterSpatialCrop", "CenterSpatialCrop.__init__"], "tokens": 228}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CenterSpatialCrop(Transform):\n    \"\"\"\n    Crop at the center of image with specified ROI size.\n    If a dimension of the expected ROI size is bigger than the input image size, will not crop that dimension.\n    So the cropped result may be smaller than the expected ROI, and the cropped results of several images may\n    not have exactly the same shape.\n\n    Args:\n        roi_size: the spatial size of the crop region e.g. [224,224,128]\n            if a dimension of ROI size is bigger than image size, will not crop that dimension of the image.\n            If its components have non-positive values, the corresponding size of input image will be used.\n            for example: if the spatial size of input data is [40, 40, 40] and `roi_size=[32, 64, -1]`,\n            the spatial size of output data will be [32, 40, 40].\n    \"\"\"\n\n    backend = SpatialCrop.backend\n\n    def __init__(self, roi_size: Union[Sequence[int], int]) -> None:\n        self.roi_size = roi_size", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_CenterSpatialCrop.__call___CenterSpatialCrop.__call__.return.cropper_img_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_CenterSpatialCrop.__call___CenterSpatialCrop.__call__.return.cropper_img_", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 449, "end_line": 457, "span_ids": ["CenterSpatialCrop.__call__"], "tokens": 114}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CenterSpatialCrop(Transform):\n\n    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:\n        \"\"\"\n        Apply the transform to `img`, assuming `img` is channel-first and\n        slicing doesn't apply to the channel dim.\n        \"\"\"\n        roi_size = fall_back_tuple(self.roi_size, img.shape[1:])\n        center = [i // 2 for i in img.shape[1:]]\n        cropper = SpatialCrop(roi_center=center, roi_size=roi_size)\n        return cropper(img)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_RandSpatialCrop.randomize_RandSpatialCrop.randomize.if_self_random_center_.self._slices._slice_None_get_rand": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_RandSpatialCrop.randomize_RandSpatialCrop.randomize.if_self_random_center_.self._slices._slice_None_get_rand", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 523, "end_line": 532, "span_ids": ["RandSpatialCrop.randomize"], "tokens": 193}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandSpatialCrop(Randomizable, Transform):\n\n    def randomize(self, img_size: Sequence[int]) -> None:\n        self._size = fall_back_tuple(self.roi_size, img_size)\n        if self.random_size:\n            max_size = img_size if self.max_roi_size is None else fall_back_tuple(self.max_roi_size, img_size)\n            if any(i > j for i, j in zip(self._size, max_size)):\n                raise ValueError(f\"min ROI size: {self._size} is bigger than max ROI size: {max_size}.\")\n            self._size = tuple(self.R.randint(low=self._size[i], high=max_size[i] + 1) for i in range(len(img_size)))\n        if self.random_center:\n            valid_size = get_valid_patch_size(img_size, self._size)\n            self._slices = (slice(None),) + get_random_patch(img_size, valid_size, self.R)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_RandSpatialCrop.__call___RandSpatialCrop.__call__.return.cropper_img_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_RandSpatialCrop.__call___RandSpatialCrop.__call__.return.cropper_img_", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 534, "end_line": 545, "span_ids": ["RandSpatialCrop.__call__"], "tokens": 118}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandSpatialCrop(Randomizable, Transform):\n\n    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:\n        \"\"\"\n        Apply the transform to `img`, assuming `img` is channel-first and\n        slicing doesn't apply to the channel dim.\n        \"\"\"\n        self.randomize(img.shape[1:])\n        if self._size is None:\n            raise RuntimeError(\"self._size not specified.\")\n        if self.random_center:\n            return img[self._slices]\n        cropper = CenterSpatialCrop(self._size)\n        return cropper(img)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_CropForeground_CropForeground.backend._TransformBackends_TORCH_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_CropForeground_CropForeground.backend._TransformBackends_TORCH_", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 659, "end_line": 691, "span_ids": ["CropForeground"], "tokens": 306}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CropForeground(Transform):\n    \"\"\"\n    Crop an image using a bounding box. The bounding box is generated by selecting foreground using select_fn\n    at channels channel_indices. margin is added in each spatial dimension of the bounding box.\n    The typical usage is to help training and evaluation if the valid part is small in the whole medical image.\n    Users can define arbitrary function to select expected foreground from the whole image or specified channels.\n    And it can also add margin to every dim of the bounding box of foreground object.\n    For example:\n\n    .. code-block:: python\n\n        image = np.array(\n            [[[0, 0, 0, 0, 0],\n              [0, 1, 2, 1, 0],\n              [0, 1, 3, 2, 0],\n              [0, 1, 2, 1, 0],\n              [0, 0, 0, 0, 0]]])  # 1x5x5, single channel 5x5 image\n\n\n        def threshold_at_one(x):\n            # threshold at 1\n            return x > 1\n\n\n        cropper = CropForeground(select_fn=threshold_at_one, margin=0)\n        print(cropper(image))\n        [[[2, 1],\n          [3, 2],\n          [2, 1]]]\n\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_CropForeground.__init___CropForeground.__init__.self.np_kwargs.np_kwargs": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_CropForeground.__init___CropForeground.__init__.self.np_kwargs.np_kwargs", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 693, "end_line": 728, "span_ids": ["CropForeground.__init__"], "tokens": 535}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CropForeground(Transform):\n\n    def __init__(\n        self,\n        select_fn: Callable = is_positive,\n        channel_indices: Optional[IndexSelection] = None,\n        margin: Union[Sequence[int], int] = 0,\n        return_coords: bool = False,\n        k_divisible: Union[Sequence[int], int] = 1,\n        mode: Optional[Union[NumpyPadMode, PytorchPadMode, str]] = NumpyPadMode.CONSTANT,\n        **np_kwargs,\n    ) -> None:\n        \"\"\"\n        Args:\n            select_fn: function to select expected foreground, default is to select values > 0.\n            channel_indices: if defined, select foreground only on the specified channels\n                of image. if None, select foreground on the whole image.\n            margin: add margin value to spatial dims of the bounding box, if only 1 value provided, use it for all dims.\n            return_coords: whether return the coordinates of spatial bounding box for foreground.\n            k_divisible: make each spatial dimension to be divisible by k, default to 1.\n                if `k_divisible` is an int, the same `k` be applied to all the input spatial dimensions.\n            mode: available modes for numpy array:{``\"constant\"``, ``\"edge\"``, ``\"linear_ramp\"``, ``\"maximum\"``,\n                ``\"mean\"``, ``\"median\"``, ``\"minimum\"``, ``\"reflect\"``, ``\"symmetric\"``, ``\"wrap\"``, ``\"empty\"``}\n                available modes for PyTorch Tensor: {``\"constant\"``, ``\"reflect\"``, ``\"replicate\"``, ``\"circular\"``}.\n                One of the listed string values or a user supplied function. Defaults to ``\"constant\"``.\n                See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html\n                https://pytorch.org/docs/stable/generated/torch.nn.functional.pad.html\n            np_kwargs: other args for `np.pad` API, note that `np.pad` treats channel dimension as the first dimension.\n                more details: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html\n\n        \"\"\"\n        self.select_fn = select_fn\n        self.channel_indices = ensure_tuple(channel_indices) if channel_indices is not None else None\n        self.margin = margin\n        self.return_coords = return_coords\n        self.k_divisible = k_divisible\n        self.mode: NumpyPadMode = look_up_option(mode, NumpyPadMode)\n        self.np_kwargs = np_kwargs", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_CropForeground.crop_pad_CropForeground.crop_pad.return.BorderPad_spatial_border_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_CropForeground.crop_pad_CropForeground.crop_pad.return.BorderPad_spatial_border_", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 747, "end_line": 762, "span_ids": ["CropForeground.crop_pad"], "tokens": 170}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CropForeground(Transform):\n\n    def crop_pad(\n        self,\n        img: NdarrayOrTensor,\n        box_start: np.ndarray,\n        box_end: np.ndarray,\n        mode: Optional[Union[NumpyPadMode, PytorchPadMode, str]] = None,\n    ):\n        \"\"\"\n        Crop and pad based on the bounding box.\n\n        \"\"\"\n        cropped = SpatialCrop(roi_start=box_start, roi_end=box_end)(img)\n        pad_to_start = np.maximum(-box_start, 0)\n        pad_to_end = np.maximum(box_end - np.asarray(img.shape[1:]), 0)\n        pad = list(chain(*zip(pad_to_start.tolist(), pad_to_end.tolist())))\n        return BorderPad(spatial_border=pad, mode=mode or self.mode, **self.np_kwargs)(cropped)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_CropForeground.__call___CropForeground.__call__.return.cropped": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_CropForeground.__call___CropForeground.__call__.return.cropped", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 764, "end_line": 774, "span_ids": ["CropForeground.__call__"], "tokens": 113}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CropForeground(Transform):\n\n    def __call__(self, img: NdarrayOrTensor, mode: Optional[Union[NumpyPadMode, str]] = None):\n        \"\"\"\n        Apply the transform to `img`, assuming `img` is channel-first and\n        slicing doesn't change the channel dim.\n        \"\"\"\n        box_start, box_end = self.compute_bounding_box(img)\n        cropped = self.crop_pad(img, box_start, box_end, mode)\n\n        if self.return_coords:\n            return cropped, box_start, box_end\n        return cropped", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_RandCropByPosNegLabel.backend_RandCropByPosNegLabel.__init__.self.allow_smaller.allow_smaller": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_RandCropByPosNegLabel.backend_RandCropByPosNegLabel.__init__.self.allow_smaller.allow_smaller", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 888, "end_line": 916, "span_ids": ["RandCropByPosNegLabel.__init__", "RandCropByPosNegLabel"], "tokens": 308}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandCropByPosNegLabel(Randomizable, Transform):\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __init__(\n        self,\n        spatial_size: Union[Sequence[int], int],\n        label: Optional[NdarrayOrTensor] = None,\n        pos: float = 1.0,\n        neg: float = 1.0,\n        num_samples: int = 1,\n        image: Optional[NdarrayOrTensor] = None,\n        image_threshold: float = 0.0,\n        fg_indices: Optional[NdarrayOrTensor] = None,\n        bg_indices: Optional[NdarrayOrTensor] = None,\n        allow_smaller: bool = False,\n    ) -> None:\n        self.spatial_size = ensure_tuple(spatial_size)\n        self.label = label\n        if pos < 0 or neg < 0:\n            raise ValueError(f\"pos and neg must be nonnegative, got pos={pos} neg={neg}.\")\n        if pos + neg == 0:\n            raise ValueError(\"Incompatible values: pos=0 and neg=0.\")\n        self.pos_ratio = pos / (pos + neg)\n        self.num_samples = num_samples\n        self.image = image\n        self.image_threshold = image_threshold\n        self.centers: Optional[List[List[int]]] = None\n        self.fg_indices = fg_indices\n        self.bg_indices = bg_indices\n        self.allow_smaller = allow_smaller", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_RandCropByLabelClasses_RandCropByLabelClasses._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_RandCropByLabelClasses_RandCropByLabelClasses._", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 985, "end_line": 1045, "span_ids": ["RandCropByLabelClasses"], "tokens": 924}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandCropByLabelClasses(Randomizable, Transform):\n    \"\"\"\n    Crop random fixed sized regions with the center being a class based on the specified ratios of every class.\n    The label data can be One-Hot format array or Argmax data. And will return a list of arrays for all the\n    cropped images. For example, crop two (3 x 3) arrays from (5 x 5) array with `ratios=[1, 2, 3, 1]`::\n\n        image = np.array([\n            [[0.0, 0.3, 0.4, 0.2, 0.0],\n            [0.0, 0.1, 0.2, 0.1, 0.4],\n            [0.0, 0.3, 0.5, 0.2, 0.0],\n            [0.1, 0.2, 0.1, 0.1, 0.0],\n            [0.0, 0.1, 0.2, 0.1, 0.0]]\n        ])\n        label = np.array([\n            [[0, 0, 0, 0, 0],\n            [0, 1, 2, 1, 0],\n            [0, 1, 3, 0, 0],\n            [0, 0, 0, 0, 0],\n            [0, 0, 0, 0, 0]]\n        ])\n        cropper = RandCropByLabelClasses(\n            spatial_size=[3, 3],\n            ratios=[1, 2, 3, 1],\n            num_classes=4,\n            num_samples=2,\n        )\n        label_samples = cropper(img=label, label=label, image=image)\n\n        The 2 randomly cropped samples of `label` can be:\n        [[0, 1, 2],     [[0, 0, 0],\n         [0, 1, 3],      [1, 2, 1],\n         [0, 0, 0]]      [1, 3, 0]]\n\n    If a dimension of the expected spatial size is bigger than the input image size,\n    will not crop that dimension. So the cropped result may be smaller than expected size, and the cropped\n    results of several images may not have exactly same shape.\n\n    Args:\n        spatial_size: the spatial size of the crop region e.g. [224, 224, 128].\n            if a dimension of ROI size is bigger than image size, will not crop that dimension of the image.\n            if its components have non-positive values, the corresponding size of `label` will be used.\n            for example: if the spatial size of input data is [40, 40, 40] and `spatial_size=[32, 64, -1]`,\n            the spatial size of output data will be [32, 40, 40].\n        ratios: specified ratios of every class in the label to generate crop centers, including background class.\n            if None, every class will have the same ratio to generate crop centers.\n        label: the label image that is used for finding every classes, if None, must set at `self.__call__`.\n        num_classes: number of classes for argmax label, not necessary for One-Hot label.\n        num_samples: number of samples (crop regions) to take in each list.\n        image: if image is not None, only return the indices of every class that are within the valid\n            region of the image (``image > image_threshold``).\n        image_threshold: if enabled `image`, use ``image > image_threshold`` to\n            determine the valid image content area and select class indices only in this area.\n        indices: if provided pre-computed indices of every class, will ignore above `image` and\n            `image_threshold`, and randomly select crop centers based on them, expect to be 1 dim array\n            of spatial indices after flattening. a typical usage is to call `ClassesToIndices` transform first\n            and cache the results for better performance.\n        allow_smaller: if `False`, an exception will be raised if the image is smaller than\n            the requested ROI in any dimension. If `True`, any smaller dimensions will remain\n            unchanged.\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_RandCropByLabelClasses.backend_RandCropByLabelClasses.__init__.self.allow_smaller.allow_smaller": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_RandCropByLabelClasses.backend_RandCropByLabelClasses.__init__.self.allow_smaller.allow_smaller", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1047, "end_line": 1070, "span_ids": ["RandCropByLabelClasses.__init__", "RandCropByLabelClasses"], "tokens": 231}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandCropByLabelClasses(Randomizable, Transform):\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __init__(\n        self,\n        spatial_size: Union[Sequence[int], int],\n        ratios: Optional[List[Union[float, int]]] = None,\n        label: Optional[NdarrayOrTensor] = None,\n        num_classes: Optional[int] = None,\n        num_samples: int = 1,\n        image: Optional[NdarrayOrTensor] = None,\n        image_threshold: float = 0.0,\n        indices: Optional[List[NdarrayOrTensor]] = None,\n        allow_smaller: bool = False,\n    ) -> None:\n        self.spatial_size = ensure_tuple(spatial_size)\n        self.ratios = ratios\n        self.label = label\n        self.num_classes = num_classes\n        self.num_samples = num_samples\n        self.image = image\n        self.image_threshold = image_threshold\n        self.centers: Optional[List[List[int]]] = None\n        self.indices = indices\n        self.allow_smaller = allow_smaller", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_RandCropByLabelClasses.randomize_RandCropByLabelClasses.randomize.self.centers.generate_label_classes_cr": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_RandCropByLabelClasses.randomize_RandCropByLabelClasses.randomize.self.centers.generate_label_classes_cr", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1072, "end_line": 1089, "span_ids": ["RandCropByLabelClasses.randomize"], "tokens": 182}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandCropByLabelClasses(Randomizable, Transform):\n\n    def randomize(\n        self,\n        label: NdarrayOrTensor,\n        indices: Optional[List[NdarrayOrTensor]] = None,\n        image: Optional[NdarrayOrTensor] = None,\n    ) -> None:\n        self.spatial_size = fall_back_tuple(self.spatial_size, default=label.shape[1:])\n        indices_: Sequence[NdarrayOrTensor]\n        if indices is None:\n            if self.indices is not None:\n                indices_ = self.indices\n            else:\n                indices_ = map_classes_to_indices(label, self.num_classes, image, self.image_threshold)\n        else:\n            indices_ = indices\n        self.centers = generate_label_classes_crop_centers(\n            self.spatial_size, self.num_samples, label.shape[1:], indices_, self.ratios, self.R, self.allow_smaller\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_RandCropByLabelClasses.__call___RandCropByLabelClasses.__call__.return.results": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_RandCropByLabelClasses.__call___RandCropByLabelClasses.__call__.return.results", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1091, "end_line": 1122, "span_ids": ["RandCropByLabelClasses.__call__"], "tokens": 314}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandCropByLabelClasses(Randomizable, Transform):\n\n    def __call__(\n        self,\n        img: NdarrayOrTensor,\n        label: Optional[NdarrayOrTensor] = None,\n        image: Optional[NdarrayOrTensor] = None,\n        indices: Optional[List[NdarrayOrTensor]] = None,\n    ) -> List[NdarrayOrTensor]:\n        \"\"\"\n        Args:\n            img: input data to crop samples from based on the ratios of every class, assumes `img` is a\n                channel-first array.\n            label: the label image that is used for finding indices of every class, if None, use `self.label`.\n            image: optional image data to help select valid area, can be same as `img` or another image array.\n                use ``image > image_threshold`` to select the centers only in valid region. if None, use `self.image`.\n            indices: list of indices for every class in the image, used to randomly select crop centers.\n\n        \"\"\"\n        if label is None:\n            label = self.label\n        if label is None:\n            raise ValueError(\"label should be provided.\")\n        if image is None:\n            image = self.image\n\n        self.randomize(label, indices, image)\n        results: List[NdarrayOrTensor] = []\n        if self.centers is not None:\n            for center in self.centers:\n                cropper = SpatialCrop(roi_center=tuple(center), roi_size=self.spatial_size)\n                results.append(cropper(img))\n\n        return results", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_ResizeWithPadOrCrop.__call___ResizeWithPadOrCrop.__call__._type_ignore": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_ResizeWithPadOrCrop.__call___ResizeWithPadOrCrop.__call__._type_ignore", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1158, "end_line": 1169, "span_ids": ["ResizeWithPadOrCrop.__call__"], "tokens": 212}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ResizeWithPadOrCrop(Transform):\n\n    def __call__(self, img: NdarrayOrTensor, mode: Optional[Union[NumpyPadMode, str]] = None) -> NdarrayOrTensor:\n        \"\"\"\n        Args:\n            img: data to pad or crop, assuming `img` is channel-first and\n                padding or cropping doesn't apply to the channel dim.\n            mode: {``\"constant\"``, ``\"edge\"``, ``\"linear_ramp\"``, ``\"maximum\"``, ``\"mean\"``,\n                ``\"median\"``, ``\"minimum\"``, ``\"reflect\"``, ``\"symmetric\"``, ``\"wrap\"``, ``\"empty\"``}\n                One of the listed string values or a user supplied function for padding.\n                If None, defaults to the ``mode`` in construction.\n                See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html\n        \"\"\"\n        return self.padder(self.cropper(img), mode=mode)  # type: ignore", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_BoundingRect_BoundingRect.__init__.self.select_fn.select_fn": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_BoundingRect_BoundingRect.__init__.self.select_fn.select_fn", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1172, "end_line": 1199, "span_ids": ["BoundingRect", "BoundingRect.__init__"], "tokens": 248}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class BoundingRect(Transform):\n    \"\"\"\n    Compute coordinates of axis-aligned bounding rectangles from input image `img`.\n    The output format of the coordinates is (shape is [channel, 2 * spatial dims]):\n\n        [[1st_spatial_dim_start, 1st_spatial_dim_end,\n         2nd_spatial_dim_start, 2nd_spatial_dim_end,\n         ...,\n         Nth_spatial_dim_start, Nth_spatial_dim_end],\n\n         ...\n\n         [1st_spatial_dim_start, 1st_spatial_dim_end,\n         2nd_spatial_dim_start, 2nd_spatial_dim_end,\n         ...,\n         Nth_spatial_dim_start, Nth_spatial_dim_end]]\n\n    The bounding boxes edges are aligned with the input image edges.\n    This function returns [-1, -1, ...] if there's no positive intensity.\n\n    Args:\n        select_fn: function to select expected foreground, default is to select values > 0.\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __init__(self, select_fn: Callable = is_positive) -> None:\n        self.select_fn = select_fn", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_BoundingRect.__call___": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/array.py_BoundingRect.__call___", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1201, "end_line": 1212, "span_ids": ["BoundingRect.__call__"], "tokens": 114}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class BoundingRect(Transform):\n\n    def __call__(self, img: NdarrayOrTensor) -> np.ndarray:\n        \"\"\"\n        See also: :py:class:`monai.transforms.utils.generate_spatial_bounding_box`.\n        \"\"\"\n        bbox = []\n\n        for channel in range(img.shape[0]):\n            start_, end_ = generate_spatial_bounding_box(img, select_fn=self.select_fn, channel_indices=channel)\n            bbox.append([i for k in zip(start_, end_) for i in k])\n\n        return np.stack(bbox, axis=0)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/batch.py_PadListDataCollate_PadListDataCollate.__init__.self.np_kwargs.np_kwargs": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/batch.py_PadListDataCollate_PadListDataCollate.__init__.self.np_kwargs.np_kwargs", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/batch.py", "file_name": "batch.py", "file_type": "text/x-python", "category": "implementation", "start_line": 44, "end_line": 74, "span_ids": ["PadListDataCollate.__init__", "PadListDataCollate"], "tokens": 357}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class PadListDataCollate(InvertibleTransform):\n    \"\"\"\n    Same as MONAI's ``list_data_collate``, except any tensors are centrally padded to match the shape of the biggest\n    tensor in each dimension. This transform is useful if some of the applied transforms generate batch data of\n    different sizes.\n\n    This can be used on both list and dictionary data. In the case of the dictionary data, this transform will be added\n    to the list of invertible transforms.\n\n    Note that normally, a user won't explicitly use the `__call__` method. Rather this would be passed to the `DataLoader`.\n    This means that `__call__` handles data as it comes out of a `DataLoader`, containing batch dimension. However, the\n    `inverse` operates on dictionaries containing images of shape `C,H,W,[D]`. This asymmetry is necessary so that we can\n    pass the inverse through multiprocessing.\n\n    Args:\n        method: padding method (see :py:class:`monai.transforms.SpatialPad`)\n        mode: padding mode (see :py:class:`monai.transforms.SpatialPad`)\n        np_kwargs: other args for `np.pad` API, note that `np.pad` treats channel dimension as the first dimension.\n            more details: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html\n\n    \"\"\"\n\n    def __init__(\n        self,\n        method: Union[Method, str] = Method.SYMMETRIC,\n        mode: Union[NumpyPadMode, str] = NumpyPadMode.CONSTANT,\n        **np_kwargs,\n    ) -> None:\n        self.method = method\n        self.mode = mode\n        self.np_kwargs = np_kwargs", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_contextlib_PadModeSequence.Union_Sequence_Union_Nump": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_contextlib_PadModeSequence.Union_Sequence_Union_Nump", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 18, "end_line": 107, "span_ids": ["impl:3", "docstring"], "tokens": 675}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import contextlib\nfrom copy import deepcopy\nfrom enum import Enum\nfrom itertools import chain\nfrom math import ceil, floor\nfrom typing import Any, Callable, Dict, Hashable, List, Mapping, Optional, Sequence, Tuple, Union\n\nimport numpy as np\n\nfrom monai.config import IndexSelection, KeysCollection\nfrom monai.config.type_definitions import NdarrayOrTensor\nfrom monai.data.utils import get_random_patch, get_valid_patch_size\nfrom monai.transforms.croppad.array import (\n    BorderPad,\n    BoundingRect,\n    CenterSpatialCrop,\n    CropForeground,\n    DivisiblePad,\n    RandCropByLabelClasses,\n    RandCropByPosNegLabel,\n    ResizeWithPadOrCrop,\n    SpatialCrop,\n    SpatialPad,\n)\nfrom monai.transforms.inverse import InvertibleTransform\nfrom monai.transforms.transform import MapTransform, Randomizable\nfrom monai.transforms.utils import (\n    allow_missing_keys_mode,\n    generate_label_classes_crop_centers,\n    generate_pos_neg_label_crop_centers,\n    is_positive,\n    map_binary_to_indices,\n    map_classes_to_indices,\n    weighted_patch_samples,\n)\nfrom monai.utils import ImageMetaKey as Key\nfrom monai.utils import Method, NumpyPadMode, PytorchPadMode, ensure_tuple, ensure_tuple_rep, fall_back_tuple\nfrom monai.utils.enums import TraceKeys\n\n__all__ = [\n    \"PadModeSequence\",\n    \"SpatialPadd\",\n    \"BorderPadd\",\n    \"DivisiblePadd\",\n    \"SpatialCropd\",\n    \"CenterSpatialCropd\",\n    \"CenterScaleCropd\",\n    \"RandScaleCropd\",\n    \"RandSpatialCropd\",\n    \"RandSpatialCropSamplesd\",\n    \"CropForegroundd\",\n    \"RandWeightedCropd\",\n    \"RandCropByPosNegLabeld\",\n    \"ResizeWithPadOrCropd\",\n    \"BoundingRectd\",\n    \"SpatialPadD\",\n    \"SpatialPadDict\",\n    \"BorderPadD\",\n    \"BorderPadDict\",\n    \"DivisiblePadD\",\n    \"DivisiblePadDict\",\n    \"SpatialCropD\",\n    \"SpatialCropDict\",\n    \"CenterSpatialCropD\",\n    \"CenterSpatialCropDict\",\n    \"CenterScaleCropD\",\n    \"CenterScaleCropDict\",\n    \"RandScaleCropD\",\n    \"RandScaleCropDict\",\n    \"RandSpatialCropD\",\n    \"RandSpatialCropDict\",\n    \"RandSpatialCropSamplesD\",\n    \"RandSpatialCropSamplesDict\",\n    \"CropForegroundD\",\n    \"CropForegroundDict\",\n    \"RandWeightedCropD\",\n    \"RandWeightedCropDict\",\n    \"RandCropByPosNegLabelD\",\n    \"RandCropByPosNegLabelDict\",\n    \"ResizeWithPadOrCropD\",\n    \"ResizeWithPadOrCropDict\",\n    \"BoundingRectD\",\n    \"BoundingRectDict\",\n    \"RandCropByLabelClassesd\",\n    \"RandCropByLabelClassesD\",\n    \"RandCropByLabelClassesDict\",\n]\n\nNumpyPadModeSequence = Union[Sequence[Union[NumpyPadMode, str]], NumpyPadMode, str]\nPadModeSequence = Union[Sequence[Union[NumpyPadMode, PytorchPadMode, str]], NumpyPadMode, PytorchPadMode, str]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_RandSpatialCropSamplesd_RandSpatialCropSamplesd.backend.RandSpatialCropd_backend": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_RandSpatialCropSamplesd_RandSpatialCropSamplesd.backend.RandSpatialCropd_backend", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 687, "end_line": 732, "span_ids": ["RandSpatialCropSamplesd"], "tokens": 690}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandSpatialCropSamplesd(Randomizable, MapTransform, InvertibleTransform):\n    \"\"\"\n    Dictionary-based version :py:class:`monai.transforms.RandSpatialCropSamples`.\n    Crop image with random size or specific size ROI to generate a list of N samples.\n    It can crop at a random position as center or at the image center. And allows to set\n    the minimum size to limit the randomly generated ROI. Suppose all the expected fields\n    specified by `keys` have same shape, and add `patch_index` to the corresponding meta data.\n    It will return a list of dictionaries for all the cropped images.\n\n    Note: even `random_size=False`, if a dimension of the expected ROI size is bigger than the input image size,\n    will not crop that dimension. So the cropped result may be smaller than the expected ROI, and the cropped\n    results of several images may not have exactly the same shape.\n\n    Args:\n        keys: keys of the corresponding items to be transformed.\n            See also: monai.transforms.MapTransform\n        roi_size: if `random_size` is True, it specifies the minimum crop region.\n            if `random_size` is False, it specifies the expected ROI size to crop. e.g. [224, 224, 128]\n            if a dimension of ROI size is bigger than image size, will not crop that dimension of the image.\n            If its components have non-positive values, the corresponding size of input image will be used.\n            for example: if the spatial size of input data is [40, 40, 40] and `roi_size=[32, 64, -1]`,\n            the spatial size of output data will be [32, 40, 40].\n        num_samples: number of samples (crop regions) to take in the returned list.\n        max_roi_size: if `random_size` is True and `roi_size` specifies the min crop region size, `max_roi_size`\n            can specify the max crop region size. if None, defaults to the input image size.\n            if its components have non-positive values, the corresponding size of input image will be used.\n        random_center: crop at random position as center or the image center.\n        random_size: crop with random size or specific size ROI.\n            The actual size is sampled from `randint(roi_size, img_size)`.\n        meta_keys: explicitly indicate the key of the corresponding meta data dictionary.\n            used to add `patch_index` to the meta dict.\n            for example, for data with key `image`, the metadata by default is in `image_meta_dict`.\n            the meta data is a dictionary object which contains: filename, original_shape, etc.\n            it can be a sequence of string, map to the `keys`.\n            if None, will try to construct meta_keys by `key_{meta_key_postfix}`.\n        meta_key_postfix: if meta_keys is None, use `key_{postfix}` to to fetch the meta data according\n            to the key data, default is `meta_dict`, the meta data is a dictionary object.\n            used to add `patch_index` to the meta dict.\n        allow_missing_keys: don't raise exception if key is missing.\n\n    Raises:\n        ValueError: When ``num_samples`` is nonpositive.\n\n    \"\"\"\n\n    backend = RandSpatialCropd.backend", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_CropForegroundd_CropForegroundd.backend.CropForeground_backend": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_CropForegroundd_CropForegroundd.backend.CropForeground_backend", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 799, "end_line": 812, "span_ids": ["CropForegroundd"], "tokens": 204}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CropForegroundd(MapTransform, InvertibleTransform):\n    \"\"\"\n    Dictionary-based version :py:class:`monai.transforms.CropForeground`.\n    Crop only the foreground object of the expected images.\n    The typical usage is to help training and evaluation if the valid part is small in the whole medical image.\n    The valid part can be determined by any field in the data with `source_key`, for example:\n    - Select values > 0 in image field as the foreground and crop on all fields specified by `keys`.\n    - Select label = 3 in label field as the foreground to crop on all fields specified by `keys`.\n    - Select label > 0 in the third channel of a One-Hot label field as the foreground to crop all `keys` fields.\n    Users can define arbitrary function to select expected foreground from the whole source image or specified\n    channels. And it can also add margin to every dim of the bounding box of foreground object.\n    \"\"\"\n\n    backend = CropForeground.backend", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_CropForegroundd.__init___CropForegroundd.__init__.self.mode.ensure_tuple_rep_mode_le": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_CropForegroundd.__init___CropForegroundd.__init__.self.mode.ensure_tuple_rep_mode_le", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 814, "end_line": 860, "span_ids": ["CropForegroundd.__init__"], "tokens": 697}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CropForegroundd(MapTransform, InvertibleTransform):\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        source_key: str,\n        select_fn: Callable = is_positive,\n        channel_indices: Optional[IndexSelection] = None,\n        margin: Union[Sequence[int], int] = 0,\n        k_divisible: Union[Sequence[int], int] = 1,\n        mode: Optional[Union[NumpyPadMode, PytorchPadMode, str]] = NumpyPadMode.CONSTANT,\n        start_coord_key: str = \"foreground_start_coord\",\n        end_coord_key: str = \"foreground_end_coord\",\n        allow_missing_keys: bool = False,\n        **np_kwargs,\n    ) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to be transformed.\n                See also: :py:class:`monai.transforms.compose.MapTransform`\n            source_key: data source to generate the bounding box of foreground, can be image or label, etc.\n            select_fn: function to select expected foreground, default is to select values > 0.\n            channel_indices: if defined, select foreground only on the specified channels\n                of image. if None, select foreground on the whole image.\n            margin: add margin value to spatial dims of the bounding box, if only 1 value provided, use it for all dims.\n            k_divisible: make each spatial dimension to be divisible by k, default to 1.\n                if `k_divisible` is an int, the same `k` be applied to all the input spatial dimensions.\n            mode: available modes for numpy array:{``\"constant\"``, ``\"edge\"``, ``\"linear_ramp\"``, ``\"maximum\"``,\n                ``\"mean\"``, ``\"median\"``, ``\"minimum\"``, ``\"reflect\"``, ``\"symmetric\"``, ``\"wrap\"``, ``\"empty\"``}\n                available modes for PyTorch Tensor: {``\"constant\"``, ``\"reflect\"``, ``\"replicate\"``, ``\"circular\"``}.\n                One of the listed string values or a user supplied function. Defaults to ``\"constant\"``.\n                See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html\n                https://pytorch.org/docs/stable/generated/torch.nn.functional.pad.html\n                it also can be a sequence of string, each element corresponds to a key in ``keys``.\n            start_coord_key: key to record the start coordinate of spatial bounding box for foreground.\n            end_coord_key: key to record the end coordinate of spatial bounding box for foreground.\n            allow_missing_keys: don't raise exception if key is missing.\n            np_kwargs: other args for `np.pad` API, note that `np.pad` treats channel dimension as the first dimension.\n                more details: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html\n\n        \"\"\"\n        super().__init__(keys, allow_missing_keys)\n        self.source_key = source_key\n        self.start_coord_key = start_coord_key\n        self.end_coord_key = end_coord_key\n        self.cropper = CropForeground(\n            select_fn=select_fn, channel_indices=channel_indices, margin=margin, k_divisible=k_divisible, **np_kwargs\n        )\n        self.mode = ensure_tuple_rep(mode, len(self.keys))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_RandCropByPosNegLabeld.backend_RandCropByPosNegLabeld.__init__.self.allow_smaller.allow_smaller": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_RandCropByPosNegLabeld.backend_RandCropByPosNegLabeld.__init__.self.allow_smaller.allow_smaller", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1076, "end_line": 1113, "span_ids": ["RandCropByPosNegLabeld", "RandCropByPosNegLabeld.__init__"], "tokens": 433}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandCropByPosNegLabeld(Randomizable, MapTransform, InvertibleTransform):\n\n    backend = RandCropByPosNegLabel.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        label_key: str,\n        spatial_size: Union[Sequence[int], int],\n        pos: float = 1.0,\n        neg: float = 1.0,\n        num_samples: int = 1,\n        image_key: Optional[str] = None,\n        image_threshold: float = 0.0,\n        fg_indices_key: Optional[str] = None,\n        bg_indices_key: Optional[str] = None,\n        meta_keys: Optional[KeysCollection] = None,\n        meta_key_postfix: str = \"meta_dict\",\n        allow_smaller: bool = False,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        MapTransform.__init__(self, keys, allow_missing_keys)\n        self.label_key = label_key\n        self.spatial_size: Union[Tuple[int, ...], Sequence[int], int] = spatial_size\n        if pos < 0 or neg < 0:\n            raise ValueError(f\"pos and neg must be nonnegative, got pos={pos} neg={neg}.\")\n        if pos + neg == 0:\n            raise ValueError(\"Incompatible values: pos=0 and neg=0.\")\n        self.pos_ratio = pos / (pos + neg)\n        self.num_samples = num_samples\n        self.image_key = image_key\n        self.image_threshold = image_threshold\n        self.fg_indices_key = fg_indices_key\n        self.bg_indices_key = bg_indices_key\n        self.meta_keys = ensure_tuple_rep(None, len(self.keys)) if meta_keys is None else ensure_tuple(meta_keys)\n        if len(self.keys) != len(self.meta_keys):\n            raise ValueError(\"meta_keys should have the same length as keys.\")\n        self.meta_key_postfix = ensure_tuple_rep(meta_key_postfix, len(self.keys))\n        self.centers: Optional[List[List[int]]] = None\n        self.allow_smaller = allow_smaller", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_RandCropByLabelClassesd_RandCropByLabelClassesd._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_RandCropByLabelClassesd_RandCropByLabelClassesd._", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1197, "end_line": 1274, "span_ids": ["RandCropByLabelClassesd"], "tokens": 1168}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandCropByLabelClassesd(Randomizable, MapTransform, InvertibleTransform):\n    \"\"\"\n    Dictionary-based version :py:class:`monai.transforms.RandCropByLabelClasses`.\n    Crop random fixed sized regions with the center being a class based on the specified ratios of every class.\n    The label data can be One-Hot format array or Argmax data. And will return a list of arrays for all the\n    cropped images. For example, crop two (3 x 3) arrays from (5 x 5) array with `ratios=[1, 2, 3, 1]`::\n\n        cropper = RandCropByLabelClassesd(\n            keys=[\"image\", \"label\"],\n            label_key=\"label\",\n            spatial_size=[3, 3],\n            ratios=[1, 2, 3, 1],\n            num_classes=4,\n            num_samples=2,\n        )\n        data = {\n            \"image\": np.array([\n                [[0.0, 0.3, 0.4, 0.2, 0.0],\n                [0.0, 0.1, 0.2, 0.1, 0.4],\n                [0.0, 0.3, 0.5, 0.2, 0.0],\n                [0.1, 0.2, 0.1, 0.1, 0.0],\n                [0.0, 0.1, 0.2, 0.1, 0.0]]\n            ]),\n            \"label\": np.array([\n                [[0, 0, 0, 0, 0],\n                [0, 1, 2, 1, 0],\n                [0, 1, 3, 0, 0],\n                [0, 0, 0, 0, 0],\n                [0, 0, 0, 0, 0]]\n            ]),\n        }\n        result = cropper(data)\n\n        The 2 randomly cropped samples of `label` can be:\n        [[0, 1, 2],     [[0, 0, 0],\n         [0, 1, 3],      [1, 2, 1],\n         [0, 0, 0]]      [1, 3, 0]]\n\n    If a dimension of the expected spatial size is bigger than the input image size,\n    will not crop that dimension. So the cropped result may be smaller than expected size, and the cropped\n    results of several images may not have exactly same shape.\n\n    Args:\n        keys: keys of the corresponding items to be transformed.\n            See also: :py:class:`monai.transforms.compose.MapTransform`\n        label_key: name of key for label image, this will be used for finding indices of every class.\n        spatial_size: the spatial size of the crop region e.g. [224, 224, 128].\n            if a dimension of ROI size is bigger than image size, will not crop that dimension of the image.\n            if its components have non-positive values, the corresponding size of `label` will be used.\n            for example: if the spatial size of input data is [40, 40, 40] and `spatial_size=[32, 64, -1]`,\n            the spatial size of output data will be [32, 40, 40].\n        ratios: specified ratios of every class in the label to generate crop centers, including background class.\n            if None, every class will have the same ratio to generate crop centers.\n        num_classes: number of classes for argmax label, not necessary for One-Hot label.\n        num_samples: number of samples (crop regions) to take in each list.\n        image_key: if image_key is not None, only return the indices of every class that are within the valid\n            region of the image (``image > image_threshold``).\n        image_threshold: if enabled `image_key`, use ``image > image_threshold`` to\n            determine the valid image content area and select class indices only in this area.\n        indices_key: if provided pre-computed indices of every class, will ignore above `image` and\n            `image_threshold`, and randomly select crop centers based on them, expect to be 1 dim array\n            of spatial indices after flattening. a typical usage is to call `ClassesToIndices` transform first\n            and cache the results for better performance.\n        meta_keys: explicitly indicate the key of the corresponding meta data dictionary.\n            used to add `patch_index` to the meta dict.\n            for example, for data with key `image`, the metadata by default is in `image_meta_dict`.\n            the meta data is a dictionary object which contains: filename, original_shape, etc.\n            it can be a sequence of string, map to the `keys`.\n            if None, will try to construct meta_keys by `key_{meta_key_postfix}`.\n        meta_key_postfix: if meta_keys is None, use `key_{postfix}` to to fetch the meta data according\n            to the key data, default is `meta_dict`, the meta data is a dictionary object.\n            used to add `patch_index` to the meta dict.\n        allow_smaller: if `False`, an exception will be raised if the image is smaller than\n            the requested ROI in any dimension. If `True`, any smaller dimensions will remain\n            unchanged.\n        allow_missing_keys: don't raise exception if key is missing.\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_RandCropByLabelClassesd.backend_RandCropByLabelClassesd.__init__.self.allow_smaller.allow_smaller": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_RandCropByLabelClassesd.backend_RandCropByLabelClassesd.__init__.self.allow_smaller.allow_smaller", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1276, "end_line": 1308, "span_ids": ["RandCropByLabelClassesd", "RandCropByLabelClassesd.__init__"], "tokens": 355}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandCropByLabelClassesd(Randomizable, MapTransform, InvertibleTransform):\n\n    backend = RandCropByLabelClasses.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        label_key: str,\n        spatial_size: Union[Sequence[int], int],\n        ratios: Optional[List[Union[float, int]]] = None,\n        num_classes: Optional[int] = None,\n        num_samples: int = 1,\n        image_key: Optional[str] = None,\n        image_threshold: float = 0.0,\n        indices_key: Optional[str] = None,\n        meta_keys: Optional[KeysCollection] = None,\n        meta_key_postfix: str = \"meta_dict\",\n        allow_smaller: bool = False,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        MapTransform.__init__(self, keys, allow_missing_keys)\n        self.label_key = label_key\n        self.spatial_size: Union[Tuple[int, ...], Sequence[int], int] = spatial_size\n        self.ratios = ratios\n        self.num_classes = num_classes\n        self.num_samples = num_samples\n        self.image_key = image_key\n        self.image_threshold = image_threshold\n        self.indices_key = indices_key\n        self.meta_keys = ensure_tuple_rep(None, len(self.keys)) if meta_keys is None else ensure_tuple(meta_keys)\n        if len(self.keys) != len(self.meta_keys):\n            raise ValueError(\"meta_keys should have the same length as keys.\")\n        self.meta_key_postfix = ensure_tuple_rep(meta_key_postfix, len(self.keys))\n        self.centers: Optional[List[List[int]]] = None\n        self.allow_smaller = allow_smaller", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_RandCropByLabelClassesd.randomize_RandCropByLabelClassesd.randomize.self.centers.generate_label_classes_cr": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_RandCropByLabelClassesd.randomize_RandCropByLabelClassesd.randomize.self.centers.generate_label_classes_cr", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1310, "end_line": 1323, "span_ids": ["RandCropByLabelClassesd.randomize"], "tokens": 161}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandCropByLabelClassesd(Randomizable, MapTransform, InvertibleTransform):\n\n    def randomize(\n        self,\n        label: NdarrayOrTensor,\n        indices: Optional[List[NdarrayOrTensor]] = None,\n        image: Optional[NdarrayOrTensor] = None,\n    ) -> None:\n        self.spatial_size = fall_back_tuple(self.spatial_size, default=label.shape[1:])\n        if indices is None:\n            indices_ = map_classes_to_indices(label, self.num_classes, image, self.image_threshold)\n        else:\n            indices_ = indices\n        self.centers = generate_label_classes_crop_centers(\n            self.spatial_size, self.num_samples, label.shape[1:], indices_, self.ratios, self.R, self.allow_smaller\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_RandCropByLabelClassesd.__call___RandCropByLabelClassesd.__call__.return.results": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_RandCropByLabelClassesd.__call___RandCropByLabelClassesd.__call__.return.results", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1325, "end_line": 1357, "span_ids": ["RandCropByLabelClassesd.__call__"], "tokens": 415}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandCropByLabelClassesd(Randomizable, MapTransform, InvertibleTransform):\n\n    def __call__(self, data: Mapping[Hashable, Any]) -> List[Dict[Hashable, NdarrayOrTensor]]:\n        d = dict(data)\n        label = d[self.label_key]\n        image = d[self.image_key] if self.image_key else None\n        indices = d.pop(self.indices_key, None) if self.indices_key is not None else None\n\n        self.randomize(label, indices, image)\n        if not isinstance(self.spatial_size, tuple):\n            raise ValueError(\"spatial_size must be a valid tuple.\")\n        if self.centers is None:\n            raise ValueError(\"no available ROI centers to crop.\")\n\n        # initialize returned list with shallow copy to preserve key ordering\n        results: List[Dict[Hashable, NdarrayOrTensor]] = [dict(d) for _ in range(self.num_samples)]\n\n        for i, center in enumerate(self.centers):\n            # fill in the extra keys with unmodified data\n            for key in set(d.keys()).difference(set(self.keys)):\n                results[i][key] = deepcopy(d[key])\n            for key in self.key_iterator(d):\n                img = d[key]\n                cropper = SpatialCrop(roi_center=tuple(center), roi_size=self.spatial_size)\n                orig_size = img.shape[1:]\n                results[i][key] = cropper(img)\n                self.push_transform(results[i], key, extra_info={\"center\": center}, orig_size=orig_size)\n            # add `patch_index` to the meta data\n            for key, meta_key, meta_key_postfix in self.key_iterator(d, self.meta_keys, self.meta_key_postfix):\n                meta_key = meta_key or f\"{key}_{meta_key_postfix}\"\n                if meta_key not in results[i]:\n                    results[i][meta_key] = {}  # type: ignore\n                results[i][meta_key][Key.PATCH_INDEX] = i  # type: ignore\n\n        return results", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_RandCropByLabelClassesd.inverse_RandCropByLabelClassesd.inverse.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/croppad/dictionary.py_RandCropByLabelClassesd.inverse_RandCropByLabelClassesd.inverse.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/croppad/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1362, "end_line": 1382, "span_ids": ["RandCropByLabelClassesd.inverse"], "tokens": 266}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandCropByLabelClassesd(Randomizable, MapTransform, InvertibleTransform):\n\n    def inverse(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = deepcopy(dict(data))\n        for key in self.key_iterator(d):\n            transform = self.get_most_recent_transform(d, key)\n            # Create inverse transform\n            orig_size = np.asarray(transform[TraceKeys.ORIG_SIZE])\n            current_size = np.asarray(d[key].shape[1:])\n            center = transform[TraceKeys.EXTRA_INFO][\"center\"]\n            cropper = SpatialCrop(roi_center=tuple(center), roi_size=self.spatial_size)  # type: ignore\n            # get required pad to start and end\n            pad_to_start = np.array([s.indices(o)[0] for s, o in zip(cropper.slices, orig_size)])\n            pad_to_end = orig_size - current_size - pad_to_start\n            # interleave mins and maxes\n            pad = list(chain(*zip(pad_to_start.tolist(), pad_to_end.tolist())))\n            inverse_transform = BorderPad(pad)\n            # Apply inverse transform\n            d[key] = inverse_transform(d[key])\n            # Remove the applied transform\n            self.pop_transform(d, key)\n\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_from_abc_import_abstractm___all__._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_from_abc_import_abstractm___all__._", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 16, "end_line": 78, "span_ids": ["docstring"], "tokens": 454}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from abc import abstractmethod\nfrom collections.abc import Iterable\nfrom functools import partial\nfrom typing import Any, Callable, List, Optional, Sequence, Tuple, Union\nfrom warnings import warn\n\nimport numpy as np\nimport torch\n\nfrom monai.config import DtypeLike\nfrom monai.config.type_definitions import NdarrayOrTensor\nfrom monai.data.utils import get_random_patch, get_valid_patch_size\nfrom monai.networks.layers import GaussianFilter, HilbertTransform, SavitzkyGolayFilter\nfrom monai.transforms.transform import RandomizableTransform, Transform\nfrom monai.transforms.utils import Fourier, equalize_hist, is_positive, rescale_array\nfrom monai.transforms.utils_pytorch_numpy_unification import clip, percentile, where\nfrom monai.utils import (\n    InvalidPyTorchVersionError,\n    convert_data_type,\n    convert_to_dst_type,\n    ensure_tuple,\n    ensure_tuple_rep,\n    ensure_tuple_size,\n    fall_back_tuple,\n    pytorch_after,\n)\nfrom monai.utils.deprecate_utils import deprecated_arg\nfrom monai.utils.enums import TransformBackends\nfrom monai.utils.type_conversion import convert_to_tensor, get_equivalent_dtype\n\n__all__ = [\n    \"RandGaussianNoise\",\n    \"RandRicianNoise\",\n    \"ShiftIntensity\",\n    \"RandShiftIntensity\",\n    \"StdShiftIntensity\",\n    \"RandStdShiftIntensity\",\n    \"RandBiasField\",\n    \"ScaleIntensity\",\n    \"RandScaleIntensity\",\n    \"NormalizeIntensity\",\n    \"ThresholdIntensity\",\n    \"ScaleIntensityRange\",\n    \"AdjustContrast\",\n    \"RandAdjustContrast\",\n    \"ScaleIntensityRangePercentiles\",\n    \"MaskIntensity\",\n    \"DetectEnvelope\",\n    \"SavitzkyGolaySmooth\",\n    \"GaussianSmooth\",\n    \"RandGaussianSmooth\",\n    \"GaussianSharpen\",\n    \"RandGaussianSharpen\",\n    \"RandHistogramShift\",\n    \"GibbsNoise\",\n    \"RandGibbsNoise\",\n    \"KSpaceSpikeNoise\",\n    \"RandKSpaceSpikeNoise\",\n    \"RandCoarseTransform\",\n    \"RandCoarseDropout\",\n    \"RandCoarseShuffle\",\n    \"HistogramNormalize\",\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandGaussianNoise.__call___RandGaussianNoise.__call__.return.img_noise": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandGaussianNoise.__call___RandGaussianNoise.__call__.return.img_noise", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 111, "end_line": 125, "span_ids": ["RandGaussianNoise.__call__"], "tokens": 146}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandGaussianNoise(RandomizableTransform):\n\n    def __call__(self, img: NdarrayOrTensor, mean: Optional[float] = None, randomize: bool = True) -> NdarrayOrTensor:\n        \"\"\"\n        Apply the transform to `img`.\n        \"\"\"\n        if randomize:\n            self.randomize(img=img, mean=self.mean if mean is None else mean)\n\n        if not self._do_transform:\n            return img\n\n        if self.noise is None:\n            raise RuntimeError(\"please call the `randomize()` function first.\")\n        img, *_ = convert_data_type(img, dtype=self.dtype)\n        noise, *_ = convert_to_dst_type(self.noise, img)\n        return img + noise", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandRicianNoise.__call___RandRicianNoise.__call__.return.img": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandRicianNoise.__call___RandRicianNoise.__call__.return.img", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 189, "end_line": 214, "span_ids": ["RandRicianNoise.__call__"], "tokens": 288}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandRicianNoise(RandomizableTransform):\n\n    def __call__(self, img: NdarrayOrTensor, randomize: bool = True) -> NdarrayOrTensor:\n        \"\"\"\n        Apply the transform to `img`.\n        \"\"\"\n        if randomize:\n            super().randomize(None)\n\n        if not self._do_transform:\n            return img\n\n        img, *_ = convert_data_type(img, dtype=self.dtype)\n        if self.channel_wise:\n            _mean = ensure_tuple_rep(self.mean, len(img))\n            _std = ensure_tuple_rep(self.std, len(img))\n            for i, d in enumerate(img):\n                img[i] = self._add_noise(d, mean=_mean[i], std=_std[i] * d.std() if self.relative else _std[i])\n        else:\n            if not isinstance(self.mean, (int, float)):\n                raise RuntimeError(\"If channel_wise is False, mean must be a float or int number.\")\n            if not isinstance(self.std, (int, float)):\n                raise RuntimeError(\"If channel_wise is False, std must be a float or int number.\")\n            std = self.std * img.std() if self.relative else self.std\n            if not isinstance(std, (int, float)):\n                raise RuntimeError(\"std must be a float or int number.\")\n            img = self._add_noise(img, mean=self.mean, std=std)\n        return img", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_ShiftIntensity_ShiftIntensity.__call__.return.out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_ShiftIntensity_ShiftIntensity.__call__.return.out", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 207, "end_line": 229, "span_ids": ["ShiftIntensity.__init__", "ShiftIntensity", "ShiftIntensity.__call__"], "tokens": 152}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ShiftIntensity(Transform):\n    \"\"\"\n    Shift intensity uniformly for the entire image with specified `offset`.\n\n    Args:\n        offset: offset value to shift the intensity of image.\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __init__(self, offset: float) -> None:\n        self.offset = offset\n\n    def __call__(self, img: NdarrayOrTensor, offset: Optional[float] = None) -> NdarrayOrTensor:\n        \"\"\"\n        Apply the transform to `img`.\n        \"\"\"\n\n        offset = self.offset if offset is None else offset\n        out = img + offset\n        out, *_ = convert_data_type(data=out, dtype=img.dtype)\n\n        return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandShiftIntensity_RandShiftIntensity.randomize.self._offset.self_R_uniform_low_self_o": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandShiftIntensity_RandShiftIntensity.randomize.self._offset.self_R_uniform_low_self_o", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 232, "end_line": 260, "span_ids": ["RandShiftIntensity", "RandShiftIntensity.randomize", "RandShiftIntensity.__init__"], "tokens": 275}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandShiftIntensity(RandomizableTransform):\n    \"\"\"\n    Randomly shift intensity with randomly picked offset.\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __init__(self, offsets: Union[Tuple[float, float], float], prob: float = 0.1) -> None:\n        \"\"\"\n        Args:\n            offsets: offset range to randomly shift.\n                if single number, offset value is picked from (-offsets, offsets).\n            prob: probability of shift.\n        \"\"\"\n        RandomizableTransform.__init__(self, prob)\n        if isinstance(offsets, (int, float)):\n            self.offsets = (min(-offsets, offsets), max(-offsets, offsets))\n        elif len(offsets) != 2:\n            raise ValueError(\"offsets should be a number or pair of numbers.\")\n        else:\n            self.offsets = (min(offsets), max(offsets))\n        self._offset = self.offsets[0]\n        self._shfiter = ShiftIntensity(self._offset)\n\n    def randomize(self, data: Optional[Any] = None) -> None:\n        super().randomize(None)\n        if not self._do_transform:\n            return None\n        self._offset = self.R.uniform(low=self.offsets[0], high=self.offsets[1])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandShiftIntensity.__call___RandShiftIntensity.__call__.return.self__shfiter_img_self__": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandShiftIntensity.__call___RandShiftIntensity.__call__.return.self__shfiter_img_self__", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 262, "end_line": 278, "span_ids": ["RandShiftIntensity.__call__"], "tokens": 143}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandShiftIntensity(RandomizableTransform):\n\n    def __call__(self, img: NdarrayOrTensor, factor: Optional[float] = None, randomize: bool = True) -> NdarrayOrTensor:\n        \"\"\"\n        Apply the transform to `img`.\n\n        Args:\n            img: input image to shift intensity.\n            factor: a factor to multiply the random offset, then shift.\n                can be some image specific value at runtime, like: max(img), etc.\n\n        \"\"\"\n        if randomize:\n            self.randomize()\n\n        if not self._do_transform:\n            return img\n\n        return self._shfiter(img, self._offset if factor is None else self._offset * factor)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_StdShiftIntensity_StdShiftIntensity.__init__.self.dtype.dtype": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_StdShiftIntensity_StdShiftIntensity.__init__.self.dtype.dtype", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 281, "end_line": 304, "span_ids": ["StdShiftIntensity.__init__", "StdShiftIntensity"], "tokens": 237}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class StdShiftIntensity(Transform):\n    \"\"\"\n    Shift intensity for the image with a factor and the standard deviation of the image\n    by: ``v = v + factor * std(v)``.\n    This transform can focus on only non-zero values or the entire image,\n    and can also calculate the std on each channel separately.\n\n    Args:\n        factor: factor shift by ``v = v + factor * std(v)``.\n        nonzero: whether only count non-zero values.\n        channel_wise: if True, calculate on each channel separately. Please ensure\n            that the first dimension represents the channel of the image if True.\n        dtype: output data type, if None, same as input image. defaults to float32.\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __init__(\n        self, factor: float, nonzero: bool = False, channel_wise: bool = False, dtype: DtypeLike = np.float32\n    ) -> None:\n        self.factor = factor\n        self.nonzero = nonzero\n        self.channel_wise = channel_wise\n        self.dtype = dtype", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_StdShiftIntensity._stdshift_StdShiftIntensity.__call__.return.img": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_StdShiftIntensity._stdshift_StdShiftIntensity.__call__.return.img", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 306, "end_line": 333, "span_ids": ["StdShiftIntensity._stdshift", "StdShiftIntensity.__call__"], "tokens": 231}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class StdShiftIntensity(Transform):\n\n    def _stdshift(self, img: NdarrayOrTensor) -> NdarrayOrTensor:\n        ones: Callable\n        std: Callable\n        if isinstance(img, torch.Tensor):\n            ones = torch.ones\n            std = partial(torch.std, unbiased=False)\n        else:\n            ones = np.ones\n            std = np.std\n\n        slices = (img != 0) if self.nonzero else ones(img.shape, dtype=bool)\n        if slices.any():\n            offset = self.factor * std(img[slices])\n            img[slices] = img[slices] + offset\n        return img\n\n    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:\n        \"\"\"\n        Apply the transform to `img`.\n        \"\"\"\n        if self.dtype is not None:\n            img, *_ = convert_data_type(img, dtype=self.dtype)\n        if self.channel_wise:\n            for i, d in enumerate(img):\n                img[i] = self._stdshift(d)  # type: ignore\n        else:\n            img = self._stdshift(img)\n        return img", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandStdShiftIntensity_RandStdShiftIntensity.__call__.return.shifter_img_img_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandStdShiftIntensity_RandStdShiftIntensity.__call__.return.shifter_img_img_", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 336, "end_line": 393, "span_ids": ["RandStdShiftIntensity", "RandStdShiftIntensity.randomize", "RandStdShiftIntensity.__init__", "RandStdShiftIntensity.__call__"], "tokens": 499}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandStdShiftIntensity(RandomizableTransform):\n    \"\"\"\n    Shift intensity for the image with a factor and the standard deviation of the image\n    by: ``v = v + factor * std(v)`` where the `factor` is randomly picked.\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __init__(\n        self,\n        factors: Union[Tuple[float, float], float],\n        prob: float = 0.1,\n        nonzero: bool = False,\n        channel_wise: bool = False,\n        dtype: DtypeLike = np.float32,\n    ) -> None:\n        \"\"\"\n        Args:\n            factors: if tuple, the randomly picked range is (min(factors), max(factors)).\n                If single number, the range is (-factors, factors).\n            prob: probability of std shift.\n            nonzero: whether only count non-zero values.\n            channel_wise: if True, calculate on each channel separately.\n            dtype: output data type, if None, same as input image. defaults to float32.\n\n        \"\"\"\n        RandomizableTransform.__init__(self, prob)\n        if isinstance(factors, (int, float)):\n            self.factors = (min(-factors, factors), max(-factors, factors))\n        elif len(factors) != 2:\n            raise ValueError(\"factors should be a number or pair of numbers.\")\n        else:\n            self.factors = (min(factors), max(factors))\n        self.factor = self.factors[0]\n        self.nonzero = nonzero\n        self.channel_wise = channel_wise\n        self.dtype = dtype\n\n    def randomize(self, data: Optional[Any] = None) -> None:\n        super().randomize(None)\n        if not self._do_transform:\n            return None\n        self.factor = self.R.uniform(low=self.factors[0], high=self.factors[1])\n\n    def __call__(self, img: NdarrayOrTensor, randomize: bool = True) -> NdarrayOrTensor:\n        \"\"\"\n        Apply the transform to `img`.\n        \"\"\"\n        if randomize:\n            self.randomize()\n\n        if not self._do_transform:\n            return img\n\n        shifter = StdShiftIntensity(\n            factor=self.factor, nonzero=self.nonzero, channel_wise=self.channel_wise, dtype=self.dtype\n        )\n        return shifter(img=img)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_ScaleIntensity_ScaleIntensity.__init__.self.dtype.dtype": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_ScaleIntensity_ScaleIntensity.__init__.self.dtype.dtype", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 396, "end_line": 426, "span_ids": ["ScaleIntensity", "ScaleIntensity.__init__"], "tokens": 304}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ScaleIntensity(Transform):\n    \"\"\"\n    Scale the intensity of input image to the given value range (minv, maxv).\n    If `minv` and `maxv` not provided, use `factor` to scale image by ``v = v * (1 + factor)``.\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __init__(\n        self,\n        minv: Optional[float] = 0.0,\n        maxv: Optional[float] = 1.0,\n        factor: Optional[float] = None,\n        channel_wise: bool = False,\n        dtype: DtypeLike = np.float32,\n    ) -> None:\n        \"\"\"\n        Args:\n            minv: minimum value of output data.\n            maxv: maximum value of output data.\n            factor: factor scale by ``v = v * (1 + factor)``. In order to use\n                this parameter, please set both `minv` and `maxv` into None.\n            channel_wise: if True, scale on each channel separately. Please ensure\n                that the first dimension represents the channel of the image if True.\n            dtype: output data type, if None, same as input image. defaults to float32.\n        \"\"\"\n        self.minv = minv\n        self.maxv = maxv\n        self.factor = factor\n        self.channel_wise = channel_wise\n        self.dtype = dtype", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandScaleIntensity_RandScaleIntensity.__call__.return.ScaleIntensity_minv_None_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandScaleIntensity_RandScaleIntensity.__call__.return.ScaleIntensity_minv_None_", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 449, "end_line": 494, "span_ids": ["RandScaleIntensity.__init__", "RandScaleIntensity", "RandScaleIntensity.randomize", "RandScaleIntensity.__call__"], "tokens": 403}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandScaleIntensity(RandomizableTransform):\n    \"\"\"\n    Randomly scale the intensity of input image by ``v = v * (1 + factor)`` where the `factor`\n    is randomly picked.\n    \"\"\"\n\n    backend = ScaleIntensity.backend\n\n    def __init__(\n        self, factors: Union[Tuple[float, float], float], prob: float = 0.1, dtype: DtypeLike = np.float32\n    ) -> None:\n        \"\"\"\n        Args:\n            factors: factor range to randomly scale by ``v = v * (1 + factor)``.\n                if single number, factor value is picked from (-factors, factors).\n            prob: probability of scale.\n            dtype: output data type, if None, same as input image. defaults to float32.\n\n        \"\"\"\n        RandomizableTransform.__init__(self, prob)\n        if isinstance(factors, (int, float)):\n            self.factors = (min(-factors, factors), max(-factors, factors))\n        elif len(factors) != 2:\n            raise ValueError(\"factors should be a number or pair of numbers.\")\n        else:\n            self.factors = (min(factors), max(factors))\n        self.factor = self.factors[0]\n        self.dtype = dtype\n\n    def randomize(self, data: Optional[Any] = None) -> None:\n        super().randomize(None)\n        if not self._do_transform:\n            return None\n        self.factor = self.R.uniform(low=self.factors[0], high=self.factors[1])\n\n    def __call__(self, img: NdarrayOrTensor, randomize: bool = True) -> NdarrayOrTensor:\n        \"\"\"\n        Apply the transform to `img`.\n        \"\"\"\n        if randomize:\n            self.randomize()\n\n        if not self._do_transform:\n            return img\n\n        return ScaleIntensity(minv=None, maxv=None, factor=self.factor, dtype=self.dtype)(img)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandBiasField_RandBiasField.__init__.self._coeff._1_0_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandBiasField_RandBiasField.__init__.self._coeff._1_0_", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 497, "end_line": 533, "span_ids": ["RandBiasField.__init__", "RandBiasField"], "tokens": 378}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandBiasField(RandomizableTransform):\n    \"\"\"\n    Random bias field augmentation for MR images.\n    The bias field is considered as a linear combination of smoothly varying basis (polynomial)\n    functions, as described in `Automated Model-Based Tissue Classification of MR Images of the Brain\n    <https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=811270>`_.\n    This implementation adapted from `NiftyNet\n    <https://github.com/NifTK/NiftyNet>`_.\n    Referred to `Longitudinal segmentation of age-related white matter hyperintensities\n    <https://www.sciencedirect.com/science/article/pii/S1361841517300257?via%3Dihub>`_.\n\n    Args:\n        degree: degree of freedom of the polynomials. The value should be no less than 1.\n            Defaults to 3.\n        coeff_range: range of the random coefficients. Defaults to (0.0, 0.1).\n        dtype: output data type, if None, same as input image. defaults to float32.\n        prob: probability to do random bias field.\n\n    \"\"\"\n\n    backend = [TransformBackends.NUMPY]\n\n    def __init__(\n        self,\n        degree: int = 3,\n        coeff_range: Tuple[float, float] = (0.0, 0.1),\n        dtype: DtypeLike = np.float32,\n        prob: float = 0.1,\n    ) -> None:\n        RandomizableTransform.__init__(self, prob)\n        if degree < 1:\n            raise ValueError(\"degree should be no less than 1.\")\n        self.degree = degree\n        self.coeff_range = coeff_range\n        self.dtype = dtype\n\n        self._coeff = [1.0]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandBiasField._generate_random_field_RandBiasField._generate_random_field.raise_NotImplementedError": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandBiasField._generate_random_field_RandBiasField._generate_random_field.raise_NotImplementedError", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 533, "end_line": 554, "span_ids": ["RandBiasField._generate_random_field"], "tokens": 307}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandBiasField(RandomizableTransform):\n\n    def _generate_random_field(self, spatial_shape: Sequence[int], degree: int, coeff: Sequence[float]):\n        \"\"\"\n        products of polynomials as bias field estimations\n        \"\"\"\n        rank = len(spatial_shape)\n        coeff_mat = np.zeros((degree + 1,) * rank)\n        coords = [np.linspace(-1.0, 1.0, dim, dtype=np.float32) for dim in spatial_shape]\n        if rank == 2:\n            coeff_mat[np.tril_indices(degree + 1)] = coeff\n            return np.polynomial.legendre.leggrid2d(coords[0], coords[1], coeff_mat)\n        if rank == 3:\n            pts: List[List[int]] = [[0, 0, 0]]\n            for i in range(degree + 1):\n                for j in range(degree + 1 - i):\n                    for k in range(degree + 1 - i - j):\n                        pts.append([i, j, k])\n            if len(pts) > 1:\n                pts = pts[1:]\n            np_pts = np.stack(pts)\n            coeff_mat[np_pts[:, 0], np_pts[:, 1], np_pts[:, 2]] = coeff\n            return np.polynomial.legendre.leggrid3d(coords[0], coords[1], coords[2], coeff_mat)\n        raise NotImplementedError(\"only supports 2D or 3D fields\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandBiasField.randomize_RandBiasField.__call__.return.out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandBiasField.randomize_RandBiasField.__call__.return.out", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 569, "end_line": 597, "span_ids": ["RandBiasField.__call__", "RandBiasField.randomize"], "tokens": 277}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandBiasField(RandomizableTransform):\n\n    def randomize(self, img_size: Sequence[int]) -> None:\n        super().randomize(None)\n        if not self._do_transform:\n            return None\n        n_coeff = int(np.prod([(self.degree + k) / k for k in range(1, len(img_size) + 1)]))\n        self._coeff = self.R.uniform(*self.coeff_range, n_coeff).tolist()\n\n    def __call__(self, img: NdarrayOrTensor, randomize: bool = True) -> NdarrayOrTensor:\n        \"\"\"\n        Apply the transform to `img`.\n        \"\"\"\n        if randomize:\n            self.randomize(img_size=img.shape[1:])\n\n        if not self._do_transform:\n            return img\n\n        num_channels, *spatial_shape = img.shape\n        _bias_fields = np.stack(\n            [\n                self._generate_random_field(spatial_shape=spatial_shape, degree=self.degree, coeff=self._coeff)\n                for _ in range(num_channels)\n            ],\n            axis=0,\n        )\n        img_np, *_ = convert_data_type(img, np.ndarray)\n        out: NdarrayOrTensor = img_np * np.exp(_bias_fields)\n        out, *_ = convert_to_dst_type(src=out, dst=img, dtype=self.dtype or img.dtype)\n        return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_NormalizeIntensity_NormalizeIntensity._std.return.x_item_if_x_numel_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_NormalizeIntensity_NormalizeIntensity._std.return.x_item_if_x_numel_", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 599, "end_line": 644, "span_ids": ["NormalizeIntensity._std", "NormalizeIntensity.__init__", "NormalizeIntensity", "NormalizeIntensity._mean"], "tokens": 419}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class NormalizeIntensity(Transform):\n    \"\"\"\n    Normalize input based on provided args, using calculated mean and std if not provided.\n    This transform can normalize only non-zero values or entire image, and can also calculate\n    mean and std on each channel separately.\n    When `channel_wise` is True, the first dimension of `subtrahend` and `divisor` should\n    be the number of image channels if they are not None.\n\n    Args:\n        subtrahend: the amount to subtract by (usually the mean).\n        divisor: the amount to divide by (usually the standard deviation).\n        nonzero: whether only normalize non-zero values.\n        channel_wise: if True, calculate on each channel separately, otherwise, calculate on\n            the entire image directly. default to False.\n        dtype: output data type, if None, same as input image. defaults to float32.\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __init__(\n        self,\n        subtrahend: Union[Sequence, NdarrayOrTensor, None] = None,\n        divisor: Union[Sequence, NdarrayOrTensor, None] = None,\n        nonzero: bool = False,\n        channel_wise: bool = False,\n        dtype: DtypeLike = np.float32,\n    ) -> None:\n        self.subtrahend = subtrahend\n        self.divisor = divisor\n        self.nonzero = nonzero\n        self.channel_wise = channel_wise\n        self.dtype = dtype\n\n    @staticmethod\n    def _mean(x):\n        if isinstance(x, np.ndarray):\n            return np.mean(x)\n        x = torch.mean(x.float())\n        return x.item() if x.numel() == 1 else x\n\n    @staticmethod\n    def _std(x):\n        if isinstance(x, np.ndarray):\n            return np.std(x)\n        x = torch.std(x.float(), unbiased=False)\n        return x.item() if x.numel() == 1 else x", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_NormalizeIntensity.__call___NormalizeIntensity.__call__.return.out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_NormalizeIntensity.__call___NormalizeIntensity.__call__.return.out", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 666, "end_line": 687, "span_ids": ["NormalizeIntensity.__call__"], "tokens": 270}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class NormalizeIntensity(Transform):\n\n    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:\n        \"\"\"\n        Apply the transform to `img`, assuming `img` is a channel-first array if `self.channel_wise` is True,\n        \"\"\"\n        dtype = self.dtype or img.dtype\n        if self.channel_wise:\n            if self.subtrahend is not None and len(self.subtrahend) != len(img):\n                raise ValueError(f\"img has {len(img)} channels, but subtrahend has {len(self.subtrahend)} components.\")\n            if self.divisor is not None and len(self.divisor) != len(img):\n                raise ValueError(f\"img has {len(img)} channels, but divisor has {len(self.divisor)} components.\")\n\n            for i, d in enumerate(img):\n                img[i] = self._normalize(  # type: ignore\n                    d,\n                    sub=self.subtrahend[i] if self.subtrahend is not None else None,\n                    div=self.divisor[i] if self.divisor is not None else None,\n                )\n        else:\n            img = self._normalize(img, self.subtrahend, self.divisor)\n\n        out, *_ = convert_data_type(img, dtype=dtype)\n        return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_ThresholdIntensity_ThresholdIntensity.__call__.return.res": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_ThresholdIntensity_ThresholdIntensity.__call__.return.res", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 687, "end_line": 714, "span_ids": ["ThresholdIntensity", "ThresholdIntensity.__call__", "ThresholdIntensity.__init__"], "tokens": 262}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ThresholdIntensity(Transform):\n    \"\"\"\n    Filter the intensity values of whole image to below threshold or above threshold.\n    And fill the remaining parts of the image to the `cval` value.\n\n    Args:\n        threshold: the threshold to filter intensity values.\n        above: filter values above the threshold or below the threshold, default is True.\n        cval: value to fill the remaining parts of the image, default is 0.\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __init__(self, threshold: float, above: bool = True, cval: float = 0.0) -> None:\n        if not isinstance(threshold, (int, float)):\n            raise ValueError(\"threshold must be a float or int number.\")\n        self.threshold = threshold\n        self.above = above\n        self.cval = cval\n\n    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:\n        \"\"\"\n        Apply the transform to `img`.\n        \"\"\"\n        mask = img > self.threshold if self.above else img < self.threshold\n        res = where(mask, img, self.cval)\n        res, *_ = convert_data_type(res, dtype=img.dtype)\n        return res", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_AdjustContrast_AdjustContrast.__call__.return.ret": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_AdjustContrast_AdjustContrast.__call__.return.ret", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 754, "end_line": 779, "span_ids": ["AdjustContrast.__init__", "AdjustContrast", "AdjustContrast.__call__"], "tokens": 218}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AdjustContrast(Transform):\n    \"\"\"\n    Changes image intensity by gamma. Each pixel/voxel intensity is updated as::\n\n        x = ((x - min) / intensity_range) ^ gamma * intensity_range + min\n\n    Args:\n        gamma: gamma value to adjust the contrast as function.\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __init__(self, gamma: float) -> None:\n        if not isinstance(gamma, (int, float)):\n            raise ValueError(\"gamma must be a float or int number.\")\n        self.gamma = gamma\n\n    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:\n        \"\"\"\n        Apply the transform to `img`.\n        \"\"\"\n        epsilon = 1e-7\n        img_min = img.min()\n        img_range = img.max() - img_min\n        ret: NdarrayOrTensor = ((img - img_min) / float(img_range + epsilon)) ** self.gamma * img_range + img_min\n        return ret", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandAdjustContrast_RandAdjustContrast.__call__.return.AdjustContrast_self_gamma": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandAdjustContrast_RandAdjustContrast.__call__.return.AdjustContrast_self_gamma", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 782, "end_line": 830, "span_ids": ["RandAdjustContrast.randomize", "RandAdjustContrast", "RandAdjustContrast.__call__", "RandAdjustContrast.__init__"], "tokens": 426}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandAdjustContrast(RandomizableTransform):\n    \"\"\"\n    Randomly changes image intensity by gamma. Each pixel/voxel intensity is updated as::\n\n        x = ((x - min) / intensity_range) ^ gamma * intensity_range + min\n\n    Args:\n        prob: Probability of adjustment.\n        gamma: Range of gamma values.\n            If single number, value is picked from (0.5, gamma), default is (0.5, 4.5).\n    \"\"\"\n\n    backend = AdjustContrast.backend\n\n    def __init__(self, prob: float = 0.1, gamma: Union[Sequence[float], float] = (0.5, 4.5)) -> None:\n        RandomizableTransform.__init__(self, prob)\n\n        if isinstance(gamma, (int, float)):\n            if gamma <= 0.5:\n                raise ValueError(\n                    \"if gamma is single number, must greater than 0.5 and value is picked from (0.5, gamma)\"\n                )\n            self.gamma = (0.5, gamma)\n        elif len(gamma) != 2:\n            raise ValueError(\"gamma should be a number or pair of numbers.\")\n        else:\n            self.gamma = (min(gamma), max(gamma))\n\n        self.gamma_value: Optional[float] = None\n\n    def randomize(self, data: Optional[Any] = None) -> None:\n        super().randomize(None)\n        if not self._do_transform:\n            return None\n        self.gamma_value = self.R.uniform(low=self.gamma[0], high=self.gamma[1])\n\n    def __call__(self, img: NdarrayOrTensor, randomize: bool = True) -> NdarrayOrTensor:\n        \"\"\"\n        Apply the transform to `img`.\n        \"\"\"\n        if randomize:\n            self.randomize()\n\n        if not self._do_transform:\n            return img\n\n        if self.gamma_value is None:\n            raise RuntimeError(\"gamma_value is not set, please call `randomize` function first.\")\n        return AdjustContrast(self.gamma_value)(img)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_ScaleIntensityRangePercentiles_ScaleIntensityRangePercentiles.backend.ScaleIntensityRange_backe": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_ScaleIntensityRangePercentiles_ScaleIntensityRangePercentiles.backend.ScaleIntensityRange_backe", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 865, "end_line": 927, "span_ids": ["ScaleIntensityRangePercentiles"], "tokens": 709}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ScaleIntensityRangePercentiles(Transform):\n    \"\"\"\n    Apply range scaling to a numpy array based on the intensity distribution of the input.\n\n    By default this transform will scale from [lower_intensity_percentile, upper_intensity_percentile] to\n    `[b_min, b_max]`, where {lower,upper}_intensity_percentile are the intensity values at the corresponding\n    percentiles of ``img``.\n\n    The ``relative`` parameter can also be set to scale from [lower_intensity_percentile, upper_intensity_percentile]\n    to the lower and upper percentiles of the output range [b_min, b_max].\n\n    For example:\n\n    .. code-block:: python\n        :emphasize-lines: 11, 22\n\n        image = np.array(\n            [[[1, 2, 3, 4, 5],\n              [1, 2, 3, 4, 5],\n              [1, 2, 3, 4, 5],\n              [1, 2, 3, 4, 5],\n              [1, 2, 3, 4, 5],\n              [1, 2, 3, 4, 5]]])\n\n        # Scale from lower and upper image intensity percentiles\n        # to output range [b_min, b_max]\n        scaler = ScaleIntensityRangePercentiles(10, 90, 0, 200, False, False)\n        print(scaler(image))\n        [[[0., 50., 100., 150., 200.],\n          [0., 50., 100., 150., 200.],\n          [0., 50., 100., 150., 200.],\n          [0., 50., 100., 150., 200.],\n          [0., 50., 100., 150., 200.],\n          [0., 50., 100., 150., 200.]]]\n\n        # Scale from lower and upper image intensity percentiles\n        # to lower and upper percentiles of the output range [b_min, b_max]\n        rel_scaler = ScaleIntensityRangePercentiles(10, 90, 0, 200, False, True)\n        print(rel_scaler(image))\n        [[[20., 60., 100., 140., 180.],\n          [20., 60., 100., 140., 180.],\n          [20., 60., 100., 140., 180.],\n          [20., 60., 100., 140., 180.],\n          [20., 60., 100., 140., 180.],\n          [20., 60., 100., 140., 180.]]]\n\n    See Also:\n\n        - :py:class:`monai.transforms.ScaleIntensityRange`\n\n    Args:\n        lower: lower intensity percentile.\n        upper: upper intensity percentile.\n        b_min: intensity target range min.\n        b_max: intensity target range max.\n        clip: whether to perform clip after scaling.\n        relative: whether to scale to the corresponding percentiles of [b_min, b_max].\n        channel_wise: if True, compute intensity percentile and normalize every channel separately.\n            default to False.\n        dtype: output data type, if None, same as input image. defaults to float32.\n    \"\"\"\n\n    backend = ScaleIntensityRange.backend", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_MaskIntensity_MaskIntensity.__init__.self.select_fn.select_fn": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_MaskIntensity_MaskIntensity.__init__.self.select_fn.select_fn", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 926, "end_line": 948, "span_ids": ["MaskIntensity.__init__", "MaskIntensity"], "tokens": 250}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class MaskIntensity(Transform):\n    \"\"\"\n    Mask the intensity values of input image with the specified mask data.\n    Mask data must have the same spatial size as the input image, and all\n    the intensity values of input image corresponding to the selected values\n    in the mask data will keep the original value, others will be set to `0`.\n\n    Args:\n        mask_data: if `mask_data` is single channel, apply to every channel\n            of input image. if multiple channels, the number of channels must\n            match the input data. the intensity values of input image corresponding\n            to the selected values in the mask data will keep the original value,\n            others will be set to `0`. if None, must specify the `mask_data` at runtime.\n        select_fn: function to select valid values of the `mask_data`, default is\n            to select `values > 0`.\n\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __init__(self, mask_data: Optional[NdarrayOrTensor] = None, select_fn: Callable = is_positive) -> None:\n        self.mask_data = mask_data\n        self.select_fn = select_fn", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_MaskIntensity.__call___MaskIntensity.__call__.return.img_mask_data_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_MaskIntensity.__call___MaskIntensity.__call__.return.img_mask_data_", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 950, "end_line": 976, "span_ids": ["MaskIntensity.__call__"], "tokens": 305}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class MaskIntensity(Transform):\n\n    def __call__(self, img: NdarrayOrTensor, mask_data: Optional[NdarrayOrTensor] = None) -> NdarrayOrTensor:\n        \"\"\"\n        Args:\n            mask_data: if mask data is single channel, apply to every channel\n                of input image. if multiple channels, the channel number must\n                match input data. mask_data will be converted to `bool` values\n                by `mask_data > 0` before applying transform to input image.\n\n        Raises:\n            - ValueError: When both ``mask_data`` and ``self.mask_data`` are None.\n            - ValueError: When ``mask_data`` and ``img`` channels differ and ``mask_data`` is not single channel.\n\n        \"\"\"\n        mask_data = self.mask_data if mask_data is None else mask_data\n        if mask_data is None:\n            raise ValueError(\"must provide the mask_data when initializing the transform or at runtime.\")\n\n        mask_data_, *_ = convert_to_dst_type(src=mask_data, dst=img)\n\n        mask_data_ = self.select_fn(mask_data_)\n        if mask_data_.shape[0] != 1 and mask_data_.shape[0] != img.shape[0]:\n            raise ValueError(\n                \"When mask_data is not single channel, mask_data channels must match img, \"\n                f\"got img channels={img.shape[0]} mask_data channels={mask_data_.shape[0]}.\"\n            )\n\n        return img * mask_data_", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_SavitzkyGolaySmooth_SavitzkyGolaySmooth.__init__.self.img_t.torch_tensor_0_0_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_SavitzkyGolaySmooth_SavitzkyGolaySmooth.__init__.self.img_t.torch_tensor_0_0_", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 979, "end_line": 1002, "span_ids": ["SavitzkyGolaySmooth.__init__", "SavitzkyGolaySmooth"], "tokens": 255}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SavitzkyGolaySmooth(Transform):\n    \"\"\"\n    Smooth the input data along the given axis using a Savitzky-Golay filter.\n\n    Args:\n        window_length: Length of the filter window, must be a positive odd integer.\n        order: Order of the polynomial to fit to each window, must be less than ``window_length``.\n        axis: Optional axis along which to apply the filter kernel. Default 1 (first spatial dimension).\n        mode: Optional padding mode, passed to convolution class. ``'zeros'``, ``'reflect'``, ``'replicate'``\n            or ``'circular'``. Default: ``'zeros'``. See ``torch.nn.Conv1d()`` for more information.\n    \"\"\"\n\n    backend = [TransformBackends.TORCH]\n\n    def __init__(self, window_length: int, order: int, axis: int = 1, mode: str = \"zeros\"):\n\n        if axis < 0:\n            raise ValueError(\"axis must be zero or positive.\")\n\n        self.window_length = window_length\n        self.order = order\n        self.axis = axis\n        self.mode = mode\n        self.img_t: torch.Tensor = torch.tensor(0.0)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_SavitzkyGolaySmooth.__call___SavitzkyGolaySmooth.__call__.return.out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_SavitzkyGolaySmooth.__call___SavitzkyGolaySmooth.__call__.return.out", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1004, "end_line": 1021, "span_ids": ["SavitzkyGolaySmooth.__call__"], "tokens": 181}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SavitzkyGolaySmooth(Transform):\n\n    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:\n        \"\"\"\n        Args:\n            img: array containing input data. Must be real and in shape [channels, spatial1, spatial2, ...].\n\n        Returns:\n            array containing smoothed result.\n\n        \"\"\"\n        self.img_t = convert_to_tensor(img)\n\n        # add one to transform axis because a batch axis will be added at dimension 0\n        savgol_filter = SavitzkyGolayFilter(self.window_length, self.order, self.axis + 1, self.mode)\n        # convert to Tensor and add Batch axis expected by HilbertTransform\n        smoothed = savgol_filter(self.img_t.unsqueeze(0)).squeeze(0)\n        out, *_ = convert_to_dst_type(smoothed, dst=img)\n\n        return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_DetectEnvelope.__call___DetectEnvelope.__call__.return.out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_DetectEnvelope.__call___DetectEnvelope.__call__.return.out", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1049, "end_line": 1067, "span_ids": ["DetectEnvelope.__call__"], "tokens": 183}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DetectEnvelope(Transform):\n\n    def __call__(self, img: NdarrayOrTensor):\n        \"\"\"\n\n        Args:\n            img: numpy.ndarray containing input data. Must be real and in shape [channels, spatial1, spatial2, ...].\n\n        Returns:\n            np.ndarray containing envelope of data in img along the specified axis.\n\n        \"\"\"\n        img_t: torch.Tensor\n        img_t, *_ = convert_data_type(img, torch.Tensor)  # type: ignore\n        # add one to transform axis because a batch axis will be added at dimension 0\n        hilbert_transform = HilbertTransform(self.axis + 1, self.n)\n        # convert to Tensor and add Batch axis expected by HilbertTransform\n        out = hilbert_transform(img_t.unsqueeze(0)).squeeze(0).abs()\n        out, *_ = convert_to_dst_type(src=out, dst=img)\n\n        return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_GaussianSmooth_GaussianSmooth.__init__.self.approx.approx": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_GaussianSmooth_GaussianSmooth.__init__.self.approx.approx", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1070, "end_line": 1088, "span_ids": ["GaussianSmooth", "GaussianSmooth.__init__"], "tokens": 191}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GaussianSmooth(Transform):\n    \"\"\"\n    Apply Gaussian smooth to the input data based on specified `sigma` parameter.\n    A default value `sigma=1.0` is provided for reference.\n\n    Args:\n        sigma: if a list of values, must match the count of spatial dimensions of input data,\n            and apply every value in the list to 1 spatial dimension. if only 1 value provided,\n            use it for all spatial dimensions.\n        approx: discrete Gaussian kernel type, available options are \"erf\", \"sampled\", and \"scalespace\".\n            see also :py:meth:`monai.networks.layers.GaussianFilter`.\n\n    \"\"\"\n\n    backend = [TransformBackends.TORCH]\n\n    def __init__(self, sigma: Union[Sequence[float], float] = 1.0, approx: str = \"erf\") -> None:\n        self.sigma = sigma\n        self.approx = approx", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_GaussianSmooth.__call___GaussianSmooth.__call__.return.out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_GaussianSmooth.__call___GaussianSmooth.__call__.return.out", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1090, "end_line": 1102, "span_ids": ["GaussianSmooth.__call__"], "tokens": 183}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GaussianSmooth(Transform):\n\n    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:\n        img_t: torch.Tensor\n        img_t, *_ = convert_data_type(img, torch.Tensor, dtype=torch.float)  # type: ignore\n        sigma: Union[Sequence[torch.Tensor], torch.Tensor]\n        if isinstance(self.sigma, Sequence):\n            sigma = [torch.as_tensor(s, device=img_t.device) for s in self.sigma]\n        else:\n            sigma = torch.as_tensor(self.sigma, device=img_t.device)\n        gaussian_filter = GaussianFilter(img_t.ndim - 1, sigma, approx=self.approx)\n        out_t: torch.Tensor = gaussian_filter(img_t.unsqueeze(0)).squeeze(0)\n        out, *_ = convert_data_type(out_t, type(img), device=img.device if isinstance(img, torch.Tensor) else None)\n\n        return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_GaussianSharpen.__call___GaussianSharpen.__call__.return.out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_GaussianSharpen.__call___GaussianSharpen.__call__.return.out", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1199, "end_line": 1211, "span_ids": ["GaussianSharpen.__call__"], "tokens": 186}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GaussianSharpen(Transform):\n\n    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:\n        img_t: torch.Tensor\n        img_t, *_ = convert_data_type(img, torch.Tensor, dtype=torch.float32)  # type: ignore\n\n        gf1, gf2 = (\n            GaussianFilter(img_t.ndim - 1, sigma, approx=self.approx).to(img_t.device)\n            for sigma in (self.sigma1, self.sigma2)\n        )\n        blurred_f = gf1(img_t.unsqueeze(0))\n        filter_blurred_f = gf2(blurred_f)\n        out_t: torch.Tensor = (blurred_f + self.alpha * (blurred_f - filter_blurred_f)).squeeze(0)\n        out, *_ = convert_data_type(out_t, type(img), device=img.device if isinstance(img, torch.Tensor) else None)\n        return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandGaussianSharpen_RandGaussianSharpen.__init__.self.a.None": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandGaussianSharpen_RandGaussianSharpen.__init__.self.a.None", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1214, "end_line": 1265, "span_ids": ["RandGaussianSharpen", "RandGaussianSharpen.__init__"], "tokens": 684}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandGaussianSharpen(RandomizableTransform):\n    \"\"\"\n    Sharpen images using the Gaussian Blur filter based on randomly selected `sigma1`, `sigma2` and `alpha`.\n    The algorithm is :py:class:`monai.transforms.GaussianSharpen`.\n\n    Args:\n        sigma1_x: randomly select sigma value for the first spatial dimension of first gaussian kernel.\n        sigma1_y: randomly select sigma value for the second spatial dimension(if have) of first gaussian kernel.\n        sigma1_z: randomly select sigma value for the third spatial dimension(if have) of first gaussian kernel.\n        sigma2_x: randomly select sigma value for the first spatial dimension of second gaussian kernel.\n            if only 1 value `X` provided, it must be smaller than `sigma1_x` and randomly select from [X, sigma1_x].\n        sigma2_y: randomly select sigma value for the second spatial dimension(if have) of second gaussian kernel.\n            if only 1 value `Y` provided, it must be smaller than `sigma1_y` and randomly select from [Y, sigma1_y].\n        sigma2_z: randomly select sigma value for the third spatial dimension(if have) of second gaussian kernel.\n            if only 1 value `Z` provided, it must be smaller than `sigma1_z` and randomly select from [Z, sigma1_z].\n        alpha: randomly select weight parameter to compute the final result.\n        approx: discrete Gaussian kernel type, available options are \"erf\", \"sampled\", and \"scalespace\".\n            see also :py:meth:`monai.networks.layers.GaussianFilter`.\n        prob: probability of Gaussian sharpen.\n\n    \"\"\"\n\n    backend = GaussianSharpen.backend\n\n    def __init__(\n        self,\n        sigma1_x: Tuple[float, float] = (0.5, 1.0),\n        sigma1_y: Tuple[float, float] = (0.5, 1.0),\n        sigma1_z: Tuple[float, float] = (0.5, 1.0),\n        sigma2_x: Union[Tuple[float, float], float] = 0.5,\n        sigma2_y: Union[Tuple[float, float], float] = 0.5,\n        sigma2_z: Union[Tuple[float, float], float] = 0.5,\n        alpha: Tuple[float, float] = (10.0, 30.0),\n        approx: str = \"erf\",\n        prob: float = 0.1,\n    ) -> None:\n        RandomizableTransform.__init__(self, prob)\n        self.sigma1_x = sigma1_x\n        self.sigma1_y = sigma1_y\n        self.sigma1_z = sigma1_z\n        self.sigma2_x = sigma2_x\n        self.sigma2_y = sigma2_y\n        self.sigma2_z = sigma2_z\n        self.alpha = alpha\n        self.approx = approx\n        self.x1: Optional[float] = None\n        self.y1: Optional[float] = None\n        self.z1: Optional[float] = None\n        self.x2: Optional[float] = None\n        self.y2: Optional[float] = None\n        self.z2: Optional[float] = None\n        self.a: Optional[float] = None", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandHistogramShift_RandHistogramShift.__init__.self.floating_control_points": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandHistogramShift_RandHistogramShift.__init__.self.floating_control_points", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1296, "end_line": 1323, "span_ids": ["RandHistogramShift", "RandHistogramShift.__init__"], "tokens": 286}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandHistogramShift(RandomizableTransform):\n    \"\"\"\n    Apply random nonlinear transform to the image's intensity histogram.\n\n    Args:\n        num_control_points: number of control points governing the nonlinear intensity mapping.\n            a smaller number of control points allows for larger intensity shifts. if two values provided, number of\n            control points selecting from range (min_value, max_value).\n        prob: probability of histogram shift.\n    \"\"\"\n\n    backend = [TransformBackends.NUMPY]\n\n    def __init__(self, num_control_points: Union[Tuple[int, int], int] = 10, prob: float = 0.1) -> None:\n        RandomizableTransform.__init__(self, prob)\n\n        if isinstance(num_control_points, int):\n            if num_control_points <= 2:\n                raise ValueError(\"num_control_points should be greater than or equal to 3\")\n            self.num_control_points = (num_control_points, num_control_points)\n        else:\n            if len(num_control_points) != 2:\n                raise ValueError(\"num_control points should be a number or a pair of numbers\")\n            if min(num_control_points) <= 2:\n                raise ValueError(\"num_control_points should be greater than or equal to 3\")\n            self.num_control_points = (min(num_control_points), max(num_control_points))\n        self.reference_control_points: np.ndarray\n        self.floating_control_points: np.ndarray", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandHistogramShift.__call___RandHistogramShift.__call__.return.img": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandHistogramShift.__call___RandHistogramShift.__call__.return.img", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1337, "end_line": 1355, "span_ids": ["RandHistogramShift.__call__"], "tokens": 224}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandHistogramShift(RandomizableTransform):\n\n    def __call__(self, img: NdarrayOrTensor, randomize: bool = True) -> NdarrayOrTensor:\n        if randomize:\n            self.randomize()\n\n        if not self._do_transform:\n            return img\n\n        if self.reference_control_points is None or self.floating_control_points is None:\n            raise RuntimeError(\"please call the `randomize()` function first.\")\n        img_np: np.ndarray\n        img_np, *_ = convert_data_type(img, np.ndarray)  # type: ignore\n        img_min, img_max = img_np.min(), img_np.max()\n        reference_control_points_scaled = self.reference_control_points * (img_max - img_min) + img_min\n        floating_control_points_scaled = self.floating_control_points * (img_max - img_min) + img_min\n        img_np = np.asarray(\n            np.interp(img_np, reference_control_points_scaled, floating_control_points_scaled), dtype=img_np.dtype\n        )\n        img, *_ = convert_to_dst_type(img_np, dst=img)\n        return img", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_GibbsNoise_GibbsNoise.__call__.return.img": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_GibbsNoise_GibbsNoise.__call__.return.img", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1358, "end_line": 1397, "span_ids": ["GibbsNoise", "GibbsNoise.__call__", "GibbsNoise.__init__"], "tokens": 377}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GibbsNoise(Transform, Fourier):\n    \"\"\"\n    The transform applies Gibbs noise to 2D/3D MRI images. Gibbs artifacts\n    are one of the common type of type artifacts appearing in MRI scans.\n\n    The transform is applied to all the channels in the data.\n\n    For general information on Gibbs artifacts, please refer to:\n\n    `An Image-based Approach to Understanding the Physics of MR Artifacts\n    <https://pubs.rsna.org/doi/full/10.1148/rg.313105115>`_.\n\n    `The AAPM/RSNA Physics Tutorial for Residents\n    <https://pubs.rsna.org/doi/full/10.1148/radiographics.22.4.g02jl14949>`_\n\n    Args:\n        alpha: Parametrizes the intensity of the Gibbs noise filter applied. Takes\n            values in the interval [0,1] with alpha = 0 acting as the identity mapping.\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    @deprecated_arg(name=\"as_tensor_output\", since=\"0.6\")\n    def __init__(self, alpha: float = 0.1, as_tensor_output: bool = True) -> None:\n\n        if alpha > 1 or alpha < 0:\n            raise ValueError(\"alpha must take values in the interval [0, 1].\")\n        self.alpha = alpha\n\n    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:\n        n_dims = len(img.shape[1:])\n\n        # FT\n        k = self.shift_fourier(img, n_dims)\n        # build and apply mask\n        k = self._apply_mask(k)\n        # map back\n        img = self.inv_shift_fourier(k, n_dims)\n\n        return img", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandGibbsNoise_RandGibbsNoise.__call__.return.GibbsNoise_self_sampled_a": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandGibbsNoise_RandGibbsNoise.__call__.return.GibbsNoise_self_sampled_a", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1433, "end_line": 1488, "span_ids": ["RandGibbsNoise", "RandGibbsNoise.__init__", "RandGibbsNoise.randomize", "RandGibbsNoise.__call__"], "tokens": 562}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandGibbsNoise(RandomizableTransform):\n    \"\"\"\n    Naturalistic image augmentation via Gibbs artifacts. The transform\n    randomly applies Gibbs noise to 2D/3D MRI images. Gibbs artifacts\n    are one of the common type of type artifacts appearing in MRI scans.\n\n    The transform is applied to all the channels in the data.\n\n    For general information on Gibbs artifacts, please refer to:\n    https://pubs.rsna.org/doi/full/10.1148/rg.313105115\n    https://pubs.rsna.org/doi/full/10.1148/radiographics.22.4.g02jl14949\n\n\n    Args:\n        prob (float): probability of applying the transform.\n        alpha (Sequence(float)): Parametrizes the intensity of the Gibbs noise filter applied. Takes\n            values in the interval [0,1] with alpha = 0 acting as the identity mapping.\n            If a length-2 list is given as [a,b] then the value of alpha will be\n            sampled uniformly from the interval [a,b]. 0 <= a <= b <= 1.\n    \"\"\"\n\n    backend = GibbsNoise.backend\n\n    @deprecated_arg(name=\"as_tensor_output\", since=\"0.6\")\n    def __init__(self, prob: float = 0.1, alpha: Sequence[float] = (0.0, 1.0), as_tensor_output: bool = True) -> None:\n        if len(alpha) != 2:\n            raise ValueError(\"alpha length must be 2.\")\n        if alpha[1] > 1 or alpha[0] < 0:\n            raise ValueError(\"alpha must take values in the interval [0, 1]\")\n        if alpha[0] > alpha[1]:\n            raise ValueError(\"When alpha = [a,b] we need a < b.\")\n\n        self.alpha = alpha\n        self.sampled_alpha = -1.0  # stores last alpha sampled by randomize()\n\n        RandomizableTransform.__init__(self, prob=prob)\n\n    def randomize(self, data: Any) -> None:\n        \"\"\"\n        (1) Set random variable to apply the transform.\n        (2) Get alpha from uniform distribution.\n        \"\"\"\n        super().randomize(None)\n        if not self._do_transform:\n            return None\n        self.sampled_alpha = self.R.uniform(self.alpha[0], self.alpha[1])\n\n    def __call__(self, img: NdarrayOrTensor, randomize: bool = True):\n        if randomize:\n            # randomize application and possibly alpha\n            self.randomize(None)\n\n        if not self._do_transform:\n            return img\n\n        return GibbsNoise(self.sampled_alpha)(img)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_KSpaceSpikeNoise_KSpaceSpikeNoise.__init__.if_isinstance_self_loc_0_.raise_ValueError_There_m": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_KSpaceSpikeNoise_KSpaceSpikeNoise.__init__.if_isinstance_self_loc_0_.raise_ValueError_There_m", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1491, "end_line": 1548, "span_ids": ["KSpaceSpikeNoise", "KSpaceSpikeNoise.__init__"], "tokens": 673}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class KSpaceSpikeNoise(Transform, Fourier):\n    \"\"\"\n    Apply localized spikes in `k`-space at the given locations and intensities.\n    Spike (Herringbone) artifact is a type of data acquisition artifact which\n    may occur during MRI scans.\n\n    For general information on spike artifacts, please refer to:\n\n    `AAPM/RSNA physics tutorial for residents: fundamental physics of MR imaging\n    <https://pubmed.ncbi.nlm.nih.gov/16009826>`_.\n\n    `Body MRI artifacts in clinical practice: A physicist's and radiologist's\n    perspective <https://doi.org/10.1002/jmri.24288>`_.\n\n    Args:\n        loc: spatial location for the spikes. For\n            images with 3D spatial dimensions, the user can provide (C, X, Y, Z)\n            to fix which channel C is affected, or (X, Y, Z) to place the same\n            spike in all channels. For 2D cases, the user can provide (C, X, Y)\n            or (X, Y).\n        k_intensity: value for the log-intensity of the\n            `k`-space version of the image. If one location is passed to ``loc`` or the\n            channel is not specified, then this argument should receive a float. If\n            ``loc`` is given a sequence of locations, then this argument should\n            receive a sequence of intensities. This value should be tested as it is\n            data-dependent. The default values are the 2.5 the mean of the\n            log-intensity for each channel.\n\n    Example:\n        When working with 4D data, ``KSpaceSpikeNoise(loc = ((3,60,64,32), (64,60,32)), k_intensity = (13,14))``\n        will place a spike at `[3, 60, 64, 32]` with `log-intensity = 13`, and\n        one spike per channel located respectively at `[: , 64, 60, 32]`\n        with `log-intensity = 14`.\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    @deprecated_arg(name=\"as_tensor_output\", since=\"0.6\")\n    def __init__(\n        self,\n        loc: Union[Tuple, Sequence[Tuple]],\n        k_intensity: Optional[Union[Sequence[float], float]] = None,\n        as_tensor_output: bool = True,\n    ):\n\n        self.loc = ensure_tuple(loc)\n        self.k_intensity = k_intensity\n\n        # assert one-to-one relationship between factors and locations\n        if isinstance(k_intensity, Sequence):\n            if not isinstance(loc[0], Sequence):\n                raise ValueError(\n                    \"If a sequence is passed to k_intensity, then a sequence of locations must be passed to loc\"\n                )\n            if len(k_intensity) != len(loc):\n                raise ValueError(\"There must be one intensity_factor value for each tuple of indices in loc.\")\n        if isinstance(self.loc[0], Sequence) and k_intensity is not None and not isinstance(self.k_intensity, Sequence):\n            raise ValueError(\"There must be one intensity_factor value for each tuple of indices in loc.\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_KSpaceSpikeNoise.__call___KSpaceSpikeNoise.__call__.return.img": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_KSpaceSpikeNoise.__call___KSpaceSpikeNoise.__call__.return.img", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1550, "end_line": 1588, "span_ids": ["KSpaceSpikeNoise.__call__"], "tokens": 445}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class KSpaceSpikeNoise(Transform, Fourier):\n\n    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:\n        \"\"\"\n        Args:\n            img: image with dimensions (C, H, W) or (C, H, W, D)\n        \"\"\"\n        # checking that tuples in loc are consistent with img size\n        self._check_indices(img)\n\n        if len(img.shape) < 3:\n            raise RuntimeError(\"Image needs a channel direction.\")\n        if isinstance(self.loc[0], int) and len(img.shape) == 4 and len(self.loc) == 2:\n            raise RuntimeError(\"Input images of dimension 4 need location tuple to be length 3 or 4\")\n        if isinstance(self.loc[0], Sequence) and len(img.shape) == 4 and min(map(len, self.loc)) == 2:\n            raise RuntimeError(\"Input images of dimension 4 need location tuple to be length 3 or 4\")\n\n        n_dims = len(img.shape[1:])\n\n        # FT\n        k = self.shift_fourier(img, n_dims)\n        lib = np if isinstance(k, np.ndarray) else torch\n        log_abs = lib.log(lib.abs(k) + 1e-10)  # type: ignore\n        phase = lib.angle(k)  # type: ignore\n\n        k_intensity = self.k_intensity\n        # default log intensity\n        if k_intensity is None:\n            k_intensity = tuple(lib.mean(log_abs, axis=tuple(range(-n_dims, 0))) * 2.5)  # type: ignore\n\n        # highlight\n        if isinstance(self.loc[0], Sequence):\n            for idx, val in zip(self.loc, ensure_tuple(k_intensity)):\n                self._set_spike(log_abs, idx, val)\n        else:\n            self._set_spike(log_abs, self.loc, k_intensity)\n        # map back\n        k = lib.exp(log_abs) * lib.exp(1j * phase)  # type: ignore\n        img, *_ = convert_to_dst_type(self.inv_shift_fourier(k, n_dims), dst=img)\n\n        return img", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_KSpaceSpikeNoise._check_indices_KSpaceSpikeNoise._check_indices.for_i_in_range_len_img_sh.if_img_shape_i_max_x_.raise_ValueError_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_KSpaceSpikeNoise._check_indices_KSpaceSpikeNoise._check_indices.for_i_in_range_len_img_sh.if_img_shape_i_max_x_.raise_ValueError_", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1590, "end_line": 1606, "span_ids": ["KSpaceSpikeNoise._check_indices"], "tokens": 166}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class KSpaceSpikeNoise(Transform, Fourier):\n\n    def _check_indices(self, img) -> None:\n        \"\"\"Helper method to check consistency of self.loc and input image.\n\n        Raises assertion error if any index in loc is out of bounds.\"\"\"\n\n        loc = list(self.loc)\n        if not isinstance(loc[0], Sequence):\n            loc = [loc]\n        for i in range(len(loc)):\n            if len(loc[i]) < len(img.shape):\n                loc[i] = [0] + list(loc[i])\n\n        for i in range(len(img.shape)):\n            if img.shape[i] <= max(x[i] for x in loc):\n                raise ValueError(\n                    f\"The index value at position {i} of one of the tuples in loc = {self.loc} is out of bounds for current image.\"\n                )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_KSpaceSpikeNoise._set_spike_KSpaceSpikeNoise._set_spike.if_len_k_shape_len_id.elif_len_k_shape_3_an._type_ignore": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_KSpaceSpikeNoise._set_spike_KSpaceSpikeNoise._set_spike.if_len_k_shape_len_id.elif_len_k_shape_3_an._type_ignore", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1608, "end_line": 1622, "span_ids": ["KSpaceSpikeNoise._set_spike"], "tokens": 190}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class KSpaceSpikeNoise(Transform, Fourier):\n\n    def _set_spike(self, k: NdarrayOrTensor, idx: Tuple, val: Union[Sequence[float], float]):\n        \"\"\"\n        Helper function to introduce a given intensity at given location.\n\n        Args:\n            k: intensity array to alter.\n            idx: index of location where to apply change.\n            val: value of intensity to write in.\n        \"\"\"\n        if len(k.shape) == len(idx):\n            k[idx] = val[idx[0]] if isinstance(val, Sequence) else val\n        elif len(k.shape) == 4 and len(idx) == 3:\n            k[:, idx[0], idx[1], idx[2]] = val  # type: ignore\n        elif len(k.shape) == 3 and len(idx) == 2:\n            k[:, idx[0], idx[1]] = val  # type: ignore", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandKSpaceSpikeNoise.__call___RandKSpaceSpikeNoise.__call__.return.KSpaceSpikeNoise_self_sam": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandKSpaceSpikeNoise.__call___RandKSpaceSpikeNoise.__call__.return.KSpaceSpikeNoise_self_sam", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1681, "end_line": 1708, "span_ids": ["RandKSpaceSpikeNoise.__call__"], "tokens": 222}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandKSpaceSpikeNoise(RandomizableTransform, Fourier):\n\n    def __call__(self, img: NdarrayOrTensor, randomize: bool = True):\n        \"\"\"\n        Apply transform to `img`. Assumes data is in channel-first form.\n\n        Args:\n            img: image with dimensions (C, H, W) or (C, H, W, D)\n        \"\"\"\n\n        if (\n            self.intensity_range is not None\n            and isinstance(self.intensity_range[0], Sequence)\n            and len(self.intensity_range) != img.shape[0]\n        ):\n            raise RuntimeError(\n                \"If intensity_range is a sequence of sequences, then there must be one (low, high) tuple for each channel.\"\n            )\n\n        self.sampled_k_intensity = []\n        self.sampled_locs = []\n\n        if randomize:\n            intensity_range = self._make_sequence(img)\n            self.randomize(img, intensity_range)\n\n        if not self._do_transform:\n            return img\n\n        return KSpaceSpikeNoise(self.sampled_locs, self.sampled_k_intensity)(img)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandKSpaceSpikeNoise.randomize_RandKSpaceSpikeNoise.randomize.if_self_channel_wise_.else_.if_isinstance_intensity_r.else_.self.sampled_k_intensity._self_R_uniform_intensity": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandKSpaceSpikeNoise.randomize_RandKSpaceSpikeNoise.randomize.if_self_channel_wise_.else_.if_isinstance_intensity_r.else_.self.sampled_k_intensity._self_R_uniform_intensity", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1710, "end_line": 1735, "span_ids": ["RandKSpaceSpikeNoise.randomize"], "tokens": 332}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandKSpaceSpikeNoise(RandomizableTransform, Fourier):\n\n    def randomize(self, img: NdarrayOrTensor, intensity_range: Sequence[Sequence[float]]) -> None:  # type: ignore\n        \"\"\"\n        Helper method to sample both the location and intensity of the spikes.\n        When not working channel wise (channel_wise=False) it use the random\n        variable ``self._do_transform`` to decide whether to sample a location\n        and intensity.\n\n        When working channel wise, the method randomly samples a location and\n        intensity for each channel depending on ``self._do_transform``.\n        \"\"\"\n        super().randomize(None)\n        if not self._do_transform:\n            return None\n        if self.channel_wise:\n            # randomizing per channel\n            for i, chan in enumerate(img):\n                self.sampled_locs.append((i,) + tuple(self.R.randint(0, k) for k in chan.shape))\n                self.sampled_k_intensity.append(self.R.uniform(intensity_range[i][0], intensity_range[i][1]))\n        else:\n            # working with all channels together\n            spatial = tuple(self.R.randint(0, k) for k in img.shape[1:])\n            self.sampled_locs = [(i,) + spatial for i in range(img.shape[0])]\n            if isinstance(intensity_range[0], Sequence):\n                self.sampled_k_intensity = [self.R.uniform(p[0], p[1]) for p in intensity_range]\n            else:\n                self.sampled_k_intensity = [self.R.uniform(intensity_range[0], intensity_range[1])] * len(img)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandKSpaceSpikeNoise._make_sequence_RandKSpaceSpikeNoise._set_default_range.return.tuple_i_0_95_i_1_1_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandKSpaceSpikeNoise._make_sequence_RandKSpaceSpikeNoise._set_default_range.return.tuple_i_0_95_i_1_1_", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1737, "end_line": 1762, "span_ids": ["RandKSpaceSpikeNoise._make_sequence", "RandKSpaceSpikeNoise._set_default_range"], "tokens": 278}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandKSpaceSpikeNoise(RandomizableTransform, Fourier):\n\n    def _make_sequence(self, x: NdarrayOrTensor) -> Sequence[Sequence[float]]:\n        \"\"\"\n        Formats the sequence of intensities ranges to Sequence[Sequence[float]].\n        \"\"\"\n        if self.intensity_range is None:\n            # set default range if one not provided\n            return self._set_default_range(x)\n\n        if not isinstance(self.intensity_range[0], Sequence):\n            return (ensure_tuple(self.intensity_range),) * x.shape[0]\n        return ensure_tuple(self.intensity_range)\n\n    def _set_default_range(self, img: NdarrayOrTensor) -> Sequence[Sequence[float]]:\n        \"\"\"\n        Sets default intensity ranges to be sampled.\n\n        Args:\n            img: image to transform.\n        \"\"\"\n        n_dims = len(img.shape[1:])\n\n        k = self.shift_fourier(img, n_dims)\n        mod = torch if isinstance(k, torch.Tensor) else np\n        log_abs = mod.log(mod.absolute(k) + 1e-10)  # type: ignore\n        shifted_means = mod.mean(log_abs, dim=tuple(range(-n_dims, 0))) * 2.5  # type: ignore\n        return tuple((i * 0.95, i * 1.1) for i in shifted_means)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandCoarseTransform_RandCoarseTransform.__init__.self.hole_coords._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandCoarseTransform_RandCoarseTransform.__init__.self.hole_coords._", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1765, "end_line": 1805, "span_ids": ["RandCoarseTransform.__init__", "RandCoarseTransform"], "tokens": 491}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandCoarseTransform(RandomizableTransform):\n    \"\"\"\n    Randomly select coarse regions in the image, then execute transform operations for the regions.\n    It's the base class of all kinds of region transforms.\n    Refer to papers: https://arxiv.org/abs/1708.04552\n\n    Args:\n        holes: number of regions to dropout, if `max_holes` is not None, use this arg as the minimum number to\n            randomly select the expected number of regions.\n        spatial_size: spatial size of the regions to dropout, if `max_spatial_size` is not None, use this arg\n            as the minimum spatial size to randomly select size for every region.\n            if some components of the `spatial_size` are non-positive values, the transform will use the\n            corresponding components of input img size. For example, `spatial_size=(32, -1)` will be adapted\n            to `(32, 64)` if the second spatial dimension size of img is `64`.\n        max_holes: if not None, define the maximum number to randomly select the expected number of regions.\n        max_spatial_size: if not None, define the maximum spatial size to randomly select size for every region.\n            if some components of the `max_spatial_size` are non-positive values, the transform will use the\n            corresponding components of input img size. For example, `max_spatial_size=(32, -1)` will be adapted\n            to `(32, 64)` if the second spatial dimension size of img is `64`.\n        prob: probability of applying the transform.\n\n    \"\"\"\n\n    backend = [TransformBackends.NUMPY]\n\n    def __init__(\n        self,\n        holes: int,\n        spatial_size: Union[Sequence[int], int],\n        max_holes: Optional[int] = None,\n        max_spatial_size: Optional[Union[Sequence[int], int]] = None,\n        prob: float = 0.1,\n    ) -> None:\n        RandomizableTransform.__init__(self, prob)\n        if holes < 1:\n            raise ValueError(\"number of holes must be greater than 0.\")\n        self.holes = holes\n        self.spatial_size = spatial_size\n        self.max_holes = max_holes\n        self.max_spatial_size = max_spatial_size\n        self.hole_coords: List = []", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandCoarseTransform.randomize_RandCoarseTransform.randomize.for___in_range_num_holes_.self_hole_coords_append_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandCoarseTransform.randomize_RandCoarseTransform.randomize.for___in_range_num_holes_.self_hole_coords_append_", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1807, "end_line": 1819, "span_ids": ["RandCoarseTransform.randomize"], "tokens": 202}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandCoarseTransform(RandomizableTransform):\n\n    def randomize(self, img_size: Sequence[int]) -> None:\n        super().randomize(None)\n        if not self._do_transform:\n            return None\n        size = fall_back_tuple(self.spatial_size, img_size)\n        self.hole_coords = []  # clear previously computed coords\n        num_holes = self.holes if self.max_holes is None else self.R.randint(self.holes, self.max_holes + 1)\n        for _ in range(num_holes):\n            if self.max_spatial_size is not None:\n                max_size = fall_back_tuple(self.max_spatial_size, img_size)\n                size = tuple(self.R.randint(low=size[i], high=max_size[i] + 1) for i in range(len(img_size)))\n            valid_size = get_valid_patch_size(img_size, size)\n            self.hole_coords.append((slice(None),) + get_random_patch(img_size, valid_size, self.R))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandCoarseTransform._transform_holes_RandCoarseTransform.__call__.return.ret": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandCoarseTransform._transform_holes_RandCoarseTransform.__call__.return.ret", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1821, "end_line": 1840, "span_ids": ["RandCoarseTransform._transform_holes", "RandCoarseTransform.__call__"], "tokens": 180}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandCoarseTransform(RandomizableTransform):\n\n    @abstractmethod\n    def _transform_holes(self, img: np.ndarray) -> np.ndarray:\n        \"\"\"\n        Transform the randomly selected `self.hole_coords` in input images.\n\n        \"\"\"\n        raise NotImplementedError(f\"Subclass {self.__class__.__name__} must implement this method.\")\n\n    def __call__(self, img: NdarrayOrTensor, randomize: bool = True) -> NdarrayOrTensor:\n        if randomize:\n            self.randomize(img.shape[1:])\n\n        if not self._do_transform:\n            return img\n\n        img_np: np.ndarray\n        img_np, *_ = convert_data_type(img, np.ndarray)  # type: ignore\n        out = self._transform_holes(img=img_np)\n        ret, *_ = convert_to_dst_type(src=out, dst=img)\n        return ret", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandCoarseDropout_RandCoarseDropout.__init__.self.fill_value.fill_value": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandCoarseDropout_RandCoarseDropout.__init__.self.fill_value.fill_value", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1843, "end_line": 1891, "span_ids": ["RandCoarseDropout", "RandCoarseDropout.__init__"], "tokens": 720}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandCoarseDropout(RandCoarseTransform):\n    \"\"\"\n    Randomly coarse dropout regions in the image, then fill in the rectangular regions with specified value.\n    Or keep the rectangular regions and fill in the other areas with specified value.\n    Refer to papers: https://arxiv.org/abs/1708.04552, https://arxiv.org/pdf/1604.07379\n    And other implementation: https://albumentations.ai/docs/api_reference/augmentations/transforms/\n    #albumentations.augmentations.transforms.CoarseDropout.\n\n    Args:\n        holes: number of regions to dropout, if `max_holes` is not None, use this arg as the minimum number to\n            randomly select the expected number of regions.\n        spatial_size: spatial size of the regions to dropout, if `max_spatial_size` is not None, use this arg\n            as the minimum spatial size to randomly select size for every region.\n            if some components of the `spatial_size` are non-positive values, the transform will use the\n            corresponding components of input img size. For example, `spatial_size=(32, -1)` will be adapted\n            to `(32, 64)` if the second spatial dimension size of img is `64`.\n        dropout_holes: if `True`, dropout the regions of holes and fill value, if `False`, keep the holes and\n            dropout the outside and fill value. default to `True`.\n        fill_value: target value to fill the dropout regions, if providing a number, will use it as constant\n            value to fill all the regions. if providing a tuple for the `min` and `max`, will randomly select\n            value for every pixel / voxel from the range `[min, max)`. if None, will compute the `min` and `max`\n            value of input image then randomly select value to fill, default to None.\n        max_holes: if not None, define the maximum number to randomly select the expected number of regions.\n        max_spatial_size: if not None, define the maximum spatial size to randomly select size for every region.\n            if some components of the `max_spatial_size` are non-positive values, the transform will use the\n            corresponding components of input img size. For example, `max_spatial_size=(32, -1)` will be adapted\n            to `(32, 64)` if the second spatial dimension size of img is `64`.\n        prob: probability of applying the transform.\n\n    \"\"\"\n\n    def __init__(\n        self,\n        holes: int,\n        spatial_size: Union[Sequence[int], int],\n        dropout_holes: bool = True,\n        fill_value: Optional[Union[Tuple[float, float], float]] = None,\n        max_holes: Optional[int] = None,\n        max_spatial_size: Optional[Union[Sequence[int], int]] = None,\n        prob: float = 0.1,\n    ) -> None:\n        super().__init__(\n            holes=holes, spatial_size=spatial_size, max_holes=max_holes, max_spatial_size=max_spatial_size, prob=prob\n        )\n        self.dropout_holes = dropout_holes\n        if isinstance(fill_value, (tuple, list)):\n            if len(fill_value) != 2:\n                raise ValueError(\"fill value should contain 2 numbers if providing the `min` and `max`.\")\n        self.fill_value = fill_value", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandCoarseDropout._transform_holes_RandCoarseDropout._transform_holes.return.ret": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandCoarseDropout._transform_holes_RandCoarseDropout._transform_holes.return.ret", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1938, "end_line": 1960, "span_ids": ["RandCoarseDropout._transform_holes"], "tokens": 233}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandCoarseDropout(RandCoarseTransform):\n\n    def _transform_holes(self, img: np.ndarray):\n        \"\"\"\n        Fill the randomly selected `self.hole_coords` in input images.\n        Please note that we usually only use `self.R` in `randomize()` method, here is a special case.\n\n        \"\"\"\n        fill_value = (img.min(), img.max()) if self.fill_value is None else self.fill_value\n\n        if self.dropout_holes:\n            for h in self.hole_coords:\n                if isinstance(fill_value, (tuple, list)):\n                    img[h] = self.R.uniform(fill_value[0], fill_value[1], size=img[h].shape)\n                else:\n                    img[h] = fill_value\n            ret = img\n        else:\n            if isinstance(fill_value, (tuple, list)):\n                ret = self.R.uniform(fill_value[0], fill_value[1], size=img.shape).astype(img.dtype, copy=False)\n            else:\n                ret = np.full_like(img, fill_value)\n            for h in self.hole_coords:\n                ret[h] = img[h]\n        return ret", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandCoarseShuffle_RandCoarseShuffle._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandCoarseShuffle_RandCoarseShuffle._", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1918, "end_line": 1941, "span_ids": ["RandCoarseShuffle"], "tokens": 365}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandCoarseShuffle(RandCoarseTransform):\n    \"\"\"\n    Randomly select regions in the image, then shuffle the pixels within every region.\n    It shuffles every channel separately.\n    Refer to paper:\n    Kang, Guoliang, et al. \"Patchshuffle regularization.\" arXiv preprint arXiv:1707.07103 (2017).\n    https://arxiv.org/abs/1707.07103\n\n    Args:\n        holes: number of regions to dropout, if `max_holes` is not None, use this arg as the minimum number to\n            randomly select the expected number of regions.\n        spatial_size: spatial size of the regions to dropout, if `max_spatial_size` is not None, use this arg\n            as the minimum spatial size to randomly select size for every region.\n            if some components of the `spatial_size` are non-positive values, the transform will use the\n            corresponding components of input img size. For example, `spatial_size=(32, -1)` will be adapted\n            to `(32, 64)` if the second spatial dimension size of img is `64`.\n        max_holes: if not None, define the maximum number to randomly select the expected number of regions.\n        max_spatial_size: if not None, define the maximum spatial size to randomly select size for every region.\n            if some components of the `max_spatial_size` are non-positive values, the transform will use the\n            corresponding components of input img size. For example, `max_spatial_size=(32, -1)` will be adapted\n            to `(32, 64)` if the second spatial dimension size of img is `64`.\n        prob: probability of applying the transform.\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandCoarseShuffle._transform_holes_RandCoarseShuffle._transform_holes.return.img": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandCoarseShuffle._transform_holes_RandCoarseShuffle._transform_holes.return.img", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1943, "end_line": 1955, "span_ids": ["RandCoarseShuffle._transform_holes"], "tokens": 127}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandCoarseShuffle(RandCoarseTransform):\n\n    def _transform_holes(self, img: np.ndarray):\n        \"\"\"\n        Shuffle the content of randomly selected `self.hole_coords` in input images.\n        Please note that we usually only use `self.R` in `randomize()` method, here is a special case.\n\n        \"\"\"\n        for h in self.hole_coords:\n            # shuffle every channel separately\n            for i, c in enumerate(img[h]):\n                patch_channel = c.flatten()\n                self.R.shuffle(patch_channel)\n                img[h][i] = patch_channel.reshape(c.shape)\n        return img", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_from_typing_import_Callab_from_monai_utils_deprecat": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_from_typing_import_Callab_from_monai_utils_deprecat", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 18, "end_line": 58, "span_ids": ["docstring"], "tokens": 266}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Callable, Dict, Hashable, List, Mapping, Optional, Sequence, Tuple, Union\n\nimport numpy as np\n\nfrom monai.config import DtypeLike, KeysCollection\nfrom monai.config.type_definitions import NdarrayOrTensor\nfrom monai.transforms.intensity.array import (\n    AdjustContrast,\n    GaussianSharpen,\n    GaussianSmooth,\n    GibbsNoise,\n    HistogramNormalize,\n    KSpaceSpikeNoise,\n    MaskIntensity,\n    NormalizeIntensity,\n    RandAdjustContrast,\n    RandBiasField,\n    RandCoarseDropout,\n    RandCoarseShuffle,\n    RandGaussianNoise,\n    RandGaussianSharpen,\n    RandGaussianSmooth,\n    RandGibbsNoise,\n    RandHistogramShift,\n    RandKSpaceSpikeNoise,\n    RandRicianNoise,\n    RandScaleIntensity,\n    RandShiftIntensity,\n    RandStdShiftIntensity,\n    SavitzkyGolaySmooth,\n    ScaleIntensity,\n    ScaleIntensityRange,\n    ScaleIntensityRangePercentiles,\n    ShiftIntensity,\n    StdShiftIntensity,\n    ThresholdIntensity,\n)\nfrom monai.transforms.transform import MapTransform, RandomizableTransform\nfrom monai.transforms.utils import is_positive\nfrom monai.utils import ensure_tuple, ensure_tuple_rep\nfrom monai.utils.deprecate_utils import deprecated_arg", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py___all_____all__._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py___all_____all__._", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 60, "end_line": 148, "span_ids": ["docstring"], "tokens": 676}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "__all__ = [\n    \"RandGaussianNoised\",\n    \"RandRicianNoised\",\n    \"ShiftIntensityd\",\n    \"RandShiftIntensityd\",\n    \"ScaleIntensityd\",\n    \"RandScaleIntensityd\",\n    \"StdShiftIntensityd\",\n    \"RandStdShiftIntensityd\",\n    \"RandBiasFieldd\",\n    \"NormalizeIntensityd\",\n    \"ThresholdIntensityd\",\n    \"ScaleIntensityRanged\",\n    \"AdjustContrastd\",\n    \"RandAdjustContrastd\",\n    \"ScaleIntensityRangePercentilesd\",\n    \"MaskIntensityd\",\n    \"SavitzkyGolaySmoothd\",\n    \"GaussianSmoothd\",\n    \"RandGaussianSmoothd\",\n    \"GaussianSharpend\",\n    \"RandGaussianSharpend\",\n    \"GibbsNoised\",\n    \"RandGibbsNoised\",\n    \"KSpaceSpikeNoised\",\n    \"RandKSpaceSpikeNoised\",\n    \"RandHistogramShiftd\",\n    \"RandCoarseDropoutd\",\n    \"RandCoarseShuffled\",\n    \"HistogramNormalized\",\n    \"RandGaussianNoiseD\",\n    \"RandGaussianNoiseDict\",\n    \"ShiftIntensityD\",\n    \"ShiftIntensityDict\",\n    \"RandShiftIntensityD\",\n    \"RandShiftIntensityDict\",\n    \"ScaleIntensityD\",\n    \"ScaleIntensityDict\",\n    \"StdShiftIntensityD\",\n    \"StdShiftIntensityDict\",\n    \"RandScaleIntensityD\",\n    \"RandScaleIntensityDict\",\n    \"RandStdShiftIntensityD\",\n    \"RandStdShiftIntensityDict\",\n    \"RandBiasFieldD\",\n    \"RandBiasFieldDict\",\n    \"NormalizeIntensityD\",\n    \"NormalizeIntensityDict\",\n    \"ThresholdIntensityD\",\n    \"ThresholdIntensityDict\",\n    \"ScaleIntensityRangeD\",\n    \"ScaleIntensityRangeDict\",\n    \"AdjustContrastD\",\n    \"AdjustContrastDict\",\n    \"RandAdjustContrastD\",\n    \"RandAdjustContrastDict\",\n    \"ScaleIntensityRangePercentilesD\",\n    \"ScaleIntensityRangePercentilesDict\",\n    \"MaskIntensityD\",\n    \"MaskIntensityDict\",\n    \"SavitzkyGolaySmoothD\",\n    \"SavitzkyGolaySmoothDict\",\n    \"GaussianSmoothD\",\n    \"GaussianSmoothDict\",\n    \"RandGaussianSmoothD\",\n    \"RandGaussianSmoothDict\",\n    \"GaussianSharpenD\",\n    \"GaussianSharpenDict\",\n    \"RandGaussianSharpenD\",\n    \"RandGaussianSharpenDict\",\n    \"GibbsNoiseD\",\n    \"GibbsNoiseDict\",\n    \"RandGibbsNoiseD\",\n    \"RandGibbsNoiseDict\",\n    \"KSpaceSpikeNoiseD\",\n    \"KSpaceSpikeNoiseDict\",\n    \"RandHistogramShiftD\",\n    \"RandHistogramShiftDict\",\n    \"RandRicianNoiseD\",\n    \"RandRicianNoiseDict\",\n    \"RandCoarseDropoutD\",\n    \"RandCoarseDropoutDict\",\n    \"RandCoarseShuffleD\",\n    \"RandCoarseShuffleDict\",\n    \"HistogramNormalizeD\",\n    \"HistogramNormalizeDict\",\n    \"RandKSpaceSpikeNoiseD\",\n    \"RandKSpaceSpikeNoiseDict\",\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_ShiftIntensityd_ShiftIntensityd.__init__.self.shifter.ShiftIntensity_offset_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_ShiftIntensityd_ShiftIntensityd.__init__.self.shifter.ShiftIntensity_offset_", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 257, "end_line": 300, "span_ids": ["ShiftIntensityd", "ShiftIntensityd.__init__"], "tokens": 518}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ShiftIntensityd(MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.ShiftIntensity`.\n    \"\"\"\n\n    backend = ShiftIntensity.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        offset: float,\n        factor_key: Optional[str] = None,\n        meta_keys: Optional[KeysCollection] = None,\n        meta_key_postfix: str = \"meta_dict\",\n        allow_missing_keys: bool = False,\n    ) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to be transformed.\n                See also: :py:class:`monai.transforms.compose.MapTransform`\n            offset: offset value to shift the intensity of image.\n            factor_key: if not None, use it as the key to extract a value from the corresponding\n                meta data dictionary of `key` at runtime, and multiply the `offset` to shift intensity.\n                Usually, `IntensityStatsd` transform can pre-compute statistics of intensity values\n                and store in the meta data.\n                it also can be a sequence of strings, map to `keys`.\n            meta_keys: explicitly indicate the key of the corresponding meta data dictionary.\n                used to extract the factor value is `factor_key` is not None.\n                for example, for data with key `image`, the metadata by default is in `image_meta_dict`.\n                the meta data is a dictionary object which contains: filename, original_shape, etc.\n                it can be a sequence of string, map to the `keys`.\n                if None, will try to construct meta_keys by `key_{meta_key_postfix}`.\n            meta_key_postfix: if meta_keys is None, use `key_{postfix}` to to fetch the meta data according\n                to the key data, default is `meta_dict`, the meta data is a dictionary object.\n                used to extract the factor value is `factor_key` is not None.\n            allow_missing_keys: don't raise exception if key is missing.\n        \"\"\"\n        super().__init__(keys, allow_missing_keys)\n        self.factor_key = ensure_tuple_rep(factor_key, len(self.keys))\n        self.meta_keys = ensure_tuple_rep(None, len(self.keys)) if meta_keys is None else ensure_tuple(meta_keys)\n        if len(self.keys) != len(self.meta_keys):\n            raise ValueError(\"meta_keys should have the same length as keys.\")\n        self.meta_key_postfix = ensure_tuple_rep(meta_key_postfix, len(self.keys))\n        self.shifter = ShiftIntensity(offset)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_ShiftIntensityd.__call___ShiftIntensityd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_ShiftIntensityd.__call___ShiftIntensityd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 302, "end_line": 311, "span_ids": ["ShiftIntensityd.__call__"], "tokens": 145}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ShiftIntensityd(MapTransform):\n\n    def __call__(self, data) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key, factor_key, meta_key, meta_key_postfix in self.key_iterator(\n            d, self.factor_key, self.meta_keys, self.meta_key_postfix\n        ):\n            meta_key = meta_key or f\"{key}_{meta_key_postfix}\"\n            factor: Optional[float] = d[meta_key].get(factor_key) if meta_key in d else None\n            offset = None if factor is None else self.shifter.offset * factor\n            d[key] = self.shifter(d[key], offset=offset)\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_RandShiftIntensityd_RandShiftIntensityd.set_random_state.return.self": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_RandShiftIntensityd_RandShiftIntensityd.set_random_state.return.self", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 314, "end_line": 370, "span_ids": ["RandShiftIntensityd.set_random_state", "RandShiftIntensityd.__init__", "RandShiftIntensityd"], "tokens": 667}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandShiftIntensityd(RandomizableTransform, MapTransform):\n    \"\"\"\n    Dictionary-based version :py:class:`monai.transforms.RandShiftIntensity`.\n    \"\"\"\n\n    backend = RandShiftIntensity.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        offsets: Union[Tuple[float, float], float],\n        factor_key: Optional[str] = None,\n        meta_keys: Optional[KeysCollection] = None,\n        meta_key_postfix: str = \"meta_dict\",\n        prob: float = 0.1,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to be transformed.\n                See also: :py:class:`monai.transforms.compose.MapTransform`\n            offsets: offset range to randomly shift.\n                if single number, offset value is picked from (-offsets, offsets).\n            factor_key: if not None, use it as the key to extract a value from the corresponding\n                meta data dictionary of `key` at runtime, and multiply the random `offset` to shift intensity.\n                Usually, `IntensityStatsd` transform can pre-compute statistics of intensity values\n                and store in the meta data.\n                it also can be a sequence of strings, map to `keys`.\n            meta_keys: explicitly indicate the key of the corresponding meta data dictionary.\n                used to extract the factor value is `factor_key` is not None.\n                for example, for data with key `image`, the metadata by default is in `image_meta_dict`.\n                the meta data is a dictionary object which contains: filename, original_shape, etc.\n                it can be a sequence of string, map to the `keys`.\n                if None, will try to construct meta_keys by `key_{meta_key_postfix}`.\n            meta_key_postfix: if meta_keys is None, use `key_{postfix}` to to fetch the meta data according\n                to the key data, default is `meta_dict`, the meta data is a dictionary object.\n                used to extract the factor value is `factor_key` is not None.\n            prob: probability of rotating.\n                (Default 0.1, with 10% probability it returns a rotated array.)\n            allow_missing_keys: don't raise exception if key is missing.\n        \"\"\"\n        MapTransform.__init__(self, keys, allow_missing_keys)\n        RandomizableTransform.__init__(self, prob)\n\n        self.factor_key = ensure_tuple_rep(factor_key, len(self.keys))\n        self.meta_keys = ensure_tuple_rep(None, len(self.keys)) if meta_keys is None else ensure_tuple(meta_keys)\n        if len(self.keys) != len(self.meta_keys):\n            raise ValueError(\"meta_keys should have the same length as keys.\")\n        self.meta_key_postfix = ensure_tuple_rep(meta_key_postfix, len(self.keys))\n        self.shifter = RandShiftIntensity(offsets=offsets, prob=1.0)\n\n    def set_random_state(\n        self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None\n    ) -> \"RandShiftIntensityd\":\n        super().set_random_state(seed, state)\n        self.shifter.set_random_state(seed, state)\n        return self", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_RandShiftIntensityd.__call___RandShiftIntensityd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_RandShiftIntensityd.__call___RandShiftIntensityd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 372, "end_line": 386, "span_ids": ["RandShiftIntensityd.__call__"], "tokens": 176}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandShiftIntensityd(RandomizableTransform, MapTransform):\n\n    def __call__(self, data) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        self.randomize(None)\n        if not self._do_transform:\n            return d\n\n        # all the keys share the same random shift factor\n        self.shifter.randomize(None)\n        for key, factor_key, meta_key, meta_key_postfix in self.key_iterator(\n            d, self.factor_key, self.meta_keys, self.meta_key_postfix\n        ):\n            meta_key = meta_key or f\"{key}_{meta_key_postfix}\"\n            factor: Optional[float] = d[meta_key].get(factor_key) if meta_key in d else None\n            d[key] = self.shifter(d[key], factor=factor, randomize=False)\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_RandBiasFieldd_RandBiasFieldd.set_random_state.return.self": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_RandBiasFieldd_RandBiasFieldd.set_random_state.return.self", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 577, "end_line": 615, "span_ids": ["RandBiasFieldd.set_random_state", "RandBiasFieldd", "RandBiasFieldd.__init__"], "tokens": 362}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandBiasFieldd(RandomizableTransform, MapTransform):\n    \"\"\"\n    Dictionary-based version :py:class:`monai.transforms.RandBiasField`.\n    \"\"\"\n\n    backend = RandBiasField.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        degree: int = 3,\n        coeff_range: Tuple[float, float] = (0.0, 0.1),\n        dtype: DtypeLike = np.float32,\n        prob: float = 0.1,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to be transformed.\n                See also: :py:class:`monai.transforms.compose.MapTransform`\n            degree: degree of freedom of the polynomials. The value should be no less than 1.\n                Defaults to 3.\n            coeff_range: range of the random coefficients. Defaults to (0.0, 0.1).\n            dtype: output data type, if None, same as input image. defaults to float32.\n            prob: probability to do random bias field.\n            allow_missing_keys: don't raise exception if key is missing.\n\n        \"\"\"\n        MapTransform.__init__(self, keys, allow_missing_keys)\n        RandomizableTransform.__init__(self, prob)\n\n        self.rand_bias_field = RandBiasField(degree=degree, coeff_range=coeff_range, dtype=dtype, prob=1.0)\n\n    def set_random_state(\n        self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None\n    ) -> \"RandBiasFieldd\":\n        super().set_random_state(seed, state)\n        self.rand_bias_field.set_random_state(seed, state)\n        return self", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_RandBiasFieldd.__call___RandBiasFieldd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_RandBiasFieldd.__call___RandBiasFieldd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 629, "end_line": 643, "span_ids": ["RandBiasFieldd.__call__"], "tokens": 157}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandBiasFieldd(RandomizableTransform, MapTransform):\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        self.randomize(None)\n        if not self._do_transform:\n            return d\n\n        # all the keys share the same random bias factor\n        first_key: Union[Hashable, List] = self.first_key(d)\n        if first_key == []:\n            return d\n\n        self.rand_bias_field.randomize(img_size=d[first_key].shape[1:])  # type: ignore\n        for key in self.key_iterator(d):\n            d[key] = self.rand_bias_field(d[key], randomize=False)\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_RandGaussianSharpend_RandGaussianSharpend.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_RandGaussianSharpend_RandGaussianSharpend.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1026, "end_line": 1097, "span_ids": ["RandGaussianSharpend.set_random_state", "RandGaussianSharpend.__init__", "RandGaussianSharpend.__call__", "RandGaussianSharpend"], "tokens": 845}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandGaussianSharpend(RandomizableTransform, MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.GaussianSharpen`.\n\n    Args:\n        keys: keys of the corresponding items to be transformed.\n            See also: :py:class:`monai.transforms.compose.MapTransform`\n        sigma1_x: randomly select sigma value for the first spatial dimension of first gaussian kernel.\n        sigma1_y: randomly select sigma value for the second spatial dimension(if have) of first gaussian kernel.\n        sigma1_z: randomly select sigma value for the third spatial dimension(if have) of first gaussian kernel.\n        sigma2_x: randomly select sigma value for the first spatial dimension of second gaussian kernel.\n            if only 1 value `X` provided, it must be smaller than `sigma1_x` and randomly select from [X, sigma1_x].\n        sigma2_y: randomly select sigma value for the second spatial dimension(if have) of second gaussian kernel.\n            if only 1 value `Y` provided, it must be smaller than `sigma1_y` and randomly select from [Y, sigma1_y].\n        sigma2_z: randomly select sigma value for the third spatial dimension(if have) of second gaussian kernel.\n            if only 1 value `Z` provided, it must be smaller than `sigma1_z` and randomly select from [Z, sigma1_z].\n        alpha: randomly select weight parameter to compute the final result.\n        approx: discrete Gaussian kernel type, available options are \"erf\", \"sampled\", and \"scalespace\".\n            see also :py:meth:`monai.networks.layers.GaussianFilter`.\n        prob: probability of Gaussian sharpen.\n        allow_missing_keys: don't raise exception if key is missing.\n\n    \"\"\"\n\n    backend = RandGaussianSharpen.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        sigma1_x: Tuple[float, float] = (0.5, 1.0),\n        sigma1_y: Tuple[float, float] = (0.5, 1.0),\n        sigma1_z: Tuple[float, float] = (0.5, 1.0),\n        sigma2_x: Union[Tuple[float, float], float] = 0.5,\n        sigma2_y: Union[Tuple[float, float], float] = 0.5,\n        sigma2_z: Union[Tuple[float, float], float] = 0.5,\n        alpha: Tuple[float, float] = (10.0, 30.0),\n        approx: str = \"erf\",\n        prob: float = 0.1,\n        allow_missing_keys: bool = False,\n    ):\n        MapTransform.__init__(self, keys, allow_missing_keys)\n        RandomizableTransform.__init__(self, prob)\n        self.rand_sharpen = RandGaussianSharpen(\n            sigma1_x=sigma1_x,\n            sigma1_y=sigma1_y,\n            sigma1_z=sigma1_z,\n            sigma2_x=sigma2_x,\n            sigma2_y=sigma2_y,\n            sigma2_z=sigma2_z,\n            alpha=alpha,\n            approx=approx,\n            prob=1.0,\n        )\n\n    def set_random_state(\n        self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None\n    ) -> \"RandGaussianSharpend\":\n        super().set_random_state(seed, state)\n        self.rand_sharpen.set_random_state(seed, state)\n        return self\n\n    def __call__(self, data: Dict[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        self.randomize(None)\n        if not self._do_transform:\n            return d\n\n        # all the keys share the same random sigma1, sigma2, etc.\n        self.rand_sharpen.randomize(None)\n        for key in self.key_iterator(d):\n            d[key] = self.rand_sharpen(d[key], randomize=False)\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_RandHistogramShiftd_RandHistogramShiftd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_RandHistogramShiftd_RandHistogramShiftd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1100, "end_line": 1145, "span_ids": ["RandHistogramShiftd.set_random_state", "RandHistogramShiftd.__call__", "RandHistogramShiftd", "RandHistogramShiftd.__init__"], "tokens": 415}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandHistogramShiftd(RandomizableTransform, MapTransform):\n    \"\"\"\n    Dictionary-based version :py:class:`monai.transforms.RandHistogramShift`.\n    Apply random nonlinear transform the the image's intensity histogram.\n\n    Args:\n        keys: keys of the corresponding items to be transformed.\n            See also: monai.transforms.MapTransform\n        num_control_points: number of control points governing the nonlinear intensity mapping.\n            a smaller number of control points allows for larger intensity shifts. if two values provided, number of\n            control points selecting from range (min_value, max_value).\n        prob: probability of histogram shift.\n        allow_missing_keys: don't raise exception if key is missing.\n    \"\"\"\n\n    backend = RandHistogramShift.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        num_control_points: Union[Tuple[int, int], int] = 10,\n        prob: float = 0.1,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        MapTransform.__init__(self, keys, allow_missing_keys)\n        RandomizableTransform.__init__(self, prob)\n        self.shifter = RandHistogramShift(num_control_points=num_control_points, prob=1.0)\n\n    def set_random_state(\n        self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None\n    ) -> \"RandHistogramShiftd\":\n        super().set_random_state(seed, state)\n        self.shifter.set_random_state(seed, state)\n        return self\n\n    def __call__(self, data: Dict[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        self.randomize(None)\n        if not self._do_transform:\n            return d\n\n        # all the keys share the same random shift params\n        self.shifter.randomize(None)\n        for key in self.key_iterator(d):\n            d[key] = self.shifter(d[key], randomize=False)\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_RandGibbsNoised_RandGibbsNoised.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_RandGibbsNoised_RandGibbsNoised.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1148, "end_line": 1206, "span_ids": ["RandGibbsNoised.__init__", "RandGibbsNoised", "RandGibbsNoised.__call__", "RandGibbsNoised.set_random_state"], "tokens": 595}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandGibbsNoised(RandomizableTransform, MapTransform):\n    \"\"\"\n    Dictionary-based version of RandGibbsNoise.\n\n    Naturalistic image augmentation via Gibbs artifacts. The transform\n    randomly applies Gibbs noise to 2D/3D MRI images. Gibbs artifacts\n    are one of the common type of type artifacts appearing in MRI scans.\n\n    The transform is applied to all the channels in the data.\n\n    For general information on Gibbs artifacts, please refer to:\n    https://pubs.rsna.org/doi/full/10.1148/rg.313105115\n    https://pubs.rsna.org/doi/full/10.1148/radiographics.22.4.g02jl14949\n\n    Args:\n        keys: 'image', 'label', or ['image', 'label'] depending on which data\n                you need to transform.\n        prob (float): probability of applying the transform.\n        alpha (float, List[float]): Parametrizes the intensity of the Gibbs noise filter applied. Takes\n            values in the interval [0,1] with alpha = 0 acting as the identity mapping.\n            If a length-2 list is given as [a,b] then the value of alpha will be sampled\n            uniformly from the interval [a,b].\n        allow_missing_keys: do not raise exception if key is missing.\n    \"\"\"\n\n    backend = RandGibbsNoise.backend\n\n    @deprecated_arg(name=\"as_tensor_output\", since=\"0.6\")\n    def __init__(\n        self,\n        keys: KeysCollection,\n        prob: float = 0.1,\n        alpha: Sequence[float] = (0.0, 1.0),\n        allow_missing_keys: bool = False,\n        as_tensor_output: bool = True,\n    ) -> None:\n\n        MapTransform.__init__(self, keys, allow_missing_keys)\n        RandomizableTransform.__init__(self, prob=prob)\n        self.rand_gibbs_noise = RandGibbsNoise(alpha=alpha, prob=1.0)\n\n    def set_random_state(\n        self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None\n    ) -> \"RandGibbsNoised\":\n        super().set_random_state(seed, state)\n        self.rand_gibbs_noise.set_random_state(seed, state)\n        return self\n\n    def __call__(self, data: Dict[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        self.randomize(None)\n        if not self._do_transform:\n            return d\n\n        # all the keys share the same random noise params\n        self.rand_gibbs_noise.randomize(None)\n        for key in self.key_iterator(d):\n            d[key] = self.rand_gibbs_noise(d[key], randomize=False)\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_KSpaceSpikeNoised_KSpaceSpikeNoised.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_KSpaceSpikeNoised_KSpaceSpikeNoised.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1246, "end_line": 1311, "span_ids": ["KSpaceSpikeNoised", "KSpaceSpikeNoised.__call__", "KSpaceSpikeNoised.__init__"], "tokens": 726}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class KSpaceSpikeNoised(MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.KSpaceSpikeNoise`.\n\n    Applies localized spikes in `k`-space at the given locations and intensities.\n    Spike (Herringbone) artifact is a type of data acquisition artifact which\n    may occur during MRI scans.\n\n    For general information on spike artifacts, please refer to:\n\n    `AAPM/RSNA physics tutorial for residents: fundamental physics of MR imaging\n    <https://pubmed.ncbi.nlm.nih.gov/16009826>`_.\n\n    `Body MRI artifacts in clinical practice: A physicist's and radiologist's\n    perspective <https://doi.org/10.1002/jmri.24288>`_.\n\n    Args:\n        keys: \"image\", \"label\", or [\"image\", \"label\"] depending\n             on which data you need to transform.\n        loc: spatial location for the spikes. For\n            images with 3D spatial dimensions, the user can provide (C, X, Y, Z)\n            to fix which channel C is affected, or (X, Y, Z) to place the same\n            spike in all channels. For 2D cases, the user can provide (C, X, Y)\n            or (X, Y).\n        k_intensity: value for the log-intensity of the\n            `k`-space version of the image. If one location is passed to ``loc`` or the\n            channel is not specified, then this argument should receive a float. If\n            ``loc`` is given a sequence of locations, then this argument should\n            receive a sequence of intensities. This value should be tested as it is\n            data-dependent. The default values are the 2.5 the mean of the\n            log-intensity for each channel.\n        allow_missing_keys: do not raise exception if key is missing.\n\n    Example:\n        When working with 4D data,\n        ``KSpaceSpikeNoised(\"image\", loc = ((3,60,64,32), (64,60,32)), k_intensity = (13,14))``\n        will place a spike at `[3, 60, 64, 32]` with `log-intensity = 13`, and\n        one spike per channel located respectively at `[: , 64, 60, 32]`\n        with `log-intensity = 14`.\n    \"\"\"\n\n    backend = KSpaceSpikeNoise.backend\n\n    @deprecated_arg(name=\"as_tensor_output\", since=\"0.6\")\n    def __init__(\n        self,\n        keys: KeysCollection,\n        loc: Union[Tuple, Sequence[Tuple]],\n        k_intensity: Optional[Union[Sequence[float], float]] = None,\n        allow_missing_keys: bool = False,\n        as_tensor_output: bool = True,\n    ) -> None:\n\n        super().__init__(keys, allow_missing_keys)\n        self.transform = KSpaceSpikeNoise(loc, k_intensity)\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        \"\"\"\n        Args:\n            data: Expects image/label to have dimensions (C, H, W) or\n                (C, H, W, D), where C is the channel.\n        \"\"\"\n        d = dict(data)\n        for key in self.key_iterator(d):\n            d[key] = self.transform(d[key])\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_RandKSpaceSpikeNoised_RandKSpaceSpikeNoised.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_RandKSpaceSpikeNoised_RandKSpaceSpikeNoised.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1320, "end_line": 1394, "span_ids": ["RandKSpaceSpikeNoised.set_random_state", "RandKSpaceSpikeNoised", "RandKSpaceSpikeNoised.__init__", "RandKSpaceSpikeNoised.__call__"], "tokens": 810}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandKSpaceSpikeNoised(RandomizableTransform, MapTransform):\n    \"\"\"\n    Dictionary-based version of :py:class:`monai.transforms.RandKSpaceSpikeNoise`.\n\n    Naturalistic data augmentation via spike artifacts. The transform applies\n    localized spikes in `k`-space.\n\n    For general information on spike artifacts, please refer to:\n\n    `AAPM/RSNA physics tutorial for residents: fundamental physics of MR imaging\n    <https://pubmed.ncbi.nlm.nih.gov/16009826>`_.\n\n    `Body MRI artifacts in clinical practice: A physicist's and radiologist's\n    perspective <https://doi.org/10.1002/jmri.24288>`_.\n\n    Args:\n        keys: \"image\", \"label\", or [\"image\", \"label\"] depending\n             on which data you need to transform.\n        prob: probability to add spike artifact to each item in the\n            dictionary provided it is realized that the noise will be applied\n            to the dictionary.\n        intensity_range: pass a tuple (a, b) to sample the log-intensity from the interval (a, b)\n            uniformly for all channels. Or pass sequence of intervals\n            ((a0, b0), (a1, b1), ...) to sample for each respective channel.\n            In the second case, the number of 2-tuples must match the number of channels.\n            Default ranges is `(0.95x, 1.10x)` where `x` is the mean\n            log-intensity for each channel.\n        channel_wise: treat each channel independently. True by default.\n        allow_missing_keys: do not raise exception if key is missing.\n\n    Example:\n        To apply `k`-space spikes randomly on the image only, with probability\n        0.5, and log-intensity sampled from the interval [13, 15] for each\n        channel independently, one uses\n        ``RandKSpaceSpikeNoised(\"image\", prob=0.5, intensity_ranges=(13, 15), channel_wise=True)``.\n    \"\"\"\n\n    backend = RandKSpaceSpikeNoise.backend\n\n    @deprecated_arg(name=\"as_tensor_output\", since=\"0.6\")\n    @deprecated_arg(name=\"common_sampling\", since=\"0.6\")\n    @deprecated_arg(name=\"common_seed\", since=\"0.6\")\n    @deprecated_arg(name=\"global_prob\", since=\"0.6\")\n    def __init__(\n        self,\n        keys: KeysCollection,\n        global_prob: float = 1.0,\n        prob: float = 0.1,\n        intensity_range: Optional[Sequence[Union[Sequence[float], float]]] = None,\n        channel_wise: bool = True,\n        common_sampling: bool = False,\n        common_seed: int = 42,\n        allow_missing_keys: bool = False,\n        as_tensor_output: bool = True,\n    ):\n        MapTransform.__init__(self, keys, allow_missing_keys)\n        RandomizableTransform.__init__(self, prob=prob)\n        self.rand_noise = RandKSpaceSpikeNoise(prob=1.0, intensity_range=intensity_range, channel_wise=channel_wise)\n\n    def set_random_state(\n        self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None\n    ) -> \"RandKSpaceSpikeNoised\":\n        super().set_random_state(seed, state)\n        self.rand_noise.set_random_state(seed, state)\n        return self\n\n    def __call__(self, data: Dict[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        self.randomize(None)\n        if not self._do_transform:\n            return d\n\n        for key in self.key_iterator(d):\n            d[key] = self.rand_noise(d[key], randomize=True)\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_RandCoarseDropoutd_RandCoarseDropoutd.__init__.self.dropper.RandCoarseDropout_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_RandCoarseDropoutd_RandCoarseDropoutd.__init__.self.dropper.RandCoarseDropout_", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1391, "end_line": 1447, "span_ids": ["RandCoarseDropoutd.__init__", "RandCoarseDropoutd"], "tokens": 736}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandCoarseDropoutd(RandomizableTransform, MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.RandCoarseDropout`.\n    Expect all the data specified by `keys` have same spatial shape and will randomly dropout the same regions\n    for every key, if want to dropout differently for every key, please use this transform separately.\n\n    Args:\n        keys: keys of the corresponding items to be transformed.\n            See also: :py:class:`monai.transforms.compose.MapTransform`\n        holes: number of regions to dropout, if `max_holes` is not None, use this arg as the minimum number to\n            randomly select the expected number of regions.\n        spatial_size: spatial size of the regions to dropout, if `max_spatial_size` is not None, use this arg\n            as the minimum spatial size to randomly select size for every region.\n            if some components of the `spatial_size` are non-positive values, the transform will use the\n            corresponding components of input img size. For example, `spatial_size=(32, -1)` will be adapted\n            to `(32, 64)` if the second spatial dimension size of img is `64`.\n        dropout_holes: if `True`, dropout the regions of holes and fill value, if `False`, keep the holes and\n            dropout the outside and fill value. default to `True`.\n        fill_value: target value to fill the dropout regions, if providing a number, will use it as constant\n            value to fill all the regions. if providing a tuple for the `min` and `max`, will randomly select\n            value for every pixel / voxel from the range `[min, max)`. if None, will compute the `min` and `max`\n            value of input image then randomly select value to fill, default to None.\n        max_holes: if not None, define the maximum number to randomly select the expected number of regions.\n        max_spatial_size: if not None, define the maximum spatial size to randomly select size for every region.\n            if some components of the `max_spatial_size` are non-positive values, the transform will use the\n            corresponding components of input img size. For example, `max_spatial_size=(32, -1)` will be adapted\n            to `(32, 64)` if the second spatial dimension size of img is `64`.\n        prob: probability of applying the transform.\n        allow_missing_keys: don't raise exception if key is missing.\n\n    \"\"\"\n\n    backend = RandCoarseDropout.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        holes: int,\n        spatial_size: Union[Sequence[int], int],\n        dropout_holes: bool = True,\n        fill_value: Optional[Union[Tuple[float, float], float]] = None,\n        max_holes: Optional[int] = None,\n        max_spatial_size: Optional[Union[Sequence[int], int]] = None,\n        prob: float = 0.1,\n        allow_missing_keys: bool = False,\n    ):\n        MapTransform.__init__(self, keys, allow_missing_keys)\n        RandomizableTransform.__init__(self, prob=prob)\n        self.dropper = RandCoarseDropout(\n            holes=holes,\n            spatial_size=spatial_size,\n            dropout_holes=dropout_holes,\n            fill_value=fill_value,\n            max_holes=max_holes,\n            max_spatial_size=max_spatial_size,\n            prob=1.0,\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_RandCoarseDropoutd.set_random_state_RandCoarseDropoutd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_RandCoarseDropoutd.set_random_state_RandCoarseDropoutd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1471, "end_line": 1493, "span_ids": ["RandCoarseDropoutd.set_random_state", "RandCoarseDropoutd.__call__"], "tokens": 206}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandCoarseDropoutd(RandomizableTransform, MapTransform):\n\n    def set_random_state(\n        self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None\n    ) -> \"RandCoarseDropoutd\":\n        super().set_random_state(seed, state)\n        self.dropper.set_random_state(seed, state)\n        return self\n\n    def __call__(self, data):\n        d = dict(data)\n        self.randomize(None)\n        if not self._do_transform:\n            return d\n\n        # expect all the specified keys have same spatial shape and share same random holes\n        first_key: Union[Hashable, List] = self.first_key(d)\n        if first_key == []:\n            return d\n\n        self.dropper.randomize(d[first_key].shape[1:])  # type: ignore\n        for key in self.key_iterator(d):\n            d[key] = self.dropper(img=d[key], randomize=False)\n\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_HistogramNormalized_HistogramNormalized.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_HistogramNormalized_HistogramNormalized.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1541, "end_line": 1583, "span_ids": ["HistogramNormalized.__call__", "HistogramNormalized.__init__", "HistogramNormalized"], "tokens": 457}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class HistogramNormalized(MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.HistogramNormalize`.\n\n    Args:\n        keys: keys of the corresponding items to be transformed.\n            See also: :py:class:`monai.transforms.compose.MapTransform`\n        num_bins: number of the bins to use in histogram, default to `256`. for more details:\n            https://numpy.org/doc/stable/reference/generated/numpy.histogram.html.\n        min: the min value to normalize input image, default to `255`.\n        max: the max value to normalize input image, default to `255`.\n        mask: if provided, must be ndarray of bools or 0s and 1s, and same shape as `image`.\n            only points at which `mask==True` are used for the equalization.\n            can also provide the mask by `mask_key` at runtime.\n        mask_key: if mask is None, will try to get the mask with `mask_key`.\n        dtype: output data type, if None, same as input image. defaults to float32.\n        allow_missing_keys: do not raise exception if key is missing.\n\n    \"\"\"\n\n    backend = HistogramNormalize.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        num_bins: int = 256,\n        min: int = 0,\n        max: int = 255,\n        mask: Optional[NdarrayOrTensor] = None,\n        mask_key: Optional[str] = None,\n        dtype: DtypeLike = np.float32,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        super().__init__(keys, allow_missing_keys)\n        self.transform = HistogramNormalize(num_bins=num_bins, min=min, max=max, mask=mask, dtype=dtype)\n        self.mask_key = mask_key if mask is None else None\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key in self.key_iterator(d):\n            d[key] = self.transform(d[key], d[self.mask_key]) if self.mask_key is not None else self.transform(d[key])\n\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/inverse_batch_transform.py_BatchInverseTransform.__call___BatchInverseTransform.__call__.try_.except_RuntimeError_as_re.raise_RuntimeError_re_str": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/inverse_batch_transform.py_BatchInverseTransform.__call___BatchInverseTransform.__call__.try_.except_RuntimeError_as_re.raise_RuntimeError_re_str", "embedding": null, "metadata": {"file_path": "monai/transforms/inverse_batch_transform.py", "file_name": "inverse_batch_transform.py", "file_type": "text/x-python", "category": "implementation", "start_line": 93, "end_line": 105, "span_ids": ["BatchInverseTransform.__call__"], "tokens": 172}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class BatchInverseTransform(Transform):\n\n    def __call__(self, data: Dict[str, Any]) -> Any:\n        decollated_data = decollate_batch(data, detach=self.detach, pad=self.pad_batch, fill_value=self.fill_value)\n        inv_ds = _BatchInverseDataset(decollated_data, self.transform, self.pad_collation_used)\n        inv_loader = DataLoader(\n            inv_ds, batch_size=self.batch_size, num_workers=self.num_workers, collate_fn=self.collate_fn\n        )\n        try:\n            return first(inv_loader)\n        except RuntimeError as re:\n            re_str = str(re)\n            if \"equal size\" in re_str:\n                re_str += \"\\nMONAI hint: try creating `BatchInverseTransform` with `collate_fn=lambda x: x`.\"\n            raise RuntimeError(re_str) from re", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/inverse_batch_transform.py_Decollated.__call___": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/inverse_batch_transform.py_Decollated.__call___", "embedding": null, "metadata": {"file_path": "monai/transforms/inverse_batch_transform.py", "file_name": "inverse_batch_transform.py", "file_type": "text/x-python", "category": "implementation", "start_line": 141, "end_line": 154, "span_ids": ["Decollated.__call__"], "tokens": 141}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Decollated(MapTransform):\n\n    def __call__(self, data: Union[Dict, List]):\n        d: Union[Dict, List]\n        if len(self.keys) == 1 and self.keys[0] is None:\n            # it doesn't support `None` as the key\n            d = data\n        else:\n            if not isinstance(data, dict):\n                raise TypeError(\"input data is not a dictionary, but specified keys to decollate.\")\n            d = {}\n            for key in self.key_iterator(data):\n                d[key] = data[key]\n\n        return decollate_batch(d, detach=self.detach, pad=self.pad_batch, fill_value=self.fill_value)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/io/array.py_inspect_SUPPORTED_READERS._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/io/array.py_inspect_SUPPORTED_READERS._", "embedding": null, "metadata": {"file_path": "monai/transforms/io/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 16, "end_line": 45, "span_ids": ["docstring"], "tokens": 247}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import inspect\nimport logging\nimport sys\nimport warnings\nfrom pathlib import Path\nfrom typing import Dict, List, Optional, Sequence, Union\n\nimport numpy as np\nimport torch\n\nfrom monai.config import DtypeLike, PathLike\nfrom monai.data.image_reader import ImageReader, ITKReader, NibabelReader, NumpyReader, PILReader\nfrom monai.data.nifti_saver import NiftiSaver\nfrom monai.data.png_saver import PNGSaver\nfrom monai.transforms.transform import Transform\nfrom monai.utils import GridSampleMode, GridSamplePadMode\nfrom monai.utils import ImageMetaKey as Key\nfrom monai.utils import InterpolateMode, OptionalImportError, ensure_tuple, look_up_option, optional_import\n\nnib, _ = optional_import(\"nibabel\")\nImage, _ = optional_import(\"PIL.Image\")\n\n__all__ = [\"LoadImage\", \"SaveImage\", \"SUPPORTED_READERS\"]\n\nSUPPORTED_READERS = {\n    \"itkreader\": ITKReader,\n    \"numpyreader\": NumpyReader,\n    \"pilreader\": PILReader,\n    \"nibabelreader\": NibabelReader,\n}", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/io/array.py_LoadImage_LoadImage._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/io/array.py_LoadImage_LoadImage._", "embedding": null, "metadata": {"file_path": "monai/transforms/io/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 76, "end_line": 92, "span_ids": ["LoadImage"], "tokens": 167}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LoadImage(Transform):\n    \"\"\"\n    Load image file or files from provided path based on reader.\n    If reader is not specified, this class automatically chooses readers\n    based on the supported suffixes and in the following order:\n\n        - User-specified reader at runtime when calling this loader.\n        - User-specified reader in the constructor of `LoadImage`.\n        - Readers from the last to the first in the registered list.\n        - Current default readers: (nii, nii.gz -> NibabelReader), (png, jpg, bmp -> PILReader),\n          (npz, npy -> NumpyReader), (others -> ITKReader).\n\n    See also:\n\n        - tutorial: https://github.com/Project-MONAI/tutorials/blob/master/modules/load_medical_images.ipynb\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/io/array.py_LoadImage.__init___LoadImage.register.self_readers_append_reade": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/io/array.py_LoadImage.__init___LoadImage.register.self_readers_append_reade", "embedding": null, "metadata": {"file_path": "monai/transforms/io/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 93, "end_line": 168, "span_ids": ["LoadImage.__init__", "LoadImage.register"], "tokens": 787}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LoadImage(Transform):\n\n    def __init__(self, reader=None, image_only: bool = False, dtype: DtypeLike = np.float32, *args, **kwargs) -> None:\n        \"\"\"\n        Args:\n            reader: reader to load image file and meta data\n\n                - if `reader` is None, a default set of `SUPPORTED_READERS` will be used.\n                - if `reader` is a string, the corresponding item in `SUPPORTED_READERS` will be used,\n                  and a reader instance will be constructed with the `*args` and `**kwargs` parameters.\n                  the supported reader names are: \"nibabelreader\", \"pilreader\", \"itkreader\", \"numpyreader\".\n                - if `reader` is a reader class/instance, it will be registered to this loader accordingly.\n\n            image_only: if True return only the image volume, otherwise return image data array and header dict.\n            dtype: if not None convert the loaded image to this data type.\n            args: additional parameters for reader if providing a reader name.\n            kwargs: additional parameters for reader if providing a reader name.\n\n        Note:\n\n            - The transform returns an image data array if `image_only` is True,\n              or a tuple of two elements containing the data array, and the meta data in a dictionary format otherwise.\n            - If `reader` is specified, the loader will attempt to use the specified readers and the default supported\n              readers. This might introduce overheads when handling the exceptions of trying the incompatible loaders.\n              In this case, it is therefore recommended to set the most appropriate reader as\n              the last item of the `reader` parameter.\n\n        \"\"\"\n\n        self.auto_select = reader is None\n        self.image_only = image_only\n        self.dtype = dtype\n\n        self.readers: List[ImageReader] = []\n        for r in SUPPORTED_READERS:  # set predefined readers as default\n            try:\n                self.register(SUPPORTED_READERS[r](*args, **kwargs))\n            except OptionalImportError:\n                logging.getLogger(self.__class__.__name__).debug(\n                    f\"required package for reader {r} is not installed, or the version doesn't match requirement.\"\n                )\n            except TypeError:  # the reader doesn't have the corresponding args/kwargs\n                logging.getLogger(self.__class__.__name__).debug(\n                    f\"{r} is not supported with the given parameters {args} {kwargs}.\"\n                )\n                self.register(SUPPORTED_READERS[r]())\n        if reader is None:\n            return  # no user-specified reader, no need to register\n\n        for _r in ensure_tuple(reader):\n            if isinstance(_r, str):\n                the_reader = look_up_option(_r.lower(), SUPPORTED_READERS)\n                try:\n                    self.register(the_reader(*args, **kwargs))\n                except OptionalImportError:\n                    warnings.warn(\n                        f\"required package for reader {r} is not installed, or the version doesn't match requirement.\"\n                    )\n                except TypeError:  # the reader doesn't have the corresponding args/kwargs\n                    warnings.warn(f\"{r} is not supported with the given parameters {args} {kwargs}.\")\n                    self.register(the_reader())\n            elif inspect.isclass(_r):\n                self.register(_r(*args, **kwargs))\n            else:\n                self.register(_r)  # reader instance, ignoring the constructor args/kwargs\n        return\n\n    def register(self, reader: ImageReader):\n        \"\"\"\n        Register image reader to load image file and meta data.\n\n        Args:\n            reader: reader instance to be registered with this loader.\n\n        \"\"\"\n        if not isinstance(reader, ImageReader):\n            warnings.warn(f\"Preferably the reader should inherit ImageReader, but got {type(reader)}.\")\n        self.readers.append(reader)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/io/array.py_SaveImage.__init___": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/io/array.py_SaveImage.__init___", "embedding": null, "metadata": {"file_path": "monai/transforms/io/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 293, "end_line": 350, "span_ids": ["SaveImage.__init__", "SaveImage.__call__"], "tokens": 460}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SaveImage(Transform):\n\n    def __init__(\n        self,\n        output_dir: PathLike = \"./\",\n        output_postfix: str = \"trans\",\n        output_ext: str = \".nii.gz\",\n        resample: bool = True,\n        mode: Union[GridSampleMode, InterpolateMode, str] = \"nearest\",\n        padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER,\n        scale: Optional[int] = None,\n        dtype: DtypeLike = np.float64,\n        output_dtype: DtypeLike = np.float32,\n        squeeze_end_dims: bool = True,\n        data_root_dir: PathLike = \"\",\n        separate_folder: bool = True,\n        print_log: bool = True,\n    ) -> None:\n        self.saver: Union[NiftiSaver, PNGSaver]\n        if output_ext in {\".nii.gz\", \".nii\"}:\n            self.saver = NiftiSaver(\n                output_dir=output_dir,\n                output_postfix=output_postfix,\n                output_ext=output_ext,\n                resample=resample,\n                mode=GridSampleMode(mode),\n                padding_mode=padding_mode,\n                dtype=dtype,\n                output_dtype=output_dtype,\n                squeeze_end_dims=squeeze_end_dims,\n                data_root_dir=data_root_dir,\n                separate_folder=separate_folder,\n                print_log=print_log,\n            )\n        elif output_ext == \".png\":\n            self.saver = PNGSaver(\n                output_dir=output_dir,\n                output_postfix=output_postfix,\n                output_ext=output_ext,\n                resample=resample,\n                mode=InterpolateMode(mode),\n                scale=scale,\n                data_root_dir=data_root_dir,\n                separate_folder=separate_folder,\n                print_log=print_log,\n            )\n        else:\n            raise ValueError(f\"unsupported output extension: {output_ext}.\")\n\n    def __call__(self, img: Union[torch.Tensor, np.ndarray], meta_data: Optional[Dict] = None):\n        \"\"\"\n        Args:\n            img: target data content that save into file.\n            meta_data: key-value pairs of meta_data corresponding to the data.\n\n        \"\"\"\n        self.saver.save(img, meta_data)\n\n        return img", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/io/dictionary.py_from_pathlib_import_Path___all__._LoadImaged_LoadImage": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/io/dictionary.py_from_pathlib_import_Path___all__._LoadImaged_LoadImage", "embedding": null, "metadata": {"file_path": "monai/transforms/io/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 18, "end_line": 29, "span_ids": ["docstring"], "tokens": 119}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from pathlib import Path\nfrom typing import Optional, Union\n\nimport numpy as np\n\nfrom monai.config import DtypeLike, KeysCollection\nfrom monai.data.image_reader import ImageReader\nfrom monai.transforms.io.array import LoadImage, SaveImage\nfrom monai.transforms.transform import MapTransform\nfrom monai.utils import GridSampleMode, GridSamplePadMode, InterpolateMode, ensure_tuple, ensure_tuple_rep\n\n__all__ = [\"LoadImaged\", \"LoadImageD\", \"LoadImageDict\", \"SaveImaged\", \"SaveImageD\", \"SaveImageDict\"]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/nvtx.py_from_monai_transforms_tra_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/nvtx.py_from_monai_transforms_tra_", "embedding": null, "metadata": {"file_path": "monai/transforms/nvtx.py", "file_name": "nvtx.py", "file_type": "text/x-python", "category": "implementation", "start_line": 15, "end_line": 128, "span_ids": ["Mark.__call__", "Mark", "impl:4", "Mark.__init__", "RandRangePop", "RangePop", "RangePush", "docstring", "RangePop.__call__", "RandMark", "RangePush.__call__", "RandRangePush", "RangePush.__init__"], "tokens": 676}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from monai.transforms.transform import RandomizableTransform, Transform\nfrom monai.utils import optional_import\n\n_nvtx, _ = optional_import(\"torch._C._nvtx\", descriptor=\"NVTX is not installed. Are you sure you have a CUDA build?\")\n\n__all__ = [\n    \"Mark\",\n    \"Markd\",\n    \"MarkD\",\n    \"MarkDict\",\n    \"RandMark\",\n    \"RandMarkd\",\n    \"RandMarkD\",\n    \"RandMarkDict\",\n    \"RandRangePop\",\n    \"RandRangePopd\",\n    \"RandRangePopD\",\n    \"RandRangePopDict\",\n    \"RandRangePush\",\n    \"RandRangePushd\",\n    \"RandRangePushD\",\n    \"RandRangePushDict\",\n    \"RangePop\",\n    \"RangePopd\",\n    \"RangePopD\",\n    \"RangePopDict\",\n    \"RangePush\",\n    \"RangePushd\",\n    \"RangePushD\",\n    \"RangePushDict\",\n]\n\n\nclass RangePush(Transform):\n    \"\"\"\n    Pushes a range onto a stack of nested range span.\n    Stores zero-based depth of the range that is started.\n\n    Args:\n        msg: ASCII message to associate with range\n    \"\"\"\n\n    def __init__(self, msg: str) -> None:\n        self.msg = msg\n        self.depth = None\n\n    def __call__(self, data):\n        self.depth = _nvtx.rangePushA(self.msg)\n        return data\n\n\nclass RandRangePush(RangePush, RandomizableTransform):\n    \"\"\"\n    Pushes a range onto a stack of nested range span (RandomizableTransform).\n    Stores zero-based depth of the range that is started.\n\n    Args:\n        msg: ASCII message to associate with range\n    \"\"\"\n\n\nclass RangePop(Transform):\n    \"\"\"\n    Pops a range off of a stack of nested range spans.\n    Stores zero-based depth of the range that is ended.\n    \"\"\"\n\n    def __call__(self, data):\n        _nvtx.rangePop()\n        return data\n\n\nclass RandRangePop(RangePop, RandomizableTransform):\n    \"\"\"\n    Pops a range off of a stack of nested range spans (RandomizableTransform).\n    Stores zero-based depth of the range that is ended.\n    \"\"\"\n\n\nclass Mark(Transform):\n    \"\"\"\n    Mark an instantaneous event that occurred at some point.\n\n    Args:\n        msg: ASCII message to associate with the event.\n    \"\"\"\n\n    def __init__(self, msg: str) -> None:\n        self.msg = msg\n\n    def __call__(self, data):\n        _nvtx.markA(self.msg)\n        return data\n\n\nclass RandMark(Mark, RandomizableTransform):\n    \"\"\"\n    Mark an instantaneous event that occurred at some point.\n    (RandomizableTransform)\n\n    Args:\n        msg: ASCII message to associate with the event.\n    \"\"\"\n\n\nRangePushDict = RangePushD = RangePushd = RangePush\nRandRangePushDict = RandRangePushD = RandRangePushd = RandRangePush\n\nRangePopDict = RangePopD = RangePopd = RangePop\nRandRangePopDict = RandRangePopD = RandRangePopd = RandRangePop\n\nMarkDict = MarkD = Markd = Mark\nRandMarkDict = RandMarkD = RandMarkd = RandMark", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/array.py_LabelFilter_LabelFilter.__init__.self.applied_labels.ensure_tuple_applied_labe": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/array.py_LabelFilter_LabelFilter.__init__.self.applied_labels.ensure_tuple_applied_labe", "embedding": null, "metadata": {"file_path": "monai/transforms/post/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 326, "end_line": 353, "span_ids": ["LabelFilter", "LabelFilter.__init__"], "tokens": 229}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LabelFilter:\n    \"\"\"\n    This transform filters out labels and can be used as a processing step to view only certain labels.\n\n    The list of applied labels defines which labels will be kept.\n\n    Note:\n        All labels which do not match the `applied_labels` are set to the background label (0).\n\n    For example:\n\n    Use LabelFilter with applied_labels=[1, 5, 9]::\n\n        [1, 2, 3]         [1, 0, 0]\n        [4, 5, 6]    =>   [0, 5 ,0]\n        [7, 8, 9]         [0, 0, 9]\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __init__(self, applied_labels: Union[Iterable[int], int]) -> None:\n        \"\"\"\n        Initialize the LabelFilter class with the labels to filter on.\n\n        Args:\n            applied_labels: Label(s) to filter on.\n        \"\"\"\n        self.applied_labels = ensure_tuple(applied_labels)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/array.py_FillHoles_FillHoles.__init__.self.connectivity.connectivity": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/array.py_FillHoles_FillHoles.__init__.self.connectivity.connectivity", "embedding": null, "metadata": {"file_path": "monai/transforms/post/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 379, "end_line": 434, "span_ids": ["FillHoles", "FillHoles.__init__"], "tokens": 689}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class FillHoles(Transform):\n    r\"\"\"\n    This transform fills holes in the image and can be used to remove artifacts inside segments.\n\n    An enclosed hole is defined as a background pixel/voxel which is only enclosed by a single class.\n    The definition of enclosed can be defined with the connectivity parameter::\n\n        1-connectivity     2-connectivity     diagonal connection close-up\n\n             [ ]           [ ]  [ ]  [ ]             [ ]\n              |               \\  |  /                 |  <- hop 2\n        [ ]--[x]--[ ]      [ ]--[x]--[ ]        [x]--[ ]\n              |               /  |  \\             hop 1\n             [ ]           [ ]  [ ]  [ ]\n\n    It is possible to define for which labels the hole filling should be applied.\n    The input image is assumed to be a PyTorch Tensor or numpy array with shape [C, spatial_dim1[, spatial_dim2, ...]].\n    If C = 1, then the values correspond to expected labels.\n    If C > 1, then a one-hot-encoding is expected where the index of C matches the label indexing.\n\n    Note:\n\n        The label 0 will be treated as background and the enclosed holes will be set to the neighboring class label.\n\n        The performance of this method heavily depends on the number of labels.\n        It is a bit faster if the list of `applied_labels` is provided.\n        Limiting the number of `applied_labels` results in a big decrease in processing time.\n\n    For example:\n\n        Use FillHoles with default parameters::\n\n            [1, 1, 1, 2, 2, 2, 3, 3]         [1, 1, 1, 2, 2, 2, 3, 3]\n            [1, 0, 1, 2, 0, 0, 3, 0]    =>   [1, 1 ,1, 2, 0, 0, 3, 0]\n            [1, 1, 1, 2, 2, 2, 3, 3]         [1, 1, 1, 2, 2, 2, 3, 3]\n\n        The hole in label 1 is fully enclosed and therefore filled with label 1.\n        The background label near label 2 and 3 is not fully enclosed and therefore not filled.\n    \"\"\"\n\n    backend = [TransformBackends.NUMPY]\n\n    def __init__(\n        self, applied_labels: Optional[Union[Iterable[int], int]] = None, connectivity: Optional[int] = None\n    ) -> None:\n        \"\"\"\n        Initialize the connectivity and limit the labels for which holes are filled.\n\n        Args:\n            applied_labels: Labels for which to fill holes. Defaults to None, that is filling holes for all labels.\n            connectivity: Maximum number of orthogonal hops to consider a pixel/voxel as a neighbor.\n                Accepted values are ranging from  1 to input.ndim. Defaults to a full connectivity of ``input.ndim``.\n        \"\"\"\n        super().__init__()\n        self.applied_labels = ensure_tuple(applied_labels) if applied_labels else None\n        self.connectivity = connectivity", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/array.py_FillHoles.__call___FillHoles.__call__.return.out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/array.py_FillHoles.__call___FillHoles.__call__.return.out", "embedding": null, "metadata": {"file_path": "monai/transforms/post/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 436, "end_line": 458, "span_ids": ["FillHoles.__call__"], "tokens": 226}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class FillHoles(Transform):\n\n    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:\n        \"\"\"\n        Fill the holes in the provided image.\n\n        Note:\n            The value 0 is assumed as background label.\n\n        Args:\n            img: Pytorch Tensor or numpy array of shape [C, spatial_dim1[, spatial_dim2, ...]].\n\n        Raises:\n            NotImplementedError: The provided image was not a Pytorch Tensor or numpy array.\n\n        Returns:\n            Pytorch Tensor or numpy array of shape [C, spatial_dim1[, spatial_dim2, ...]].\n        \"\"\"\n        if not isinstance(img, (np.ndarray, torch.Tensor)):\n            raise NotImplementedError(f\"{self.__class__} can not handle data of type {type(img)}.\")\n        img_np: np.ndarray\n        img_np, *_ = convert_data_type(img, np.ndarray)  # type: ignore\n        out_np: np.ndarray = fill_holes(img_np, self.applied_labels, self.connectivity)\n        out, *_ = convert_to_dst_type(out_np, img)\n        return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/array.py_Ensemble_Ensemble.post_convert.return.out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/array.py_Ensemble_Ensemble.post_convert.return.out", "embedding": null, "metadata": {"file_path": "monai/transforms/post/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 516, "end_line": 531, "span_ids": ["Ensemble.post_convert", "Ensemble.get_stacked_torch", "Ensemble"], "tokens": 203}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Ensemble:\n    @staticmethod\n    def get_stacked_torch(img: Union[Sequence[NdarrayOrTensor], NdarrayOrTensor]) -> torch.Tensor:\n        \"\"\"Get either a sequence or single instance of np.ndarray/torch.Tensor. Return single torch.Tensor.\"\"\"\n        if isinstance(img, Sequence) and isinstance(img[0], np.ndarray):\n            img = [torch.as_tensor(i) for i in img]\n        elif isinstance(img, np.ndarray):\n            img = torch.as_tensor(img)\n        out: torch.Tensor = torch.stack(img) if isinstance(img, Sequence) else img  # type: ignore\n        return out\n\n    @staticmethod\n    def post_convert(img: torch.Tensor, orig_img: Union[Sequence[NdarrayOrTensor], NdarrayOrTensor]) -> NdarrayOrTensor:\n        orig_img_ = orig_img[0] if isinstance(orig_img, Sequence) else orig_img\n        out, *_ = convert_to_dst_type(img, orig_img_)\n        return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/array.py_MeanEnsemble.__call___MeanEnsemble.__call__.return.self_post_convert_out_pt_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/array.py_MeanEnsemble.__call___MeanEnsemble.__call__.return.self_post_convert_out_pt_", "embedding": null, "metadata": {"file_path": "monai/transforms/post/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 562, "end_line": 574, "span_ids": ["MeanEnsemble.__call__"], "tokens": 150}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class MeanEnsemble(Ensemble, Transform):\n\n    def __call__(self, img: Union[Sequence[NdarrayOrTensor], NdarrayOrTensor]) -> NdarrayOrTensor:\n        img_ = self.get_stacked_torch(img)\n        if self.weights is not None:\n            self.weights = self.weights.to(img_.device)\n            shape = tuple(self.weights.shape)\n            for _ in range(img_.ndimension() - self.weights.ndimension()):\n                shape += (1,)\n            weights = self.weights.reshape(*shape)\n\n            img_ = img_ * weights / weights.mean(dim=0, keepdim=True)\n\n        out_pt = torch.mean(img_, dim=0)\n        return self.post_convert(out_pt, img)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/array.py_VoteEnsemble.__call___VoteEnsemble.__call__.return.self_post_convert_out_pt_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/array.py_VoteEnsemble.__call___VoteEnsemble.__call__.return.self_post_convert_out_pt_", "embedding": null, "metadata": {"file_path": "monai/transforms/post/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 602, "end_line": 623, "span_ids": ["VoteEnsemble.__call__"], "tokens": 259}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class VoteEnsemble(Ensemble, Transform):\n\n    def __call__(self, img: Union[Sequence[NdarrayOrTensor], NdarrayOrTensor]) -> NdarrayOrTensor:\n        img_ = self.get_stacked_torch(img)\n\n        if self.num_classes is not None:\n            has_ch_dim = True\n            if img_.ndimension() > 1 and img_.shape[1] > 1:\n                warnings.warn(\"no need to specify num_classes for One-Hot format data.\")\n            else:\n                if img_.ndimension() == 1:\n                    # if no channel dim, need to remove channel dim after voting\n                    has_ch_dim = False\n                img_ = one_hot(img_, self.num_classes, dim=1)\n\n        img_ = torch.mean(img_.float(), dim=0)\n\n        if self.num_classes is not None:\n            # if not One-Hot, use \"argmax\" to vote the most common class\n            out_pt = torch.argmax(img_, dim=0, keepdim=has_ch_dim)\n        else:\n            # for One-Hot data, round the float number to 0 or 1\n            out_pt = torch.round(img_)\n        return self.post_convert(out_pt, img)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/dictionary.py_LabelFilterd_LabelFilterd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/dictionary.py_LabelFilterd_LabelFilterd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/post/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 232, "end_line": 257, "span_ids": ["LabelFilterd", "LabelFilterd.__call__", "LabelFilterd.__init__"], "tokens": 207}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LabelFilterd(MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.LabelFilter`.\n    \"\"\"\n\n    backend = LabelFilter.backend\n\n    def __init__(\n        self, keys: KeysCollection, applied_labels: Union[Sequence[int], int], allow_missing_keys: bool = False\n    ) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to be transformed.\n                See also: :py:class:`monai.transforms.compose.MapTransform`\n            applied_labels: Label(s) to filter on.\n            allow_missing_keys: don't raise exception if key is missing.\n\n        \"\"\"\n        super().__init__(keys, allow_missing_keys)\n        self.converter = LabelFilter(applied_labels)\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key in self.key_iterator(d):\n            d[key] = self.converter(d[key])\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/dictionary.py_FillHolesd_FillHolesd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/dictionary.py_FillHolesd_FillHolesd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/post/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 256, "end_line": 290, "span_ids": ["FillHolesd", "FillHolesd.__call__", "FillHolesd.__init__"], "tokens": 324}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class FillHolesd(MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.FillHoles`.\n    \"\"\"\n\n    backend = FillHoles.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        applied_labels: Optional[Union[Iterable[int], int]] = None,\n        connectivity: Optional[int] = None,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        \"\"\"\n        Initialize the connectivity and limit the labels for which holes are filled.\n\n        Args:\n            keys: keys of the corresponding items to be transformed.\n                See also: :py:class:`monai.transforms.compose.MapTransform`\n            applied_labels (Optional[Union[Iterable[int], int]], optional): Labels for which to fill holes. Defaults to None,\n                that is filling holes for all labels.\n            connectivity (int, optional): Maximum number of orthogonal hops to consider a pixel/voxel as a neighbor.\n                Accepted values are ranging from  1 to input.ndim. Defaults to a full\n                connectivity of ``input.ndim``.\n            allow_missing_keys: don't raise exception if key is missing.\n        \"\"\"\n        super().__init__(keys, allow_missing_keys)\n        self.converter = FillHoles(applied_labels=applied_labels, connectivity=connectivity)\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key in self.key_iterator(d):\n            d[key] = self.converter(d[key])\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/dictionary.py_SaveClassificationd_SaveClassificationd.__init__.self.meta_key_postfix.ensure_tuple_rep_meta_key": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/dictionary.py_SaveClassificationd_SaveClassificationd.__init__.self.meta_key_postfix.ensure_tuple_rep_meta_key", "embedding": null, "metadata": {"file_path": "monai/transforms/post/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 656, "end_line": 707, "span_ids": ["SaveClassificationd.__init__", "SaveClassificationd"], "tokens": 662}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SaveClassificationd(MapTransform):\n    \"\"\"\n    Save the classification results and meta data into CSV file or other storage.\n\n    \"\"\"\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        meta_keys: Optional[KeysCollection] = None,\n        meta_key_postfix: str = \"meta_dict\",\n        saver: Optional[CSVSaver] = None,\n        output_dir: PathLike = \"./\",\n        filename: str = \"predictions.csv\",\n        overwrite: bool = True,\n        flush: bool = True,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to model output, this transform only supports 1 key.\n                See also: :py:class:`monai.transforms.compose.MapTransform`\n            meta_keys: explicitly indicate the key of the corresponding meta data dictionary.\n                for example, for data with key `image`, the metadata by default is in `image_meta_dict`.\n                the meta data is a dictionary object which contains: filename, original_shape, etc.\n                it can be a sequence of string, map to the `keys`.\n                if None, will try to construct meta_keys by `key_{meta_key_postfix}`.\n                will extract the filename of input image to save classification results.\n            meta_key_postfix: `key_{postfix}` was used to store the metadata in `LoadImaged`.\n                so need the key to extract the metadata of input image, like filename, etc. default is `meta_dict`.\n                for example, for data with key `image`, the metadata by default is in `image_meta_dict`.\n                the meta data is a dictionary object which contains: filename, original_shape, etc.\n                this arg only works when `meta_keys=None`. if no corresponding metadata, set to `None`.\n            saver: the saver instance to save classification results, if None, create a CSVSaver internally.\n                the saver must provide `save(data, meta_data)` and `finalize()` APIs.\n            output_dir: if `saver=None`, specify the directory to save the CSV file.\n            filename: if `saver=None`, specify the name of the saved CSV file.\n            overwrite: if `saver=None`, indicate whether to overwriting existing CSV file content, if True,\n                will clear the file before saving. otherwise, will append new content to the CSV file.\n            flush: if `saver=None`, indicate whether to write the cache data to CSV file immediately\n                in this transform and clear the cache. default to True.\n                If False, may need user to call `saver.finalize()` manually or use `ClassificationSaver` handler.\n            allow_missing_keys: don't raise exception if key is missing.\n\n        \"\"\"\n        super().__init__(keys, allow_missing_keys)\n        if len(self.keys) != 1:\n            raise ValueError(\"only 1 key is allowed when saving the classification result.\")\n        self.saver = saver or CSVSaver(output_dir, filename, overwrite, flush)\n        self.flush = flush\n        self.meta_keys = ensure_tuple_rep(meta_keys, len(self.keys))\n        self.meta_key_postfix = ensure_tuple_rep(meta_key_postfix, len(self.keys))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/dictionary.py_SaveClassificationd.__call___SaveClassificationd.get_saver.return.self_saver": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/dictionary.py_SaveClassificationd.__call___SaveClassificationd.get_saver.return.self_saver", "embedding": null, "metadata": {"file_path": "monai/transforms/post/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 676, "end_line": 694, "span_ids": ["SaveClassificationd.__call__", "SaveClassificationd.get_saver"], "tokens": 184}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SaveClassificationd(MapTransform):\n\n    def __call__(self, data):\n        d = dict(data)\n        for key, meta_key, meta_key_postfix in self.key_iterator(d, self.meta_keys, self.meta_key_postfix):\n            if meta_key is None and meta_key_postfix is not None:\n                meta_key = f\"{key}_{meta_key_postfix}\"\n            meta_data = d[meta_key] if meta_key is not None else None\n            self.saver.save(data=d[key], meta_data=meta_data)\n            if self.flush:\n                self.saver.finalize()\n\n        return d\n\n    def get_saver(self):\n        \"\"\"\n        If want to write content into file, may need to call `finalize` of saver when epoch completed.\n        Or users can also get the cache content from `saver` instead of writing into file.\n\n        \"\"\"\n        return self.saver", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_Spacing_Spacing.__init__.self.image_only.image_only": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_Spacing_Spacing.__init__.self.image_only.image_only", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 86, "end_line": 143, "span_ids": ["Spacing.__init__", "Spacing"], "tokens": 689}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Spacing(Transform):\n    \"\"\"\n    Resample input image into the specified `pixdim`.\n    \"\"\"\n\n    backend = [TransformBackends.TORCH]\n\n    def __init__(\n        self,\n        pixdim: Union[Sequence[float], float],\n        diagonal: bool = False,\n        mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,\n        padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER,\n        align_corners: bool = False,\n        dtype: DtypeLike = np.float64,\n        image_only: bool = False,\n    ) -> None:\n        \"\"\"\n        Args:\n            pixdim: output voxel spacing. if providing a single number, will use it for the first dimension.\n                items of the pixdim sequence map to the spatial dimensions of input image, if length\n                of pixdim sequence is longer than image spatial dimensions, will ignore the longer part,\n                if shorter, will pad with `1.0`.\n                if the components of the `pixdim` are non-positive values, the transform will use the\n                corresponding components of the original pixdim, which is computed from the `affine`\n                matrix of input image.\n            diagonal: whether to resample the input to have a diagonal affine matrix.\n                If True, the input data is resampled to the following affine::\n\n                    np.diag((pixdim_0, pixdim_1, ..., pixdim_n, 1))\n\n                This effectively resets the volume to the world coordinate system (RAS+ in nibabel).\n                The original orientation, rotation, shearing are not preserved.\n\n                If False, this transform preserves the axes orientation, orthogonal rotation and\n                translation components from the original affine. This option will not flip/swap axes\n                of the original data.\n            mode: {``\"bilinear\"``, ``\"nearest\"``}\n                Interpolation mode to calculate output values. Defaults to ``\"bilinear\"``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            padding_mode: {``\"zeros\"``, ``\"border\"``, ``\"reflection\"``}\n                Padding mode for outside grid values. Defaults to ``\"border\"``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            align_corners: Geometrically, we consider the pixels of the input as squares rather than points.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            dtype: data type for resampling computation. Defaults to ``np.float64`` for best precision.\n                If None, use the data type of input data. To be compatible with other modules,\n                the output data type is always ``np.float32``.\n            image_only: return just the image or the image, the old affine and new affine. Default is `False`.\n\n        \"\"\"\n        self.pixdim = np.array(ensure_tuple(pixdim), dtype=np.float64)\n        self.diagonal = diagonal\n        self.mode: GridSampleMode = look_up_option(mode, GridSampleMode)\n        self.padding_mode: GridSamplePadMode = look_up_option(padding_mode, GridSamplePadMode)\n        self.align_corners = align_corners\n        self.dtype = dtype\n        self.image_only = image_only", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_Spacing.__call___Spacing.__call__._no_resampling_if_it_s_i": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_Spacing.__call___Spacing.__call__._no_resampling_if_it_s_i", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 145, "end_line": 206, "span_ids": ["Spacing.__call__"], "tokens": 764}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Spacing(Transform):\n\n    def __call__(\n        self,\n        data_array: NdarrayOrTensor,\n        affine: Optional[NdarrayOrTensor] = None,\n        mode: Optional[Union[GridSampleMode, str]] = None,\n        padding_mode: Optional[Union[GridSamplePadMode, str]] = None,\n        align_corners: Optional[bool] = None,\n        dtype: DtypeLike = None,\n        output_spatial_shape: Optional[np.ndarray] = None,\n    ) -> Union[NdarrayOrTensor, Tuple[NdarrayOrTensor, NdarrayOrTensor, NdarrayOrTensor]]:\n        \"\"\"\n        Args:\n            data_array: in shape (num_channels, H[, W, ...]).\n            affine (matrix): (N+1)x(N+1) original affine matrix for spatially ND `data_array`. Defaults to identity.\n            mode: {``\"bilinear\"``, ``\"nearest\"``}\n                Interpolation mode to calculate output values. Defaults to ``self.mode``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            padding_mode: {``\"zeros\"``, ``\"border\"``, ``\"reflection\"``}\n                Padding mode for outside grid values. Defaults to ``self.padding_mode``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            align_corners: Geometrically, we consider the pixels of the input as squares rather than points.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            dtype: data type for resampling computation. Defaults to ``self.dtype``.\n                If None, use the data type of input data. To be compatible with other modules,\n                the output data type is always ``np.float32``.\n            output_spatial_shape: specify the shape of the output data_array. This is typically useful for\n                the inverse of `Spacingd` where sometimes we could not compute the exact shape due to the quantization\n                error with the affine.\n\n        Raises:\n            ValueError: When ``data_array`` has no spatial dimensions.\n            ValueError: When ``pixdim`` is nonpositive.\n\n        Returns:\n            data_array (resampled into `self.pixdim`), original affine, current affine.\n\n        \"\"\"\n        _dtype = dtype or self.dtype or data_array.dtype\n        sr = int(data_array.ndim - 1)\n        if sr <= 0:\n            raise ValueError(\"data_array must have at least one spatial dimension.\")\n        if affine is None:\n            # default to identity\n            affine_np = affine = np.eye(sr + 1, dtype=np.float64)\n            affine_ = np.eye(sr + 1, dtype=np.float64)\n        else:\n            affine_np, *_ = convert_data_type(affine, np.ndarray)  # type: ignore\n            affine_ = to_affine_nd(sr, affine_np)\n\n        out_d = self.pixdim[:sr]\n        if out_d.size < sr:\n            out_d = np.append(out_d, [1.0] * (sr - out_d.size))\n\n        # compute output affine, shape and offset\n        new_affine = zoom_affine(affine_, out_d, diagonal=self.diagonal)\n        output_shape, offset = compute_shape_offset(data_array.shape[1:], affine_, new_affine)\n        new_affine[:sr, -1] = offset[:sr]\n        transform = np.linalg.inv(affine_) @ new_affine\n        # adapt to the actual rank\n        transform = to_affine_nd(sr, transform)\n\n        # no resampling if it's identity transform\n        # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_Spacing.__call__.if_np_allclose_transform__Spacing.__call__.return.output_data_affine_new_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_Spacing.__call__.if_np_allclose_transform__Spacing.__call__.return.output_data_affine_new_", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 207, "end_line": 233, "span_ids": ["Spacing.__call__"], "tokens": 434}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Spacing(Transform):\n\n    def __call__(\n        self,\n        data_array: NdarrayOrTensor,\n        affine: Optional[NdarrayOrTensor] = None,\n        mode: Optional[Union[GridSampleMode, str]] = None,\n        padding_mode: Optional[Union[GridSamplePadMode, str]] = None,\n        align_corners: Optional[bool] = None,\n        dtype: DtypeLike = None,\n        output_spatial_shape: Optional[np.ndarray] = None,\n    ) -> Union[NdarrayOrTensor, Tuple[NdarrayOrTensor, NdarrayOrTensor, NdarrayOrTensor]]:\n        # ... other code\n        if np.allclose(transform, np.diag(np.ones(len(transform))), atol=1e-3):\n            output_data = data_array\n        else:\n            # resample\n            affine_xform = AffineTransform(\n                normalized=False,\n                mode=look_up_option(mode or self.mode, GridSampleMode),\n                padding_mode=look_up_option(padding_mode or self.padding_mode, GridSamplePadMode),\n                align_corners=self.align_corners if align_corners is None else align_corners,\n                reverse_indexing=True,\n            )\n            data_array_t: torch.Tensor\n            data_array_t, *_ = convert_data_type(data_array, torch.Tensor, dtype=_dtype)  # type: ignore\n            output_data = affine_xform(\n                # AffineTransform requires a batch dim\n                data_array_t.unsqueeze(0),\n                convert_data_type(transform, torch.Tensor, data_array_t.device, dtype=_dtype)[0],\n                spatial_size=output_shape if output_spatial_shape is None else output_spatial_shape,\n            ).squeeze(0)\n\n        output_data, *_ = convert_to_dst_type(output_data, data_array, dtype=torch.float32)\n        new_affine = to_affine_nd(affine_np, new_affine)  # type: ignore\n        new_affine, *_ = convert_to_dst_type(src=new_affine, dst=affine, dtype=torch.float32)\n\n        if self.image_only:\n            return output_data\n        return output_data, affine, new_affine", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_Orientation_Orientation.__init__.self.image_only.image_only": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_Orientation_Orientation.__init__.self.image_only.image_only", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 236, "end_line": 276, "span_ids": ["Orientation.__init__", "Orientation"], "tokens": 440}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Orientation(Transform):\n    \"\"\"\n    Change the input image's orientation into the specified based on `axcodes`.\n    \"\"\"\n\n    backend = [TransformBackends.NUMPY]\n\n    def __init__(\n        self,\n        axcodes: Optional[str] = None,\n        as_closest_canonical: bool = False,\n        labels: Optional[Sequence[Tuple[str, str]]] = tuple(zip(\"LPI\", \"RAS\")),\n        image_only: bool = False,\n    ) -> None:\n        \"\"\"\n        Args:\n            axcodes: N elements sequence for spatial ND input's orientation.\n                e.g. axcodes='RAS' represents 3D orientation:\n                (Left, Right), (Posterior, Anterior), (Inferior, Superior).\n                default orientation labels options are: 'L' and 'R' for the first dimension,\n                'P' and 'A' for the second, 'I' and 'S' for the third.\n            as_closest_canonical: if True, load the image as closest to canonical axis format.\n            labels: optional, None or sequence of (2,) sequences\n                (2,) sequences are labels for (beginning, end) of output axis.\n                Defaults to ``(('L', 'R'), ('P', 'A'), ('I', 'S'))``.\n            image_only: if True return only the image volume, otherwise return (image, affine, new_affine).\n\n        Raises:\n            ValueError: When ``axcodes=None`` and ``as_closest_canonical=True``. Incompatible values.\n\n        See Also: `nibabel.orientations.ornt2axcodes`.\n\n        \"\"\"\n        if axcodes is None and not as_closest_canonical:\n            raise ValueError(\"Incompatible values: axcodes=None and as_closest_canonical=True.\")\n        if axcodes is not None and as_closest_canonical:\n            warnings.warn(\"using as_closest_canonical=True, axcodes ignored.\")\n        self.axcodes = axcodes\n        self.as_closest_canonical = as_closest_canonical\n        self.labels = labels\n        self.image_only = image_only", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_Orientation.__call___Orientation.__call__.return.out_affine_new_affine": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_Orientation.__call___Orientation.__call__.return.out_affine_new_affine", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 278, "end_line": 333, "span_ids": ["Orientation.__call__"], "tokens": 646}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Orientation(Transform):\n\n    def __call__(\n        self, data_array: NdarrayOrTensor, affine: Optional[NdarrayOrTensor] = None\n    ) -> Union[NdarrayOrTensor, Tuple[NdarrayOrTensor, NdarrayOrTensor, NdarrayOrTensor]]:\n        \"\"\"\n        original orientation of `data_array` is defined by `affine`.\n\n        Args:\n            data_array: in shape (num_channels, H[, W, ...]).\n            affine (matrix): (N+1)x(N+1) original affine matrix for spatially ND `data_array`. Defaults to identity.\n\n        Raises:\n            ValueError: When ``data_array`` has no spatial dimensions.\n            ValueError: When ``axcodes`` spatiality differs from ``data_array``.\n\n        Returns:\n            data_array [reoriented in `self.axcodes`] if `self.image_only`, else\n            (data_array [reoriented in `self.axcodes`], original axcodes, current axcodes).\n\n        \"\"\"\n        data_array_np, *_ = convert_data_type(data_array, np.ndarray)  # type: ignore\n        sr = data_array_np.ndim - 1\n        if sr <= 0:\n            raise ValueError(\"data_array must have at least one spatial dimension.\")\n        if affine is None:\n            # default to identity\n            affine_np = affine = np.eye(sr + 1, dtype=np.float64)\n            affine_ = np.eye(sr + 1, dtype=np.float64)\n        else:\n            affine_np, *_ = convert_data_type(affine, np.ndarray)  # type: ignore\n            affine_ = to_affine_nd(sr, affine_np)\n\n        src = nib.io_orientation(affine_)\n        if self.as_closest_canonical:\n            spatial_ornt = src\n        else:\n            if self.axcodes is None:\n                raise AssertionError\n            dst = nib.orientations.axcodes2ornt(self.axcodes[:sr], labels=self.labels)\n            if len(dst) < sr:\n                raise ValueError(\n                    f\"axcodes must match data_array spatially, got axcodes={len(self.axcodes)}D data_array={sr}D\"\n                )\n            spatial_ornt = nib.orientations.ornt_transform(src, dst)\n        ornt = spatial_ornt.copy()\n        ornt[:, 0] += 1  # skip channel dim\n        ornt = np.concatenate([np.array([[0, 1]]), ornt])\n        shape = data_array_np.shape[1:]\n        data_array_np = np.ascontiguousarray(nib.orientations.apply_orientation(data_array_np, ornt))\n        new_affine = affine_ @ nib.orientations.inv_ornt_aff(spatial_ornt, shape)\n        new_affine = to_affine_nd(affine_np, new_affine)\n        out, *_ = convert_to_dst_type(src=data_array_np, dst=data_array)\n        new_affine, *_ = convert_to_dst_type(src=new_affine, dst=affine, dtype=torch.float32)\n\n        if self.image_only:\n            return out\n        return out, affine, new_affine", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_Flip_Flip.__call__.return.torch_flip_img_map_spati": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_Flip_Flip.__call__.return.torch_flip_img_map_spati", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 336, "end_line": 363, "span_ids": ["Flip.__init__", "Flip", "Flip.__call__"], "tokens": 282}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Flip(Transform):\n    \"\"\"\n    Reverses the order of elements along the given spatial axis. Preserves shape.\n    Uses ``np.flip`` in practice. See numpy.flip for additional details:\n    https://docs.scipy.org/doc/numpy/reference/generated/numpy.flip.html.\n\n    Args:\n        spatial_axis: spatial axes along which to flip over. Default is None.\n            The default `axis=None` will flip over all of the axes of the input array.\n            If axis is negative it counts from the last to the first axis.\n            If axis is a tuple of ints, flipping is performed on all of the axes\n            specified in the tuple.\n\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __init__(self, spatial_axis: Optional[Union[Sequence[int], int]] = None) -> None:\n        self.spatial_axis = spatial_axis\n\n    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:\n        \"\"\"\n        Args:\n            img: channel first array, must have shape: (num_channels, H[, W, ..., ]),\n        \"\"\"\n        if isinstance(img, np.ndarray):\n            return np.ascontiguousarray(np.flip(img, map_spatial_axes(img.ndim, self.spatial_axis)))\n        return torch.flip(img, map_spatial_axes(img.ndim, self.spatial_axis))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_Resize.__call___Resize.__call__.return.out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_Resize.__call___Resize.__call__.return.out", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 403, "end_line": 449, "span_ids": ["Resize.__call__"], "tokens": 584}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Resize(Transform):\n\n    def __call__(\n        self,\n        img: NdarrayOrTensor,\n        mode: Optional[Union[InterpolateMode, str]] = None,\n        align_corners: Optional[bool] = None,\n    ) -> NdarrayOrTensor:\n        \"\"\"\n        Args:\n            img: channel first array, must have shape: (num_channels, H[, W, ..., ]).\n            mode: {``\"nearest\"``, ``\"linear\"``, ``\"bilinear\"``, ``\"bicubic\"``, ``\"trilinear\"``, ``\"area\"``}\n                The interpolation mode. Defaults to ``self.mode``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#interpolate\n            align_corners: This only has an effect when mode is\n                'linear', 'bilinear', 'bicubic' or 'trilinear'. Defaults to ``self.align_corners``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#interpolate\n\n        Raises:\n            ValueError: When ``self.spatial_size`` length is less than ``img`` spatial dimensions.\n\n        \"\"\"\n        img_, *_ = convert_data_type(img, torch.Tensor, dtype=torch.float)  # type: ignore\n        if self.size_mode == \"all\":\n            input_ndim = img_.ndim - 1  # spatial ndim\n            output_ndim = len(ensure_tuple(self.spatial_size))\n            if output_ndim > input_ndim:\n                input_shape = ensure_tuple_size(img_.shape, output_ndim + 1, 1)\n                img_ = img_.reshape(input_shape)\n            elif output_ndim < input_ndim:\n                raise ValueError(\n                    \"len(spatial_size) must be greater or equal to img spatial dimensions, \"\n                    f\"got spatial_size={output_ndim} img={input_ndim}.\"\n                )\n            spatial_size_ = fall_back_tuple(self.spatial_size, img_.shape[1:])\n        else:  # for the \"longest\" mode\n            img_size = img_.shape[1:]\n            if not isinstance(self.spatial_size, int):\n                raise ValueError(\"spatial_size must be an int number if size_mode is 'longest'.\")\n            scale = self.spatial_size / max(img_size)\n            spatial_size_ = tuple(int(round(s * scale)) for s in img_size)\n        resized = torch.nn.functional.interpolate(  # type: ignore\n            input=img_.unsqueeze(0),  # type: ignore\n            size=spatial_size_,\n            mode=look_up_option(self.mode if mode is None else mode, InterpolateMode).value,\n            align_corners=self.align_corners if align_corners is None else align_corners,\n        )\n        out, *_ = convert_to_dst_type(resized.squeeze(0), img)\n        return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_Zoom_Zoom.__init__.self.kwargs.kwargs": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_Zoom_Zoom.__init__.self.kwargs.kwargs", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 569, "end_line": 616, "span_ids": ["Zoom.__init__", "Zoom"], "tokens": 618}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Zoom(Transform):\n    \"\"\"\n    Zooms an ND image using :py:class:`torch.nn.functional.interpolate`.\n    For details, please see https://pytorch.org/docs/stable/nn.functional.html#interpolate.\n\n    Different from :py:class:`monai.transforms.resize`, this transform takes scaling factors\n    as input, and provides an option of preserving the input spatial size.\n\n    Args:\n        zoom: The zoom factor along the spatial axes.\n            If a float, zoom is the same for each spatial axis.\n            If a sequence, zoom should contain one value for each spatial axis.\n        mode: {``\"nearest\"``, ``\"linear\"``, ``\"bilinear\"``, ``\"bicubic\"``, ``\"trilinear\"``, ``\"area\"``}\n            The interpolation mode. Defaults to ``\"area\"``.\n            See also: https://pytorch.org/docs/stable/nn.functional.html#interpolate\n        padding_mode: available modes for numpy array:{``\"constant\"``, ``\"edge\"``, ``\"linear_ramp\"``, ``\"maximum\"``,\n            ``\"mean\"``, ``\"median\"``, ``\"minimum\"``, ``\"reflect\"``, ``\"symmetric\"``, ``\"wrap\"``, ``\"empty\"``}\n            available modes for PyTorch Tensor: {``\"constant\"``, ``\"reflect\"``, ``\"replicate\"``, ``\"circular\"``}.\n            One of the listed string values or a user supplied function. Defaults to ``\"constant\"``.\n            The mode to pad data after zooming.\n            See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html\n            https://pytorch.org/docs/stable/generated/torch.nn.functional.pad.html\n        align_corners: This only has an effect when mode is\n            'linear', 'bilinear', 'bicubic' or 'trilinear'. Default: None.\n            See also: https://pytorch.org/docs/stable/nn.functional.html#interpolate\n        keep_size: Should keep original size (padding/slicing if needed), default is True.\n        kwargs: other arguments for the `np.pad` or `torch.pad` function.\n            note that `np.pad` treats channel dimension as the first dimension.\n\n    \"\"\"\n\n    backend = [TransformBackends.TORCH]\n\n    def __init__(\n        self,\n        zoom: Union[Sequence[float], float],\n        mode: Union[InterpolateMode, str] = InterpolateMode.AREA,\n        padding_mode: Union[NumpyPadMode, PytorchPadMode, str] = NumpyPadMode.EDGE,\n        align_corners: Optional[bool] = None,\n        keep_size: bool = True,\n        **kwargs,\n    ) -> None:\n        self.zoom = zoom\n        self.mode: InterpolateMode = InterpolateMode(mode)\n        self.padding_mode = padding_mode\n        self.align_corners = align_corners\n        self.keep_size = keep_size\n        self.kwargs = kwargs", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_Zoom.__call___Zoom.__call__.return.out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_Zoom.__call___Zoom.__call__.return.out", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 618, "end_line": 673, "span_ids": ["Zoom.__call__"], "tokens": 742}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Zoom(Transform):\n\n    def __call__(\n        self,\n        img: NdarrayOrTensor,\n        mode: Optional[Union[InterpolateMode, str]] = None,\n        padding_mode: Optional[Union[NumpyPadMode, PytorchPadMode, str]] = None,\n        align_corners: Optional[bool] = None,\n    ) -> NdarrayOrTensor:\n        \"\"\"\n        Args:\n            img: channel first array, must have shape: (num_channels, H[, W, ..., ]).\n            mode: {``\"nearest\"``, ``\"linear\"``, ``\"bilinear\"``, ``\"bicubic\"``, ``\"trilinear\"``, ``\"area\"``}\n                The interpolation mode. Defaults to ``self.mode``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#interpolate\n            padding_mode: available modes for numpy array:{``\"constant\"``, ``\"edge\"``, ``\"linear_ramp\"``, ``\"maximum\"``,\n                ``\"mean\"``, ``\"median\"``, ``\"minimum\"``, ``\"reflect\"``, ``\"symmetric\"``, ``\"wrap\"``, ``\"empty\"``}\n                available modes for PyTorch Tensor: {``\"constant\"``, ``\"reflect\"``, ``\"replicate\"``, ``\"circular\"``}.\n                One of the listed string values or a user supplied function. Defaults to ``\"constant\"``.\n                The mode to pad data after zooming.\n                See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html\n                https://pytorch.org/docs/stable/generated/torch.nn.functional.pad.html\n            align_corners: This only has an effect when mode is\n                'linear', 'bilinear', 'bicubic' or 'trilinear'. Defaults to ``self.align_corners``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#interpolate\n\n        \"\"\"\n        img_t: torch.Tensor\n        img_t, *_ = convert_data_type(img, torch.Tensor, dtype=torch.float32)  # type: ignore\n\n        _zoom = ensure_tuple_rep(self.zoom, img.ndim - 1)  # match the spatial image dim\n        zoomed: NdarrayOrTensor = torch.nn.functional.interpolate(  # type: ignore\n            recompute_scale_factor=True,\n            input=img_t.unsqueeze(0),\n            scale_factor=list(_zoom),\n            mode=look_up_option(self.mode if mode is None else mode, InterpolateMode).value,\n            align_corners=self.align_corners if align_corners is None else align_corners,\n        )\n        zoomed = zoomed.squeeze(0)\n\n        if self.keep_size and not np.allclose(img_t.shape, zoomed.shape):\n\n            pad_vec = [(0, 0)] * len(img_t.shape)\n            slice_vec = [slice(None)] * len(img_t.shape)\n            for idx, (od, zd) in enumerate(zip(img_t.shape, zoomed.shape)):\n                diff = od - zd\n                half = abs(diff) // 2\n                if diff > 0:  # need padding\n                    pad_vec[idx] = (half, diff - half)\n                elif diff < 0:  # need slicing\n                    slice_vec[idx] = slice(half, half + od)\n\n            padder = Pad(pad_vec, padding_mode or self.padding_mode)\n            zoomed = padder(zoomed)\n            zoomed = zoomed[tuple(slice_vec)]\n\n        out, *_ = convert_to_dst_type(zoomed, dst=img)\n        return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_Rotate90_Rotate90.__init__.self.spatial_axes.spatial_axes_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_Rotate90_Rotate90.__init__.self.spatial_axes.spatial_axes_", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 676, "end_line": 698, "span_ids": ["Rotate90.__init__", "Rotate90"], "tokens": 244}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Rotate90(Transform):\n    \"\"\"\n    Rotate an array by 90 degrees in the plane specified by `axes`.\n    See np.rot90 for additional details:\n    https://numpy.org/doc/stable/reference/generated/numpy.rot90.html.\n\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __init__(self, k: int = 1, spatial_axes: Tuple[int, int] = (0, 1)) -> None:\n        \"\"\"\n        Args:\n            k: number of times to rotate by 90 degrees.\n            spatial_axes: 2 int numbers, defines the plane to rotate with 2 spatial axes.\n                Default: (0, 1), this is the first two axis in spatial dimensions.\n                If axis is negative it counts from the last to the first axis.\n        \"\"\"\n        self.k = k\n        spatial_axes_: Tuple[int, int] = ensure_tuple(spatial_axes)  # type: ignore\n        if len(spatial_axes_) != 2:\n            raise ValueError(\"spatial_axes must be 2 int numbers to indicate the axes to rotate 90 degrees.\")\n        self.spatial_axes = spatial_axes_", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_Rotate90.__call___Rotate90.__call__.return.out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_Rotate90.__call___Rotate90.__call__.return.out", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 698, "end_line": 706, "span_ids": ["Rotate90.__call__"], "tokens": 122}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Rotate90(Transform):\n\n    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:\n        \"\"\"\n        Args:\n            img: channel first array, must have shape: (num_channels, H[, W, ..., ]),\n        \"\"\"\n        rot90: Callable = torch.rot90 if isinstance(img, torch.Tensor) else np.rot90  # type: ignore\n        out: NdarrayOrTensor = rot90(img, self.k, map_spatial_axes(img.ndim, self.spatial_axes))\n        out, *_ = convert_data_type(out, dtype=img.dtype)\n        return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_RandRotate90_RandRotate90.randomize.self._rand_k.self_R_randint_self_max_k": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_RandRotate90_RandRotate90.randomize.self._rand_k.self_R_randint_self_max_k", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 711, "end_line": 738, "span_ids": ["RandRotate90.randomize", "RandRotate90", "RandRotate90.__init__"], "tokens": 274}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandRotate90(RandomizableTransform):\n    \"\"\"\n    With probability `prob`, input arrays are rotated by 90 degrees\n    in the plane specified by `spatial_axes`.\n    \"\"\"\n\n    backend = Rotate90.backend\n\n    def __init__(self, prob: float = 0.1, max_k: int = 3, spatial_axes: Tuple[int, int] = (0, 1)) -> None:\n        \"\"\"\n        Args:\n            prob: probability of rotating.\n                (Default 0.1, with 10% probability it returns a rotated array)\n            max_k: number of rotations will be sampled from `np.random.randint(max_k) + 1`, (Default 3).\n            spatial_axes: 2 int numbers, defines the plane to rotate with 2 spatial axes.\n                Default: (0, 1), this is the first two axis in spatial dimensions.\n        \"\"\"\n        RandomizableTransform.__init__(self, prob)\n        self.max_k = max_k\n        self.spatial_axes = spatial_axes\n\n        self._rand_k = 0\n\n    def randomize(self, data: Optional[Any] = None) -> None:\n        super().randomize(None)\n        if not self._do_transform:\n            return None\n        self._rand_k = self.R.randint(self.max_k) + 1", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_RandRotate90.__call___RandRotate90.__call__.return.Rotate90_self__rand_k_se": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_RandRotate90.__call___RandRotate90.__call__.return.Rotate90_self__rand_k_se", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 740, "end_line": 752, "span_ids": ["RandRotate90.__call__"], "tokens": 118}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandRotate90(RandomizableTransform):\n\n    def __call__(self, img: NdarrayOrTensor, randomize: bool = True) -> NdarrayOrTensor:\n        \"\"\"\n        Args:\n            img: channel first array, must have shape: (num_channels, H[, W, ..., ]),\n            randomize: whether to execute `randomize()` function first, default to True.\n        \"\"\"\n        if randomize:\n            self.randomize()\n\n        if not self._do_transform:\n            return img\n\n        return Rotate90(self._rand_k, self.spatial_axes)(img)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_RandRotate.randomize_RandRotate.__call__.return._img_rotator_get_rotatio": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_RandRotate.randomize_RandRotate.__call__.return._img_rotator_get_rotatio", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 818, "end_line": 868, "span_ids": ["RandRotate.randomize", "RandRotate.__call__"], "tokens": 611}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandRotate(RandomizableTransform):\n\n    def randomize(self, data: Optional[Any] = None) -> None:\n        super().randomize(None)\n        if not self._do_transform:\n            return None\n        self.x = self.R.uniform(low=self.range_x[0], high=self.range_x[1])\n        self.y = self.R.uniform(low=self.range_y[0], high=self.range_y[1])\n        self.z = self.R.uniform(low=self.range_z[0], high=self.range_z[1])\n\n    def __call__(\n        self,\n        img: NdarrayOrTensor,\n        mode: Optional[Union[GridSampleMode, str]] = None,\n        padding_mode: Optional[Union[GridSamplePadMode, str]] = None,\n        align_corners: Optional[bool] = None,\n        dtype: Union[DtypeLike, torch.dtype] = None,\n        randomize: bool = True,\n        get_matrix: bool = False,\n    ):\n        \"\"\"\n        Args:\n            img: channel first array, must have shape 2D: (nchannels, H, W), or 3D: (nchannels, H, W, D).\n            mode: {``\"bilinear\"``, ``\"nearest\"``}\n                Interpolation mode to calculate output values. Defaults to ``self.mode``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            padding_mode: {``\"zeros\"``, ``\"border\"``, ``\"reflection\"``}\n                Padding mode for outside grid values. Defaults to ``self.padding_mode``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            align_corners: Defaults to ``self.align_corners``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            dtype: data type for resampling computation. Defaults to ``self.dtype``.\n                If None, use the data type of input data. To be compatible with other modules,\n                the output data type is always ``np.float32``.\n            randomize: whether to execute `randomize()` function first, default to True.\n            get_matrix: whether to return the rotated image and rotate matrix together, default to False.\n        \"\"\"\n        if randomize:\n            self.randomize()\n\n        if not self._do_transform:\n            return img\n\n        rotator = Rotate(\n            angle=self.x if img.ndim == 3 else (self.x, self.y, self.z),\n            keep_size=self.keep_size,\n            mode=look_up_option(mode or self.mode, GridSampleMode),\n            padding_mode=look_up_option(padding_mode or self.padding_mode, GridSamplePadMode),\n            align_corners=self.align_corners if align_corners is None else align_corners,\n            dtype=dtype or self.dtype or img.dtype,\n        )\n        img = rotator(img)\n        return (img, rotator.get_rotation_matrix()) if get_matrix else img", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_RandAxisFlip_RandAxisFlip.randomize.self._axis.self_R_randint_data_ndim_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_RandAxisFlip_RandAxisFlip.randomize.self._axis.self_R_randint_data_ndim_", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 903, "end_line": 924, "span_ids": ["RandAxisFlip.randomize", "RandAxisFlip", "RandAxisFlip.__init__"], "tokens": 155}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandAxisFlip(RandomizableTransform):\n    \"\"\"\n    Randomly select a spatial axis and flip along it.\n    See numpy.flip for additional details.\n    https://docs.scipy.org/doc/numpy/reference/generated/numpy.flip.html\n\n    Args:\n        prob: Probability of flipping.\n\n    \"\"\"\n\n    backend = Flip.backend\n\n    def __init__(self, prob: float = 0.1) -> None:\n        RandomizableTransform.__init__(self, prob)\n        self._axis: Optional[int] = None\n\n    def randomize(self, data: NdarrayOrTensor) -> None:\n        super().randomize(None)\n        if not self._do_transform:\n            return None\n        self._axis = self.R.randint(data.ndim - 1)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_RandAxisFlip.__call___RandAxisFlip.__call__.return.Flip_spatial_axis_self__a": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_RandAxisFlip.__call___RandAxisFlip.__call__.return.Flip_spatial_axis_self__a", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 926, "end_line": 938, "span_ids": ["RandAxisFlip.__call__"], "tokens": 117}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandAxisFlip(RandomizableTransform):\n\n    def __call__(self, img: NdarrayOrTensor, randomize: bool = True) -> NdarrayOrTensor:\n        \"\"\"\n        Args:\n            img: channel first array, must have shape: (num_channels, H[, W, ..., ]),\n            randomize: whether to execute `randomize()` function first, default to True.\n        \"\"\"\n        if randomize:\n            self.randomize(data=img)\n\n        if not self._do_transform:\n            return img\n\n        return Flip(spatial_axis=self._axis)(img)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_RandZoom_RandZoom.backend.Zoom_backend": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_RandZoom_RandZoom.backend.Zoom_backend", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 941, "end_line": 976, "span_ids": ["RandZoom"], "tokens": 582}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandZoom(RandomizableTransform):\n    \"\"\"\n    Randomly zooms input arrays with given probability within given zoom range.\n\n    Args:\n        prob: Probability of zooming.\n        min_zoom: Min zoom factor. Can be float or sequence same size as image.\n            If a float, select a random factor from `[min_zoom, max_zoom]` then apply to all spatial dims\n            to keep the original spatial shape ratio.\n            If a sequence, min_zoom should contain one value for each spatial axis.\n            If 2 values provided for 3D data, use the first value for both H & W dims to keep the same zoom ratio.\n        max_zoom: Max zoom factor. Can be float or sequence same size as image.\n            If a float, select a random factor from `[min_zoom, max_zoom]` then apply to all spatial dims\n            to keep the original spatial shape ratio.\n            If a sequence, max_zoom should contain one value for each spatial axis.\n            If 2 values provided for 3D data, use the first value for both H & W dims to keep the same zoom ratio.\n        mode: {``\"nearest\"``, ``\"linear\"``, ``\"bilinear\"``, ``\"bicubic\"``, ``\"trilinear\"``, ``\"area\"``}\n            The interpolation mode. Defaults to ``\"area\"``.\n            See also: https://pytorch.org/docs/stable/nn.functional.html#interpolate\n        padding_mode: available modes for numpy array:{``\"constant\"``, ``\"edge\"``, ``\"linear_ramp\"``, ``\"maximum\"``,\n            ``\"mean\"``, ``\"median\"``, ``\"minimum\"``, ``\"reflect\"``, ``\"symmetric\"``, ``\"wrap\"``, ``\"empty\"``}\n            available modes for PyTorch Tensor: {``\"constant\"``, ``\"reflect\"``, ``\"replicate\"``, ``\"circular\"``}.\n            One of the listed string values or a user supplied function. Defaults to ``\"constant\"``.\n            The mode to pad data after zooming.\n            See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html\n            https://pytorch.org/docs/stable/generated/torch.nn.functional.pad.html\n        align_corners: This only has an effect when mode is\n            'linear', 'bilinear', 'bicubic' or 'trilinear'. Default: None.\n            See also: https://pytorch.org/docs/stable/nn.functional.html#interpolate\n        keep_size: Should keep original size (pad if needed), default is True.\n        kwargs: other arguments for the `np.pad` or `torch.pad` function.\n            note that `np.pad` treats channel dimension as the first dimension.\n\n    \"\"\"\n\n    backend = Zoom.backend", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_RandZoom.__init___RandZoom.__init__.self._zoom._1_0_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_RandZoom.__init___RandZoom.__init__.self._zoom._1_0_", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 978, "end_line": 1000, "span_ids": ["RandZoom.__init__"], "tokens": 249}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandZoom(RandomizableTransform):\n\n    def __init__(\n        self,\n        prob: float = 0.1,\n        min_zoom: Union[Sequence[float], float] = 0.9,\n        max_zoom: Union[Sequence[float], float] = 1.1,\n        mode: Union[InterpolateMode, str] = InterpolateMode.AREA,\n        padding_mode: Union[NumpyPadMode, PytorchPadMode, str] = NumpyPadMode.EDGE,\n        align_corners: Optional[bool] = None,\n        keep_size: bool = True,\n        **kwargs,\n    ) -> None:\n        RandomizableTransform.__init__(self, prob)\n        self.min_zoom = ensure_tuple(min_zoom)\n        self.max_zoom = ensure_tuple(max_zoom)\n        if len(self.min_zoom) != len(self.max_zoom):\n            raise AssertionError(\"min_zoom and max_zoom must have same length.\")\n        self.mode: InterpolateMode = look_up_option(mode, InterpolateMode)\n        self.padding_mode = padding_mode\n        self.align_corners = align_corners\n        self.keep_size = keep_size\n        self.kwargs = kwargs\n\n        self._zoom: Sequence[float] = [1.0]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_RandZoom.randomize_RandZoom.randomize.if_len_self__zoom_1_.elif_len_self__zoom_2.self._zoom.ensure_tuple_rep_self__zo": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_RandZoom.randomize_RandZoom.randomize.if_len_self__zoom_1_.elif_len_self__zoom_2.self._zoom.ensure_tuple_rep_self__zo", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1002, "end_line": 1012, "span_ids": ["RandZoom.randomize"], "tokens": 194}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandZoom(RandomizableTransform):\n\n    def randomize(self, img: NdarrayOrTensor) -> None:\n        super().randomize(None)\n        if not self._do_transform:\n            return None\n        self._zoom = [self.R.uniform(l, h) for l, h in zip(self.min_zoom, self.max_zoom)]\n        if len(self._zoom) == 1:\n            # to keep the spatial shape ratio, use same random zoom factor for all dims\n            self._zoom = ensure_tuple_rep(self._zoom[0], img.ndim - 1)\n        elif len(self._zoom) == 2 and img.ndim > 3:\n            # if 2 zoom factors provided for 3D data, use the first factor for H and W dims, second factor for D dim\n            self._zoom = ensure_tuple_rep(self._zoom[0], img.ndim - 2) + ensure_tuple(self._zoom[-1])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_RandZoom.__call___RandZoom.__call__.return.Zoom_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_RandZoom.__call___RandZoom.__call__.return.Zoom_", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1014, "end_line": 1055, "span_ids": ["RandZoom.__call__"], "tokens": 543}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandZoom(RandomizableTransform):\n\n    def __call__(\n        self,\n        img: NdarrayOrTensor,\n        mode: Optional[Union[InterpolateMode, str]] = None,\n        padding_mode: Optional[Union[NumpyPadMode, PytorchPadMode, str]] = None,\n        align_corners: Optional[bool] = None,\n        randomize: bool = True,\n    ) -> NdarrayOrTensor:\n        \"\"\"\n        Args:\n            img: channel first array, must have shape 2D: (nchannels, H, W), or 3D: (nchannels, H, W, D).\n            mode: {``\"nearest\"``, ``\"linear\"``, ``\"bilinear\"``, ``\"bicubic\"``, ``\"trilinear\"``, ``\"area\"``}\n                The interpolation mode. Defaults to ``self.mode``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#interpolate\n            padding_mode: available modes for numpy array:{``\"constant\"``, ``\"edge\"``, ``\"linear_ramp\"``, ``\"maximum\"``,\n                ``\"mean\"``, ``\"median\"``, ``\"minimum\"``, ``\"reflect\"``, ``\"symmetric\"``, ``\"wrap\"``, ``\"empty\"``}\n                available modes for PyTorch Tensor: {``\"constant\"``, ``\"reflect\"``, ``\"replicate\"``, ``\"circular\"``}.\n                One of the listed string values or a user supplied function. Defaults to ``\"constant\"``.\n                The mode to pad data after zooming.\n                See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html\n                https://pytorch.org/docs/stable/generated/torch.nn.functional.pad.html\n            align_corners: This only has an effect when mode is\n                'linear', 'bilinear', 'bicubic' or 'trilinear'. Defaults to ``self.align_corners``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#interpolate\n            randomize: whether to execute `randomize()` function first, default to True.\n\n        \"\"\"\n        # match the spatial image dim\n        if randomize:\n            self.randomize(img=img)\n\n        if not self._do_transform:\n            return img\n\n        return Zoom(\n            self._zoom,\n            keep_size=self.keep_size,\n            mode=look_up_option(mode or self.mode, InterpolateMode),\n            padding_mode=padding_mode or self.padding_mode,\n            align_corners=align_corners or self.align_corners,\n            **self.kwargs,\n        )(img)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_AffineGrid.__call___AffineGrid.__call__.return.grid_affine": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_AffineGrid.__call___AffineGrid.__call__.return.grid_affine", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1108, "end_line": 1155, "span_ids": ["AffineGrid.__call__"], "tokens": 548}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AffineGrid(Transform):\n\n    def __call__(\n        self, spatial_size: Optional[Sequence[int]] = None, grid: Optional[NdarrayOrTensor] = None\n    ) -> Tuple[NdarrayOrTensor, NdarrayOrTensor]:\n        \"\"\"\n        The grid can be initialized with a `spatial_size` parameter, or provided directly as `grid`.\n        Therefore, either `spatial_size` or `grid` must be provided.\n        When initialising from `spatial_size`, the backend \"torch\" will be used.\n\n        Args:\n            spatial_size: output grid size.\n            grid: grid to be transformed. Shape must be (3, H, W) for 2D or (4, H, W, D) for 3D.\n\n        Raises:\n            ValueError: When ``grid=None`` and ``spatial_size=None``. Incompatible values.\n\n        \"\"\"\n        if grid is None:\n            if spatial_size is not None:\n                grid = create_grid(spatial_size, device=self.device, backend=\"torch\")\n            else:\n                raise ValueError(\"Incompatible values: grid=None and spatial_size=None.\")\n\n        _b = TransformBackends.TORCH if isinstance(grid, torch.Tensor) else TransformBackends.NUMPY\n        _device = grid.device if isinstance(grid, torch.Tensor) else self.device\n        affine: NdarrayOrTensor\n        if self.affine is None:\n            spatial_dims = len(grid.shape) - 1\n            affine = (\n                torch.eye(spatial_dims + 1, device=_device)\n                if _b == TransformBackends.TORCH\n                else np.eye(spatial_dims + 1)\n            )\n            if self.rotate_params:\n                affine = affine @ create_rotate(spatial_dims, self.rotate_params, device=_device, backend=_b)\n            if self.shear_params:\n                affine = affine @ create_shear(spatial_dims, self.shear_params, device=_device, backend=_b)\n            if self.translate_params:\n                affine = affine @ create_translate(spatial_dims, self.translate_params, device=_device, backend=_b)\n            if self.scale_params:\n                affine = affine @ create_scale(spatial_dims, self.scale_params, device=_device, backend=_b)\n        else:\n            affine = self.affine\n\n        grid, *_ = convert_data_type(grid, torch.Tensor, device=_device, dtype=float)\n        affine, *_ = convert_to_dst_type(affine, grid)\n\n        grid = (affine @ grid.reshape((grid.shape[0], -1))).reshape([-1] + list(grid.shape[1:]))\n        return grid, affine", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_Resample.__call___Resample.__call__.return.out_val": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_Resample.__call___Resample.__call__.return.out_val", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1354, "end_line": 1418, "span_ids": ["Resample.__call__"], "tokens": 774}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Resample(Transform):\n\n    def __call__(\n        self,\n        img: NdarrayOrTensor,\n        grid: Optional[NdarrayOrTensor] = None,\n        mode: Optional[Union[GridSampleMode, str]] = None,\n        padding_mode: Optional[Union[GridSamplePadMode, str]] = None,\n    ) -> NdarrayOrTensor:\n        \"\"\"\n        Args:\n            img: shape must be (num_channels, H, W[, D]).\n            grid: shape must be (3, H, W) for 2D or (4, H, W, D) for 3D.\n            mode: {``\"bilinear\"``, ``\"nearest\"``}\n                Interpolation mode to calculate output values. Defaults to ``self.mode``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            padding_mode: {``\"zeros\"``, ``\"border\"``, ``\"reflection\"``}\n                Padding mode for outside grid values. Defaults to ``self.padding_mode``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n        \"\"\"\n        if grid is None:\n            raise ValueError(\"Unknown grid.\")\n        _device = img.device if isinstance(img, torch.Tensor) else self.device\n        img_t: torch.Tensor\n        grid_t: torch.Tensor\n        img_t, *_ = convert_data_type(img, torch.Tensor, device=_device, dtype=torch.float32)  # type: ignore\n        grid_t, *_ = convert_to_dst_type(grid, img_t)  # type: ignore\n\n        if USE_COMPILED:\n            for i, dim in enumerate(img_t.shape[1:]):\n                grid_t[i] += (dim - 1.0) / 2.0\n            grid_t = grid_t[:-1] / grid_t[-1:]\n            grid_t = grid_t.permute(list(range(grid_t.ndimension()))[1:] + [0])\n            _padding_mode = look_up_option(\n                self.padding_mode if padding_mode is None else padding_mode, GridSamplePadMode\n            ).value\n            if _padding_mode == \"zeros\":\n                bound = 7\n            elif _padding_mode == \"border\":\n                bound = 0\n            else:\n                bound = 1\n            _interp_mode = look_up_option(self.mode if mode is None else mode, GridSampleMode).value\n            out = grid_pull(\n                img_t.unsqueeze(0),\n                grid_t.unsqueeze(0),\n                bound=bound,\n                extrapolate=True,\n                interpolation=1 if _interp_mode == \"bilinear\" else _interp_mode,\n            )[0]\n        else:\n            for i, dim in enumerate(img_t.shape[1:]):\n                grid_t[i] = 2.0 * grid_t[i] / (dim - 1.0)\n            grid_t = grid_t[:-1] / grid_t[-1:]\n            index_ordering: List[int] = list(range(img_t.ndimension() - 2, -1, -1))\n            grid_t = grid_t[index_ordering]\n            grid_t = grid_t.permute(list(range(grid_t.ndimension()))[1:] + [0])\n            out = torch.nn.functional.grid_sample(\n                img_t.unsqueeze(0),\n                grid_t.unsqueeze(0),\n                mode=self.mode.value if mode is None else GridSampleMode(mode).value,\n                padding_mode=self.padding_mode.value if padding_mode is None else GridSamplePadMode(padding_mode).value,\n                align_corners=True,\n            )[0]\n        out_val: NdarrayOrTensor\n        out_val, *_ = convert_to_dst_type(out, dst=img, dtype=out.dtype)\n        return out_val", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_Affine_Affine.__init__.self.padding_mode.look_up_option_padding_mo": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_Affine_Affine.__init__.self.padding_mode.look_up_option_padding_mo", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1422, "end_line": 1500, "span_ids": ["Affine", "Affine.__init__"], "tokens": 972}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Affine(Transform):\n    \"\"\"\n    Transform ``img`` given the affine parameters.\n    A tutorial is available: https://github.com/Project-MONAI/tutorials/blob/0.6.0/modules/transforms_demo_2d.ipynb.\n\n    \"\"\"\n\n    backend = list(set(AffineGrid.backend) & set(Resample.backend))\n\n    @deprecated_arg(name=\"as_tensor_output\", since=\"0.6\")\n    def __init__(\n        self,\n        rotate_params: Optional[Union[Sequence[float], float]] = None,\n        shear_params: Optional[Union[Sequence[float], float]] = None,\n        translate_params: Optional[Union[Sequence[float], float]] = None,\n        scale_params: Optional[Union[Sequence[float], float]] = None,\n        affine: Optional[NdarrayOrTensor] = None,\n        spatial_size: Optional[Union[Sequence[int], int]] = None,\n        mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,\n        padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.REFLECTION,\n        as_tensor_output: bool = True,\n        device: Optional[torch.device] = None,\n        image_only: bool = False,\n    ) -> None:\n        \"\"\"\n        The affine transformations are applied in rotate, shear, translate, scale order.\n\n        Args:\n            rotate_params: a rotation angle in radians, a scalar for 2D image, a tuple of 3 floats for 3D.\n                Defaults to no rotation.\n            shear_params: shearing factors for affine matrix, take a 3D affine as example::\n\n                [\n                    [1.0, params[0], params[1], 0.0],\n                    [params[2], 1.0, params[3], 0.0],\n                    [params[4], params[5], 1.0, 0.0],\n                    [0.0, 0.0, 0.0, 1.0],\n                ]\n\n                a tuple of 2 floats for 2D, a tuple of 6 floats for 3D. Defaults to no shearing.\n            translate_params: a tuple of 2 floats for 2D, a tuple of 3 floats for 3D. Translation is in\n                pixel/voxel relative to the center of the input image. Defaults to no translation.\n            scale_params: scale factor for every spatial dims. a tuple of 2 floats for 2D,\n                a tuple of 3 floats for 3D. Defaults to `1.0`.\n            affine: If applied, ignore the params (`rotate_params`, etc.) and use the\n                supplied matrix. Should be square with each side = num of image spatial\n                dimensions + 1.\n            spatial_size: output image spatial size.\n                if `spatial_size` and `self.spatial_size` are not defined, or smaller than 1,\n                the transform will use the spatial size of `img`.\n                if some components of the `spatial_size` are non-positive values, the transform will use the\n                corresponding components of img size. For example, `spatial_size=(32, -1)` will be adapted\n                to `(32, 64)` if the second spatial dimension size of img is `64`.\n            mode: {``\"bilinear\"``, ``\"nearest\"``}\n                Interpolation mode to calculate output values. Defaults to ``\"bilinear\"``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            padding_mode: {``\"zeros\"``, ``\"border\"``, ``\"reflection\"``}\n                Padding mode for outside grid values. Defaults to ``\"reflection\"``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            device: device on which the tensor will be allocated.\n            image_only: if True return only the image volume, otherwise return (image, affine).\n\n        .. deprecated:: 0.6.0\n            ``as_tensor_output`` is deprecated.\n\n        \"\"\"\n        self.affine_grid = AffineGrid(\n            rotate_params=rotate_params,\n            shear_params=shear_params,\n            translate_params=translate_params,\n            scale_params=scale_params,\n            affine=affine,\n            device=device,\n        )\n        self.image_only = image_only\n        self.resampler = Resample(device=device)\n        self.spatial_size = spatial_size\n        self.mode: GridSampleMode = look_up_option(mode, GridSampleMode)\n        self.padding_mode: GridSamplePadMode = look_up_option(padding_mode, GridSamplePadMode)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_RandAffine._init_identity_cache_RandAffine._init_identity_cache.return.create_grid_spatial_size_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_RandAffine._init_identity_cache_RandAffine._init_identity_cache.return.create_grid_spatial_size_", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1618, "end_line": 1638, "span_ids": ["RandAffine._init_identity_cache"], "tokens": 229}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandAffine(RandomizableTransform):\n\n    def _init_identity_cache(self):\n        \"\"\"\n        Create cache of the identity grid if cache_grid=True and spatial_size is known.\n        \"\"\"\n        if self.spatial_size is None:\n            if self.cache_grid:\n                warnings.warn(\n                    \"cache_grid=True is not compatible with the dynamic spatial_size, please specify 'spatial_size'.\"\n                )\n            return None\n        _sp_size = ensure_tuple(self.spatial_size)\n        _ndim = len(_sp_size)\n        if _sp_size != fall_back_tuple(_sp_size, [1] * _ndim) or _sp_size != fall_back_tuple(_sp_size, [2] * _ndim):\n            # dynamic shape because it falls back to different outcomes\n            if self.cache_grid:\n                warnings.warn(\n                    \"cache_grid=True is not compatible with the dynamic spatial_size \"\n                    f\"'spatial_size={self.spatial_size}', please specify 'spatial_size'.\"\n                )\n            return None\n        return create_grid(spatial_size=_sp_size, device=self.rand_affine_grid.device, backend=\"torch\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_RandAffine.__call___RandAffine.__call__.return.out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_RandAffine.__call___RandAffine.__call__.return.out", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1671, "end_line": 1711, "span_ids": ["RandAffine.__call__"], "tokens": 513}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandAffine(RandomizableTransform):\n\n    def __call__(\n        self,\n        img: NdarrayOrTensor,\n        spatial_size: Optional[Union[Sequence[int], int]] = None,\n        mode: Optional[Union[GridSampleMode, str]] = None,\n        padding_mode: Optional[Union[GridSamplePadMode, str]] = None,\n        randomize: bool = True,\n    ) -> NdarrayOrTensor:\n        \"\"\"\n        Args:\n            img: shape must be (num_channels, H, W[, D]),\n            spatial_size: output image spatial size.\n                if `spatial_size` and `self.spatial_size` are not defined, or smaller than 1,\n                the transform will use the spatial size of `img`.\n                if `img` has two spatial dimensions, `spatial_size` should have 2 elements [h, w].\n                if `img` has three spatial dimensions, `spatial_size` should have 3 elements [h, w, d].\n            mode: {``\"bilinear\"``, ``\"nearest\"``}\n                Interpolation mode to calculate output values. Defaults to ``self.mode``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            padding_mode: {``\"zeros\"``, ``\"border\"``, ``\"reflection\"``}\n                Padding mode for outside grid values. Defaults to ``self.padding_mode``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            randomize: whether to execute `randomize()` function first, default to True.\n\n        \"\"\"\n        if randomize:\n            self.randomize()\n\n        # if not doing transform and spatial size doesn't change, nothing to do\n        # except convert to float and device\n        sp_size = fall_back_tuple(spatial_size or self.spatial_size, img.shape[1:])\n        do_resampling = self._do_transform or (sp_size != ensure_tuple(img.shape[1:]))\n        if not do_resampling:\n            img, *_ = convert_data_type(img, dtype=torch.float32, device=self.resampler.device)\n        grid = self.get_identity_grid(sp_size)\n        if self._do_transform:\n            grid = self.rand_affine_grid(grid=grid)\n        out: NdarrayOrTensor = self.resampler(\n            img=img, grid=grid, mode=mode or self.mode, padding_mode=padding_mode or self.padding_mode\n        )\n        return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_Rand2DElastic_Rand2DElastic.__init__.self.padding_mode.look_up_option_padding_mo": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_Rand2DElastic_Rand2DElastic.__init__.self.padding_mode.look_up_option_padding_mo", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1714, "end_line": 1804, "span_ids": ["Rand2DElastic", "Rand2DElastic.__init__"], "tokens": 1139}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Rand2DElastic(RandomizableTransform):\n    \"\"\"\n    Random elastic deformation and affine in 2D.\n    A tutorial is available: https://github.com/Project-MONAI/tutorials/blob/0.6.0/modules/transforms_demo_2d.ipynb.\n\n    \"\"\"\n\n    backend = Resample.backend\n\n    @deprecated_arg(name=\"as_tensor_output\", since=\"0.6\")\n    def __init__(\n        self,\n        spacing: Union[Tuple[float, float], float],\n        magnitude_range: Tuple[float, float],\n        prob: float = 0.1,\n        rotate_range: RandRange = None,\n        shear_range: RandRange = None,\n        translate_range: RandRange = None,\n        scale_range: RandRange = None,\n        spatial_size: Optional[Union[Tuple[int, int], int]] = None,\n        mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,\n        padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.REFLECTION,\n        as_tensor_output: bool = False,\n        device: Optional[torch.device] = None,\n    ) -> None:\n        \"\"\"\n        Args:\n            spacing : distance in between the control points.\n            magnitude_range: the random offsets will be generated from ``uniform[magnitude[0], magnitude[1])``.\n            prob: probability of returning a randomized elastic transform.\n                defaults to 0.1, with 10% chance returns a randomized elastic transform,\n                otherwise returns a ``spatial_size`` centered area extracted from the input image.\n            rotate_range: angle range in radians. If element `i` is a pair of (min, max) values, then\n                `uniform[-rotate_range[i][0], rotate_range[i][1])` will be used to generate the rotation parameter\n                for the `i`th spatial dimension. If not, `uniform[-rotate_range[i], rotate_range[i])` will be used.\n                This can be altered on a per-dimension basis. E.g., `((0,3), 1, ...)`: for dim0, rotation will be\n                in range `[0, 3]`, and for dim1 `[-1, 1]` will be used. Setting a single value will use `[-x, x]`\n                for dim0 and nothing for the remaining dimensions.\n            shear_range: shear range with format matching `rotate_range`, it defines the range to randomly select\n                shearing factors(a tuple of 2 floats for 2D) for affine matrix, take a 2D affine as example::\n\n                    [\n                        [1.0, params[0], 0.0],\n                        [params[1], 1.0, 0.0],\n                        [0.0, 0.0, 1.0],\n                    ]\n\n            translate_range: translate range with format matching `rotate_range`, it defines the range to randomly\n                select pixel to translate for every spatial dims.\n            scale_range: scaling range with format matching `rotate_range`. it defines the range to randomly select\n                the scale factor to translate for every spatial dims. A value of 1.0 is added to the result.\n                This allows 0 to correspond to no change (i.e., a scaling of 1.0).\n            spatial_size: specifying output image spatial size [h, w].\n                if `spatial_size` and `self.spatial_size` are not defined, or smaller than 1,\n                the transform will use the spatial size of `img`.\n                if some components of the `spatial_size` are non-positive values, the transform will use the\n                corresponding components of img size. For example, `spatial_size=(32, -1)` will be adapted\n                to `(32, 64)` if the second spatial dimension size of img is `64`.\n            mode: {``\"bilinear\"``, ``\"nearest\"``}\n                Interpolation mode to calculate output values. Defaults to ``\"bilinear\"``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            padding_mode: {``\"zeros\"``, ``\"border\"``, ``\"reflection\"``}\n                Padding mode for outside grid values. Defaults to ``\"reflection\"``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            device: device on which the tensor will be allocated.\n\n        See also:\n            - :py:class:`RandAffineGrid` for the random affine parameters configurations.\n            - :py:class:`Affine` for the affine transformation parameters configurations.\n\n        .. deprecated:: 0.6.0\n            ``as_tensor_output`` is deprecated.\n\n        \"\"\"\n        RandomizableTransform.__init__(self, prob)\n        self.deform_grid = RandDeformGrid(\n            spacing=spacing, magnitude_range=magnitude_range, as_tensor_output=True, device=device\n        )\n        self.rand_affine_grid = RandAffineGrid(\n            rotate_range=rotate_range,\n            shear_range=shear_range,\n            translate_range=translate_range,\n            scale_range=scale_range,\n            device=device,\n        )\n        self.resampler = Resample(device=device)\n\n        self.device = device\n        self.spatial_size = spatial_size\n        self.mode: GridSampleMode = look_up_option(mode, GridSampleMode)\n        self.padding_mode: GridSamplePadMode = look_up_option(padding_mode, GridSamplePadMode)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_Rand2DElastic.__call___Rand2DElastic.__call__.return.out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_Rand2DElastic.__call___Rand2DElastic.__call__.return.out", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1821, "end_line": 1864, "span_ids": ["Rand2DElastic.__call__"], "tokens": 516}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Rand2DElastic(RandomizableTransform):\n\n    def __call__(\n        self,\n        img: NdarrayOrTensor,\n        spatial_size: Optional[Union[Tuple[int, int], int]] = None,\n        mode: Optional[Union[GridSampleMode, str]] = None,\n        padding_mode: Optional[Union[GridSamplePadMode, str]] = None,\n        randomize: bool = True,\n    ) -> NdarrayOrTensor:\n        \"\"\"\n        Args:\n            img: shape must be (num_channels, H, W),\n            spatial_size: specifying output image spatial size [h, w].\n                if `spatial_size` and `self.spatial_size` are not defined, or smaller than 1,\n                the transform will use the spatial size of `img`.\n            mode: {``\"bilinear\"``, ``\"nearest\"``}\n                Interpolation mode to calculate output values. Defaults to ``self.mode``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            padding_mode: {``\"zeros\"``, ``\"border\"``, ``\"reflection\"``}\n                Padding mode for outside grid values. Defaults to ``self.padding_mode``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            randomize: whether to execute `randomize()` function first, default to True.\n        \"\"\"\n        sp_size = fall_back_tuple(spatial_size or self.spatial_size, img.shape[1:])\n        if randomize:\n            self.randomize(spatial_size=sp_size)\n\n        if self._do_transform:\n            grid = self.deform_grid(spatial_size=sp_size)\n            grid = self.rand_affine_grid(grid=grid)\n            grid = torch.nn.functional.interpolate(  # type: ignore\n                recompute_scale_factor=True,\n                input=grid.unsqueeze(0),\n                scale_factor=list(ensure_tuple(self.deform_grid.spacing)),\n                mode=InterpolateMode.BICUBIC.value,\n                align_corners=False,\n            )\n            grid = CenterSpatialCrop(roi_size=sp_size)(grid[0])\n        else:\n            _device = img.device if isinstance(img, torch.Tensor) else self.device\n            grid = create_grid(spatial_size=sp_size, device=_device, backend=\"torch\")\n        out: NdarrayOrTensor = self.resampler(\n            img, grid, mode=mode or self.mode, padding_mode=padding_mode or self.padding_mode\n        )\n        return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_Rand3DElastic.__call___Rand3DElastic.__call__.return.out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_Rand3DElastic.__call___Rand3DElastic.__call__.return.out", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1981, "end_line": 2019, "span_ids": ["Rand3DElastic.__call__"], "tokens": 502}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Rand3DElastic(RandomizableTransform):\n\n    def __call__(\n        self,\n        img: NdarrayOrTensor,\n        spatial_size: Optional[Union[Tuple[int, int, int], int]] = None,\n        mode: Optional[Union[GridSampleMode, str]] = None,\n        padding_mode: Optional[Union[GridSamplePadMode, str]] = None,\n        randomize: bool = True,\n    ) -> NdarrayOrTensor:\n        \"\"\"\n        Args:\n            img: shape must be (num_channels, H, W, D),\n            spatial_size: specifying spatial 3D output image spatial size [h, w, d].\n                if `spatial_size` and `self.spatial_size` are not defined, or smaller than 1,\n                the transform will use the spatial size of `img`.\n            mode: {``\"bilinear\"``, ``\"nearest\"``}\n                Interpolation mode to calculate output values. Defaults to ``self.mode``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            padding_mode: {``\"zeros\"``, ``\"border\"``, ``\"reflection\"``}\n                Padding mode for outside grid values. Defaults to ``self.padding_mode``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            randomize: whether to execute `randomize()` function first, default to True.\n        \"\"\"\n        sp_size = fall_back_tuple(spatial_size or self.spatial_size, img.shape[1:])\n        if randomize:\n            self.randomize(grid_size=sp_size)\n\n        _device = img.device if isinstance(img, torch.Tensor) else self.device\n        grid = create_grid(spatial_size=sp_size, device=_device, backend=\"torch\")\n        if self._do_transform:\n            if self.rand_offset is None:\n                raise RuntimeError(\"rand_offset is not initialized.\")\n            gaussian = GaussianFilter(3, self.sigma, 3.0).to(device=_device)\n            offset = torch.as_tensor(self.rand_offset, device=_device).unsqueeze(0)\n            grid[:3] += gaussian(offset)[0] * self.magnitude\n            grid = self.rand_affine_grid(grid=grid)\n        out: NdarrayOrTensor = self.resampler(\n            img, grid, mode=mode or self.mode, padding_mode=padding_mode or self.padding_mode\n        )\n        return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_GridDistortion_GridDistortion.__init__.self.device.device": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_GridDistortion_GridDistortion.__init__.self.device.device", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2065, "end_line": 2098, "span_ids": ["GridDistortion.__init__", "GridDistortion"], "tokens": 386}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GridDistortion(Transform):\n\n    backend = [TransformBackends.TORCH]\n\n    def __init__(\n        self,\n        num_cells: Union[Tuple[int], int],\n        distort_steps: Sequence[Sequence[float]],\n        mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,\n        padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER,\n        device: Optional[torch.device] = None,\n    ) -> None:\n        \"\"\"\n        Grid distortion transform. Refer to:\n        https://github.com/albumentations-team/albumentations/blob/master/albumentations/augmentations/transforms.py\n\n        Args:\n            num_cells: number of grid cells on each dimension.\n            distort_steps: This argument is a list of tuples, where each tuple contains the distort steps of the\n                corresponding dimensions (in the order of H, W[, D]). The length of each tuple equals to `num_cells + 1`.\n                Each value in the tuple represents the distort step of the related cell.\n            mode: {``\"bilinear\"``, ``\"nearest\"``}\n                Interpolation mode to calculate output values. Defaults to ``\"bilinear\"``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            padding_mode: {``\"zeros\"``, ``\"border\"``, ``\"reflection\"``}\n                Padding mode for outside grid values. Defaults to ``\"border\"``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            device: device on which the tensor will be allocated.\n\n        \"\"\"\n        self.resampler = Resample(mode=mode, padding_mode=padding_mode, device=device)\n        self.num_cells = num_cells\n        self.distort_steps = distort_steps\n        self.device = device", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_GridDistortion.__call___GridDistortion.__call__._type_ignore": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_GridDistortion.__call___GridDistortion.__call__._type_ignore", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2100, "end_line": 2148, "span_ids": ["GridDistortion.__call__"], "tokens": 582}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GridDistortion(Transform):\n\n    def __call__(\n        self,\n        img: NdarrayOrTensor,\n        distort_steps: Optional[Sequence[Sequence]] = None,\n        mode: Optional[Union[GridSampleMode, str]] = None,\n        padding_mode: Optional[Union[GridSamplePadMode, str]] = None,\n    ) -> NdarrayOrTensor:\n        \"\"\"\n        Args:\n            img: shape must be (num_channels, H, W[, D]).\n            distort_steps: This argument is a list of tuples, where each tuple contains the distort steps of the\n                corresponding dimensions (in the order of H, W[, D]). The length of each tuple equals to `num_cells + 1`.\n                Each value in the tuple represents the distort step of the related cell.\n            mode: {``\"bilinear\"``, ``\"nearest\"``}\n                Interpolation mode to calculate output values. Defaults to ``\"bilinear\"``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            padding_mode: {``\"zeros\"``, ``\"border\"``, ``\"reflection\"``}\n                Padding mode for outside grid values. Defaults to ``\"border\"``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n\n        \"\"\"\n        distort_steps = self.distort_steps if distort_steps is None else distort_steps\n        if len(img.shape) != len(distort_steps) + 1:\n            raise ValueError(\"the spatial size of `img` does not match with the length of `distort_steps`\")\n\n        all_ranges = []\n        num_cells = ensure_tuple_rep(self.num_cells, len(img.shape) - 1)\n        for dim_idx, dim_size in enumerate(img.shape[1:]):\n            dim_distort_steps = distort_steps[dim_idx]\n            ranges = torch.zeros(dim_size, dtype=torch.float32)\n            cell_size = dim_size // num_cells[dim_idx]\n            prev = 0\n            for idx in range(num_cells[dim_idx] + 1):\n                start = int(idx * cell_size)\n                end = start + cell_size\n                if end > dim_size:\n                    end = dim_size\n                    cur = dim_size\n                else:\n                    cur = prev + cell_size * dim_distort_steps[idx]\n                ranges[start:end] = torch.linspace(prev, cur, end - start)\n                prev = cur\n            ranges = ranges - (dim_size - 1.0) / 2.0\n            all_ranges.append(ranges)\n\n        coords = torch.meshgrid(*all_ranges)\n        grid = torch.stack([*coords, torch.ones_like(coords[0])])\n\n        return self.resampler(img, grid=grid, mode=mode, padding_mode=padding_mode)  # type: ignore", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_RandGridDistortion_RandGridDistortion.randomize.self.distort_steps.tuple_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_RandGridDistortion_RandGridDistortion.randomize.self.distort_steps.tuple_", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2151, "end_line": 2201, "span_ids": ["RandGridDistortion.randomize", "RandGridDistortion.__init__", "RandGridDistortion"], "tokens": 596}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandGridDistortion(RandomizableTransform):\n\n    backend = [TransformBackends.TORCH]\n\n    def __init__(\n        self,\n        num_cells: Union[Tuple[int], int] = 5,\n        prob: float = 0.1,\n        distort_limit: Union[Tuple[float, float], float] = (-0.03, 0.03),\n        mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,\n        padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER,\n        device: Optional[torch.device] = None,\n    ) -> None:\n        \"\"\"\n        Random grid distortion transform. Refer to:\n        https://github.com/albumentations-team/albumentations/blob/master/albumentations/augmentations/transforms.py\n\n        Args:\n            num_cells: number of grid cells on each dimension.\n            prob: probability of returning a randomized grid distortion transform. Defaults to 0.1.\n            distort_limit: range to randomly distort.\n                If single number, distort_limit is picked from (-distort_limit, distort_limit).\n                Defaults to (-0.03, 0.03).\n            mode: {``\"bilinear\"``, ``\"nearest\"``}\n                Interpolation mode to calculate output values. Defaults to ``\"bilinear\"``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            padding_mode: {``\"zeros\"``, ``\"border\"``, ``\"reflection\"``}\n                Padding mode for outside grid values. Defaults to ``\"border\"``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            device: device on which the tensor will be allocated.\n\n        \"\"\"\n        RandomizableTransform.__init__(self, prob)\n        self.num_cells = num_cells\n        if isinstance(distort_limit, (int, float)):\n            self.distort_limit = (min(-distort_limit, distort_limit), max(-distort_limit, distort_limit))\n        else:\n            self.distort_limit = (min(distort_limit), max(distort_limit))\n        self.distort_steps: Sequence[Sequence[float]] = ((1.0,),)\n        self.grid_distortion = GridDistortion(\n            num_cells=num_cells, distort_steps=self.distort_steps, mode=mode, padding_mode=padding_mode, device=device\n        )\n\n    def randomize(self, spatial_shape: Sequence[int]) -> None:\n        super().randomize(None)\n        if not self._do_transform:\n            return\n        self.distort_steps = tuple(\n            tuple(1.0 + self.R.uniform(low=self.distort_limit[0], high=self.distort_limit[1], size=n_cells + 1))\n            for n_cells in ensure_tuple_rep(self.num_cells, len(spatial_shape))\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_RandGridDistortion.__call___": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/array.py_RandGridDistortion.__call___", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2203, "end_line": 2226, "span_ids": ["RandGridDistortion.__call__"], "tokens": 280}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandGridDistortion(RandomizableTransform):\n\n    def __call__(\n        self,\n        img: NdarrayOrTensor,\n        mode: Optional[Union[GridSampleMode, str]] = None,\n        padding_mode: Optional[Union[GridSamplePadMode, str]] = None,\n        randomize: bool = True,\n    ) -> NdarrayOrTensor:\n        \"\"\"\n        Args:\n            img: shape must be (num_channels, H, W[, D]).\n            mode: {``\"bilinear\"``, ``\"nearest\"``}\n                Interpolation mode to calculate output values. Defaults to ``\"bilinear\"``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            padding_mode: {``\"zeros\"``, ``\"border\"``, ``\"reflection\"``}\n                Padding mode for outside grid values. Defaults to ``\"border\"``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            randomize: whether to shuffle the random factors using `randomize()`, default to True.\n        \"\"\"\n        if randomize:\n            self.randomize(img.shape[1:])\n        if not self._do_transform:\n            return img\n        return self.grid_distortion(img, distort_steps=self.distort_steps, mode=mode, padding_mode=padding_mode)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_RandRotate90d_RandRotate90d.randomize.super_randomize_None_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_RandRotate90d_RandRotate90d.randomize.super_randomize_None_", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 441, "end_line": 480, "span_ids": ["RandRotate90d", "RandRotate90d.__init__", "RandRotate90d.randomize"], "tokens": 365}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandRotate90d(RandomizableTransform, MapTransform, InvertibleTransform):\n    \"\"\"\n    Dictionary-based version :py:class:`monai.transforms.RandRotate90`.\n    With probability `prob`, input arrays are rotated by 90 degrees\n    in the plane specified by `spatial_axes`.\n    \"\"\"\n\n    backend = Rotate90.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        prob: float = 0.1,\n        max_k: int = 3,\n        spatial_axes: Tuple[int, int] = (0, 1),\n        allow_missing_keys: bool = False,\n    ) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to be transformed.\n                See also: :py:class:`monai.transforms.compose.MapTransform`\n            prob: probability of rotating.\n                (Default 0.1, with 10% probability it returns a rotated array.)\n            max_k: number of rotations will be sampled from `np.random.randint(max_k) + 1`.\n                (Default 3)\n            spatial_axes: 2 int numbers, defines the plane to rotate with 2 spatial axes.\n                Default: (0, 1), this is the first two axis in spatial dimensions.\n            allow_missing_keys: don't raise exception if key is missing.\n        \"\"\"\n        MapTransform.__init__(self, keys, allow_missing_keys)\n        RandomizableTransform.__init__(self, prob)\n\n        self.max_k = max_k\n        self.spatial_axes = spatial_axes\n\n        self._rand_k = 0\n\n    def randomize(self, data: Optional[Any] = None) -> None:\n        self._rand_k = self.R.randint(self.max_k) + 1\n        super().randomize(None)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_RandRotate90d.__call___RandRotate90d.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_RandRotate90d.__call___RandRotate90d.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 482, "end_line": 493, "span_ids": ["RandRotate90d.__call__"], "tokens": 160}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandRotate90d(RandomizableTransform, MapTransform, InvertibleTransform):\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Mapping[Hashable, NdarrayOrTensor]:\n        self.randomize()\n        d = dict(data)\n\n        # FIXME: here we didn't use array version `RandRotate90` transform as others, because we need\n        # to be compatible with the random status of some previous integration tests\n        rotator = Rotate90(self._rand_k, self.spatial_axes)\n        for key in self.key_iterator(d):\n            if self._do_transform:\n                d[key] = rotator(d[key])\n            self.push_transform(d, key, extra_info={\"rand_k\": self._rand_k})\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Flipd_Flipd.inverse.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_Flipd_Flipd.inverse.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1127, "end_line": 1167, "span_ids": ["Flipd.inverse", "Flipd.__init__", "Flipd", "Flipd.__call__"], "tokens": 337}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Flipd(MapTransform, InvertibleTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.Flip`.\n\n    See `numpy.flip` for additional details.\n    https://docs.scipy.org/doc/numpy/reference/generated/numpy.flip.html\n\n    Args:\n        keys: Keys to pick data for transformation.\n        spatial_axis: Spatial axes along which to flip over. Default is None.\n        allow_missing_keys: don't raise exception if key is missing.\n    \"\"\"\n\n    backend = Flip.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        spatial_axis: Optional[Union[Sequence[int], int]] = None,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        super().__init__(keys, allow_missing_keys)\n        self.flipper = Flip(spatial_axis=spatial_axis)\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key in self.key_iterator(d):\n            self.push_transform(d, key)\n            d[key] = self.flipper(d[key])\n        return d\n\n    def inverse(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = deepcopy(dict(data))\n        for key in self.key_iterator(d):\n            _ = self.get_most_recent_transform(d, key)\n            # Inverse is same as forward\n            d[key] = self.flipper(d[key])\n            # Remove the applied transform\n            self.pop_transform(d, key)\n\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_RandAxisFlipd_RandAxisFlipd.set_random_state.return.self": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_RandAxisFlipd_RandAxisFlipd.set_random_state.return.self", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1227, "end_line": 1253, "span_ids": ["RandAxisFlipd.__init__", "RandAxisFlipd.set_random_state", "RandAxisFlipd"], "tokens": 238}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandAxisFlipd(RandomizableTransform, MapTransform, InvertibleTransform):\n    \"\"\"\n    Dictionary-based version :py:class:`monai.transforms.RandAxisFlip`.\n\n    See `numpy.flip` for additional details.\n    https://docs.scipy.org/doc/numpy/reference/generated/numpy.flip.html\n\n    Args:\n        keys: Keys to pick data for transformation.\n        prob: Probability of flipping.\n        allow_missing_keys: don't raise exception if key is missing.\n\n    \"\"\"\n\n    backend = RandAxisFlip.backend\n\n    def __init__(self, keys: KeysCollection, prob: float = 0.1, allow_missing_keys: bool = False) -> None:\n        MapTransform.__init__(self, keys, allow_missing_keys)\n        RandomizableTransform.__init__(self, prob)\n        self.flipper = RandAxisFlip(prob=1.0)\n\n    def set_random_state(\n        self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None\n    ) -> \"RandAxisFlipd\":\n        super().set_random_state(seed, state)\n        self.flipper.set_random_state(seed, state)\n        return self", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_RandAxisFlipd.__call___RandAxisFlipd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_RandAxisFlipd.__call___RandAxisFlipd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1275, "end_line": 1289, "span_ids": ["RandAxisFlipd.__call__"], "tokens": 167}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandAxisFlipd(RandomizableTransform, MapTransform, InvertibleTransform):\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        first_key: Union[Hashable, List] = self.first_key(d)\n        if first_key == []:\n            return d\n\n        self.randomize(None)\n\n        # all the keys share the same random selected axis\n        self.flipper.randomize(d[first_key])  # type: ignore\n        for key in self.key_iterator(d):\n            if self._do_transform:\n                d[key] = self.flipper(d[key], randomize=False)\n            self.push_transform(d, key, extra_info={\"axis\": self.flipper._axis})\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_RandRotated_RandRotated.backend.RandRotate_backend": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_RandRotated_RandRotated.backend.RandRotate_backend", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1385, "end_line": 1420, "span_ids": ["RandRotated"], "tokens": 523}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandRotated(RandomizableTransform, MapTransform, InvertibleTransform):\n    \"\"\"\n    Dictionary-based version :py:class:`monai.transforms.RandRotate`\n    Randomly rotates the input arrays.\n\n    Args:\n        keys: Keys to pick data for transformation.\n        range_x: Range of rotation angle in radians in the plane defined by the first and second axes.\n            If single number, angle is uniformly sampled from (-range_x, range_x).\n        range_y: Range of rotation angle in radians in the plane defined by the first and third axes.\n            If single number, angle is uniformly sampled from (-range_y, range_y).\n        range_z: Range of rotation angle in radians in the plane defined by the second and third axes.\n            If single number, angle is uniformly sampled from (-range_z, range_z).\n        prob: Probability of rotation.\n        keep_size: If it is False, the output shape is adapted so that the\n            input array is contained completely in the output.\n            If it is True, the output shape is the same as the input. Default is True.\n        mode: {``\"bilinear\"``, ``\"nearest\"``}\n            Interpolation mode to calculate output values. Defaults to ``\"bilinear\"``.\n            See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            It also can be a sequence of string, each element corresponds to a key in ``keys``.\n        padding_mode: {``\"zeros\"``, ``\"border\"``, ``\"reflection\"``}\n            Padding mode for outside grid values. Defaults to ``\"border\"``.\n            See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n            It also can be a sequence of string, each element corresponds to a key in ``keys``.\n        align_corners: Defaults to False.\n            See also: https://pytorch.org/docs/stable/nn.functional.html#interpolate\n            It also can be a sequence of bool, each element corresponds to a key in ``keys``.\n        dtype: data type for resampling computation. Defaults to ``np.float64`` for best precision.\n            If None, use the data type of input data. To be compatible with other modules,\n            the output data type is always ``np.float32``.\n            It also can be a sequence of dtype or None, each element corresponds to a key in ``keys``.\n        allow_missing_keys: don't raise exception if key is missing.\n    \"\"\"\n\n    backend = RandRotate.backend", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_RandRotated.__init___RandRotated.set_random_state.return.self": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_RandRotated.__init___RandRotated.set_random_state.return.self", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1422, "end_line": 1449, "span_ids": ["RandRotated.set_random_state", "RandRotated.__init__"], "tokens": 373}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandRotated(RandomizableTransform, MapTransform, InvertibleTransform):\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        range_x: Union[Tuple[float, float], float] = 0.0,\n        range_y: Union[Tuple[float, float], float] = 0.0,\n        range_z: Union[Tuple[float, float], float] = 0.0,\n        prob: float = 0.1,\n        keep_size: bool = True,\n        mode: GridSampleModeSequence = GridSampleMode.BILINEAR,\n        padding_mode: GridSamplePadModeSequence = GridSamplePadMode.BORDER,\n        align_corners: Union[Sequence[bool], bool] = False,\n        dtype: Union[Sequence[Union[DtypeLike, torch.dtype]], Union[DtypeLike, torch.dtype]] = np.float64,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        MapTransform.__init__(self, keys, allow_missing_keys)\n        RandomizableTransform.__init__(self, prob)\n        self.rand_rotate = RandRotate(range_x=range_x, range_y=range_y, range_z=range_z, prob=1.0, keep_size=keep_size)\n        self.mode = ensure_tuple_rep(mode, len(self.keys))\n        self.padding_mode = ensure_tuple_rep(padding_mode, len(self.keys))\n        self.align_corners = ensure_tuple_rep(align_corners, len(self.keys))\n        self.dtype = ensure_tuple_rep(dtype, len(self.keys))\n\n    def set_random_state(\n        self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None\n    ) -> \"RandRotated\":\n        super().set_random_state(seed, state)\n        self.rand_rotate.set_random_state(seed, state)\n        return self", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_RandZoomd_RandZoomd.backend.RandZoom_backend": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_RandZoomd_RandZoomd.backend.RandZoom_backend", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1611, "end_line": 1650, "span_ids": ["RandZoomd"], "tokens": 673}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandZoomd(RandomizableTransform, MapTransform, InvertibleTransform):\n    \"\"\"\n    Dict-based version :py:class:`monai.transforms.RandZoom`.\n\n    Args:\n        keys: Keys to pick data for transformation.\n        prob: Probability of zooming.\n        min_zoom: Min zoom factor. Can be float or sequence same size as image.\n            If a float, select a random factor from `[min_zoom, max_zoom]` then apply to all spatial dims\n            to keep the original spatial shape ratio.\n            If a sequence, min_zoom should contain one value for each spatial axis.\n            If 2 values provided for 3D data, use the first value for both H & W dims to keep the same zoom ratio.\n        max_zoom: Max zoom factor. Can be float or sequence same size as image.\n            If a float, select a random factor from `[min_zoom, max_zoom]` then apply to all spatial dims\n            to keep the original spatial shape ratio.\n            If a sequence, max_zoom should contain one value for each spatial axis.\n            If 2 values provided for 3D data, use the first value for both H & W dims to keep the same zoom ratio.\n        mode: {``\"nearest\"``, ``\"linear\"``, ``\"bilinear\"``, ``\"bicubic\"``, ``\"trilinear\"``, ``\"area\"``}\n            The interpolation mode. Defaults to ``\"area\"``.\n            See also: https://pytorch.org/docs/stable/nn.functional.html#interpolate\n            It also can be a sequence of string, each element corresponds to a key in ``keys``.\n        padding_mode: available modes for numpy array:{``\"constant\"``, ``\"edge\"``, ``\"linear_ramp\"``, ``\"maximum\"``,\n            ``\"mean\"``, ``\"median\"``, ``\"minimum\"``, ``\"reflect\"``, ``\"symmetric\"``, ``\"wrap\"``, ``\"empty\"``}\n            available modes for PyTorch Tensor: {``\"constant\"``, ``\"reflect\"``, ``\"replicate\"``, ``\"circular\"``}.\n            One of the listed string values or a user supplied function. Defaults to ``\"constant\"``.\n            The mode to pad data after zooming.\n            See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html\n            https://pytorch.org/docs/stable/generated/torch.nn.functional.pad.html\n        align_corners: This only has an effect when mode is\n            'linear', 'bilinear', 'bicubic' or 'trilinear'. Default: None.\n            See also: https://pytorch.org/docs/stable/nn.functional.html#interpolate\n            It also can be a sequence of bool or None, each element corresponds to a key in ``keys``.\n        keep_size: Should keep original size (pad if needed), default is True.\n        allow_missing_keys: don't raise exception if key is missing.\n        kwargs: other args for `np.pad` API, note that `np.pad` treats channel dimension as the first dimension.\n            more details: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html\n\n    \"\"\"\n\n    backend = RandZoom.backend", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_RandZoomd.__init___RandZoomd.set_random_state.return.self": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_RandZoomd.__init___RandZoomd.set_random_state.return.self", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1652, "end_line": 1677, "span_ids": ["RandZoomd.__init__", "RandZoomd.set_random_state"], "tokens": 309}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandZoomd(RandomizableTransform, MapTransform, InvertibleTransform):\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        prob: float = 0.1,\n        min_zoom: Union[Sequence[float], float] = 0.9,\n        max_zoom: Union[Sequence[float], float] = 1.1,\n        mode: InterpolateModeSequence = InterpolateMode.AREA,\n        padding_mode: PadModeSequence = NumpyPadMode.EDGE,\n        align_corners: Union[Sequence[Optional[bool]], Optional[bool]] = None,\n        keep_size: bool = True,\n        allow_missing_keys: bool = False,\n        **kwargs,\n    ) -> None:\n        MapTransform.__init__(self, keys, allow_missing_keys)\n        RandomizableTransform.__init__(self, prob)\n        self.rand_zoom = RandZoom(prob=1.0, min_zoom=min_zoom, max_zoom=max_zoom, keep_size=keep_size, **kwargs)\n        self.mode = ensure_tuple_rep(mode, len(self.keys))\n        self.padding_mode = ensure_tuple_rep(padding_mode, len(self.keys))\n        self.align_corners = ensure_tuple_rep(align_corners, len(self.keys))\n\n    def set_random_state(\n        self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None\n    ) -> \"RandZoomd\":\n        super().set_random_state(seed, state)\n        self.rand_zoom.set_random_state(seed, state)\n        return self", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_GridDistortiond_GridDistortiond.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_GridDistortiond_GridDistortiond.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1759, "end_line": 1804, "span_ids": ["GridDistortiond", "GridDistortiond.__call__", "GridDistortiond.__init__"], "tokens": 554}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class GridDistortiond(MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.GridDistortion`.\n    \"\"\"\n\n    backend = GridDistortion.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        num_cells: Union[Tuple[int], int],\n        distort_steps: List[Tuple],\n        mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,\n        padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER,\n        device: Optional[torch.device] = None,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to be transformed.\n            num_cells: number of grid cells on each dimension.\n            distort_steps: This argument is a list of tuples, where each tuple contains the distort steps of the\n                corresponding dimensions (in the order of H, W[, D]). The length of each tuple equals to `num_cells + 1`.\n                Each value in the tuple represents the distort step of the related cell.\n            mode: {``\"bilinear\"``, ``\"nearest\"``}\n                Interpolation mode to calculate output values. Defaults to ``\"bilinear\"``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n                It also can be a sequence of string, each element corresponds to a key in ``keys``.\n            padding_mode: {``\"zeros\"``, ``\"border\"``, ``\"reflection\"``}\n                Padding mode for outside grid values. Defaults to ``\"reflection\"``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n                It also can be a sequence of string, each element corresponds to a key in ``keys``.\n            device: device on which the tensor will be allocated.\n            allow_missing_keys: don't raise exception if key is missing.\n\n        \"\"\"\n        super().__init__(keys, allow_missing_keys)\n        self.grid_distortion = GridDistortion(num_cells=num_cells, distort_steps=distort_steps, device=device)\n        self.mode = ensure_tuple_rep(mode, len(self.keys))\n        self.padding_mode = ensure_tuple_rep(padding_mode, len(self.keys))\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key, mode, padding_mode in self.key_iterator(d, self.mode, self.padding_mode):\n            d[key] = self.grid_distortion(d[key], mode=mode, padding_mode=padding_mode)\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_RandGridDistortiond_RandGridDistortiond.set_random_state.return.self": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_RandGridDistortiond_RandGridDistortiond.set_random_state.return.self", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1807, "end_line": 1858, "span_ids": ["RandGridDistortiond.__init__", "RandGridDistortiond", "RandGridDistortiond.set_random_state"], "tokens": 594}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandGridDistortiond(RandomizableTransform, MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.RandGridDistortion`.\n    \"\"\"\n\n    backend = RandGridDistortion.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        num_cells: Union[Tuple[int], int] = 5,\n        prob: float = 0.1,\n        distort_limit: Union[Tuple[float, float], float] = (-0.03, 0.03),\n        mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,\n        padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER,\n        device: Optional[torch.device] = None,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to be transformed.\n            num_cells: number of grid cells on each dimension.\n            prob: probability of returning a randomized grid distortion transform. Defaults to 0.1.\n            distort_limit: range to randomly distort.\n                If single number, distort_limit is picked from (-distort_limit, distort_limit).\n                Defaults to (-0.03, 0.03).\n            mode: {``\"bilinear\"``, ``\"nearest\"``}\n                Interpolation mode to calculate output values. Defaults to ``\"bilinear\"``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n                It also can be a sequence of string, each element corresponds to a key in ``keys``.\n            padding_mode: {``\"zeros\"``, ``\"border\"``, ``\"reflection\"``}\n                Padding mode for outside grid values. Defaults to ``\"reflection\"``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n                It also can be a sequence of string, each element corresponds to a key in ``keys``.\n            device: device on which the tensor will be allocated.\n            allow_missing_keys: don't raise exception if key is missing.\n\n        \"\"\"\n        MapTransform.__init__(self, keys, allow_missing_keys)\n        RandomizableTransform.__init__(self, prob)\n        self.rand_grid_distortion = RandGridDistortion(\n            num_cells=num_cells, prob=1.0, distort_limit=distort_limit, device=device\n        )\n        self.mode = ensure_tuple_rep(mode, len(self.keys))\n        self.padding_mode = ensure_tuple_rep(padding_mode, len(self.keys))\n\n    def set_random_state(\n        self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None\n    ) -> \"RandGridDistortiond\":\n        super().set_random_state(seed, state)\n        self.rand_grid_distortion.set_random_state(seed, state)\n        return self", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_RandGridDistortiond.__call___RandGridDistortiond.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_RandGridDistortiond.__call___RandGridDistortiond.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1888, "end_line": 1901, "span_ids": ["RandGridDistortiond.__call__"], "tokens": 167}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandGridDistortiond(RandomizableTransform, MapTransform):\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        self.randomize(None)\n        if not self._do_transform:\n            return d\n\n        first_key: Union[Hashable, List] = self.first_key(d)\n        if first_key == []:\n            return d\n\n        self.rand_grid_distortion.randomize(d[first_key].shape[1:])  # type: ignore\n        for key, mode, padding_mode in self.key_iterator(d, self.mode, self.padding_mode):\n            d[key] = self.rand_grid_distortion(d[key], mode=mode, padding_mode=padding_mode, randomize=False)\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/transform.py_logging_ReturnType.TypeVar_ReturnType_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/transform.py_logging_ReturnType.TypeVar_ReturnType_", "embedding": null, "metadata": {"file_path": "monai/transforms/transform.py", "file_name": "transform.py", "file_type": "text/x-python", "category": "implementation", "start_line": 15, "end_line": 29, "span_ids": ["docstring"], "tokens": 119}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import logging\nfrom abc import ABC, abstractmethod\nfrom typing import Any, Callable, Dict, Generator, Hashable, Iterable, List, Optional, Tuple, TypeVar, Union\n\nimport numpy as np\nimport torch\n\nfrom monai import transforms\nfrom monai.config import KeysCollection\nfrom monai.utils import MAX_SEED, ensure_tuple, first\nfrom monai.utils.enums import TransformBackends\n\n__all__ = [\"ThreadUnsafe\", \"apply_transform\", \"Randomizable\", \"RandomizableTransform\", \"Transform\", \"MapTransform\"]\n\nReturnType = TypeVar(\"ReturnType\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/transform.py__apply_transform__apply_transform.return.transform_parameters_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/transform.py__apply_transform__apply_transform.return.transform_parameters_", "embedding": null, "metadata": {"file_path": "monai/transforms/transform.py", "file_name": "transform.py", "file_type": "text/x-python", "category": "implementation", "start_line": 32, "end_line": 53, "span_ids": ["_apply_transform"], "tokens": 167}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _apply_transform(\n    transform: Callable[..., ReturnType], parameters: Any, unpack_parameters: bool = False\n) -> ReturnType:\n    \"\"\"\n    Perform transformation `transform` with the provided parameters `parameters`.\n\n    If `parameters` is a tuple and `unpack_items` is True, each parameter of `parameters` is unpacked\n    as arguments to `transform`.\n    Otherwise `parameters` is considered as single argument to `transform`.\n\n    Args:\n        transform: a callable to be used to transform `data`.\n        parameters: parameters for the `transform`.\n        unpack_parameters: whether to unpack parameters for `transform`. Defaults to False.\n\n    Returns:\n        ReturnType: The return type of `transform`.\n    \"\"\"\n    if isinstance(parameters, tuple) and unpack_parameters:\n        return transform(*parameters)\n\n    return transform(parameters)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_logging_Identity.__call__.return.img": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_logging_Identity.__call__.return.img", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 16, "end_line": 104, "span_ids": ["Identity.__call__", "Identity", "docstring"], "tokens": 561}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import logging\nimport sys\nimport time\nimport warnings\nfrom typing import Callable, Dict, List, Mapping, Optional, Sequence, Tuple, Union\n\nimport numpy as np\nimport torch\n\nfrom monai.config import DtypeLike\nfrom monai.config.type_definitions import NdarrayOrTensor\nfrom monai.transforms.transform import Randomizable, RandomizableTransform, Transform\nfrom monai.transforms.utils import (\n    extreme_points_to_image,\n    get_extreme_points,\n    map_binary_to_indices,\n    map_classes_to_indices,\n)\nfrom monai.transforms.utils_pytorch_numpy_unification import concatenate, in1d, moveaxis, unravel_indices\nfrom monai.utils import (\n    convert_data_type,\n    convert_to_cupy,\n    convert_to_numpy,\n    convert_to_tensor,\n    deprecated_arg,\n    ensure_tuple,\n    look_up_option,\n    min_version,\n    optional_import,\n)\nfrom monai.utils.enums import TransformBackends\nfrom monai.utils.misc import is_module_ver_at_least\nfrom monai.utils.type_conversion import convert_to_dst_type, get_equivalent_dtype\n\nPILImageImage, has_pil = optional_import(\"PIL.Image\", name=\"Image\")\npil_image_fromarray, _ = optional_import(\"PIL.Image\", name=\"fromarray\")\ncp, has_cp = optional_import(\"cupy\")\n\n\n__all__ = [\n    \"Identity\",\n    \"AsChannelFirst\",\n    \"AsChannelLast\",\n    \"AddChannel\",\n    \"AddCoordinateChannels\",\n    \"EnsureChannelFirst\",\n    \"EnsureType\",\n    \"RepeatChannel\",\n    \"RemoveRepeatedChannel\",\n    \"SplitChannel\",\n    \"CastToType\",\n    \"ToTensor\",\n    \"ToNumpy\",\n    \"ToPIL\",\n    \"Transpose\",\n    \"SqueezeDim\",\n    \"DataStats\",\n    \"SimulateDelay\",\n    \"Lambda\",\n    \"RandLambda\",\n    \"LabelToMask\",\n    \"FgBgToIndices\",\n    \"ClassesToIndices\",\n    \"ConvertToMultiChannelBasedOnBratsClasses\",\n    \"AddExtremePointsChannel\",\n    \"TorchVision\",\n    \"MapLabelValue\",\n    \"IntensityStats\",\n    \"ToDevice\",\n    \"CuCIM\",\n    \"RandCuCIM\",\n    \"ToCupy\",\n]\n\n\nclass Identity(Transform):\n    \"\"\"\n    Do nothing to the data.\n    As the output value is same as input, it can be used as a testing tool to verify the transform chain,\n    Compose or transform adaptor, etc.\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:\n        \"\"\"\n        Apply the transform to `img`.\n        \"\"\"\n        return img", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_AsChannelFirst_AsChannelFirst.__call__.return.moveaxis_img_self_channe": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_AsChannelFirst_AsChannelFirst.__call__.return.moveaxis_img_self_channe", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 102, "end_line": 129, "span_ids": ["AsChannelFirst.__init__", "AsChannelFirst", "AsChannelFirst.__call__"], "tokens": 268}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AsChannelFirst(Transform):\n    \"\"\"\n    Change the channel dimension of the image to the first dimension.\n\n    Most of the image transformations in ``monai.transforms``\n    assume the input image is in the channel-first format, which has the shape\n    (num_channels, spatial_dim_1[, spatial_dim_2, ...]).\n\n    This transform could be used to convert, for example, a channel-last image array in shape\n    (spatial_dim_1[, spatial_dim_2, ...], num_channels) into the channel-first format,\n    so that the multidimensional image array can be correctly interpreted by the other transforms.\n\n    Args:\n        channel_dim: which dimension of input image is the channel, default is the last dimension.\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __init__(self, channel_dim: int = -1) -> None:\n        if not (isinstance(channel_dim, int) and channel_dim >= -1):\n            raise AssertionError(\"invalid channel dimension.\")\n        self.channel_dim = channel_dim\n\n    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:\n        \"\"\"\n        Apply the transform to `img`.\n        \"\"\"\n        return moveaxis(img, self.channel_dim, 0)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_AsChannelLast_AsChannelLast.__call__.return.moveaxis_img_self_channe": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_AsChannelLast_AsChannelLast.__call__.return.moveaxis_img_self_channe", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 132, "end_line": 158, "span_ids": ["AsChannelLast.__init__", "AsChannelLast.__call__", "AsChannelLast"], "tokens": 262}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AsChannelLast(Transform):\n    \"\"\"\n    Change the channel dimension of the image to the last dimension.\n\n    Some of other 3rd party transforms assume the input image is in the channel-last format with shape\n    (spatial_dim_1[, spatial_dim_2, ...], num_channels).\n\n    This transform could be used to convert, for example, a channel-first image array in shape\n    (num_channels, spatial_dim_1[, spatial_dim_2, ...]) into the channel-last format,\n    so that MONAI transforms can construct a chain with other 3rd party transforms together.\n\n    Args:\n        channel_dim: which dimension of input image is the channel, default is the first dimension.\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __init__(self, channel_dim: int = 0) -> None:\n        if not (isinstance(channel_dim, int) and channel_dim >= -1):\n            raise AssertionError(\"invalid channel dimension.\")\n        self.channel_dim = channel_dim\n\n    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:\n        \"\"\"\n        Apply the transform to `img`.\n        \"\"\"\n        return moveaxis(img, self.channel_dim, -1)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_RepeatChannel_RepeatChannel.__call__._type_ignore": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_RepeatChannel_RepeatChannel.__call__._type_ignore", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 228, "end_line": 250, "span_ids": ["RepeatChannel.__call__", "RepeatChannel.__init__", "RepeatChannel"], "tokens": 242}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RepeatChannel(Transform):\n    \"\"\"\n    Repeat channel data to construct expected input shape for models.\n    The `repeats` count includes the origin data, for example:\n    ``RepeatChannel(repeats=2)([[1, 2], [3, 4]])`` generates: ``[[1, 2], [1, 2], [3, 4], [3, 4]]``\n\n    Args:\n        repeats: the number of repetitions for each element.\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __init__(self, repeats: int) -> None:\n        if repeats <= 0:\n            raise AssertionError(\"repeats count must be greater than 0.\")\n        self.repeats = repeats\n\n    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:\n        \"\"\"\n        Apply the transform to `img`, assuming `img` is a \"channel-first\" array.\n        \"\"\"\n        repeat_fn = torch.repeat_interleave if isinstance(img, torch.Tensor) else np.repeat\n        return repeat_fn(img, self.repeats, 0)  # type: ignore", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_RemoveRepeatedChannel_RemoveRepeatedChannel.__call__.return.img_self_repeats_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_RemoveRepeatedChannel_RemoveRepeatedChannel.__call__.return.img_self_repeats_", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 250, "end_line": 275, "span_ids": ["RemoveRepeatedChannel.__init__", "RemoveRepeatedChannel.__call__", "RemoveRepeatedChannel"], "tokens": 244}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RemoveRepeatedChannel(Transform):\n    \"\"\"\n    RemoveRepeatedChannel data to undo RepeatChannel\n    The `repeats` count specifies the deletion of the origin data, for example:\n    ``RemoveRepeatedChannel(repeats=2)([[1, 2], [1, 2], [3, 4], [3, 4]])`` generates: ``[[1, 2], [3, 4]]``\n\n    Args:\n        repeats: the number of repetitions to be deleted for each element.\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __init__(self, repeats: int) -> None:\n        if repeats <= 0:\n            raise AssertionError(\"repeats count must be greater than 0.\")\n\n        self.repeats = repeats\n\n    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:\n        \"\"\"\n        Apply the transform to `img`, assuming `img` is a \"channel-first\" array.\n        \"\"\"\n        if img.shape[0] < 2:\n            raise AssertionError(\"Image must have more than one channel\")\n\n        return img[:: self.repeats, :]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_SplitChannel_SplitChannel.__call__.return.outputs": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_SplitChannel_SplitChannel.__call__.return.outputs", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 278, "end_line": 304, "span_ids": ["SplitChannel.__call__", "SplitChannel", "SplitChannel.__init__"], "tokens": 206}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SplitChannel(Transform):\n    \"\"\"\n    Split Numpy array or PyTorch Tensor data according to the channel dim.\n    It can help applying different following transforms to different channels.\n\n    Args:\n        channel_dim: which dimension of input image is the channel, default to 0.\n\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __init__(self, channel_dim: int = 0) -> None:\n        self.channel_dim = channel_dim\n\n    def __call__(self, img: NdarrayOrTensor) -> List[NdarrayOrTensor]:\n        num_classes = img.shape[self.channel_dim]\n        if num_classes <= 1:\n            raise RuntimeError(\"input image does not contain multiple channels.\")\n\n        outputs = []\n        slices = [slice(None)] * len(img.shape)\n        for i in range(num_classes):\n            slices[self.channel_dim] = slice(i, i + 1)\n            outputs.append(img[tuple(slices)])\n\n        return outputs", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_CastToType_CastToType.__call__.return.img_out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_CastToType_CastToType.__call__.return.img_out", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 307, "end_line": 334, "span_ids": ["CastToType.__call__", "CastToType", "CastToType.__init__"], "tokens": 235}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CastToType(Transform):\n    \"\"\"\n    Cast the Numpy data to specified numpy data type, or cast the PyTorch Tensor to\n    specified PyTorch data type.\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __init__(self, dtype=np.float32) -> None:\n        \"\"\"\n        Args:\n            dtype: convert image to this data type, default is `np.float32`.\n        \"\"\"\n        self.dtype = dtype\n\n    def __call__(self, img: NdarrayOrTensor, dtype: Optional[Union[DtypeLike, torch.dtype]] = None) -> NdarrayOrTensor:\n        \"\"\"\n        Apply the transform to `img`, assuming `img` is a numpy array or PyTorch Tensor.\n\n        Args:\n            dtype: convert image to this data type, default is `self.dtype`.\n\n        Raises:\n            TypeError: When ``img`` type is not in ``Union[numpy.ndarray, torch.Tensor]``.\n\n        \"\"\"\n        img_out, *_ = convert_data_type(img, output_type=type(img), dtype=dtype or self.dtype)\n        return img_out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_EnsureType.__call___EnsureType.__call__.return.out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_EnsureType.__call___EnsureType.__call__.return.out", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 402, "end_line": 415, "span_ids": ["EnsureType.__call__"], "tokens": 163}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class EnsureType(Transform):\n\n    def __call__(self, data: NdarrayOrTensor):\n        \"\"\"\n        Args:\n            data: input data can be PyTorch Tensor, numpy array, list, dictionary, int, float, bool, str, etc.\n                will ensure Tensor, Numpy array, float, int, bool as Tensors or numpy arrays, strings and\n                objects keep the original. for dictionary, list or tuple, ensure every item as expected type\n                if applicable and `wrap_sequence=False`.\n\n        \"\"\"\n        output_type = torch.Tensor if self.data_type == \"tensor\" else np.ndarray\n        out, *_ = convert_data_type(\n            data=data, output_type=output_type, dtype=self.dtype, device=self.device, wrap_sequence=self.wrap_sequence\n        )\n        return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_ToPIL_ToPIL.__call__.return.pil_image_fromarray_img_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_ToPIL_ToPIL.__call__.return.pil_image_fromarray_img_", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 435, "end_line": 450, "span_ids": ["ToPIL.__call__", "ToPIL"], "tokens": 111}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ToPIL(Transform):\n    \"\"\"\n    Converts the input image (in the form of NumPy array or PyTorch Tensor) to PIL image\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __call__(self, img):\n        \"\"\"\n        Apply the transform to `img`.\n        \"\"\"\n        if isinstance(img, PILImageImage):\n            return img\n        if isinstance(img, torch.Tensor):\n            img = img.detach().cpu().numpy()\n        return pil_image_fromarray(img)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_Transpose_Transpose.__call__._type_ignore": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_Transpose_Transpose.__call__._type_ignore", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 453, "end_line": 469, "span_ids": ["Transpose.__init__", "Transpose.__call__", "Transpose"], "tokens": 141}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Transpose(Transform):\n    \"\"\"\n    Transposes the input image based on the given `indices` dimension ordering.\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __init__(self, indices: Optional[Sequence[int]]) -> None:\n        self.indices = None if indices is None else tuple(indices)\n\n    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:\n        \"\"\"\n        Apply the transform to `img`.\n        \"\"\"\n        if isinstance(img, torch.Tensor):\n            return img.permute(self.indices or tuple(range(img.ndim)[::-1]))\n        return img.transpose(self.indices)  # type: ignore", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_SqueezeDim_SqueezeDim.__call__.return.img_squeeze_self_dim_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_SqueezeDim_SqueezeDim.__call__.return.img_squeeze_self_dim_", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 472, "end_line": 503, "span_ids": ["SqueezeDim.__call__", "SqueezeDim.__init__", "SqueezeDim"], "tokens": 237}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SqueezeDim(Transform):\n    \"\"\"\n    Squeeze a unitary dimension.\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __init__(self, dim: Optional[int] = 0) -> None:\n        \"\"\"\n        Args:\n            dim: dimension to be squeezed. Default = 0\n                \"None\" works when the input is numpy array.\n\n        Raises:\n            TypeError: When ``dim`` is not an ``Optional[int]``.\n\n        \"\"\"\n        if dim is not None and not isinstance(dim, int):\n            raise TypeError(f\"dim must be None or a int but is {type(dim).__name__}.\")\n        self.dim = dim\n\n    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:\n        \"\"\"\n        Args:\n            img: numpy arrays with required dimension `dim` removed\n        \"\"\"\n        if self.dim is None:\n            return img.squeeze()\n        # for pytorch/numpy unification\n        if img.shape[self.dim] != 1:\n            raise ValueError(\"Can only squeeze singleton dimension\")\n        return img.squeeze(self.dim)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_RandLambda_RandLambda.__call__.return.super___call___img_img_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_RandLambda_RandLambda.__call__.return.super___call___img_img_", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 683, "end_line": 703, "span_ids": ["RandLambda.__call__", "RandLambda.__init__", "RandLambda"], "tokens": 212}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandLambda(Lambda, RandomizableTransform):\n    \"\"\"\n    Randomizable version :py:class:`monai.transforms.Lambda`, the input `func` may contain random logic,\n    or randomly execute the function based on `prob`.\n\n    Args:\n        func: Lambda/function to be applied.\n        prob: probability of executing the random function, default to 1.0, with 100% probability to execute.\n\n    For more details, please check :py:class:`monai.transforms.Lambda`.\n    \"\"\"\n\n    backend = Lambda.backend\n\n    def __init__(self, func: Optional[Callable] = None, prob: float = 1.0) -> None:\n        Lambda.__init__(self=self, func=func)\n        RandomizableTransform.__init__(self=self, prob=prob)\n\n    def __call__(self, img: NdarrayOrTensor, func: Optional[Callable] = None):\n        self.randomize(img)\n        return super().__call__(img=img, func=func) if self._do_transform else img", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_LabelToMask.__call___LabelToMask.__call__.return.data": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_LabelToMask.__call___LabelToMask.__call__.return.data", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 770, "end_line": 807, "span_ids": ["LabelToMask.__call__"], "tokens": 419}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LabelToMask(Transform):\n\n    def __call__(\n        self,\n        img: NdarrayOrTensor,\n        select_labels: Optional[Union[Sequence[int], int]] = None,\n        merge_channels: bool = False,\n    ) -> NdarrayOrTensor:\n        \"\"\"\n        Args:\n            select_labels: labels to generate mask from. for 1 channel label, the `select_labels`\n                is the expected label values, like: [1, 2, 3]. for One-Hot format label, the\n                `select_labels` is the expected channel indices.\n            merge_channels: whether to use `np.any()` to merge the result on channel dim. if yes,\n                will return a single channel mask with binary data.\n        \"\"\"\n        if select_labels is None:\n            select_labels = self.select_labels\n        else:\n            select_labels = ensure_tuple(select_labels)\n\n        if img.shape[0] > 1:\n            data = img[[*select_labels]]\n        else:\n            where: Callable = np.where if isinstance(img, np.ndarray) else torch.where  # type: ignore\n            if isinstance(img, np.ndarray) or is_module_ver_at_least(torch, (1, 8, 0)):\n                data = where(in1d(img, select_labels), True, False).reshape(img.shape)\n            # pre pytorch 1.8.0, need to use 1/0 instead of True/False\n            else:\n                data = where(\n                    in1d(img, select_labels), torch.tensor(1, device=img.device), torch.tensor(0, device=img.device)\n                ).reshape(img.shape)\n\n        if merge_channels or self.merge_channels:\n            if isinstance(img, np.ndarray) or is_module_ver_at_least(torch, (1, 8, 0)):\n                return data.any(0)[None]\n            # pre pytorch 1.8.0 compatibility\n            return data.to(torch.uint8).any(0)[None].to(bool)  # type: ignore\n\n        return data", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_ClassesToIndices_ClassesToIndices.__init__.self.output_shape.output_shape": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_ClassesToIndices_ClassesToIndices.__init__.self.output_shape.output_shape", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 817, "end_line": 843, "span_ids": ["ClassesToIndices", "ClassesToIndices.__init__"], "tokens": 267}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ClassesToIndices(Transform):\n\n    backend = [TransformBackends.NUMPY, TransformBackends.TORCH]\n\n    def __init__(\n        self,\n        num_classes: Optional[int] = None,\n        image_threshold: float = 0.0,\n        output_shape: Optional[Sequence[int]] = None,\n    ) -> None:\n        \"\"\"\n        Compute indices of every class of the input label data, return a list of indices.\n        If no output_shape specified, output data will be 1 dim indices after flattening.\n        This transform can help pre-compute indices of the class regions for other transforms.\n        A typical usage is to randomly select indices of classes to crop.\n        The main logic is based on :py:class:`monai.transforms.utils.map_classes_to_indices`.\n\n        Args:\n            num_classes: number of classes for argmax label, not necessary for One-Hot label.\n            image_threshold: if enabled `image` at runtime, use ``image > image_threshold`` to\n                determine the valid image content area and select only the indices of classes in this area.\n            output_shape: expected shape of output indices. if not None, unravel indices to specified shape.\n\n        \"\"\"\n        self.num_classes = num_classes\n        self.image_threshold = image_threshold\n        self.output_shape = output_shape", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_ClassesToIndices.__call___ClassesToIndices.__call__.return.indices": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_ClassesToIndices.__call___ClassesToIndices.__call__.return.indices", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 845, "end_line": 867, "span_ids": ["ClassesToIndices.__call__"], "tokens": 206}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ClassesToIndices(Transform):\n\n    def __call__(\n        self,\n        label: NdarrayOrTensor,\n        image: Optional[NdarrayOrTensor] = None,\n        output_shape: Optional[Sequence[int]] = None,\n    ) -> List[NdarrayOrTensor]:\n        \"\"\"\n        Args:\n            label: input data to compute the indices of every class.\n            image: if image is not None, use ``image > image_threshold`` to define valid region, and only select\n                the indices within the valid region.\n            output_shape: expected shape of output indices. if None, use `self.output_shape` instead.\n\n        \"\"\"\n\n        if output_shape is None:\n            output_shape = self.output_shape\n        indices: List[NdarrayOrTensor]\n        indices = map_classes_to_indices(label, self.num_classes, image, self.image_threshold)\n        if output_shape is not None:\n            indices = [unravel_indices(cls_indices, output_shape) for cls_indices in indices]\n\n        return indices", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_ConvertToMultiChannelBasedOnBratsClasses_ConvertToMultiChannelBasedOnBratsClasses.__call__.return.torch_stack_result_dim_0": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_ConvertToMultiChannelBasedOnBratsClasses_ConvertToMultiChannelBasedOnBratsClasses.__call__.return.torch_stack_result_dim_0", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 873, "end_line": 893, "span_ids": ["ConvertToMultiChannelBasedOnBratsClasses.__call__", "ConvertToMultiChannelBasedOnBratsClasses"], "tokens": 274}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ConvertToMultiChannelBasedOnBratsClasses(Transform):\n    \"\"\"\n    Convert labels to multi channels based on brats18 classes:\n    label 1 is the necrotic and non-enhancing tumor core\n    label 2 is the the peritumoral edema\n    label 4 is the GD-enhancing tumor\n    The possible classes are TC (Tumor core), WT (Whole tumor)\n    and ET (Enhancing tumor).\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:\n        # if img has channel dim, squeeze it\n        if img.ndim == 4 and img.shape[0] == 1:\n            img = img.squeeze(0)\n\n        result = [(img == 1) | (img == 4), (img == 1) | (img == 4) | (img == 2), img == 4]\n        # merge labels 1 (tumor non-enh) and 4 (tumor enh) and 2 (large edema) to WT\n        # label 4 is ET\n        return torch.stack(result, dim=0) if isinstance(img, torch.Tensor) else np.stack(result, axis=0)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_AddExtremePointsChannel.__call___AddExtremePointsChannel.__call__.return.concatenate_img_points_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_AddExtremePointsChannel.__call___AddExtremePointsChannel.__call__.return.concatenate_img_points_", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 925, "end_line": 956, "span_ids": ["AddExtremePointsChannel.__call__"], "tokens": 350}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AddExtremePointsChannel(Randomizable, Transform):\n\n    def __call__(\n        self,\n        img: NdarrayOrTensor,\n        label: Optional[NdarrayOrTensor] = None,\n        sigma: Union[Sequence[float], float, Sequence[torch.Tensor], torch.Tensor] = 3.0,\n        rescale_min: float = -1.0,\n        rescale_max: float = 1.0,\n    ) -> NdarrayOrTensor:\n        \"\"\"\n        Args:\n            img: the image that we want to add new channel to.\n            label: label image to get extreme points from. Shape must be\n                (1, spatial_dim1, [, spatial_dim2, ...]). Doesn't support one-hot labels.\n            sigma: if a list of values, must match the count of spatial dimensions of input data,\n                and apply every value in the list to 1 spatial dimension. if only 1 value provided,\n                use it for all spatial dimensions.\n            rescale_min: minimum value of output data.\n            rescale_max: maximum value of output data.\n        \"\"\"\n        if label is None:\n            raise ValueError(\"This transform requires a label array!\")\n        if label.shape[0] != 1:\n            raise ValueError(\"Only supports single channel labels!\")\n\n        # Generate extreme points\n        self.randomize(label[0, :])\n\n        points_image = extreme_points_to_image(\n            points=self._points, label=label, sigma=sigma, rescale_min=rescale_min, rescale_max=rescale_max\n        )\n        points_image, *_ = convert_to_dst_type(points_image, img)  # type: ignore\n        return concatenate((img, points_image), axis=0)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_TorchVision_TorchVision.__call__.return.out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_TorchVision_TorchVision.__call__.return.out", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 994, "end_line": 1026, "span_ids": ["TorchVision.__call__", "TorchVision.__init__", "TorchVision"], "tokens": 278}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TorchVision:\n    \"\"\"\n    This is a wrapper transform for PyTorch TorchVision transform based on the specified transform name and args.\n    As most of the TorchVision transforms only work for PIL image and PyTorch Tensor, this transform expects input\n    data to be PyTorch Tensor, users can easily call `ToTensor` transform to convert a Numpy array to Tensor.\n\n    \"\"\"\n\n    backend = [TransformBackends.TORCH]\n\n    def __init__(self, name: str, *args, **kwargs) -> None:\n        \"\"\"\n        Args:\n            name: The transform name in TorchVision package.\n            args: parameters for the TorchVision transform.\n            kwargs: parameters for the TorchVision transform.\n\n        \"\"\"\n        super().__init__()\n        self.name = name\n        transform, _ = optional_import(\"torchvision.transforms\", \"0.8.0\", min_version, name=name)\n        self.trans = transform(*args, **kwargs)\n\n    def __call__(self, img: NdarrayOrTensor):\n        \"\"\"\n        Args:\n            img: PyTorch Tensor data for the TorchVision transform.\n\n        \"\"\"\n        img_t, *_ = convert_data_type(img, torch.Tensor)  # type: ignore\n        out = self.trans(img_t)\n        out, *_ = convert_to_dst_type(src=out, dst=img)\n        return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_MapLabelValue_MapLabelValue.__init__.self.dtype.get_equivalent_dtype_dtyp": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_MapLabelValue_MapLabelValue.__init__.self.dtype.get_equivalent_dtype_dtyp", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 993, "end_line": 1019, "span_ids": ["MapLabelValue.__init__", "MapLabelValue"], "tokens": 332}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class MapLabelValue:\n    \"\"\"\n    Utility to map label values to another set of values.\n    For example, map [3, 2, 1] to [0, 1, 2], [1, 2, 3] -> [0.5, 1.5, 2.5], [\"label3\", \"label2\", \"label1\"] -> [0, 1, 2],\n    [3.5, 2.5, 1.5] -> [\"label0\", \"label1\", \"label2\"], etc.\n    The label data must be numpy array or array-like data and the output data will be numpy array.\n\n    \"\"\"\n\n    backend = [TransformBackends.NUMPY]\n\n    def __init__(self, orig_labels: Sequence, target_labels: Sequence, dtype: DtypeLike = np.float32) -> None:\n        \"\"\"\n        Args:\n            orig_labels: original labels that map to others.\n            target_labels: expected label values, 1: 1 map to the `orig_labels`.\n            dtype: convert the output data to dtype, default to float32.\n\n        \"\"\"\n        if len(orig_labels) != len(target_labels):\n            raise ValueError(\"orig_labels and target_labels must have the same length.\")\n        if all(o == z for o, z in zip(orig_labels, target_labels)):\n            raise ValueError(\"orig_labels and target_labels are exactly the same, should be different to map.\")\n\n        self.orig_labels = orig_labels\n        self.target_labels = target_labels\n        self.dtype = get_equivalent_dtype(dtype, data_type=np.ndarray)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_MapLabelValue.__call___MapLabelValue.__call__.return.out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_MapLabelValue.__call___MapLabelValue.__call__.return.out", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1052, "end_line": 1068, "span_ids": ["MapLabelValue.__call__"], "tokens": 170}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class MapLabelValue:\n\n    def __call__(self, img: NdarrayOrTensor):\n        img_np, *_ = convert_data_type(img, np.ndarray)\n        img_flat = img_np.flatten()\n        try:\n            out_flat = np.array(img_flat, dtype=self.dtype)\n        except ValueError:\n            # can't copy unchanged labels as the expected dtype is not supported, must map all the label values\n            out_flat = np.zeros(shape=img_flat.shape, dtype=self.dtype)\n\n        for o, t in zip(self.orig_labels, self.target_labels):\n            if o == t:\n                continue\n            np.place(out_flat, img_flat == o, t)\n\n        reshaped = out_flat.reshape(img_np.shape)\n        out, *_ = convert_to_dst_type(src=reshaped, dst=img, dtype=self.dtype)\n        return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_IntensityStats_IntensityStats.__init__.self.channel_wise.channel_wise": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_IntensityStats_IntensityStats.__init__.self.channel_wise.channel_wise", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1040, "end_line": 1064, "span_ids": ["IntensityStats", "IntensityStats.__init__"], "tokens": 332}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class IntensityStats(Transform):\n    \"\"\"\n    Compute statistics for the intensity values of input image and store into the meta data dictionary.\n    For example: if `ops=[lambda x: np.mean(x), \"max\"]` and `key_prefix=\"orig\"`, may generate below stats:\n    `{\"orig_custom_0\": 1.5, \"orig_max\": 3.0}`.\n\n    Args:\n        ops: expected operations to compute statistics for the intensity.\n            if a string, will map to the predefined operations, supported: [\"mean\", \"median\", \"max\", \"min\", \"std\"]\n            mapping to `np.nanmean`, `np.nanmedian`, `np.nanmax`, `np.nanmin`, `np.nanstd`.\n            if a callable function, will execute the function on input image.\n        key_prefix: the prefix to combine with `ops` name to generate the key to store the results in the\n            meta data dictionary. if some `ops` are callable functions, will use \"{key_prefix}_custom_{index}\"\n            as the key, where index counts from 0.\n        channel_wise: whether to compute statistics for every channel of input image separately.\n            if True, return a list of values for every operation, default to False.\n\n    \"\"\"\n\n    backend = [TransformBackends.NUMPY]\n\n    def __init__(self, ops: Sequence[Union[str, Callable]], key_prefix: str, channel_wise: bool = False) -> None:\n        self.ops = ensure_tuple(ops)\n        self.key_prefix = key_prefix\n        self.channel_wise = channel_wise", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_IntensityStats.__call___IntensityStats.__call__.return.img_meta_data": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_IntensityStats.__call___IntensityStats.__call__.return.img_meta_data", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1066, "end_line": 1113, "span_ids": ["IntensityStats.__call__"], "tokens": 424}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class IntensityStats(Transform):\n\n    def __call__(\n        self, img: NdarrayOrTensor, meta_data: Optional[Dict] = None, mask: Optional[np.ndarray] = None\n    ) -> Tuple[NdarrayOrTensor, Dict]:\n        \"\"\"\n        Compute statistics for the intensity of input image.\n\n        Args:\n            img: input image to compute intensity stats.\n            meta_data: meta data dictionary to store the statistics data, if None, will create an empty dictionary.\n            mask: if not None, mask the image to extract only the interested area to compute statistics.\n                mask must have the same shape as input `img`.\n\n        \"\"\"\n        img_np: np.ndarray\n        img_np, *_ = convert_data_type(img, np.ndarray)  # type: ignore\n        if meta_data is None:\n            meta_data = {}\n\n        if mask is not None:\n            if mask.shape != img_np.shape or mask.dtype != bool:\n                raise TypeError(\"mask must be bool array with the same shape as input `img`.\")\n            img_np = img_np[mask]\n\n        supported_ops = {\n            \"mean\": np.nanmean,\n            \"median\": np.nanmedian,\n            \"max\": np.nanmax,\n            \"min\": np.nanmin,\n            \"std\": np.nanstd,\n        }\n\n        def _compute(op: Callable, data: np.ndarray):\n            if self.channel_wise:\n                return [op(c) for c in data]\n            return op(data)\n\n        custom_index = 0\n        for o in self.ops:\n            if isinstance(o, str):\n                o = look_up_option(o, supported_ops.keys())\n                meta_data[self.key_prefix + \"_\" + o] = _compute(supported_ops[o], img_np)  # type: ignore\n            elif callable(o):\n                meta_data[self.key_prefix + \"_custom_\" + str(custom_index)] = _compute(o, img_np)\n                custom_index += 1\n            else:\n                raise ValueError(\"ops must be key string for predefined operations or callable function.\")\n\n        return img, meta_data", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_ToDevice_ToDevice.__call__.return.img_to_self_device_sel": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_ToDevice_ToDevice.__call__.return.img_to_self_device_sel", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1116, "end_line": 1146, "span_ids": ["ToDevice.__init__", "ToDevice.__call__", "ToDevice"], "tokens": 277}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ToDevice(Transform):\n    \"\"\"\n    Move PyTorch Tensor to the specified device.\n    It can help cache data into GPU and execute following logic on GPU directly.\n\n    Note:\n        If moving data to GPU device in the multi-processing workers of DataLoader, may got below CUDA error:\n        \"RuntimeError: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing,\n        you must use the 'spawn' start method.\"\n        So usually suggest to set `num_workers=0` in the `DataLoader` or `ThreadDataLoader`.\n\n    \"\"\"\n\n    backend = [TransformBackends.TORCH]\n\n    def __init__(self, device: Union[torch.device, str], **kwargs) -> None:\n        \"\"\"\n        Args:\n            device: target device to move the Tensor, for example: \"cuda:1\".\n            kwargs: other args for the PyTorch `Tensor.to()` API, for more details:\n                https://pytorch.org/docs/stable/generated/torch.Tensor.to.html.\n\n        \"\"\"\n        self.device = device\n        self.kwargs = kwargs\n\n    def __call__(self, img: torch.Tensor):\n        if not isinstance(img, torch.Tensor):\n            raise ValueError(\"img must be PyTorch Tensor, consider converting img by `EnsureType` transform first.\")\n\n        return img.to(self.device, **self.kwargs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_CuCIM_CuCIM.__call__.return.self_transform_data_sel": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_CuCIM_CuCIM.__call__.return.self_transform_data_sel", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1185, "end_line": 1216, "span_ids": ["CuCIM.__init__", "CuCIM", "CuCIM.__call__"], "tokens": 265}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CuCIM(Transform):\n    \"\"\"\n    Wrap a non-randomized cuCIM transform, defined based on the transform name and args.\n    For randomized transforms (or randomly applying a transform) use :py:class:`monai.transforms.RandCuCIM`.\n\n    Args:\n        name: the transform name in CuCIM package\n        args: parameters for the CuCIM transform\n        kwargs: parameters for the CuCIM transform\n\n    Note:\n        CuCIM transform only work with CuPy arrays, so this transform expects input data to be `cupy.ndarray`.\n        Users can call `ToCuPy` transform to convert a numpy array or torch tensor to cupy array.\n    \"\"\"\n\n    def __init__(self, name: str, *args, **kwargs) -> None:\n        super().__init__()\n        self.name = name\n        self.transform, _ = optional_import(\"cucim.core.operations.expose.transform\", name=name)\n        self.args = args\n        self.kwargs = kwargs\n\n    def __call__(self, data):\n        \"\"\"\n        Args:\n            data: a CuPy array (`cupy.ndarray`) for the cuCIM transform\n\n        Returns:\n            `cupy.ndarray`\n\n        \"\"\"\n        return self.transform(data, *self.args, **self.kwargs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_logging_from_monai_utils_type_con": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_logging_from_monai_utils_type_con", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 18, "end_line": 67, "span_ids": ["docstring"], "tokens": 330}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import logging\nimport re\nfrom copy import deepcopy\nfrom typing import Any, Callable, Dict, Hashable, List, Mapping, Optional, Sequence, Tuple, Union\n\nimport numpy as np\nimport torch\n\nfrom monai.config import DtypeLike, KeysCollection\nfrom monai.config.type_definitions import NdarrayOrTensor\nfrom monai.data.utils import no_collation\nfrom monai.transforms.inverse import InvertibleTransform\nfrom monai.transforms.transform import MapTransform, Randomizable, RandomizableTransform\nfrom monai.transforms.utility.array import (\n    AddChannel,\n    AddCoordinateChannels,\n    AddExtremePointsChannel,\n    AsChannelFirst,\n    AsChannelLast,\n    CastToType,\n    ClassesToIndices,\n    ConvertToMultiChannelBasedOnBratsClasses,\n    CuCIM,\n    DataStats,\n    EnsureChannelFirst,\n    EnsureType,\n    FgBgToIndices,\n    Identity,\n    IntensityStats,\n    LabelToMask,\n    Lambda,\n    MapLabelValue,\n    RemoveRepeatedChannel,\n    RepeatChannel,\n    SimulateDelay,\n    SplitChannel,\n    SqueezeDim,\n    ToCupy,\n    ToDevice,\n    ToNumpy,\n    ToPIL,\n    TorchVision,\n    ToTensor,\n    Transpose,\n)\nfrom monai.transforms.utils import extreme_points_to_image, get_extreme_points\nfrom monai.transforms.utils_pytorch_numpy_unification import concatenate\nfrom monai.utils import convert_to_numpy, deprecated_arg, ensure_tuple, ensure_tuple_rep\nfrom monai.utils.enums import TraceKeys, TransformBackends\nfrom monai.utils.type_conversion import convert_to_dst_type", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_EnsureTyped_EnsureTyped.inverse.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_EnsureTyped_EnsureTyped.inverse.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 498, "end_line": 551, "span_ids": ["EnsureTyped.__init__", "EnsureTyped.inverse", "EnsureTyped.__call__", "EnsureTyped"], "tokens": 592}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class EnsureTyped(MapTransform, InvertibleTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.EnsureType`.\n\n    Ensure the input data to be a PyTorch Tensor or numpy array, support: `numpy array`, `PyTorch Tensor`,\n    `float`, `int`, `bool`, `string` and `object` keep the original.\n    If passing a dictionary, list or tuple, still return dictionary, list or tuple and recursively convert\n    every item to the expected data type if `wrap_sequence=False`.\n\n    Note: Currently, we only convert tensor data to numpy array or scalar number in the inverse operation.\n\n    \"\"\"\n\n    backend = EnsureType.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        data_type: str = \"tensor\",\n        dtype: Optional[Union[DtypeLike, torch.dtype]] = None,\n        device: Optional[torch.device] = None,\n        wrap_sequence: bool = True,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to be transformed.\n                See also: :py:class:`monai.transforms.compose.MapTransform`\n            data_type: target data type to convert, should be \"tensor\" or \"numpy\".\n            dtype: target data content type to convert, for example: np.float32, torch.float, etc.\n            device: for Tensor data type, specify the target device.\n            wrap_sequence: if `False`, then lists will recursively call this function, default to `True`.\n                E.g., if `False`, `[1, 2]` -> `[tensor(1), tensor(2)]`, if `True`, then `[1, 2]` -> `tensor([1, 2])`.\n            allow_missing_keys: don't raise exception if key is missing.\n        \"\"\"\n        super().__init__(keys, allow_missing_keys)\n        self.converter = EnsureType(data_type=data_type, dtype=dtype, device=device, wrap_sequence=wrap_sequence)\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key in self.key_iterator(d):\n            self.push_transform(d, key)\n            d[key] = self.converter(d[key])\n        return d\n\n    def inverse(self, data: Mapping[Hashable, Any]) -> Dict[Hashable, Any]:\n        d = deepcopy(dict(data))\n        for key in self.key_iterator(d):\n            # FIXME: currently, only convert tensor data to numpy array or scalar number,\n            # need to also invert numpy array but it's not easy to determine the previous data type\n            d[key] = convert_to_numpy(d[key])\n            # Remove the applied transform\n            self.pop_transform(d, key)\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_DeleteItemsd_DeleteItemsd.__init__.self.use_re.ensure_tuple_rep_use_re_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_DeleteItemsd_DeleteItemsd.__init__.self.use_re.ensure_tuple_rep_use_re_", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 657, "end_line": 677, "span_ids": ["DeleteItemsd.__init__", "DeleteItemsd"], "tokens": 230}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DeleteItemsd(MapTransform):\n    \"\"\"\n    Delete specified items from data dictionary to release memory.\n    It will remove the key-values and copy the others to construct a new dictionary.\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __init__(self, keys: KeysCollection, sep: str = \".\", use_re: Union[Sequence[bool], bool] = False) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to delete, can be \"A{sep}B{sep}C\"\n                to delete key `C` in nested dictionary, `C` can be regular expression.\n                See also: :py:class:`monai.transforms.compose.MapTransform`\n            sep: the separator tag to define nested dictionary keys, default to \".\".\n            use_re: whether the specified key is a regular expression, it also can be\n                a list of bool values, map the to keys.\n        \"\"\"\n        super().__init__(keys)\n        self.sep = sep\n        self.use_re = ensure_tuple_rep(use_re, len(self.keys))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_DeleteItemsd.__call___SelectItemsd.__call__.return._key_data_key_for_key_i": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_DeleteItemsd.__call___SelectItemsd.__call__.return._key_data_key_for_key_i", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 679, "end_line": 703, "span_ids": ["SelectItemsd.__call__", "DeleteItemsd.__call__", "SelectItemsd"], "tokens": 227}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DeleteItemsd(MapTransform):\n\n    def __call__(self, data):\n        def _delete_item(keys, d, use_re: bool = False):\n            key = keys[0]\n            if len(keys) > 1:\n                d[key] = _delete_item(keys[1:], d[key], use_re)\n                return d\n            return {k: v for k, v in d.items() if (use_re and not re.search(key, k)) or (not use_re and k != key)}\n\n        d = dict(data)\n        for key, use_re in zip(self.keys, self.use_re):\n            d = _delete_item(key.split(self.sep), d, use_re)\n\n        return d\n\n\nclass SelectItemsd(MapTransform):\n    \"\"\"\n    Select only specified items from data dictionary to release memory.\n    It will copy the selected key-values and construct and new dictionary.\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __call__(self, data):\n        return {key: data[key] for key in self.key_iterator(data)}", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_Lambdad_Lambdad.inverse.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_Lambdad_Lambdad.inverse.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 928, "end_line": 997, "span_ids": ["Lambdad", "Lambdad.__call__", "Lambdad._inverse_transform", "Lambdad.inverse", "Lambdad._transform", "Lambdad.__init__"], "tokens": 717}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Lambdad(MapTransform, InvertibleTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.Lambda`.\n\n    For example:\n\n    .. code-block:: python\n        :emphasize-lines: 2\n\n        input_data={'image': np.zeros((10, 2, 2)), 'label': np.ones((10, 2, 2))}\n        lambd = Lambdad(keys='label', func=lambda x: x[:4, :, :])\n        print(lambd(input_data)['label'].shape)\n        (4, 2, 2)\n\n    Args:\n        keys: keys of the corresponding items to be transformed.\n            See also: :py:class:`monai.transforms.compose.MapTransform`\n        func: Lambda/function to be applied. It also can be a sequence of Callable,\n            each element corresponds to a key in ``keys``.\n        inv_func: Lambda/function of inverse operation if want to invert transforms, default to `lambda x: x`.\n            It also can be a sequence of Callable, each element corresponds to a key in ``keys``.\n        overwrite: whether to overwrite the original data in the input dictionary with lamdbda function output.\n            default to True. it also can be a sequence of bool, each element corresponds to a key in ``keys``.\n        allow_missing_keys: don't raise exception if key is missing.\n\n    Note: The inverse operation doesn't allow to define `extra_info` or access other information, such as the\n        image's original size. If need these complicated information, please write a new InvertibleTransform directly.\n\n    \"\"\"\n\n    backend = Lambda.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        func: Union[Sequence[Callable], Callable],\n        inv_func: Union[Sequence[Callable], Callable] = no_collation,\n        overwrite: Union[Sequence[bool], bool] = True,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        super().__init__(keys, allow_missing_keys)\n        self.func = ensure_tuple_rep(func, len(self.keys))\n        self.inv_func = ensure_tuple_rep(inv_func, len(self.keys))\n        self.overwrite = ensure_tuple_rep(overwrite, len(self.keys))\n        self._lambd = Lambda()\n\n    def _transform(self, data: Any, func: Callable):\n        return self._lambd(data, func=func)\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key, func, overwrite in self.key_iterator(d, self.func, self.overwrite):\n            ret = self._transform(data=d[key], func=func)\n            if overwrite:\n                d[key] = ret\n            self.push_transform(d, key)\n        return d\n\n    def _inverse_transform(self, transform_info: Dict, data: Any, func: Callable):\n        return self._lambd(data, func=func)\n\n    def inverse(self, data):\n        d = deepcopy(dict(data))\n        for key, inv_func, overwrite in self.key_iterator(d, self.inv_func, self.overwrite):\n            transform = self.get_most_recent_transform(d, key)\n            ret = self._inverse_transform(transform_info=transform, data=d[key], func=inv_func)\n            if overwrite:\n                d[key] = ret\n            self.pop_transform(d, key)\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_RandLambdad_RandLambdad._inverse_transform.return.self__lambd_data_func_fu": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_RandLambdad_RandLambdad._inverse_transform.return.self__lambd_data_func_fu", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1001, "end_line": 1054, "span_ids": ["RandLambdad", "RandLambdad._transform", "RandLambdad.__call__", "RandLambdad.__init__", "RandLambdad._inverse_transform"], "tokens": 590}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandLambdad(Lambdad, RandomizableTransform):\n    \"\"\"\n    Randomizable version :py:class:`monai.transforms.Lambdad`, the input `func` may contain random logic,\n    or randomly execute the function based on `prob`. so `CacheDataset` will not execute it and cache the results.\n\n    Args:\n        keys: keys of the corresponding items to be transformed.\n            See also: :py:class:`monai.transforms.compose.MapTransform`\n        func: Lambda/function to be applied. It also can be a sequence of Callable,\n            each element corresponds to a key in ``keys``.\n        inv_func: Lambda/function of inverse operation if want to invert transforms, default to `lambda x: x`.\n            It also can be a sequence of Callable, each element corresponds to a key in ``keys``.\n        overwrite: whether to overwrite the original data in the input dictionary with lamdbda function output.\n            default to True. it also can be a sequence of bool, each element corresponds to a key in ``keys``.\n        prob: probability of executing the random function, default to 1.0, with 100% probability to execute.\n            note that all the data specified by `keys` will share the same random probability to execute or not.\n        allow_missing_keys: don't raise exception if key is missing.\n\n    For more details, please check :py:class:`monai.transforms.Lambdad`.\n\n    Note: The inverse operation doesn't allow to define `extra_info` or access other information, such as the\n        image's original size. If need these complicated information, please write a new InvertibleTransform directly.\n    \"\"\"\n\n    backend = Lambda.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        func: Union[Sequence[Callable], Callable],\n        inv_func: Union[Sequence[Callable], Callable] = no_collation,\n        overwrite: Union[Sequence[bool], bool] = True,\n        prob: float = 1.0,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        Lambdad.__init__(\n            self=self,\n            keys=keys,\n            func=func,\n            inv_func=inv_func,\n            overwrite=overwrite,\n            allow_missing_keys=allow_missing_keys,\n        )\n        RandomizableTransform.__init__(self=self, prob=prob, do_transform=True)\n\n    def _transform(self, data: Any, func: Callable):\n        return self._lambd(data, func=func) if self._do_transform else data\n\n    def __call__(self, data):\n        self.randomize(data)\n        return super().__call__(data)\n\n    def _inverse_transform(self, transform_info: Dict, data: Any, func: Callable):\n        return self._lambd(data, func=func) if transform_info[TraceKeys.DO_TRANSFORM] else data", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_ClassesToIndicesd_ClassesToIndicesd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_ClassesToIndicesd_ClassesToIndicesd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1139, "end_line": 1181, "span_ids": ["ClassesToIndicesd.__init__", "ClassesToIndicesd.__call__", "ClassesToIndicesd"], "tokens": 432}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ClassesToIndicesd(MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.ClassesToIndices`.\n\n    Args:\n        keys: keys of the corresponding items to be transformed.\n            See also: :py:class:`monai.transforms.compose.MapTransform`\n        indices_postfix: postfix to save the computed indices of all classes in dict.\n            for example, if computed on `label` and `postfix = \"_cls_indices\"`, the key will be `label_cls_indices`.\n        num_classes: number of classes for argmax label, not necessary for One-Hot label.\n        image_key: if image_key is not None, use ``image > image_threshold`` to define valid region, and only select\n            the indices within the valid region.\n        image_threshold: if enabled image_key, use ``image > image_threshold`` to determine the valid image content\n            area and select only the indices of classes in this area.\n        output_shape: expected shape of output indices. if not None, unravel indices to specified shape.\n        allow_missing_keys: don't raise exception if key is missing.\n\n    \"\"\"\n\n    backend = ClassesToIndices.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        indices_postfix: str = \"_cls_indices\",\n        num_classes: Optional[int] = None,\n        image_key: Optional[str] = None,\n        image_threshold: float = 0.0,\n        output_shape: Optional[Sequence[int]] = None,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        super().__init__(keys, allow_missing_keys)\n        self.indices_postfix = indices_postfix\n        self.image_key = image_key\n        self.converter = ClassesToIndices(num_classes, image_threshold, output_shape)\n\n    def __call__(self, data: Mapping[Hashable, Any]):\n        d = dict(data)\n        image = d[self.image_key] if self.image_key else None\n        for key in self.key_iterator(d):\n            d[str(key) + self.indices_postfix] = self.converter(d[key], image)\n\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_IntensityStatsd_IntensityStatsd.backend.IntensityStats_backend": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_IntensityStatsd_IntensityStatsd.backend.IntensityStats_backend", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1382, "end_line": 1417, "span_ids": ["IntensityStatsd"], "tokens": 544}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class IntensityStatsd(MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.IntensityStats`.\n    Compute statistics for the intensity values of input image and store into the meta data dictionary.\n    For example: if `ops=[lambda x: np.mean(x), \"max\"]` and `key_prefix=\"orig\"`, may generate below stats:\n    `{\"orig_custom_0\": 1.5, \"orig_max\": 3.0}`.\n\n    Args:\n        keys: keys of the corresponding items to be transformed.\n            See also: :py:class:`monai.transforms.compose.MapTransform`\n        ops: expected operations to compute statistics for the intensity.\n            if a string, will map to the predefined operations, supported: [\"mean\", \"median\", \"max\", \"min\", \"std\"]\n            mapping to `np.nanmean`, `np.nanmedian`, `np.nanmax`, `np.nanmin`, `np.nanstd`.\n            if a callable function, will execute the function on input image.\n        key_prefix: the prefix to combine with `ops` name to generate the key to store the results in the\n            meta data dictionary. if some `ops` are callable functions, will use \"{key_prefix}_custom_{index}\"\n            as the key, where index counts from 0.\n        mask_keys: if not None, specify the mask array for the image to extract only the interested area to compute\n            statistics, mask must have the same shape as the image.\n            it should be a sequence of strings or None, map to the `keys`.\n        channel_wise: whether to compute statistics for every channel of input image separately.\n            if True, return a list of values for every operation, default to False.\n        meta_keys: explicitly indicate the key of the corresponding meta data dictionary.\n            used to store the computed statistics to the meta dict.\n            for example, for data with key `image`, the metadata by default is in `image_meta_dict`.\n            the meta data is a dictionary object which contains: filename, original_shape, etc.\n            it can be a sequence of string, map to the `keys`.\n            if None, will try to construct meta_keys by `key_{meta_key_postfix}`.\n        meta_key_postfix: if meta_keys is None, use `key_{postfix}` to to fetch the meta data according\n            to the key data, default is `meta_dict`, the meta data is a dictionary object.\n            used to store the computed statistics to the meta dict.\n        allow_missing_keys: don't raise exception if key is missing.\n\n    \"\"\"\n\n    backend = IntensityStats.backend", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_IntensityStatsd.__init___IntensityStatsd.__init__.self.meta_key_postfix.ensure_tuple_rep_meta_key": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_IntensityStatsd.__init___IntensityStatsd.__init__.self.meta_key_postfix.ensure_tuple_rep_meta_key", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1419, "end_line": 1436, "span_ids": ["IntensityStatsd.__init__"], "tokens": 228}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class IntensityStatsd(MapTransform):\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        ops: Sequence[Union[str, Callable]],\n        key_prefix: str,\n        mask_keys: Optional[KeysCollection] = None,\n        channel_wise: bool = False,\n        meta_keys: Optional[KeysCollection] = None,\n        meta_key_postfix: str = \"meta_dict\",\n        allow_missing_keys: bool = False,\n    ) -> None:\n        super().__init__(keys, allow_missing_keys)\n        self.stats = IntensityStats(ops=ops, key_prefix=key_prefix, channel_wise=channel_wise)\n        self.mask_keys = ensure_tuple_rep(None, len(self.keys)) if mask_keys is None else ensure_tuple(mask_keys)\n        self.meta_keys = ensure_tuple_rep(None, len(self.keys)) if meta_keys is None else ensure_tuple(meta_keys)\n        if len(self.keys) != len(self.meta_keys):\n            raise ValueError(\"meta_keys should have the same length as keys.\")\n        self.meta_key_postfix = ensure_tuple_rep(meta_key_postfix, len(self.keys))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_IntensityStatsd.__call___IntensityStatsd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_IntensityStatsd.__call___IntensityStatsd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1438, "end_line": 1447, "span_ids": ["IntensityStatsd.__call__"], "tokens": 133}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class IntensityStatsd(MapTransform):\n\n    def __call__(self, data) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key, mask_key, meta_key, meta_key_postfix in self.key_iterator(\n            d, self.mask_keys, self.meta_keys, self.meta_key_postfix\n        ):\n            meta_key = meta_key or f\"{key}_{meta_key_postfix}\"\n            d[key], d[meta_key] = self.stats(\n                img=d[key], meta_data=d.get(meta_key), mask=d.get(mask_key) if mask_key is not None else None\n            )\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_ToDeviced_ToDeviced.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_ToDeviced_ToDeviced.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1450, "end_line": 1476, "span_ids": ["ToDeviced.__call__", "ToDeviced", "ToDeviced.__init__"], "tokens": 250}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ToDeviced(MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.ToDevice`.\n    \"\"\"\n\n    backend = ToDevice.backend\n\n    def __init__(\n        self, keys: KeysCollection, device: Union[torch.device, str], allow_missing_keys: bool = False, **kwargs\n    ) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to be transformed.\n                See also: :py:class:`monai.transforms.compose.MapTransform`\n            device: target device to move the Tensor, for example: \"cuda:1\".\n            allow_missing_keys: don't raise exception if key is missing.\n            kwargs: other args for the PyTorch `Tensor.to()` API, for more details:\n                https://pytorch.org/docs/stable/generated/torch.Tensor.to.html.\n        \"\"\"\n        super().__init__(keys, allow_missing_keys)\n        self.converter = ToDevice(device=device, **kwargs)\n\n    def __call__(self, data: Mapping[Hashable, torch.Tensor]) -> Dict[Hashable, torch.Tensor]:\n        d = dict(data)\n        for key in self.key_iterator(d):\n            d[key] = self.converter(d[key])\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_CuCIMd_CuCIMd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_CuCIMd_CuCIMd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1514, "end_line": 1549, "span_ids": ["CuCIMd", "CuCIMd.__call__", "CuCIMd.__init__"], "tokens": 329}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CuCIMd(MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.CuCIM` for non-randomized transforms.\n    For randomized transforms of CuCIM use :py:class:`monai.transforms.RandCuCIMd`.\n\n    Args:\n        keys: keys of the corresponding items to be transformed.\n            See also: :py:class:`monai.transforms.compose.MapTransform`\n        name: The transform name in CuCIM package.\n        allow_missing_keys: don't raise exception if key is missing.\n        args: parameters for the CuCIM transform.\n        kwargs: parameters for the CuCIM transform.\n\n    Note:\n        CuCIM transforms only work with CuPy arrays, this transform expects input data to be `cupy.ndarray`.\n        Users can call `ToCuPy` transform to convert a numpy array or torch tensor to cupy array.\n    \"\"\"\n\n    def __init__(self, keys: KeysCollection, name: str, allow_missing_keys: bool = False, *args, **kwargs) -> None:\n        super().__init__(keys=keys, allow_missing_keys=allow_missing_keys)\n        self.name = name\n        self.trans = CuCIM(name, *args, **kwargs)\n\n    def __call__(self, data):\n        \"\"\"\n        Args:\n            data: Dict[Hashable, `cupy.ndarray`]\n\n        Returns:\n            Dict[Hashable, `cupy.ndarray`]\n\n        \"\"\"\n        d = dict(data)\n        for key in self.key_iterator(d):\n            d[key] = self.trans(d[key])\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_RandCuCIMd_RandCuCIMd.__call__.return.super___call___data_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_RandCuCIMd_RandCuCIMd.__call__.return.super___call___data_", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1539, "end_line": 1580, "span_ids": ["RandCuCIMd", "RandCuCIMd.__call__", "RandCuCIMd.__init__"], "tokens": 474}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandCuCIMd(CuCIMd, RandomizableTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.CuCIM` for randomized transforms.\n    For deterministic non-randomized transforms of CuCIM use :py:class:`monai.transforms.CuCIMd`.\n\n    Args:\n        keys: keys of the corresponding items to be transformed.\n            See also: :py:class:`monai.transforms.compose.MapTransform`\n        name: The transform name in CuCIM package.\n        apply_prob: the probability to apply the transform (default=1.0)\n        allow_missing_keys: don't raise exception if key is missing.\n        args: parameters for the CuCIM transform.\n        kwargs: parameters for the CuCIM transform.\n\n    Note:\n        - CuCIM transform only work with CuPy arrays, so this transform expects input data to be `cupy.ndarray`.\n          Users can call `ToCuPy` transform to convert a numpy array or torch tensor to cupy array.\n        - If the cuCIM transform is already randomized the `apply_prob` argument has nothing to do with\n          the randomness of the underlying cuCIM transform. `apply_prob` defines if the transform (either randomized\n          or non-randomized) being applied randomly, so it can apply non-randomized transforms randomly but be careful\n          with setting `apply_prob` to anything than 1.0 when using along with cuCIM's randomized transforms.\n        - If the random factor of the underlying cuCIM transform is not derived from `self.R`,\n          the results may not be deterministic. See Also: :py:class:`monai.transforms.Randomizable`.\n    \"\"\"\n\n    def __init__(self, apply_prob: float = 1.0, *args, **kwargs) -> None:\n        CuCIMd.__init__(self, *args, **kwargs)\n        RandomizableTransform.__init__(self, prob=apply_prob)\n\n    def __call__(self, data):\n        \"\"\"\n        Args:\n            data: Dict[Hashable, `cupy.ndarray`]\n\n        Returns:\n            Dict[Hashable, `cupy.ndarray`]\n\n        \"\"\"\n        self.randomize(data)\n        if not self._do_transform:\n            return dict(data)\n        return super().__call__(data)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_itertools_img_bounds.return.np_concatenate_np_where_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_itertools_img_bounds.return.np_concatenate_np_where_", "embedding": null, "metadata": {"file_path": "monai/transforms/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 117, "span_ids": ["img_bounds", "rand_choice", "docstring"], "tokens": 739}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import itertools\nimport random\nimport warnings\nfrom contextlib import contextmanager\nfrom inspect import getmembers, isclass\nfrom typing import Any, Callable, Hashable, Iterable, List, Optional, Sequence, Tuple, Union\n\nimport numpy as np\nimport torch\n\nimport monai\nfrom monai.config import DtypeLike, IndexSelection\nfrom monai.config.type_definitions import NdarrayOrTensor\nfrom monai.networks.layers import GaussianFilter\nfrom monai.transforms.compose import Compose, OneOf\nfrom monai.transforms.transform import MapTransform, Transform, apply_transform\nfrom monai.transforms.utils_pytorch_numpy_unification import (\n    any_np_pt,\n    cumsum,\n    isfinite,\n    nonzero,\n    ravel,\n    searchsorted,\n    unravel_index,\n    where,\n)\nfrom monai.utils import (\n    GridSampleMode,\n    InterpolateMode,\n    NumpyPadMode,\n    PytorchPadMode,\n    TraceKeys,\n    deprecated_arg,\n    ensure_tuple,\n    ensure_tuple_rep,\n    ensure_tuple_size,\n    fall_back_tuple,\n    issequenceiterable,\n    look_up_option,\n    min_version,\n    optional_import,\n)\nfrom monai.utils.enums import TransformBackends\nfrom monai.utils.type_conversion import convert_data_type, convert_to_dst_type\n\nmeasure, _ = optional_import(\"skimage.measure\", \"0.14.2\", min_version)\nndimage, _ = optional_import(\"scipy.ndimage\")\ncp, has_cp = optional_import(\"cupy\")\ncp_ndarray, _ = optional_import(\"cupy\", name=\"ndarray\")\nexposure, has_skimage = optional_import(\"skimage.exposure\")\n\n__all__ = [\n    \"allow_missing_keys_mode\",\n    \"compute_divisible_spatial_size\",\n    \"convert_inverse_interp_mode\",\n    \"copypaste_arrays\",\n    \"create_control_grid\",\n    \"create_grid\",\n    \"create_rotate\",\n    \"create_scale\",\n    \"create_shear\",\n    \"create_translate\",\n    \"extreme_points_to_image\",\n    \"fill_holes\",\n    \"Fourier\",\n    \"generate_label_classes_crop_centers\",\n    \"generate_pos_neg_label_crop_centers\",\n    \"generate_spatial_bounding_box\",\n    \"get_extreme_points\",\n    \"get_largest_connected_component_mask\",\n    \"img_bounds\",\n    \"in_bounds\",\n    \"is_empty\",\n    \"is_positive\",\n    \"map_binary_to_indices\",\n    \"map_classes_to_indices\",\n    \"map_spatial_axes\",\n    \"rand_choice\",\n    \"rescale_array\",\n    \"rescale_array_int_max\",\n    \"rescale_instance_array\",\n    \"resize_center\",\n    \"weighted_patch_samples\",\n    \"zero_margins\",\n    \"equalize_hist\",\n    \"get_number_image_type_conversions\",\n    \"get_transform_backends\",\n    \"print_transform_backends\",\n    \"convert_pad_mode\",\n]\n\n\ndef rand_choice(prob: float = 0.5) -> bool:\n    \"\"\"\n    Returns True if a randomly chosen number is less than or equal to `prob`, by default this is a 50/50 chance.\n    \"\"\"\n    return bool(random.random() <= prob)\n\n\ndef img_bounds(img: np.ndarray):\n    \"\"\"\n    Returns the minimum and maximum indices of non-zero lines in axis 0 of `img`, followed by that for axis 1.\n    \"\"\"\n    ax0 = np.any(img, axis=0)\n    ax1 = np.any(img, axis=1)\n    return np.concatenate((np.where(ax0)[0][[0, -1]], np.where(ax1)[0][[0, -1]]))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_in_bounds_zero_margins.return.not_np_any_img_margin": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_in_bounds_zero_margins.return.not_np_any_img_margin", "embedding": null, "metadata": {"file_path": "monai/transforms/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 120, "end_line": 148, "span_ids": ["is_empty", "zero_margins", "in_bounds", "is_positive"], "tokens": 288}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def in_bounds(x: float, y: float, margin: float, maxx: float, maxy: float) -> bool:\n    \"\"\"\n    Returns True if (x,y) is within the rectangle (margin, margin, maxx-margin, maxy-margin).\n    \"\"\"\n    return bool(margin <= x < (maxx - margin) and margin <= y < (maxy - margin))\n\n\ndef is_empty(img: Union[np.ndarray, torch.Tensor]) -> bool:\n    \"\"\"\n    Returns True if `img` is empty, that is its maximum value is not greater than its minimum.\n    \"\"\"\n    return not (img.max() > img.min())  # use > instead of <= so that an image full of NaNs will result in True\n\n\ndef is_positive(img):\n    \"\"\"\n    Returns a boolean version of `img` where the positive values are converted into True, the other values are False.\n    \"\"\"\n    return img > 0\n\n\ndef zero_margins(img: np.ndarray, margin: int) -> bool:\n    \"\"\"\n    Returns True if the values within `margin` indices of the edges of `img` in dimensions 1 and 2 are 0.\n    \"\"\"\n    if np.any(img[:, :, :margin]) or np.any(img[:, :, -margin:]):\n        return False\n\n    return not np.any(img[:, :margin, :]) and not np.any(img[:, -margin:, :])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_map_classes_to_indices_map_classes_to_indices.return.indices": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_map_classes_to_indices_map_classes_to_indices.return.indices", "embedding": null, "metadata": {"file_path": "monai/transforms/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 303, "end_line": 349, "span_ids": ["map_classes_to_indices"], "tokens": 543}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def map_classes_to_indices(\n    label: NdarrayOrTensor,\n    num_classes: Optional[int] = None,\n    image: Optional[NdarrayOrTensor] = None,\n    image_threshold: float = 0.0,\n) -> List[NdarrayOrTensor]:\n    \"\"\"\n    Filter out indices of every class of the input label data, return the indices after fattening.\n    It can handle both One-Hot format label and Argmax format label, must provide `num_classes` for\n    Argmax label.\n\n    For example:\n    ``label = np.array([[[0, 1, 2], [2, 0, 1], [1, 2, 0]]])`` and `num_classes=3`, will return a list\n    which contains the indices of the 3 classes:\n    ``[np.array([0, 4, 8]), np.array([1, 5, 6]), np.array([2, 3, 7])]``\n\n    Args:\n        label: use the label data to get the indices of every class.\n        num_classes: number of classes for argmax label, not necessary for One-Hot label.\n        image: if image is not None, only return the indices of every class that are within the valid\n            region of the image (``image > image_threshold``).\n        image_threshold: if enabled `image`, use ``image > image_threshold`` to\n            determine the valid image content area and select class indices only in this area.\n\n    \"\"\"\n    img_flat: Optional[NdarrayOrTensor] = None\n    if image is not None:\n        img_flat = ravel((image > image_threshold).any(0))\n\n    indices: List[NdarrayOrTensor] = []\n    # assuming the first dimension is channel\n    channels = len(label)\n\n    num_classes_: int = channels\n    if channels == 1:\n        if num_classes is None:\n            raise ValueError(\"if not One-Hot format label, must provide the num_classes.\")\n        num_classes_ = num_classes\n\n    for c in range(num_classes_):\n        label_flat = ravel(any_np_pt(label[c : c + 1] if channels > 1 else label == c, 0))\n        label_flat = img_flat & label_flat if img_flat is not None else label_flat\n        # no need to save the indices in GPU, otherwise, still need to move to CPU at runtime when crop by indices\n        cls_indices, *_ = convert_data_type(nonzero(label_flat), device=torch.device(\"cpu\"))\n        indices.append(cls_indices)\n\n    return indices", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_weighted_patch_samples_weighted_patch_samples.return._unravel_index_i_v_size_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_weighted_patch_samples_weighted_patch_samples.return._unravel_index_i_v_size_", "embedding": null, "metadata": {"file_path": "monai/transforms/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 352, "end_line": 396, "span_ids": ["weighted_patch_samples"], "tokens": 540}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def weighted_patch_samples(\n    spatial_size: Union[int, Sequence[int]],\n    w: NdarrayOrTensor,\n    n_samples: int = 1,\n    r_state: Optional[np.random.RandomState] = None,\n) -> List:\n    \"\"\"\n    Computes `n_samples` of random patch sampling locations, given the sampling weight map `w` and patch `spatial_size`.\n\n    Args:\n        spatial_size: length of each spatial dimension of the patch.\n        w: weight map, the weights must be non-negative. each element denotes a sampling weight of the spatial location.\n            0 indicates no sampling.\n            The weight map shape is assumed ``(spatial_dim_0, spatial_dim_1, ..., spatial_dim_n)``.\n        n_samples: number of patch samples\n        r_state: a random state container\n\n    Returns:\n        a list of `n_samples` N-D integers representing the spatial sampling location of patches.\n\n    \"\"\"\n    if w is None:\n        raise ValueError(\"w must be an ND array.\")\n    if r_state is None:\n        r_state = np.random.RandomState()\n    img_size = np.asarray(w.shape, dtype=int)\n    win_size = np.asarray(fall_back_tuple(spatial_size, img_size), dtype=int)\n\n    s = tuple(slice(w // 2, m - w + w // 2) if m > w else slice(m // 2, m // 2 + 1) for w, m in zip(win_size, img_size))\n    v = w[s]  # weight map in the 'valid' mode\n    v_size = v.shape\n    v = ravel(v)\n    if (v < 0).any():\n        v -= v.min()  # shifting to non-negative\n    v = cumsum(v)\n    if not v[-1] or not isfinite(v[-1]) or v[-1] < 0:  # uniform sampling\n        idx = r_state.randint(0, len(v), size=n_samples)\n    else:\n        r, *_ = convert_to_dst_type(r_state.random(n_samples), v)  # type: ignore\n        idx = searchsorted(v, r * v[-1], right=True)\n    idx, *_ = convert_to_dst_type(idx, v, dtype=torch.int)  # type: ignore\n    # compensate 'valid' mode\n    diff = np.minimum(win_size, img_size) // 2\n    diff, *_ = convert_to_dst_type(diff, v)  # type: ignore\n    return [unravel_index(i, v_size) + diff for i in idx]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_generate_label_classes_crop_centers_generate_label_classes_crop_centers.return.centers": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_generate_label_classes_crop_centers_generate_label_classes_crop_centers.return.centers", "embedding": null, "metadata": {"file_path": "monai/transforms/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 503, "end_line": 556, "span_ids": ["generate_label_classes_crop_centers"], "tokens": 612}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def generate_label_classes_crop_centers(\n    spatial_size: Union[Sequence[int], int],\n    num_samples: int,\n    label_spatial_shape: Sequence[int],\n    indices: Sequence[NdarrayOrTensor],\n    ratios: Optional[List[Union[float, int]]] = None,\n    rand_state: Optional[np.random.RandomState] = None,\n    allow_smaller: bool = False,\n) -> List[List[int]]:\n    \"\"\"\n    Generate valid sample locations based on the specified ratios of label classes.\n    Valid: samples sitting entirely within image, expected input shape: [C, H, W, D] or [C, H, W]\n\n    Args:\n        spatial_size: spatial size of the ROIs to be sampled.\n        num_samples: total sample centers to be generated.\n        label_spatial_shape: spatial shape of the original label data to unravel selected centers.\n        indices: sequence of pre-computed foreground indices of every class in 1 dimension.\n        ratios: ratios of every class in the label to generate crop centers, including background class.\n            if None, every class will have the same ratio to generate crop centers.\n        rand_state: numpy randomState object to align with other modules.\n        allow_smaller: if `False`, an exception will be raised if the image is smaller than\n            the requested ROI in any dimension. If `True`, any smaller dimensions will be set to\n            match the cropped size (i.e., no cropping in that dimension).\n\n    \"\"\"\n    if rand_state is None:\n        rand_state = np.random.random.__self__  # type: ignore\n\n    if num_samples < 1:\n        raise ValueError(\"num_samples must be an int number and greater than 0.\")\n    ratios_: List[Union[float, int]] = ([1] * len(indices)) if ratios is None else ratios\n    if len(ratios_) != len(indices):\n        raise ValueError(\"random crop ratios must match the number of indices of classes.\")\n    if any(i < 0 for i in ratios_):\n        raise ValueError(\"ratios should not contain negative number.\")\n\n    for i, array in enumerate(indices):\n        if len(array) == 0:\n            warnings.warn(f\"no available indices of class {i} to crop, set the crop ratio of this class to zero.\")\n            ratios_[i] = 0\n\n    centers = []\n    classes = rand_state.choice(len(ratios_), size=num_samples, p=np.asarray(ratios_) / np.sum(ratios_))\n    for i in classes:\n        # randomly select the indices of a class based on the ratios\n        indices_to_use = indices[i]\n        random_int = rand_state.randint(len(indices_to_use))\n        center = unravel_index(indices_to_use[random_int], label_spatial_shape)\n        # shift center to range of valid centers\n        center_ori = list(center)\n        centers.append(correct_crop_centers(center_ori, spatial_size, label_spatial_shape, allow_smaller))\n\n    return centers", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_create_grid_create_grid.raise_ValueError_f_backen": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_create_grid_create_grid.raise_ValueError_f_backen", "embedding": null, "metadata": {"file_path": "monai/transforms/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 559, "end_line": 584, "span_ids": ["create_grid"], "tokens": 239}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def create_grid(\n    spatial_size: Sequence[int],\n    spacing: Optional[Sequence[float]] = None,\n    homogeneous: bool = True,\n    dtype=float,\n    device: Optional[torch.device] = None,\n    backend=TransformBackends.NUMPY,\n):\n    \"\"\"\n    compute a `spatial_size` mesh.\n\n    Args:\n        spatial_size: spatial size of the grid.\n        spacing: same len as ``spatial_size``, defaults to 1.0 (dense grid).\n        homogeneous: whether to make homogeneous coordinates.\n        dtype: output grid data type.\n        device: device to compute and store the output (when the backend is \"torch\").\n        backend: APIs to use, ``numpy`` or ``torch``.\n\n    \"\"\"\n    _backend = look_up_option(backend, TransformBackends)\n    if _backend == TransformBackends.NUMPY:\n        return _create_grid_numpy(spatial_size, spacing, homogeneous, dtype)\n    if _backend == TransformBackends.TORCH:\n        return _create_grid_torch(spatial_size, spacing, homogeneous, dtype, device)\n    raise ValueError(f\"backend {backend} is not supported\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py__create_grid_numpy__create_grid_numpy.return.np_concatenate_coords_n": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py__create_grid_numpy__create_grid_numpy.return.np_concatenate_coords_n", "embedding": null, "metadata": {"file_path": "monai/transforms/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 587, "end_line": 601, "span_ids": ["_create_grid_numpy"], "tokens": 170}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _create_grid_numpy(\n    spatial_size: Sequence[int],\n    spacing: Optional[Sequence[float]] = None,\n    homogeneous: bool = True,\n    dtype: DtypeLike = float,\n):\n    \"\"\"\n    compute a `spatial_size` mesh with the numpy API.\n    \"\"\"\n    spacing = spacing or tuple(1.0 for _ in spatial_size)\n    ranges = [np.linspace(-(d - 1.0) / 2.0 * s, (d - 1.0) / 2.0 * s, int(d)) for d, s in zip(spatial_size, spacing)]\n    coords = np.asarray(np.meshgrid(*ranges, indexing=\"ij\"), dtype=dtype)\n    if not homogeneous:\n        return coords\n    return np.concatenate([coords, np.ones_like(coords[:1])])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py__create_grid_torch__create_grid_torch.return.torch_stack_coords_tor": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py__create_grid_torch__create_grid_torch.return.torch_stack_coords_tor", "embedding": null, "metadata": {"file_path": "monai/transforms/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 604, "end_line": 622, "span_ids": ["_create_grid_torch"], "tokens": 184}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _create_grid_torch(\n    spatial_size: Sequence[int],\n    spacing: Optional[Sequence[float]] = None,\n    homogeneous: bool = True,\n    dtype=torch.float32,\n    device: Optional[torch.device] = None,\n):\n    \"\"\"\n    compute a `spatial_size` mesh with the torch API.\n    \"\"\"\n    spacing = spacing or tuple(1.0 for _ in spatial_size)\n    ranges = [\n        torch.linspace(-(d - 1.0) / 2.0 * s, (d - 1.0) / 2.0 * s, int(d), device=device, dtype=dtype)\n        for d, s in zip(spatial_size, spacing)\n    ]\n    coords = torch.meshgrid(*ranges)\n    if not homogeneous:\n        return torch.stack(coords)\n    return torch.stack([*coords, torch.ones_like(coords[0])])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_create_control_grid_create_control_grid.return.create_grid_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_create_control_grid_create_control_grid.return.create_grid_", "embedding": null, "metadata": {"file_path": "monai/transforms/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 625, "end_line": 647, "span_ids": ["create_control_grid"], "tokens": 275}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def create_control_grid(\n    spatial_shape: Sequence[int],\n    spacing: Sequence[float],\n    homogeneous: bool = True,\n    dtype: DtypeLike = float,\n    device: Optional[torch.device] = None,\n    backend=TransformBackends.NUMPY,\n):\n    \"\"\"\n    control grid with two additional point in each direction\n    \"\"\"\n    torch_backend = look_up_option(backend, TransformBackends) == TransformBackends.TORCH\n    ceil_func: Callable = torch.ceil if torch_backend else np.ceil  # type: ignore\n    grid_shape = []\n    for d, s in zip(spatial_shape, spacing):\n        d = torch.as_tensor(d, device=device) if torch_backend else int(d)  # type: ignore\n        if d % 2 == 0:\n            grid_shape.append(ceil_func((d - 1.0) / (2.0 * s) + 0.5) * 2.0 + 2.0)\n        else:\n            grid_shape.append(ceil_func((d - 1.0) / (2.0 * s)) * 2.0 + 3.0)\n    return create_grid(\n        spatial_size=grid_shape, spacing=spacing, homogeneous=homogeneous, dtype=dtype, device=device, backend=backend\n    )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_create_rotate_create_rotate.raise_ValueError_f_backen": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_create_rotate_create_rotate.raise_ValueError_f_backen", "embedding": null, "metadata": {"file_path": "monai/transforms/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 650, "end_line": 685, "span_ids": ["create_rotate"], "tokens": 353}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def create_rotate(\n    spatial_dims: int,\n    radians: Union[Sequence[float], float],\n    device: Optional[torch.device] = None,\n    backend=TransformBackends.NUMPY,\n) -> NdarrayOrTensor:\n    \"\"\"\n    create a 2D or 3D rotation matrix\n\n    Args:\n        spatial_dims: {``2``, ``3``} spatial rank\n        radians: rotation radians\n            when spatial_dims == 3, the `radians` sequence corresponds to\n            rotation in the 1st, 2nd, and 3rd dim respectively.\n        device: device to compute and store the output (when the backend is \"torch\").\n        backend: APIs to use, ``numpy`` or ``torch``.\n\n    Raises:\n        ValueError: When ``radians`` is empty.\n        ValueError: When ``spatial_dims`` is not one of [2, 3].\n\n    \"\"\"\n    _backend = look_up_option(backend, TransformBackends)\n    if _backend == TransformBackends.NUMPY:\n        return _create_rotate(\n            spatial_dims=spatial_dims, radians=radians, sin_func=np.sin, cos_func=np.cos, eye_func=np.eye\n        )\n    if _backend == TransformBackends.TORCH:\n        return _create_rotate(\n            spatial_dims=spatial_dims,\n            radians=radians,\n            sin_func=lambda th: torch.sin(torch.as_tensor(th, dtype=torch.float32, device=device)),\n            cos_func=lambda th: torch.cos(torch.as_tensor(th, dtype=torch.float32, device=device)),\n            eye_func=lambda rank: torch.eye(rank, device=device),\n        )\n    raise ValueError(f\"backend {backend} is not supported\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py__create_rotate__create_rotate.raise_ValueError_f_Unsupp": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py__create_rotate__create_rotate.raise_ValueError_f_Unsupp", "embedding": null, "metadata": {"file_path": "monai/transforms/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 688, "end_line": 732, "span_ids": ["_create_rotate"], "tokens": 542}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _create_rotate(\n    spatial_dims: int,\n    radians: Union[Sequence[float], float],\n    sin_func: Callable = np.sin,\n    cos_func: Callable = np.cos,\n    eye_func: Callable = np.eye,\n) -> NdarrayOrTensor:\n    radians = ensure_tuple(radians)\n    if spatial_dims == 2:\n        if len(radians) >= 1:\n            sin_, cos_ = sin_func(radians[0]), cos_func(radians[0])\n            out = eye_func(3)\n            out[0, 0], out[0, 1] = cos_, -sin_\n            out[1, 0], out[1, 1] = sin_, cos_\n            return out  # type: ignore\n        raise ValueError(\"radians must be non empty.\")\n\n    if spatial_dims == 3:\n        affine = None\n        if len(radians) >= 1:\n            sin_, cos_ = sin_func(radians[0]), cos_func(radians[0])\n            affine = eye_func(4)\n            affine[1, 1], affine[1, 2] = cos_, -sin_\n            affine[2, 1], affine[2, 2] = sin_, cos_\n        if len(radians) >= 2:\n            sin_, cos_ = sin_func(radians[1]), cos_func(radians[1])\n            if affine is None:\n                raise ValueError(\"Affine should be a matrix.\")\n            _affine = eye_func(4)\n            _affine[0, 0], _affine[0, 2] = cos_, sin_\n            _affine[2, 0], _affine[2, 2] = -sin_, cos_\n            affine = affine @ _affine\n        if len(radians) >= 3:\n            sin_, cos_ = sin_func(radians[2]), cos_func(radians[2])\n            if affine is None:\n                raise ValueError(\"Affine should be a matrix.\")\n            _affine = eye_func(4)\n            _affine[0, 0], _affine[0, 1] = cos_, -sin_\n            _affine[1, 0], _affine[1, 1] = sin_, cos_\n            affine = affine @ _affine\n        if affine is None:\n            raise ValueError(\"radians must be non empty.\")\n        return affine  # type: ignore\n\n    raise ValueError(f\"Unsupported spatial_dims: {spatial_dims}, available options are [2, 3].\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_create_shear_create_shear.raise_ValueError_f_backen": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_create_shear_create_shear.raise_ValueError_f_backen", "embedding": null, "metadata": {"file_path": "monai/transforms/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 735, "end_line": 770, "span_ids": ["create_shear"], "tokens": 367}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def create_shear(\n    spatial_dims: int,\n    coefs: Union[Sequence[float], float],\n    device: Optional[torch.device] = None,\n    backend=TransformBackends.NUMPY,\n) -> NdarrayOrTensor:\n    \"\"\"\n    create a shearing matrix\n\n    Args:\n        spatial_dims: spatial rank\n        coefs: shearing factors, a tuple of 2 floats for 2D, a tuple of 6 floats for 3D),\n            take a 3D affine as example::\n\n                [\n                    [1.0, coefs[0], coefs[1], 0.0],\n                    [coefs[2], 1.0, coefs[3], 0.0],\n                    [coefs[4], coefs[5], 1.0, 0.0],\n                    [0.0, 0.0, 0.0, 1.0],\n                ]\n\n        device: device to compute and store the output (when the backend is \"torch\").\n        backend: APIs to use, ``numpy`` or ``torch``.\n\n    Raises:\n        NotImplementedError: When ``spatial_dims`` is not one of [2, 3].\n\n    \"\"\"\n    _backend = look_up_option(backend, TransformBackends)\n    if _backend == TransformBackends.NUMPY:\n        return _create_shear(spatial_dims=spatial_dims, coefs=coefs, eye_func=np.eye)\n    if _backend == TransformBackends.TORCH:\n        return _create_shear(\n            spatial_dims=spatial_dims, coefs=coefs, eye_func=lambda rank: torch.eye(rank, device=device)\n        )\n    raise ValueError(f\"backend {backend} is not supported\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py__create_shear__create_shear.raise_NotImplementedError": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py__create_shear__create_shear.raise_NotImplementedError", "embedding": null, "metadata": {"file_path": "monai/transforms/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 773, "end_line": 786, "span_ids": ["_create_shear"], "tokens": 246}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _create_shear(spatial_dims: int, coefs: Union[Sequence[float], float], eye_func=np.eye) -> NdarrayOrTensor:\n    if spatial_dims == 2:\n        coefs = ensure_tuple_size(coefs, dim=2, pad_val=0.0)\n        out = eye_func(3)\n        out[0, 1], out[1, 0] = coefs[0], coefs[1]\n        return out  # type: ignore\n    if spatial_dims == 3:\n        coefs = ensure_tuple_size(coefs, dim=6, pad_val=0.0)\n        out = eye_func(4)\n        out[0, 1], out[0, 2] = coefs[0], coefs[1]\n        out[1, 0], out[1, 2] = coefs[2], coefs[3]\n        out[2, 0], out[2, 1] = coefs[4], coefs[5]\n        return out  # type: ignore\n    raise NotImplementedError(\"Currently only spatial_dims in [2, 3] are supported.\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_create_scale__create_scale._type_ignore": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_create_scale__create_scale._type_ignore", "embedding": null, "metadata": {"file_path": "monai/transforms/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 789, "end_line": 820, "span_ids": ["create_scale", "_create_scale"], "tokens": 313}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def create_scale(\n    spatial_dims: int,\n    scaling_factor: Union[Sequence[float], float],\n    device: Optional[torch.device] = None,\n    backend=TransformBackends.NUMPY,\n) -> NdarrayOrTensor:\n    \"\"\"\n    create a scaling matrix\n\n    Args:\n        spatial_dims: spatial rank\n        scaling_factor: scaling factors for every spatial dim, defaults to 1.\n        device: device to compute and store the output (when the backend is \"torch\").\n        backend: APIs to use, ``numpy`` or ``torch``.\n    \"\"\"\n    _backend = look_up_option(backend, TransformBackends)\n    if _backend == TransformBackends.NUMPY:\n        return _create_scale(spatial_dims=spatial_dims, scaling_factor=scaling_factor, array_func=np.diag)\n    if _backend == TransformBackends.TORCH:\n        return _create_scale(\n            spatial_dims=spatial_dims,\n            scaling_factor=scaling_factor,\n            array_func=lambda x: torch.diag(torch.as_tensor(x, device=device)),\n        )\n    raise ValueError(f\"backend {backend} is not supported\")\n\n\ndef _create_scale(\n    spatial_dims: int, scaling_factor: Union[Sequence[float], float], array_func=np.diag\n) -> NdarrayOrTensor:\n    scaling_factor = ensure_tuple_size(scaling_factor, dim=spatial_dims, pad_val=1.0)\n    return array_func(scaling_factor[:spatial_dims] + (1.0,))  # type: ignore", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_create_translate__create_translate._type_ignore": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_create_translate__create_translate._type_ignore", "embedding": null, "metadata": {"file_path": "monai/transforms/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 823, "end_line": 858, "span_ids": ["_create_translate", "create_translate"], "tokens": 352}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def create_translate(\n    spatial_dims: int,\n    shift: Union[Sequence[float], float],\n    device: Optional[torch.device] = None,\n    backend=TransformBackends.NUMPY,\n) -> NdarrayOrTensor:\n    \"\"\"\n    create a translation matrix\n\n    Args:\n        spatial_dims: spatial rank\n        shift: translate pixel/voxel for every spatial dim, defaults to 0.\n        device: device to compute and store the output (when the backend is \"torch\").\n        backend: APIs to use, ``numpy`` or ``torch``.\n    \"\"\"\n    _backend = look_up_option(backend, TransformBackends)\n    if _backend == TransformBackends.NUMPY:\n        return _create_translate(spatial_dims=spatial_dims, shift=shift, eye_func=np.eye, array_func=np.asarray)\n    if _backend == TransformBackends.TORCH:\n        return _create_translate(\n            spatial_dims=spatial_dims,\n            shift=shift,\n            eye_func=lambda x: torch.eye(torch.as_tensor(x), device=device),  # type: ignore\n            array_func=lambda x: torch.as_tensor(x, device=device),  # type: ignore\n        )\n    raise ValueError(f\"backend {backend} is not supported\")\n\n\ndef _create_translate(\n    spatial_dims: int, shift: Union[Sequence[float], float], eye_func=np.eye, array_func=np.asarray\n) -> NdarrayOrTensor:\n    shift = ensure_tuple(shift)\n    affine = eye_func(spatial_dims + 1)\n    for i, a in enumerate(shift[:spatial_dims]):\n        affine[i, spatial_dims] = a\n    return array_func(affine)  # type: ignore", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_fill_holes_fill_holes.return.img_arr": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_fill_holes_fill_holes.return.img_arr", "embedding": null, "metadata": {"file_path": "monai/transforms/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 935, "end_line": 991, "span_ids": ["fill_holes"], "tokens": 522}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def fill_holes(\n    img_arr: np.ndarray, applied_labels: Optional[Iterable[int]] = None, connectivity: Optional[int] = None\n) -> np.ndarray:\n    \"\"\"\n    Fill the holes in the provided image.\n\n    The label 0 will be treated as background and the enclosed holes will be set to the neighboring class label.\n    What is considered to be an enclosed hole is defined by the connectivity.\n    Holes on the edge are always considered to be open (not enclosed).\n\n    Note:\n\n        The performance of this method heavily depends on the number of labels.\n        It is a bit faster if the list of `applied_labels` is provided.\n        Limiting the number of `applied_labels` results in a big decrease in processing time.\n\n        If the image is one-hot-encoded, then the `applied_labels` need to match the channel index.\n\n    Args:\n        img_arr: numpy array of shape [C, spatial_dim1[, spatial_dim2, ...]].\n        applied_labels: Labels for which to fill holes. Defaults to None,\n            that is filling holes for all labels.\n        connectivity: Maximum number of orthogonal hops to\n            consider a pixel/voxel as a neighbor. Accepted values are ranging from  1 to input.ndim.\n            Defaults to a full connectivity of ``input.ndim``.\n\n    Returns:\n        numpy array of shape [C, spatial_dim1[, spatial_dim2, ...]].\n    \"\"\"\n    channel_axis = 0\n    num_channels = img_arr.shape[channel_axis]\n    is_one_hot = num_channels > 1\n    spatial_dims = img_arr.ndim - 1\n    structure = ndimage.generate_binary_structure(spatial_dims, connectivity or spatial_dims)\n\n    # Get labels if not provided. Exclude background label.\n    applied_labels = set(applied_labels or (range(num_channels) if is_one_hot else np.unique(img_arr)))\n    background_label = 0\n    applied_labels.discard(background_label)\n\n    for label in applied_labels:\n        tmp = np.zeros(img_arr.shape[1:], dtype=bool)\n        ndimage.binary_dilation(\n            tmp,\n            structure=structure,\n            iterations=-1,\n            mask=np.logical_not(img_arr[label]) if is_one_hot else img_arr[0] != label,\n            origin=0,\n            border_value=1,\n            output=tmp,\n        )\n        if is_one_hot:\n            img_arr[label] = np.logical_not(tmp)\n        else:\n            img_arr[0, np.logical_not(tmp)] = label\n\n    return img_arr", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_extreme_points_to_image_extreme_points_to_image.return.points_image_rescale_m": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_extreme_points_to_image_extreme_points_to_image.return.points_image_rescale_m", "embedding": null, "metadata": {"file_path": "monai/transforms/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1051, "end_line": 1094, "span_ids": ["extreme_points_to_image"], "tokens": 481}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def extreme_points_to_image(\n    points: List[Tuple[int, ...]],\n    label: NdarrayOrTensor,\n    sigma: Union[Sequence[float], float, Sequence[torch.Tensor], torch.Tensor] = 0.0,\n    rescale_min: float = -1.0,\n    rescale_max: float = 1.0,\n) -> torch.Tensor:\n    \"\"\"\n    Please refer to :py:class:`monai.transforms.AddExtremePointsChannel` for the usage.\n\n    Applies a gaussian filter to the extreme points image. Then the pixel values in points image are rescaled\n    to range [rescale_min, rescale_max].\n\n    Args:\n        points: Extreme points of the object/organ.\n        label: label image to get extreme points from. Shape must be\n            (1, spatial_dim1, [, spatial_dim2, ...]). Doesn't support one-hot labels.\n        sigma: if a list of values, must match the count of spatial dimensions of input data,\n            and apply every value in the list to 1 spatial dimension. if only 1 value provided,\n            use it for all spatial dimensions.\n        rescale_min: minimum value of output data.\n        rescale_max: maximum value of output data.\n    \"\"\"\n    # points to image\n    # points_image = torch.zeros(label.shape[1:], dtype=torch.float)\n    points_image = torch.zeros_like(torch.as_tensor(label[0]), dtype=torch.float)\n    for p in points:\n        points_image[p] = 1.0\n\n    if isinstance(sigma, Sequence):\n        sigma = [torch.as_tensor(s, device=points_image.device) for s in sigma]\n    else:\n        sigma = torch.as_tensor(sigma, device=points_image.device)\n\n    # add channel and add batch\n    points_image = points_image.unsqueeze(0).unsqueeze(0)\n    gaussian_filter = GaussianFilter(label.ndim - 1, sigma=sigma)\n    points_image = gaussian_filter(points_image).squeeze(0).detach()\n\n    # rescale the points image to [rescale_min, rescale_max]\n    min_intensity = points_image.min()\n    max_intensity = points_image.max()\n    points_image = (points_image - min_intensity) / (max_intensity - min_intensity)\n    return points_image * (rescale_max - rescale_min) + rescale_min", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_compute_divisible_spatial_size_compute_divisible_spatial_size.return.new_size": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_compute_divisible_spatial_size_compute_divisible_spatial_size.return.new_size", "embedding": null, "metadata": {"file_path": "monai/transforms/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1216, "end_line": 1233, "span_ids": ["compute_divisible_spatial_size"], "tokens": 174}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def compute_divisible_spatial_size(spatial_shape: Sequence[int], k: Union[Sequence[int], int]):\n    \"\"\"\n    Compute the target spatial size which should be divisible by `k`.\n\n    Args:\n        spatial_shape: original spatial shape.\n        k: the target k for each spatial dimension.\n            if `k` is negative or 0, the original size is preserved.\n            if `k` is an int, the same `k` be applied to all the input spatial dimensions.\n\n    \"\"\"\n    k = fall_back_tuple(k, (1,) * len(spatial_shape))\n    new_size = []\n    for k_d, dim in zip(k, spatial_shape):\n        new_dim = int(np.ceil(dim / k_d) * k_d) if k_d > 0 else dim\n        new_size.append(new_dim)\n\n    return new_size", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_Fourier_Fourier.shift_fourier.return.k": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_Fourier_Fourier.shift_fourier.return.k", "embedding": null, "metadata": {"file_path": "monai/transforms/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1280, "end_line": 1316, "span_ids": ["Fourier", "Fourier.shift_fourier"], "tokens": 338}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Fourier:\n    \"\"\"\n    Helper class storing Fourier mappings\n    \"\"\"\n\n    @staticmethod\n    @deprecated_arg(\n        name=\"n_dims\", new_name=\"spatial_dims\", since=\"0.6\", msg_suffix=\"Please use `spatial_dims` instead.\"\n    )\n    def shift_fourier(x: NdarrayOrTensor, spatial_dims: int, n_dims: Optional[int] = None) -> NdarrayOrTensor:\n        \"\"\"\n        Applies fourier transform and shifts the zero-frequency component to the\n        center of the spectrum. Only the spatial dimensions get transformed.\n\n        Args:\n            x: Image to transform.\n            spatial_dims: Number of spatial dimensions.\n\n        .. deprecated:: 0.6.0\n            ``n_dims`` is deprecated, use ``spatial_dims`` instead.\n\n        Returns\n            k: K-space data.\n        \"\"\"\n        if n_dims is not None:\n            spatial_dims = n_dims\n        dims = tuple(range(-spatial_dims, 0))\n        k: NdarrayOrTensor\n        if isinstance(x, torch.Tensor):\n            if hasattr(torch.fft, \"fftshift\"):  # `fftshift` is new in torch 1.8.0\n                k = torch.fft.fftshift(torch.fft.fftn(x, dim=dims), dim=dims)\n            else:\n                # if using old PyTorch, will convert to numpy array and return\n                k = np.fft.fftshift(np.fft.fftn(x.cpu().numpy(), axes=dims), axes=dims)\n        else:\n            k = np.fft.fftshift(np.fft.fftn(x, axes=dims), axes=dims)\n        return k", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_Fourier.inv_shift_fourier_Fourier.inv_shift_fourier.return.out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_Fourier.inv_shift_fourier_Fourier.inv_shift_fourier.return.out", "embedding": null, "metadata": {"file_path": "monai/transforms/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1318, "end_line": 1349, "span_ids": ["Fourier.inv_shift_fourier"], "tokens": 338}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Fourier:\n\n    @staticmethod\n    @deprecated_arg(\n        name=\"n_dims\", new_name=\"spatial_dims\", since=\"0.6\", msg_suffix=\"Please use `spatial_dims` instead.\"\n    )\n    def inv_shift_fourier(k: NdarrayOrTensor, spatial_dims: int, n_dims: Optional[int] = None) -> NdarrayOrTensor:\n        \"\"\"\n        Applies inverse shift and fourier transform. Only the spatial\n        dimensions are transformed.\n\n        Args:\n            k: K-space data.\n            spatial_dims: Number of spatial dimensions.\n\n        .. deprecated:: 0.6.0\n            ``n_dims`` is deprecated, use ``spatial_dims`` instead.\n\n        Returns:\n            x: Tensor in image space.\n        \"\"\"\n        if n_dims is not None:\n            spatial_dims = n_dims\n        dims = tuple(range(-spatial_dims, 0))\n        out: NdarrayOrTensor\n        if isinstance(k, torch.Tensor):\n            if hasattr(torch.fft, \"ifftshift\"):  # `ifftshift` is new in torch 1.8.0\n                out = torch.fft.ifftn(torch.fft.ifftshift(k, dim=dims), dim=dims, norm=\"backward\").real\n            else:\n                # if using old PyTorch, will convert to numpy array and return\n                out = np.fft.ifftn(np.fft.ifftshift(k.cpu().numpy(), axes=dims), axes=dims).real\n        else:\n            out = np.fft.ifftn(np.fft.ifftshift(k, axes=dims), axes=dims).real\n        return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_get_number_image_type_conversions_get_number_image_type_conversions.return.num_conversions": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_get_number_image_type_conversions_get_number_image_type_conversions.return.num_conversions", "embedding": null, "metadata": {"file_path": "monai/transforms/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1356, "end_line": 1389, "span_ids": ["get_number_image_type_conversions"], "tokens": 389}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_number_image_type_conversions(transform: Compose, test_data: Any, key: Optional[Hashable] = None) -> int:\n    \"\"\"\n    Get the number of times that the data need to be converted (e.g., numpy to torch).\n    Conversions between different devices are also counted (e.g., CPU to GPU).\n\n    Args:\n        transform: composed transforms to be tested\n        test_data: data to be used to count the number of conversions\n        key: if using dictionary transforms, this key will be used to check the number of conversions.\n    \"\"\"\n\n    def _get_data(obj, key):\n        return obj if key is None else obj[key]\n\n    # if the starting point is a string (e.g., input to LoadImage), start\n    # at -1 since we don't want to count the string -> image conversion.\n    num_conversions = 0 if not isinstance(_get_data(test_data, key), str) else -1\n\n    tr = transform.flatten().transforms\n\n    if isinstance(transform, OneOf) or any(isinstance(i, OneOf) for i in tr):\n        raise RuntimeError(\"Not compatible with `OneOf`, as the applied transform is deterministically chosen.\")\n\n    for _transform in tr:\n        prev_data = _get_data(test_data, key)\n        prev_type = type(prev_data)\n        prev_device = prev_data.device if isinstance(prev_data, torch.Tensor) else None\n        test_data = apply_transform(_transform, test_data, transform.map_items, transform.unpack_items)\n        # every time the type or device changes, increment the counter\n        curr_data = _get_data(test_data, key)\n        curr_device = curr_data.device if isinstance(curr_data, torch.Tensor) else None\n        if not isinstance(curr_data, prev_type) or curr_device != prev_device:\n            num_conversions += 1\n    return num_conversions", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_get_transform_backends_get_transform_backends.return.backends": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_get_transform_backends_get_transform_backends.return.backends", "embedding": null, "metadata": {"file_path": "monai/transforms/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1392, "end_line": 1434, "span_ids": ["get_transform_backends"], "tokens": 273}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_transform_backends():\n    \"\"\"Get the backends of all MONAI transforms.\n\n    Returns:\n        Dictionary, where each key is a transform, and its\n        corresponding values are a boolean list, stating\n        whether that transform supports (1) `torch.Tensor`,\n        and (2) `np.ndarray` as input without needing to\n        convert.\n    \"\"\"\n    backends = {}\n    unique_transforms = []\n    for n, obj in getmembers(monai.transforms):\n        # skip aliases\n        if obj in unique_transforms:\n            continue\n        unique_transforms.append(obj)\n\n        if (\n            isclass(obj)\n            and issubclass(obj, Transform)\n            and n\n            not in [\n                \"BatchInverseTransform\",\n                \"Compose\",\n                \"Decollated\",\n                \"InvertD\",\n                \"InvertibleTransform\",\n                \"Lambda\",\n                \"LambdaD\",\n                \"MapTransform\",\n                \"OneOf\",\n                \"PadListDataCollate\",\n                \"RandLambda\",\n                \"RandLambdaD\",\n                \"RandTorchVisionD\",\n                \"RandomizableTransform\",\n                \"TorchVisionD\",\n                \"Transform\",\n            ]\n        ):\n            backends[n] = [TransformBackends.TORCH in obj.backend, TransformBackends.NUMPY in obj.backend]\n    return backends", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_print_transform_backends_print_transform_backends.print_color_f_Number_of_u": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_print_transform_backends_print_transform_backends.print_color_f_Number_of_u", "embedding": null, "metadata": {"file_path": "monai/transforms/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1437, "end_line": 1475, "span_ids": ["print_transform_backends"], "tokens": 352}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def print_transform_backends():\n    \"\"\"Prints a list of backends of all MONAI transforms.\"\"\"\n\n    class Colors:\n        none = \"\"\n        red = \"91\"\n        green = \"92\"\n        yellow = \"93\"\n\n    def print_color(t, color):\n        print(f\"\\033[{color}m{t}\\033[00m\")\n\n    def print_table_column(name, torch, numpy, color=Colors.none):\n        print_color(f\"{name:<50} {torch:<8} {numpy:<8}\", color)\n\n    backends = get_transform_backends()\n    n_total = len(backends)\n    n_t_or_np, n_t, n_np, n_uncategorized = 0, 0, 0, 0\n    print_table_column(\"Transform\", \"Torch?\", \"Numpy?\")\n    for k, v in backends.items():\n        if all(v):\n            color = Colors.green\n            n_t_or_np += 1\n        elif v[0]:\n            color = Colors.green\n            n_t += 1\n        elif v[1]:\n            color = Colors.yellow\n            n_np += 1\n        else:\n            color = Colors.red\n            n_uncategorized += 1\n        print_table_column(k, *v, color)\n\n    print(\"Total number of transforms:\", n_total)\n    print_color(f\"Number transforms allowing both torch and numpy: {n_t_or_np}\", Colors.green)\n    print_color(f\"Number of TorchTransform: {n_t}\", Colors.green)\n    print_color(f\"Number of NumpyTransform: {n_np}\", Colors.yellow)\n    print_color(f\"Number of uncategorised: {n_uncategorized}\", Colors.red)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_convert_pad_mode_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_convert_pad_mode_", "embedding": null, "metadata": {"file_path": "monai/transforms/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1478, "end_line": 1505, "span_ids": ["convert_pad_mode", "impl:8"], "tokens": 224}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def convert_pad_mode(dst: NdarrayOrTensor, mode: Union[NumpyPadMode, PytorchPadMode, str]):\n    \"\"\"\n    Utility to convert padding mode between numpy array and PyTorch Tensor.\n\n    Args:\n        dst: target data to convert padding mode for, should be numpy array or PyTorch Tensor.\n        mode: current padding mode.\n\n    \"\"\"\n    mode = mode.value if isinstance(mode, (NumpyPadMode, PytorchPadMode)) else mode\n    if isinstance(dst, torch.Tensor):\n        if mode == \"wrap\":\n            mode = \"circular\"\n        if mode == \"edge\":\n            mode = \"replicate\"\n        return look_up_option(mode, PytorchPadMode)\n    if isinstance(dst, np.ndarray):\n        if mode == \"circular\":\n            mode = \"wrap\"\n        if mode == \"replicate\":\n            mode = \"edge\"\n        return look_up_option(mode, NumpyPadMode)\n    raise ValueError(f\"unsupported data type: {type(dst)}.\")\n\n\nif __name__ == \"__main__\":\n    print_transform_backends()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils_create_transform_ims.py_get_data_get_data.return.padder_data_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils_create_transform_ims.py_get_data_get_data.return.padder_data_", "embedding": null, "metadata": {"file_path": "monai/transforms/utils_create_transform_ims.py", "file_name": "utils_create_transform_ims.py", "file_type": "text/x-python", "category": "implementation", "start_line": 174, "end_line": 198, "span_ids": ["get_data"], "tokens": 268}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_data(keys):\n    \"\"\"Get the example data to be used.\n\n    Use MarsAtlas as it only contains 1 image for quick download and\n    that image is parcellated.\n    \"\"\"\n    cache_dir = os.environ.get(\"MONAI_DATA_DIRECTORY\") or tempfile.mkdtemp()\n    fname = \"MarsAtlas-MNI-Colin27.zip\"\n    url = \"https://www.dropbox.com/s/ndz8qtqblkciole/\" + fname + \"?dl=1\"\n    out_path = os.path.join(cache_dir, \"MarsAtlas-MNI-Colin27\")\n    zip_path = os.path.join(cache_dir, fname)\n\n    download_and_extract(url, zip_path, out_path)\n\n    image, label = sorted(glob(os.path.join(out_path, \"*.nii\")))\n\n    data = {CommonKeys.IMAGE: image, CommonKeys.LABEL: label}\n\n    transforms = Compose(\n        [LoadImaged(keys), AddChanneld(keys), ScaleIntensityd(CommonKeys.IMAGE), Rotate90d(keys, spatial_axes=[0, 2])]\n    )\n    data = transforms(data)\n    max_size = max(data[keys[0]].shape)\n    padder = SpatialPadd(keys, (max_size, max_size, max_size))\n    return padder(data)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils_create_transform_ims.py_update_docstring_update_docstring.with_open_code_path_w_.f_writelines_contents_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils_create_transform_ims.py_update_docstring_update_docstring.with_open_code_path_w_.f_writelines_contents_", "embedding": null, "metadata": {"file_path": "monai/transforms/utils_create_transform_ims.py", "file_name": "utils_create_transform_ims.py", "file_type": "text/x-python", "category": "implementation", "start_line": 201, "end_line": 235, "span_ids": ["update_docstring"], "tokens": 293}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def update_docstring(code_path, transform_name):\n    \"\"\"\n    Find the documentation for a given transform and if it's missing,\n    add a pointer to the transform's example image.\n    \"\"\"\n    with open(code_path) as f:\n        contents = f.readlines()\n    doc_start = None\n    for i, line in enumerate(contents):\n        # find the line containing start of the transform documentation\n        if \"`\" + transform_name + \"`\" in line:\n            doc_start = i\n            break\n    if doc_start is None:\n        raise RuntimeError(\"Couldn't find transform documentation\")\n\n    # if image is already in docs, nothing to do\n    image_line = doc_start + 2\n    if \".. image\" in contents[image_line]:\n        return\n\n    # add the line for the image and the alt text\n    contents_orig = deepcopy(contents)\n    contents.insert(\n        image_line,\n        \".. image:: https://github.com/Project-MONAI/DocImages/raw/main/transforms/\" + transform_name + \".png\\n\",\n    )\n    contents.insert(image_line + 1, \"    :alt: example of \" + transform_name + \"\\n\")\n\n    # check that we've only added two lines\n    assert len(contents) == len(contents_orig) + 2\n\n    # write the updated doc to overwrite the original\n    with open(code_path, \"w\") as f:\n        f.writelines(contents)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils_create_transform_ims.py_get_stacked_2d_ims_get_stacked_before_after.return._get_stacked_2d_ims_d_is": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils_create_transform_ims.py_get_stacked_2d_ims_get_stacked_before_after.return._get_stacked_2d_ims_d_is", "embedding": null, "metadata": {"file_path": "monai/transforms/utils_create_transform_ims.py", "file_name": "utils_create_transform_ims.py", "file_type": "text/x-python", "category": "implementation", "start_line": 270, "end_line": 282, "span_ids": ["get_stacked_before_after", "get_stacked_2d_ims"], "tokens": 149}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_stacked_2d_ims(im, is_label):\n    \"\"\"Get the 3 orthogonal views and stack them into 1 image.\n    Requires that all images be same size, but this is taken care\n    of by the `SpatialPadd` earlier.\n    \"\"\"\n    return [get_2d_slice(im, i, is_label) for i in range(3)]\n\n\ndef get_stacked_before_after(before, after, is_label=False):\n    \"\"\"Stack before and after images into 1 image if 3d.\n    Requires that before and after images be the same size.\n    \"\"\"\n    return [get_stacked_2d_ims(d, is_label) for d in (before, after)]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils_create_transform_ims.py_save_image_save_image.plt_close_fig_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils_create_transform_ims.py_save_image_save_image.plt_close_fig_", "embedding": null, "metadata": {"file_path": "monai/transforms/utils_create_transform_ims.py", "file_name": "utils_create_transform_ims.py", "file_type": "text/x-python", "category": "implementation", "start_line": 285, "end_line": 340, "span_ids": ["save_image"], "tokens": 624}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def save_image(images, labels, filename, transform_name, transform_args, shapes, colorbar=False):\n    \"\"\"Save image to file, ensuring there's no whitespace around the edge.\"\"\"\n    plt.rcParams.update({\"font.family\": \"monospace\"})\n    plt.style.use(\"dark_background\")\n    nrow = len(images)  # before and after (should always be 2)\n    ncol = len(images[0])  # num orthogonal views (either 1 or 3)\n    # roughly estimate the height_ratios of the first:second row\n    hs = [float(r[0].shape[0]) for r in images]\n    fig = plt.figure(tight_layout=True)\n    spec = fig.add_gridspec(nrow, ncol, hspace=0, wspace=0, height_ratios=hs)\n    for row in range(nrow):\n        vmin = min(i.min() for i in images[row])\n        vmax = max(i.max() for i in images[row])\n        for col in range(ncol):\n            ax = fig.add_subplot(spec[row, col])\n            imshow = ax.imshow(images[row][col], cmap=\"gray\", vmin=vmin, vmax=vmax)\n            ax.set_aspect(\"equal\")\n            if colorbar and col == ncol - 1:\n                plt.colorbar(imshow, ax=ax)\n            if col == 0:\n                y_label = \"After\" if row else \"Before\"\n                y_label += (\"\\n\" + shapes[row]) if shapes[0] != shapes[1] else \"\"\n                ax.set_ylabel(y_label)\n            # print yticks for the right most column\n            if col != ncol - 1 or colorbar:\n                ax.set_yticks([])\n            else:\n                ax.yaxis.tick_right()\n                for n, label in enumerate(ax.yaxis.get_ticklabels()):\n                    if n > 2:\n                        label.set_visible(False)\n            ax.set_xticks([])\n            ax.set_frame_on(False)\n            if labels is not None:\n                ax.imshow(labels[row][col], cmap=\"hsv\", alpha=0.9, interpolation=\"nearest\")\n    # title is e.g., Flipd(keys=keys, spatial_axis=0)\n    title = transform_name + \"(\"\n    for k, v in transform_args.items():\n        title += k + \"=\"\n        if isinstance(v, str):\n            title += \"'\" + v + \"'\"\n        elif isinstance(v, (np.ndarray, torch.Tensor)):\n            title += \"[array]\"\n        elif isinstance(v, Callable):\n            title += \"[callable]\"\n        else:\n            title += str(v)\n        title += \", \"\n    if len(transform_args) > 0:\n        title = title[:-2]\n    title += \")\"\n    # shorten the lines\n    title = textwrap.fill(title, 50, break_long_words=False, subsequent_indent=\" \" * (len(transform_name) + 1))\n    fig.suptitle(title, x=0.1, horizontalalignment=\"left\")\n    fig.savefig(filename)\n    plt.close(fig)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils_create_transform_ims.py_get_images_get_images.return.out_shape_str": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils_create_transform_ims.py_get_images_get_images.return.out_shape_str", "embedding": null, "metadata": {"file_path": "monai/transforms/utils_create_transform_ims.py", "file_name": "utils_create_transform_ims.py", "file_type": "text/x-python", "category": "implementation", "start_line": 343, "end_line": 383, "span_ids": ["get_images"], "tokens": 585}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_images(data, is_label=False):\n    \"\"\"Get image. If is dictionary, extract key. If is list, stack. If both dictionary and list, do both.\n    Also return the image size as string to be used im the imshow. If it's a list, return `N x (H,W,D)`.\n    \"\"\"\n    # If not a list, convert\n    if not isinstance(data, list):\n        data = [data]\n    key = CommonKeys.LABEL if is_label else CommonKeys.IMAGE\n    is_map = isinstance(data[0], dict)\n    # length of the list will be equal to number of samples produced. This will be 1 except for transforms that\n    # produce `num_samples`.\n    data = [d[key] if is_map else d for d in data]\n    data = [d[0] for d in data]  # remove channel component\n\n    # for each sample, create a list of the orthogonal views. If image is 2d, length will be 1. If 3d, there\n    # will be three orthogonal views\n    num_samples = len(data)\n    num_orthog_views = 3 if data[0].ndim == 3 else 1\n    shape_str = (f\"{num_samples} x \" if num_samples > 1 else \"\") + str(data[0].shape)\n    for i in range(num_samples):\n        data[i] = [get_2d_slice(data[i], view, is_label) for view in range(num_orthog_views)]\n\n    out = []\n    if num_samples == 1:\n        out = data[0]\n    else:\n        # we might need to panel the images. this happens if a transform produces e.g. 4 output images.\n        # In this case, we create a 2-by-2 grid from them. Output will be a list containing n_orthog_views,\n        # each element being either the image (if num_samples is 1) or the panelled image.\n        nrows = int(np.floor(num_samples ** 0.5))\n        for view in range(num_orthog_views):\n            result = np.asarray([d[view] for d in data])\n            nindex, height, width = result.shape\n            ncols = nindex // nrows\n            # only implemented for square number of images (e.g. 4 images goes to a 2-by-2 panel)\n            if nindex != nrows * ncols:\n                raise NotImplementedError\n            # want result.shape = (height*nrows, width*ncols), have to be careful about striding\n            result = result.reshape(nrows, ncols, height, width).swapaxes(1, 2).reshape(height * nrows, width * ncols)\n            out.append(result)\n    return out, shape_str", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils_create_transform_ims.py_create_transform_im_create_transform_im.if_update_doc_.update_docstring_rst_path": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils_create_transform_ims.py_create_transform_im_create_transform_im.if_update_doc_.update_docstring_rst_path", "embedding": null, "metadata": {"file_path": "monai/transforms/utils_create_transform_ims.py", "file_name": "utils_create_transform_ims.py", "file_type": "text/x-python", "category": "implementation", "start_line": 383, "end_line": 433, "span_ids": ["create_transform_im"], "tokens": 450}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def create_transform_im(\n    transform, transform_args, data, ndim=3, colorbar=False, update_doc=True, seed=0, is_post=False\n):\n    \"\"\"Create an image with the before and after of the transform.\n    Also update the transform's documentation to point to this image.\"\"\"\n\n    transform = transform(**transform_args)\n\n    if not has_matplotlib:\n        raise RuntimeError\n\n    if isinstance(transform, Randomizable):\n        # increment the seed for map transforms so they're different to the array versions.\n        seed = seed + 1 if isinstance(transform, MapTransform) else seed\n        transform.set_random_state(seed)\n\n    out_dir = os.environ.get(\"MONAI_DOC_IMAGES\")\n    if out_dir is None:\n        raise RuntimeError(\n            \"Please git clone https://github.com/Project-MONAI/DocImages\"\n            + \" and then set the environment variable `MONAI_DOC_IMAGES`\"\n        )\n    out_dir = os.path.join(out_dir, \"transforms\")\n\n    # Path is transform name\n    transform_name = transform.__class__.__name__\n    out_fname = transform_name + \".png\"\n    out_file = os.path.join(out_dir, out_fname)\n\n    is_map = isinstance(transform, MapTransform)\n    data_in = pre_process_data(deepcopy(data), ndim, is_map, is_post)\n\n    data_tr = transform(deepcopy(data_in))\n\n    images_before, before_shape = get_images(data_in)\n    images_after, after_shape = get_images(data_tr)\n    images = (images_before, images_after)\n    shapes = (before_shape, after_shape)\n\n    labels = None\n    if is_map:\n        labels_before, *_ = get_images(data_in, is_label=True)\n        labels_after, *_ = get_images(data_tr, is_label=True)\n        labels = (labels_before, labels_after)\n\n    save_image(images, labels, out_file, transform_name, transform_args, shapes, colorbar)\n\n    if update_doc:\n        base_dir = pathlib.Path(__file__).parent.parent.parent\n        rst_path = os.path.join(base_dir, \"docs\", \"source\", \"transforms.rst\")\n        update_docstring(rst_path, transform_name)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils_pytorch_numpy_unification.py_moveaxis_moveaxis.raise_RuntimeError_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils_pytorch_numpy_unification.py_moveaxis_moveaxis.raise_RuntimeError_", "embedding": null, "metadata": {"file_path": "monai/transforms/utils_pytorch_numpy_unification.py", "file_name": "utils_pytorch_numpy_unification.py", "file_type": "text/x-python", "category": "implementation", "start_line": 42, "end_line": 50, "span_ids": ["moveaxis"], "tokens": 139}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def moveaxis(x: NdarrayOrTensor, src: int, dst: int) -> NdarrayOrTensor:\n    \"\"\"`moveaxis` for pytorch and numpy, using `permute` for pytorch ver < 1.8\"\"\"\n    if isinstance(x, torch.Tensor):\n        if hasattr(torch, \"moveaxis\"):  # `moveaxis` is new in torch 1.8.0\n            return torch.moveaxis(x, src, dst)\n        return _moveaxis_with_permute(x, src, dst)  # type: ignore\n    if isinstance(x, np.ndarray):\n        return np.moveaxis(x, src, dst)\n    raise RuntimeError()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils_pytorch_numpy_unification.py__moveaxis_with_permute_clip.return.result": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils_pytorch_numpy_unification.py__moveaxis_with_permute_clip.return.result", "embedding": null, "metadata": {"file_path": "monai/transforms/utils_pytorch_numpy_unification.py", "file_name": "utils_pytorch_numpy_unification.py", "file_type": "text/x-python", "category": "implementation", "start_line": 54, "end_line": 82, "span_ids": ["clip", "_moveaxis_with_permute", "in1d"], "tokens": 244}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _moveaxis_with_permute(x, src, dst):\n    # get original indices\n    indices = list(range(x.ndim))\n    # make src and dst positive\n    if src < 0:\n        src = len(indices) + src\n    if dst < 0:\n        dst = len(indices) + dst\n    # remove desired index and insert it in new position\n    indices.pop(src)\n    indices.insert(dst, src)\n    return x.permute(indices)\n\n\ndef in1d(x, y):\n    \"\"\"`np.in1d` with equivalent implementation for torch.\"\"\"\n    if isinstance(x, np.ndarray):\n        return np.in1d(x, y)\n    return (x[..., None] == torch.tensor(y, device=x.device)).any(-1).view(-1)\n\n\ndef clip(a: NdarrayOrTensor, a_min, a_max) -> NdarrayOrTensor:\n    \"\"\"`np.clip` with equivalent implementation for torch.\"\"\"\n    result: NdarrayOrTensor\n    if isinstance(a, np.ndarray):\n        result = np.clip(a, a_min, a_max)\n    else:\n        result = torch.clamp(a, a_min, a_max)\n    return result", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils_pytorch_numpy_unification.py_percentile_percentile.return.result": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils_pytorch_numpy_unification.py_percentile_percentile.return.result", "embedding": null, "metadata": {"file_path": "monai/transforms/utils_pytorch_numpy_unification.py", "file_name": "utils_pytorch_numpy_unification.py", "file_type": "text/x-python", "category": "implementation", "start_line": 84, "end_line": 125, "span_ids": ["percentile"], "tokens": 511}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def percentile(x: NdarrayOrTensor, q, dim: Optional[int] = None) -> Union[NdarrayOrTensor, float, int]:\n    \"\"\"`np.percentile` with equivalent implementation for torch.\n\n    Pytorch uses `quantile`, but this functionality is only available from v1.7.\n    For earlier methods, we calculate it ourselves. This doesn't do interpolation,\n    so is the equivalent of ``numpy.percentile(..., interpolation=\"nearest\")``.\n    For more details, please refer to:\n    https://pytorch.org/docs/stable/generated/torch.quantile.html.\n    https://numpy.org/doc/stable/reference/generated/numpy.percentile.html.\n\n    Args:\n        x: input data\n        q: percentile to compute (should in range 0 <= q <= 100)\n        dim: the dim along which the percentiles are computed. default is to compute the percentile\n            along a flattened version of the array. only work for numpy array or Tensor with PyTorch >= 1.7.0.\n\n    Returns:\n        Resulting value (scalar)\n    \"\"\"\n    if np.isscalar(q):\n        if not 0 <= q <= 100:  # type: ignore\n            raise ValueError\n    elif any(q < 0) or any(q > 100):\n        raise ValueError\n    result: Union[NdarrayOrTensor, float, int]\n    if isinstance(x, np.ndarray):\n        result = np.percentile(x, q, axis=dim)\n    else:\n        q = torch.tensor(q, device=x.device)\n        if hasattr(torch, \"quantile\"):  # `quantile` is new in torch 1.7.0\n            result = torch.quantile(x, q / 100.0, dim=dim)\n        else:\n            # Note that ``kthvalue()`` works one-based, i.e., the first sorted value\n            # corresponds to k=1, not k=0. Thus, we need the `1 +`.\n            k = 1 + (0.01 * q * (x.numel() - 1)).round().int()\n            if k.numel() > 1:\n                r = [x.view(-1).kthvalue(int(_k)).values.item() for _k in k]\n                result = torch.tensor(r, device=x.device)\n            else:\n                result = x.view(-1).kthvalue(int(k)).values.item()\n\n    return result", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils_pytorch_numpy_unification.py_where_nonzero.return.torch_nonzero_x_flatten_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils_pytorch_numpy_unification.py_where_nonzero.return.torch_nonzero_x_flatten_", "embedding": null, "metadata": {"file_path": "monai/transforms/utils_pytorch_numpy_unification.py", "file_name": "utils_pytorch_numpy_unification.py", "file_type": "text/x-python", "category": "implementation", "start_line": 128, "end_line": 159, "span_ids": ["where", "nonzero"], "tokens": 232}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def where(condition: NdarrayOrTensor, x=None, y=None) -> NdarrayOrTensor:\n    \"\"\"\n    Note that `torch.where` may convert y.dtype to x.dtype.\n    \"\"\"\n    result: NdarrayOrTensor\n    if isinstance(condition, np.ndarray):\n        if x is not None:\n            result = np.where(condition, x, y)\n        else:\n            result = np.where(condition)  # type: ignore\n    else:\n        if x is not None:\n            x = torch.as_tensor(x, device=condition.device)\n            y = torch.as_tensor(y, device=condition.device, dtype=x.dtype)\n            result = torch.where(condition, x, y)\n        else:\n            result = torch.where(condition)  # type: ignore\n    return result\n\n\ndef nonzero(x: NdarrayOrTensor):\n    \"\"\"`np.nonzero` with equivalent implementation for torch.\n\n    Args:\n        x: array/tensor\n\n    Returns:\n        Index unravelled for given shape\n    \"\"\"\n    if isinstance(x, np.ndarray):\n        return np.nonzero(x)[0]\n    return torch.nonzero(x).flatten()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils_pytorch_numpy_unification.py_floor_divide_floor_divide.return.np_floor_divide_a_b_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils_pytorch_numpy_unification.py_floor_divide_floor_divide.return.np_floor_divide_a_b_", "embedding": null, "metadata": {"file_path": "monai/transforms/utils_pytorch_numpy_unification.py", "file_name": "utils_pytorch_numpy_unification.py", "file_type": "text/x-python", "category": "implementation", "start_line": 155, "end_line": 172, "span_ids": ["floor_divide"], "tokens": 160}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def floor_divide(a: NdarrayOrTensor, b) -> NdarrayOrTensor:\n    \"\"\"`np.floor_divide` with equivalent implementation for torch.\n\n    As of pt1.8, use `torch.div(..., rounding_mode=\"floor\")`, and\n    before that, use `torch.floor_divide`.\n\n    Args:\n        a: first array/tensor\n        b: scalar to divide by\n\n    Returns:\n        Element-wise floor division between two arrays/tensors.\n    \"\"\"\n    if isinstance(a, torch.Tensor):\n        if is_module_ver_at_least(torch, (1, 8, 0)):\n            return torch.div(a, b, rounding_mode=\"floor\")\n        return torch.floor_divide(a, b)\n    return np.floor_divide(a, b)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils_pytorch_numpy_unification.py_unravel_index_unravel_index.return.np_asarray_np_unravel_ind": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils_pytorch_numpy_unification.py_unravel_index_unravel_index.return.np_asarray_np_unravel_ind", "embedding": null, "metadata": {"file_path": "monai/transforms/utils_pytorch_numpy_unification.py", "file_name": "utils_pytorch_numpy_unification.py", "file_type": "text/x-python", "category": "implementation", "start_line": 175, "end_line": 191, "span_ids": ["unravel_index"], "tokens": 111}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def unravel_index(idx, shape):\n    \"\"\"`np.unravel_index` with equivalent implementation for torch.\n\n    Args:\n        idx: index to unravel\n        shape: shape of array/tensor\n\n    Returns:\n        Index unravelled for given shape\n    \"\"\"\n    if isinstance(idx, torch.Tensor):\n        coord = []\n        for dim in reversed(shape):\n            coord.append(idx % dim)\n            idx = floor_divide(idx, dim)\n        return torch.stack(coord[::-1])\n    return np.asarray(np.unravel_index(idx, shape))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils_pytorch_numpy_unification.py_unravel_indices_ravel.return.np_ravel_x_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils_pytorch_numpy_unification.py_unravel_indices_ravel.return.np_ravel_x_", "embedding": null, "metadata": {"file_path": "monai/transforms/utils_pytorch_numpy_unification.py", "file_name": "utils_pytorch_numpy_unification.py", "file_type": "text/x-python", "category": "implementation", "start_line": 201, "end_line": 228, "span_ids": ["ravel", "unravel_indices"], "tokens": 192}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def unravel_indices(idx, shape):\n    \"\"\"Computing unravel coordinates from indices.\n\n    Args:\n        idx: a sequence of indices to unravel\n        shape: shape of array/tensor\n\n    Returns:\n        Stacked indices unravelled for given shape\n    \"\"\"\n    lib_stack = torch.stack if isinstance(idx[0], torch.Tensor) else np.stack\n    return lib_stack([unravel_index(i, shape) for i in idx])\n\n\ndef ravel(x: NdarrayOrTensor):\n    \"\"\"`np.ravel` with equivalent implementation for torch.\n\n    Args:\n        x: array/tensor to ravel\n\n    Returns:\n        Return a contiguous flattened array/tensor.\n    \"\"\"\n    if isinstance(x, torch.Tensor):\n        if hasattr(torch, \"ravel\"):  # `ravel` is new in torch 1.8.0\n            return x.ravel()\n        return x.flatten().contiguous()\n    return np.ravel(x)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils_pytorch_numpy_unification.py_any_np_pt_any_np_pt.return.x": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils_pytorch_numpy_unification.py_any_np_pt_any_np_pt.return.x", "embedding": null, "metadata": {"file_path": "monai/transforms/utils_pytorch_numpy_unification.py", "file_name": "utils_pytorch_numpy_unification.py", "file_type": "text/x-python", "category": "implementation", "start_line": 224, "end_line": 247, "span_ids": ["any_np_pt"], "tokens": 183}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def any_np_pt(x: NdarrayOrTensor, axis: Union[int, Sequence[int]]):\n    \"\"\"`np.any` with equivalent implementation for torch.\n\n    For pytorch, convert to boolean for compatibility with older versions.\n\n    Args:\n        x: input array/tensor\n        axis: axis to perform `any` over\n\n    Returns:\n        Return a contiguous flattened array/tensor.\n    \"\"\"\n    if isinstance(x, np.ndarray):\n        return np.any(x, axis)\n\n    # pytorch can't handle multiple dimensions to `any` so loop across them\n    axis = [axis] if not isinstance(axis, Sequence) else axis\n    for ax in axis:\n        try:\n            x = torch.any(x, ax)\n        except RuntimeError:\n            # older versions of pytorch require the input to be cast to boolean\n            x = torch.any(x.bool(), ax)\n    return x", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils_pytorch_numpy_unification.py_maximum_maximum.return.np_maximum_a_b_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils_pytorch_numpy_unification.py_maximum_maximum.return.np_maximum_a_b_", "embedding": null, "metadata": {"file_path": "monai/transforms/utils_pytorch_numpy_unification.py", "file_name": "utils_pytorch_numpy_unification.py", "file_type": "text/x-python", "category": "implementation", "start_line": 257, "end_line": 274, "span_ids": ["maximum"], "tokens": 179}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def maximum(a: NdarrayOrTensor, b: NdarrayOrTensor) -> NdarrayOrTensor:\n    \"\"\"`np.maximum` with equivalent implementation for torch.\n\n    `torch.maximum` only available from pt>1.6, else use `torch.stack` and `torch.max`.\n\n    Args:\n        a: first array/tensor\n        b: second array/tensor\n\n    Returns:\n        Element-wise maximum between two arrays/tensors.\n    \"\"\"\n    if isinstance(a, torch.Tensor) and isinstance(b, torch.Tensor):\n        # is torch and has torch.maximum (pt>1.6)\n        if hasattr(torch, \"maximum\"):  # `maximum` is new in torch 1.7.0\n            return torch.maximum(a, b)\n        return torch.stack((a, b)).max(dim=0)[0]\n    return np.maximum(a, b)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/deprecate_utils.py_inspect_deprecated.if_since_is_None_and_remo.else_.is_removed.removed_is_not_None_and_v": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/deprecate_utils.py_inspect_deprecated.if_since_is_None_and_remo.else_.is_removed.removed_is_not_None_and_v", "embedding": null, "metadata": {"file_path": "monai/utils/deprecate_utils.py", "file_name": "deprecate_utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 81, "span_ids": ["warn_deprecated", "DeprecatedError", "deprecated", "docstring"], "tokens": 669}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import inspect\nimport sys\nimport warnings\nfrom functools import wraps\nfrom types import FunctionType\nfrom typing import Optional\n\nfrom monai.utils.module import version_leq\n\nfrom .. import __version__\n\n__all__ = [\"deprecated\", \"deprecated_arg\", \"DeprecatedError\"]\n\n\nclass DeprecatedError(Exception):\n    pass\n\n\ndef warn_deprecated(obj, msg):\n    \"\"\"\n    Issue the warning message `msg`.\n    \"\"\"\n    warnings.warn(msg, category=DeprecationWarning, stacklevel=2)\n\n\ndef deprecated(\n    since: Optional[str] = None, removed: Optional[str] = None, msg_suffix: str = \"\", version_val: str = __version__\n):\n    \"\"\"\n    Marks a function or class as deprecated. If `since` is given this should be a version at or earlier than the\n    current version and states at what version of the definition was marked as deprecated. If `removed` is given\n    this can be any version and marks when the definition was removed.\n\n    When the decorated definition is called, that is when the function is called or the class instantiated,\n    a `DeprecationWarning` is issued if `since` is given and the current version is at or later than that given.\n    a `DeprecatedError` exception is instead raised if `removed` is given and the current version is at or later\n    than that, or if neither `since` nor `removed` is provided.\n\n    The relevant docstring of the deprecating function should also be updated accordingly,\n    using the Sphinx directives such as `.. versionchanged:: version` and `.. deprecated:: version`.\n    https://www.sphinx-doc.org/en/master/usage/restructuredtext/directives.html#directive-versionadded\n\n    Args:\n        since: version at which the definition was marked deprecated but not removed.\n        removed: version at which the definition was removed and no longer usable.\n        msg_suffix: message appended to warning/exception detailing reasons for deprecation and what to use instead.\n        version_val: (used for testing) version to compare since and removed against, default is MONAI version.\n\n    Returns:\n        Decorated definition which warns or raises exception when used\n    \"\"\"\n\n    # if version_val.startswith(\"0+\"):\n    #     # version unknown, set version_val to a large value (assuming the latest version)\n    #     version_val = f\"{sys.maxsize}\"\n    if since is not None and removed is not None and not version_leq(since, removed):\n        raise ValueError(f\"since must be less or equal to removed, got since={since}, removed={removed}.\")\n    is_not_yet_deprecated = since is not None and version_val != since and version_leq(version_val, since)\n    if is_not_yet_deprecated:\n        # smaller than `since`, do nothing\n        return lambda obj: obj\n\n    if since is None and removed is None:\n        # raise a DeprecatedError directly\n        is_removed = True\n        is_deprecated = True\n    else:\n        # compare the numbers\n        is_deprecated = since is not None and version_leq(since, version_val)\n        is_removed = removed is not None and version_leq(removed, version_val)\n    # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/deprecate_utils.py_deprecated._decorator_deprecated.return._decorator": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/deprecate_utils.py_deprecated._decorator_deprecated.return._decorator", "embedding": null, "metadata": {"file_path": "monai/utils/deprecate_utils.py", "file_name": "deprecate_utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 82, "end_line": 113, "span_ids": ["deprecated"], "tokens": 272}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def deprecated(\n    since: Optional[str] = None, removed: Optional[str] = None, msg_suffix: str = \"\", version_val: str = __version__\n):\n    # ... other code\n\n    def _decorator(obj):\n        is_func = isinstance(obj, FunctionType)\n        call_obj = obj if is_func else obj.__init__\n\n        msg_prefix = f\"{'Function' if is_func else 'Class'} `{obj.__name__}`\"\n\n        if is_removed:\n            msg_infix = f\"was removed in version {removed}.\"\n        elif is_deprecated:\n            msg_infix = f\"has been deprecated since version {since}.\"\n            if removed is not None:\n                msg_infix += f\" It will be removed in version {removed}.\"\n        else:\n            msg_infix = \"has been deprecated.\"\n\n        msg = f\"{msg_prefix} {msg_infix} {msg_suffix}\".strip()\n\n        @wraps(call_obj)\n        def _wrapper(*args, **kwargs):\n            if is_removed:\n                raise DeprecatedError(msg)\n            if is_deprecated:\n                warn_deprecated(obj, msg)\n\n            return call_obj(*args, **kwargs)\n\n        if is_func:\n            return _wrapper\n        obj.__init__ = _wrapper\n        return obj\n\n    return _decorator", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/deprecate_utils.py_deprecated_arg_deprecated_arg.if_since_is_None_and_remo.else_.is_removed.removed_is_not_None_and_v": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/deprecate_utils.py_deprecated_arg_deprecated_arg.if_since_is_None_and_remo.else_.is_removed.removed_is_not_None_and_v", "embedding": null, "metadata": {"file_path": "monai/utils/deprecate_utils.py", "file_name": "deprecate_utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 117, "end_line": 171, "span_ids": ["deprecated_arg"], "tokens": 639}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def deprecated_arg(\n    name,\n    since: Optional[str] = None,\n    removed: Optional[str] = None,\n    msg_suffix: str = \"\",\n    version_val: str = __version__,\n    new_name: Optional[str] = None,\n):\n    \"\"\"\n    Marks a particular named argument of a callable as deprecated. The same conditions for `since` and `removed` as\n    described in the `deprecated` decorator.\n\n    When the decorated definition is called, that is when the function is called or the class instantiated with args,\n    a `DeprecationWarning` is issued if `since` is given and the current version is at or later than that given.\n    a `DeprecatedError` exception is instead raised if `removed` is given and the current version is at or later\n    than that, or if neither `since` nor `removed` is provided.\n\n    The relevant docstring of the deprecating function should also be updated accordingly,\n    using the Sphinx directives such as `.. versionchanged:: version` and `.. deprecated:: version`.\n    https://www.sphinx-doc.org/en/master/usage/restructuredtext/directives.html#directive-versionadded\n\n    In the current implementation type annotations are not preserved.\n\n\n    Args:\n        name: name of position or keyword argument to mark as deprecated.\n        since: version at which the argument was marked deprecated but not removed.\n        removed: version at which the argument was removed and no longer usable.\n        msg_suffix: message appended to warning/exception detailing reasons for deprecation and what to use instead.\n        version_val: (used for testing) version to compare since and removed against, default is MONAI version.\n        new_name: name of position or keyword argument to replace the deprecated argument.\n            if it is specified and the signature of the decorated function has a `kwargs`, the value to the\n            deprecated argument `name` will be removed.\n\n    Returns:\n        Decorated callable which warns or raises exception when deprecated argument used.\n    \"\"\"\n\n    if version_val.startswith(\"0+\") or not f\"{version_val}\".strip()[0].isdigit():\n        # version unknown, set version_val to a large value (assuming the latest version)\n        version_val = f\"{sys.maxsize}\"\n    if since is not None and removed is not None and not version_leq(since, removed):\n        raise ValueError(f\"since must be less or equal to removed, got since={since}, removed={removed}.\")\n    is_not_yet_deprecated = since is not None and version_val != since and version_leq(version_val, since)\n    if is_not_yet_deprecated:\n        # smaller than `since`, do nothing\n        return lambda obj: obj\n    if since is None and removed is None:\n        # raise a DeprecatedError directly\n        is_removed = True\n        is_deprecated = True\n    else:\n        # compare the numbers\n        is_deprecated = since is not None and version_leq(since, version_val)\n        is_removed = removed is not None and version_leq(removed, version_val)\n    # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/deprecate_utils.py_deprecated_arg._decorator_deprecated_arg._decorator.sig.inspect_signature_func_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/deprecate_utils.py_deprecated_arg._decorator_deprecated_arg._decorator.sig.inspect_signature_func_", "embedding": null, "metadata": {"file_path": "monai/utils/deprecate_utils.py", "file_name": "deprecate_utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 170, "end_line": 186, "span_ids": ["deprecated_arg"], "tokens": 205}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def deprecated_arg(\n    name,\n    since: Optional[str] = None,\n    removed: Optional[str] = None,\n    msg_suffix: str = \"\",\n    version_val: str = __version__,\n    new_name: Optional[str] = None,\n):\n    # ... other code\n\n    def _decorator(func):\n        argname = f\"{func.__name__}_{name}\"\n\n        msg_prefix = f\"Argument `{name}`\"\n\n        if is_removed:\n            msg_infix = f\"was removed in version {removed}.\"\n        elif is_deprecated:\n            msg_infix = f\"has been deprecated since version {since}.\"\n            if removed is not None:\n                msg_infix += f\" It will be removed in version {removed}.\"\n        else:\n            msg_infix = \"has been deprecated.\"\n\n        msg = f\"{msg_prefix} {msg_infix} {msg_suffix}\".strip()\n\n        sig = inspect.signature(func)\n        # ... other code\n    # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/deprecate_utils.py_deprecated_arg._decorator._wrapper_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/deprecate_utils.py_deprecated_arg._decorator._wrapper_", "embedding": null, "metadata": {"file_path": "monai/utils/deprecate_utils.py", "file_name": "deprecate_utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 191, "end_line": 222, "span_ids": ["deprecated_arg"], "tokens": 315}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def deprecated_arg(\n    name,\n    since: Optional[str] = None,\n    removed: Optional[str] = None,\n    msg_suffix: str = \"\",\n    version_val: str = __version__,\n    new_name: Optional[str] = None,\n):\n\n    def _decorator(func):\n        # ... other code\n\n        @wraps(func)\n        def _wrapper(*args, **kwargs):\n            if new_name is not None and name in kwargs and new_name not in kwargs:\n                # replace the deprecated arg \"name\" with \"new_name\"\n                # if name is specified and new_name is not specified\n                kwargs[new_name] = kwargs[name]\n                try:\n                    sig.bind(*args, **kwargs).arguments\n                except TypeError:\n                    # multiple values for new_name using both args and kwargs\n                    kwargs.pop(new_name, None)\n            binding = sig.bind(*args, **kwargs).arguments\n            positional_found = name in binding\n            kw_found = False\n            for k, param in sig.parameters.items():\n                if param.kind == inspect.Parameter.VAR_KEYWORD and k in binding and name in binding[k]:\n                    kw_found = True\n                    # if the deprecated arg is found in the **kwargs, it should be removed\n                    kwargs.pop(name, None)\n\n            if positional_found or kw_found:\n                if is_removed:\n                    raise DeprecatedError(msg)\n                if is_deprecated:\n                    warn_deprecated(argname, msg)\n\n            return func(*args, **kwargs)\n\n        return _wrapper\n\n    return _decorator", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/dist.py_from_typing_import_List_get_dist_device.return.None": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/dist.py_from_typing_import_List_get_dist_device.return.None", "embedding": null, "metadata": {"file_path": "monai/utils/dist.py", "file_name": "dist.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 39, "span_ids": ["get_dist_device", "docstring"], "tokens": 218}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import List\n\nimport torch\nimport torch.distributed as dist\n\nfrom monai.config import IgniteInfo\nfrom monai.utils.module import min_version, optional_import\n\nidist, has_ignite = optional_import(\"ignite\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"distributed\")\n\n__all__ = [\"get_dist_device\", \"evenly_divisible_all_gather\", \"string_list_all_gather\"]\n\n\ndef get_dist_device():\n    \"\"\"\n    Get the expected target device in the native PyTorch distributed data parallel.\n    For NCCL backend, return GPU device of current process.\n    For GLOO backend, return CPU.\n    For any other backends, return None as the default, tensor.to(None) will not change the device.\n\n    \"\"\"\n    if dist.is_initialized():\n        backend = dist.get_backend()\n        if backend == \"nccl\" and torch.cuda.is_available():\n            return torch.device(f\"cuda:{torch.cuda.current_device()}\")\n        if backend == \"gloo\":\n            return torch.device(\"cpu\")\n    return None", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/dist.py_evenly_divisible_all_gather_evenly_divisible_all_gather.length.data_shape_0_if_ndims_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/dist.py_evenly_divisible_all_gather_evenly_divisible_all_gather.length.data_shape_0_if_ndims_", "embedding": null, "metadata": {"file_path": "monai/utils/dist.py", "file_name": "dist.py", "file_type": "text/x-python", "category": "implementation", "start_line": 42, "end_line": 63, "span_ids": ["evenly_divisible_all_gather"], "tokens": 254}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def evenly_divisible_all_gather(data: torch.Tensor, concat: bool = True):\n    \"\"\"\n    Utility function for distributed data parallel to pad at first dim to make it evenly divisible and all_gather.\n    The input data of every rank should have the same number of dimensions, only the first dim can be different.\n\n    Note: If has ignite installed, will execute based on ignite distributed APIs, otherwise, if the native\n    PyTorch distributed group initialized, will execute based on native PyTorch distributed APIs.\n\n    Args:\n        data: source tensor to pad and execute all_gather in distributed data parallel.\n        concat: whether to concat the gathered list to be a Tensor, if False, return a list\n            of Tensors, similar behavior as torch.distributed.all_gather(). default to True.\n\n    Note:\n        The input data on different ranks must have exactly same `dtype`.\n\n    \"\"\"\n    if not isinstance(data, torch.Tensor):\n        raise ValueError(\"input data must be PyTorch Tensor.\")\n    # data of all the ranks must have same number of dimensions\n    ndims = data.ndimension()\n    length: int = data.shape[0] if ndims > 0 else 1\n    # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/dist.py_evenly_divisible_all_gather._torch_all_gather_evenly_divisible_all_gather._torch_all_gather.return._o_squeeze_0_if_ndims_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/dist.py_evenly_divisible_all_gather._torch_all_gather_evenly_divisible_all_gather._torch_all_gather.return._o_squeeze_0_if_ndims_", "embedding": null, "metadata": {"file_path": "monai/utils/dist.py", "file_name": "dist.py", "file_type": "text/x-python", "category": "implementation", "start_line": 65, "end_line": 89, "span_ids": ["evenly_divisible_all_gather"], "tokens": 331}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def evenly_divisible_all_gather(data: torch.Tensor, concat: bool = True):\n    # ... other code\n\n    def _torch_all_gather(data: torch.Tensor) -> List[torch.Tensor]:\n        \"\"\"\n        Implementation based on native PyTorch distributed data parallel APIs.\n\n        \"\"\"\n        device = get_dist_device()\n        orig_device = data.device\n        data = data.to(device)\n        data = data.unsqueeze(0) if ndims == 0 else data\n\n        # make sure the data is evenly-divisible on multi-GPUs\n        length_tensor = torch.as_tensor([length], device=device)\n        all_lens = [torch.zeros_like(length_tensor) for _ in range(dist.get_world_size())]\n        dist.all_gather(all_lens, length_tensor)\n        all_lens_: List[int] = [int(i.item()) for i in all_lens]\n\n        max_len: int = max(all_lens_)\n        if length < max_len:\n            size = [max_len - length] + list(data.shape[1:])\n            data = torch.cat([data, data.new_full(size, 0)], dim=0)\n        # all gather across all processes\n        output = [torch.zeros_like(data) for _ in range(dist.get_world_size())]\n        dist.all_gather(output, data)\n        # remove the padding items, if all the input data doesn't have batch dim, squeeze the first dim\n        return [(o.squeeze(0) if ndims == 0 else o[:l, ...]).to(orig_device) for o, l in zip(output, all_lens_)]\n    # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/dist.py_evenly_divisible_all_gather._ignite_all_gather_evenly_divisible_all_gather.return.torch_cat_output_dim_0_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/dist.py_evenly_divisible_all_gather._ignite_all_gather_evenly_divisible_all_gather.return.torch_cat_output_dim_0_", "embedding": null, "metadata": {"file_path": "monai/utils/dist.py", "file_name": "dist.py", "file_type": "text/x-python", "category": "implementation", "start_line": 91, "end_line": 123, "span_ids": ["evenly_divisible_all_gather"], "tokens": 363}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def evenly_divisible_all_gather(data: torch.Tensor, concat: bool = True):\n    # ... other code\n\n    def _ignite_all_gather(data: torch.Tensor) -> List[torch.Tensor]:\n        \"\"\"\n        Implementation based on PyTorch ignite package, it can support more kinds of backends.\n\n        \"\"\"\n        data = data.unsqueeze(0) if ndims == 0 else data\n        # make sure the data is evenly-divisible on multi-GPUs\n        all_lens: List[int] = idist.all_gather(length)\n        max_len: int = max(all_lens)\n        if length < max_len:\n            size = [max_len - length] + list(data.shape[1:])\n            data = torch.cat([data, data.new_full(size, 0)], dim=0)\n        # all gather across all processes\n        output = idist.all_gather(data)\n        # delete the padding NaN items\n        if ndims == 0:\n            # if all the input data doesn't have batch dim, unbind to a list of 0-dim Tensors\n            return list(torch.unbind(output, dim=0))\n        return [output[i * max_len : i * max_len + l, ...] for i, l in enumerate(all_lens)]\n\n    output: List[torch.Tensor]\n    if has_ignite:\n        if idist.get_world_size() <= 1:\n            return data\n        output = _ignite_all_gather(data=data)\n    elif dist.is_available() and dist.is_initialized():\n        if dist.get_world_size() <= 1:\n            return data\n        output = _torch_all_gather(data=data)\n    else:\n        return data\n\n    return torch.cat(output, dim=0) if concat else output", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/dist.py_string_list_all_gather_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/dist.py_string_list_all_gather_", "embedding": null, "metadata": {"file_path": "monai/utils/dist.py", "file_name": "dist.py", "file_type": "text/x-python", "category": "implementation", "start_line": 126, "end_line": 155, "span_ids": ["string_list_all_gather"], "tokens": 299}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def string_list_all_gather(strings: List[str], delimiter: str = \"\\t\") -> List[str]:\n    \"\"\"\n    Utility function for distributed data parallel to all gather a list of strings.\n    Refer to the idea of ignite `all_gather(string)`:\n    https://pytorch.org/ignite/v0.4.5/distributed.html#ignite.distributed.utils.all_gather.\n\n    Note: If has ignite installed, will execute based on ignite distributed APIs, otherwise, if the native\n    PyTorch distributed group initialized, will execute based on native PyTorch distributed APIs.\n\n    Args:\n        strings: a list of strings to all gather.\n        delimiter: use the delimiter to join the string list to be a long string,\n            then all gather across ranks and split to a list. default to \"\\t\".\n\n    \"\"\"\n    world_size: int = 1\n    if has_ignite:\n        world_size = idist.get_world_size()\n    elif dist.is_available() and dist.is_initialized():\n        world_size = dist.get_world_size()\n\n    if world_size <= 1:\n        return strings\n\n    joined = delimiter.join(strings)\n    gathered = evenly_divisible_all_gather(torch.tensor(bytearray(joined, \"utf-8\"), dtype=torch.long), concat=False)\n    gathered = [bytearray(g.tolist()).decode(\"utf-8\").split(delimiter) for g in gathered]\n\n    return [i for k in gathered for i in k]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/jupyter_utils.py_from_enum_import_Enum_LOSS_NAME._loss_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/jupyter_utils.py_from_enum_import_Enum_LOSS_NAME._loss_", "embedding": null, "metadata": {"file_path": "monai/utils/jupyter_utils.py", "file_name": "jupyter_utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 17, "end_line": 40, "span_ids": ["docstring"], "tokens": 161}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from enum import Enum\nfrom threading import RLock, Thread\nfrom typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\n\nfrom monai.config import IgniteInfo\nfrom monai.utils.module import min_version, optional_import\n\ntry:\n    import matplotlib.pyplot as plt\n\n    has_matplotlib = True\nexcept ImportError:\n    has_matplotlib = False\n\nif TYPE_CHECKING:\n    from ignite.engine import Engine, Events\nelse:\n    Engine, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Engine\")\n    Events, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Events\")\n\nLOSS_NAME = \"loss\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/jupyter_utils.py_plot_metric_graph_plot_metric_graph.ax_xaxis_set_major_locato": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/jupyter_utils.py_plot_metric_graph_plot_metric_graph.ax_xaxis_set_major_locato", "embedding": null, "metadata": {"file_path": "monai/utils/jupyter_utils.py", "file_name": "jupyter_utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 43, "end_line": 87, "span_ids": ["plot_metric_graph"], "tokens": 502}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def plot_metric_graph(\n    ax,\n    title: str,\n    graphmap: Dict[str, Union[List[float], Tuple[List[float], List[float]]]],\n    yscale: str = \"log\",\n    avg_keys: Tuple[str] = (LOSS_NAME,),\n    window_fraction: int = 20,\n):\n    \"\"\"\n    Plot metrics on a single graph with running averages plotted for selected keys. The values in `graphmap`\n    should be lists of (timepoint, value) pairs as stored in MetricLogger objects.\n\n    Args:\n        ax: Axes object to plot into\n        title: graph title\n        graphmap: dictionary of named graph values, which are lists of values or (index, value) pairs\n        yscale: scale for y-axis compatible with `Axes.set_yscale`\n        avg_keys: tuple of keys in `graphmap` to provide running average plots for\n        window_fraction: what fraction of the graph value length to use as the running average window\n    \"\"\"\n    from matplotlib.ticker import MaxNLocator\n\n    for n, v in graphmap.items():\n        if len(v) > 0:\n            if isinstance(v[0], (tuple, list)):  # values are (x,y) pairs\n                inds, vals = zip(*v)  # separate values into list of indices in X dimension and values\n            else:\n                inds, vals = tuple(range(len(v))), tuple(v)  # values are without indices, make indices for them\n\n            ax.plot(inds, vals, label=f\"{n} = {vals[-1]:.5g}\")\n\n            # if requested compute and plot a running average for the values using a fractional window size\n            if n in avg_keys and len(v) > window_fraction:\n                window = len(v) // window_fraction\n                kernel = np.ones((window,)) / window\n                ra = np.convolve((vals[0],) * (window - 1) + vals, kernel, mode=\"valid\")\n\n                ax.plot(inds, ra, label=f\"{n} Avg = {ra[-1]:.5g}\")\n\n    ax.set_title(title)\n    ax.set_yscale(yscale)\n    ax.axis(\"on\")\n    ax.legend(bbox_to_anchor=(1, 1), loc=1, borderaxespad=0.0)\n    ax.grid(True, \"both\", \"both\")\n    ax.xaxis.set_major_locator(MaxNLocator(integer=True))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/jupyter_utils.py_tensor_to_images_tensor_to_images.return.None": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/jupyter_utils.py_tensor_to_images_tensor_to_images.return.None", "embedding": null, "metadata": {"file_path": "monai/utils/jupyter_utils.py", "file_name": "jupyter_utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 138, "end_line": 152, "span_ids": ["tensor_to_images"], "tokens": 213}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def tensor_to_images(name: str, tensor: torch.Tensor):\n    \"\"\"\n    Return an tuple of images derived from the given tensor. The `name` value indices which key from the\n    output or batch value the tensor was stored as, or is \"Batch\" or \"Output\" if these were single tensors\n    instead of dictionaries. Returns a tuple of 2D images of shape HW, or 3D images of shape CHW where C is\n    color channels RGB or RGBA. This allows multiple images to be created from a single tensor, ie. to show\n    each channel separately.\n    \"\"\"\n    if tensor.ndim == 3 and tensor.shape[1] > 2 and tensor.shape[2] > 2:\n        return tensor.cpu().data.numpy()\n    if tensor.ndim == 4 and tensor.shape[2] > 2 and tensor.shape[3] > 2:\n        dmid = tensor.shape[1] // 2\n        return tensor[:, dmid].cpu().data.numpy()\n\n    return None", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/misc.py_set_determinism_set_determinism.if_use_deterministic_algo.if_hasattr_torch_use_de.else_.warnings_warn_use_determ": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/misc.py_set_determinism_set_determinism.if_use_deterministic_algo.if_hasattr_torch_use_de.else_.warnings_warn_use_determ", "embedding": null, "metadata": {"file_path": "monai/utils/misc.py", "file_name": "misc.py", "file_type": "text/x-python", "category": "implementation", "start_line": 219, "end_line": 270, "span_ids": ["set_determinism"], "tokens": 573}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def set_determinism(\n    seed: Optional[int] = np.iinfo(np.uint32).max,\n    use_deterministic_algorithms: Optional[bool] = None,\n    additional_settings: Optional[Union[Sequence[Callable[[int], Any]], Callable[[int], Any]]] = None,\n) -> None:\n    \"\"\"\n    Set random seed for modules to enable or disable deterministic training.\n\n    Args:\n        seed: the random seed to use, default is np.iinfo(np.int32).max.\n            It is recommended to set a large seed, i.e. a number that has a good balance\n            of 0 and 1 bits. Avoid having many 0 bits in the seed.\n            if set to None, will disable deterministic training.\n        use_deterministic_algorithms: Set whether PyTorch operations must use \"deterministic\" algorithms.\n        additional_settings: additional settings that need to set random seed.\n\n    \"\"\"\n    if seed is None:\n        # cast to 32 bit seed for CUDA\n        seed_ = torch.default_generator.seed() % (np.iinfo(np.int32).max + 1)\n        torch.manual_seed(seed_)\n    else:\n        seed = int(seed) % MAX_SEED\n        torch.manual_seed(seed)\n\n    global _seed\n    _seed = seed\n    random.seed(seed)\n    np.random.seed(seed)\n\n    if additional_settings is not None:\n        additional_settings = ensure_tuple(additional_settings)\n        for func in additional_settings:\n            func(seed)\n\n    if torch.backends.flags_frozen():\n        warnings.warn(\"PyTorch global flag support of backends is disabled, enable it to set global `cudnn` flags.\")\n        torch.backends.__allow_nonbracketed_mutation_flag = True\n\n    if seed is not None:\n        torch.backends.cudnn.deterministic = True\n        torch.backends.cudnn.benchmark = False\n    else:  # restore the original flags\n        torch.backends.cudnn.deterministic = _flag_deterministic\n        torch.backends.cudnn.benchmark = _flag_cudnn_benchmark\n    if use_deterministic_algorithms is not None:\n        if hasattr(torch, \"use_deterministic_algorithms\"):  # `use_deterministic_algorithms` is new in torch 1.8.0\n            torch.use_deterministic_algorithms(use_deterministic_algorithms)\n        elif hasattr(torch, \"set_deterministic\"):  # `set_deterministic` is new in torch 1.7.0\n            torch.set_deterministic(use_deterministic_algorithms)  # type: ignore\n        else:\n            warnings.warn(\"use_deterministic_algorithms=True, but PyTorch version is too old to set the mode.\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/misc.py_ImageMetaKey_has_option.return.all_key_in_sig_parameters": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/misc.py_ImageMetaKey_has_option.return.all_key_in_sig_parameters", "embedding": null, "metadata": {"file_path": "monai/utils/misc.py", "file_name": "misc.py", "file_type": "text/x-python", "category": "implementation", "start_line": 334, "end_line": 350, "span_ids": ["has_option", "ImageMetaKey"], "tokens": 113}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ImageMetaKey:\n    \"\"\"\n    Common key names in the meta data header of images\n    \"\"\"\n\n    FILENAME_OR_OBJ = \"filename_or_obj\"\n    PATCH_INDEX = \"patch_index\"\n\n\ndef has_option(obj, keywords: Union[str, Sequence[str]]) -> bool:\n    \"\"\"\n    Return a boolean indicating whether the given callable `obj` has the `keywords` in its signature.\n    \"\"\"\n    if not callable(obj):\n        return False\n    sig = inspect.signature(obj)\n    return all(key in sig.parameters for key in ensure_tuple(keywords))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/misc.py_is_module_ver_at_least_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/misc.py_is_module_ver_at_least_", "embedding": null, "metadata": {"file_path": "monai/utils/misc.py", "file_name": "misc.py", "file_type": "text/x-python", "category": "implementation", "start_line": 353, "end_line": 364, "span_ids": ["is_module_ver_at_least"], "tokens": 123}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def is_module_ver_at_least(module, version):\n    \"\"\"Determine if a module's version is at least equal to the given value.\n\n    Args:\n        module: imported module's name, e.g., `np` or `torch`.\n        version: required version, given as a tuple, e.g., `(1, 8, 0)`.\n    Returns:\n        `True` if module is the given version or newer.\n    \"\"\"\n    test_ver = \".\".join(map(str, version))\n    return module.__version__ != test_ver and version_leq(test_ver, module.__version__)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/module.py_enum___all__._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/module.py_enum___all__._", "embedding": null, "metadata": {"file_path": "monai/utils/module.py", "file_name": "module.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 44, "span_ids": ["docstring"], "tokens": 180}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import enum\nimport os\nimport re\nimport sys\nimport warnings\nfrom functools import wraps\nfrom importlib import import_module\nfrom pkgutil import walk_packages\nfrom re import match\nfrom types import FunctionType\nfrom typing import Any, Callable, Collection, Hashable, Iterable, List, Mapping, Tuple, cast\n\nimport torch\n\nOPTIONAL_IMPORT_MSG_FMT = \"{}\"\n\n__all__ = [\n    \"InvalidPyTorchVersionError\",\n    \"OptionalImportError\",\n    \"exact_version\",\n    \"export\",\n    \"damerau_levenshtein_distance\",\n    \"look_up_option\",\n    \"min_version\",\n    \"optional_import\",\n    \"require_pkg\",\n    \"load_submodules\",\n    \"get_full_type_name\",\n    \"get_package_version\",\n    \"get_torch_version_tuple\",\n    \"version_leq\",\n    \"pytorch_after\",\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/module.py_look_up_option_look_up_option.raise_ValueError_f_Unsupp": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/module.py_look_up_option_look_up_option.raise_ValueError_f_Unsupp", "embedding": null, "metadata": {"file_path": "monai/utils/module.py", "file_name": "module.py", "file_type": "text/x-python", "category": "implementation", "start_line": 41, "end_line": 115, "span_ids": ["look_up_option"], "tokens": 769}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def look_up_option(opt_str, supported: Collection, default=\"no_default\"):\n    \"\"\"\n    Look up the option in the supported collection and return the matched item.\n    Raise a value error possibly with a guess of the closest match.\n\n    Args:\n        opt_str: The option string or Enum to look up.\n        supported: The collection of supported options, it can be list, tuple, set, dict, or Enum.\n        default: If it is given, this method will return `default` when `opt_str` is not found,\n            instead of raising a `ValueError`. Otherwise, it defaults to `\"no_default\"`,\n            so that the method may raise a `ValueError`.\n\n    Examples:\n\n    .. code-block:: python\n\n        from enum import Enum\n        from monai.utils import look_up_option\n        class Color(Enum):\n            RED = \"red\"\n            BLUE = \"blue\"\n        look_up_option(\"red\", Color)  # <Color.RED: 'red'>\n        look_up_option(Color.RED, Color)  # <Color.RED: 'red'>\n        look_up_option(\"read\", Color)\n        # ValueError: By 'read', did you mean 'red'?\n        # 'read' is not a valid option.\n        # Available options are {'blue', 'red'}.\n        look_up_option(\"red\", {\"red\", \"blue\"})  # \"red\"\n\n    Adapted from https://github.com/NifTK/NiftyNet/blob/v0.6.0/niftynet/utilities/util_common.py#L249\n    \"\"\"\n    if not isinstance(opt_str, Hashable):\n        raise ValueError(f\"Unrecognized option type: {type(opt_str)}:{opt_str}.\")\n    if isinstance(opt_str, str):\n        opt_str = opt_str.strip()\n    if isinstance(supported, enum.EnumMeta):\n        if isinstance(opt_str, str) and opt_str in {item.value for item in cast(Iterable[enum.Enum], supported)}:\n            # such as: \"example\" in MyEnum\n            return supported(opt_str)\n        if isinstance(opt_str, enum.Enum) and opt_str in supported:\n            # such as: MyEnum.EXAMPLE in MyEnum\n            return opt_str\n    elif isinstance(supported, Mapping) and opt_str in supported:\n        # such as: MyDict[key]\n        return supported[opt_str]\n    elif isinstance(supported, Collection) and opt_str in supported:\n        return opt_str\n\n    if default != \"no_default\":\n        return default\n\n    # find a close match\n    set_to_check: set\n    if isinstance(supported, enum.EnumMeta):\n        set_to_check = {item.value for item in cast(Iterable[enum.Enum], supported)}\n    else:\n        set_to_check = set(supported) if supported is not None else set()\n    if not set_to_check:\n        raise ValueError(f\"No options available: {supported}.\")\n    edit_dists = {}\n    opt_str = f\"{opt_str}\"\n    for key in set_to_check:\n        edit_dist = damerau_levenshtein_distance(f\"{key}\", opt_str)\n        if edit_dist <= 3:\n            edit_dists[key] = edit_dist\n\n    supported_msg = f\"Available options are {set_to_check}.\\n\"\n    if edit_dists:\n        guess_at_spelling = min(edit_dists, key=edit_dists.get)  # type: ignore\n        raise ValueError(\n            f\"By '{opt_str}', did you mean '{guess_at_spelling}'?\\n\"\n            + f\"'{opt_str}' is not a valid option.\\n\"\n            + supported_msg\n        )\n    raise ValueError(f\"Unsupported option '{opt_str}', \" + supported_msg)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/module.py_damerau_levenshtein_distance_damerau_levenshtein_distance.return.d_string_1_length_1_st": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/module.py_damerau_levenshtein_distance_damerau_levenshtein_distance.return.d_string_1_length_1_st", "embedding": null, "metadata": {"file_path": "monai/utils/module.py", "file_name": "module.py", "file_type": "text/x-python", "category": "implementation", "start_line": 118, "end_line": 144, "span_ids": ["damerau_levenshtein_distance"], "tokens": 364}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def damerau_levenshtein_distance(s1: str, s2: str):\n    \"\"\"\n    Calculates the Damerau\u2013Levenshtein distance between two strings for spelling correction.\n    https://en.wikipedia.org/wiki/Damerau\u2013Levenshtein_distance\n    \"\"\"\n    if s1 == s2:\n        return 0\n    string_1_length = len(s1)\n    string_2_length = len(s2)\n    if not s1:\n        return string_2_length\n    if not s2:\n        return string_1_length\n    d = {(i, -1): i + 1 for i in range(-1, string_1_length + 1)}\n    for j in range(-1, string_2_length + 1):\n        d[(-1, j)] = j + 1\n\n    for i, s1i in enumerate(s1):\n        for j, s2j in enumerate(s2):\n            cost = 0 if s1i == s2j else 1\n            d[(i, j)] = min(\n                d[(i - 1, j)] + 1, d[(i, j - 1)] + 1, d[(i - 1, j - 1)] + cost  # deletion  # insertion  # substitution\n            )\n            if i and j and s1i == s2[j - 1] and s1[i - 1] == s2j:\n                d[(i, j)] = min(d[(i, j)], d[i - 2, j - 2] + cost)  # transposition\n\n    return d[string_1_length - 1, string_2_length - 1]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/module.py_get_package_version_get_torch_version_tuple.return.tuple_int_x_for_x_in_tor": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/module.py_get_package_version_get_torch_version_tuple.return.tuple_int_x_for_x_in_tor", "embedding": null, "metadata": {"file_path": "monai/utils/module.py", "file_name": "module.py", "file_type": "text/x-python", "category": "implementation", "start_line": 350, "end_line": 365, "span_ids": ["get_package_version", "get_torch_version_tuple"], "tokens": 116}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_package_version(dep_name, default=\"NOT INSTALLED or UNKNOWN VERSION.\"):\n    \"\"\"\n    Try to load package and get version. If not found, return `default`.\n    \"\"\"\n    dep, has_dep = optional_import(dep_name)\n    if has_dep and hasattr(dep, \"__version__\"):\n        return dep.__version__\n    return default\n\n\ndef get_torch_version_tuple():\n    \"\"\"\n    Returns:\n        tuple of ints represents the pytorch major/minor version.\n    \"\"\"\n    return tuple(int(x) for x in torch.__version__.split(\".\")[:2])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/nvtx.py_from_collections_import_d_Range.__init__.self.append_method_name.append_method_name": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/nvtx.py_from_collections_import_d_Range.__init__.self.append_method_name.append_method_name", "embedding": null, "metadata": {"file_path": "monai/utils/nvtx.py", "file_name": "nvtx.py", "file_type": "text/x-python", "category": "implementation", "start_line": 15, "end_line": 59, "span_ids": ["Range", "Range.__init__", "docstring"], "tokens": 419}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from collections import defaultdict\nfrom functools import wraps\nfrom typing import Any, Optional, Tuple, Union\n\nfrom torch.autograd import Function\nfrom torch.nn import Module\nfrom torch.optim import Optimizer\nfrom torch.utils.data import Dataset\n\nfrom monai.utils import ensure_tuple, optional_import\n\n_nvtx, _ = optional_import(\"torch._C._nvtx\", descriptor=\"NVTX is not installed. Are you sure you have a CUDA build?\")\n\n__all__ = [\"Range\"]\n\n\nclass Range:\n    \"\"\"\n    A decorator and context manager for NVIDIA Tools Extension (NVTX) Range for profiling.\n    When used as a decorator it encloses a specific method of the object with an NVTX Range.\n    When used as a context manager, it encloses the runtime context (created by with statement) with an NVTX Range.\n\n    Args:\n        name: the name to be associated to the range\n        methods: (only when used as decorator) the name of a method (or a list of the name of the methods)\n            to be wrapped by NVTX range.\n            If None (default), the method(s) will be inferred based on the object's type for various MONAI components,\n            such as Networks, Losses, Functions, Transforms, and Datasets.\n            Otherwise, it look up predefined methods: \"forward\", \"__call__\", \"__next__\", \"__getitem__\"\n        append_method_name: if append the name of the methods to be decorated to the range's name\n            If None (default), it appends the method's name only if we are annotating more than one method.\n\n    \"\"\"\n\n    name_counter: dict = defaultdict(int)\n\n    def __init__(\n        self,\n        name: Optional[str] = None,\n        methods: Optional[Union[str, Tuple[str, ...]]] = None,\n        append_method_name: Optional[bool] = None,\n    ) -> None:\n        self.name = name\n        self.methods = methods\n        self.append_method_name = append_method_name", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/nvtx.py_Range.__call___Range.__call__.return.obj": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/nvtx.py_Range.__call___Range.__call__.return.obj", "embedding": null, "metadata": {"file_path": "monai/utils/nvtx.py", "file_name": "nvtx.py", "file_type": "text/x-python", "category": "implementation", "start_line": 61, "end_line": 92, "span_ids": ["Range.__call__"], "tokens": 268}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Range:\n\n    def __call__(self, obj: Any):\n        # Define the name to be associated to the range if not provided\n        if self.name is None:\n            name = type(obj).__name__\n            # If CuCIM or TorchVision transform wrappers are being used,\n            # append the underlying transform to the name for more clarity\n            if \"CuCIM\" in name or \"TorchVision\" in name:\n                name = f\"{name}_{obj.name}\"\n            self.name_counter[name] += 1\n            if self.name_counter[name] > 1:\n                self.name = f\"{name}_{self.name_counter[name]}\"\n            else:\n                self.name = name\n\n        # Define the methods to be wrapped if not provided\n        if self.methods is None:\n            self.methods = self._get_method(obj)\n        else:\n            self.methods = ensure_tuple(self.methods)\n\n        # Check if to append method's name to the range's name\n        if self.append_method_name is None:\n            if len(self.methods) > 1:\n                self.append_method_name = True\n            else:\n                self.append_method_name = False\n\n        # Decorate the methods\n        for method in self.methods:\n            self._decorate_method(obj, method, self.append_method_name)\n\n        return obj", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/nvtx.py_Range._decorate_method_Range._decorate_method.None_2.else_.setattr_owner_method_ra": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/nvtx.py_Range._decorate_method_Range._decorate_method.None_2.else_.setattr_owner_method_ra", "embedding": null, "metadata": {"file_path": "monai/utils/nvtx.py", "file_name": "nvtx.py", "file_type": "text/x-python", "category": "implementation", "start_line": 87, "end_line": 121, "span_ids": ["Range._decorate_method"], "tokens": 253}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Range:\n\n    def _decorate_method(self, obj, method, append_method_name):\n        # Append the method's name to the range's name\n        if append_method_name:\n            name = f\"{self.name}.{method}\"\n        else:\n            name = self.name\n\n        # Get the class for special functions\n        if method.startswith(\"__\"):\n            owner = type(obj)\n        else:\n            owner = obj\n\n        # Get the method to be wrapped\n        _temp_func = getattr(owner, method)\n\n        # Wrap the method with NVTX range (range push/pop)\n        @wraps(_temp_func)\n        def range_wrapper(*args, **kwargs):\n            _nvtx.rangePushA(name)\n            output = _temp_func(*args, **kwargs)\n            _nvtx.rangePop()\n            return output\n\n        # Replace the method with the wrapped version\n        if method.startswith(\"__\"):\n            # If it is a special method, it requires special attention\n            class NVTXRangeDecoratedClass(owner):\n                ...\n\n            setattr(NVTXRangeDecoratedClass, method, range_wrapper)\n            obj.__class__ = NVTXRangeDecoratedClass\n\n        else:\n            setattr(owner, method, range_wrapper)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/nvtx.py_Range._get_method_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/nvtx.py_Range._get_method_", "embedding": null, "metadata": {"file_path": "monai/utils/nvtx.py", "file_name": "nvtx.py", "file_type": "text/x-python", "category": "implementation", "start_line": 123, "end_line": 156, "span_ids": ["Range.__exit__", "Range.__enter__", "Range._get_method"], "tokens": 277}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Range:\n\n    def _get_method(self, obj: Any) -> tuple:\n        if isinstance(obj, Module):\n            method_list = [\"forward\"]\n        elif isinstance(obj, Optimizer):\n            method_list = [\"step\"]\n        elif isinstance(obj, Function):\n            method_list = [\"forward\", \"backward\"]\n        elif isinstance(obj, Dataset):\n            method_list = [\"__getitem__\"]\n        else:\n            default_methods = [\"forward\", \"__call__\", \"__next__\", \"__getitem__\"]\n            method_list = []\n            for method in default_methods:\n                if hasattr(obj, method):\n                    method_list.append(method)\n            if len(method_list) < 1:\n                raise ValueError(\n                    f\"The method to be wrapped for this object [{type(obj)}] is not recognized.\"\n                    \"The name of the method should be provided or the object should have one of these methods:\"\n                    f\"{default_methods}\"\n                )\n        return ensure_tuple(method_list)\n\n    def __enter__(self):\n        if self.name is None:\n            # Number the range with class variable counter to avoid duplicate names.\n            self.name_counter[\"context\"] += 1\n            self.name = f\"context_{self.name_counter['context']}\"\n\n        _nvtx.rangePushA(self.name)\n\n    def __exit__(self, type, value, traceback):\n        _nvtx.rangePop()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/type_conversion.py_re_dtype_numpy_to_torch.return.look_up_option_dtype__np": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/type_conversion.py_re_dtype_numpy_to_torch.return.look_up_option_dtype__np", "embedding": null, "metadata": {"file_path": "monai/utils/type_conversion.py", "file_name": "type_conversion.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 63, "span_ids": ["dtype_numpy_to_torch", "dtype_torch_to_numpy", "docstring"], "tokens": 411}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import re\nfrom typing import Any, Optional, Sequence, Tuple, Union\n\nimport numpy as np\nimport torch\n\nfrom monai.config.type_definitions import DtypeLike, NdarrayOrTensor\nfrom monai.utils import optional_import\nfrom monai.utils.module import look_up_option\n\ncp, has_cp = optional_import(\"cupy\")\ncp_ndarray, _ = optional_import(\"cupy\", name=\"ndarray\")\n\n__all__ = [\n    \"dtype_torch_to_numpy\",\n    \"dtype_numpy_to_torch\",\n    \"get_equivalent_dtype\",\n    \"convert_data_type\",\n    \"get_dtype\",\n    \"convert_to_cupy\",\n    \"convert_to_numpy\",\n    \"convert_to_tensor\",\n    \"convert_to_dst_type\",\n]\n\n\n_torch_to_np_dtype = {\n    torch.bool: np.dtype(bool),\n    torch.uint8: np.dtype(np.uint8),\n    torch.int8: np.dtype(np.int8),\n    torch.int16: np.dtype(np.int16),\n    torch.int32: np.dtype(np.int32),\n    torch.int64: np.dtype(np.int64),\n    torch.float16: np.dtype(np.float16),\n    torch.float32: np.dtype(np.float32),\n    torch.float64: np.dtype(np.float64),\n    torch.complex64: np.dtype(np.complex64),\n    torch.complex128: np.dtype(np.complex128),\n}\n_np_to_torch_dtype = {value: key for key, value in _torch_to_np_dtype.items()}\n\n\ndef dtype_torch_to_numpy(dtype):\n    \"\"\"Convert a torch dtype to its numpy equivalent.\"\"\"\n    return look_up_option(dtype, _torch_to_np_dtype)\n\n\ndef dtype_numpy_to_torch(dtype):\n    \"\"\"Convert a numpy dtype to its torch equivalent.\"\"\"\n    # np dtypes can be given as np.float32 and np.dtype(np.float32) so unify them\n    dtype = np.dtype(dtype) if isinstance(dtype, (type, str)) else dtype\n    return look_up_option(dtype, _np_to_torch_dtype)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/type_conversion.py_get_equivalent_dtype_get_dtype.return.type_data_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/type_conversion.py_get_equivalent_dtype_get_dtype.return.type_data_", "embedding": null, "metadata": {"file_path": "monai/utils/type_conversion.py", "file_name": "type_conversion.py", "file_type": "text/x-python", "category": "implementation", "start_line": 55, "end_line": 88, "span_ids": ["get_equivalent_dtype", "get_dtype"], "tokens": 259}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_equivalent_dtype(dtype, data_type):\n    \"\"\"Convert to the `dtype` that corresponds to `data_type`.\n\n    Example::\n\n        im = torch.tensor(1)\n        dtype = get_equivalent_dtype(np.float32, type(im))\n\n    \"\"\"\n    if dtype is None:\n        return None\n    if data_type is torch.Tensor:\n        if isinstance(dtype, torch.dtype):\n            # already a torch dtype and target `data_type` is torch.Tensor\n            return dtype\n        return dtype_numpy_to_torch(dtype)\n    if not isinstance(dtype, torch.dtype):\n        # assuming the dtype is ok if it is not a torch dtype and target `data_type` is not torch.Tensor\n        return dtype\n    return dtype_torch_to_numpy(dtype)\n\n\ndef get_dtype(data: Any):\n    \"\"\"Get the dtype of an image, or if there is a sequence, recursively call the method on the 0th element.\n\n    This therefore assumes that in a `Sequence`, all types are the same.\n    \"\"\"\n    if hasattr(data, \"dtype\"):\n        return data.dtype\n    # need recursion\n    if isinstance(data, Sequence):\n        return get_dtype(data[0])\n    # objects like float don't have dtype, so return their type\n    return type(data)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/type_conversion.py_convert_to_tensor_convert_to_tensor.return.data": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/type_conversion.py_convert_to_tensor_convert_to_tensor.return.data", "embedding": null, "metadata": {"file_path": "monai/utils/type_conversion.py", "file_name": "type_conversion.py", "file_type": "text/x-python", "category": "implementation", "start_line": 102, "end_line": 141, "span_ids": ["convert_to_tensor"], "tokens": 606}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def convert_to_tensor(\n    data, dtype: Optional[torch.dtype] = None, device: Optional[torch.device] = None, wrap_sequence: bool = False\n):\n    \"\"\"\n    Utility to convert the input data to a PyTorch Tensor. If passing a dictionary, list or tuple,\n    recursively check every item and convert it to PyTorch Tensor.\n\n    Args:\n        data: input data can be PyTorch Tensor, numpy array, list, dictionary, int, float, bool, str, etc.\n            will convert Tensor, Numpy array, float, int, bool to Tensor, strings and objects keep the original.\n            for dictionary, list or tuple, convert every item to a Tensor if applicable.\n        dtype: target data type to when converting to Tensor.\n        device: target device to put the converted Tensor data.\n        wrap_sequence: if `False`, then lists will recursively call this function.\n            E.g., `[1, 2]` -> `[tensor(1), tensor(2)]`. If `True`, then `[1, 2]` -> `tensor([1, 2])`.\n\n    \"\"\"\n    if isinstance(data, torch.Tensor):\n        return data.to(dtype=dtype, device=device, memory_format=torch.contiguous_format)  # type: ignore\n    if isinstance(data, np.ndarray):\n        # skip array of string classes and object, refer to:\n        # https://github.com/pytorch/pytorch/blob/v1.9.0/torch/utils/data/_utils/collate.py#L13\n        if re.search(r\"[SaUO]\", data.dtype.str) is None:\n            # numpy array with 0 dims is also sequence iterable,\n            # `ascontiguousarray` will add 1 dim if img has no dim, so we only apply on data with dims\n            if data.ndim > 0:\n                data = np.ascontiguousarray(data)\n            return torch.as_tensor(data, dtype=dtype, device=device)  # type: ignore\n    elif (has_cp and isinstance(data, cp_ndarray)) or isinstance(data, (float, int, bool)):\n        return torch.as_tensor(data, dtype=dtype, device=device)  # type: ignore\n    elif isinstance(data, list):\n        list_ret = [convert_to_tensor(i, dtype=dtype, device=device) for i in data]\n        return torch.as_tensor(list_ret, dtype=dtype, device=device) if wrap_sequence else list_ret  # type: ignore\n    elif isinstance(data, tuple):\n        tuple_ret = tuple(convert_to_tensor(i, dtype=dtype, device=device) for i in data)\n        return torch.as_tensor(tuple_ret, dtype=dtype, device=device) if wrap_sequence else tuple_ret  # type: ignore\n    elif isinstance(data, dict):\n        return {k: convert_to_tensor(v, dtype=dtype, device=device) for k, v in data.items()}\n\n    return data", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/type_conversion.py_convert_to_numpy_convert_to_numpy.return.data": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/type_conversion.py_convert_to_numpy_convert_to_numpy.return.data", "embedding": null, "metadata": {"file_path": "monai/utils/type_conversion.py", "file_name": "type_conversion.py", "file_type": "text/x-python", "category": "implementation", "start_line": 144, "end_line": 175, "span_ids": ["convert_to_numpy"], "tokens": 425}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def convert_to_numpy(data, dtype: DtypeLike = None, wrap_sequence: bool = False):\n    \"\"\"\n    Utility to convert the input data to a numpy array. If passing a dictionary, list or tuple,\n    recursively check every item and convert it to numpy array.\n\n    Args:\n        data: input data can be PyTorch Tensor, numpy array, list, dictionary, int, float, bool, str, etc.\n            will convert Tensor, Numpy array, float, int, bool to numpy arrays, strings and objects keep the original.\n            for dictionary, list or tuple, convert every item to a numpy array if applicable.\n        dtype: target data type when converting to numpy array.\n        wrap_sequence: if `False`, then lists will recursively call this function.\n            E.g., `[1, 2]` -> `[array(1), array(2)]`. If `True`, then `[1, 2]` -> `array([1, 2])`.\n    \"\"\"\n    if isinstance(data, torch.Tensor):\n        data = data.detach().to(dtype=get_equivalent_dtype(dtype, torch.Tensor), device=\"cpu\").numpy()\n    elif has_cp and isinstance(data, cp_ndarray):\n        data = cp.asnumpy(data).astype(dtype, copy=False)\n    elif isinstance(data, (np.ndarray, float, int, bool)):\n        data = np.asarray(data, dtype=dtype)\n    elif isinstance(data, list):\n        list_ret = [convert_to_numpy(i, dtype=dtype) for i in data]\n        return np.asarray(list_ret) if wrap_sequence else list_ret\n    elif isinstance(data, tuple):\n        tuple_ret = tuple(convert_to_numpy(i, dtype=dtype) for i in data)\n        return np.asarray(tuple_ret) if wrap_sequence else tuple_ret\n    elif isinstance(data, dict):\n        return {k: convert_to_numpy(v, dtype=dtype) for k, v in data.items()}\n\n    if isinstance(data, np.ndarray) and data.ndim > 0:\n        data = np.ascontiguousarray(data)\n\n    return data", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/type_conversion.py_convert_to_cupy_convert_to_cupy.return.data": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/type_conversion.py_convert_to_cupy_convert_to_cupy.return.data", "embedding": null, "metadata": {"file_path": "monai/utils/type_conversion.py", "file_name": "type_conversion.py", "file_type": "text/x-python", "category": "implementation", "start_line": 178, "end_line": 210, "span_ids": ["convert_to_cupy"], "tokens": 441}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def convert_to_cupy(data, dtype: Optional[np.dtype] = None, wrap_sequence: bool = False):\n    \"\"\"\n    Utility to convert the input data to a cupy array. If passing a dictionary, list or tuple,\n    recursively check every item and convert it to cupy array.\n\n    Args:\n        data: input data can be PyTorch Tensor, numpy array, cupy array, list, dictionary, int, float, bool, str, etc.\n            Tensor, numpy array, cupy array, float, int, bool are converted to cupy arrays,\n            for dictionary, list or tuple, convert every item to a numpy array if applicable.\n        dtype: target data type when converting to Cupy array, tt must be an argument of `numpy.dtype`,\n            for more details: https://docs.cupy.dev/en/stable/reference/generated/cupy.array.html.\n        wrap_sequence: if `False`, then lists will recursively call this function.\n            E.g., `[1, 2]` -> `[array(1), array(2)]`. If `True`, then `[1, 2]` -> `array([1, 2])`.\n    \"\"\"\n\n    # direct calls\n    if isinstance(data, (cp_ndarray, np.ndarray, torch.Tensor, float, int, bool)):\n        data = cp.asarray(data, dtype)\n    elif isinstance(data, list):\n        list_ret = [convert_to_cupy(i, dtype) for i in data]\n        return cp.asarray(list_ret) if wrap_sequence else list_ret\n    elif isinstance(data, tuple):\n        tuple_ret = tuple(convert_to_cupy(i, dtype) for i in data)\n        return cp.asarray(tuple_ret) if wrap_sequence else tuple_ret\n    elif isinstance(data, dict):\n        return {k: convert_to_cupy(v, dtype) for k, v in data.items()}\n    # make it contiguous\n    if not isinstance(data, cp.ndarray):\n        raise ValueError(f\"The input data type [{type(data)}] cannot be converted into cupy arrays!\")\n\n    if data.ndim > 0:\n        data = cp.ascontiguousarray(data)\n    return data", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/type_conversion.py_convert_data_type_convert_data_type.return.data_orig_type_orig_dev": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/type_conversion.py_convert_data_type_convert_data_type.return.data_orig_type_orig_dev", "embedding": null, "metadata": {"file_path": "monai/utils/type_conversion.py", "file_name": "type_conversion.py", "file_type": "text/x-python", "category": "implementation", "start_line": 213, "end_line": 268, "span_ids": ["convert_data_type"], "tokens": 574}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def convert_data_type(\n    data: Any,\n    output_type: Optional[type] = None,\n    device: Optional[torch.device] = None,\n    dtype: Optional[Union[DtypeLike, torch.dtype]] = None,\n    wrap_sequence: bool = False,\n) -> Tuple[NdarrayOrTensor, type, Optional[torch.device]]:\n    \"\"\"\n    Convert to `torch.Tensor`/`np.ndarray` from `torch.Tensor`/`np.ndarray`/`float`/`int` etc.\n\n    Args:\n        data: data to be converted\n        output_type: `torch.Tensor` or `np.ndarray` (if blank, unchanged)\n        device: if output is `torch.Tensor`, select device (if blank, unchanged)\n        dtype: dtype of output data. Converted to correct library type (e.g.,\n            `np.float32` is converted to `torch.float32` if output type is `torch.Tensor`).\n            If left blank, it remains unchanged.\n        wrap_sequence: if `False`, then lists will recursively call this function.\n            E.g., `[1, 2]` -> `[array(1), array(2)]`. If `True`, then `[1, 2]` -> `array([1, 2])`.\n    Returns:\n        modified data, orig_type, orig_device\n\n    Note:\n        When both `output_type` and `dtype` are specified with different backend\n        (e.g., `torch.Tensor` and `np.float32`), the `output_type` will be used as the primary type,\n        for example::\n\n            >>> convert_data_type(1, torch.Tensor, dtype=np.float32)\n            (1.0, <class 'torch.Tensor'>, None)\n\n    \"\"\"\n    orig_type: Any\n    if isinstance(data, torch.Tensor):\n        orig_type = torch.Tensor\n    elif isinstance(data, np.ndarray):\n        orig_type = np.ndarray\n    elif has_cp and isinstance(data, cp.ndarray):\n        orig_type = cp.ndarray\n    else:\n        orig_type = type(data)\n\n    orig_device = data.device if isinstance(data, torch.Tensor) else None\n\n    output_type = output_type or orig_type\n\n    dtype_ = get_equivalent_dtype(dtype, output_type)\n\n    if output_type is torch.Tensor:\n        data = convert_to_tensor(data, dtype=dtype_, device=device, wrap_sequence=wrap_sequence)\n    elif output_type is np.ndarray:\n        data = convert_to_numpy(data, dtype=dtype_, wrap_sequence=wrap_sequence)\n    elif has_cp and output_type is cp.ndarray:\n        data = convert_to_cupy(data, dtype=dtype_, wrap_sequence=wrap_sequence)\n    else:\n        raise ValueError(f\"Unsupported output type: {output_type}\")\n    return data, orig_type, orig_device", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/type_conversion.py_convert_to_dst_type_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/type_conversion.py_convert_to_dst_type_", "embedding": null, "metadata": {"file_path": "monai/utils/type_conversion.py", "file_name": "type_conversion.py", "file_type": "text/x-python", "category": "implementation", "start_line": 271, "end_line": 302, "span_ids": ["convert_to_dst_type"], "tokens": 380}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def convert_to_dst_type(\n    src: Any, dst: NdarrayOrTensor, dtype: Optional[Union[DtypeLike, torch.dtype]] = None, wrap_sequence: bool = False\n) -> Tuple[NdarrayOrTensor, type, Optional[torch.device]]:\n    \"\"\"\n    Convert source data to the same data type and device as the destination data.\n    If `dst` is an instance of `torch.Tensor` or its subclass, convert `src` to `torch.Tensor` with the same data type as `dst`,\n    if `dst` is an instance of `numpy.ndarray` or its subclass, convert to `numpy.ndarray` with the same data type as `dst`,\n    otherwise, convert to the type of `dst` directly.\n\n    Args:\n        src: source data to convert type.\n        dst: destination data that convert to the same data type as it.\n        dtype: an optional argument if the target `dtype` is different from the original `dst`'s data type.\n        wrap_sequence: if `False`, then lists will recursively call this function. E.g., `[1, 2]` -> `[array(1), array(2)]`.\n            If `True`, then `[1, 2]` -> `array([1, 2])`.\n\n    See Also:\n        :func:`convert_data_type`\n    \"\"\"\n    device = dst.device if isinstance(dst, torch.Tensor) else None\n    if dtype is None:\n        dtype = dst.dtype\n\n    output_type: Any\n    if isinstance(dst, torch.Tensor):\n        output_type = torch.Tensor\n    elif isinstance(dst, np.ndarray):\n        output_type = np.ndarray\n    else:\n        output_type = type(dst)\n    return convert_data_type(data=src, output_type=output_type, device=device, dtype=dtype, wrap_sequence=wrap_sequence)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/hvd_evenly_divisible_all_gather.py_hvd_HvdEvenlyDivisibleAllGather._run.for_r_in_result3_.torch_testing_assert_allc": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/hvd_evenly_divisible_all_gather.py_hvd_HvdEvenlyDivisibleAllGather._run.for_r_in_result3_.torch_testing_assert_allc", "embedding": null, "metadata": {"file_path": "tests/hvd_evenly_divisible_all_gather.py", "file_name": "hvd_evenly_divisible_all_gather.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 44, "span_ids": ["HvdEvenlyDivisibleAllGather", "HvdEvenlyDivisibleAllGather._run", "HvdEvenlyDivisibleAllGather.test_data", "docstring"], "tokens": 350}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import horovod.torch as hvd\nimport torch\n\nfrom monai.utils import evenly_divisible_all_gather\n\n\nclass HvdEvenlyDivisibleAllGather:\n    def test_data(self):\n        # initialize Horovod\n        hvd.init()\n        if torch.cuda.is_available():\n            torch.cuda.set_device(hvd.local_rank())\n        self._run()\n\n    def _run(self):\n        if hvd.rank() == 0:\n            data1 = torch.tensor([[1, 2], [3, 4]])\n            data2 = torch.tensor([[1.0, 2.0]])\n            data3 = torch.tensor(7)\n\n        if hvd.rank() == 1:\n            data1 = torch.tensor([[5, 6]])\n            data2 = torch.tensor([[3.0, 4.0], [5.0, 6.0]])\n            data3 = torch.tensor(8)\n\n        result1 = evenly_divisible_all_gather(data=data1, concat=True)\n        torch.testing.assert_allclose(result1, torch.tensor([[1, 2], [3, 4], [5, 6]]))\n        result2 = evenly_divisible_all_gather(data=data2, concat=False)\n        for r, e in zip(result2, [torch.tensor([[1.0, 2.0]]), torch.tensor([[3.0, 4.0], [5.0, 6.0]])]):\n            torch.testing.assert_allclose(r, e)\n        result3 = evenly_divisible_all_gather(data=data3, concat=False)\n        for r in result3:\n            torch.testing.assert_allclose(r.ndimension(), 0)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/hvd_evenly_divisible_all_gather.py_if___name_____main____": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/hvd_evenly_divisible_all_gather.py_if___name_____main____", "embedding": null, "metadata": {"file_path": "tests/hvd_evenly_divisible_all_gather.py", "file_name": "hvd_evenly_divisible_all_gather.py", "file_type": "text/x-python", "category": "implementation", "start_line": 47, "end_line": 58, "span_ids": ["impl"], "tokens": 129}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "if __name__ == \"__main__\":\n    \"\"\"\n    1. Install Horovod:\n    `HOROVOD_NCCL_INCLUDE=/usr/include HOROVOD_NCCL_LIB=/usr/lib/x86_64-linux-gnu HOROVOD_GPU_OPERATIONS=NCCL \\\n    HOROVOD_NCCL_LINK=SHARED pip install --no-cache-dir horovod`\n\n    2. Execute on 2 GPUs in a single machine:\n    `horovodrun -np 2 python test_evenly_divisible_all_gather_hvd.py`\n\n    \"\"\"\n    HvdEvenlyDivisibleAllGather().test_data()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/ngc_mmar_loading.py_os_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/ngc_mmar_loading.py_os_", "embedding": null, "metadata": {"file_path": "tests/ngc_mmar_loading.py", "file_name": "ngc_mmar_loading.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 38, "span_ids": ["TestAllDownloadingMMAR.test_loading_mmar", "TestAllDownloadingMMAR", "impl", "TestAllDownloadingMMAR.tearDown", "docstring", "TestAllDownloadingMMAR.setUp"], "tokens": 148}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.apps.mmars import MODEL_DESC, load_from_mmar\nfrom monai.config import print_debug_info\n\n\nclass TestAllDownloadingMMAR(unittest.TestCase):\n    def setUp(self):\n        print_debug_info()\n        self.test_dir = \"./\"\n\n    @parameterized.expand((item,) for item in MODEL_DESC)\n    def test_loading_mmar(self, item):\n        pretrained_model = load_from_mmar(item=item, mmar_dir=\"./\", map_location=\"cpu\")\n        self.assertTrue(isinstance(pretrained_model, torch.nn.Module))\n\n    def tearDown(self):\n        print(os.listdir(self.test_dir))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_add_coordinate_channels.py_unittest_for_p_in_TEST_NDARRAYS_.TEST_CASES_ERROR_2_append": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_add_coordinate_channels.py_unittest_for_p_in_TEST_NDARRAYS_.TEST_CASES_ERROR_2_append", "embedding": null, "metadata": {"file_path": "tests/test_add_coordinate_channels.py", "file_name": "test_add_coordinate_channels.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 26, "span_ids": ["docstring"], "tokens": 256}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import AddCoordinateChannels\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTESTS, TEST_CASES_ERROR_1, TEST_CASES_ERROR_2 = [], [], []\nfor p in TEST_NDARRAYS:\n    TESTS.append([{\"spatial_dims\": (0, 1, 2)}, p(np.random.randint(0, 2, size=(1, 3, 3, 3))), (4, 3, 3, 3)])\n    TESTS.append([{\"spatial_dims\": (0,)}, p(np.random.randint(0, 2, size=(1, 3, 3, 3))), (2, 3, 3, 3)])\n    TEST_CASES_ERROR_1.append([{\"spatial_dims\": (2,)}, p(np.random.randint(0, 2, size=(1, 3, 3)))])\n    TEST_CASES_ERROR_2.append([{\"spatial_dims\": (-1, 0, 1)}, p(np.random.randint(0, 2, size=(1, 3, 3)))])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_add_coordinate_channelsd.py_unittest_for_p_in_TEST_NDARRAYS_.TEST_CASES_ERROR_2_append": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_add_coordinate_channelsd.py_unittest_for_p_in_TEST_NDARRAYS_.TEST_CASES_ERROR_2_append", "embedding": null, "metadata": {"file_path": "tests/test_add_coordinate_channelsd.py", "file_name": "test_add_coordinate_channelsd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 39, "span_ids": ["docstring"], "tokens": 315}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import AddCoordinateChannelsd\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTESTS, TEST_CASES_ERROR_1, TEST_CASES_ERROR_2 = [], [], []\nfor p in TEST_NDARRAYS:\n    TESTS.append(\n        [\n            {\"spatial_dims\": (0, 1, 2), \"keys\": [\"img\"]},\n            {\"img\": p(np.random.randint(0, 2, size=(1, 3, 3, 3)))},\n            (4, 3, 3, 3),\n        ]\n    )\n    TESTS.append(\n        [{\"spatial_dims\": (0,), \"keys\": [\"img\"]}, {\"img\": p(np.random.randint(0, 2, size=(1, 3, 3, 3)))}, (2, 3, 3, 3)]\n    )\n\n    TEST_CASES_ERROR_1.append(\n        [{\"spatial_dims\": (2,), \"keys\": [\"img\"]}, {\"img\": p(np.random.randint(0, 2, size=(1, 3, 3)))}]\n    )\n    TEST_CASES_ERROR_2.append(\n        [{\"spatial_dims\": (-1, 0, 1), \"keys\": [\"img\"]}, {\"img\": p(np.random.randint(0, 2, size=(1, 3, 3)))}]\n    )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_add_extreme_points_channeld.py_unittest_for_p_in_TEST_NDARRAYS_.for_q_in_TEST_NDARRAYS_.None_1": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_add_extreme_points_channeld.py_unittest_for_p_in_TEST_NDARRAYS_.for_q_in_TEST_NDARRAYS_.None_1", "embedding": null, "metadata": {"file_path": "tests/test_add_extreme_points_channeld.py", "file_name": "test_add_extreme_points_channeld.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 61, "span_ids": ["docstring"], "tokens": 420}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import AddExtremePointsChanneld\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nIMG_CHANNEL = 3\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    for q in TEST_NDARRAYS:\n        TESTS.append(\n            [\n                {\n                    \"img\": p(np.zeros((IMG_CHANNEL, 4, 3))),\n                    \"label\": q(np.array([[[0, 1, 0], [0, 0, 1], [0, 1, 0], [1, 0, 0]]])),\n                },\n                p(\n                    np.array(\n                        [\n                            [0.38318458, 0.98615628, 0.85551184],\n                            [0.35422316, 0.94430935, 1.0],\n                            [0.46000731, 0.57319659, 0.46000722],\n                            [0.64577687, 0.38318464, 0.0],\n                        ]\n                    )\n                ),\n            ]\n        )\n\n        TESTS.append(\n            [\n                {\n                    \"img\": p(np.zeros((IMG_CHANNEL, 4, 3))),\n                    \"label\": q(np.array([[[0, 1, 0], [1, 1, 1], [0, 1, 0], [0, 1, 0]]])),\n                },\n                p(\n                    np.array(\n                        [\n                            [0.44628328, 0.80495411, 0.44628328],\n                            [0.6779086, 1.0, 0.67790854],\n                            [0.33002687, 0.62079221, 0.33002687],\n                            [0.0, 0.31848389, 0.0],\n                        ]\n                    )\n                ),\n            ]\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affine_grid.py_unittest_for_p_in_TEST_NDARRAYS_.for_device_in_None_cpu.None_4": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affine_grid.py_unittest_for_p_in_TEST_NDARRAYS_.for_device_in_None_cpu.None_4", "embedding": null, "metadata": {"file_path": "tests/test_affine_grid.py", "file_name": "test_affine_grid.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 85, "span_ids": ["docstring"], "tokens": 790}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import AffineGrid\nfrom tests.utils import TEST_NDARRAYS, assert_allclose, is_tf32_env\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    for device in [None, \"cpu\", \"cuda\"] if torch.cuda.is_available() else [None, \"cpu\"]:\n        TESTS.append(\n            [\n                {\"device\": device},\n                {\"spatial_size\": (2, 2)},\n                np.array([[[-0.5, -0.5], [0.5, 0.5]], [[-0.5, 0.5], [-0.5, 0.5]], [[1.0, 1.0], [1.0, 1.0]]]),\n            ]\n        )\n\n        TESTS.append([{\"device\": device}, {\"grid\": p(np.ones((3, 3, 3)))}, p(np.ones((3, 3, 3)))])\n        TESTS.append([{\"device\": device}, {\"grid\": p(torch.ones((3, 3, 3)))}, p(np.ones((3, 3, 3)))])\n        TESTS.append(\n            [\n                {\"rotate_params\": (1.0, 1.0), \"scale_params\": (-20, 10), \"device\": device},\n                {\"grid\": p(torch.ones((3, 3, 3)))},\n                p(\n                    torch.tensor(\n                        [\n                            [\n                                [-19.2208, -19.2208, -19.2208],\n                                [-19.2208, -19.2208, -19.2208],\n                                [-19.2208, -19.2208, -19.2208],\n                            ],\n                            [\n                                [-11.4264, -11.4264, -11.4264],\n                                [-11.4264, -11.4264, -11.4264],\n                                [-11.4264, -11.4264, -11.4264],\n                            ],\n                            [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],\n                        ]\n                    )\n                ),\n            ]\n        )\n        TESTS.append(\n            [\n                {\n                    \"affine\": p(\n                        torch.tensor(\n                            [[-10.8060, -8.4147, 0.0000], [-16.8294, 5.4030, 0.0000], [0.0000, 0.0000, 1.0000]]\n                        )\n                    )\n                },\n                {\"grid\": p(torch.ones((3, 3, 3)))},\n                p(\n                    torch.tensor(\n                        [\n                            [\n                                [-19.2208, -19.2208, -19.2208],\n                                [-19.2208, -19.2208, -19.2208],\n                                [-19.2208, -19.2208, -19.2208],\n                            ],\n                            [\n                                [-11.4264, -11.4264, -11.4264],\n                                [-11.4264, -11.4264, -11.4264],\n                                [-11.4264, -11.4264, -11.4264],\n                            ],\n                            [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],\n                        ]\n                    )\n                ),\n            ]\n        )\n        # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affine_grid.py__rtol_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affine_grid.py__rtol_", "embedding": null, "metadata": {"file_path": "tests/test_affine_grid.py", "file_name": "test_affine_grid.py", "file_type": "text/x-python", "category": "test", "start_line": 132, "end_line": 147, "span_ids": ["impl:11", "impl:13", "TestAffineGrid.test_affine_grid", "TestAffineGrid"], "tokens": 125}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "_rtol = 5e-2 if is_tf32_env() else 1e-4\n\n\nclass TestAffineGrid(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_affine_grid(self, input_param, input_data, expected_val):\n        g = AffineGrid(**input_param)\n        result, _ = g(**input_data)\n        if \"device\" in input_data:\n            self.assertEqual(result.device, input_data[device])\n        assert_allclose(result, expected_val, type_test=False, rtol=_rtol)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ahnet.py_TestAHNET_TestAHNET.test_ahnet_shape_3d.with_eval_mode_net_.self_assertEqual_result_s": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ahnet.py_TestAHNET_TestAHNET.test_ahnet_shape_3d.with_eval_mode_net_.self_assertEqual_result_s", "embedding": null, "metadata": {"file_path": "tests/test_ahnet.py", "file_name": "test_ahnet.py", "file_type": "text/x-python", "category": "test", "start_line": 164, "end_line": 178, "span_ids": ["TestAHNET.test_ahnet_shape_3d", "TestAHNET", "TestAHNET.test_ahnet_shape_2d"], "tokens": 213}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestAHNET(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_AHNET_2D_1, TEST_CASE_AHNET_2D_2, TEST_CASE_AHNET_2D_3])\n    def test_ahnet_shape_2d(self, input_param, input_shape, expected_shape):\n        net = AHNet(**input_param).to(device)\n        with eval_mode(net):\n            result = net.forward(torch.randn(input_shape).to(device))\n            self.assertEqual(result.shape, expected_shape)\n\n    @parameterized.expand([TEST_CASE_AHNET_3D_1, TEST_CASE_AHNET_3D_2, TEST_CASE_AHNET_3D_3])\n    @skip_if_quick\n    def test_ahnet_shape_3d(self, input_param, input_shape, expected_shape):\n        net = AHNet(**input_param).to(device)\n        with eval_mode(net):\n            result = net.forward(torch.randn(input_shape).to(device))\n            self.assertEqual(result.shape, expected_shape)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_alias.py_glob_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_alias.py_glob_", "embedding": null, "metadata": {"file_path": "tests/test_alias.py", "file_name": "test_alias.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 40, "span_ids": ["TestModuleAlias", "TestModuleAlias.test_files", "impl", "docstring"], "tokens": 210}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import glob\nimport inspect\nimport os\nimport unittest\n\nfrom monai.utils import optional_import\n\n\nclass TestModuleAlias(unittest.TestCase):\n    \"\"\"check that 'import monai.xx.file_name' returns a module\"\"\"\n\n    def test_files(self):\n        src_dir = os.path.dirname(os.path.dirname(__file__))\n        monai_dir = os.path.join(src_dir, \"monai\")\n        py_files = glob.glob(os.path.join(monai_dir, \"**\", \"*.py\"), recursive=True)\n        for x in py_files:\n            if os.path.basename(x).startswith(\"_\"):\n                continue\n            mod_name = x[len(src_dir) : -3]  # create relative path\n            mod_name = mod_name[1:].replace(mod_name[0], \".\")\n            mod, cls = mod_name.rsplit(\".\", 1)\n            obj, exist = optional_import(mod, name=cls)\n            if exist:\n                self.assertTrue(inspect.ismodule(obj), msg=mod_name)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_as_channel_first.py_unittest_for_p_in_TEST_NDARRAYS_.None_2": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_as_channel_first.py_unittest_for_p_in_TEST_NDARRAYS_.None_2", "embedding": null, "metadata": {"file_path": "tests/test_as_channel_first.py", "file_name": "test_as_channel_first.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 25, "span_ids": ["docstring"], "tokens": 132}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import AsChannelFirst\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    TESTS.append([p, {\"channel_dim\": -1}, (4, 1, 2, 3)])\n    TESTS.append([p, {\"channel_dim\": 3}, (4, 1, 2, 3)])\n    TESTS.append([p, {\"channel_dim\": 2}, (3, 1, 2, 4)])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_as_channel_first.py_TestAsChannelFirst_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_as_channel_first.py_TestAsChannelFirst_", "embedding": null, "metadata": {"file_path": "tests/test_as_channel_first.py", "file_name": "test_as_channel_first.py", "file_type": "text/x-python", "category": "test", "start_line": 28, "end_line": 42, "span_ids": ["TestAsChannelFirst.test_value", "TestAsChannelFirst", "impl:7"], "tokens": 144}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestAsChannelFirst(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_value(self, in_type, input_param, expected_shape):\n        test_data = in_type(np.random.randint(0, 2, size=[1, 2, 3, 4]))\n        result = AsChannelFirst(**input_param)(test_data)\n        self.assertTupleEqual(result.shape, expected_shape)\n        if isinstance(test_data, torch.Tensor):\n            test_data = test_data.cpu().numpy()\n        expected = np.moveaxis(test_data, input_param[\"channel_dim\"], 0)\n        assert_allclose(result, expected, type_test=False)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_as_channel_firstd.py_unittest_for_p_in_TEST_NDARRAYS_.None_2": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_as_channel_firstd.py_unittest_for_p_in_TEST_NDARRAYS_.None_2", "embedding": null, "metadata": {"file_path": "tests/test_as_channel_firstd.py", "file_name": "test_as_channel_firstd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 24, "span_ids": ["docstring"], "tokens": 162}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import AsChannelFirstd\nfrom tests.utils import TEST_NDARRAYS\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    TESTS.append([p, {\"keys\": [\"image\", \"label\", \"extra\"], \"channel_dim\": -1}, (4, 1, 2, 3)])\n    TESTS.append([p, {\"keys\": [\"image\", \"label\", \"extra\"], \"channel_dim\": 3}, (4, 1, 2, 3)])\n    TESTS.append([p, {\"keys\": [\"image\", \"label\", \"extra\"], \"channel_dim\": 2}, (3, 1, 2, 4)])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_as_channel_lastd.py_unittest_for_p_in_TEST_NDARRAYS_.None_2": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_as_channel_lastd.py_unittest_for_p_in_TEST_NDARRAYS_.None_2", "embedding": null, "metadata": {"file_path": "tests/test_as_channel_lastd.py", "file_name": "test_as_channel_lastd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 24, "span_ids": ["docstring"], "tokens": 162}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import AsChannelLastd\nfrom tests.utils import TEST_NDARRAYS\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    TESTS.append([p, {\"keys\": [\"image\", \"label\", \"extra\"], \"channel_dim\": 0}, (2, 3, 4, 1)])\n    TESTS.append([p, {\"keys\": [\"image\", \"label\", \"extra\"], \"channel_dim\": 1}, (1, 3, 4, 2)])\n    TESTS.append([p, {\"keys\": [\"image\", \"label\", \"extra\"], \"channel_dim\": 3}, (1, 2, 3, 4)])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_border_pad.py_unittest_TEST_CASE_4._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_border_pad.py_unittest_TEST_CASE_4._", "embedding": null, "metadata": {"file_path": "tests/test_border_pad.py", "file_name": "test_border_pad.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 35, "span_ids": ["docstring"], "tokens": 284}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import BorderPad\nfrom monai.utils import NumpyPadMode\nfrom tests.utils import TEST_NDARRAYS\n\nTEST_CASE_1 = [{\"spatial_border\": 2, \"mode\": \"constant\"}, np.zeros((3, 8, 8, 4)), np.zeros((3, 12, 12, 8))]\n\nTEST_CASE_2 = [{\"spatial_border\": [1, 2, 3], \"mode\": \"constant\"}, np.zeros((3, 8, 8, 4)), np.zeros((3, 10, 12, 10))]\n\nTEST_CASE_3 = [\n    {\"spatial_border\": [1, 2, 3, 4, 5, 6], \"mode\": \"constant\"},\n    np.zeros((3, 8, 8, 4)),\n    np.zeros((3, 11, 15, 15)),\n]\n\nTEST_CASE_4 = [\n    {\"spatial_border\": [1, 2, 3, 4, 5, 6], \"mode\": NumpyPadMode.CONSTANT},\n    np.zeros((3, 8, 8, 4)),\n    np.zeros((3, 11, 15, 15)),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_border_pad.py_TestBorderPad_TestBorderPad.test_pad_shape.for_p_in_TEST_NDARRAYS_.self_assertAlmostEqual_r2": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_border_pad.py_TestBorderPad_TestBorderPad.test_pad_shape.for_p_in_TEST_NDARRAYS_.self_assertAlmostEqual_r2", "embedding": null, "metadata": {"file_path": "tests/test_border_pad.py", "file_name": "test_border_pad.py", "file_type": "text/x-python", "category": "test", "start_line": 38, "end_line": 46, "span_ids": ["TestBorderPad.test_pad_shape", "TestBorderPad"], "tokens": 116}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestBorderPad(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4])\n    def test_pad_shape(self, input_param, input_data, expected_val):\n        for p in TEST_NDARRAYS:\n            padder = BorderPad(**input_param)\n            r1 = padder(p(input_data))\n            r2 = padder(input_data, mode=input_param[\"mode\"])\n            self.assertAlmostEqual(r1.shape, expected_val.shape)\n            self.assertAlmostEqual(r2.shape, expected_val.shape)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_border_pad.py_TestBorderPad.test_pad_kwargs_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_border_pad.py_TestBorderPad.test_pad_kwargs_", "embedding": null, "metadata": {"file_path": "tests/test_border_pad.py", "file_name": "test_border_pad.py", "file_type": "text/x-python", "category": "test", "start_line": 48, "end_line": 57, "span_ids": ["impl:9", "TestBorderPad.test_pad_kwargs"], "tokens": 132}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestBorderPad(unittest.TestCase):\n\n    def test_pad_kwargs(self):\n        padder = BorderPad(spatial_border=2, mode=\"constant\", constant_values=((0, 0), (1, 1), (2, 2)))\n        result = padder(np.zeros((3, 8, 4)))\n        np.testing.assert_allclose(result[:, :2, 2:6], np.ones((3, 2, 4)))\n        np.testing.assert_allclose(result[:, :, :2], np.ones((3, 12, 2)) + 1)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cachedataset.py_TestCacheDataset_TestCacheDataset.test_shape.if_transform_is_None_.else_.for_d_in_data3_.self_assertTupleEqual_d_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cachedataset.py_TestCacheDataset_TestCacheDataset.test_shape.if_transform_is_None_.else_.for_d_in_data3_.self_assertTupleEqual_d_", "embedding": null, "metadata": {"file_path": "tests/test_cachedataset.py", "file_name": "test_cachedataset.py", "file_type": "text/x-python", "category": "test", "start_line": 40, "end_line": 82, "span_ids": ["TestCacheDataset.test_shape", "TestCacheDataset"], "tokens": 557}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestCacheDataset(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2])\n    def test_shape(self, transform, expected_shape):\n        test_image = nib.Nifti1Image(np.random.randint(0, 2, size=[128, 128, 128]), np.eye(4))\n        with tempfile.TemporaryDirectory() as tempdir:\n            nib.save(test_image, os.path.join(tempdir, \"test_image1.nii.gz\"))\n            nib.save(test_image, os.path.join(tempdir, \"test_label1.nii.gz\"))\n            nib.save(test_image, os.path.join(tempdir, \"test_extra1.nii.gz\"))\n            nib.save(test_image, os.path.join(tempdir, \"test_image2.nii.gz\"))\n            nib.save(test_image, os.path.join(tempdir, \"test_label2.nii.gz\"))\n            nib.save(test_image, os.path.join(tempdir, \"test_extra2.nii.gz\"))\n            test_data = [\n                {\n                    \"image\": os.path.join(tempdir, \"test_image1.nii.gz\"),\n                    \"label\": os.path.join(tempdir, \"test_label1.nii.gz\"),\n                    \"extra\": os.path.join(tempdir, \"test_extra1.nii.gz\"),\n                },\n                {\n                    \"image\": os.path.join(tempdir, \"test_image2.nii.gz\"),\n                    \"label\": os.path.join(tempdir, \"test_label2.nii.gz\"),\n                    \"extra\": os.path.join(tempdir, \"test_extra2.nii.gz\"),\n                },\n            ]\n            dataset = CacheDataset(data=test_data, transform=transform, cache_rate=0.5)\n            data1 = dataset[0]\n            data2 = dataset[1]\n            data3 = dataset[0:-1]\n            data4 = dataset[-1]\n            self.assertEqual(len(data3), 1)\n\n        if transform is None:\n            self.assertEqual(data1[\"image\"], os.path.join(tempdir, \"test_image1.nii.gz\"))\n            self.assertEqual(data2[\"label\"], os.path.join(tempdir, \"test_label2.nii.gz\"))\n            self.assertEqual(data4[\"image\"], os.path.join(tempdir, \"test_image2.nii.gz\"))\n        else:\n            self.assertTupleEqual(data1[\"image\"].shape, expected_shape)\n            self.assertTupleEqual(data1[\"label\"].shape, expected_shape)\n            self.assertTupleEqual(data1[\"extra\"].shape, expected_shape)\n            self.assertTupleEqual(data2[\"image\"].shape, expected_shape)\n            self.assertTupleEqual(data2[\"label\"].shape, expected_shape)\n            self.assertTupleEqual(data2[\"extra\"].shape, expected_shape)\n            for d in data3:\n                self.assertTupleEqual(d[\"image\"].shape, expected_shape)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cachedataset.py_TestCacheDataset.test_set_data__StatefulTransform.__call__.return.data_100_self_propert": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cachedataset.py_TestCacheDataset.test_set_data__StatefulTransform.__call__.return.data_100_self_propert", "embedding": null, "metadata": {"file_path": "tests/test_cachedataset.py", "file_name": "test_cachedataset.py", "file_type": "text/x-python", "category": "test", "start_line": 84, "end_line": 125, "span_ids": ["_StatefulTransform.__call__", "_StatefulTransform", "_StatefulTransform.__init__", "TestCacheDataset.test_set_data"], "tokens": 365}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestCacheDataset(unittest.TestCase):\n\n    def test_set_data(self):\n        data_list1 = list(range(10))\n\n        transform = Compose([Lambda(func=lambda x: np.array([x * 10])), RandLambda(func=lambda x: x + 1)])\n\n        dataset = CacheDataset(\n            data=data_list1,\n            transform=transform,\n            cache_rate=1.0,\n            num_workers=4,\n            progress=True,\n            copy_cache=False if sys.platform == \"linux\" else True,\n        )\n\n        num_workers = 2 if sys.platform == \"linux\" else 0\n        dataloader = DataLoader(dataset=dataset, num_workers=num_workers, batch_size=1)\n        for i, d in enumerate(dataloader):\n            np.testing.assert_allclose([[data_list1[i] * 10 + 1]], d)\n        # simulate another epoch, the cache content should not be modified\n        for i, d in enumerate(dataloader):\n            np.testing.assert_allclose([[data_list1[i] * 10 + 1]], d)\n\n        # update the datalist and fill the cache content\n        data_list2 = list(range(-10, 0))\n        dataset.set_data(data=data_list2)\n        # rerun with updated cache content\n        for i, d in enumerate(dataloader):\n            np.testing.assert_allclose([[data_list2[i] * 10 + 1]], d)\n\n\nclass _StatefulTransform(Transform, ThreadUnsafe):\n    \"\"\"\n    A transform with an internal state.\n    The state is changing at each call.\n    \"\"\"\n\n    def __init__(self):\n        self.property = 1\n\n    def __call__(self, data):\n        self.property = self.property + 1\n        return data * 100 + self.property", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cast_to_type.py_unittest_TESTS_CUPY._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cast_to_type.py_unittest_TESTS_CUPY._", "embedding": null, "metadata": {"file_path": "tests/test_cast_to_type.py", "file_name": "test_cast_to_type.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 34, "span_ids": ["docstring"], "tokens": 225}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import CastToType\nfrom monai.utils import optional_import\nfrom monai.utils.type_conversion import get_equivalent_dtype\nfrom tests.utils import HAS_CUPY, TEST_NDARRAYS\n\ncp, _ = optional_import(\"cupy\")\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    for out_dtype in (np.float64, torch.float64):\n        TESTS.append([out_dtype, p(np.array([[0, 1], [1, 2]], dtype=np.float32)), out_dtype])\n\nTESTS_CUPY = [\n    [np.float32, np.array([[0, 1], [1, 2]], dtype=np.float32), np.float32],\n    [np.float32, np.array([[0, 1], [1, 2]], dtype=np.uint8), np.float32],\n    [np.uint8, np.array([[0, 1], [1, 2]], dtype=np.float32), np.uint8],\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cast_to_type.py_TestCastToType_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cast_to_type.py_TestCastToType_", "embedding": null, "metadata": {"file_path": "tests/test_cast_to_type.py", "file_name": "test_cast_to_type.py", "file_type": "text/x-python", "category": "test", "start_line": 37, "end_line": 63, "span_ids": ["impl:9", "TestCastToType.test_type", "TestCastToType", "TestCastToType.test_type_cupy"], "tokens": 230}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestCastToType(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_type(self, out_dtype, input_data, expected_type):\n\n        result = CastToType(dtype=out_dtype)(input_data)\n        self.assertEqual(result.dtype, get_equivalent_dtype(expected_type, type(result)))\n\n        result = CastToType()(input_data, out_dtype)\n        self.assertEqual(result.dtype, get_equivalent_dtype(expected_type, type(result)))\n\n    @parameterized.expand(TESTS_CUPY)\n    @unittest.skipUnless(HAS_CUPY, \"Requires CuPy\")\n    def test_type_cupy(self, out_dtype, input_data, expected_type):\n        input_data = cp.asarray(input_data)\n\n        result = CastToType(dtype=out_dtype)(input_data)\n        self.assertTrue(isinstance(result, cp.ndarray))\n        self.assertEqual(result.dtype, get_equivalent_dtype(expected_type, type(result)))\n\n        result = CastToType()(input_data, out_dtype)\n        self.assertTrue(isinstance(result, cp.ndarray))\n        self.assertEqual(result.dtype, get_equivalent_dtype(expected_type, type(result)))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cast_to_typed.py_unittest_TESTS_CUPY._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cast_to_typed.py_unittest_TESTS_CUPY._", "embedding": null, "metadata": {"file_path": "tests/test_cast_to_typed.py", "file_name": "test_cast_to_typed.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 51, "span_ids": ["docstring"], "tokens": 388}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import CastToTyped\nfrom monai.utils import optional_import\nfrom tests.utils import HAS_CUPY\n\ncp, _ = optional_import(\"cupy\")\n\nTEST_CASE_1 = [\n    {\"keys\": [\"img\"], \"dtype\": np.float64},\n    {\"img\": np.array([[0, 1], [1, 2]], dtype=np.float32), \"seg\": np.array([[0, 1], [1, 2]], dtype=np.int8)},\n    {\"img\": np.float64, \"seg\": np.int8},\n]\n\nTEST_CASE_2 = [\n    {\"keys\": [\"img\"], \"dtype\": torch.float64},\n    {\n        \"img\": torch.tensor([[0, 1], [1, 2]], dtype=torch.float32),\n        \"seg\": torch.tensor([[0, 1], [1, 2]], dtype=torch.int8),\n    },\n    {\"img\": torch.float64, \"seg\": torch.int8},\n]\n\n\nTESTS_CUPY = [\n    [\n        {\"keys\": \"image\", \"dtype\": np.uint8},\n        {\"image\": np.array([[0, 1], [1, 2]], dtype=np.float32), \"label\": np.array([[0, 1], [1, 1]], dtype=np.float32)},\n        {\"image\": np.uint8, \"label\": np.float32},\n    ],\n    [\n        {\"keys\": [\"image\", \"label\"], \"dtype\": np.float32},\n        {\"image\": np.array([[0, 1], [1, 2]], dtype=np.uint8), \"label\": np.array([[0, 1], [1, 1]], dtype=np.uint8)},\n        {\"image\": np.float32, \"label\": np.float32},\n    ],\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cast_to_typed.py_TestCastToTyped_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cast_to_typed.py_TestCastToTyped_", "embedding": null, "metadata": {"file_path": "tests/test_cast_to_typed.py", "file_name": "test_cast_to_typed.py", "file_type": "text/x-python", "category": "test", "start_line": 54, "end_line": 74, "span_ids": ["TestCastToTyped", "TestCastToTyped.test_type", "TestCastToTyped.test_type_cupy", "impl:8"], "tokens": 189}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestCastToTyped(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2])\n    def test_type(self, input_param, input_data, expected_type):\n        result = CastToTyped(**input_param)(input_data)\n        for k, v in result.items():\n            self.assertEqual(v.dtype, expected_type[k])\n\n    @parameterized.expand(TESTS_CUPY)\n    @unittest.skipUnless(HAS_CUPY, \"Requires CuPy\")\n    def test_type_cupy(self, input_param, input_data, expected_type):\n        input_data = {k: cp.asarray(v) for k, v in input_data.items()}\n\n        result = CastToTyped(**input_param)(input_data)\n        for k, v in result.items():\n            self.assertTrue(isinstance(v, cp.ndarray))\n            self.assertEqual(v.dtype, expected_type[k])\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_center_spatial_cropd.py_unittest_None_1.TEST_CASES_append_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_center_spatial_cropd.py_unittest_None_1.TEST_CASES_append_", "embedding": null, "metadata": {"file_path": "tests/test_center_spatial_cropd.py", "file_name": "test_center_spatial_cropd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 42, "span_ids": ["docstring"], "tokens": 343}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import CenterSpatialCropd\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTEST_SHAPES = []\nfor p in TEST_NDARRAYS:\n    TEST_SHAPES.append(\n        [{\"keys\": \"img\", \"roi_size\": [2, -1, -1]}, {\"img\": p(np.random.randint(0, 2, size=[3, 3, 3, 3]))}, (3, 2, 3, 3)]\n    )\n\n    TEST_SHAPES.append(\n        [{\"keys\": \"img\", \"roi_size\": [2, 2, 2]}, {\"img\": p(np.random.randint(0, 2, size=[3, 3, 3, 3]))}, (3, 2, 2, 2)]\n    )\n\nTEST_CASES = []\nfor p in TEST_NDARRAYS:\n    TEST_CASES.append(\n        [\n            {\"keys\": \"img\", \"roi_size\": [2, 2]},\n            {\n                \"img\": p(\n                    np.array([[[0, 0, 0, 0, 0], [0, 1, 2, 1, 0], [0, 2, 3, 2, 0], [0, 1, 2, 1, 0], [0, 0, 0, 0, 0]]])\n                )\n            },\n            p(np.array([[[1, 2], [2, 3]]])),\n        ]\n    )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_classes_to_indices.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_classes_to_indices.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_classes_to_indices.py", "file_name": "test_classes_to_indices.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 92, "span_ids": ["TestClassesToIndices", "impl:9", "TestClassesToIndices.test_value", "docstring"], "tokens": 825}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nfrom parameterized import parameterized\n\nfrom monai.transforms import ClassesToIndices\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTESTS_CASES = []\nfor p in TEST_NDARRAYS:\n    TESTS_CASES.append(\n        [\n            # test Argmax data\n            {\"num_classes\": 3, \"image_threshold\": 0.0},\n            p([[[0, 1, 2], [2, 0, 1], [1, 2, 0]]]),\n            None,\n            [p([0, 4, 8]), p([1, 5, 6]), p([2, 3, 7])],\n        ]\n    )\n\n    TESTS_CASES.append(\n        [\n            {\"num_classes\": 3, \"image_threshold\": 60},\n            p([[[0, 1, 2], [2, 0, 1], [1, 2, 0]]]),\n            p([[[132, 1434, 51], [61, 0, 133], [523, 44, 232]]]),\n            [p([0, 8]), p([1, 5, 6]), p([3])],\n        ]\n    )\n\n    TESTS_CASES.append(\n        [\n            # test One-Hot data\n            {\"image_threshold\": 0.0},\n            p(\n                [\n                    [[1, 0, 0], [0, 1, 0], [0, 0, 1]],\n                    [[0, 1, 0], [0, 0, 1], [1, 0, 0]],\n                    [[0, 0, 1], [1, 0, 0], [0, 1, 0]],\n                ]\n            ),\n            None,\n            [p([0, 4, 8]), p([1, 5, 6]), p([2, 3, 7])],\n        ]\n    )\n\n    TESTS_CASES.append(\n        [\n            {\"num_classes\": None, \"image_threshold\": 60},\n            p(\n                [\n                    [[1, 0, 0], [0, 1, 0], [0, 0, 1]],\n                    [[0, 1, 0], [0, 0, 1], [1, 0, 0]],\n                    [[0, 0, 1], [1, 0, 0], [0, 1, 0]],\n                ]\n            ),\n            p([[[132, 1434, 51], [61, 0, 133], [523, 44, 232]]]),\n            [p([0, 8]), p([1, 5, 6]), p([3])],\n        ]\n    )\n\n    TESTS_CASES.append(\n        [\n            # test output_shape\n            {\"num_classes\": 3, \"image_threshold\": 0.0, \"output_shape\": [3, 3]},\n            p([[[0, 1, 2], [2, 0, 1], [1, 2, 0]]]),\n            None,\n            [p([[0, 0], [1, 1], [2, 2]]), p([[0, 1], [1, 2], [2, 0]]), p([[0, 2], [1, 0], [2, 1]])],\n        ]\n    )\n\n\nclass TestClassesToIndices(unittest.TestCase):\n    @parameterized.expand(TESTS_CASES)\n    def test_value(self, input_args, label, image, expected_indices):\n        indices = ClassesToIndices(**input_args)(label, image)\n        for i, e in zip(indices, expected_indices):\n            assert_allclose(i, e)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_classes_to_indicesd.py_unittest_for_p_in_TEST_NDARRAYS_.None_4": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_classes_to_indicesd.py_unittest_for_p_in_TEST_NDARRAYS_.None_4", "embedding": null, "metadata": {"file_path": "tests/test_classes_to_indicesd.py", "file_name": "test_classes_to_indicesd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 88, "span_ids": ["docstring"], "tokens": 832}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nfrom parameterized import parameterized\n\nfrom monai.transforms import ClassesToIndicesd\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTESTS_CASES = []\nfor p in TEST_NDARRAYS:\n    TESTS_CASES.append(\n        [\n            # test Argmax data\n            {\"keys\": \"label\", \"num_classes\": 3, \"image_threshold\": 0.0},\n            {\"label\": p([[[0, 1, 2], [2, 0, 1], [1, 2, 0]]])},\n            [p([0, 4, 8]), p([1, 5, 6]), p([2, 3, 7])],\n        ]\n    )\n\n    TESTS_CASES.append(\n        [\n            {\"keys\": \"label\", \"image_key\": \"image\", \"num_classes\": 3, \"image_threshold\": 60},\n            {\n                \"label\": p([[[0, 1, 2], [2, 0, 1], [1, 2, 0]]]),\n                \"image\": p([[[132, 1434, 51], [61, 0, 133], [523, 44, 232]]]),\n            },\n            [p([0, 8]), p([1, 5, 6]), p([3])],\n        ]\n    )\n\n    TESTS_CASES.append(\n        [\n            # test One-Hot data\n            {\"keys\": \"label\", \"image_threshold\": 0.0},\n            {\n                \"label\": p(\n                    [\n                        [[1, 0, 0], [0, 1, 0], [0, 0, 1]],\n                        [[0, 1, 0], [0, 0, 1], [1, 0, 0]],\n                        [[0, 0, 1], [1, 0, 0], [0, 1, 0]],\n                    ]\n                )\n            },\n            [p([0, 4, 8]), p([1, 5, 6]), p([2, 3, 7])],\n        ]\n    )\n\n    TESTS_CASES.append(\n        [\n            {\"keys\": \"label\", \"image_key\": \"image\", \"num_classes\": None, \"image_threshold\": 60},\n            {\n                \"label\": p(\n                    [\n                        [[1, 0, 0], [0, 1, 0], [0, 0, 1]],\n                        [[0, 1, 0], [0, 0, 1], [1, 0, 0]],\n                        [[0, 0, 1], [1, 0, 0], [0, 1, 0]],\n                    ]\n                ),\n                \"image\": p([[[132, 1434, 51], [61, 0, 133], [523, 44, 232]]]),\n            },\n            [p([0, 8]), p([1, 5, 6]), p([3])],\n        ]\n    )\n\n    TESTS_CASES.append(\n        [\n            # test output_shape\n            {\n                \"keys\": \"label\",\n                \"indices_postfix\": \"cls\",\n                \"num_classes\": 3,\n                \"image_threshold\": 0.0,\n                \"output_shape\": [3, 3],\n            },\n            {\"label\": p([[[0, 1, 2], [2, 0, 1], [1, 2, 0]]])},\n            [p([[0, 0], [1, 1], [2, 2]]), p([[0, 1], [1, 2], [2, 0]]), p([[0, 2], [1, 0], [2, 1]])],\n        ]\n    )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_classes_to_indicesd.py_TestClassesToIndicesd_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_classes_to_indicesd.py_TestClassesToIndicesd_", "embedding": null, "metadata": {"file_path": "tests/test_classes_to_indicesd.py", "file_name": "test_classes_to_indicesd.py", "file_type": "text/x-python", "category": "test", "start_line": 91, "end_line": 103, "span_ids": ["impl:9", "TestClassesToIndicesd.test_value", "TestClassesToIndicesd"], "tokens": 120}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestClassesToIndicesd(unittest.TestCase):\n    @parameterized.expand(TESTS_CASES)\n    def test_value(self, input_args, input_data, expected_indices):\n        result = ClassesToIndicesd(**input_args)(input_data)\n        key_postfix = input_args.get(\"indices_postfix\")\n        key_postfix = \"_cls_indices\" if key_postfix is None else key_postfix\n        for i, e in zip(result[\"label\" + key_postfix], expected_indices):\n            assert_allclose(i, e)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compose_get_number_conversions.py_unittest_for_is_dict_in_False_Tr.if_torch_cuda_is_availabl.TESTS_append_TCPU_TG": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compose_get_number_conversions.py_unittest_for_is_dict_in_False_Tr.if_torch_cuda_is_availabl.TESTS_append_TCPU_TG", "embedding": null, "metadata": {"file_path": "tests/test_compose_get_number_conversions.py", "file_name": "test_compose_get_number_conversions.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 101, "span_ids": ["_apply", "T.__call__", "Load.__call__", "TCPU.__call__", "NT.__call__", "NT", "Load.__init__", "docstring", "TCPU", "impl:7", "N", "TGPU", "TGPU.__call__", "Load", "N.__call__", "T"], "tokens": 749}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom copy import deepcopy\nfrom typing import List, Tuple\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import Compose\nfrom monai.transforms.compose import OneOf\nfrom monai.transforms.transform import Transform\nfrom monai.transforms.utils import get_number_image_type_conversions\nfrom monai.utils import convert_to_numpy, convert_to_tensor\n\nNP_ARR = np.ones((10, 10, 10))\nPT_ARR = torch.as_tensor(NP_ARR)\nKEY = \"IMAGE\"\n\n\ndef _apply(x, fn):\n    if isinstance(x, dict):\n        d = deepcopy(x)\n        d[KEY] = fn(d[KEY])\n        return d\n    return fn(x)\n\n\nclass Load(Transform):\n    def __init__(self, as_tensor):\n        self.fn = lambda _: PT_ARR if as_tensor else NP_ARR\n\n    def __call__(self, x):\n        return _apply(x, self.fn)\n\n\nclass N(Transform):\n    def __call__(self, x):\n        return _apply(x, convert_to_numpy)\n\n\nclass T(Transform):\n    def __call__(self, x):\n        return _apply(x, convert_to_tensor)\n\n\nclass NT(Transform):\n    def __call__(self, x):\n        return _apply(x, lambda x: x)\n\n\nclass TCPU(Transform):\n    def __call__(self, x):\n        return _apply(x, lambda x: convert_to_tensor(x).cpu())\n\n\nclass TGPU(Transform):\n    def __call__(self, x):\n        return _apply(x, lambda x: convert_to_tensor(x).cuda())\n\n\nTESTS: List[Tuple] = []\nfor is_dict in (False, True):\n    # same type depends on input\n    TESTS.append(((N(), N()), is_dict, NP_ARR, 0))\n    TESTS.append(((N(), N()), is_dict, PT_ARR, 1))\n    TESTS.append(((T(), T()), is_dict, NP_ARR, 1))\n    TESTS.append(((T(), T()), is_dict, PT_ARR, 0))\n\n    # loading depends on loader's output type and following transform\n    TESTS.append(((Load(as_tensor=False), N()), is_dict, \"fname.nii\", 0))\n    TESTS.append(((Load(as_tensor=True), N()), is_dict, \"fname.nii\", 1))\n    TESTS.append(((Load(as_tensor=False), T()), is_dict, \"fname.nii\", 1))\n    TESTS.append(((Load(as_tensor=True), T()), is_dict, \"fname.nii\", 0))\n    TESTS.append(((Load(as_tensor=True), NT()), is_dict, \"fname.nii\", 0))\n    TESTS.append(((Load(as_tensor=True), NT()), is_dict, \"fname.nii\", 0))\n\n    # no changes for ambivalent transforms\n    TESTS.append(((NT(), NT()), is_dict, NP_ARR, 0))\n    TESTS.append(((NT(), NT()), is_dict, PT_ARR, 0))\n\n    # multiple conversions\n    TESTS.append(((N(), T(), N()), is_dict, PT_ARR, 3))\n    TESTS.append(((N(), NT(), T(), T(), NT(), NT(), N()), is_dict, PT_ARR, 3))\n\n    # shouldn't matter that there are nested composes\n    TESTS.append(((N(), NT(), T(), Compose([T(), NT(), NT(), N()])), is_dict, PT_ARR, 3))\n\n    # changing device also counts\n    if torch.cuda.is_available():\n        TESTS.append(((TCPU(), TGPU(), TCPU()), is_dict, PT_ARR, 2))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compose_get_number_conversions.py_TestComposeNumConversions_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_compose_get_number_conversions.py_TestComposeNumConversions_", "embedding": null, "metadata": {"file_path": "tests/test_compose_get_number_conversions.py", "file_name": "test_compose_get_number_conversions.py", "file_type": "text/x-python", "category": "test", "start_line": 104, "end_line": 120, "span_ids": ["TestComposeNumConversions.test_get_number_of_conversions", "TestComposeNumConversions.test_raises", "impl:27", "TestComposeNumConversions"], "tokens": 147}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestComposeNumConversions(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_get_number_of_conversions(self, transforms, is_dict, input, expected):\n        input = input if not is_dict else {KEY: input, \"Other\": NP_ARR}\n        tr = Compose(transforms)\n        n = get_number_image_type_conversions(tr, input, key=KEY if is_dict else None)\n        self.assertEqual(n, expected)\n\n    def test_raises(self):\n        tr = Compose([N(), OneOf([T(), T()])])\n        with self.assertRaises(RuntimeError):\n            get_number_image_type_conversions(tr, NP_ARR)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_convert_data_type.py_unittest_TestTensor.pass": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_convert_data_type.py_unittest_TestTensor.pass", "embedding": null, "metadata": {"file_path": "tests/test_convert_data_type.py", "file_name": "test_convert_data_type.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 43, "span_ids": ["TestTensor", "docstring"], "tokens": 273}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom typing import List, Tuple\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.utils.type_conversion import convert_data_type, convert_to_dst_type\nfrom tests.utils import TEST_NDARRAYS\n\nTESTS: List[Tuple] = []\nfor in_type in TEST_NDARRAYS + (int, float):\n    for out_type in TEST_NDARRAYS:\n        TESTS.append((in_type(np.array(1.0)), out_type(np.array(1.0))))  # type: ignore\n\nTESTS_LIST: List[Tuple] = []\nfor in_type in TEST_NDARRAYS + (int, float):\n    for out_type in TEST_NDARRAYS:\n        TESTS_LIST.append(\n            ([in_type(np.array(1.0)), in_type(np.array(1.0))], out_type(np.array([1.0, 1.0])), True)  # type: ignore\n        )\n        TESTS_LIST.append(\n            (\n                [in_type(np.array(1.0)), in_type(np.array(1.0))],  # type: ignore\n                [out_type(np.array(1.0)), out_type(np.array(1.0))],\n                False,\n            )\n        )\n\n\nclass TestTensor(torch.Tensor):\n    pass", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_convert_data_type.py_TestConvertDataType_TestConvertDataType.test_neg_stride._.convert_data_type_np_arra": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_convert_data_type.py_TestConvertDataType_TestConvertDataType.test_neg_stride._.convert_data_type_np_arra", "embedding": null, "metadata": {"file_path": "tests/test_convert_data_type.py", "file_name": "test_convert_data_type.py", "file_type": "text/x-python", "category": "test", "start_line": 46, "end_line": 61, "span_ids": ["TestConvertDataType", "TestConvertDataType.test_convert_data_type", "TestConvertDataType.test_neg_stride"], "tokens": 164}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestConvertDataType(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_convert_data_type(self, in_image, im_out):\n        converted_im, orig_type, orig_device = convert_data_type(in_image, type(im_out))\n        # check input is unchanged\n        self.assertEqual(type(in_image), orig_type)\n        if isinstance(in_image, torch.Tensor):\n            self.assertEqual(in_image.device, orig_device)\n        # check output is desired type\n        self.assertEqual(type(converted_im), type(im_out))\n        # check dtype is unchanged\n        if isinstance(in_type, (np.ndarray, torch.Tensor)):\n            self.assertEqual(converted_im.dtype, im_out.dtype)\n\n    def test_neg_stride(self):\n        _ = convert_data_type(np.array((1, 2))[::-1], torch.Tensor)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_convert_data_type.py_TestConvertDataType.test_convert_list_TestConvertDataType.test_convert_list.if_isinstance_in_type_n.self_assertEqual_converte": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_convert_data_type.py_TestConvertDataType.test_convert_list_TestConvertDataType.test_convert_list.if_isinstance_in_type_n.self_assertEqual_converte", "embedding": null, "metadata": {"file_path": "tests/test_convert_data_type.py", "file_name": "test_convert_data_type.py", "file_type": "text/x-python", "category": "test", "start_line": 63, "end_line": 74, "span_ids": ["TestConvertDataType.test_convert_list"], "tokens": 143}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestConvertDataType(unittest.TestCase):\n\n    @parameterized.expand(TESTS_LIST)\n    def test_convert_list(self, in_image, im_out, wrap):\n        output_type = type(im_out) if wrap else type(im_out[0])\n        converted_im, *_ = convert_data_type(in_image, output_type, wrap_sequence=wrap)\n        # check output is desired type\n        if not wrap:\n            converted_im = converted_im[0]\n            im_out = im_out[0]\n        self.assertEqual(type(converted_im), type(im_out))\n        # check dtype is unchanged\n        if isinstance(in_type, (np.ndarray, torch.Tensor)):\n            self.assertEqual(converted_im.dtype, im_out.dtype)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_convert_data_type.py_TestConvertDataSame_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_convert_data_type.py_TestConvertDataSame_", "embedding": null, "metadata": {"file_path": "tests/test_convert_data_type.py", "file_name": "test_convert_data_type.py", "file_type": "text/x-python", "category": "test", "start_line": 77, "end_line": 99, "span_ids": ["TestConvertDataSame.test_convert_data_type", "impl:12", "TestConvertDataSame"], "tokens": 202}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestConvertDataSame(unittest.TestCase):\n    # add test for subclass of Tensor\n    @parameterized.expand(TESTS + [(np.array(1.0), TestTensor(np.array(1.0)))])\n    def test_convert_data_type(self, in_image, im_out):\n        converted_im, orig_type, orig_device = convert_to_dst_type(in_image, im_out)\n        # check input is unchanged\n        self.assertEqual(type(in_image), orig_type)\n        if isinstance(in_image, torch.Tensor):\n            self.assertEqual(in_image.device, orig_device)\n        # check output is desired type\n        if isinstance(im_out, torch.Tensor):\n            output_type = torch.Tensor\n        else:\n            output_type = np.ndarray\n        self.assertEqual(type(converted_im), output_type)\n        # check dtype is unchanged\n        if isinstance(in_type, (np.ndarray, torch.Tensor)):\n            self.assertEqual(converted_im.dtype, im_out.dtype)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_convert_to_torchscript.py_os_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_convert_to_torchscript.py_os_", "embedding": null, "metadata": {"file_path": "tests/test_convert_to_torchscript.py", "file_name": "test_convert_to_torchscript.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 44, "span_ids": ["TestConvertToTorchScript", "TestConvertToTorchScript.test_value", "impl", "docstring"], "tokens": 236}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport tempfile\nimport unittest\n\nimport torch\n\nfrom monai.networks import convert_to_torchscript\nfrom monai.networks.nets import UNet\n\n\nclass TestConvertToTorchScript(unittest.TestCase):\n    def test_value(self):\n        model = UNet(\n            spatial_dims=2, in_channels=1, out_channels=3, channels=(16, 32, 64), strides=(2, 2), num_res_units=0\n        )\n        with tempfile.TemporaryDirectory() as tempdir:\n            torchscript_model = convert_to_torchscript(\n                model=model,\n                filename_or_obj=os.path.join(tempdir, \"model.ts\"),\n                extra_files={\"foo.txt\": b\"bar\"},\n                verify=True,\n                inputs=[torch.randn((16, 1, 32, 32), requires_grad=False)],\n                device=\"cuda\" if torch.cuda.is_available() else \"cpu\",\n                rtol=1e-3,\n                atol=1e-4,\n                optimize=None,\n            )\n            self.assertTrue(isinstance(torchscript_model, torch.nn.Module))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_copy_model_state.py_unittest_for__x_in___devices_.for__y_in___devices_.TEST_CASES_append__x__y": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_copy_model_state.py_unittest_for__x_in___devices_.for__y_in___devices_.TEST_CASES_append__x__y", "embedding": null, "metadata": {"file_path": "tests/test_copy_model_state.py", "file_name": "test_copy_model_state.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 52, "span_ids": ["impl", "_TestModelTwo.forward", "_TestModelOne.__init__", "_TestModelTwo", "_TestModelTwo.__init__", "docstring", "_TestModelOne", "_TestModelOne.forward"], "tokens": 283}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks.utils import copy_model_state\nfrom monai.utils import set_determinism\n\n\nclass _TestModelOne(torch.nn.Module):\n    def __init__(self, n_n, n_m, n_class):\n        super().__init__()\n        self.layer = torch.nn.Linear(n_n, n_m)\n        self.class_layer = torch.nn.Linear(n_m, n_class)\n\n    def forward(self, x):\n        x = self.layer(x)\n        x = self.class_layer(x)\n        return x\n\n\nclass _TestModelTwo(torch.nn.Module):\n    def __init__(self, n_n, n_m, n_d, n_class):\n        super().__init__()\n        self.layer = torch.nn.Linear(n_n, n_m)\n        self.layer_1 = torch.nn.Linear(n_m, n_d)\n        self.class_layer = torch.nn.Linear(n_d, n_class)\n\n    def forward(self, x):\n        x = self.layer(x)\n        x = self.layer_1(x)\n        x = self.class_layer(x)\n        return x\n\n\nTEST_CASES = []\n__devices = (\"cpu\", \"cuda\") if torch.cuda.is_available() else (\"cpu\",)\nfor _x in __devices:\n    for _y in __devices:\n        TEST_CASES.append((_x, _y))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_correct_crop_centers.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_correct_crop_centers.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_correct_crop_centers.py", "file_name": "test_correct_crop_centers.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 35, "span_ids": ["impl:3", "TestCorrectCropCenters", "TestCorrectCropCenters.test_torch", "docstring"], "tokens": 210}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms.utils import correct_crop_centers\nfrom tests.utils import assert_allclose\n\nTESTS = [[[1, 5, 0], [2, 2, 2], [10, 10, 10]], [[4, 4, 4], [2, 2, 1], [10, 10, 10]]]\n\n\nclass TestCorrectCropCenters(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_torch(self, spatial_size, centers, label_spatial_shape):\n        result1 = correct_crop_centers(centers, spatial_size, label_spatial_shape)\n        centers = [torch.tensor(i) for i in centers]\n        result2 = correct_crop_centers(centers, spatial_size, label_spatial_shape)\n        assert_allclose(result1, result2)\n        self.assertEqual(type(result1[0]), type(result2[0]))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_create_grid_and_affine.py_unittest_TestCreateGrid.test_create_grid.test_assert_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_create_grid_and_affine.py_unittest_TestCreateGrid.test_create_grid.test_assert_", "embedding": null, "metadata": {"file_path": "tests/test_create_grid_and_affine.py", "file_name": "test_create_grid_and_affine.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 64, "span_ids": ["TestCreateGrid", "TestCreateGrid.test_create_grid", "docstring"], "tokens": 657}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\n\nfrom monai.transforms import (\n    create_control_grid,\n    create_grid,\n    create_rotate,\n    create_scale,\n    create_shear,\n    create_translate,\n)\nfrom tests.utils import assert_allclose, is_tf32_env\n\n\nclass TestCreateGrid(unittest.TestCase):\n    def test_create_grid(self):\n        with self.assertRaisesRegex(TypeError, \"\"):\n            create_grid(None)\n        with self.assertRaisesRegex(TypeError, \"\"):\n            create_grid((1, 1), spacing=2.0)\n        with self.assertRaisesRegex(TypeError, \"\"):\n            create_grid((1, 1), spacing=2.0)\n\n        test_assert(create_grid, ((1, 1),), np.array([[[0.0]], [[0.0]], [[1.0]]]))\n\n        test_assert(create_grid, ((1, 1), None, False), np.array([[[0.0]], [[0.0]]]))\n\n        test_assert(create_grid, ((1, 1), (1.2, 1.3)), np.array([[[0.0]], [[0.0]], [[1.0]]]))\n\n        test_assert(create_grid, ((1, 1, 1), (1.2, 1.3, 1.0)), np.array([[[[0.0]]], [[[0.0]]], [[[0.0]]], [[[1.0]]]]))\n\n        test_assert(create_grid, ((1, 1, 1), (1.2, 1.3, 1.0), False), np.array([[[[0.0]]], [[[0.0]]], [[[0.0]]]]))\n\n        g = create_grid((1, 1, 1), spacing=(1.2, 1.3, 1.0), dtype=np.int32)\n        np.testing.assert_equal(g.dtype, np.int32)\n\n        g = create_grid((1, 1, 1), spacing=(1.2, 1.3, 1.0), dtype=torch.float64, backend=\"torch\")\n        np.testing.assert_equal(g.dtype, torch.float64)\n\n        test_assert(\n            create_grid,\n            ((2, 2, 2),),\n            np.array(\n                [\n                    [[[-0.5, -0.5], [-0.5, -0.5]], [[0.5, 0.5], [0.5, 0.5]]],\n                    [[[-0.5, -0.5], [0.5, 0.5]], [[-0.5, -0.5], [0.5, 0.5]]],\n                    [[[-0.5, 0.5], [-0.5, 0.5]], [[-0.5, 0.5], [-0.5, 0.5]]],\n                    [[[1.0, 1.0], [1.0, 1.0]], [[1.0, 1.0], [1.0, 1.0]]],\n                ]\n            ),\n        )\n        # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_create_grid_and_affine.py_TestCreateGrid.test_create_grid.None_8_TestCreateGrid.test_create_grid.None_8": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_create_grid_and_affine.py_TestCreateGrid.test_create_grid.None_8_TestCreateGrid.test_create_grid.None_8", "embedding": null, "metadata": {"file_path": "tests/test_create_grid_and_affine.py", "file_name": "test_create_grid_and_affine.py", "file_type": "text/x-python", "category": "test", "start_line": 66, "end_line": 77, "span_ids": ["TestCreateGrid.test_create_grid"], "tokens": 238}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestCreateGrid(unittest.TestCase):\n    def test_create_grid(self):\n        # ... other code\n\n        test_assert(\n            create_grid,\n            ((2, 2, 2), (1.2, 1.3, 1.0)),\n            np.array(\n                [\n                    [[[-0.6, -0.6], [-0.6, -0.6]], [[0.6, 0.6], [0.6, 0.6]]],\n                    [[[-0.65, -0.65], [0.65, 0.65]], [[-0.65, -0.65], [0.65, 0.65]]],\n                    [[[-0.5, 0.5], [-0.5, 0.5]], [[-0.5, 0.5], [-0.5, 0.5]]],\n                    [[[1.0, 1.0], [1.0, 1.0]], [[1.0, 1.0], [1.0, 1.0]]],\n                ]\n            ),\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_create_grid_and_affine.py_TestCreateGrid.test_create_control_grid_TestCreateGrid.test_create_control_grid.None_2": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_create_grid_and_affine.py_TestCreateGrid.test_create_control_grid_TestCreateGrid.test_create_control_grid.None_2", "embedding": null, "metadata": {"file_path": "tests/test_create_grid_and_affine.py", "file_name": "test_create_grid_and_affine.py", "file_type": "text/x-python", "category": "test", "start_line": 79, "end_line": 119, "span_ids": ["TestCreateGrid.test_create_control_grid"], "tokens": 712}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestCreateGrid(unittest.TestCase):\n\n    def test_create_control_grid(self):\n        with self.assertRaisesRegex(TypeError, \"\"):\n            create_control_grid(None, None)\n        with self.assertRaisesRegex(TypeError, \"\"):\n            create_control_grid((1, 1), 2.0)\n\n        test_assert(\n            create_control_grid,\n            ((1.0, 1.0), (1.0, 1.0)),\n            np.array(\n                [\n                    [[-1.0, -1.0, -1.0], [0.0, 0.0, 0.0], [1.0, 1.0, 1.0]],\n                    [[-1.0, 0.0, 1.0], [-1.0, 0.0, 1.0], [-1.0, 0.0, 1.0]],\n                    [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],\n                ]\n            ),\n        )\n\n        test_assert(\n            create_control_grid,\n            ((1.0, 1.0), (2.0, 2.0)),\n            np.array(\n                [\n                    [[-2.0, -2.0, -2.0], [0.0, 0.0, 0.0], [2.0, 2.0, 2.0]],\n                    [[-2.0, 0.0, 2.0], [-2.0, 0.0, 2.0], [-2.0, 0.0, 2.0]],\n                    [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],\n                ]\n            ),\n        )\n\n        test_assert(\n            create_control_grid,\n            ((2.0, 2.0), (1.0, 1.0)),\n            np.array(\n                [\n                    [[-1.5, -1.5, -1.5, -1.5], [-0.5, -0.5, -0.5, -0.5], [0.5, 0.5, 0.5, 0.5], [1.5, 1.5, 1.5, 1.5]],\n                    [[-1.5, -0.5, 0.5, 1.5], [-1.5, -0.5, 0.5, 1.5], [-1.5, -0.5, 0.5, 1.5], [-1.5, -0.5, 0.5, 1.5]],\n                    [[1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0]],\n                ]\n            ),\n        )\n        # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_create_grid_and_affine.py_TestCreateGrid.test_create_control_grid.None_3_TestCreateGrid.test_create_control_grid.None_3": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_create_grid_and_affine.py_TestCreateGrid.test_create_control_grid.None_3_TestCreateGrid.test_create_control_grid.None_3", "embedding": null, "metadata": {"file_path": "tests/test_create_grid_and_affine.py", "file_name": "test_create_grid_and_affine.py", "file_type": "text/x-python", "category": "test", "start_line": 121, "end_line": 131, "span_ids": ["TestCreateGrid.test_create_control_grid"], "tokens": 313}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestCreateGrid(unittest.TestCase):\n\n    def test_create_control_grid(self):\n        # ... other code\n\n        test_assert(\n            create_control_grid,\n            ((2.0, 2.0), (2.0, 2.0)),\n            np.array(\n                [\n                    [[-3.0, -3.0, -3.0, -3.0], [-1.0, -1.0, -1.0, -1.0], [1.0, 1.0, 1.0, 1.0], [3.0, 3.0, 3.0, 3.0]],\n                    [[-3.0, -1.0, 1.0, 3.0], [-3.0, -1.0, 1.0, 3.0], [-3.0, -1.0, 1.0, 3.0], [-3.0, -1.0, 1.0, 3.0]],\n                    [[1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0]],\n                ]\n            ),\n        )\n        # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_create_grid_and_affine.py_TestCreateGrid.test_create_control_grid.None_4_TestCreateGrid.test_create_control_grid.None_4": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_create_grid_and_affine.py_TestCreateGrid.test_create_control_grid.None_4_TestCreateGrid.test_create_control_grid.None_4", "embedding": null, "metadata": {"file_path": "tests/test_create_grid_and_affine.py", "file_name": "test_create_grid_and_affine.py", "file_type": "text/x-python", "category": "test", "start_line": 133, "end_line": 155, "span_ids": ["TestCreateGrid.test_create_control_grid"], "tokens": 508}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestCreateGrid(unittest.TestCase):\n\n    def test_create_control_grid(self):\n        # ... other code\n\n        test_assert(\n            create_control_grid,\n            ((1.0, 1.0, 1.0), (2.0, 2.0, 2.0), False),\n            np.array(\n                [\n                    [\n                        [[-2.0, -2.0, -2.0], [-2.0, -2.0, -2.0], [-2.0, -2.0, -2.0]],\n                        [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],\n                        [[2.0, 2.0, 2.0], [2.0, 2.0, 2.0], [2.0, 2.0, 2.0]],\n                    ],\n                    [\n                        [[-2.0, -2.0, -2.0], [0.0, 0.0, 0.0], [2.0, 2.0, 2.0]],\n                        [[-2.0, -2.0, -2.0], [0.0, 0.0, 0.0], [2.0, 2.0, 2.0]],\n                        [[-2.0, -2.0, -2.0], [0.0, 0.0, 0.0], [2.0, 2.0, 2.0]],\n                    ],\n                    [\n                        [[-2.0, 0.0, 2.0], [-2.0, 0.0, 2.0], [-2.0, 0.0, 2.0]],\n                        [[-2.0, 0.0, 2.0], [-2.0, 0.0, 2.0], [-2.0, 0.0, 2.0]],\n                        [[-2.0, 0.0, 2.0], [-2.0, 0.0, 2.0], [-2.0, 0.0, 2.0]],\n                    ],\n                ]\n            ),\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_create_grid_and_affine.py_test_assert_test_assert.for_b_in_torch_numpy.assert_allclose_m_expect": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_create_grid_and_affine.py_test_assert_test_assert.for_b_in_torch_numpy.assert_allclose_m_expect", "embedding": null, "metadata": {"file_path": "tests/test_create_grid_and_affine.py", "file_name": "test_create_grid_and_affine.py", "file_type": "text/x-python", "category": "test", "start_line": 158, "end_line": 165, "span_ids": ["test_assert"], "tokens": 117}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def test_assert(func, params, expected):\n    gpu_test = (\"torch_gpu\",) if torch.cuda.is_available() else ()\n    for b in (\"torch\", \"numpy\") + gpu_test:\n        if b == \"torch_gpu\":\n            m = func(*params, device=\"cuda:0\", backend=\"torch\")\n        else:\n            m = func(*params, backend=b)\n        assert_allclose(m, expected, type_test=False, rtol=1e-2 if is_tf32_env() else 1e-5, atol=1e-5)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_crop_foreground.py_unittest_for_p_in_TEST_NDARRAYS_.None_6": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_crop_foreground.py_unittest_for_p_in_TEST_NDARRAYS_.None_6", "embedding": null, "metadata": {"file_path": "tests/test_crop_foreground.py", "file_name": "test_crop_foreground.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 78, "span_ids": ["docstring"], "tokens": 1162}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import CropForeground\nfrom tests.utils import TEST_NDARRAYS\n\nTEST_COORDS, TESTS = [], []\n\nfor p in TEST_NDARRAYS:\n    TEST_COORDS.append(\n        [\n            {\"select_fn\": lambda x: x > 0, \"channel_indices\": None, \"margin\": 0},\n            p([[[0, 0, 0, 0, 0], [0, 1, 2, 1, 0], [0, 2, 3, 2, 0], [0, 1, 2, 1, 0], [0, 0, 0, 0, 0]]]),\n            p([[[1, 2, 1], [2, 3, 2], [1, 2, 1]]]),\n        ]\n    )\n\n    TESTS.append(\n        [\n            {\"select_fn\": lambda x: x > 1, \"channel_indices\": None, \"margin\": 0},\n            p([[[0, 0, 0, 0, 0], [0, 1, 1, 1, 0], [0, 1, 3, 1, 0], [0, 1, 1, 1, 0], [0, 0, 0, 0, 0]]]),\n            p([[[3]]]),\n        ]\n    )\n\n    TESTS.append(\n        [\n            {\"select_fn\": lambda x: x > 0, \"channel_indices\": 0, \"margin\": 0},\n            p([[[0, 0, 0, 0, 0], [0, 1, 2, 1, 0], [0, 2, 3, 2, 0], [0, 1, 2, 1, 0], [0, 0, 0, 0, 0]]]),\n            p([[[1, 2, 1], [2, 3, 2], [1, 2, 1]]]),\n        ]\n    )\n\n    TESTS.append(\n        [\n            {\"select_fn\": lambda x: x > 0, \"channel_indices\": None, \"margin\": 1},\n            p([[[0, 0, 0, 0, 0], [0, 1, 2, 1, 0], [0, 2, 3, 2, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]]),\n            p([[[0, 0, 0, 0, 0], [0, 1, 2, 1, 0], [0, 2, 3, 2, 0], [0, 0, 0, 0, 0]]]),\n        ]\n    )\n\n    TESTS.append(\n        [\n            {\"select_fn\": lambda x: x > 0, \"channel_indices\": None, \"margin\": [2, 1]},\n            p([[[0, 0, 0, 0, 0], [0, 1, 2, 1, 0], [0, 2, 3, 2, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]]),\n            p([[[0, 0, 0, 0, 0], [0, 1, 2, 1, 0], [0, 2, 3, 2, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]]),\n        ]\n    )\n\n    TESTS.append(\n        [\n            {\"select_fn\": lambda x: x > 0, \"channel_indices\": None, \"margin\": 0, \"k_divisible\": 4},\n            p([[[0, 0, 0, 0, 0], [0, 1, 2, 1, 0], [0, 2, 3, 2, 0], [0, 1, 2, 1, 0], [0, 0, 0, 0, 0]]]),\n            p([[[1, 2, 1, 0], [2, 3, 2, 0], [1, 2, 1, 0], [0, 0, 0, 0]]]),\n        ]\n    )\n\n    TESTS.append(\n        [\n            {\"select_fn\": lambda x: x > 0, \"channel_indices\": None, \"margin\": 0, \"k_divisible\": 10},\n            p([[[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]]),\n            p(np.zeros((1, 0, 0), dtype=np.int64)),\n        ]\n    )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_crop_foregroundd.py_unittest_for_p_in_TEST_NDARRAYS_.None_5": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_crop_foregroundd.py_unittest_for_p_in_TEST_NDARRAYS_.None_5", "embedding": null, "metadata": {"file_path": "tests/test_crop_foregroundd.py", "file_name": "test_crop_foregroundd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 115, "span_ids": ["docstring"], "tokens": 1352}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import CropForegroundd\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTEST_POSITION, TESTS = [], []\nfor p in TEST_NDARRAYS:\n\n    TEST_POSITION.append(\n        [\n            {\n                \"keys\": [\"img\", \"label\"],\n                \"source_key\": \"label\",\n                \"select_fn\": lambda x: x > 0,\n                \"channel_indices\": None,\n                \"margin\": 0,\n            },\n            {\n                \"img\": p(\n                    np.array([[[1, 0, 2, 0, 1], [0, 1, 2, 1, 0], [2, 2, 3, 2, 2], [0, 1, 2, 1, 0], [1, 0, 2, 0, 1]]])\n                ),\n                \"label\": p(\n                    np.array([[[0, 0, 0, 0, 0], [0, 1, 0, 1, 0], [0, 0, 1, 0, 0], [0, 1, 0, 1, 0], [0, 0, 0, 0, 0]]])\n                ),\n            },\n            p(np.array([[[1, 2, 1], [2, 3, 2], [1, 2, 1]]])),\n        ]\n    )\n    TESTS.append(\n        [\n            {\"keys\": [\"img\"], \"source_key\": \"img\", \"select_fn\": lambda x: x > 1, \"channel_indices\": None, \"margin\": 0},\n            {\n                \"img\": p(\n                    np.array([[[0, 0, 0, 0, 0], [0, 1, 1, 1, 0], [0, 1, 3, 1, 0], [0, 1, 1, 1, 0], [0, 0, 0, 0, 0]]])\n                )\n            },\n            p(np.array([[[3]]])),\n        ]\n    )\n    TESTS.append(\n        [\n            {\"keys\": [\"img\"], \"source_key\": \"img\", \"select_fn\": lambda x: x > 0, \"channel_indices\": 0, \"margin\": 0},\n            {\n                \"img\": p(\n                    np.array([[[0, 0, 0, 0, 0], [0, 1, 2, 1, 0], [0, 2, 3, 2, 0], [0, 1, 2, 1, 0], [0, 0, 0, 0, 0]]])\n                )\n            },\n            p(np.array([[[1, 2, 1], [2, 3, 2], [1, 2, 1]]])),\n        ]\n    )\n    TESTS.append(\n        [\n            {\"keys\": [\"img\"], \"source_key\": \"img\", \"select_fn\": lambda x: x > 0, \"channel_indices\": None, \"margin\": 1},\n            {\n                \"img\": p(\n                    np.array([[[0, 0, 0, 0, 0], [0, 1, 2, 1, 0], [0, 2, 3, 2, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]])\n                )\n            },\n            p(np.array([[[0, 0, 0, 0, 0], [0, 1, 2, 1, 0], [0, 2, 3, 2, 0], [0, 0, 0, 0, 0]]])),\n        ]\n    )\n    TESTS.append(\n        [\n            {\n                \"keys\": [\"img\"],\n                \"source_key\": \"img\",\n                \"select_fn\": lambda x: x > 0,\n                \"channel_indices\": None,\n                \"margin\": [2, 1],\n            },\n            {\n                \"img\": p(\n                    np.array([[[0, 0, 0, 0, 0], [0, 1, 2, 1, 0], [0, 2, 3, 2, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]])\n                )\n            },\n            p(np.array([[[0, 0, 0, 0, 0], [0, 1, 2, 1, 0], [0, 2, 3, 2, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]])),\n        ]\n    )\n    TESTS.append(\n        [\n            {\n                \"keys\": [\"img\"],\n                \"source_key\": \"img\",\n                \"select_fn\": lambda x: x > 0,\n                \"channel_indices\": 0,\n                \"margin\": 0,\n                \"k_divisible\": [4, 6],\n                \"mode\": \"edge\",\n            },\n            {\n                \"img\": p(\n                    np.array(\n                        [[[0, 2, 1, 2, 0], [1, 1, 2, 1, 1], [2, 2, 3, 2, 2], [1, 1, 2, 1, 1], [0, 0, 0, 0, 0]]],\n                        dtype=np.float32,\n                    )\n                )\n            },\n            p(np.array([[[0, 2, 1, 2, 0, 0], [1, 1, 2, 1, 1, 1], [2, 2, 3, 2, 2, 2], [1, 1, 2, 1, 1, 1]]])),\n        ]\n    )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cucim_dict_transform.py_unittest_TEST_CASE_FLIP_1._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cucim_dict_transform.py_unittest_TEST_CASE_FLIP_1._", "embedding": null, "metadata": {"file_path": "tests/test_cucim_dict_transform.py", "file_name": "test_cucim_dict_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 42, "span_ids": ["impl:8", "docstring"], "tokens": 587}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import CuCIMd\nfrom monai.utils import optional_import, set_determinism\nfrom tests.utils import HAS_CUPY, skip_if_no_cuda\n\n_, has_cut = optional_import(\"cucim.core.operations.expose.transform\")\ncp, _ = optional_import(\"cupy\")\n\nset_determinism(seed=0)\n\nTEST_CASE_COLOR_JITTER_1 = [\n    {\"name\": \"color_jitter\", \"brightness\": 0.0, \"contrast\": 0.0, \"saturation\": 0.0, \"hue\": 0.0},\n    np.array([[[0.0, 1.0], [1.0, 2.0]], [[0.0, 1.0], [1.0, 2.0]], [[0.0, 1.0], [1.0, 2.0]]], dtype=np.float32),\n    np.array([[[0.0, 1.0], [1.0, 2.0]], [[0.0, 1.0], [1.0, 2.0]], [[0.0, 1.0], [1.0, 2.0]]], dtype=np.float32),\n]\n\nTEST_CASE_COLOR_JITTER_2 = [\n    {\"name\": \"color_jitter\", \"brightness\": 0.0, \"contrast\": 0.0, \"saturation\": 0.0, \"hue\": 0.0},\n    np.array([[[0, 1], [2, 3]], [[0, 1], [2, 3]], [[0, 1], [2, 3]]], dtype=np.uint8),\n    np.array([[[0, 1], [2, 3]], [[0, 1], [2, 3]], [[0, 1], [2, 3]]], dtype=np.uint8),\n]\n\nTEST_CASE_FLIP_1 = [\n    {\"name\": \"image_flip\", \"spatial_axis\": -1},\n    np.array([[[0.0, 1.0], [2.0, 3.0]], [[0.0, 1.0], [2.0, 3.0]], [[0.0, 1.0], [2.0, 3.0]]], dtype=np.float32),\n    np.array([[[1.0, 0.0], [3.0, 2.0]], [[1.0, 0.0], [3.0, 2.0]], [[1.0, 0.0], [3.0, 2.0]]], dtype=np.float32),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cucim_dict_transform.py_TEST_CASE_ROTATE_1_TEST_CASE_ZOOM_1._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cucim_dict_transform.py_TEST_CASE_ROTATE_1_TEST_CASE_ZOOM_1._", "embedding": null, "metadata": {"file_path": "tests/test_cucim_dict_transform.py", "file_name": "test_cucim_dict_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 45, "end_line": 61, "span_ids": ["impl:14", "impl:8"], "tokens": 472}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "TEST_CASE_ROTATE_1 = [\n    {\"name\": \"image_rotate_90\", \"k\": 1, \"spatial_axis\": (-2, -1)},\n    np.array([[[0.0, 1.0], [2.0, 3.0]], [[0.0, 1.0], [2.0, 3.0]], [[0.0, 1.0], [2.0, 3.0]]], dtype=np.float32),\n    np.array([[[1.0, 3.0], [0.0, 2.0]], [[1.0, 3.0], [0.0, 2.0]], [[1.0, 3.0], [0.0, 2.0]]], dtype=np.float32),\n]\n\nTEST_CASE_SCALE_INTENSITY_1 = [\n    {\"name\": \"scale_intensity_range\", \"a_min\": 0.0, \"a_max\": 4.0, \"b_min\": 0.0, \"b_max\": 1.0, \"clip\": False},\n    np.array([[[0.0, 1.0], [2.0, 3.0]], [[0.0, 1.0], [2.0, 3.0]], [[0.0, 1.0], [2.0, 3.0]]], dtype=np.float32),\n    np.array([[[0.0, 0.25], [0.5, 0.75]], [[0.0, 0.25], [0.5, 0.75]], [[0.0, 0.25], [0.5, 0.75]]], dtype=np.float32),\n]\n\nTEST_CASE_ZOOM_1 = [\n    {\"name\": \"zoom\", \"zoom_factor\": (0.5, 0.5)},\n    np.mgrid[:3, 1:4].astype(dtype=np.float32),\n    np.concatenate([np.ones((1, 3, 3), dtype=np.float32) * 1.0, np.ones((1, 3, 3), dtype=np.float32) * 2.0]),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cucim_dict_transform.py_TestCuCIMDict_TestCuCIMDict.test_tramsforms_numpy_single.cp_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cucim_dict_transform.py_TestCuCIMDict_TestCuCIMDict.test_tramsforms_numpy_single.cp_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_cucim_dict_transform.py", "file_name": "test_cucim_dict_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 64, "end_line": 83, "span_ids": ["TestCuCIMDict.test_tramsforms_numpy_single", "TestCuCIMDict"], "tokens": 179}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_no_cuda\n@unittest.skipUnless(HAS_CUPY, \"CuPy is required.\")\n@unittest.skipUnless(has_cut, \"cuCIM transforms are required.\")\nclass TestCuCIMDict(unittest.TestCase):\n    @parameterized.expand(\n        [\n            TEST_CASE_COLOR_JITTER_1,\n            TEST_CASE_COLOR_JITTER_2,\n            TEST_CASE_FLIP_1,\n            TEST_CASE_ROTATE_1,\n            TEST_CASE_SCALE_INTENSITY_1,\n            TEST_CASE_ZOOM_1,\n        ]\n    )\n    def test_tramsforms_numpy_single(self, params, input, expected):\n        input = {\"image\": input}\n        output = CuCIMd(keys=\"image\", **params)(input)[\"image\"]\n        self.assertTrue(output.dtype == expected.dtype)\n        self.assertTrue(isinstance(output, np.ndarray))\n        cp.testing.assert_allclose(output, expected)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cucim_dict_transform.py_TestCuCIMDict.test_tramsforms_numpy_batch_TestCuCIMDict.test_tramsforms_numpy_batch.cp_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cucim_dict_transform.py_TestCuCIMDict.test_tramsforms_numpy_batch_TestCuCIMDict.test_tramsforms_numpy_batch.cp_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_cucim_dict_transform.py", "file_name": "test_cucim_dict_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 85, "end_line": 101, "span_ids": ["TestCuCIMDict.test_tramsforms_numpy_batch"], "tokens": 194}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_no_cuda\n@unittest.skipUnless(HAS_CUPY, \"CuPy is required.\")\n@unittest.skipUnless(has_cut, \"cuCIM transforms are required.\")\nclass TestCuCIMDict(unittest.TestCase):\n\n    @parameterized.expand(\n        [\n            TEST_CASE_COLOR_JITTER_1,\n            TEST_CASE_COLOR_JITTER_2,\n            TEST_CASE_FLIP_1,\n            TEST_CASE_ROTATE_1,\n            TEST_CASE_SCALE_INTENSITY_1,\n            TEST_CASE_ZOOM_1,\n        ]\n    )\n    def test_tramsforms_numpy_batch(self, params, input, expected):\n        input = {\"image\": input[cp.newaxis, ...]}\n        expected = expected[cp.newaxis, ...]\n        output = CuCIMd(keys=\"image\", **params)(input)[\"image\"]\n        self.assertTrue(output.dtype == expected.dtype)\n        self.assertTrue(isinstance(output, np.ndarray))\n        cp.testing.assert_allclose(output, expected)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cucim_dict_transform.py_TestCuCIMDict.test_tramsforms_cupy_single_TestCuCIMDict.test_tramsforms_cupy_single.cp_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cucim_dict_transform.py_TestCuCIMDict.test_tramsforms_cupy_single_TestCuCIMDict.test_tramsforms_cupy_single.cp_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_cucim_dict_transform.py", "file_name": "test_cucim_dict_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 103, "end_line": 119, "span_ids": ["TestCuCIMDict.test_tramsforms_cupy_single"], "tokens": 189}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_no_cuda\n@unittest.skipUnless(HAS_CUPY, \"CuPy is required.\")\n@unittest.skipUnless(has_cut, \"cuCIM transforms are required.\")\nclass TestCuCIMDict(unittest.TestCase):\n\n    @parameterized.expand(\n        [\n            TEST_CASE_COLOR_JITTER_1,\n            TEST_CASE_COLOR_JITTER_2,\n            TEST_CASE_FLIP_1,\n            TEST_CASE_ROTATE_1,\n            TEST_CASE_SCALE_INTENSITY_1,\n            TEST_CASE_ZOOM_1,\n        ]\n    )\n    def test_tramsforms_cupy_single(self, params, input, expected):\n        input = {\"image\": cp.asarray(input)}\n        expected = cp.asarray(expected)\n        output = CuCIMd(keys=\"image\", **params)(input)[\"image\"]\n        self.assertTrue(output.dtype == expected.dtype)\n        self.assertTrue(isinstance(output, cp.ndarray))\n        cp.testing.assert_allclose(output, expected)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cucim_dict_transform.py_TestCuCIMDict.test_tramsforms_cupy_batch_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cucim_dict_transform.py_TestCuCIMDict.test_tramsforms_cupy_batch_", "embedding": null, "metadata": {"file_path": "tests/test_cucim_dict_transform.py", "file_name": "test_cucim_dict_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 121, "end_line": 142, "span_ids": ["impl:16", "TestCuCIMDict.test_tramsforms_cupy_batch"], "tokens": 211}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_no_cuda\n@unittest.skipUnless(HAS_CUPY, \"CuPy is required.\")\n@unittest.skipUnless(has_cut, \"cuCIM transforms are required.\")\nclass TestCuCIMDict(unittest.TestCase):\n\n    @parameterized.expand(\n        [\n            TEST_CASE_COLOR_JITTER_1,\n            TEST_CASE_COLOR_JITTER_2,\n            TEST_CASE_FLIP_1,\n            TEST_CASE_ROTATE_1,\n            TEST_CASE_SCALE_INTENSITY_1,\n            TEST_CASE_ZOOM_1,\n        ]\n    )\n    def test_tramsforms_cupy_batch(self, params, input, expected):\n        input = {\"image\": cp.asarray(input)[cp.newaxis, ...]}\n        expected = cp.asarray(expected)[cp.newaxis, ...]\n        output = CuCIMd(keys=\"image\", **params)(input)[\"image\"]\n        self.assertTrue(output.dtype == expected.dtype)\n        self.assertTrue(isinstance(output, cp.ndarray))\n        cp.testing.assert_allclose(output, expected)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cucim_transform.py_unittest_TEST_CASE_FLIP_1._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cucim_transform.py_unittest_TEST_CASE_FLIP_1._", "embedding": null, "metadata": {"file_path": "tests/test_cucim_transform.py", "file_name": "test_cucim_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 42, "span_ids": ["impl:8", "docstring"], "tokens": 586}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import CuCIM\nfrom monai.utils import optional_import, set_determinism\nfrom tests.utils import HAS_CUPY, skip_if_no_cuda\n\n_, has_cut = optional_import(\"cucim.core.operations.expose.transform\")\ncp, _ = optional_import(\"cupy\")\n\nset_determinism(seed=0)\n\nTEST_CASE_COLOR_JITTER_1 = [\n    {\"name\": \"color_jitter\", \"brightness\": 0.0, \"contrast\": 0.0, \"saturation\": 0.0, \"hue\": 0.0},\n    np.array([[[0.0, 1.0], [1.0, 2.0]], [[0.0, 1.0], [1.0, 2.0]], [[0.0, 1.0], [1.0, 2.0]]], dtype=np.float32),\n    np.array([[[0.0, 1.0], [1.0, 2.0]], [[0.0, 1.0], [1.0, 2.0]], [[0.0, 1.0], [1.0, 2.0]]], dtype=np.float32),\n]\n\nTEST_CASE_COLOR_JITTER_2 = [\n    {\"name\": \"color_jitter\", \"brightness\": 0.0, \"contrast\": 0.0, \"saturation\": 0.0, \"hue\": 0.0},\n    np.array([[[0, 1], [2, 3]], [[0, 1], [2, 3]], [[0, 1], [2, 3]]], dtype=np.uint8),\n    np.array([[[0, 1], [2, 3]], [[0, 1], [2, 3]], [[0, 1], [2, 3]]], dtype=np.uint8),\n]\n\nTEST_CASE_FLIP_1 = [\n    {\"name\": \"image_flip\", \"spatial_axis\": -1},\n    np.array([[[0.0, 1.0], [2.0, 3.0]], [[0.0, 1.0], [2.0, 3.0]], [[0.0, 1.0], [2.0, 3.0]]], dtype=np.float32),\n    np.array([[[1.0, 0.0], [3.0, 2.0]], [[1.0, 0.0], [3.0, 2.0]], [[1.0, 0.0], [3.0, 2.0]]], dtype=np.float32),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cucim_transform.py_TEST_CASE_ROTATE_1_TEST_CASE_ZOOM_1._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cucim_transform.py_TEST_CASE_ROTATE_1_TEST_CASE_ZOOM_1._", "embedding": null, "metadata": {"file_path": "tests/test_cucim_transform.py", "file_name": "test_cucim_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 45, "end_line": 61, "span_ids": ["impl:14", "impl:8"], "tokens": 472}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "TEST_CASE_ROTATE_1 = [\n    {\"name\": \"image_rotate_90\", \"k\": 1, \"spatial_axis\": (-2, -1)},\n    np.array([[[0.0, 1.0], [2.0, 3.0]], [[0.0, 1.0], [2.0, 3.0]], [[0.0, 1.0], [2.0, 3.0]]], dtype=np.float32),\n    np.array([[[1.0, 3.0], [0.0, 2.0]], [[1.0, 3.0], [0.0, 2.0]], [[1.0, 3.0], [0.0, 2.0]]], dtype=np.float32),\n]\n\nTEST_CASE_SCALE_INTENSITY_1 = [\n    {\"name\": \"scale_intensity_range\", \"a_min\": 0.0, \"a_max\": 4.0, \"b_min\": 0.0, \"b_max\": 1.0, \"clip\": False},\n    np.array([[[0.0, 1.0], [2.0, 3.0]], [[0.0, 1.0], [2.0, 3.0]], [[0.0, 1.0], [2.0, 3.0]]], dtype=np.float32),\n    np.array([[[0.0, 0.25], [0.5, 0.75]], [[0.0, 0.25], [0.5, 0.75]], [[0.0, 0.25], [0.5, 0.75]]], dtype=np.float32),\n]\n\nTEST_CASE_ZOOM_1 = [\n    {\"name\": \"zoom\", \"zoom_factor\": (0.5, 0.5)},\n    np.mgrid[:3, 1:4].astype(dtype=np.float32),\n    np.concatenate([np.ones((1, 3, 3), dtype=np.float32) * 1.0, np.ones((1, 3, 3), dtype=np.float32) * 2.0]),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cucim_transform.py_TestCuCIM_TestCuCIM.test_tramsforms_numpy_single.cp_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cucim_transform.py_TestCuCIM_TestCuCIM.test_tramsforms_numpy_single.cp_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_cucim_transform.py", "file_name": "test_cucim_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 64, "end_line": 82, "span_ids": ["TestCuCIM.test_tramsforms_numpy_single", "TestCuCIM"], "tokens": 163}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_no_cuda\n@unittest.skipUnless(HAS_CUPY, \"CuPy is required.\")\n@unittest.skipUnless(has_cut, \"cuCIM transforms are required.\")\nclass TestCuCIM(unittest.TestCase):\n    @parameterized.expand(\n        [\n            TEST_CASE_COLOR_JITTER_1,\n            TEST_CASE_COLOR_JITTER_2,\n            TEST_CASE_FLIP_1,\n            TEST_CASE_ROTATE_1,\n            TEST_CASE_SCALE_INTENSITY_1,\n            TEST_CASE_ZOOM_1,\n        ]\n    )\n    def test_tramsforms_numpy_single(self, params, input, expected):\n        output = CuCIM(**params)(input)\n        self.assertTrue(output.dtype == expected.dtype)\n        self.assertTrue(isinstance(output, np.ndarray))\n        cp.testing.assert_allclose(output, expected)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cucim_transform.py_TestCuCIM.test_tramsforms_numpy_batch_TestCuCIM.test_tramsforms_numpy_batch.cp_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cucim_transform.py_TestCuCIM.test_tramsforms_numpy_batch_TestCuCIM.test_tramsforms_numpy_batch.cp_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_cucim_transform.py", "file_name": "test_cucim_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 84, "end_line": 100, "span_ids": ["TestCuCIM.test_tramsforms_numpy_batch"], "tokens": 183}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_no_cuda\n@unittest.skipUnless(HAS_CUPY, \"CuPy is required.\")\n@unittest.skipUnless(has_cut, \"cuCIM transforms are required.\")\nclass TestCuCIM(unittest.TestCase):\n\n    @parameterized.expand(\n        [\n            TEST_CASE_COLOR_JITTER_1,\n            TEST_CASE_COLOR_JITTER_2,\n            TEST_CASE_FLIP_1,\n            TEST_CASE_ROTATE_1,\n            TEST_CASE_SCALE_INTENSITY_1,\n            TEST_CASE_ZOOM_1,\n        ]\n    )\n    def test_tramsforms_numpy_batch(self, params, input, expected):\n        input = input[cp.newaxis, ...]\n        expected = expected[cp.newaxis, ...]\n        output = CuCIM(**params)(input)\n        self.assertTrue(output.dtype == expected.dtype)\n        self.assertTrue(isinstance(output, np.ndarray))\n        cp.testing.assert_allclose(output, expected)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cucim_transform.py_TestCuCIM.test_tramsforms_cupy_single_TestCuCIM.test_tramsforms_cupy_single.cp_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cucim_transform.py_TestCuCIM.test_tramsforms_cupy_single_TestCuCIM.test_tramsforms_cupy_single.cp_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_cucim_transform.py", "file_name": "test_cucim_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 102, "end_line": 118, "span_ids": ["TestCuCIM.test_tramsforms_cupy_single"], "tokens": 178}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_no_cuda\n@unittest.skipUnless(HAS_CUPY, \"CuPy is required.\")\n@unittest.skipUnless(has_cut, \"cuCIM transforms are required.\")\nclass TestCuCIM(unittest.TestCase):\n\n    @parameterized.expand(\n        [\n            TEST_CASE_COLOR_JITTER_1,\n            TEST_CASE_COLOR_JITTER_2,\n            TEST_CASE_FLIP_1,\n            TEST_CASE_ROTATE_1,\n            TEST_CASE_SCALE_INTENSITY_1,\n            TEST_CASE_ZOOM_1,\n        ]\n    )\n    def test_tramsforms_cupy_single(self, params, input, expected):\n        input = cp.asarray(input)\n        expected = cp.asarray(expected)\n        output = CuCIM(**params)(input)\n        self.assertTrue(output.dtype == expected.dtype)\n        self.assertTrue(isinstance(output, cp.ndarray))\n        cp.testing.assert_allclose(output, expected)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cucim_transform.py_TestCuCIM.test_tramsforms_cupy_batch_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cucim_transform.py_TestCuCIM.test_tramsforms_cupy_batch_", "embedding": null, "metadata": {"file_path": "tests/test_cucim_transform.py", "file_name": "test_cucim_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 120, "end_line": 141, "span_ids": ["impl:16", "TestCuCIM.test_tramsforms_cupy_batch"], "tokens": 200}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_no_cuda\n@unittest.skipUnless(HAS_CUPY, \"CuPy is required.\")\n@unittest.skipUnless(has_cut, \"cuCIM transforms are required.\")\nclass TestCuCIM(unittest.TestCase):\n\n    @parameterized.expand(\n        [\n            TEST_CASE_COLOR_JITTER_1,\n            TEST_CASE_COLOR_JITTER_2,\n            TEST_CASE_FLIP_1,\n            TEST_CASE_ROTATE_1,\n            TEST_CASE_SCALE_INTENSITY_1,\n            TEST_CASE_ZOOM_1,\n        ]\n    )\n    def test_tramsforms_cupy_batch(self, params, input, expected):\n        input = cp.asarray(input)[cp.newaxis, ...]\n        expected = cp.asarray(expected)[cp.newaxis, ...]\n        output = CuCIM(**params)(input)\n        self.assertTrue(output.dtype == expected.dtype)\n        self.assertTrue(isinstance(output, cp.ndarray))\n        cp.testing.assert_allclose(output, expected)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_data_statsd.py_logging_TEST_CASE_5._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_data_statsd.py_logging_TEST_CASE_5._", "embedding": null, "metadata": {"file_path": "tests/test_data_statsd.py", "file_name": "test_data_statsd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 97, "span_ids": ["impl:9", "docstring"], "tokens": 606}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import logging\nimport os\nimport sys\nimport tempfile\nimport unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import DataStatsd\n\nTEST_CASE_1 = [\n    {\n        \"keys\": \"img\",\n        \"prefix\": \"test data\",\n        \"data_type\": False,\n        \"data_shape\": False,\n        \"value_range\": False,\n        \"data_value\": False,\n        \"additional_info\": None,\n        \"logger_handler\": None,\n    },\n    {\"img\": np.array([[0, 1], [1, 2]])},\n    \"test data statistics:\",\n]\n\nTEST_CASE_2 = [\n    {\n        \"keys\": \"img\",\n        \"prefix\": \"test data\",\n        \"data_type\": True,\n        \"data_shape\": False,\n        \"value_range\": False,\n        \"data_value\": False,\n        \"additional_info\": None,\n        \"logger_handler\": None,\n    },\n    {\"img\": np.array([[0, 1], [1, 2]])},\n    \"test data statistics:\\nType: <class 'numpy.ndarray'>\",\n]\n\nTEST_CASE_3 = [\n    {\n        \"keys\": \"img\",\n        \"prefix\": \"test data\",\n        \"data_type\": True,\n        \"data_shape\": True,\n        \"value_range\": False,\n        \"data_value\": False,\n        \"additional_info\": None,\n        \"logger_handler\": None,\n    },\n    {\"img\": np.array([[0, 1], [1, 2]])},\n    \"test data statistics:\\nType: <class 'numpy.ndarray'>\\nShape: (2, 2)\",\n]\n\nTEST_CASE_4 = [\n    {\n        \"keys\": \"img\",\n        \"prefix\": \"test data\",\n        \"data_type\": True,\n        \"data_shape\": True,\n        \"value_range\": True,\n        \"data_value\": False,\n        \"additional_info\": None,\n        \"logger_handler\": None,\n    },\n    {\"img\": np.array([[0, 1], [1, 2]])},\n    \"test data statistics:\\nType: <class 'numpy.ndarray'>\\nShape: (2, 2)\\nValue range: (0, 2)\",\n]\n\nTEST_CASE_5 = [\n    {\n        \"keys\": \"img\",\n        \"prefix\": \"test data\",\n        \"data_type\": True,\n        \"data_shape\": True,\n        \"value_range\": True,\n        \"data_value\": True,\n        \"additional_info\": None,\n        \"logger_handler\": None,\n    },\n    {\"img\": np.array([[0, 1], [1, 2]])},\n    \"test data statistics:\\nType: <class 'numpy.ndarray'>\\nShape: (2, 2)\\nValue range: (0, 2)\\nValue: [[0 1]\\n [1 2]]\",\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_data_statsd.py_TEST_CASE_6_TEST_CASE_9._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_data_statsd.py_TEST_CASE_6_TEST_CASE_9._", "embedding": null, "metadata": {"file_path": "tests/test_data_statsd.py", "file_name": "test_data_statsd.py", "file_type": "text/x-python", "category": "test", "start_line": 99, "end_line": 153, "span_ids": ["impl:9", "impl:17"], "tokens": 578}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "TEST_CASE_6 = [\n    {\n        \"keys\": \"img\",\n        \"prefix\": \"test data\",\n        \"data_type\": True,\n        \"data_shape\": True,\n        \"value_range\": True,\n        \"data_value\": True,\n        \"additional_info\": np.mean,\n        \"logger_handler\": None,\n    },\n    {\"img\": np.array([[0, 1], [1, 2]])},\n    (\n        \"test data statistics:\\nType: <class 'numpy.ndarray'>\\nShape: (2, 2)\\n\"\n        \"Value range: (0, 2)\\nValue: [[0 1]\\n [1 2]]\\nAdditional info: 1.0\"\n    ),\n]\n\nTEST_CASE_7 = [\n    {\n        \"keys\": \"img\",\n        \"prefix\": \"test data\",\n        \"data_type\": True,\n        \"data_shape\": True,\n        \"value_range\": True,\n        \"data_value\": True,\n        \"additional_info\": lambda x: torch.mean(x.float()),\n        \"logger_handler\": None,\n    },\n    {\"img\": torch.tensor([[0, 1], [1, 2]]).to(\"cuda\" if torch.cuda.is_available() else \"cpu\")},\n    (\n        \"test data statistics:\\nType: <class 'torch.Tensor'>\\nShape: torch.Size([2, 2])\\nValue range: (0, 2)\\n\"\n        \"Value: tensor([[0, 1],\\n        [1, 2]])\\nAdditional info: 1.0\"\n    ),\n]\n\nTEST_CASE_8 = [\n    {\n        \"keys\": (\"img\", \"affine\"),\n        \"prefix\": (\"image\", \"affine\"),\n        \"data_type\": True,\n        \"data_shape\": True,\n        \"value_range\": (True, False),\n        \"data_value\": (False, True),\n        \"additional_info\": (np.mean, None),\n    },\n    {\"img\": np.array([[0, 1], [1, 2]]), \"affine\": np.eye(2, 2)},\n    \"affine statistics:\\nType: <class 'numpy.ndarray'>\\nShape: (2, 2)\\nValue: [[1. 0.]\\n [0. 1.]]\",\n]\n\nTEST_CASE_9 = [\n    {\"img\": np.array([[0, 1], [1, 2]])},\n    \"test data statistics:\\nType: <class 'numpy.ndarray'> int64\\nShape: (2, 2)\\nValue range: (0, 2)\\n\"\n    \"Value: [[0 1]\\n [1 2]]\\nAdditional info: 1.0\\n\",\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_data_statsd.py_TestDataStatsd_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_data_statsd.py_TestDataStatsd_", "embedding": null, "metadata": {"file_path": "tests/test_data_statsd.py", "file_name": "test_data_statsd.py", "file_type": "text/x-python", "category": "test", "start_line": 156, "end_line": 195, "span_ids": ["TestDataStatsd.test_file", "TestDataStatsd", "TestDataStatsd.test_value", "impl:19"], "tokens": 322}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestDataStatsd(unittest.TestCase):\n    @parameterized.expand(\n        [TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5, TEST_CASE_6, TEST_CASE_7, TEST_CASE_8]\n    )\n    def test_value(self, input_param, input_data, expected_print):\n        transform = DataStatsd(**input_param)\n        _ = transform(input_data)\n        # self.assertEqual(transform.printer.output, expected_print)\n\n    @parameterized.expand([TEST_CASE_9])\n    def test_file(self, input_data, expected_print):\n        with tempfile.TemporaryDirectory() as tempdir:\n            filename = os.path.join(tempdir, \"test_stats.log\")\n            handler = logging.FileHandler(filename, mode=\"w\")\n            handler.setLevel(logging.INFO)\n            input_param = {\n                \"keys\": \"img\",\n                \"prefix\": \"test data\",\n                \"data_shape\": True,\n                \"value_range\": True,\n                \"data_value\": True,\n                \"additional_info\": np.mean,\n                \"logger_handler\": handler,\n            }\n            transform = DataStatsd(**input_param)\n            _ = transform(input_data)\n            _logger = logging.getLogger(transform.printer._logger_name)\n            for h in _logger.handlers[:]:\n                h.close()\n                _logger.removeHandler(h)\n            del handler\n            with open(filename) as f:\n                content = f.read()\n            if sys.platform != \"win32\":\n                self.assertEqual(content, expected_print)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dataloader.py_sys_TEST_CASE_2._label_torch_as_tens": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dataloader.py_sys_TEST_CASE_2._label_torch_as_tens", "embedding": null, "metadata": {"file_path": "tests/test_dataloader.py", "file_name": "test_dataloader.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 25, "span_ids": ["docstring"], "tokens": 129}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import sys\nimport unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.data import CacheDataset, DataLoader, Dataset\nfrom monai.transforms import Compose, DataStatsd, Randomizable, SimulateDelayd\nfrom monai.utils import set_determinism\n\nTEST_CASE_1 = [[{\"image\": np.asarray([1, 2, 3])}, {\"image\": np.asarray([4, 5])}]]\n\nTEST_CASE_2 = [[{\"label\": torch.as_tensor([[3], [2]])}, {\"label\": np.asarray([[1], [2]])}]]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dataset_summary.py_TestDatasetSummary.test_anisotropic_spacing_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dataset_summary.py_TestDatasetSummary.test_anisotropic_spacing_", "embedding": null, "metadata": {"file_path": "tests/test_dataset_summary.py", "file_name": "test_dataset_summary.py", "file_type": "text/x-python", "category": "test", "start_line": 68, "end_line": 97, "span_ids": ["impl", "TestDatasetSummary.test_anisotropic_spacing"], "tokens": 437}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestDatasetSummary(unittest.TestCase):\n\n    def test_anisotropic_spacing(self):\n        with tempfile.TemporaryDirectory() as tempdir:\n\n            pixdims = [[1.0, 1.0, 5.0], [1.0, 1.0, 4.0], [1.0, 1.0, 4.5], [1.0, 1.0, 2.0], [1.0, 1.0, 1.0]]\n            for i in range(5):\n                im, seg = create_test_image_3d(32, 32, 32, num_seg_classes=1, num_objs=3, rad_max=6, channel_dim=0)\n                n = nib.Nifti1Image(im, np.eye(4))\n                n.header[\"pixdim\"][1:4] = pixdims[i]\n                nib.save(n, os.path.join(tempdir, f\"img{i:d}.nii.gz\"))\n                n = nib.Nifti1Image(seg, np.eye(4))\n                n.header[\"pixdim\"][1:4] = pixdims[i]\n                nib.save(n, os.path.join(tempdir, f\"seg{i:d}.nii.gz\"))\n\n            train_images = sorted(glob.glob(os.path.join(tempdir, \"img*.nii.gz\")))\n            train_labels = sorted(glob.glob(os.path.join(tempdir, \"seg*.nii.gz\")))\n            data_dicts = [\n                {\"image\": image_name, \"label\": label_name} for image_name, label_name in zip(train_images, train_labels)\n            ]\n\n            dataset = Dataset(data=data_dicts, transform=LoadImaged(keys=[\"image\", \"label\"]))\n\n            calculator = DatasetSummary(dataset, num_workers=4, meta_key_postfix=\"meta_dict\")\n\n            target_spacing = calculator.get_target_spacing(anisotropic_threshold=4.0, percentile=20.0)\n            np.testing.assert_allclose(target_spacing, (1.0, 1.0, 1.8))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_decollate.py_TestBasicDeCollate.test_decollated_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_decollate.py_TestBasicDeCollate.test_decollated_", "embedding": null, "metadata": {"file_path": "tests/test_decollate.py", "file_name": "test_decollate.py", "file_type": "text/x-python", "category": "test", "start_line": 221, "end_line": 264, "span_ids": ["impl:16", "TestBasicDeCollate.test_decollated"], "tokens": 498}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestBasicDeCollate(unittest.TestCase):\n\n    def test_decollated(self):\n        test_case = {\n            \"image\": torch.tensor([[[1, 2]], [[3, 4]]]),\n            \"meta\": {\"out\": [\"test\", \"test\"]},\n            \"image_meta_dict\": {\"scl_slope\": torch.Tensor((0.0, 0.0))},\n            \"loss\": 0.85,\n        }\n        transform = Decollated(keys=[\"meta\", \"image_meta_dict\"], detach=False)\n        out = transform(test_case)\n        self.assertFalse(\"loss\" in out)\n        self.assertEqual(out[0][\"meta\"][\"out\"], \"test\")\n        self.assertEqual(out[0][\"image_meta_dict\"][\"scl_slope\"], 0.0)\n        self.assertTrue(isinstance(out[0][\"image_meta_dict\"][\"scl_slope\"], torch.Tensor))\n        # decollate all data with keys=None\n        transform = Decollated(keys=None, detach=True)\n        out = transform(test_case)\n        self.assertEqual(out[1][\"loss\"], 0.85)\n        self.assertEqual(out[0][\"meta\"][\"out\"], \"test\")\n        self.assertEqual(out[0][\"image_meta_dict\"][\"scl_slope\"], 0.0)\n        self.assertTrue(isinstance(out[0][\"image_meta_dict\"][\"scl_slope\"], float))\n\n        # test list input\n        test_case = [\n            torch.tensor([[[1, 2]], [[3, 4]]]),\n            {\"out\": [\"test\", \"test\"]},\n            {\"scl_slope\": torch.Tensor((0.0, 0.0))},\n            {\"out2\": [\"test1\"]},\n            0.85,\n            [],\n        ]\n        transform = Decollated(keys=None, detach=False, fill_value=-1)\n        out = transform(test_case)\n\n        self.assertEqual(out[0][-2], 0.85)  # scalar replicates\n        self.assertEqual(out[1][-2], 0.85)  # scalar replicates\n        self.assertEqual(out[1][-3], -1)  # fill value for the dictionary item\n        self.assertEqual(out[0][1][\"out\"], \"test\")\n        self.assertEqual(out[0][2][\"scl_slope\"], 0.0)\n        self.assertTrue(isinstance(out[0][2][\"scl_slope\"], torch.Tensor))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deepedit_transforms.py_unittest_CLICK_RATIO_ADD_RANDOM_GUIDANCE_TEST_CASE_1._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deepedit_transforms.py_unittest_CLICK_RATIO_ADD_RANDOM_GUIDANCE_TEST_CASE_1._", "embedding": null, "metadata": {"file_path": "tests/test_deepedit_transforms.py", "file_name": "test_deepedit_transforms.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 50, "span_ids": ["impl:11", "docstring"], "tokens": 633}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.apps.deepedit.transforms import ClickRatioAddRandomGuidanced, DiscardAddGuidanced, ResizeGuidanceCustomd\n\nIMAGE = np.array([[[[1, 0, 2, 0, 1], [0, 1, 2, 1, 0], [2, 2, 3, 2, 2], [0, 1, 2, 1, 0], [1, 0, 2, 0, 1]]]])\nLABEL = np.array([[[[0, 0, 0, 0, 0], [0, 1, 0, 1, 0], [0, 0, 1, 0, 0], [0, 1, 0, 1, 0], [0, 0, 0, 0, 0]]]])\n\nDATA_1 = {\n    \"image\": IMAGE,\n    \"label\": LABEL,\n    \"image_meta_dict\": {\"dim\": IMAGE.shape},\n    \"label_meta_dict\": {},\n    \"foreground\": [0, 0, 0],\n    \"background\": [0, 0, 0],\n}\n\nDISCARD_ADD_GUIDANCE_TEST_CASE = [{\"image\": IMAGE, \"label\": LABEL}, DATA_1, (3, 1, 5, 5)]\n\nDATA_2 = {\n    \"image\": IMAGE,\n    \"label\": LABEL,\n    \"guidance\": np.array([[[1, 0, 2, 2]], [[-1, -1, -1, -1]]]),\n    \"discrepancy\": np.array(\n        [\n            [[[[0, 0, 0, 0, 0], [0, 0, 0, 1, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]]],\n            [[[[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 1, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]]],\n        ]\n    ),\n    \"probability\": 1.0,\n}\n\nCLICK_RATIO_ADD_RANDOM_GUIDANCE_TEST_CASE_1 = [\n    {\"guidance\": \"guidance\", \"discrepancy\": \"discrepancy\", \"probability\": \"probability\"},\n    DATA_2,\n    \"[[[1, 0, 2, 2], [-1, -1, -1, -1]], [[-1, -1, -1, -1], [1, 0, 2, 1]]]\",\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deepedit_transforms.py_DATA_3_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deepedit_transforms.py_DATA_3_", "embedding": null, "metadata": {"file_path": "tests/test_deepedit_transforms.py", "file_name": "test_deepedit_transforms.py", "file_type": "text/x-python", "category": "test", "start_line": 52, "end_line": 94, "span_ids": ["TestDiscardAddGuidanced.test_correct_results", "impl:11", "TestClickRatioAddRandomGuidanced.test_correct_results", "impl:17", "TestResizeGuidanced", "TestClickRatioAddRandomGuidanced", "TestDiscardAddGuidanced", "TestResizeGuidanced.test_correct_results"], "tokens": 437}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "DATA_3 = {\n    \"image\": np.arange(1000).reshape((1, 5, 10, 20)),\n    \"image_meta_dict\": {\"foreground_cropped_shape\": (1, 10, 20, 40), \"dim\": [3, 512, 512, 128]},\n    \"guidance\": [[[6, 10, 14], [8, 10, 14]], [[8, 10, 16]]],\n    \"foreground\": [[10, 14, 6], [10, 14, 8]],\n    \"background\": [[10, 16, 8]],\n}\n\nRESIZE_GUIDANCE_TEST_CASE_1 = [\n    {\"ref_image\": \"image\", \"guidance\": \"guidance\"},\n    DATA_3,\n    [[[0, 0, 0], [0, 0, 1]], [[0, 0, 1]]],\n]\n\n\nclass TestDiscardAddGuidanced(unittest.TestCase):\n    @parameterized.expand([DISCARD_ADD_GUIDANCE_TEST_CASE])\n    def test_correct_results(self, arguments, input_data, expected_result):\n        add_fn = DiscardAddGuidanced(arguments)\n        result = add_fn(input_data)\n        self.assertEqual(result[\"image\"].shape, expected_result)\n\n\nclass TestClickRatioAddRandomGuidanced(unittest.TestCase):\n    @parameterized.expand([CLICK_RATIO_ADD_RANDOM_GUIDANCE_TEST_CASE_1])\n    def test_correct_results(self, arguments, input_data, expected_result):\n        seed = 0\n        add_fn = ClickRatioAddRandomGuidanced(**arguments)\n        add_fn.set_random_state(seed)\n        result = add_fn(input_data)\n        self.assertEqual(result[arguments[\"guidance\"]], expected_result)\n\n\nclass TestResizeGuidanced(unittest.TestCase):\n    @parameterized.expand([RESIZE_GUIDANCE_TEST_CASE_1])\n    def test_correct_results(self, arguments, input_data, expected_result):\n        result = ResizeGuidanceCustomd(**arguments)(input_data)\n        self.assertEqual(result[arguments[\"guidance\"]], expected_result)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deepgrow_interaction.py_unittest_add_one.if_engine_state_best_metr.else_.engine.state.best_metric.engine_state_best_metric_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deepgrow_interaction.py_unittest_add_one.if_engine_state_best_metr.else_.engine.state.best_metric.engine_state_best_metric_", "embedding": null, "metadata": {"file_path": "tests/test_deepgrow_interaction.py", "file_name": "test_deepgrow_interaction.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 35, "span_ids": ["add_one", "docstring"], "tokens": 160}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\n\nfrom monai.apps.deepgrow.interaction import Interaction\nfrom monai.apps.deepgrow.transforms import (\n    AddGuidanceSignald,\n    AddInitialSeedPointd,\n    AddRandomGuidanced,\n    FindAllValidSlicesd,\n    FindDiscrepancyRegionsd,\n)\nfrom monai.data import Dataset\nfrom monai.engines import SupervisedTrainer\nfrom monai.engines.utils import IterationEvents\nfrom monai.transforms import Activationsd, Compose, ToNumpyd, ToTensord\n\n\ndef add_one(engine):\n    if engine.state.best_metric == -1:\n        engine.state.best_metric = 0\n    else:\n        engine.state.best_metric = engine.state.best_metric + 1", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deepgrow_interaction.py_TestInteractions_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deepgrow_interaction.py_TestInteractions_", "embedding": null, "metadata": {"file_path": "tests/test_deepgrow_interaction.py", "file_name": "test_deepgrow_interaction.py", "file_type": "text/x-python", "category": "test", "start_line": 38, "end_line": 95, "span_ids": ["impl", "TestInteractions.run_interaction", "TestInteractions.test_val_interaction", "TestInteractions", "TestInteractions.test_train_interaction"], "tokens": 550}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestInteractions(unittest.TestCase):\n    def run_interaction(self, train, compose):\n        data = [{\"image\": np.ones((1, 2, 2, 2)).astype(np.float32), \"label\": np.ones((1, 2, 2, 2))} for _ in range(5)]\n        network = torch.nn.Linear(2, 2)\n        lr = 1e-3\n        opt = torch.optim.SGD(network.parameters(), lr)\n        loss = torch.nn.L1Loss()\n        train_transforms = Compose(\n            [\n                FindAllValidSlicesd(label=\"label\", sids=\"sids\"),\n                AddInitialSeedPointd(label=\"label\", guidance=\"guidance\", sids=\"sids\"),\n                AddGuidanceSignald(image=\"image\", guidance=\"guidance\"),\n                ToTensord(keys=(\"image\", \"label\")),\n            ]\n        )\n        dataset = Dataset(data, transform=train_transforms)\n        data_loader = torch.utils.data.DataLoader(dataset, batch_size=5)\n\n        iteration_transforms = [\n            Activationsd(keys=\"pred\", sigmoid=True),\n            ToNumpyd(keys=[\"image\", \"label\", \"pred\"]),\n            FindDiscrepancyRegionsd(label=\"label\", pred=\"pred\", discrepancy=\"discrepancy\"),\n            AddRandomGuidanced(guidance=\"guidance\", discrepancy=\"discrepancy\", probability=\"probability\"),\n            AddGuidanceSignald(image=\"image\", guidance=\"guidance\"),\n            ToTensord(keys=(\"image\", \"label\")),\n        ]\n        iteration_transforms = Compose(iteration_transforms) if compose else iteration_transforms\n\n        i = Interaction(transforms=iteration_transforms, train=train, max_interactions=5)\n        self.assertEqual(len(i.transforms.transforms), 6, \"Mismatch in expected transforms\")\n\n        # set up engine\n        engine = SupervisedTrainer(\n            device=torch.device(\"cpu\"),\n            max_epochs=1,\n            train_data_loader=data_loader,\n            network=network,\n            optimizer=opt,\n            loss_function=loss,\n            iteration_update=i,\n        )\n        engine.add_event_handler(IterationEvents.INNER_ITERATION_STARTED, add_one)\n        engine.add_event_handler(IterationEvents.INNER_ITERATION_COMPLETED, add_one)\n\n        engine.run()\n        self.assertIsNotNone(engine.state.batch[0].get(\"guidance\"), \"guidance is missing\")\n        self.assertEqual(engine.state.best_metric, 9)\n\n    def test_train_interaction(self):\n        self.run_interaction(train=True, compose=True)\n\n    def test_val_interaction(self):\n        self.run_interaction(train=False, compose=False)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deepgrow_transforms.py_unittest_DATA_2._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deepgrow_transforms.py_unittest_DATA_2._", "embedding": null, "metadata": {"file_path": "tests/test_deepgrow_transforms.py", "file_name": "test_deepgrow_transforms.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 54, "span_ids": ["docstring"], "tokens": 691}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.apps.deepgrow.transforms import (\n    AddGuidanceFromPointsd,\n    AddGuidanceSignald,\n    AddInitialSeedPointd,\n    AddRandomGuidanced,\n    Fetch2DSliced,\n    FindAllValidSlicesd,\n    FindDiscrepancyRegionsd,\n    ResizeGuidanced,\n    RestoreLabeld,\n    SpatialCropForegroundd,\n    SpatialCropGuidanced,\n)\n\nIMAGE = np.array([[[[1, 0, 2, 0, 1], [0, 1, 2, 1, 0], [2, 2, 3, 2, 2], [0, 1, 2, 1, 0], [1, 0, 2, 0, 1]]]])\nLABEL = np.array([[[[0, 0, 0, 0, 0], [0, 1, 0, 1, 0], [0, 0, 1, 0, 0], [0, 1, 0, 1, 0], [0, 0, 0, 0, 0]]]])\n\nDATA_1 = {\"image\": IMAGE, \"label\": LABEL, \"image_meta_dict\": {}, \"label_meta_dict\": {}}\n\nDATA_2 = {\n    \"image\": np.array(\n        [\n            [\n                [[1, 2, 3, 2, 1], [1, 1, 3, 2, 1], [0, 0, 0, 0, 0], [1, 1, 1, 2, 1], [0, 2, 2, 2, 1]],\n                [[1, 0, 2, 0, 1], [0, 1, 2, 1, 0], [2, 2, 3, 2, 2], [0, 1, 2, 1, 0], [1, 0, 2, 0, 1]],\n            ]\n        ]\n    ),\n    \"label\": np.array(\n        [\n            [\n                [[0, 0, 1, 0, 0], [0, 1, 1, 1, 0], [1, 1, 1, 1, 1], [0, 1, 1, 1, 0], [0, 0, 1, 0, 0]],\n                [[0, 0, 0, 0, 0], [0, 1, 0, 1, 0], [0, 0, 1, 0, 0], [0, 1, 0, 1, 0], [0, 0, 0, 0, 0]],\n            ]\n        ]\n    ),\n    \"guidance\": np.array([[[1, 0, 2, 2], [1, 1, 2, 2]], [[-1, -1, -1, -1], [-1, -1, -1, -1]]]),\n}", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deepgrow_transforms.py_DATA_3_DATA_9._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deepgrow_transforms.py_DATA_3_DATA_9._", "embedding": null, "metadata": {"file_path": "tests/test_deepgrow_transforms.py", "file_name": "test_deepgrow_transforms.py", "file_type": "text/x-python", "category": "test", "start_line": 56, "end_line": 107, "span_ids": ["impl:9", "impl:19"], "tokens": 724}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "DATA_3 = {\n    \"image\": IMAGE,\n    \"label\": LABEL,\n    \"pred\": np.array([[[[0, 0, 0, 0, 0], [0, 1, 0, 0, 0], [0, 1, 1, 0, 0], [0, 1, 0, 1, 0], [0, 0, 0, 0, 0]]]]),\n}\n\nDATA_4 = {\n    \"image\": IMAGE,\n    \"label\": LABEL,\n    \"guidance\": np.array([[[1, 0, 2, 2]], [[-1, -1, -1, -1]]]),\n    \"discrepancy\": np.array(\n        [\n            [[[[0, 0, 0, 0, 0], [0, 0, 0, 1, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]]],\n            [[[[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 1, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]]],\n        ]\n    ),\n    \"probability\": 1.0,\n}\n\nDATA_5 = {\n    \"image\": np.arange(25).reshape((1, 5, 5)),\n    \"image_meta_dict\": {\"spatial_shape\": [5, 5, 1]},\n    \"foreground\": [[2, 2, 0]],\n    \"background\": [],\n}\n\nDATA_6 = {\n    \"image\": np.arange(25).reshape((1, 5, 5)),\n    \"image_meta_dict\": {\"spatial_shape\": [5, 2, 1]},\n    \"foreground\": [[2, 1, 0]],\n    \"background\": [[1, 0, 0]],\n}\n\nDATA_7 = {\n    \"image\": np.arange(500).reshape((5, 10, 10)),\n    \"image_meta_dict\": {\"spatial_shape\": [20, 20, 10]},\n    \"foreground\": [[10, 14, 6], [10, 14, 8]],\n    \"background\": [[10, 16, 8]],\n    \"slice\": 6,\n}\n\nDATA_8 = {\n    \"image\": np.arange(500).reshape((1, 5, 10, 10)),\n    \"image_meta_dict\": {\"spatial_shape\": [20, 20, 10]},\n    \"guidance\": [[[3, 5, 7], [4, 5, 7]], [[4, 5, 8]]],\n}\n\nDATA_9 = {\n    \"image\": np.arange(1000).reshape((1, 5, 10, 20)),\n    \"image_meta_dict\": {\"foreground_cropped_shape\": (1, 10, 20, 40)},\n    \"guidance\": [[[6, 10, 14], [8, 10, 14]], [[8, 10, 16]]],\n}", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deepgrow_transforms.py_DATA_10_CROP_TEST_CASE_1._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deepgrow_transforms.py_DATA_10_CROP_TEST_CASE_1._", "embedding": null, "metadata": {"file_path": "tests/test_deepgrow_transforms.py", "file_name": "test_deepgrow_transforms.py", "file_type": "text/x-python", "category": "test", "start_line": 109, "end_line": 156, "span_ids": ["impl:27", "impl:19"], "tokens": 732}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "DATA_10 = {\n    \"image\": np.arange(9).reshape((1, 1, 3, 3)),\n    \"image_meta_dict\": {\n        \"spatial_shape\": [3, 3, 1],\n        \"foreground_start_coord\": np.array([0, 0, 0]),\n        \"foreground_end_coord\": np.array([1, 3, 3]),\n        \"foreground_original_shape\": (1, 1, 3, 3),\n        \"foreground_cropped_shape\": (1, 1, 3, 3),\n        \"original_affine\": np.array(\n            [[[1.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 0.0]]]\n        ),\n    },\n    \"pred\": np.array([[[[1, 2, 3], [4, 5, 6], [7, 8, 9]]]]),\n}\n\nDATA_11 = {\n    \"image\": np.arange(500).reshape((1, 5, 10, 10)),\n    \"image_meta_dict\": {\n        \"spatial_shape\": [20, 20, 10],\n        \"foreground_start_coord\": np.array([2, 2, 2]),\n        \"foreground_end_coord\": np.array([4, 4, 4]),\n        \"foreground_original_shape\": (1, 5, 10, 10),\n        \"foreground_cropped_shape\": (1, 2, 2, 2),\n        \"original_affine\": np.array(\n            [[[1.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 0.0]]]\n        ),\n    },\n    \"pred\": np.array([[[[1, 2], [3, 4]], [[5, 6], [7, 8]]]]),\n}\n\nDATA_12 = {\"image\": np.arange(27).reshape(3, 3, 3), \"image_meta_dict\": {}, \"guidance\": [[0, 0, 0], [0, 1, 1], 1]}\n\nFIND_SLICE_TEST_CASE_1 = [{\"label\": \"label\", \"sids\": \"sids\"}, DATA_1, [0]]\n\nFIND_SLICE_TEST_CASE_2 = [{\"label\": \"label\", \"sids\": \"sids\"}, DATA_2, [0, 1]]\n\nCROP_TEST_CASE_1 = [\n    {\n        \"keys\": [\"image\", \"label\"],\n        \"source_key\": \"label\",\n        \"select_fn\": lambda x: x > 0,\n        \"channel_indices\": None,\n        \"margin\": 0,\n        \"spatial_size\": [1, 4, 4],\n    },\n    DATA_1,\n    np.array([[[[1, 2, 1], [2, 3, 2], [1, 2, 1]]]]),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deepgrow_transforms.py_CROP_TEST_CASE_2_ADD_INITIAL_POINT_TEST_CASE_1._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deepgrow_transforms.py_CROP_TEST_CASE_2_ADD_INITIAL_POINT_TEST_CASE_1._", "embedding": null, "metadata": {"file_path": "tests/test_deepgrow_transforms.py", "file_name": "test_deepgrow_transforms.py", "file_type": "text/x-python", "category": "test", "start_line": 158, "end_line": 175, "span_ids": ["impl:27"], "tokens": 159}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "CROP_TEST_CASE_2 = [\n    {\n        \"keys\": [\"image\", \"label\"],\n        \"source_key\": \"label\",\n        \"select_fn\": lambda x: x > 0,\n        \"channel_indices\": None,\n        \"margin\": 0,\n        \"spatial_size\": [2, 4, 4],\n    },\n    DATA_1,\n    np.array([1, 1, 4, 4]),\n]\n\nADD_INITIAL_POINT_TEST_CASE_1 = [\n    {\"label\": \"label\", \"guidance\": \"guidance\", \"sids\": \"sids\"},\n    DATA_1,\n    \"[[[1, 0, 2, 2]], [[-1, -1, -1, -1]]]\",\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deepgrow_transforms.py_FIND_DISCREPANCY_TEST_CASE_1_SPATIAL_CROP_GUIDANCE_TEST_CASE_1._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deepgrow_transforms.py_FIND_DISCREPANCY_TEST_CASE_1_SPATIAL_CROP_GUIDANCE_TEST_CASE_1._", "embedding": null, "metadata": {"file_path": "tests/test_deepgrow_transforms.py", "file_name": "test_deepgrow_transforms.py", "file_type": "text/x-python", "category": "test", "start_line": 210, "end_line": 273, "span_ids": ["impl:41", "impl:51"], "tokens": 748}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "FIND_DISCREPANCY_TEST_CASE_1 = [\n    {\"label\": \"label\", \"pred\": \"pred\", \"discrepancy\": \"discrepancy\"},\n    DATA_3,\n    np.array(\n        [\n            [[[[0, 0, 0, 0, 0], [0, 0, 0, 1, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]]],\n            [[[[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 1, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]]],\n        ]\n    ),\n]\n\nADD_RANDOM_GUIDANCE_TEST_CASE_1 = [\n    {\"guidance\": \"guidance\", \"discrepancy\": \"discrepancy\", \"probability\": \"probability\"},\n    DATA_4,\n    \"[[[1, 0, 2, 2], [1, 0, 1, 3]], [[-1, -1, -1, -1], [-1, -1, -1, -1]]]\",\n]\n\nADD_GUIDANCE_FROM_POINTS_TEST_CASE_1 = [\n    {\"ref_image\": \"image\", \"dimensions\": 3, \"guidance\": \"guidance\", \"depth_first\": True},\n    DATA_5,\n    [[0, 2, 2]],\n    [],\n]\n\nADD_GUIDANCE_FROM_POINTS_TEST_CASE_2 = [\n    {\"ref_image\": \"image\", \"dimensions\": 3, \"guidance\": \"guidance\", \"depth_first\": True},\n    DATA_6,\n    [[0, 2, 2]],\n    [[0, 1, 0]],\n]\n\nADD_GUIDANCE_FROM_POINTS_TEST_CASE_3 = [\n    {\"ref_image\": \"image\", \"dimensions\": 3, \"guidance\": \"guidance\", \"depth_first\": True},\n    DATA_7,\n    [[3, 5, 7], [4, 5, 7]],\n    [[4, 5, 8]],\n]\n\nADD_GUIDANCE_FROM_POINTS_TEST_CASE_4 = [\n    {\"ref_image\": \"image\", \"dimensions\": 2, \"guidance\": \"guidance\", \"depth_first\": True},\n    DATA_6,\n    [[2, 2]],\n    [[1, 0]],\n]\n\nADD_GUIDANCE_FROM_POINTS_TEST_CASE_5 = [\n    {\"ref_image\": \"image\", \"dimensions\": 2, \"guidance\": \"guidance\", \"depth_first\": True, \"slice_key\": \"slice\"},\n    DATA_7,\n    [[5, 7]],\n    [],\n]\n\nADD_GUIDANCE_FROM_POINTS_TEST_CASE_6 = [\n    {\"ref_image\": \"image\", \"dimensions\": 2, \"guidance\": \"guidance\", \"depth_first\": True},\n    DATA_5,\n    [[2, 2]],\n    [],\n]\n\nSPATIAL_CROP_GUIDANCE_TEST_CASE_1 = [\n    {\"keys\": [\"image\"], \"guidance\": \"guidance\", \"spatial_size\": [1, 4, 4], \"margin\": 0},\n    DATA_8,\n    np.array([[[[357, 358]], [[457, 458]]]]),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deepgrow_transforms.py_SPATIAL_CROP_GUIDANCE_TEST_CASE_2_RESULT.np_zeros_10_20_20_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deepgrow_transforms.py_SPATIAL_CROP_GUIDANCE_TEST_CASE_2_RESULT.np_zeros_10_20_20_", "embedding": null, "metadata": {"file_path": "tests/test_deepgrow_transforms.py", "file_name": "test_deepgrow_transforms.py", "file_type": "text/x-python", "category": "test", "start_line": 275, "end_line": 317, "span_ids": ["impl:63", "impl:51"], "tokens": 522}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "SPATIAL_CROP_GUIDANCE_TEST_CASE_2 = [\n    {\"keys\": [\"image\"], \"guidance\": \"guidance\", \"spatial_size\": [2, 2], \"margin\": 1},\n    DATA_8,\n    np.array(\n        [\n            [\n                [[246, 247, 248, 249], [256, 257, 258, 259], [266, 267, 268, 269]],\n                [[346, 347, 348, 349], [356, 357, 358, 359], [366, 367, 368, 369]],\n                [[446, 447, 448, 449], [456, 457, 458, 459], [466, 467, 468, 469]],\n            ]\n        ]\n    ),\n]\n\nSPATIAL_CROP_GUIDANCE_TEST_CASE_3 = [\n    {\"keys\": [\"image\"], \"guidance\": \"guidance\", \"spatial_size\": [3, 3], \"margin\": 0},\n    DATA_8,\n    np.array(\n        [\n            [\n                [[47, 48, 49], [57, 58, 59], [67, 68, 69]],\n                [[147, 148, 149], [157, 158, 159], [167, 168, 169]],\n                [[247, 248, 249], [257, 258, 259], [267, 268, 269]],\n                [[347, 348, 349], [357, 358, 359], [367, 368, 369]],\n                [[447, 448, 449], [457, 458, 459], [467, 468, 469]],\n            ]\n        ]\n    ),\n]\n\nRESIZE_GUIDANCE_TEST_CASE_1 = [\n    {\"ref_image\": \"image\", \"guidance\": \"guidance\"},\n    DATA_9,\n    [[[3, 5, 7], [4, 5, 7]], [[4, 5, 8]]],\n]\n\nRESTORE_LABEL_TEST_CASE_1 = [\n    {\"keys\": [\"pred\"], \"ref_image\": \"image\", \"mode\": \"nearest\"},\n    DATA_10,\n    np.array([[[1, 2, 3], [4, 5, 6], [7, 8, 9]]]),\n]\n\nRESULT = np.zeros((10, 20, 20))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deepgrow_transforms.py_TestAddInitialSeedPointd_TestAddRandomGuidanced.test_correct_results.self_assertEqual_result_a": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deepgrow_transforms.py_TestAddInitialSeedPointd_TestAddRandomGuidanced.test_correct_results.self_assertEqual_result_a", "embedding": null, "metadata": {"file_path": "tests/test_deepgrow_transforms.py", "file_name": "test_deepgrow_transforms.py", "file_type": "text/x-python", "category": "test", "start_line": 367, "end_line": 398, "span_ids": ["TestAddGuidanceSignald.test_correct_results", "TestAddInitialSeedPointd.test_correct_results", "TestAddRandomGuidanced.test_correct_results", "TestFindDiscrepancyRegionsd.test_correct_results", "TestAddInitialSeedPointd", "TestFindDiscrepancyRegionsd", "TestAddGuidanceSignald", "TestAddRandomGuidanced"], "tokens": 327}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestAddInitialSeedPointd(unittest.TestCase):\n    @parameterized.expand([ADD_INITIAL_POINT_TEST_CASE_1])\n    def test_correct_results(self, arguments, input_data, expected_result):\n        seed = 0\n        add_fn = AddInitialSeedPointd(**arguments)\n        add_fn.set_random_state(seed)\n        result = add_fn(input_data)\n        self.assertEqual(result[arguments[\"guidance\"]], expected_result)\n\n\nclass TestAddGuidanceSignald(unittest.TestCase):\n    @parameterized.expand([ADD_GUIDANCE_TEST_CASE_1])\n    def test_correct_results(self, arguments, input_data, expected_result):\n        result = AddGuidanceSignald(**arguments)(input_data)\n        np.testing.assert_allclose(result[\"image\"], expected_result, rtol=1e-5)\n\n\nclass TestFindDiscrepancyRegionsd(unittest.TestCase):\n    @parameterized.expand([FIND_DISCREPANCY_TEST_CASE_1])\n    def test_correct_results(self, arguments, input_data, expected_result):\n        result = FindDiscrepancyRegionsd(**arguments)(input_data)\n        np.testing.assert_allclose(result[arguments[\"discrepancy\"]], expected_result)\n\n\nclass TestAddRandomGuidanced(unittest.TestCase):\n    @parameterized.expand([ADD_RANDOM_GUIDANCE_TEST_CASE_1])\n    def test_correct_results(self, arguments, input_data, expected_result):\n        seed = 0\n        add_fn = AddRandomGuidanced(**arguments)\n        add_fn.set_random_state(seed)\n        result = add_fn(input_data)\n        self.assertEqual(result[arguments[\"guidance\"]], expected_result)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_delete_itemsd.py_sys_TEST_CASE_3._keys_meta_dict_0008": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_delete_itemsd.py_sys_TEST_CASE_3._keys_meta_dict_0008", "embedding": null, "metadata": {"file_path": "tests/test_delete_itemsd.py", "file_name": "test_delete_itemsd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 24, "span_ids": ["docstring"], "tokens": 109}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import sys\nimport time\nimport unittest\n\nfrom parameterized import parameterized\n\nfrom monai.transforms import DeleteItemsd\n\nTEST_CASE_1 = [{\"keys\": [str(i) for i in range(30)]}, 20]\n\nTEST_CASE_2 = [{\"keys\": [\"image/\" + str(i) for i in range(30)], \"sep\": \"/\"}, 20]\n\nTEST_CASE_3 = [{\"keys\": \"meta_dict%0008\\\\|[0-9]\", \"sep\": \"%\", \"use_re\": True}]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_delete_itemsd.py_TestDeleteItemsd_TestDeleteItemsd.test_memory.self_assertGreaterEqual_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_delete_itemsd.py_TestDeleteItemsd_TestDeleteItemsd.test_memory.self_assertGreaterEqual_", "embedding": null, "metadata": {"file_path": "tests/test_delete_itemsd.py", "file_name": "test_delete_itemsd.py", "file_type": "text/x-python", "category": "test", "start_line": 27, "end_line": 43, "span_ids": ["TestDeleteItemsd", "TestDeleteItemsd.test_memory"], "tokens": 191}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestDeleteItemsd(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2])\n    def test_memory(self, input_param, expected_key_size):\n        input_data = {\"image\": {}} if \"sep\" in input_param else {}\n        for i in range(50):\n            if \"sep\" in input_param:\n                input_data[\"image\"][str(i)] = [time.time()] * 100000\n            else:\n                input_data[str(i)] = [time.time()] * 100000\n        result = DeleteItemsd(**input_param)(input_data)\n        if \"sep\" in input_param:\n            self.assertEqual(len(result[\"image\"].keys()), expected_key_size)\n        else:\n            self.assertEqual(len(result.keys()), expected_key_size)\n        self.assertGreaterEqual(\n            sys.getsizeof(input_data) * float(expected_key_size) / len(input_data), sys.getsizeof(result)\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_delete_itemsd.py_TestDeleteItemsd.test_re_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_delete_itemsd.py_TestDeleteItemsd.test_re_", "embedding": null, "metadata": {"file_path": "tests/test_delete_itemsd.py", "file_name": "test_delete_itemsd.py", "file_type": "text/x-python", "category": "test", "start_line": 45, "end_line": 55, "span_ids": ["TestDeleteItemsd.test_re", "impl:7"], "tokens": 128}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestDeleteItemsd(unittest.TestCase):\n\n    @parameterized.expand([TEST_CASE_3])\n    def test_re(self, input_param):\n        input_data = {\"image\": [1, 2, 3], \"meta_dict\": {\"0008|0005\": 1, \"0008|1050\": 2, \"0008test\": 3}}\n        result = DeleteItemsd(**input_param)(input_data)\n        self.assertEqual(result[\"meta_dict\"][\"0008test\"], 3)\n        self.assertTrue(len(result[\"meta_dict\"]), 1)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deprecated.py_unittest_TestDeprecatedRC.test_warning_beyond.self_assertRaises_Depreca": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deprecated.py_unittest_TestDeprecatedRC.test_warning_beyond.self_assertRaises_Depreca", "embedding": null, "metadata": {"file_path": "tests/test_deprecated.py", "file_name": "test_deprecated.py", "file_type": "text/x-python", "category": "test", "start_line": 13, "end_line": 59, "span_ids": ["TestDeprecatedRC", "TestDeprecatedRC.setUp", "docstring", "TestDeprecatedRC.test_warning_last", "TestDeprecatedRC.test_warning_beyond", "TestDeprecatedRC.test_warning_milestone", "TestDeprecatedRC.test_warning"], "tokens": 339}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nimport warnings\n\nfrom monai.utils import DeprecatedError, deprecated, deprecated_arg\n\n\nclass TestDeprecatedRC(unittest.TestCase):\n    def setUp(self):\n        self.test_version_rc = \"0.6.0rc1\"\n        self.test_version = \"0.6.0\"\n        self.next_version = \"0.7.0\"\n\n    def test_warning(self):\n        \"\"\"Test deprecated decorator with `since` and `removed` set for an RC version\"\"\"\n\n        @deprecated(since=self.test_version, removed=self.next_version, version_val=self.test_version_rc)\n        def foo2():\n            pass\n\n        foo2()  # should not raise any warnings\n\n    def test_warning_milestone(self):\n        \"\"\"Test deprecated decorator with `since` and `removed` set for a milestone version\"\"\"\n\n        @deprecated(since=self.test_version, removed=self.next_version, version_val=self.test_version)\n        def foo2():\n            pass\n\n        self.assertWarns(DeprecationWarning, foo2)\n\n    def test_warning_last(self):\n        \"\"\"Test deprecated decorator with `since` and `removed` set, for the last version\"\"\"\n\n        @deprecated(since=self.test_version, removed=self.next_version, version_val=self.next_version)\n        def foo3():\n            pass\n\n        self.assertRaises(DeprecatedError, foo3)\n\n    def test_warning_beyond(self):\n        \"\"\"Test deprecated decorator with `since` and `removed` set, beyond the last version\"\"\"\n\n        @deprecated(since=self.test_version_rc, removed=self.test_version, version_val=self.next_version)\n        def foo3():\n            pass\n\n        self.assertRaises(DeprecatedError, foo3)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deprecated.py_TestDeprecated_TestDeprecated.test_arg_warn1.self_assertWarns_Deprecat": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deprecated.py_TestDeprecated_TestDeprecated.test_arg_warn1.self_assertWarns_Deprecat", "embedding": null, "metadata": {"file_path": "tests/test_deprecated.py", "file_name": "test_deprecated.py", "file_type": "text/x-python", "category": "test", "start_line": 62, "end_line": 169, "span_ids": ["TestDeprecated.setUp", "TestDeprecated.test_except1", "TestDeprecated.test_meth_except1", "TestDeprecated.test_meth_warning1", "TestDeprecated.test_class_warning1", "TestDeprecated.test_class_except1", "TestDeprecated", "TestDeprecated.test_class_warning2", "TestDeprecated.test_warning2", "TestDeprecated.test_class_except2", "TestDeprecated.test_warning1", "TestDeprecated.test_except2", "TestDeprecated.test_arg_warn1"], "tokens": 744}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestDeprecated(unittest.TestCase):\n    def setUp(self):\n        self.test_version = \"0.5.3+96.g1fa03c2.dirty\"\n        self.prev_version = \"0.4.3+96.g1fa03c2.dirty\"\n        self.next_version = \"0.6.3+96.g1fa03c2.dirty\"\n\n    def test_warning1(self):\n        \"\"\"Test deprecated decorator with just `since` set.\"\"\"\n\n        @deprecated(since=self.prev_version, version_val=self.test_version)\n        def foo1():\n            pass\n\n        self.assertWarns(DeprecationWarning, foo1)\n\n    def test_warning2(self):\n        \"\"\"Test deprecated decorator with `since` and `removed` set.\"\"\"\n\n        @deprecated(since=self.prev_version, removed=self.next_version, version_val=self.test_version)\n        def foo2():\n            pass\n\n        self.assertWarns(DeprecationWarning, foo2)\n\n    def test_except1(self):\n        \"\"\"Test deprecated decorator raises exception with no versions set.\"\"\"\n\n        @deprecated(version_val=self.test_version)\n        def foo3():\n            pass\n\n        self.assertRaises(DeprecatedError, foo3)\n\n    def test_except2(self):\n        \"\"\"Test deprecated decorator raises exception with `removed` set in the past.\"\"\"\n\n        @deprecated(removed=self.prev_version, version_val=self.test_version)\n        def foo4():\n            pass\n\n        self.assertRaises(DeprecatedError, foo4)\n\n    def test_class_warning1(self):\n        \"\"\"Test deprecated decorator with just `since` set.\"\"\"\n\n        @deprecated(since=self.prev_version, version_val=self.test_version)\n        class Foo1:\n            pass\n\n        self.assertWarns(DeprecationWarning, Foo1)\n\n    def test_class_warning2(self):\n        \"\"\"Test deprecated decorator with `since` and `removed` set.\"\"\"\n\n        @deprecated(since=self.prev_version, removed=self.next_version, version_val=self.test_version)\n        class Foo2:\n            pass\n\n        self.assertWarns(DeprecationWarning, Foo2)\n\n    def test_class_except1(self):\n        \"\"\"Test deprecated decorator raises exception with no versions set.\"\"\"\n\n        @deprecated(version_val=self.test_version)\n        class Foo3:\n            pass\n\n        self.assertRaises(DeprecatedError, Foo3)\n\n    def test_class_except2(self):\n        \"\"\"Test deprecated decorator raises exception with `removed` set in the past.\"\"\"\n\n        @deprecated(removed=self.prev_version, version_val=self.test_version)\n        class Foo4:\n            pass\n\n        self.assertRaises(DeprecatedError, Foo4)\n\n    def test_meth_warning1(self):\n        \"\"\"Test deprecated decorator with just `since` set.\"\"\"\n\n        class Foo5:\n            @deprecated(since=self.prev_version, version_val=self.test_version)\n            def meth1(self):\n                pass\n\n        self.assertWarns(DeprecationWarning, lambda: Foo5().meth1())\n\n    def test_meth_except1(self):\n        \"\"\"Test deprecated decorator with just `since` set.\"\"\"\n\n        class Foo6:\n            @deprecated(version_val=self.test_version)\n            def meth1(self):\n                pass\n\n        self.assertRaises(DeprecatedError, lambda: Foo6().meth1())\n\n    def test_arg_warn1(self):\n        \"\"\"Test deprecated_arg decorator with just `since` set.\"\"\"\n\n        @deprecated_arg(\"b\", since=self.prev_version, version_val=self.test_version)\n        def afoo1(a, b=None):\n            pass\n\n        afoo1(1)  # ok when no b provided\n\n        self.assertWarns(DeprecationWarning, lambda: afoo1(1, 2))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deprecated.py_TestDeprecated.test_2arg_warn1_TestDeprecated.test_2arg_warn1.None_2": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deprecated.py_TestDeprecated.test_2arg_warn1_TestDeprecated.test_2arg_warn1.None_2", "embedding": null, "metadata": {"file_path": "tests/test_deprecated.py", "file_name": "test_deprecated.py", "file_type": "text/x-python", "category": "test", "start_line": 200, "end_line": 211, "span_ids": ["TestDeprecated.test_2arg_warn1"], "tokens": 147}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestDeprecated(unittest.TestCase):\n\n    def test_2arg_warn1(self):\n        \"\"\"Test deprecated_arg decorator applied twice with just `since` set.\"\"\"\n\n        @deprecated_arg(\"b\", since=self.prev_version, version_val=self.test_version)\n        @deprecated_arg(\"c\", since=self.prev_version, version_val=self.test_version)\n        def afoo5(a, b=None, c=None):\n            pass\n\n        afoo5(1)  # ok when no b or c provided\n\n        self.assertWarns(DeprecationWarning, lambda: afoo5(1, 2))\n        self.assertWarns(DeprecationWarning, lambda: afoo5(1, 2, 3))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deprecated.py_TestDeprecated.test_future_TestDeprecated.test_arg_except2_unknown.self_assertRaises_Depreca": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deprecated.py_TestDeprecated.test_future_TestDeprecated.test_arg_except2_unknown.self_assertRaises_Depreca", "embedding": null, "metadata": {"file_path": "tests/test_deprecated.py", "file_name": "test_deprecated.py", "file_type": "text/x-python", "category": "test", "start_line": 213, "end_line": 236, "span_ids": ["TestDeprecated.test_future", "TestDeprecated.test_arg_except2_unknown"], "tokens": 188}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestDeprecated(unittest.TestCase):\n\n    def test_future(self):\n        \"\"\"Test deprecated decorator with `since` set to a future version.\"\"\"\n\n        @deprecated(since=self.next_version, version_val=self.test_version)\n        def future1():\n            pass\n\n        with self.assertWarns(DeprecationWarning) as aw:\n            future1()\n            warnings.warn(\"fake warning\", DeprecationWarning)\n\n        self.assertEqual(aw.warning.args[0], \"fake warning\")\n\n    def test_arg_except2_unknown(self):\n        \"\"\"\n        Test deprecated_arg decorator raises exception with `removed` set in the past.\n        with unknown version\n        \"\"\"\n\n        @deprecated_arg(\"b\", removed=self.prev_version, version_val=\"0+untagged.1.g3131155\")\n        def afoo4(a, b=None):\n            pass\n\n        self.assertRaises(DeprecatedError, lambda: afoo4(1, b=2))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_divisible_pad.py_unittest_for_p_in_TEST_NDARRAYS_.TESTS_append_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_divisible_pad.py_unittest_for_p_in_TEST_NDARRAYS_.TESTS_append_", "embedding": null, "metadata": {"file_path": "tests/test_divisible_pad.py", "file_name": "test_divisible_pad.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 30, "span_ids": ["docstring"], "tokens": 180}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import DivisiblePad\nfrom tests.utils import TEST_NDARRAYS\n\nTESTS = []\n\nfor p in TEST_NDARRAYS:\n    # pad first dim to be divisible by 7, the second unchanged.\n    TESTS.append([{\"k\": (7, -1), \"mode\": \"constant\"}, p(np.zeros((3, 8, 7))), p(np.zeros((3, 14, 7)))])\n\n    # pad all dimensions to be divisible by 5\n    TESTS.append(\n        [{\"k\": 5, \"mode\": \"constant\", \"method\": \"end\"}, p(np.zeros((3, 10, 5, 17))), p(np.zeros((3, 10, 5, 20)))]\n    )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_divisible_pad.py_TestDivisiblePad_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_divisible_pad.py_TestDivisiblePad_", "embedding": null, "metadata": {"file_path": "tests/test_divisible_pad.py", "file_name": "test_divisible_pad.py", "file_type": "text/x-python", "category": "test", "start_line": 33, "end_line": 55, "span_ids": ["TestDivisiblePad.test_pad_kwargs", "TestDivisiblePad.test_pad_shape", "impl:6", "TestDivisiblePad"], "tokens": 281}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestDivisiblePad(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_pad_shape(self, input_param, input_data, expected_val):\n        padder = DivisiblePad(**input_param)\n        result = padder(input_data)\n        self.assertAlmostEqual(result.shape, expected_val.shape)\n        result = padder(input_data, mode=input_param[\"mode\"])\n        self.assertAlmostEqual(result.shape, expected_val.shape)\n\n    def test_pad_kwargs(self):\n        for p in TEST_NDARRAYS:\n            input_data = p(np.zeros((3, 8, 4)))\n            if isinstance(input_data, np.ndarray):\n                result = DivisiblePad(k=5, mode=\"constant\", constant_values=((0, 0), (1, 1), (2, 2)))(input_data)\n                np.testing.assert_allclose(result[:, :1, :4], np.ones((3, 1, 4)), rtol=1e-7, atol=0)\n            else:\n                result = DivisiblePad(k=5, mode=\"constant\", value=2)(input_data).cpu()\n            torch.testing.assert_allclose(result[:, :, 4:5], np.ones((3, 10, 1)) + 1, rtol=1e-7, atol=0)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_download_and_extract.py_os_TestDownloadAndExtract.test_actions.None_1.except_RuntimeError_as_e_.self_assertTrue_str_e_st": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_download_and_extract.py_os_TestDownloadAndExtract.test_actions.None_1.except_RuntimeError_as_e_.self_assertTrue_str_e_st", "embedding": null, "metadata": {"file_path": "tests/test_download_and_extract.py", "file_name": "test_download_and_extract.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 54, "span_ids": ["TestDownloadAndExtract.test_actions", "TestDownloadAndExtract", "docstring"], "tokens": 417}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport tempfile\nimport unittest\nfrom pathlib import Path\nfrom urllib.error import ContentTooShortError, HTTPError\n\nfrom monai.apps import download_and_extract, download_url, extractall\nfrom tests.utils import skip_if_quick\n\n\nclass TestDownloadAndExtract(unittest.TestCase):\n    @skip_if_quick\n    def test_actions(self):\n        testing_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"testing_data\")\n        url = \"https://drive.google.com/uc?id=1QsnnkvZyJPcbRoV_ArW8SnE1OTuoVbKE\"\n        filepath = Path(testing_dir) / \"MedNIST.tar.gz\"\n        output_dir = Path(testing_dir)\n        md5_value = \"0bc7306e7427e00ad1c5526a6677552d\"\n        try:\n            download_and_extract(url, filepath, output_dir, md5_value)\n            download_and_extract(url, filepath, output_dir, md5_value)\n        except (ContentTooShortError, HTTPError, RuntimeError) as e:\n            print(str(e))\n            if isinstance(e, RuntimeError):\n                # FIXME: skip MD5 check as current downloading method may fail\n                self.assertTrue(str(e).startswith(\"md5 check\"))\n            return  # skipping this test due the network connection errors\n\n        wrong_md5 = \"0\"\n        with self.assertLogs(logger=\"monai.apps\", level=\"ERROR\"):\n            try:\n                download_url(url, filepath, wrong_md5)\n            except (ContentTooShortError, HTTPError, RuntimeError) as e:\n                print(str(e))\n                if isinstance(e, RuntimeError):\n                    # FIXME: skip MD5 check as current downloading method may fail\n                    self.assertTrue(str(e).startswith(\"md5 check\"))\n                return  # skipping this test due the network connection errors\n\n        try:\n            extractall(filepath, output_dir, wrong_md5)\n        except RuntimeError as e:\n            self.assertTrue(str(e).startswith(\"md5 check\"))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_efficientnet.py_os_get_model_names.return._f_efficientnet_b_d_for": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_efficientnet.py_os_get_model_names.return._f_efficientnet_b_d_for", "embedding": null, "metadata": {"file_path": "tests/test_efficientnet.py", "file_name": "test_efficientnet.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 48, "span_ids": ["get_model_names", "docstring"], "tokens": 247}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport unittest\nfrom typing import TYPE_CHECKING\nfrom unittest import skipUnless\nfrom urllib.error import ContentTooShortError, HTTPError\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks import eval_mode\nfrom monai.networks.nets import (\n    BlockArgs,\n    EfficientNetBN,\n    EfficientNetBNFeatures,\n    drop_connect,\n    get_efficientnet_image_size,\n)\nfrom monai.utils import optional_import\nfrom tests.utils import skip_if_quick, test_pretrained_networks, test_script_save\n\nif TYPE_CHECKING:\n    import torchvision\n\n    has_torchvision = True\nelse:\n    torchvision, has_torchvision = optional_import(\"torchvision\")\n\nif TYPE_CHECKING:\n    import PIL\n\n    has_pil = True\nelse:\n    PIL, has_pil = optional_import(\"PIL\")\n\n\ndef get_model_names():\n    return [f\"efficientnet-b{d}\" for d in range(8)]\nCASES_VARIATIONS.extend(\n    make_shape_cases(\n        models=[SEL_MODELS[0]], spatial_dims=[3], batches=[1], pretrained=[False], in_channels=3, num_classes=10\n    )\n)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_efficientnet.py__create_list_of_selected_None_26": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_efficientnet.py__create_list_of_selected_None_26", "embedding": null, "metadata": {"file_path": "tests/test_efficientnet.py", "file_name": "test_efficientnet.py", "file_type": "text/x-python", "category": "test", "start_line": 117, "end_line": 212, "span_ids": ["impl:11", "impl:21", "make_shape_cases"], "tokens": 752}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "if TYPE_CHECKING:\n# ... other code\n\n\n# create list of selected models to speed up redundant tests\n# only test the models B0, B3, B7\nSEL_MODELS = [get_model_names()[i] for i in [0, 3, 7]]\n\n# pretrained=False cases\n# 1D models are cheap so do test for all models in 1D\nCASES_1D = make_shape_cases(\n    models=get_model_names(), spatial_dims=[1], batches=[1, 4], pretrained=[False], in_channels=3, num_classes=1000\n)\n\n# 2D and 3D models are expensive so use selected models\nCASES_2D = make_shape_cases(\n    models=SEL_MODELS,\n    spatial_dims=[2],\n    batches=[1, 4],\n    pretrained=[False],\n    in_channels=3,\n    num_classes=1000,\n    norm=\"instance\",\n)\nCASES_3D = make_shape_cases(\n    models=[SEL_MODELS[0]],\n    spatial_dims=[3],\n    batches=[1],\n    pretrained=[False],\n    in_channels=3,\n    num_classes=1000,\n    norm=\"batch\",\n)\n\n# pretrained=True cases\n# tabby kitty test with pretrained model\n# needs 'testing_data/kitty_test.jpg'\n# image from: https://commons.wikimedia.org/wiki/File:Tabby_cat_with_blue_eyes-3336579.jpg\nCASES_KITTY_TRAINED = [\n    (\n        {\n            \"model_name\": \"efficientnet-b0\",\n            \"pretrained\": True,\n            \"progress\": False,\n            \"spatial_dims\": 2,\n            \"in_channels\": 3,\n            \"num_classes\": 1000,\n            \"norm\": (\"batch\", {\"eps\": 1e-3, \"momentum\": 0.01}),\n            \"adv_prop\": False,\n        },\n        os.path.join(os.path.dirname(__file__), \"testing_data\", \"kitty_test.jpg\"),\n        282,  # ~ tiger cat\n    ),\n    (\n        {\n            \"model_name\": \"efficientnet-b3\",\n            \"pretrained\": True,\n            \"progress\": False,\n            \"spatial_dims\": 2,\n            \"in_channels\": 3,\n            \"num_classes\": 1000,\n        },\n        os.path.join(os.path.dirname(__file__), \"testing_data\", \"kitty_test.jpg\"),\n        282,  # ~ tiger cat\n    ),\n    (\n        {\n            \"model_name\": \"efficientnet-b7\",\n            \"pretrained\": True,\n            \"progress\": False,\n            \"spatial_dims\": 2,\n            \"in_channels\": 3,\n            \"num_classes\": 1000,\n        },\n        os.path.join(os.path.dirname(__file__), \"testing_data\", \"kitty_test.jpg\"),\n        282,  # ~ tiger cat\n    ),\n]\n\n# varying num_classes and in_channels\nCASES_VARIATIONS = []\n\n# change num_classes test\n# 10 classes\n# 2D\nCASES_VARIATIONS.extend(\n    make_shape_cases(\n        models=SEL_MODELS, spatial_dims=[2], batches=[1], pretrained=[False, True], in_channels=3, num_classes=10\n    )\n)\n# 3D\nCASES_VARIATIONS.extend(\n    make_shape_cases(\n        models=[SEL_MODELS[0]], spatial_dims=[3], batches=[1], pretrained=[False], in_channels=3, num_classes=10\n    )\n)\n\n# change in_channels test\n# 1 channel\n# 2D", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_efficientnet.py_None_2_CASE_EXTRACT_FEATURES._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_efficientnet.py_None_2_CASE_EXTRACT_FEATURES._", "embedding": null, "metadata": {"file_path": "tests/test_efficientnet.py", "file_name": "test_efficientnet.py", "file_type": "text/x-python", "category": "test", "start_line": 213, "end_line": 245, "span_ids": ["impl:21"], "tokens": 297}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "CASES_VARIATIONS.extend(\n    make_shape_cases(\n        models=SEL_MODELS, spatial_dims=[2], batches=[1], pretrained=[False, True], in_channels=1, num_classes=1000\n    )\n)\n# 8 channel\n# 2D\nCASES_VARIATIONS.extend(\n    make_shape_cases(\n        models=SEL_MODELS, spatial_dims=[2], batches=[1], pretrained=[False, True], in_channels=8, num_classes=1000\n    )\n)\n# 3D\nCASES_VARIATIONS.extend(\n    make_shape_cases(\n        models=[SEL_MODELS[0]], spatial_dims=[3], batches=[1], pretrained=[False], in_channels=1, num_classes=1000\n    )\n)\n\nCASE_EXTRACT_FEATURES = [\n    (\n        {\n            \"model_name\": \"efficientnet-b8\",\n            \"pretrained\": True,\n            \"progress\": False,\n            \"spatial_dims\": 2,\n            \"in_channels\": 2,\n            \"adv_prop\": True,\n        },\n        [1, 2, 224, 224],\n        ([1, 32, 112, 112], [1, 56, 56, 56], [1, 88, 28, 28], [1, 248, 14, 14], [1, 704, 7, 7]),\n    )\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_efficientnet.py_TestEFFICIENTNET.test_block_args_decode_TestEFFICIENTNET.test_script.test_script_save_net_tes": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_efficientnet.py_TestEFFICIENTNET.test_block_args_decode_TestEFFICIENTNET.test_script.test_script_save_net_tes", "embedding": null, "metadata": {"file_path": "tests/test_efficientnet.py", "file_name": "test_efficientnet.py", "file_type": "text/x-python", "category": "test", "start_line": 353, "end_line": 381, "span_ids": ["TestEFFICIENTNET.test_ill_arg", "TestEFFICIENTNET.test_func_get_efficientnet_input_shape", "TestEFFICIENTNET.test_script", "TestEFFICIENTNET.test_block_args_decode"], "tokens": 312}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestEFFICIENTNET(unittest.TestCase):\n\n    def test_block_args_decode(self):\n        blocks_args_str = get_block_args()\n\n        # convert strings to BlockArgs\n        blocks_args = [BlockArgs.from_string(s) for s in blocks_args_str]\n        # convert BlockArgs back to string\n        blocks_args_str_convert = [s.to_string() for s in blocks_args]\n\n        # check if converted strings match original\n        [self.assertEqual(original, converted) for original, converted in zip(blocks_args_str, blocks_args_str_convert)]\n\n    def test_ill_arg(self):\n        with self.assertRaises(ValueError):\n            # wrong spatial_dims\n            EfficientNetBN(model_name=\"efficientnet-b0\", spatial_dims=4)\n            # wrong model_name\n            EfficientNetBN(model_name=\"efficientnet-b10\", spatial_dims=3)\n\n    def test_func_get_efficientnet_input_shape(self):\n        for model in get_model_names():\n            result_shape = get_efficientnet_image_size(model_name=model)\n            expected_shape = get_expected_model_shape(model)\n            self.assertEqual(result_shape, expected_shape)\n\n    def test_script(self):\n        net = EfficientNetBN(model_name=\"efficientnet-b0\", spatial_dims=2, in_channels=3, num_classes=1000)\n        net.set_swish(memory_efficient=False)  # at the moment custom memory efficient swish is not exportable with jit\n        test_data = torch.randn(1, 3, 224, 224)\n        test_script_save(net, test_data)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_efficientnet.py_TestExtractFeatures_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_efficientnet.py_TestExtractFeatures_", "embedding": null, "metadata": {"file_path": "tests/test_efficientnet.py", "file_name": "test_efficientnet.py", "file_type": "text/x-python", "category": "test", "start_line": 384, "end_line": 408, "span_ids": ["impl:30", "TestExtractFeatures.test_shape", "TestExtractFeatures"], "tokens": 183}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestExtractFeatures(unittest.TestCase):\n    @parameterized.expand(CASE_EXTRACT_FEATURES)\n    def test_shape(self, input_param, input_shape, expected_shapes):\n        device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\n        try:\n            # initialize model\n            net = EfficientNetBNFeatures(**input_param).to(device)\n        except (ContentTooShortError, HTTPError, RuntimeError) as e:\n            print(str(e))\n            return  # skipping the tests because of http errors\n\n        # run inference with random tensor\n        with eval_mode(net):\n            features = net(torch.randn(input_shape).to(device))\n\n        # check output shape\n        self.assertEqual(len(features), len(expected_shapes))\n        for feature, expected_shape in zip(features, expected_shapes):\n            self.assertEqual(feature.shape, torch.Size(expected_shape))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ensure_channel_first.py_os_TEST_CASE_7._image_only_False_r": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ensure_channel_first.py_os_TEST_CASE_7._image_only_False_r", "embedding": null, "metadata": {"file_path": "tests/test_ensure_channel_first.py", "file_name": "test_ensure_channel_first.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 42, "span_ids": ["docstring"], "tokens": 273}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport tempfile\nimport unittest\n\nimport itk\nimport nibabel as nib\nimport numpy as np\nfrom parameterized import parameterized\nfrom PIL import Image\n\nfrom monai.data import ITKReader\nfrom monai.transforms import EnsureChannelFirst, LoadImage\nfrom tests.utils import TEST_NDARRAYS\n\nTEST_CASE_1 = [{\"image_only\": False}, [\"test_image.nii.gz\"], None]\n\nTEST_CASE_2 = [{\"image_only\": False}, [\"test_image.nii.gz\"], -1]\n\nTEST_CASE_3 = [{\"image_only\": False}, [\"test_image.nii.gz\", \"test_image2.nii.gz\", \"test_image3.nii.gz\"], None]\n\nTEST_CASE_4 = [{\"reader\": ITKReader(), \"image_only\": False}, [\"test_image.nii.gz\"], None]\n\nTEST_CASE_5 = [{\"reader\": ITKReader(), \"image_only\": False}, [\"test_image.nii.gz\"], -1]\n\nTEST_CASE_6 = [\n    {\"reader\": ITKReader(), \"image_only\": False},\n    [\"test_image.nii.gz\", \"test_image2.nii.gz\", \"test_image3.nii.gz\"],\n    None,\n]\n\nTEST_CASE_7 = [{\"image_only\": False, \"reader\": ITKReader(pixel_type=itk.UC)}, \"tests/testing_data/CT_DICOM\", None]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ensure_channel_first.py_TestEnsureChannelFirst_TestEnsureChannelFirst.test_load_nifti.with_tempfile_TemporaryDi.for_p_in_TEST_NDARRAYS_.self_assertEqual_result_s": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ensure_channel_first.py_TestEnsureChannelFirst_TestEnsureChannelFirst.test_load_nifti.with_tempfile_TemporaryDi.for_p_in_TEST_NDARRAYS_.self_assertEqual_result_s", "embedding": null, "metadata": {"file_path": "tests/test_ensure_channel_first.py", "file_name": "test_ensure_channel_first.py", "file_type": "text/x-python", "category": "test", "start_line": 45, "end_line": 60, "span_ids": ["TestEnsureChannelFirst.test_load_nifti", "TestEnsureChannelFirst"], "tokens": 217}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestEnsureChannelFirst(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5, TEST_CASE_6])\n    def test_load_nifti(self, input_param, filenames, original_channel_dim):\n        if original_channel_dim is None:\n            test_image = np.random.rand(128, 128, 128)\n        elif original_channel_dim == -1:\n            test_image = np.random.rand(128, 128, 128, 1)\n\n        with tempfile.TemporaryDirectory() as tempdir:\n            for i, name in enumerate(filenames):\n                filenames[i] = os.path.join(tempdir, name)\n                nib.save(nib.Nifti1Image(test_image, np.eye(4)), filenames[i])\n            for p in TEST_NDARRAYS:\n                result, header = LoadImage(**input_param)(filenames)\n                result = EnsureChannelFirst()(p(result), header)\n                self.assertEqual(result.shape[0], len(filenames))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ensure_channel_firstd.py_os_TEST_CASE_3._keys_img_test_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ensure_channel_firstd.py_os_TEST_CASE_3._keys_img_test_", "embedding": null, "metadata": {"file_path": "tests/test_ensure_channel_firstd.py", "file_name": "test_ensure_channel_firstd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 28, "span_ids": ["docstring"], "tokens": 128}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport tempfile\nimport unittest\n\nimport nibabel as nib\nimport numpy as np\nfrom parameterized import parameterized\nfrom PIL import Image\n\nfrom monai.transforms import EnsureChannelFirstd, LoadImaged\nfrom tests.utils import TEST_NDARRAYS\n\nTEST_CASE_1 = [{\"keys\": \"img\"}, [\"test_image.nii.gz\"], None]\n\nTEST_CASE_2 = [{\"keys\": \"img\"}, [\"test_image.nii.gz\"], -1]\n\nTEST_CASE_3 = [{\"keys\": \"img\"}, [\"test_image.nii.gz\", \"test_image2.nii.gz\", \"test_image3.nii.gz\"], None]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ensure_channel_firstd.py_TestEnsureChannelFirstd_TestEnsureChannelFirstd.test_load_nifti.with_tempfile_TemporaryDi.for_p_in_TEST_NDARRAYS_.self_assertEqual_result_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ensure_channel_firstd.py_TestEnsureChannelFirstd_TestEnsureChannelFirstd.test_load_nifti.with_tempfile_TemporaryDi.for_p_in_TEST_NDARRAYS_.self_assertEqual_result_", "embedding": null, "metadata": {"file_path": "tests/test_ensure_channel_firstd.py", "file_name": "test_ensure_channel_firstd.py", "file_type": "text/x-python", "category": "test", "start_line": 31, "end_line": 47, "span_ids": ["TestEnsureChannelFirstd.test_load_nifti", "TestEnsureChannelFirstd"], "tokens": 219}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestEnsureChannelFirstd(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3])\n    def test_load_nifti(self, input_param, filenames, original_channel_dim):\n        if original_channel_dim is None:\n            test_image = np.random.rand(128, 128, 128)\n        elif original_channel_dim == -1:\n            test_image = np.random.rand(128, 128, 128, 1)\n\n        with tempfile.TemporaryDirectory() as tempdir:\n            for i, name in enumerate(filenames):\n                filenames[i] = os.path.join(tempdir, name)\n                nib.save(nib.Nifti1Image(test_image, np.eye(4)), filenames[i])\n            for p in TEST_NDARRAYS:\n                result = LoadImaged(**input_param)({\"img\": filenames})\n                result[\"img\"] = p(result[\"img\"])\n                result = EnsureChannelFirstd(**input_param)(result)\n                self.assertEqual(result[\"img\"].shape[0], len(filenames))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ensure_type.py_unittest_TestEnsureType.test_array_input.for_test_data_in_test_dat.for_dtype_in_tensor_.self_assertTupleEqual_res": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ensure_type.py_unittest_TestEnsureType.test_array_input.for_test_data_in_test_dat.for_dtype_in_tensor_.self_assertTupleEqual_res", "embedding": null, "metadata": {"file_path": "tests/test_ensure_type.py", "file_name": "test_ensure_type.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 33, "span_ids": ["TestEnsureType.test_array_input", "TestEnsureType", "docstring"], "tokens": 205}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\n\nfrom monai.transforms import EnsureType\nfrom tests.utils import assert_allclose\n\n\nclass TestEnsureType(unittest.TestCase):\n    def test_array_input(self):\n        test_datas = [np.array([[1, 2], [3, 4]]), torch.as_tensor([[1, 2], [3, 4]])]\n        if torch.cuda.is_available():\n            test_datas.append(test_datas[-1].cuda())\n        for test_data in test_datas:\n            for dtype in (\"tensor\", \"NUMPY\"):\n                result = EnsureType(dtype, dtype=np.float32 if dtype == \"NUMPY\" else None, device=\"cpu\")(test_data)\n                if dtype == \"NUMPY\":\n                    self.assertTrue(result.dtype == np.float32)\n                self.assertTrue(isinstance(result, torch.Tensor if dtype == \"tensor\" else np.ndarray))\n                assert_allclose(result, test_data, type_test=False)\n                self.assertTupleEqual(result.shape, (2, 2))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ensure_type.py_TestEnsureType.test_single_input_TestEnsureType.test_string.for_dtype_in_tensor_.self_assertEqual_result_1": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ensure_type.py_TestEnsureType.test_single_input_TestEnsureType.test_string.for_dtype_in_tensor_.self_assertEqual_result_1", "embedding": null, "metadata": {"file_path": "tests/test_ensure_type.py", "file_name": "test_ensure_type.py", "file_type": "text/x-python", "category": "test", "start_line": 35, "end_line": 58, "span_ids": ["TestEnsureType.test_single_input", "TestEnsureType.test_string"], "tokens": 248}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestEnsureType(unittest.TestCase):\n\n    def test_single_input(self):\n        test_datas = [5, 5.0, False, np.asarray(5), torch.tensor(5)]\n        if torch.cuda.is_available():\n            test_datas.append(test_datas[-1].cuda())\n        for test_data in test_datas:\n            for dtype in (\"tensor\", \"numpy\"):\n                result = EnsureType(data_type=dtype, device=\"cpu\")(test_data)\n                self.assertTrue(isinstance(result, torch.Tensor if dtype == \"tensor\" else np.ndarray))\n                if isinstance(test_data, bool):\n                    self.assertFalse(result)\n                else:\n                    assert_allclose(result, test_data, type_test=False)\n                self.assertEqual(result.ndim, 0)\n\n    def test_string(self):\n        for dtype in (\"tensor\", \"numpy\"):\n            # string input\n            result = EnsureType(data_type=dtype)(\"test_string\")\n            self.assertTrue(isinstance(result, str))\n            self.assertEqual(result, \"test_string\")\n            # numpy array of string\n            result = EnsureType(data_type=dtype)(np.array([\"test_string0\", \"test_string1\"]))\n            self.assertTrue(isinstance(result, np.ndarray))\n            self.assertEqual(result[1], \"test_string1\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ensure_type.py_TestEnsureType.test_list_tuple_TestEnsureType.test_list_tuple.for_dtype_in_tensor_.None_5": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ensure_type.py_TestEnsureType.test_list_tuple_TestEnsureType.test_list_tuple.for_dtype_in_tensor_.None_5", "embedding": null, "metadata": {"file_path": "tests/test_ensure_type.py", "file_name": "test_ensure_type.py", "file_type": "text/x-python", "category": "test", "start_line": 60, "end_line": 70, "span_ids": ["TestEnsureType.test_list_tuple"], "tokens": 182}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestEnsureType(unittest.TestCase):\n\n    def test_list_tuple(self):\n        for dtype in (\"tensor\", \"numpy\"):\n            result = EnsureType(data_type=dtype, wrap_sequence=False)([[1, 2], [3, 4]])\n            self.assertTrue(isinstance(result, list))\n            self.assertTrue(isinstance(result[0][1], torch.Tensor if dtype == \"tensor\" else np.ndarray))\n            torch.testing.assert_allclose(result[1][0], torch.as_tensor(3))\n            # tuple of numpy arrays\n            result = EnsureType(data_type=dtype, wrap_sequence=False)((np.array([1, 2]), np.array([3, 4])))\n            self.assertTrue(isinstance(result, tuple))\n            self.assertTrue(isinstance(result[0], torch.Tensor if dtype == \"tensor\" else np.ndarray))\n            torch.testing.assert_allclose(result[1], torch.as_tensor([3, 4]))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ensure_type.py_TestEnsureType.test_dict_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ensure_type.py_TestEnsureType.test_dict_", "embedding": null, "metadata": {"file_path": "tests/test_ensure_type.py", "file_name": "test_ensure_type.py", "file_type": "text/x-python", "category": "test", "start_line": 72, "end_line": 92, "span_ids": ["TestEnsureType.test_dict", "impl"], "tokens": 239}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestEnsureType(unittest.TestCase):\n\n    def test_dict(self):\n        # simulate complicated input data\n        test_data = {\n            \"img\": np.array([1.0, 2.0], dtype=np.float32),\n            \"meta\": {\"dims\": 3, \"size\": np.array([1, 2, 3]), \"path\": \"temp/test\"},\n            \"extra\": None,\n        }\n        for dtype in (\"tensor\", \"numpy\"):\n            result = EnsureType(data_type=dtype)(test_data)\n            self.assertTrue(isinstance(result, dict))\n            self.assertTrue(isinstance(result[\"img\"], torch.Tensor if dtype == \"tensor\" else np.ndarray))\n            torch.testing.assert_allclose(result[\"img\"], torch.as_tensor([1.0, 2.0]))\n            self.assertTrue(isinstance(result[\"meta\"][\"size\"], torch.Tensor if dtype == \"tensor\" else np.ndarray))\n            torch.testing.assert_allclose(result[\"meta\"][\"size\"], torch.as_tensor([1, 2, 3]))\n            self.assertEqual(result[\"meta\"][\"path\"], \"temp/test\")\n            self.assertEqual(result[\"extra\"], None)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ensure_typed.py_unittest_TestEnsureTyped.test_array_input.for_test_data_in_test_dat.for_dtype_in_tensor_.self_assertTupleEqual_res": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ensure_typed.py_unittest_TestEnsureTyped.test_array_input.for_test_data_in_test_dat.for_dtype_in_tensor_.self_assertTupleEqual_res", "embedding": null, "metadata": {"file_path": "tests/test_ensure_typed.py", "file_name": "test_ensure_typed.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 35, "span_ids": ["TestEnsureTyped.test_array_input", "TestEnsureTyped", "docstring"], "tokens": 222}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\n\nfrom monai.transforms import EnsureTyped\nfrom tests.utils import assert_allclose\n\n\nclass TestEnsureTyped(unittest.TestCase):\n    def test_array_input(self):\n        test_datas = [np.array([[1, 2], [3, 4]]), torch.as_tensor([[1, 2], [3, 4]])]\n        if torch.cuda.is_available():\n            test_datas.append(test_datas[-1].cuda())\n        for test_data in test_datas:\n            for dtype in (\"tensor\", \"NUMPY\"):\n                result = EnsureTyped(\n                    keys=\"data\", data_type=dtype, dtype=np.float32 if dtype == \"NUMPY\" else None, device=\"cpu\"\n                )({\"data\": test_data})[\"data\"]\n                if dtype == \"NUMPY\":\n                    self.assertTrue(result.dtype == np.float32)\n                self.assertTrue(isinstance(result, torch.Tensor if dtype == \"tensor\" else np.ndarray))\n                assert_allclose(result, test_data, type_test=False)\n                self.assertTupleEqual(result.shape, (2, 2))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ensure_typed.py_TestEnsureTyped.test_single_input_TestEnsureTyped.test_single_input.for_test_data_in_test_dat.for_dtype_in_tensor_.self_assertEqual_result_n": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ensure_typed.py_TestEnsureTyped.test_single_input_TestEnsureTyped.test_single_input.for_test_data_in_test_dat.for_dtype_in_tensor_.self_assertEqual_result_n", "embedding": null, "metadata": {"file_path": "tests/test_ensure_typed.py", "file_name": "test_ensure_typed.py", "file_type": "text/x-python", "category": "test", "start_line": 37, "end_line": 49, "span_ids": ["TestEnsureTyped.test_single_input"], "tokens": 153}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestEnsureTyped(unittest.TestCase):\n\n    def test_single_input(self):\n        test_datas = [5, 5.0, False, np.asarray(5), torch.tensor(5)]\n        if torch.cuda.is_available():\n            test_datas.append(test_datas[-1].cuda())\n        for test_data in test_datas:\n            for dtype in (\"tensor\", \"numpy\"):\n                result = EnsureTyped(keys=\"data\", data_type=dtype)({\"data\": test_data})[\"data\"]\n                self.assertTrue(isinstance(result, torch.Tensor if dtype == \"tensor\" else np.ndarray))\n                if isinstance(test_data, bool):\n                    self.assertFalse(result)\n                else:\n                    assert_allclose(result, test_data, type_test=False)\n                self.assertEqual(result.ndim, 0)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ensure_typed.py_TestEnsureTyped.test_string_TestEnsureTyped.test_string.for_dtype_in_tensor_.self_assertEqual_result_0": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ensure_typed.py_TestEnsureTyped.test_string_TestEnsureTyped.test_string.for_dtype_in_tensor_.self_assertEqual_result_0", "embedding": null, "metadata": {"file_path": "tests/test_ensure_typed.py", "file_name": "test_ensure_typed.py", "file_type": "text/x-python", "category": "test", "start_line": 51, "end_line": 60, "span_ids": ["TestEnsureTyped.test_string"], "tokens": 123}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestEnsureTyped(unittest.TestCase):\n\n    def test_string(self):\n        for dtype in (\"tensor\", \"numpy\"):\n            # string input\n            result = EnsureTyped(keys=\"data\", data_type=dtype)({\"data\": \"test_string\"})[\"data\"]\n            self.assertTrue(isinstance(result, str))\n            self.assertEqual(result, \"test_string\")\n            # numpy array of string\n            result = EnsureTyped(keys=\"data\", data_type=dtype)({\"data\": np.array([\"test_string\"])})[\"data\"]\n            self.assertTrue(isinstance(result, np.ndarray))\n            self.assertEqual(result[0], \"test_string\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ensure_typed.py_TestEnsureTyped.test_list_tuple_TestEnsureTyped.test_list_tuple.for_dtype_in_tensor_.None_5": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ensure_typed.py_TestEnsureTyped.test_list_tuple_TestEnsureTyped.test_list_tuple.for_dtype_in_tensor_.None_5", "embedding": null, "metadata": {"file_path": "tests/test_ensure_typed.py", "file_name": "test_ensure_typed.py", "file_type": "text/x-python", "category": "test", "start_line": 62, "end_line": 74, "span_ids": ["TestEnsureTyped.test_list_tuple"], "tokens": 208}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestEnsureTyped(unittest.TestCase):\n\n    def test_list_tuple(self):\n        for dtype in (\"tensor\", \"numpy\"):\n            result = EnsureTyped(keys=\"data\", data_type=dtype, wrap_sequence=False)({\"data\": [[1, 2], [3, 4]]})[\"data\"]\n            self.assertTrue(isinstance(result, list))\n            self.assertTrue(isinstance(result[0][1], torch.Tensor if dtype == \"tensor\" else np.ndarray))\n            torch.testing.assert_allclose(result[1][0], torch.as_tensor(3))\n            # tuple of numpy arrays\n            result = EnsureTyped(keys=\"data\", data_type=dtype, wrap_sequence=False)(\n                {\"data\": (np.array([1, 2]), np.array([3, 4]))}\n            )[\"data\"]\n            self.assertTrue(isinstance(result, tuple))\n            self.assertTrue(isinstance(result[0], torch.Tensor if dtype == \"tensor\" else np.ndarray))\n            torch.testing.assert_allclose(result[1], torch.as_tensor([3, 4]))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ensure_typed.py_TestEnsureTyped.test_dict_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_ensure_typed.py_TestEnsureTyped.test_dict_", "embedding": null, "metadata": {"file_path": "tests/test_ensure_typed.py", "file_name": "test_ensure_typed.py", "file_type": "text/x-python", "category": "test", "start_line": 74, "end_line": 94, "span_ids": ["TestEnsureTyped.test_dict", "impl"], "tokens": 253}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestEnsureTyped(unittest.TestCase):\n\n    def test_dict(self):\n        # simulate complicated input data\n        test_data = {\n            \"img\": np.array([1.0, 2.0], dtype=np.float32),\n            \"meta\": {\"dims\": 3, \"size\": np.array([1, 2, 3]), \"path\": \"temp/test\"},\n            \"extra\": None,\n        }\n        for dtype in (\"tensor\", \"numpy\"):\n            result = EnsureTyped(keys=\"data\", data_type=dtype, device=\"cpu\")({\"data\": test_data})[\"data\"]\n            self.assertTrue(isinstance(result, dict))\n            self.assertTrue(isinstance(result[\"img\"], torch.Tensor if dtype == \"tensor\" else np.ndarray))\n            torch.testing.assert_allclose(result[\"img\"], torch.as_tensor([1.0, 2.0]))\n            self.assertTrue(isinstance(result[\"meta\"][\"size\"], torch.Tensor if dtype == \"tensor\" else np.ndarray))\n            torch.testing.assert_allclose(result[\"meta\"][\"size\"], torch.as_tensor([1, 2, 3]))\n            self.assertEqual(result[\"meta\"][\"path\"], \"temp/test\")\n            self.assertEqual(result[\"extra\"], None)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_fg_bg_to_indices.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_fg_bg_to_indices.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_fg_bg_to_indices.py", "file_name": "test_fg_bg_to_indices.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 82, "span_ids": ["TestFgBgToIndices", "impl:9", "TestFgBgToIndices.test_type_shape", "docstring"], "tokens": 743}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nfrom parameterized import parameterized\n\nfrom monai.transforms import FgBgToIndices\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTESTS_CASES = []\nfor p in TEST_NDARRAYS:\n    TESTS_CASES.append(\n        [\n            {\"image_threshold\": 0.0, \"output_shape\": None},\n            p([[[0, 1, 1], [1, 0, 1], [1, 1, 0]]]),\n            None,\n            p([1, 2, 3, 5, 6, 7]),\n            p([0, 4, 8]),\n        ]\n    )\n\n    TESTS_CASES.append(\n        [\n            {\"image_threshold\": 0.0, \"output_shape\": None},\n            p([[[0, 1, 1], [1, 0, 1], [1, 1, 0]]]),\n            p([[[1, 1, 1], [1, 0, 1], [1, 1, 1]]]),\n            p([1, 2, 3, 5, 6, 7]),\n            p([0, 8]),\n        ]\n    )\n\n    TESTS_CASES.append(\n        [\n            {\"image_threshold\": 1.0, \"output_shape\": None},\n            p([[[0, 1, 1], [1, 0, 1], [1, 1, 0]]]),\n            p([[[3, 3, 3], [3, 1, 3], [3, 3, 3]]]),\n            p([1, 2, 3, 5, 6, 7]),\n            p([0, 8]),\n        ]\n    )\n\n    TESTS_CASES.append(\n        [\n            {\"image_threshold\": 1.0, \"output_shape\": None},\n            p([[[0, 1, 2], [3, 0, 4], [5, 6, 0]]]),\n            p([[[3, 3, 3], [3, 1, 3], [3, 3, 3]]]),\n            p([1, 2, 3, 5, 6, 7]),\n            p([0, 8]),\n        ]\n    )\n\n    TESTS_CASES.append(\n        [\n            {\"image_threshold\": 1.0, \"output_shape\": [3, 3]},\n            p([[[0, 1, 2], [3, 0, 4], [5, 6, 0]]]),\n            p([[[3, 3, 3], [3, 1, 3], [3, 3, 3]]]),\n            p([[0, 1], [0, 2], [1, 0], [1, 2], [2, 0], [2, 1]]),\n            p([[0, 0], [2, 2]]),\n        ]\n    )\n\n\nclass TestFgBgToIndices(unittest.TestCase):\n    @parameterized.expand(TESTS_CASES)\n    def test_type_shape(self, input_data, label, image, expected_fg, expected_bg):\n        fg_indices, bg_indices = FgBgToIndices(**input_data)(label, image)\n        assert_allclose(fg_indices, expected_fg)\n        assert_allclose(bg_indices, expected_bg)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_fg_bg_to_indicesd.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_fg_bg_to_indicesd.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_fg_bg_to_indicesd.py", "file_name": "test_fg_bg_to_indicesd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 78, "span_ids": ["impl:9", "TestFgBgToIndicesd", "TestFgBgToIndicesd.test_type_shape", "docstring"], "tokens": 825}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nfrom parameterized import parameterized\n\nfrom monai.transforms import FgBgToIndicesd\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTEST_CASES = []\nfor p in TEST_NDARRAYS:\n\n    TEST_CASES.append(\n        [\n            {\"keys\": \"label\", \"image_key\": None, \"image_threshold\": 0.0, \"output_shape\": None},\n            {\"label\": p([[[0, 1, 1], [1, 0, 1], [1, 1, 0]]])},\n            p([1, 2, 3, 5, 6, 7]),\n            p([0, 4, 8]),\n        ]\n    )\n\n    TEST_CASES.append(\n        [\n            {\"keys\": \"label\", \"image_key\": \"image\", \"image_threshold\": 0.0, \"output_shape\": None},\n            {\"label\": p([[[0, 1, 1], [1, 0, 1], [1, 1, 0]]]), \"image\": p([[[1, 1, 1], [1, 0, 1], [1, 1, 1]]])},\n            p([1, 2, 3, 5, 6, 7]),\n            p([0, 8]),\n        ]\n    )\n\n    TEST_CASES.append(\n        [\n            {\"keys\": \"label\", \"image_key\": \"image\", \"image_threshold\": 1.0, \"output_shape\": None},\n            {\"label\": p([[[0, 1, 1], [1, 0, 1], [1, 1, 0]]]), \"image\": p([[[3, 3, 3], [3, 1, 3], [3, 3, 3]]])},\n            p([1, 2, 3, 5, 6, 7]),\n            p([0, 8]),\n        ]\n    )\n\n    TEST_CASES.append(\n        [\n            {\"keys\": \"label\", \"image_key\": \"image\", \"image_threshold\": 1.0, \"output_shape\": None},\n            {\"label\": p([[[0, 1, 2], [3, 0, 4], [5, 6, 0]]]), \"image\": p([[[3, 3, 3], [3, 1, 3], [3, 3, 3]]])},\n            p([1, 2, 3, 5, 6, 7]),\n            p([0, 8]),\n        ]\n    )\n\n    TEST_CASES.append(\n        [\n            {\"keys\": \"label\", \"image_key\": \"image\", \"image_threshold\": 1.0, \"output_shape\": [3, 3]},\n            {\"label\": p([[[0, 1, 2], [3, 0, 4], [5, 6, 0]]]), \"image\": p([[[3, 3, 3], [3, 1, 3], [3, 3, 3]]])},\n            p([[0, 1], [0, 2], [1, 0], [1, 2], [2, 0], [2, 1]]),\n            p([[0, 0], [2, 2]]),\n        ]\n    )\n\n\nclass TestFgBgToIndicesd(unittest.TestCase):\n    @parameterized.expand(TEST_CASES)\n    def test_type_shape(self, input_data, data, expected_fg, expected_bg):\n        result = FgBgToIndicesd(**input_data)(data)\n        assert_allclose(result[\"label_fg_indices\"], expected_fg)\n        assert_allclose(result[\"label_bg_indices\"], expected_bg)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_fill_holes.py_unittest_TEST_CASE_9._open_full_connectivity_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_fill_holes.py_unittest_TEST_CASE_9._open_full_connectivity_", "embedding": null, "metadata": {"file_path": "tests/test_fill_holes.py", "file_name": "test_fill_holes.py", "file_type": "text/x-python", "category": "test", "start_line": 13, "end_line": 86, "span_ids": ["impl:25", "docstring"], "tokens": 740}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import FillHoles\nfrom tests.utils import TEST_NDARRAYS, assert_allclose, clone\n\ngrid_1_raw = [[1, 1, 1], [1, 0, 1], [1, 1, 1]]\n\ngrid_2_raw = [[0, 1, 0], [1, 0, 1], [0, 1, 0]]\n\ngrid_3_raw = [[1, 1, 1], [1, 1, 1], [1, 1, 1]]\n\ngrid_4_raw = [[0, 1, 0], [1, 1, 1], [0, 1, 0]]\n\ngrid_1 = torch.tensor([grid_1_raw])\n\ngrid_2 = torch.tensor([grid_2_raw])\n\ngrid_3 = torch.tensor([grid_3_raw])\n\ngrid_4 = torch.tensor([grid_4_raw])\n\ngrid_5 = torch.tensor([[[1, 1, 1], [1, 0, 0], [1, 1, 1]]])\n\ngrid_6 = torch.tensor([[[1, 1, 2, 2, 2], [1, 0, 2, 0, 2], [1, 1, 2, 2, 2]]])\n\ngrid_7 = torch.tensor([[[1, 1, 2, 2, 2], [1, 0, 2, 2, 2], [1, 1, 2, 2, 2]]])\n\nTEST_CASE_0 = [\"enclosed_default_full_connectivity_default_applied_labels\", {}, grid_1, grid_3]\n\nTEST_CASE_1 = [\"enclosed_full_connectivity_default_applied_labels\", {\"connectivity\": 2}, grid_1, grid_3]\n\nTEST_CASE_2 = [\n    \"enclosed_full_connectivity_applied_labels_same_single\",\n    {\"connectivity\": 2, \"applied_labels\": 1},\n    grid_1,\n    grid_3,\n]\n\nTEST_CASE_3 = [\n    \"enclosed_full_connectivity_applied_labels_same_list\",\n    {\"connectivity\": 2, \"applied_labels\": [1]},\n    grid_1,\n    grid_3,\n]\n\nTEST_CASE_4 = [\n    \"enclosed_full_connectivity_applied_labels_other_single\",\n    {\"connectivity\": 2, \"applied_labels\": 2},\n    grid_1,\n    grid_1,\n]\n\nTEST_CASE_5 = [\n    \"enclosed_full_connectivity_applied_labels_other_list\",\n    {\"connectivity\": 2, \"applied_labels\": [2]},\n    grid_1,\n    grid_1,\n]\n\nTEST_CASE_6 = [\n    \"enclosed_full_connectivity_applied_labels_same_and_other\",\n    {\"connectivity\": 2, \"applied_labels\": [1, 2]},\n    grid_1,\n    grid_3,\n]\n\nTEST_CASE_7 = [\"enclosed_connectivity_1_default_applied_labels\", {\"connectivity\": 1}, grid_1, grid_3]\n\nTEST_CASE_8 = [\"enclosed_connectivity_1_default_applied_labels\", {\"connectivity\": 1}, grid_2, grid_4]\n\nTEST_CASE_9 = [\"open_full_connectivity_default_applied_labels\", {\"connectivity\": 2}, grid_2, grid_2]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_fill_holes.py_TEST_CASE_10_TEST_CASE_22._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_fill_holes.py_TEST_CASE_10_TEST_CASE_22._", "embedding": null, "metadata": {"file_path": "tests/test_fill_holes.py", "file_name": "test_fill_holes.py", "file_type": "text/x-python", "category": "test", "start_line": 88, "end_line": 167, "span_ids": ["impl:67", "impl:25", "impl:51"], "tokens": 738}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "TEST_CASE_10 = [\"open_to_edge_connectivity_1_default_applied_labels\", {\"connectivity\": 1}, grid_5, grid_5]\n\nTEST_CASE_11 = [\"open_to_other_label_connectivity_1_default_applied_labels\", {\"connectivity\": 1}, grid_6, grid_7]\n\nTEST_CASE_12 = [\n    \"open_to_other_label_connectivity_1_applied_labels_other\",\n    {\"connectivity\": 1, \"applied_labels\": 1},\n    grid_6,\n    grid_6,\n]\n\nTEST_CASE_13 = [\n    \"numpy_enclosed_default_full_connectivity_default_applied_labels\",\n    {},\n    grid_1.cpu().numpy(),\n    grid_3.cpu().numpy(),\n]\n\nTEST_CASE_14 = [\n    \"3D_enclosed_full_connectivity_default_applied_labels\",\n    {\"connectivity\": 3},\n    torch.tensor([[grid_3_raw, grid_1_raw, grid_3_raw]]),\n    torch.tensor([[grid_3_raw, grid_3_raw, grid_3_raw]]),\n]\n\nTEST_CASE_15 = [\n    \"3D_enclosed_connectivity_1_default_applied_labels\",\n    {\"connectivity\": 1},\n    torch.tensor([[grid_4_raw, grid_2_raw, grid_4_raw]]),\n    torch.tensor([[grid_4_raw, grid_4_raw, grid_4_raw]]),\n]\n\nTEST_CASE_16 = [\n    \"3D_open_full_connectivity_default_applied_labels\",\n    {\"connectivity\": 3},\n    torch.tensor([[grid_4_raw, grid_2_raw, grid_4_raw]]),\n    torch.tensor([[grid_4_raw, grid_2_raw, grid_4_raw]]),\n]\n\nTEST_CASE_17 = [\n    \"3D_open_to_edge_connectivity_1_default_applied_labels\",\n    {\"connectivity\": 1},\n    torch.tensor([[grid_1_raw, grid_1_raw, grid_3_raw]]),\n    torch.tensor([[grid_1_raw, grid_1_raw, grid_3_raw]]),\n]\n\nTEST_CASE_18 = [\n    \"enclosed_full_connectivity_applied_labels_with_background\",\n    {\"connectivity\": 2, \"applied_labels\": [0, 1]},\n    grid_1,\n    grid_3,\n]\n\nTEST_CASE_19 = [\n    \"enclosed_full_connectivity_applied_labels_only_background\",\n    {\"connectivity\": 2, \"applied_labels\": [0]},\n    grid_1,\n    grid_1,\n]\n\nTEST_CASE_20 = [\n    \"one-hot_enclosed_connectivity_1_default_applied_labels\",\n    {\"connectivity\": 1},\n    torch.tensor([grid_1_raw, grid_1_raw, grid_2_raw]),\n    torch.tensor([grid_1_raw, grid_3_raw, grid_4_raw]),\n]\n\nTEST_CASE_21 = [\n    \"one-hot_enclosed_connectivity_1_applied_labels_2\",\n    {\"connectivity\": 1, \"applied_labels\": [2]},\n    torch.tensor([grid_1_raw, grid_1_raw, grid_2_raw]),\n    torch.tensor([grid_1_raw, grid_1_raw, grid_4_raw]),\n]\n\nTEST_CASE_22 = [\n    \"one-hot_full_connectivity_applied_labels_2\",\n    {\"connectivity\": 2},\n    torch.tensor([grid_1_raw, grid_1_raw, grid_2_raw]),\n    torch.tensor([grid_1_raw, grid_3_raw, grid_2_raw]),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_fill_holes.py_VALID_CASES_INVALID_CASES._ITEST_CASE_1_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_fill_holes.py_VALID_CASES_INVALID_CASES._ITEST_CASE_1_", "embedding": null, "metadata": {"file_path": "tests/test_fill_holes.py", "file_name": "test_fill_holes.py", "file_type": "text/x-python", "category": "test", "start_line": 169, "end_line": 197, "span_ids": ["impl:67"], "tokens": 181}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "VALID_CASES = [\n    TEST_CASE_0,\n    TEST_CASE_1,\n    TEST_CASE_2,\n    TEST_CASE_3,\n    TEST_CASE_4,\n    TEST_CASE_5,\n    TEST_CASE_6,\n    TEST_CASE_7,\n    TEST_CASE_8,\n    TEST_CASE_9,\n    TEST_CASE_10,\n    TEST_CASE_11,\n    TEST_CASE_12,\n    TEST_CASE_13,\n    TEST_CASE_14,\n    TEST_CASE_15,\n    TEST_CASE_16,\n    TEST_CASE_17,\n    TEST_CASE_18,\n    TEST_CASE_19,\n    TEST_CASE_20,\n    TEST_CASE_21,\n    TEST_CASE_22,\n]\n\nITEST_CASE_1 = [\"invalid_image_data_type\", {}, [[[[1, 1, 1]]]], NotImplementedError]\n\nINVALID_CASES = [ITEST_CASE_1]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_fill_holes.py_TestFillHoles_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_fill_holes.py_TestFillHoles_", "embedding": null, "metadata": {"file_path": "tests/test_fill_holes.py", "file_name": "test_fill_holes.py", "file_type": "text/x-python", "category": "test", "start_line": 200, "end_line": 220, "span_ids": ["impl:75", "TestFillHoles.test_correct_results", "TestFillHoles.test_raise_exception", "TestFillHoles"], "tokens": 160}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestFillHoles(unittest.TestCase):\n    @parameterized.expand(VALID_CASES)\n    def test_correct_results(self, _, args, input_image, expected):\n        converter = FillHoles(**args)\n        for p in TEST_NDARRAYS:\n            result = converter(p(clone(input_image)))\n            assert_allclose(result, p(expected))\n\n    @parameterized.expand(INVALID_CASES)\n    def test_raise_exception(self, _, args, input_image, expected_error):\n        with self.assertRaises(expected_error):\n            converter = FillHoles(**args)\n            if isinstance(input_image, torch.Tensor) and torch.cuda.is_available():\n                _ = converter(clone(input_image).cuda())\n            else:\n                _ = converter(clone(input_image))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_fill_holesd.py_unittest_TEST_CASE_9._open_full_connectivity_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_fill_holesd.py_unittest_TEST_CASE_9._open_full_connectivity_", "embedding": null, "metadata": {"file_path": "tests/test_fill_holesd.py", "file_name": "test_fill_holesd.py", "file_type": "text/x-python", "category": "test", "start_line": 13, "end_line": 87, "span_ids": ["impl:25", "docstring"], "tokens": 750}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import FillHolesd\nfrom monai.utils.enums import CommonKeys\nfrom tests.utils import TEST_NDARRAYS, assert_allclose, clone\n\ngrid_1_raw = [[1, 1, 1], [1, 0, 1], [1, 1, 1]]\n\ngrid_2_raw = [[0, 1, 0], [1, 0, 1], [0, 1, 0]]\n\ngrid_3_raw = [[1, 1, 1], [1, 1, 1], [1, 1, 1]]\n\ngrid_4_raw = [[0, 1, 0], [1, 1, 1], [0, 1, 0]]\n\ngrid_1 = torch.tensor([grid_1_raw])\n\ngrid_2 = torch.tensor([grid_2_raw])\n\ngrid_3 = torch.tensor([grid_3_raw])\n\ngrid_4 = torch.tensor([grid_4_raw])\n\ngrid_5 = torch.tensor([[[1, 1, 1], [1, 0, 0], [1, 1, 1]]])\n\ngrid_6 = torch.tensor([[[1, 1, 2, 2, 2], [1, 0, 2, 0, 2], [1, 1, 2, 2, 2]]])\n\ngrid_7 = torch.tensor([[[1, 1, 2, 2, 2], [1, 0, 2, 2, 2], [1, 1, 2, 2, 2]]])\n\nTEST_CASE_0 = [\"enclosed_default_full_connectivity_default_applied_labels\", {}, grid_1, grid_3]\n\nTEST_CASE_1 = [\"enclosed_full_connectivity_default_applied_labels\", {\"connectivity\": 2}, grid_1, grid_3]\n\nTEST_CASE_2 = [\n    \"enclosed_full_connectivity_applied_labels_same_single\",\n    {\"connectivity\": 2, \"applied_labels\": 1},\n    grid_1,\n    grid_3,\n]\n\nTEST_CASE_3 = [\n    \"enclosed_full_connectivity_applied_labels_same_list\",\n    {\"connectivity\": 2, \"applied_labels\": [1]},\n    grid_1,\n    grid_3,\n]\n\nTEST_CASE_4 = [\n    \"enclosed_full_connectivity_applied_labels_other_single\",\n    {\"connectivity\": 2, \"applied_labels\": 2},\n    grid_1,\n    grid_1,\n]\n\nTEST_CASE_5 = [\n    \"enclosed_full_connectivity_applied_labels_other_list\",\n    {\"connectivity\": 2, \"applied_labels\": [2]},\n    grid_1,\n    grid_1,\n]\n\nTEST_CASE_6 = [\n    \"enclosed_full_connectivity_applied_labels_same_and_other\",\n    {\"connectivity\": 2, \"applied_labels\": [1, 2]},\n    grid_1,\n    grid_3,\n]\n\nTEST_CASE_7 = [\"enclosed_connectivity_1_default_applied_labels\", {\"connectivity\": 1}, grid_1, grid_3]\n\nTEST_CASE_8 = [\"enclosed_connectivity_1_default_applied_labels\", {\"connectivity\": 1}, grid_2, grid_4]\n\nTEST_CASE_9 = [\"open_full_connectivity_default_applied_labels\", {\"connectivity\": 2}, grid_2, grid_2]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_fill_holesd.py_TEST_CASE_10_TEST_CASE_22._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_fill_holesd.py_TEST_CASE_10_TEST_CASE_22._", "embedding": null, "metadata": {"file_path": "tests/test_fill_holesd.py", "file_name": "test_fill_holesd.py", "file_type": "text/x-python", "category": "test", "start_line": 89, "end_line": 168, "span_ids": ["impl:67", "impl:25", "impl:51"], "tokens": 738}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "TEST_CASE_10 = [\"open_to_edge_connectivity_1_default_applied_labels\", {\"connectivity\": 1}, grid_5, grid_5]\n\nTEST_CASE_11 = [\"open_to_other_label_connectivity_1_default_applied_labels\", {\"connectivity\": 1}, grid_6, grid_7]\n\nTEST_CASE_12 = [\n    \"open_to_other_label_connectivity_1_applied_labels_other\",\n    {\"connectivity\": 1, \"applied_labels\": 1},\n    grid_6,\n    grid_6,\n]\n\nTEST_CASE_13 = [\n    \"numpy_enclosed_default_full_connectivity_default_applied_labels\",\n    {},\n    grid_1.cpu().numpy(),\n    grid_3.cpu().numpy(),\n]\n\nTEST_CASE_14 = [\n    \"3D_enclosed_full_connectivity_default_applied_labels\",\n    {\"connectivity\": 3},\n    torch.tensor([[grid_3_raw, grid_1_raw, grid_3_raw]]),\n    torch.tensor([[grid_3_raw, grid_3_raw, grid_3_raw]]),\n]\n\nTEST_CASE_15 = [\n    \"3D_enclosed_connectivity_1_default_applied_labels\",\n    {\"connectivity\": 1},\n    torch.tensor([[grid_4_raw, grid_2_raw, grid_4_raw]]),\n    torch.tensor([[grid_4_raw, grid_4_raw, grid_4_raw]]),\n]\n\nTEST_CASE_16 = [\n    \"3D_open_full_connectivity_default_applied_labels\",\n    {\"connectivity\": 3},\n    torch.tensor([[grid_4_raw, grid_2_raw, grid_4_raw]]),\n    torch.tensor([[grid_4_raw, grid_2_raw, grid_4_raw]]),\n]\n\nTEST_CASE_17 = [\n    \"3D_open_to_edge_connectivity_1_default_applied_labels\",\n    {\"connectivity\": 1},\n    torch.tensor([[grid_1_raw, grid_1_raw, grid_3_raw]]),\n    torch.tensor([[grid_1_raw, grid_1_raw, grid_3_raw]]),\n]\n\nTEST_CASE_18 = [\n    \"enclosed_full_connectivity_applied_labels_with_background\",\n    {\"connectivity\": 2, \"applied_labels\": [0, 1]},\n    grid_1,\n    grid_3,\n]\n\nTEST_CASE_19 = [\n    \"enclosed_full_connectivity_applied_labels_only_background\",\n    {\"connectivity\": 2, \"applied_labels\": [0]},\n    grid_1,\n    grid_1,\n]\n\nTEST_CASE_20 = [\n    \"one-hot_enclosed_connectivity_1_default_applied_labels\",\n    {\"connectivity\": 1},\n    torch.tensor([grid_1_raw, grid_1_raw, grid_2_raw]),\n    torch.tensor([grid_1_raw, grid_3_raw, grid_4_raw]),\n]\n\nTEST_CASE_21 = [\n    \"one-hot_enclosed_connectivity_1_applied_labels_2\",\n    {\"connectivity\": 1, \"applied_labels\": [2]},\n    torch.tensor([grid_1_raw, grid_1_raw, grid_2_raw]),\n    torch.tensor([grid_1_raw, grid_1_raw, grid_4_raw]),\n]\n\nTEST_CASE_22 = [\n    \"one-hot_full_connectivity_applied_labels_2\",\n    {\"connectivity\": 2},\n    torch.tensor([grid_1_raw, grid_1_raw, grid_2_raw]),\n    torch.tensor([grid_1_raw, grid_3_raw, grid_2_raw]),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_fill_holesd.py_VALID_CASES_INVALID_CASES._ITEST_CASE_1_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_fill_holesd.py_VALID_CASES_INVALID_CASES._ITEST_CASE_1_", "embedding": null, "metadata": {"file_path": "tests/test_fill_holesd.py", "file_name": "test_fill_holesd.py", "file_type": "text/x-python", "category": "test", "start_line": 170, "end_line": 198, "span_ids": ["impl:67"], "tokens": 181}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "VALID_CASES = [\n    TEST_CASE_0,\n    TEST_CASE_1,\n    TEST_CASE_2,\n    TEST_CASE_3,\n    TEST_CASE_4,\n    TEST_CASE_5,\n    TEST_CASE_6,\n    TEST_CASE_7,\n    TEST_CASE_8,\n    TEST_CASE_9,\n    TEST_CASE_10,\n    TEST_CASE_11,\n    TEST_CASE_12,\n    TEST_CASE_13,\n    TEST_CASE_14,\n    TEST_CASE_15,\n    TEST_CASE_16,\n    TEST_CASE_17,\n    TEST_CASE_18,\n    TEST_CASE_19,\n    TEST_CASE_20,\n    TEST_CASE_21,\n    TEST_CASE_22,\n]\n\nITEST_CASE_1 = [\"invalid_image_data_type\", {}, [[[[1, 1, 1]]]], NotImplementedError]\n\nINVALID_CASES = [ITEST_CASE_1]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_fill_holesd.py_TestFillHoles_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_fill_holesd.py_TestFillHoles_", "embedding": null, "metadata": {"file_path": "tests/test_fill_holesd.py", "file_name": "test_fill_holesd.py", "file_type": "text/x-python", "category": "test", "start_line": 201, "end_line": 223, "span_ids": ["impl:75", "TestFillHoles.test_correct_results", "TestFillHoles.test_raise_exception", "TestFillHoles"], "tokens": 202}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestFillHoles(unittest.TestCase):\n    @parameterized.expand(VALID_CASES)\n    def test_correct_results(self, _, args, input_image, expected):\n        key = CommonKeys.IMAGE\n        converter = FillHolesd(keys=key, **args)\n        for p in TEST_NDARRAYS:\n            result = converter({key: p(clone(input_image))})[key]\n            assert_allclose(result, p(expected))\n\n    @parameterized.expand(INVALID_CASES)\n    def test_raise_exception(self, _, args, input_image, expected_error):\n        key = CommonKeys.IMAGE\n        with self.assertRaises(expected_error):\n            converter = FillHolesd(keys=key, **args)\n            if isinstance(input_image, torch.Tensor) and torch.cuda.is_available():\n                _ = converter({key: clone(input_image).cuda()})[key]\n            else:\n                _ = converter({key: clone(input_image)})[key]\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_flip.py_unittest_VALID_CASES._no_axis_None_one": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_flip.py_unittest_VALID_CASES._no_axis_None_one", "embedding": null, "metadata": {"file_path": "tests/test_flip.py", "file_name": "test_flip.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 22, "span_ids": ["docstring"], "tokens": 105}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import Flip\nfrom tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, assert_allclose\n\nINVALID_CASES = [(\"wrong_axis\", [\"s\", 1], TypeError), (\"not_numbers\", \"s\", TypeError)]\n\nVALID_CASES = [(\"no_axis\", None), (\"one_axis\", 1), (\"many_axis\", [0, 1]), (\"negative_axis\", [0, -1])]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_flip.py_TestFlip_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_flip.py_TestFlip_", "embedding": null, "metadata": {"file_path": "tests/test_flip.py", "file_name": "test_flip.py", "file_type": "text/x-python", "category": "test", "start_line": 25, "end_line": 45, "span_ids": ["impl:5", "TestFlip.test_invalid_inputs", "TestFlip.test_correct_results", "TestFlip"], "tokens": 163}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestFlip(NumpyImageTestCase2D):\n    @parameterized.expand(INVALID_CASES)\n    def test_invalid_inputs(self, _, spatial_axis, raises):\n        with self.assertRaises(raises):\n            flip = Flip(spatial_axis)\n            flip(self.imt[0])\n\n    @parameterized.expand(VALID_CASES)\n    def test_correct_results(self, _, spatial_axis):\n        for p in TEST_NDARRAYS:\n            im = p(self.imt[0])\n            flip = Flip(spatial_axis=spatial_axis)\n            expected = [np.flip(channel, spatial_axis) for channel in self.imt[0]]\n            expected = np.stack(expected)\n            result = flip(im)\n            assert_allclose(result, p(expected))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_focal_loss.py_TestFocalLoss.test_consistency_with_cross_entropy_classification_01_TestFocalLoss.test_consistency_with_cross_entropy_classification_01.self_assertNotAlmostEqual": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_focal_loss.py_TestFocalLoss.test_consistency_with_cross_entropy_classification_01_TestFocalLoss.test_consistency_with_cross_entropy_classification_01.self_assertNotAlmostEqual", "embedding": null, "metadata": {"file_path": "tests/test_focal_loss.py", "file_name": "test_focal_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 94, "end_line": 116, "span_ids": ["TestFocalLoss.test_consistency_with_cross_entropy_classification_01"], "tokens": 276}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestFocalLoss(unittest.TestCase):\n\n    def test_consistency_with_cross_entropy_classification_01(self):\n        # for gamma=0.1 the focal loss differs from the cross entropy loss\n        focal_loss = FocalLoss(to_onehot_y=True, gamma=0.1, reduction=\"mean\")\n        ce = nn.BCEWithLogitsLoss(reduction=\"mean\")\n        max_error = 0\n        class_num = 10\n        batch_size = 128\n        for _ in range(100):\n            # Create a random scores tensor of shape (batch_size, class_num)\n            x = torch.rand(batch_size, class_num, requires_grad=True)\n            # Create a random batch of classes\n            l = torch.randint(low=0, high=class_num, size=(batch_size, 1))\n            l = l.long()\n            if torch.cuda.is_available():\n                x = x.cuda()\n                l = l.cuda()\n            output0 = focal_loss(x, l)\n            output1 = ce(x, one_hot(l, num_classes=class_num))\n            a = float(output0.cpu().detach())\n            b = float(output1.cpu().detach())\n            if abs(a - b) > max_error:\n                max_error = abs(a - b)\n        self.assertNotAlmostEqual(max_error, 0.0, places=3)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_focal_loss.py_TestFocalLoss.test_foreground_TestFocalLoss.test_foreground.None_1": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_focal_loss.py_TestFocalLoss.test_foreground_TestFocalLoss.test_foreground.None_1", "embedding": null, "metadata": {"file_path": "tests/test_focal_loss.py", "file_name": "test_focal_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 200, "end_line": 211, "span_ids": ["TestFocalLoss.test_foreground"], "tokens": 188}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestFocalLoss(unittest.TestCase):\n\n    def test_foreground(self):\n        background = torch.ones(1, 1, 5, 5)\n        foreground = torch.zeros(1, 1, 5, 5)\n        target = torch.cat((background, foreground), dim=1)\n        input = torch.cat((background, foreground), dim=1)\n        target[:, 0, 2, 2] = 0\n        target[:, 1, 2, 2] = 1\n\n        fgbg = FocalLoss(to_onehot_y=False, include_background=True)(input, target)\n        fg = FocalLoss(to_onehot_y=False, include_background=False)(input, target)\n        self.assertAlmostEqual(float(fgbg.cpu()), 0.1116, places=3)\n        self.assertAlmostEqual(float(fg.cpu()), 0.1733, places=3)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_fourier.py_unittest_TestFourier.test_forward.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_fourier.py_unittest_TestFourier.test_forward.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_fourier.py", "file_name": "test_fourier.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 53, "span_ids": ["TestFourier", "TestFourier.tearDown", "TestFourier.setUp", "TestFourier.test_forward", "TestFourier.get_data", "docstring"], "tokens": 362}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.data.synthetic import create_test_image_2d, create_test_image_3d\nfrom monai.transforms import Fourier\nfrom monai.utils.misc import set_determinism\nfrom tests.utils import SkipIfBeforePyTorchVersion, SkipIfNoModule\n\nTEST_CASES = [((128, 64),), ((64, 48, 80),)]\n# for shape in ((128, 64), (64, 48, 80)):\n#     TEST_CASES.append(shape)\n\n\n@SkipIfBeforePyTorchVersion((1, 8))\n@SkipIfNoModule(\"torch.fft\")\nclass TestFourier(unittest.TestCase):\n    def setUp(self):\n        set_determinism(0)\n        super().setUp()\n\n    def tearDown(self):\n        set_determinism(None)\n\n    @staticmethod\n    def get_data(img_shape):\n        create_test_image = create_test_image_2d if len(img_shape) == 2 else create_test_image_3d\n        im = create_test_image(*img_shape, num_objs=4, rad_max=20, noise_max=0.0, num_seg_classes=5)[0][None]\n        return torch.Tensor(im)\n\n    @parameterized.expand(TEST_CASES)\n    def test_forward(self, img_shape):\n        n_dims = len(img_shape[1:])\n        x = self.get_data(img_shape)\n        t = Fourier()\n        out = t.shift_fourier(x, n_dims)\n\n        expect = torch.fft.fftshift(torch.fft.fftn(x, dim=tuple(range(-n_dims, 0))), dim=tuple(range(-n_dims, 0)))\n\n        np.testing.assert_allclose(out, expect)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_fourier.py_TestFourier.test_backward_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_fourier.py_TestFourier.test_backward_", "embedding": null, "metadata": {"file_path": "tests/test_fourier.py", "file_name": "test_fourier.py", "file_type": "text/x-python", "category": "test", "start_line": 55, "end_line": 71, "span_ids": ["TestFourier.test_backward", "impl:3"], "tokens": 150}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@SkipIfBeforePyTorchVersion((1, 8))\n@SkipIfNoModule(\"torch.fft\")\nclass TestFourier(unittest.TestCase):\n\n    @parameterized.expand(TEST_CASES)\n    def test_backward(self, img_shape):\n        n_dims = len(img_shape[1:])\n        x = self.get_data(img_shape)\n        t = Fourier()\n        out = t.inv_shift_fourier(x, n_dims)\n\n        expect = torch.fft.ifftn(\n            torch.fft.ifftshift(x, dim=tuple(range(-n_dims, 0))), dim=tuple(range(-n_dims, 0))\n        ).real\n\n        np.testing.assert_allclose(out, expect)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_generate_label_classes_crop_centers.py_unittest_TEST_CASE_2._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_generate_label_classes_crop_centers.py_unittest_TEST_CASE_2._", "embedding": null, "metadata": {"file_path": "tests/test_generate_label_classes_crop_centers.py", "file_name": "test_generate_label_classes_crop_centers.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 45, "span_ids": ["docstring"], "tokens": 235}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom copy import deepcopy\n\nfrom parameterized import parameterized\n\nfrom monai.transforms import generate_label_classes_crop_centers\nfrom monai.utils.misc import set_determinism\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTEST_CASE_1 = [\n    {\n        \"spatial_size\": [2, 2, 2],\n        \"num_samples\": 2,\n        \"ratios\": [1, 2],\n        \"label_spatial_shape\": [3, 3, 3],\n        \"indices\": [[3, 12, 21], [1, 9, 18]],\n    },\n    list,\n    2,\n    3,\n]\n\nTEST_CASE_2 = [\n    {\n        \"spatial_size\": [2, 2, 2],\n        \"num_samples\": 1,\n        \"ratios\": None,\n        \"label_spatial_shape\": [3, 3, 3],\n        \"indices\": [[3, 12, 21], [1, 9, 18]],\n    },\n    list,\n    1,\n    3,\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_generate_label_classes_crop_centers.py_TestGenerateLabelClassesCropCenters_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_generate_label_classes_crop_centers.py_TestGenerateLabelClassesCropCenters_", "embedding": null, "metadata": {"file_path": "tests/test_generate_label_classes_crop_centers.py", "file_name": "test_generate_label_classes_crop_centers.py", "file_type": "text/x-python", "category": "test", "start_line": 48, "end_line": 70, "span_ids": ["impl:5", "TestGenerateLabelClassesCropCenters.test_type_shape", "TestGenerateLabelClassesCropCenters"], "tokens": 206}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestGenerateLabelClassesCropCenters(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2])\n    def test_type_shape(self, input_data, expected_type, expected_count, expected_shape):\n        results = []\n        for p in TEST_NDARRAYS + (None,):\n            input_data = deepcopy(input_data)\n            if p is not None:\n                input_data[\"indices\"] = p(input_data[\"indices\"])\n            set_determinism(0)\n            result = generate_label_classes_crop_centers(**input_data)\n            self.assertIsInstance(result, expected_type)\n            self.assertEqual(len(result), expected_count)\n            self.assertEqual(len(result[0]), expected_shape)\n            # check for consistency between numpy, torch and torch.cuda\n            results.append(result)\n            if len(results) > 1:\n                for x, y in zip(result[0], result[-1]):\n                    assert_allclose(x, y, type_test=False)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_generate_pos_neg_label_crop_centers.py_unittest_TESTS_append_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_generate_pos_neg_label_crop_centers.py_unittest_TESTS_append_", "embedding": null, "metadata": {"file_path": "tests/test_generate_pos_neg_label_crop_centers.py", "file_name": "test_generate_pos_neg_label_crop_centers.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 36, "span_ids": ["docstring"], "tokens": 157}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom copy import deepcopy\n\nfrom parameterized import parameterized\n\nfrom monai.transforms import generate_pos_neg_label_crop_centers\nfrom monai.utils.misc import set_determinism\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTESTS = []\nTESTS.append(\n    [\n        {\n            \"spatial_size\": [2, 2, 2],\n            \"num_samples\": 2,\n            \"pos_ratio\": 1.0,\n            \"label_spatial_shape\": [3, 3, 3],\n            \"fg_indices\": [1, 9, 18],\n            \"bg_indices\": [3, 12, 21],\n        },\n        list,\n        2,\n        3,\n    ]\n)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_generate_pos_neg_label_crop_centers.py_TestGeneratePosNegLabelCropCenters_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_generate_pos_neg_label_crop_centers.py_TestGeneratePosNegLabelCropCenters_", "embedding": null, "metadata": {"file_path": "tests/test_generate_pos_neg_label_crop_centers.py", "file_name": "test_generate_pos_neg_label_crop_centers.py", "file_type": "text/x-python", "category": "test", "start_line": 39, "end_line": 63, "span_ids": ["TestGeneratePosNegLabelCropCenters", "TestGeneratePosNegLabelCropCenters.test_type_shape", "impl:4"], "tokens": 217}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestGeneratePosNegLabelCropCenters(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_type_shape(self, input_data, expected_type, expected_count, expected_shape):\n        results = []\n        for p in TEST_NDARRAYS + (None,):\n            input_data = deepcopy(input_data)\n            if p is not None:\n                for k in [\"fg_indices\", \"bg_indices\"]:\n                    input_data[k] = p(input_data[k])\n            set_determinism(0)\n            result = generate_pos_neg_label_crop_centers(**input_data)\n            self.assertIsInstance(result, expected_type)\n            self.assertEqual(len(result), expected_count)\n            self.assertEqual(len(result[0]), expected_shape)\n            # check for consistency between numpy, torch and torch.cuda\n            results.append(result)\n            if len(results) > 1:\n                # compare every crop center\n                for x, y in zip(results[0], results[-1]):\n                    assert_allclose(x, y, type_test=False)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_get_equivalent_dtype.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_get_equivalent_dtype.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_get_equivalent_dtype.py", "file_name": "test_get_equivalent_dtype.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 46, "span_ids": ["TestGetEquivalentDtype.test_native_type", "docstring", "TestGetEquivalentDtype.test_get_equivalent_dtype", "TestGetEquivalentDtype", "impl:8"], "tokens": 237}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.utils.type_conversion import get_equivalent_dtype\nfrom tests.utils import TEST_NDARRAYS\n\nDTYPES = [torch.float32, np.float32, np.dtype(np.float32)]\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    for im_dtype in DTYPES:\n        TESTS.append((p(np.array(1.0, dtype=np.float32)), im_dtype))\n\n\nclass TestGetEquivalentDtype(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_get_equivalent_dtype(self, im, input_dtype):\n        out_dtype = get_equivalent_dtype(input_dtype, type(im))\n        self.assertEqual(out_dtype, im.dtype)\n\n    def test_native_type(self):\n        \"\"\"the get_equivalent_dtype currently doesn't change the build-in type\"\"\"\n        n_type = [float, int, bool]\n        for n in n_type:\n            for im_dtype in DTYPES:\n                out_dtype = get_equivalent_dtype(n, type(im_dtype))\n                self.assertEqual(out_dtype, n)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gibbs_noise.py_unittest_for_shape_in_128_64_.for_input_type_in_TEST_ND.TEST_CASES_append_shape_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gibbs_noise.py_unittest_for_shape_in_128_64_.for_input_type_in_TEST_ND.TEST_CASES_append_shape_", "embedding": null, "metadata": {"file_path": "tests/test_gibbs_noise.py", "file_name": "test_gibbs_noise.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 30, "span_ids": ["docstring"], "tokens": 152}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom copy import deepcopy\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.data.synthetic import create_test_image_2d, create_test_image_3d\nfrom monai.transforms import GibbsNoise\nfrom monai.utils.misc import set_determinism\nfrom monai.utils.module import optional_import\nfrom tests.utils import TEST_NDARRAYS\n\n_, has_torch_fft = optional_import(\"torch.fft\", name=\"fftshift\")\n\nTEST_CASES = []\nfor shape in ((128, 64), (64, 48, 80)):\n    for input_type in TEST_NDARRAYS if has_torch_fft else [np.array]:\n        TEST_CASES.append((shape, input_type))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gibbs_noise.py_TestGibbsNoise_TestGibbsNoise.get_data.return.input_type_im_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gibbs_noise.py_TestGibbsNoise_TestGibbsNoise.get_data.return.input_type_im_", "embedding": null, "metadata": {"file_path": "tests/test_gibbs_noise.py", "file_name": "test_gibbs_noise.py", "file_type": "text/x-python", "category": "test", "start_line": 33, "end_line": 45, "span_ids": ["TestGibbsNoise", "TestGibbsNoise.tearDown", "TestGibbsNoise.setUp", "TestGibbsNoise.get_data"], "tokens": 123}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestGibbsNoise(unittest.TestCase):\n    def setUp(self):\n        set_determinism(0)\n        super().setUp()\n\n    def tearDown(self):\n        set_determinism(None)\n\n    @staticmethod\n    def get_data(im_shape, input_type):\n        create_test_image = create_test_image_2d if len(im_shape) == 2 else create_test_image_3d\n        im = create_test_image(*im_shape, num_objs=4, rad_max=20, noise_max=0.0, num_seg_classes=5)[0][None]\n        return input_type(im)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gibbs_noise.py_TestGibbsNoise.test_same_result_TestGibbsNoise.test_same_result.self_assertIsInstance_out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gibbs_noise.py_TestGibbsNoise.test_same_result_TestGibbsNoise.test_same_result.self_assertIsInstance_out", "embedding": null, "metadata": {"file_path": "tests/test_gibbs_noise.py", "file_name": "test_gibbs_noise.py", "file_type": "text/x-python", "category": "test", "start_line": 47, "end_line": 58, "span_ids": ["TestGibbsNoise.test_same_result"], "tokens": 140}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestGibbsNoise(unittest.TestCase):\n\n    @parameterized.expand(TEST_CASES)\n    def test_same_result(self, im_shape, input_type):\n        im = self.get_data(im_shape, input_type)\n        alpha = 0.8\n        t = GibbsNoise(alpha)\n        out1 = t(deepcopy(im))\n        out2 = t(deepcopy(im))\n        self.assertEqual(type(out1), type(im))\n        if isinstance(out1, torch.Tensor):\n            self.assertEqual(out1.device, im.device)\n        torch.testing.assert_allclose(out1, out2, rtol=1e-7, atol=0)\n        self.assertIsInstance(out1, type(im))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gibbs_noise.py_TestGibbsNoise.test_identity_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gibbs_noise.py_TestGibbsNoise.test_identity_", "embedding": null, "metadata": {"file_path": "tests/test_gibbs_noise.py", "file_name": "test_gibbs_noise.py", "file_type": "text/x-python", "category": "test", "start_line": 60, "end_line": 79, "span_ids": ["TestGibbsNoise.test_alpha_1", "TestGibbsNoise.test_identity", "impl:7"], "tokens": 187}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestGibbsNoise(unittest.TestCase):\n\n    @parameterized.expand(TEST_CASES)\n    def test_identity(self, im_shape, input_type):\n        im = self.get_data(im_shape, input_type)\n        alpha = 0.0\n        t = GibbsNoise(alpha)\n        out = t(deepcopy(im))\n        torch.testing.assert_allclose(im, out, atol=1e-2, rtol=1e-7)\n\n    @parameterized.expand(TEST_CASES)\n    def test_alpha_1(self, im_shape, input_type):\n        im = self.get_data(im_shape, input_type)\n        alpha = 1.0\n        t = GibbsNoise(alpha)\n        out = t(deepcopy(im))\n        torch.testing.assert_allclose(0 * im, out, rtol=1e-7, atol=0)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gibbs_noised.py_TestGibbsNoised_TestGibbsNoised.get_data.return._k_input_type_deepcopy_v": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gibbs_noised.py_TestGibbsNoised_TestGibbsNoised.get_data.return._k_input_type_deepcopy_v", "embedding": null, "metadata": {"file_path": "tests/test_gibbs_noised.py", "file_name": "test_gibbs_noised.py", "file_type": "text/x-python", "category": "test", "start_line": 34, "end_line": 46, "span_ids": ["TestGibbsNoised.tearDown", "TestGibbsNoised.setUp", "TestGibbsNoised", "TestGibbsNoised.get_data"], "tokens": 132}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestGibbsNoised(unittest.TestCase):\n    def setUp(self):\n        set_determinism(0)\n        super().setUp()\n\n    def tearDown(self):\n        set_determinism(None)\n\n    @staticmethod\n    def get_data(im_shape, input_type):\n        create_test_image = create_test_image_2d if len(im_shape) == 2 else create_test_image_3d\n        ims = create_test_image(*im_shape, rad_max=20, noise_max=0.0, num_seg_classes=5)\n        return {k: input_type(deepcopy(v)) for k, v in zip(KEYS, ims)}", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gibbs_noised.py_TestGibbsNoised.test_identity_TestGibbsNoised.test_identity.for_k_in_KEYS_.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gibbs_noised.py_TestGibbsNoised.test_identity_TestGibbsNoised.test_identity.for_k_in_KEYS_.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_gibbs_noised.py", "file_name": "test_gibbs_noised.py", "file_type": "text/x-python", "category": "test", "start_line": 59, "end_line": 70, "span_ids": ["TestGibbsNoised.test_identity"], "tokens": 148}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestGibbsNoised(unittest.TestCase):\n\n    @parameterized.expand(TEST_CASES)\n    def test_identity(self, im_shape, input_type):\n        data = self.get_data(im_shape, input_type)\n        alpha = 0.0\n        t = GibbsNoised(KEYS, alpha)\n        out = t(deepcopy(data))\n        for k in KEYS:\n            self.assertEqual(type(out[k]), type(data[k]))\n            if isinstance(out[k], torch.Tensor):\n                self.assertEqual(out[k].device, data[k].device)\n                out[k], data[k] = out[k].cpu(), data[k].cpu()\n            np.testing.assert_allclose(data[k], out[k], atol=1e-2)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gibbs_noised.py_TestGibbsNoised.test_alpha_1_TestGibbsNoised.test_alpha_1.for_k_in_KEYS_.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_gibbs_noised.py_TestGibbsNoised.test_alpha_1_TestGibbsNoised.test_alpha_1.for_k_in_KEYS_.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_gibbs_noised.py", "file_name": "test_gibbs_noised.py", "file_type": "text/x-python", "category": "test", "start_line": 72, "end_line": 83, "span_ids": ["TestGibbsNoised.test_alpha_1"], "tokens": 155}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestGibbsNoised(unittest.TestCase):\n\n    @parameterized.expand(TEST_CASES)\n    def test_alpha_1(self, im_shape, input_type):\n        data = self.get_data(im_shape, input_type)\n        alpha = 1.0\n        t = GibbsNoised(KEYS, alpha)\n        out = t(deepcopy(data))\n        for k in KEYS:\n            self.assertEqual(type(out[k]), type(data[k]))\n            if isinstance(out[k], torch.Tensor):\n                self.assertEqual(out[k].device, data[k].device)\n                out[k], data[k] = out[k].cpu(), data[k].cpu()\n            np.testing.assert_allclose(0.0 * data[k], out[k], atol=1e-2)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_grid_distortion.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_grid_distortion.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_grid_distortion.py", "file_name": "test_grid_distortion.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 109, "span_ids": ["impl:11", "TestGridDistortion", "TestGridDistortion.test_grid_distortion", "docstring"], "tokens": 1313}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import GridDistortion\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    TESTS.append(\n        [\n            dict(num_cells=3, distort_steps=[(1.5,) * 4] * 2, mode=\"nearest\", padding_mode=\"zeros\"),\n            p(np.indices([6, 6]).astype(np.float32)),\n            p(\n                np.array(\n                    [\n                        [\n                            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n                            [3.0, 3.0, 3.0, 0.0, 0.0, 0.0],\n                            [3.0, 3.0, 3.0, 0.0, 0.0, 0.0],\n                            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n                            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n                            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n                        ],\n                        [\n                            [0.0, 3.0, 3.0, 0.0, 0.0, 0.0],\n                            [0.0, 3.0, 3.0, 0.0, 0.0, 0.0],\n                            [0.0, 3.0, 3.0, 0.0, 0.0, 0.0],\n                            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n                            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n                            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n                        ],\n                    ]\n                ).astype(np.float32)\n            ),\n        ]\n    )\n    num_cells = (2, 2)\n    distort_steps = [(1.5,) * (1 + num_cells[0]), (1.0,) * (1 + num_cells[1])]\n    TESTS.append(\n        [\n            dict(num_cells=num_cells, distort_steps=distort_steps, mode=\"bilinear\", padding_mode=\"reflection\"),\n            p(np.indices([6, 6]).astype(np.float32)),\n            p(\n                np.array(\n                    [\n                        [\n                            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n                            [2.25, 2.25, 2.25, 2.25, 2.25, 2.25],\n                            [4.5, 4.5, 4.5, 4.5, 4.5, 4.5],\n                            [4.5, 4.5, 4.5, 4.5, 4.5, 4.5],\n                            [3.25, 3.25, 3.25, 3.25, 3.25, 3.25],\n                            [1.0, 1.0, 1.0, 1.0, 1.0, 1.0],\n                        ],\n                        [\n                            [0.0, 1.5, 3.0, 3.0, 4.5, 4.0],\n                            [0.0, 1.5, 3.0, 3.0, 4.5, 4.0],\n                            [0.0, 1.5, 3.0, 3.0, 4.5, 4.0],\n                            [0.0, 1.5, 3.0, 3.0, 4.5, 4.0],\n                            [0.0, 1.5, 3.0, 3.0, 4.5, 4.0],\n                            [0.0, 1.5, 3.0, 3.0, 4.5, 4.0],\n                        ],\n                    ]\n                ).astype(np.float32)\n            ),\n        ]\n    )\n    TESTS.append(\n        [\n            dict(num_cells=2, distort_steps=[(1.25,) * 3] * 3, mode=\"nearest\", padding_mode=\"zeros\"),\n            p(np.indices([3, 3, 3])[:1].astype(np.float32)),\n            p(\n                np.array(\n                    [\n                        [\n                            [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],\n                            [[1.0, 1.0, 0.0], [1.0, 1.0, 0.0], [0.0, 0.0, 0.0]],\n                            [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],\n                        ]\n                    ]\n                ).astype(np.float32)\n            ),\n        ]\n    )\n\n\nclass TestGridDistortion(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_grid_distortion(self, input_param, input_data, expected_val):\n        g = GridDistortion(**input_param)\n        result = g(input_data)\n        assert_allclose(result, expected_val, rtol=1e-4, atol=1e-4)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_grid_distortiond.py_unittest_for_p_in_TEST_NDARRAYS_.TESTS_append_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_grid_distortiond.py_unittest_for_p_in_TEST_NDARRAYS_.TESTS_append_", "embedding": null, "metadata": {"file_path": "tests/test_grid_distortiond.py", "file_name": "test_grid_distortiond.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 72, "span_ids": ["docstring"], "tokens": 785}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import GridDistortiond\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTESTS = []\nnum_cells = (2, 2)\ndistort_steps = [(1.5,) * (1 + n_c) for n_c in num_cells]\nfor p in TEST_NDARRAYS:\n    img = np.indices([6, 6]).astype(np.float32)\n    TESTS.append(\n        [\n            dict(\n                keys=[\"img\", \"mask\"],\n                num_cells=num_cells,\n                distort_steps=distort_steps,\n                mode=[\"bilinear\", \"nearest\"],\n                padding_mode=[\"reflection\", \"zeros\"],\n            ),\n            {\"img\": p(img), \"mask\": p(np.ones_like(img[:1]))},\n            p(\n                np.array(\n                    [\n                        [\n                            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n                            [2.25, 2.25, 2.25, 2.25, 2.25, 2.25],\n                            [4.5, 4.5, 4.5, 4.5, 4.5, 4.5],\n                            [4.5, 4.5, 4.5, 4.5, 4.5, 4.5],\n                            [3.25, 3.25, 3.25, 3.25, 3.25, 3.25],\n                            [1.0, 1.0, 1.0, 1.0, 1.0, 1.0],\n                        ],\n                        [\n                            [0.0, 2.25, 4.5, 4.5, 3.25, 1.0],\n                            [0.0, 2.25, 4.5, 4.5, 3.25, 1.0],\n                            [0.0, 2.25, 4.5, 4.5, 3.25, 1.0],\n                            [0.0, 2.25, 4.5, 4.5, 3.25, 1.0],\n                            [0.0, 2.25, 4.5, 4.5, 3.25, 1.0],\n                            [0.0, 2.25, 4.5, 4.5, 3.25, 1.0],\n                        ],\n                    ]\n                ).astype(np.float32)\n            ),\n            p(\n                np.array(\n                    [\n                        [\n                            [1.0, 1.0, 1.0, 1.0, 0.0, 0.0],\n                            [1.0, 1.0, 1.0, 1.0, 0.0, 0.0],\n                            [1.0, 1.0, 1.0, 1.0, 0.0, 0.0],\n                            [1.0, 1.0, 1.0, 1.0, 0.0, 0.0],\n                            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n                            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n                        ]\n                    ]\n                ).astype(np.float32)\n            ),\n        ]\n    )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_grid_distortiond.py_TestGridDistortiond_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_grid_distortiond.py_TestGridDistortiond_", "embedding": null, "metadata": {"file_path": "tests/test_grid_distortiond.py", "file_name": "test_grid_distortiond.py", "file_type": "text/x-python", "category": "test", "start_line": 75, "end_line": 86, "span_ids": ["impl:11", "TestGridDistortiond", "TestGridDistortiond.test_grid_distortiond"], "tokens": 124}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestGridDistortiond(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_grid_distortiond(self, input_param, input_data, expected_val_img, expected_val_mask):\n        g = GridDistortiond(**input_param)\n        result = g(input_data)\n        assert_allclose(result[\"img\"], expected_val_img, rtol=1e-4, atol=1e-4)\n        assert_allclose(result[\"mask\"], expected_val_mask, rtol=1e-4, atol=1e-4)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_decollate_batch.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_decollate_batch.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_handler_decollate_batch.py", "file_name": "test_handler_decollate_batch.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 64, "span_ids": ["impl", "TestHandlerDecollateBatch.test_compute", "TestHandlerDecollateBatch", "docstring"], "tokens": 412}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\n\nfrom monai.engines import SupervisedEvaluator\nfrom monai.handlers import DecollateBatch, PostProcessing\nfrom monai.transforms import Activationsd, AsDiscreted, Compose, CopyItemsd\n\n\nclass TestHandlerDecollateBatch(unittest.TestCase):\n    def test_compute(self):\n        data = [\n            {\"image\": torch.tensor([[[[2.0], [3.0]]]]), \"filename\": [\"test1\"]},\n            {\"image\": torch.tensor([[[[6.0], [8.0]]]]), \"filename\": [\"test2\"]},\n        ]\n\n        handlers = [\n            DecollateBatch(event=\"MODEL_COMPLETED\"),\n            PostProcessing(\n                transform=Compose(\n                    [\n                        Activationsd(keys=\"pred\", sigmoid=True),\n                        CopyItemsd(keys=\"filename\", times=1, names=\"filename_bak\"),\n                        AsDiscreted(keys=\"pred\", threshold=0.5, to_onehot=2),\n                    ]\n                )\n            ),\n        ]\n        # set up engine, PostProcessing handler works together with postprocessing transforms of engine\n        engine = SupervisedEvaluator(\n            device=torch.device(\"cpu:0\"),\n            val_data_loader=data,\n            epoch_length=2,\n            network=torch.nn.PReLU(),\n            # set decollate=False and execute some postprocessing first, then decollate in handlers\n            postprocessing=lambda x: dict(pred=x[\"pred\"] + 1.0),\n            decollate=False,\n            val_handlers=handlers,\n        )\n        engine.run()\n\n        expected = torch.tensor([[[[1.0], [1.0]], [[0.0], [0.0]]]])\n\n        for o, e in zip(engine.state.output, expected):\n            torch.testing.assert_allclose(o[\"pred\"], e)\n            filename = o.get(\"filename_bak\")\n            if filename is not None:\n                self.assertEqual(filename, \"test2\")\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_early_stop.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_early_stop.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_handler_early_stop.py", "file_name": "test_handler_early_stop.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 64, "span_ids": ["impl", "TestHandlerEarlyStop.test_early_stop_train_loss", "docstring", "TestHandlerEarlyStop", "TestHandlerEarlyStop.test_early_stop_val_metric"], "tokens": 336}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nfrom ignite.engine import Engine, Events\n\nfrom monai.handlers import EarlyStopHandler\n\n\nclass TestHandlerEarlyStop(unittest.TestCase):\n    def test_early_stop_train_loss(self):\n        def _train_func(engine, batch):\n            return {\"loss\": 1.5}\n\n        trainer = Engine(_train_func)\n        EarlyStopHandler(\n            patience=5, score_function=lambda x: x.state.output[\"loss\"], trainer=trainer, epoch_level=False\n        ).attach(trainer)\n\n        trainer.run(range(4), max_epochs=2)\n        self.assertEqual(trainer.state.iteration, 6)\n        self.assertEqual(trainer.state.epoch, 2)\n\n    def test_early_stop_val_metric(self):\n        def _train_func(engine, batch):\n            pass\n\n        trainer = Engine(_train_func)\n        validator = Engine(_train_func)\n        validator.state.metrics[\"val_acc\"] = 0.90\n\n        @trainer.on(Events.EPOCH_COMPLETED)\n        def run_validation(engine):\n            validator.state.metrics[\"val_acc\"] += 0.01\n            validator.run(range(3))\n\n        handler = EarlyStopHandler(\n            patience=3,\n            score_function=lambda x: x.state.metrics[\"val_acc\"],\n            trainer=None,\n            min_delta=0.1,\n            cumulative_delta=True,\n            epoch_level=True,\n        )\n        handler.attach(validator)\n        handler.set_trainer(trainer=trainer)\n\n        trainer.run(range(3), max_epochs=5)\n        self.assertEqual(trainer.state.iteration, 12)\n        self.assertEqual(trainer.state.epoch, 4)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_mlflow.py_glob_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_mlflow.py_glob_", "embedding": null, "metadata": {"file_path": "tests/test_handler_mlflow.py", "file_name": "test_handler_mlflow.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 52, "span_ids": ["TestHandlerMLFlow", "TestHandlerMLFlow.test_metrics_track", "impl", "docstring"], "tokens": 254}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import glob\nimport os\nimport tempfile\nimport unittest\nfrom pathlib import Path\n\nfrom ignite.engine import Engine, Events\n\nfrom monai.handlers import MLFlowHandler\n\n\nclass TestHandlerMLFlow(unittest.TestCase):\n    def test_metrics_track(self):\n        with tempfile.TemporaryDirectory() as tempdir:\n\n            # set up engine\n            def _train_func(engine, batch):\n                return [batch + 1.0]\n\n            engine = Engine(_train_func)\n\n            # set up dummy metric\n            @engine.on(Events.EPOCH_COMPLETED)\n            def _update_metric(engine):\n                current_metric = engine.state.metrics.get(\"acc\", 0.1)\n                engine.state.metrics[\"acc\"] = current_metric + 0.1\n                engine.state.test = current_metric\n\n            # set up testing handler\n            test_path = os.path.join(tempdir, \"mlflow_test\")\n            handler = MLFlowHandler(tracking_uri=Path(test_path).as_uri(), state_attributes=[\"test\"])\n            handler.attach(engine)\n            engine.run(range(3), max_epochs=2)\n            handler.close()\n            # check logging output\n            self.assertTrue(len(glob.glob(test_path)) > 0)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_nvtx.py_unittest_TEST_CASE_1._image_TENSOR_1_T": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_nvtx.py_unittest_TEST_CASE_1._image_TENSOR_1_T", "embedding": null, "metadata": {"file_path": "tests/test_handler_nvtx.py", "file_name": "test_handler_nvtx.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 32, "span_ids": ["docstring"], "tokens": 245}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom ignite.engine import Events\nfrom parameterized import parameterized\n\nfrom monai.engines import SupervisedEvaluator\nfrom monai.handlers import StatsHandler, from_engine\nfrom monai.handlers.nvtx_handlers import MarkHandler, RangeHandler, RangePopHandler, RangePushHandler\nfrom monai.utils import optional_import\n\n_, has_nvtx = optional_import(\"torch._C._nvtx\", descriptor=\"NVTX is not installed. Are you sure you have a CUDA build?\")\n\nTENSOR_0 = torch.tensor([[[[1.0], [2.0]], [[3.0], [4.0]]]])\n\nTENSOR_1 = torch.tensor([[[[0.0], [-2.0]], [[-3.0], [4.0]]]])\n\nTENSOR_1_EXPECTED = torch.tensor([[[1.0], [0.5]], [[0.25], [5.0]]])\n\nTEST_CASE_0 = [[{\"image\": TENSOR_0}], TENSOR_0[0] + 1.0]\nTEST_CASE_1 = [[{\"image\": TENSOR_1}], TENSOR_1_EXPECTED]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_nvtx.py_TestHandlerDecollateBatch_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_nvtx.py_TestHandlerDecollateBatch_", "embedding": null, "metadata": {"file_path": "tests/test_handler_nvtx.py", "file_name": "test_handler_nvtx.py", "file_type": "text/x-python", "category": "test", "start_line": 35, "end_line": 91, "span_ids": ["TestHandlerDecollateBatch.test_compute", "impl:12", "TestHandlerDecollateBatch"], "tokens": 494}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestHandlerDecollateBatch(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_0, TEST_CASE_1])\n    @unittest.skipUnless(has_nvtx, \"CUDA is required for NVTX!\")\n    def test_compute(self, data, expected):\n        # Set up handlers\n        handlers = [\n            # Mark with Ignite Event\n            MarkHandler(Events.STARTED),\n            # Mark with literal\n            MarkHandler(\"EPOCH_STARTED\"),\n            # Mark with literal and providing the message\n            MarkHandler(\"EPOCH_STARTED\", \"Start of the epoch\"),\n            # Define a range using one prefix (between BATCH_STARTED and BATCH_COMPLETED)\n            RangeHandler(\"Batch\"),\n            # Define a range using a pair of events\n            RangeHandler((Events.STARTED, Events.COMPLETED)),\n            # Define a range using a pair of literals\n            RangeHandler((\"GET_BATCH_STARTED\", \"GET_BATCH_COMPLETED\"), msg=\"Batching!\"),\n            # Define a range using a pair of literal and events\n            RangeHandler((\"GET_BATCH_STARTED\", Events.COMPLETED)),\n            # Define the start of range using literal\n            RangePushHandler(\"ITERATION_STARTED\"),\n            # Define the start of range using event\n            RangePushHandler(Events.ITERATION_STARTED, \"Iteration 2\"),\n            # Define the start of range using literals and providing message\n            RangePushHandler(\"EPOCH_STARTED\", \"Epoch 2\"),\n            # Define the end of range using Ignite Event\n            RangePopHandler(Events.ITERATION_COMPLETED),\n            RangePopHandler(Events.EPOCH_COMPLETED),\n            # Define the end of range using literal\n            RangePopHandler(\"ITERATION_COMPLETED\"),\n            # Other handlers\n            StatsHandler(tag_name=\"train\", output_transform=from_engine([\"label\"], first=True)),\n        ]\n\n        # Set up an engine\n        engine = SupervisedEvaluator(\n            device=torch.device(\"cpu:0\"),\n            val_data_loader=data,\n            epoch_length=1,\n            network=torch.nn.PReLU(),\n            postprocessing=lambda x: dict(pred=x[\"pred\"] + 1.0),\n            decollate=True,\n            val_handlers=handlers,\n        )\n        # Run the engine\n        engine.run()\n\n        # Get the output from the engine\n        output = engine.state.output[0]\n\n        torch.testing.assert_allclose(output[\"pred\"], expected)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_stats.py_TestHandlerStats.test_loss_file_TestHandlerStats.test_exception.with_self_assertRaises_Ru.engine_run_range_3_max_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_stats.py_TestHandlerStats.test_loss_file_TestHandlerStats.test_exception.with_self_assertRaises_Ru.engine_run_range_3_max_", "embedding": null, "metadata": {"file_path": "tests/test_handler_stats.py", "file_name": "test_handler_stats.py", "file_type": "text/x-python", "category": "test", "start_line": 121, "end_line": 164, "span_ids": ["TestHandlerStats.test_loss_file", "TestHandlerStats.test_exception"], "tokens": 341}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestHandlerStats(unittest.TestCase):\n\n    def test_loss_file(self):\n        key_to_handler = \"test_logging\"\n        key_to_print = \"myLoss\"\n\n        with tempfile.TemporaryDirectory() as tempdir:\n            filename = os.path.join(tempdir, \"test_loss_stats.log\")\n            handler = logging.FileHandler(filename, mode=\"w\")\n            handler.setLevel(logging.INFO)\n\n            # set up engine\n            def _train_func(engine, batch):\n                return [torch.tensor(0.0)]\n\n            engine = Engine(_train_func)\n\n            # set up testing handler\n            stats_handler = StatsHandler(name=key_to_handler, tag_name=key_to_print, logger_handler=handler)\n            stats_handler.attach(engine)\n\n            engine.run(range(3), max_epochs=2)\n            handler.close()\n            stats_handler.logger.removeHandler(handler)\n            with open(filename) as f:\n                output_str = f.read()\n                grep = re.compile(f\".*{key_to_handler}.*\")\n                has_key_word = re.compile(f\".*{key_to_print}.*\")\n                for idx, line in enumerate(output_str.split(\"\\n\")):\n                    if grep.match(line):\n                        if idx in [1, 2, 3, 6, 7, 8]:\n                            self.assertTrue(has_key_word.match(line))\n\n    def test_exception(self):\n        # set up engine\n        def _train_func(engine, batch):\n            raise RuntimeError(\"test exception.\")\n\n        engine = Engine(_train_func)\n\n        # set up testing handler\n        stats_handler = StatsHandler()\n        stats_handler.attach(engine)\n\n        with self.assertRaises(RuntimeError):\n            engine.run(range(3), max_epochs=2)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_stats.py_TestHandlerStats.test_attributes_print_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_stats.py_TestHandlerStats.test_attributes_print_", "embedding": null, "metadata": {"file_path": "tests/test_handler_stats.py", "file_name": "test_handler_stats.py", "file_type": "text/x-python", "category": "test", "start_line": 166, "end_line": 208, "span_ids": ["TestHandlerStats.test_attributes_print", "impl"], "tokens": 317}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestHandlerStats(unittest.TestCase):\n\n    def test_attributes_print(self):\n        log_stream = StringIO()\n        log_handler = logging.StreamHandler(log_stream)\n        log_handler.setLevel(logging.INFO)\n        key_to_handler = \"test_logging\"\n\n        # set up engine\n        def _train_func(engine, batch):\n            return [torch.tensor(0.0)]\n\n        engine = Engine(_train_func)\n\n        # set up dummy metric\n        @engine.on(Events.EPOCH_COMPLETED)\n        def _update_metric(engine):\n            if not hasattr(engine.state, \"test1\"):\n                engine.state.test1 = 0.1\n                engine.state.test2 = 0.2\n            else:\n                engine.state.test1 += 0.1\n                engine.state.test2 += 0.2\n\n        # set up testing handler\n        stats_handler = StatsHandler(\n            name=key_to_handler, state_attributes=[\"test1\", \"test2\", \"test3\"], logger_handler=log_handler\n        )\n        stats_handler.attach(engine)\n\n        engine.run(range(3), max_epochs=2)\n\n        # check logging output\n        output_str = log_stream.getvalue()\n        log_handler.close()\n        grep = re.compile(f\".*{key_to_handler}.*\")\n        has_key_word = re.compile(\".*State values.*\")\n        for idx, line in enumerate(output_str.split(\"\\n\")):\n            if grep.match(line) and idx in [5, 10]:\n                self.assertTrue(has_key_word.match(line))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_histogram_normalize.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_histogram_normalize.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_histogram_normalize.py", "file_name": "test_histogram_normalize.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 58, "span_ids": ["TestHistogramNormalize", "TestHistogramNormalize.test_value", "impl:7", "docstring"], "tokens": 425}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import HistogramNormalize\nfrom monai.utils import get_equivalent_dtype\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    TESTS.append(\n        [\n            {\"num_bins\": 4, \"min\": 1, \"max\": 5, \"mask\": np.array([1, 1, 1, 1, 1, 0])},\n            p(np.array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0])),\n            p(np.array([1.0, 1.5, 2.5, 4.0, 5.0, 5.0])),\n        ]\n    )\n\n    TESTS.append(\n        [\n            {\"num_bins\": 4, \"max\": 4, \"dtype\": np.uint8},\n            p(np.array([0.0, 1.0, 2.0, 3.0, 4.0])),\n            p(np.array([0, 0, 1, 3, 4])),\n        ]\n    )\n\n    TESTS.append(\n        [\n            {\"num_bins\": 256, \"max\": 255, \"dtype\": np.uint8},\n            p(np.array([[[100.0, 200.0], [150.0, 250.0]]])),\n            p(np.array([[[0, 170], [70, 255]]])),\n        ]\n    )\n\n\nclass TestHistogramNormalize(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_value(self, argments, image, expected_data):\n        result = HistogramNormalize(**argments)(image)\n        assert_allclose(result, expected_data)\n        self.assertEqual(get_equivalent_dtype(result.dtype, data_type=np.ndarray), argments.get(\"dtype\", np.float32))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_histogram_normalized.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_histogram_normalized.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_histogram_normalized.py", "file_name": "test_histogram_normalized.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 58, "span_ids": ["impl:7", "TestHistogramNormalized", "TestHistogramNormalized.test_value", "docstring"], "tokens": 464}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import HistogramNormalized\nfrom monai.utils import get_equivalent_dtype\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    TESTS.append(\n        [\n            {\"keys\": \"img\", \"num_bins\": 4, \"min\": 1, \"max\": 5, \"mask_key\": \"mask\"},\n            {\"img\": p(np.array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0])), \"mask\": p(np.array([1, 1, 1, 1, 1, 0]))},\n            p(np.array([1.0, 1.5, 2.5, 4.0, 5.0, 5.0])),\n        ]\n    )\n\n    TESTS.append(\n        [\n            {\"keys\": \"img\", \"num_bins\": 4, \"max\": 4, \"dtype\": np.uint8},\n            {\"img\": p(np.array([0.0, 1.0, 2.0, 3.0, 4.0]))},\n            p(np.array([0, 0, 1, 3, 4])),\n        ]\n    )\n\n    TESTS.append(\n        [\n            {\"keys\": \"img\", \"num_bins\": 256, \"max\": 255, \"dtype\": np.uint8},\n            {\"img\": p(np.array([[[100.0, 200.0], [150.0, 250.0]]]))},\n            p(np.array([[[0, 170], [70, 255]]])),\n        ]\n    )\n\n\nclass TestHistogramNormalized(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_value(self, argments, image, expected_data):\n        result = HistogramNormalized(**argments)(image)[\"img\"]\n        assert_allclose(result, expected_data)\n        self.assertEqual(get_equivalent_dtype(result.dtype, data_type=np.ndarray), argments.get(\"dtype\", np.float32))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_fast_train.py_math_from_tests_utils_import_D": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_fast_train.py_math_from_tests_utils_import_D", "embedding": null, "metadata": {"file_path": "tests/test_integration_fast_train.py", "file_name": "test_integration_fast_train.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 55, "span_ids": ["docstring"], "tokens": 279}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import math\nimport os\nimport shutil\nimport tempfile\nimport time\nimport unittest\nfrom glob import glob\n\nimport nibabel as nib\nimport numpy as np\nimport torch\n\nimport monai\nfrom monai.data import CacheDataset, ThreadDataLoader, create_test_image_3d, decollate_batch\nfrom monai.inferers import sliding_window_inference\nfrom monai.losses import DiceCELoss\nfrom monai.metrics import DiceMetric\nfrom monai.networks.layers import Norm\nfrom monai.networks.nets import UNet\nfrom monai.optimizers import Novograd\nfrom monai.transforms import (\n    AsDiscrete,\n    Compose,\n    CropForegroundd,\n    EnsureChannelFirstd,\n    EnsureType,\n    EnsureTyped,\n    FgBgToIndicesd,\n    LoadImaged,\n    RandAffined,\n    RandAxisFlipd,\n    RandCropByPosNegLabeld,\n    RandFlipd,\n    RandGaussianNoised,\n    RandRotate90d,\n    RandRotated,\n    RandStdShiftIntensityd,\n    RandZoomd,\n    ScaleIntensityd,\n    Spacingd,\n    ToDeviced,\n)\nfrom monai.utils import set_determinism\nfrom tests.utils import DistTestCase, SkipIfBeforePyTorchVersion, TimedCall, skip_if_no_cuda, skip_if_quick", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_fast_train.py_IntegrationFastTrain_IntegrationFastTrain._test_the_fast_training_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_fast_train.py_IntegrationFastTrain_IntegrationFastTrain._test_the_fast_training_", "embedding": null, "metadata": {"file_path": "tests/test_integration_fast_train.py", "file_name": "test_integration_fast_train.py", "file_type": "text/x-python", "category": "test", "start_line": 58, "end_line": 78, "span_ids": ["IntegrationFastTrain", "IntegrationFastTrain.tearDown", "IntegrationFastTrain.setUp"], "tokens": 209}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_no_cuda\n@skip_if_quick\n@SkipIfBeforePyTorchVersion((1, 7))\nclass IntegrationFastTrain(DistTestCase):\n    def setUp(self):\n        set_determinism(seed=0)\n        monai.config.print_config()\n\n        self.data_dir = tempfile.mkdtemp()\n        for i in range(41):\n            im, seg = create_test_image_3d(128, 128, 128, num_seg_classes=1, channel_dim=-1)\n            n = nib.Nifti1Image(im, np.eye(4))\n            nib.save(n, os.path.join(self.data_dir, f\"img{i:d}.nii.gz\"))\n            n = nib.Nifti1Image(seg, np.eye(4))\n            nib.save(n, os.path.join(self.data_dir, f\"seg{i:d}.nii.gz\"))\n\n    def tearDown(self):\n        set_determinism(seed=None)\n        shutil.rmtree(self.data_dir)\n\n    # test the fast training speed is as expected", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_fast_train.py_IntegrationFastTrain.test_train_timing_IntegrationFastTrain.test_train_timing.train_transforms.Compose_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_fast_train.py_IntegrationFastTrain.test_train_timing_IntegrationFastTrain.test_train_timing.train_transforms.Compose_", "embedding": null, "metadata": {"file_path": "tests/test_integration_fast_train.py", "file_name": "test_integration_fast_train.py", "file_type": "text/x-python", "category": "test", "start_line": 79, "end_line": 131, "span_ids": ["IntegrationFastTrain.test_train_timing"], "tokens": 702}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_no_cuda\n@skip_if_quick\n@SkipIfBeforePyTorchVersion((1, 7))\nclass IntegrationFastTrain(DistTestCase):\n    @TimedCall(seconds=100, daemon=False, force_quit=False)\n    def test_train_timing(self):\n        images = sorted(glob(os.path.join(self.data_dir, \"img*.nii.gz\")))\n        segs = sorted(glob(os.path.join(self.data_dir, \"seg*.nii.gz\")))\n        train_files = [{\"image\": img, \"label\": seg} for img, seg in zip(images[:32], segs[:32])]\n        val_files = [{\"image\": img, \"label\": seg} for img, seg in zip(images[-9:], segs[-9:])]\n\n        device = torch.device(\"cuda:0\")\n        # define transforms for train and validation\n        train_transforms = Compose(\n            [\n                LoadImaged(keys=[\"image\", \"label\"]),\n                EnsureChannelFirstd(keys=[\"image\", \"label\"]),\n                Spacingd(keys=[\"image\", \"label\"], pixdim=(1.0, 1.0, 1.0), mode=(\"bilinear\", \"nearest\")),\n                ScaleIntensityd(keys=\"image\"),\n                CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\"),\n                # pre-compute foreground and background indexes\n                # and cache them to accelerate training\n                FgBgToIndicesd(keys=\"label\", fg_postfix=\"_fg\", bg_postfix=\"_bg\"),\n                # change to execute transforms with Tensor data\n                EnsureTyped(keys=[\"image\", \"label\"]),\n                # move the data to GPU and cache to avoid CPU -> GPU sync in every epoch\n                ToDeviced(keys=[\"image\", \"label\"], device=device),\n                # randomly crop out patch samples from big\n                # image based on pos / neg ratio\n                # the image centers of negative samples\n                # must be in valid image area\n                RandCropByPosNegLabeld(\n                    keys=[\"image\", \"label\"],\n                    label_key=\"label\",\n                    spatial_size=(64, 64, 64),\n                    pos=1,\n                    neg=1,\n                    num_samples=4,\n                    fg_indices_key=\"label_fg\",\n                    bg_indices_key=\"label_bg\",\n                ),\n                RandFlipd(keys=[\"image\", \"label\"], prob=0.5, spatial_axis=[1, 2]),\n                RandAxisFlipd(keys=[\"image\", \"label\"], prob=0.5),\n                RandRotate90d(keys=[\"image\", \"label\"], prob=0.5, spatial_axes=(1, 2)),\n                RandZoomd(keys=[\"image\", \"label\"], prob=0.5, min_zoom=0.8, max_zoom=1.2, keep_size=True),\n                RandRotated(\n                    keys=[\"image\", \"label\"],\n                    prob=0.5,\n                    range_x=np.pi / 4,\n                    mode=(\"bilinear\", \"nearest\"),\n                    align_corners=True,\n                ),\n                RandAffined(keys=[\"image\", \"label\"], prob=0.5, rotate_range=np.pi / 2, mode=(\"bilinear\", \"nearest\")),\n                RandGaussianNoised(keys=\"image\", prob=0.5),\n                RandStdShiftIntensityd(keys=\"image\", prob=0.5, factors=0.05, nonzero=True),\n            ]\n        )\n        # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_fast_train.py_IntegrationFastTrain.test_train_timing.val_transforms_IntegrationFastTrain.test_train_timing.total_start.time_time_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_fast_train.py_IntegrationFastTrain.test_train_timing.val_transforms_IntegrationFastTrain.test_train_timing.total_start.time_time_", "embedding": null, "metadata": {"file_path": "tests/test_integration_fast_train.py", "file_name": "test_integration_fast_train.py", "file_type": "text/x-python", "category": "test", "start_line": 133, "end_line": 179, "span_ids": ["IntegrationFastTrain.test_train_timing"], "tokens": 595}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_no_cuda\n@skip_if_quick\n@SkipIfBeforePyTorchVersion((1, 7))\nclass IntegrationFastTrain(DistTestCase):\n    @TimedCall(seconds=100, daemon=False, force_quit=False)\n    def test_train_timing(self):\n        # ... other code\n\n        val_transforms = Compose(\n            [\n                LoadImaged(keys=[\"image\", \"label\"]),\n                EnsureChannelFirstd(keys=[\"image\", \"label\"]),\n                Spacingd(keys=[\"image\", \"label\"], pixdim=(1.0, 1.0, 1.0), mode=(\"bilinear\", \"nearest\")),\n                ScaleIntensityd(keys=\"image\"),\n                CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\"),\n                EnsureTyped(keys=[\"image\", \"label\"]),\n                # move the data to GPU and cache to avoid CPU -> GPU sync in every epoch\n                ToDeviced(keys=[\"image\", \"label\"], device=device),\n            ]\n        )\n\n        max_epochs = 5\n        learning_rate = 2e-4\n        val_interval = 1  # do validation for every epoch\n\n        # set CacheDataset, ThreadDataLoader and DiceCE loss for MONAI fast training\n        train_ds = CacheDataset(data=train_files, transform=train_transforms, cache_rate=1.0, num_workers=8)\n        val_ds = CacheDataset(data=val_files, transform=val_transforms, cache_rate=1.0, num_workers=5)\n        # disable multi-workers because `ThreadDataLoader` works with multi-threads\n        train_loader = ThreadDataLoader(train_ds, num_workers=0, batch_size=4, shuffle=True)\n        val_loader = ThreadDataLoader(val_ds, num_workers=0, batch_size=1)\n\n        loss_function = DiceCELoss(to_onehot_y=True, softmax=True, squared_pred=True, batch=True)\n        model = UNet(\n            spatial_dims=3,\n            in_channels=1,\n            out_channels=2,\n            channels=(16, 32, 64, 128, 256),\n            strides=(2, 2, 2, 2),\n            num_res_units=2,\n            norm=Norm.BATCH,\n        ).to(device)\n\n        # Novograd paper suggests to use a bigger LR than Adam,\n        # because Adam does normalization by element-wise second moments\n        optimizer = Novograd(model.parameters(), learning_rate * 10)\n        scaler = torch.cuda.amp.GradScaler()\n\n        post_pred = Compose([EnsureType(), AsDiscrete(argmax=True, to_onehot=2)])\n        post_label = Compose([EnsureType(), AsDiscrete(to_onehot=2)])\n\n        dice_metric = DiceMetric(include_background=True, reduction=\"mean\", get_not_nans=False)\n\n        best_metric = -1\n        total_start = time.time()\n        # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_fast_train.py_IntegrationFastTrain.test_train_timing.for_epoch_in_range_max_ep_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_fast_train.py_IntegrationFastTrain.test_train_timing.for_epoch_in_range_max_ep_", "embedding": null, "metadata": {"file_path": "tests/test_integration_fast_train.py", "file_name": "test_integration_fast_train.py", "file_type": "text/x-python", "category": "test", "start_line": 180, "end_line": 235, "span_ids": ["IntegrationFastTrain.test_train_timing", "impl"], "tokens": 588}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_no_cuda\n@skip_if_quick\n@SkipIfBeforePyTorchVersion((1, 7))\nclass IntegrationFastTrain(DistTestCase):\n    @TimedCall(seconds=100, daemon=False, force_quit=False)\n    def test_train_timing(self):\n        # ... other code\n        for epoch in range(max_epochs):\n            epoch_start = time.time()\n            print(\"-\" * 10)\n            print(f\"epoch {epoch + 1}/{max_epochs}\")\n            model.train()\n            epoch_loss = 0\n            step = 0\n            for batch_data in train_loader:\n                step_start = time.time()\n                step += 1\n                optimizer.zero_grad()\n                # set AMP for training\n                with torch.cuda.amp.autocast():\n                    outputs = model(batch_data[\"image\"])\n                    loss = loss_function(outputs, batch_data[\"label\"])\n                scaler.scale(loss).backward()\n                scaler.step(optimizer)\n                scaler.update()\n                epoch_loss += loss.item()\n                epoch_len = math.ceil(len(train_ds) / train_loader.batch_size)\n                print(\n                    f\"{step}/{epoch_len}, train_loss: {loss.item():.4f}\" f\" step time: {(time.time() - step_start):.4f}\"\n                )\n            epoch_loss /= step\n            print(f\"epoch {epoch + 1} average loss: {epoch_loss:.4f}\")\n\n            if (epoch + 1) % val_interval == 0:\n                model.eval()\n                with torch.no_grad():\n                    for val_data in val_loader:\n                        roi_size = (96, 96, 96)\n                        sw_batch_size = 4\n                        # set AMP for validation\n                        with torch.cuda.amp.autocast():\n                            val_outputs = sliding_window_inference(val_data[\"image\"], roi_size, sw_batch_size, model)\n\n                        val_outputs = [post_pred(i) for i in decollate_batch(val_outputs)]\n                        val_labels = [post_label(i) for i in decollate_batch(val_data[\"label\"])]\n                        dice_metric(y_pred=val_outputs, y=val_labels)\n\n                    metric = dice_metric.aggregate().item()\n                    dice_metric.reset()\n                    if metric > best_metric:\n                        best_metric = metric\n                    print(f\"epoch: {epoch + 1} current mean dice: {metric:.4f}, best mean dice: {best_metric:.4f}\")\n            print(f\"time consuming of epoch {epoch + 1} is: {(time.time() - epoch_start):.4f}\")\n\n        total_time = time.time() - total_start\n        print(f\"train completed, best_metric: {best_metric:.4f} total time: {total_time:.4f}\")\n        # test expected metrics\n        self.assertGreater(best_metric, 0.95)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_stn.py_unittest_STNBenchmark.forward.return.self_stn_x_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_stn.py_unittest_STNBenchmark.forward.return.self_stn_x_", "embedding": null, "metadata": {"file_path": "tests/test_integration_stn.py", "file_name": "test_integration_stn.py", "file_type": "text/x-python", "category": "test", "start_line": 13, "end_line": 73, "span_ids": ["STNBenchmark.forward", "STNBenchmark.stn", "docstring", "STNBenchmark.__init__", "STNBenchmark", "STNBenchmark.stn_ref"], "tokens": 546}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\n\nfrom monai.data import create_test_image_2d\nfrom monai.networks.layers import AffineTransform\nfrom monai.utils import set_determinism\nfrom tests.utils import DistTestCase, TimedCall\n\n\nclass STNBenchmark(nn.Module):\n    \"\"\"\n    adapted from https://pytorch.org/tutorials/intermediate/spatial_transformer_tutorial.html\n    \"\"\"\n\n    def __init__(self, is_ref=True, reverse_indexing=False):\n        super().__init__()\n        self.is_ref = is_ref\n        self.localization = nn.Sequential(\n            nn.Conv2d(1, 8, kernel_size=7),\n            nn.MaxPool2d(2, stride=2),\n            nn.ReLU(True),\n            nn.Conv2d(8, 10, kernel_size=5),\n            nn.MaxPool2d(2, stride=2),\n            nn.ReLU(True),\n        )\n        # Regressor for the 3 * 2 affine matrix\n        self.fc_loc = nn.Sequential(nn.Linear(10 * 3 * 3, 32), nn.ReLU(True), nn.Linear(32, 3 * 2))\n        # Initialize the weights/bias with identity transformation\n        self.fc_loc[2].weight.data.zero_()\n        self.fc_loc[2].bias.data.copy_(torch.tensor([1, 0, 0, 0, 1, 0], dtype=torch.float))\n        if not self.is_ref:\n            self.xform = AffineTransform(normalized=True, reverse_indexing=reverse_indexing)\n\n    # Spatial transformer network forward function\n    def stn_ref(self, x):\n        xs = self.localization(x)\n        xs = xs.view(-1, 10 * 3 * 3)\n        theta = self.fc_loc(xs)\n        theta = theta.view(-1, 2, 3)\n\n        grid = F.affine_grid(theta, x.size(), align_corners=False)\n        x = F.grid_sample(x, grid, align_corners=False)\n        return x\n\n    def stn(self, x):\n        xs = self.localization(x)\n        xs = xs.view(-1, 10 * 3 * 3)\n        theta = self.fc_loc(xs)\n        theta = theta.view(-1, 2, 3)\n        x = self.xform(x, theta, spatial_size=x.size()[2:])\n        return x\n\n    def forward(self, x):\n        if self.is_ref:\n            return self.stn_ref(x)\n        return self.stn(x)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_workflows.py_run_training_test.val_handlers_run_training_test.train_postprocessing.Compose_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_workflows.py_run_training_test.val_handlers_run_training_test.train_postprocessing.Compose_", "embedding": null, "metadata": {"file_path": "tests/test_integration_workflows.py", "file_name": "test_integration_workflows.py", "file_type": "text/x-python", "category": "test", "start_line": 129, "end_line": 161, "span_ids": ["run_training_test"], "tokens": 353}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def run_training_test(root_dir, device=\"cuda:0\", amp=False, num_workers=4):\n    # ... other code\n\n    val_handlers = [\n        StatsHandler(output_transform=lambda x: None),\n        TensorBoardStatsHandler(summary_writer=summary_writer, output_transform=lambda x: None),\n        TensorBoardImageHandler(\n            log_dir=root_dir, batch_transform=from_engine([\"image\", \"label\"]), output_transform=from_engine(\"pred\")\n        ),\n        CheckpointSaver(save_dir=root_dir, save_dict={\"net\": net}, save_key_metric=True),\n        _TestEvalIterEvents(),\n    ]\n\n    evaluator = SupervisedEvaluator(\n        device=device,\n        val_data_loader=val_loader,\n        network=net,\n        inferer=SlidingWindowInferer(roi_size=(96, 96, 96), sw_batch_size=4, overlap=0.5),\n        postprocessing=val_postprocessing,\n        key_val_metric={\n            \"val_mean_dice\": MeanDice(include_background=True, output_transform=from_engine([\"pred\", \"label\"]))\n        },\n        additional_metrics={\"val_acc\": Accuracy(output_transform=from_engine([\"pred\", \"label\"]))},\n        metric_cmp_fn=lambda cur, prev: cur >= prev,  # if greater or equal, treat as new best metric\n        val_handlers=val_handlers,\n        amp=True if amp else False,\n    )\n\n    train_postprocessing = Compose(\n        [\n            ToTensord(keys=[\"pred\", \"label\"]),\n            Activationsd(keys=\"pred\", sigmoid=True),\n            AsDiscreted(keys=\"pred\", threshold=0.5),\n            KeepLargestConnectedComponentd(keys=\"pred\", applied_labels=[1]),\n        ]\n    )\n    # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_intensity_stats.py_unittest_for_p_in_TEST_NDARRAYS_.TESTS_extend_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_intensity_stats.py_unittest_for_p_in_TEST_NDARRAYS_.TESTS_extend_", "embedding": null, "metadata": {"file_path": "tests/test_intensity_stats.py", "file_name": "test_intensity_stats.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 50, "span_ids": ["docstring"], "tokens": 441}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import IntensityStats\nfrom tests.utils import TEST_NDARRAYS\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    TESTS.extend(\n        [\n            [\n                {\"ops\": [\"max\", \"mean\"], \"key_prefix\": \"orig\"},\n                p([[[0.0, 1.0], [2.0, 3.0]]]),\n                {\"affine\": None},\n                {\"orig_max\": 3.0, \"orig_mean\": 1.5},\n            ],\n            [{\"ops\": \"std\", \"key_prefix\": \"orig\"}, p([[[0.0, 1.0], [2.0, 3.0]]]), None, {\"orig_std\": 1.118034}],\n            [\n                {\"ops\": [np.mean, \"max\", np.min], \"key_prefix\": \"orig\"},\n                p([[[0.0, 1.0], [2.0, 3.0]]]),\n                None,\n                {\"orig_custom_0\": 1.5, \"orig_max\": 3.0, \"orig_custom_1\": 0.0},\n            ],\n            [\n                {\"ops\": [\"max\", \"mean\"], \"key_prefix\": \"orig\", \"channel_wise\": True},\n                p([[[0.0, 1.0], [2.0, 3.0]], [[4.0, 5.0], [6.0, 7.0]]]),\n                {\"affine\": None},\n                {\"orig_max\": [3.0, 7.0], \"orig_mean\": [1.5, 5.5]},\n            ],\n            [\n                {\"ops\": [\"max\", \"mean\"], \"key_prefix\": \"orig\"},\n                p([[[0.0, 1.0], [2.0, 3.0]]]),\n                {\"affine\": None},\n                {\"orig_max\": 3.0, \"orig_mean\": 1.5},\n            ],\n        ]\n    )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_intensity_stats.py_TestIntensityStats_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_intensity_stats.py_TestIntensityStats_", "embedding": null, "metadata": {"file_path": "tests/test_intensity_stats.py", "file_name": "test_intensity_stats.py", "file_type": "text/x-python", "category": "test", "start_line": 53, "end_line": 72, "span_ids": ["TestIntensityStats", "TestIntensityStats.test_value", "impl:5", "TestIntensityStats.test_mask"], "tokens": 236}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestIntensityStats(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_value(self, input_param, img, meta_dict, expected):\n        _, meta_dict = IntensityStats(**input_param)(img, meta_dict)\n        for k, v in expected.items():\n            self.assertTrue(k in meta_dict)\n            np.testing.assert_allclose(v, meta_dict[k], atol=1e-3)\n\n    def test_mask(self):\n        for p in TEST_NDARRAYS:\n            img = p([[[0.0, 1.0], [2.0, 3.0]]])\n            mask = np.array([[[1, 0], [1, 0]]], dtype=bool)\n            img, meta_dict = IntensityStats(ops=[\"max\", \"mean\"], key_prefix=\"orig\")(img, mask=mask)\n            np.testing.assert_allclose(meta_dict[\"orig_max\"], 2.0, atol=1e-3)\n            np.testing.assert_allclose(meta_dict[\"orig_mean\"], 1.0, atol=1e-3)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_intensity_statsd.py_sys_TEST_CASE_4._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_intensity_statsd.py_sys_TEST_CASE_4._", "embedding": null, "metadata": {"file_path": "tests/test_intensity_statsd.py", "file_name": "test_intensity_statsd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 48, "span_ids": ["docstring"], "tokens": 457}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import sys\nimport unittest\n\nimport numpy as np\nimport torch.multiprocessing as mp\nfrom parameterized import parameterized\n\nfrom monai.data import DataLoader, Dataset\nfrom monai.transforms import IntensityStatsd\n\nTEST_CASE_1 = [\n    {\"keys\": \"img\", \"ops\": [\"max\", \"mean\"], \"key_prefix\": \"orig\", \"meta_keys\": \"test_meta\"},\n    {\"img\": np.array([[[0.0, 1.0], [2.0, 3.0]]]), \"test_meta\": {\"affine\": None}},\n    \"test_meta\",\n    {\"orig_max\": 3.0, \"orig_mean\": 1.5},\n]\n\nTEST_CASE_2 = [\n    {\"keys\": \"img\", \"ops\": \"std\", \"key_prefix\": \"orig\"},\n    {\"img\": np.array([[[0.0, 1.0], [2.0, 3.0]]])},\n    \"img_meta_dict\",\n    {\"orig_std\": 1.118034},\n]\n\nTEST_CASE_3 = [\n    {\"keys\": \"img\", \"ops\": [np.mean, \"max\", np.min], \"key_prefix\": \"orig\"},\n    {\"img\": np.array([[[0.0, 1.0], [2.0, 3.0]]])},\n    \"img_meta_dict\",\n    {\"orig_custom_0\": 1.5, \"orig_max\": 3.0, \"orig_custom_1\": 0.0},\n]\n\nTEST_CASE_4 = [\n    {\"keys\": \"img\", \"ops\": [\"max\", \"mean\"], \"key_prefix\": \"orig\", \"channel_wise\": True, \"meta_key_postfix\": \"meta\"},\n    {\"img\": np.array([[[0.0, 1.0], [2.0, 3.0]], [[4.0, 5.0], [6.0, 7.0]]]), \"img_meta\": {\"affine\": None}},\n    \"img_meta\",\n    {\"orig_max\": [3.0, 7.0], \"orig_mean\": [1.5, 5.5]},\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_intensity_statsd.py_TestIntensityStatsd_TestIntensityStatsd.test_dataloader.mp_set_start_method_orig_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_intensity_statsd.py_TestIntensityStatsd_TestIntensityStatsd.test_dataloader.mp_set_start_method_orig_", "embedding": null, "metadata": {"file_path": "tests/test_intensity_statsd.py", "file_name": "test_intensity_statsd.py", "file_type": "text/x-python", "category": "test", "start_line": 51, "end_line": 75, "span_ids": ["TestIntensityStatsd", "TestIntensityStatsd.test_value", "TestIntensityStatsd.test_dataloader"], "tokens": 356}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestIntensityStatsd(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4])\n    def test_value(self, input_param, data, meta_key, expected):\n        meta = IntensityStatsd(**input_param)(data)[meta_key]\n        for k, v in expected.items():\n            self.assertTrue(k in meta)\n            np.testing.assert_allclose(v, meta[k], atol=1e-3)\n\n    def test_dataloader(self):\n        dataset = Dataset(\n            data=[{\"img\": np.array([[[0.0, 1.0], [2.0, 3.0]]])}, {\"img\": np.array([[[0.0, 1.0], [2.0, 3.0]]])}],\n            transform=IntensityStatsd(keys=\"img\", ops=[\"max\", \"mean\"], key_prefix=\"orig\"),\n        )\n        # set num workers = 0 for mac / win\n        num_workers = 2 if sys.platform == \"linux\" else 0\n        dataloader = DataLoader(dataset=dataset, num_workers=num_workers, batch_size=2)\n        orig_method = mp.get_start_method()\n        mp.set_start_method(\"spawn\", force=True)\n\n        for d in dataloader:\n            meta = d[\"img_meta_dict\"]\n            np.testing.assert_allclose(meta[\"orig_max\"], [3.0, 3.0], atol=1e-3)\n            np.testing.assert_allclose(meta[\"orig_mean\"], [1.5, 1.5], atol=1e-3)\n        # restore the mp method\n        mp.set_start_method(orig_method, force=True)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_intensity_statsd.py_TestIntensityStatsd.test_mask_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_intensity_statsd.py_TestIntensityStatsd.test_mask_", "embedding": null, "metadata": {"file_path": "tests/test_intensity_statsd.py", "file_name": "test_intensity_statsd.py", "file_type": "text/x-python", "category": "test", "start_line": 77, "end_line": 87, "span_ids": ["impl:9", "TestIntensityStatsd.test_mask"], "tokens": 164}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestIntensityStatsd(unittest.TestCase):\n\n    def test_mask(self):\n        data = {\"img\": np.array([[[0.0, 1.0], [2.0, 3.0]]]), \"img_mask\": np.array([[[1, 0], [1, 0]]], dtype=bool)}\n        stats = IntensityStatsd(keys=\"img\", ops=[\"max\", \"mean\"], mask_keys=\"img_mask\", key_prefix=\"orig\")\n        meta = stats(data)[\"img_meta_dict\"]\n        np.testing.assert_allclose(meta[\"orig_max\"], 2.0, atol=1e-3)\n        np.testing.assert_allclose(meta[\"orig_mean\"], 1.0, atol=1e-3)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_inverse.py_for_t_in__None_14": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_inverse.py_for_t_in__None_14", "embedding": null, "metadata": {"file_path": "tests/test_inverse.py", "file_name": "test_inverse.py", "file_type": "text/x-python", "category": "test", "start_line": 103, "end_line": 159, "span_ids": ["impl:16", "impl:28", "docstring:11"], "tokens": 745}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "for t in (\n    partial(DivisiblePadd, k=-3),\n    partial(CenterSpatialCropd, roi_size=-3),\n    partial(RandSpatialCropd, roi_size=-3),\n    partial(SpatialPadd, spatial_size=15),\n    partial(BorderPadd, spatial_border=[15, 16]),\n    partial(CenterSpatialCropd, roi_size=30),\n    partial(SpatialCropd, roi_center=10, roi_size=100),\n    partial(SpatialCropd, roi_start=3, roi_end=100),\n):\n    TESTS.append((t.func.__name__ + \"bad 1D even\", \"1D even\", 0, t(KEYS)))  # type: ignore\n\nTESTS.append(\n    (\n        \"SpatialPadd (x2) 2d\",\n        \"2D\",\n        0,\n        SpatialPadd(KEYS, spatial_size=[111, 113], method=\"end\"),\n        SpatialPadd(KEYS, spatial_size=[118, 117]),\n    )\n)\n\nTESTS.append((\"SpatialPadd 3d\", \"3D\", 0, SpatialPadd(KEYS, spatial_size=[112, 113, 116])))\n\nTESTS.append((\"SpatialCropd 2d\", \"2D\", 0, SpatialCropd(KEYS, [49, 51], [90, 89])))\n\nTESTS.append(\n    (\n        \"SpatialCropd 3d\",\n        \"3D\",\n        0,\n        SpatialCropd(KEYS, roi_slices=[slice(s, e) for s, e in zip([None, None, -99], [None, -2, None])]),\n    )\n)\n\nTESTS.append((\"SpatialCropd 2d\", \"2D\", 0, SpatialCropd(KEYS, [49, 51], [390, 89])))\n\nTESTS.append((\"SpatialCropd 3d\", \"3D\", 0, SpatialCropd(KEYS, [49, 51, 44], [90, 89, 93])))\n\nTESTS.append((\"RandSpatialCropd 2d\", \"2D\", 0, RandSpatialCropd(KEYS, [96, 93], None, True, False)))\n\nTESTS.append((\"RandSpatialCropd 3d\", \"3D\", 0, RandSpatialCropd(KEYS, [96, 93, 92], None, False, False)))\n\nTESTS.append((\"BorderPadd 2d\", \"2D\", 0, BorderPadd(KEYS, [3, 7, 2, 5])))\n\nTESTS.append((\"BorderPadd 2d\", \"2D\", 0, BorderPadd(KEYS, [3, 7])))\n\nTESTS.append((\"BorderPadd 3d\", \"3D\", 0, BorderPadd(KEYS, [4])))\n\nTESTS.append((\"DivisiblePadd 2d\", \"2D\", 0, DivisiblePadd(KEYS, k=4)))\n\nTESTS.append((\"DivisiblePadd 3d\", \"3D\", 0, DivisiblePadd(KEYS, k=[4, 8, 11])))\n\n\nTESTS.append((\"CenterSpatialCropd 2d\", \"2D\", 0, CenterSpatialCropd(KEYS, roi_size=95)))\n\nTESTS.append((\"CenterSpatialCropd 3d\", \"3D\", 0, CenterSpatialCropd(KEYS, roi_size=[95, 97, 98])))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_inverse.py_TESTS_append_CropForegr_TESTS_append_Zoomd_3d_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_inverse.py_TESTS_append_CropForegr_TESTS_append_Zoomd_3d_", "embedding": null, "metadata": {"file_path": "tests/test_inverse.py", "file_name": "test_inverse.py", "file_type": "text/x-python", "category": "test", "start_line": 161, "end_line": 208, "span_ids": ["impl:43", "impl:28"], "tokens": 753}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "TESTS.append((\"CropForegroundd 2d\", \"2D\", 0, CropForegroundd(KEYS, source_key=\"label\", margin=2)))\n\nTESTS.append((\"CropForegroundd 3d\", \"3D\", 0, CropForegroundd(KEYS, source_key=\"label\", k_divisible=[5, 101, 2])))\n\n\nTESTS.append((\"ResizeWithPadOrCropd 3d\", \"3D\", 0, ResizeWithPadOrCropd(KEYS, [201, 150, 105])))\n\nTESTS.append((\"Flipd 3d\", \"3D\", 0, Flipd(KEYS, [1, 2])))\n\nTESTS.append((\"RandFlipd 3d\", \"3D\", 0, RandFlipd(KEYS, 1, [1, 2])))\n\nTESTS.append((\"RandAxisFlipd 3d\", \"3D\", 0, RandAxisFlipd(KEYS, 1)))\n\nfor acc in [True, False]:\n    TESTS.append((\"Orientationd 3d\", \"3D\", 0, Orientationd(KEYS, \"RAS\", as_closest_canonical=acc)))\n\nTESTS.append((\"Rotate90d 2d\", \"2D\", 0, Rotate90d(KEYS)))\n\nTESTS.append((\"Rotate90d 3d\", \"3D\", 0, Rotate90d(KEYS, k=2, spatial_axes=(1, 2))))\n\nTESTS.append((\"RandRotate90d 3d\", \"3D\", 0, RandRotate90d(KEYS, prob=1, spatial_axes=(1, 2))))\n\nTESTS.append((\"Spacingd 3d\", \"3D\", 3e-2, Spacingd(KEYS, [0.5, 0.7, 0.9], diagonal=False)))\n\nTESTS.append((\"Resized 2d\", \"2D\", 2e-1, Resized(KEYS, [50, 47])))\n\nTESTS.append((\"Resized 3d\", \"3D\", 5e-2, Resized(KEYS, [201, 150, 78])))\n\nTESTS.append((\"Resized longest 2d\", \"2D\", 2e-1, Resized(KEYS, 47, \"longest\", \"area\")))\n\nTESTS.append((\"Resized longest 3d\", \"3D\", 5e-2, Resized(KEYS, 201, \"longest\", \"trilinear\", True)))\n\nTESTS.append((\"Lambdad 2d\", \"2D\", 5e-2, Lambdad(KEYS, func=lambda x: x + 5, inv_func=lambda x: x - 5, overwrite=True)))\n\nTESTS.append(\n    (\n        \"RandLambdad 3d\",\n        \"3D\",\n        5e-2,\n        RandLambdad(KEYS, func=lambda x: x * 10, inv_func=lambda x: x / 10, overwrite=True, prob=0.5),\n    )\n)\n\nTESTS.append((\"Zoomd 1d\", \"1D odd\", 0, Zoomd(KEYS, zoom=2, keep_size=False)))\n\nTESTS.append((\"Zoomd 2d\", \"2D\", 2e-1, Zoomd(KEYS, zoom=0.9)))\n\nTESTS.append((\"Zoomd 3d\", \"3D\", 3e-2, Zoomd(KEYS, zoom=[2.5, 1, 3], keep_size=False)))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_inverse.py_TESTS_append_RandZoom_3_TESTS_append_RandCropBy": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_inverse.py_TESTS_append_RandZoom_3_TESTS_append_RandCropBy", "embedding": null, "metadata": {"file_path": "tests/test_inverse.py", "file_name": "test_inverse.py", "file_type": "text/x-python", "category": "test", "start_line": 210, "end_line": 285, "span_ids": ["impl:62", "impl:55", "impl:43"], "tokens": 747}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "TESTS.append((\"RandZoom 3d\", \"3D\", 9e-2, RandZoomd(KEYS, 1, [0.5, 0.6, 0.9], [1.1, 1, 1.05], keep_size=True)))\n\nTESTS.append((\"RandRotated, prob 0\", \"2D\", 0, RandRotated(KEYS, prob=0)))\n\nTESTS.append(\n    (\"Rotated 2d\", \"2D\", 8e-2, Rotated(KEYS, random.uniform(np.pi / 6, np.pi), keep_size=True, align_corners=False))\n)\n\nTESTS.append(\n    (\n        \"Rotated 3d\",\n        \"3D\",\n        1e-1,\n        Rotated(KEYS, [random.uniform(np.pi / 6, np.pi) for _ in range(3)], True),  # type: ignore\n    )\n)\n\nTESTS.append(\n    (\n        \"RandRotated 3d\",\n        \"3D\",\n        1e-1,\n        RandRotated(KEYS, *[random.uniform(np.pi / 6, np.pi) for _ in range(3)], 1),  # type: ignore\n    )\n)\n\nTESTS.append((\"Transposed 2d\", \"2D\", 0, Transposed(KEYS, [0, 2, 1])))  # channel=0\n\nTESTS.append((\"Transposed 3d\", \"3D\", 0, Transposed(KEYS, [0, 3, 1, 2])))  # channel=0\n\nTESTS.append(\n    (\n        \"Affine 3d\",\n        \"3D\",\n        1e-1,\n        Affined(\n            KEYS,\n            spatial_size=[155, 179, 192],\n            rotate_params=[np.pi / 6, -np.pi / 5, np.pi / 7],\n            shear_params=[0.5, 0.5],\n            translate_params=[10, 5, -4],\n            scale_params=[0.8, 1.3],\n        ),\n    )\n)\n\nTESTS.append(\n    (\n        \"RandAffine 3d\",\n        \"3D\",\n        1e-1,\n        RandAffined(\n            KEYS,\n            [155, 179, 192],\n            prob=1,\n            padding_mode=\"zeros\",\n            rotate_range=[np.pi / 6, -np.pi / 5, np.pi / 7],\n            shear_range=[(0.5, 0.5)],\n            translate_range=[10, 5, -4],\n            scale_range=[(0.8, 1.2), (0.9, 1.3)],\n        ),\n    )\n)\n\nTESTS.append((\"RandAffine 3d\", \"3D\", 0, RandAffined(KEYS, spatial_size=None, prob=0)))\n\nTESTS.append(\n    (\n        \"RandCropByLabelClassesd 2d\",\n        \"2D\",\n        1e-7,\n        RandCropByLabelClassesd(KEYS, \"label\", (99, 96), ratios=[1, 2, 3, 4, 5], num_classes=5, num_samples=10),\n    )\n)\n\nTESTS.append((\"RandCropByPosNegLabeld 2d\", \"2D\", 1e-7, RandCropByPosNegLabeld(KEYS, \"label\", (99, 96), num_samples=10)))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_inverse.py_None_46_no_collation.return.x": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_inverse.py_None_46_no_collation.return.x", "embedding": null, "metadata": {"file_path": "tests/test_inverse.py", "file_name": "test_inverse.py", "file_type": "text/x-python", "category": "test", "start_line": 287, "end_line": 305, "span_ids": ["impl:62", "no_collation"], "tokens": 290}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "TESTS.append((\"RandSpatialCropSamplesd 2d\", \"2D\", 1e-7, RandSpatialCropSamplesd(KEYS, (90, 91), num_samples=10)))\n\nTESTS.append((\"RandWeightedCropd 2d\", \"2D\", 1e-7, RandWeightedCropd(KEYS, \"label\", (90, 91), num_samples=10)))\n\nTESTS_COMPOSE_X2 = [(t[0] + \" Compose\", t[1], t[2], Compose(Compose(t[3:]))) for t in TESTS]\n\nTESTS = TESTS + TESTS_COMPOSE_X2  # type: ignore\n\nNUM_SAMPLES = 5\nN_SAMPLES_TESTS = [\n    [RandCropByLabelClassesd(KEYS, \"label\", (110, 99), [1, 2, 3, 4, 5], num_classes=5, num_samples=NUM_SAMPLES)],\n    [RandCropByPosNegLabeld(KEYS, \"label\", (110, 99), num_samples=NUM_SAMPLES)],\n    [RandSpatialCropSamplesd(KEYS, (90, 91), num_samples=NUM_SAMPLES, random_size=False)],\n    [RandWeightedCropd(KEYS, \"label\", (90, 91), num_samples=NUM_SAMPLES)],\n]\n\n\ndef no_collation(x):\n    return x", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_invertd.py_TestInvertd_TestInvertd.test_invert.inverter_1.Invertd_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_invertd.py_TestInvertd_TestInvertd.test_invert.inverter_1.Invertd_", "embedding": null, "metadata": {"file_path": "tests/test_invertd.py", "file_name": "test_invertd.py", "file_type": "text/x-python", "category": "test", "start_line": 45, "end_line": 102, "span_ids": ["TestInvertd", "TestInvertd.test_invert"], "tokens": 742}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestInvertd(unittest.TestCase):\n    def test_invert(self):\n        set_determinism(seed=0)\n        im_fname, seg_fname = (make_nifti_image(i) for i in create_test_image_3d(101, 100, 107, noise_max=100))\n        transform = Compose(\n            [\n                LoadImaged(KEYS),\n                AddChanneld(KEYS),\n                Orientationd(KEYS, \"RPS\"),\n                Spacingd(KEYS, pixdim=(1.2, 1.01, 0.9), mode=[\"bilinear\", \"nearest\"], dtype=np.float32),\n                ScaleIntensityd(\"image\", minv=1, maxv=10),\n                RandFlipd(KEYS, prob=0.5, spatial_axis=[1, 2]),\n                RandAxisFlipd(KEYS, prob=0.5),\n                RandRotate90d(KEYS, spatial_axes=(1, 2)),\n                RandZoomd(KEYS, prob=0.5, min_zoom=0.5, max_zoom=1.1, keep_size=True),\n                RandRotated(KEYS, prob=0.5, range_x=np.pi, mode=\"bilinear\", align_corners=True),\n                RandAffined(KEYS, prob=0.5, rotate_range=np.pi, mode=\"nearest\"),\n                ResizeWithPadOrCropd(KEYS, 100),\n                # test EnsureTensor for complicated dict data and invert it\n                CopyItemsd(\"image_meta_dict\", times=1, names=\"test_dict\"),\n                # test to support Tensor, Numpy array and dictionary when inverting\n                EnsureTyped(keys=[\"image\", \"test_dict\"]),\n                ToTensord(\"image\"),\n                CastToTyped(KEYS, dtype=[torch.uint8, np.uint8]),\n                CopyItemsd(\"label\", times=2, names=[\"label_inverted\", \"label_inverted1\"]),\n                CopyItemsd(\"image\", times=2, names=[\"image_inverted\", \"image_inverted1\"]),\n            ]\n        )\n        data = [{\"image\": im_fname, \"label\": seg_fname} for _ in range(12)]\n\n        # num workers = 0 for mac or gpu transforms\n        num_workers = 0 if sys.platform != \"linux\" or torch.cuda.is_available() else 2\n\n        dataset = CacheDataset(data, transform=transform, progress=False)\n        loader = DataLoader(dataset, num_workers=num_workers, batch_size=5)\n        inverter = Invertd(\n            # `image` was not copied, invert the original value directly\n            keys=[\"image_inverted\", \"label_inverted\", \"test_dict\"],\n            transform=transform,\n            orig_keys=[\"label\", \"label\", \"test_dict\"],\n            meta_keys=[\"image_inverted_meta_dict\", \"label_inverted_meta_dict\", None],\n            orig_meta_keys=[\"label_meta_dict\", \"label_meta_dict\", None],\n            nearest_interp=True,\n            to_tensor=[True, False, False],\n            device=\"cpu\",\n        )\n\n        inverter_1 = Invertd(\n            # `image` was not copied, invert the original value directly\n            keys=[\"image_inverted1\", \"label_inverted1\"],\n            transform=transform,\n            orig_keys=[\"image\", \"image\"],\n            meta_keys=[\"image_inverted1_meta_dict\", \"label_inverted1_meta_dict\"],\n            orig_meta_keys=[\"image_meta_dict\", \"image_meta_dict\"],\n            nearest_interp=[True, False],\n            to_tensor=[True, True],\n            device=\"cpu\",\n        )\n        # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_invertd.py_TestInvertd.test_invert.expected_keys_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_invertd.py_TestInvertd.test_invert.expected_keys_", "embedding": null, "metadata": {"file_path": "tests/test_invertd.py", "file_name": "test_invertd.py", "file_type": "text/x-python", "category": "test", "start_line": 104, "end_line": 172, "span_ids": ["TestInvertd.test_invert", "impl:3"], "tokens": 761}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestInvertd(unittest.TestCase):\n    def test_invert(self):\n        # ... other code\n\n        expected_keys = [\n            \"image\",\n            \"image_inverted\",\n            \"image_inverted1\",\n            \"image_inverted1_meta_dict\",\n            \"image_inverted_meta_dict\",\n            \"image_meta_dict\",\n            \"image_transforms\",\n            \"label\",\n            \"label_inverted\",\n            \"label_inverted1\",\n            \"label_inverted1_meta_dict\",\n            \"label_inverted_meta_dict\",\n            \"label_meta_dict\",\n            \"label_transforms\",\n            \"test_dict\",\n            \"test_dict_transforms\",\n        ]\n        # execute 1 epoch\n        for d in loader:\n            d = decollate_batch(d)\n            for item in d:\n                item = inverter(item)\n                item = inverter_1(item)\n\n                self.assertListEqual(sorted(item), expected_keys)\n                self.assertTupleEqual(item[\"image\"].shape[1:], (100, 100, 100))\n                self.assertTupleEqual(item[\"label\"].shape[1:], (100, 100, 100))\n                # check the nearest interpolation mode\n                i = item[\"image_inverted\"]\n                torch.testing.assert_allclose(i.to(torch.uint8).to(torch.float), i.to(torch.float))\n                self.assertTupleEqual(i.shape[1:], (100, 101, 107))\n                i = item[\"label_inverted\"]\n                torch.testing.assert_allclose(i.to(torch.uint8).to(torch.float), i.to(torch.float))\n                self.assertTupleEqual(i.shape[1:], (100, 101, 107))\n                # test inverted test_dict\n                self.assertTrue(isinstance(item[\"test_dict\"][\"affine\"], np.ndarray))\n                self.assertTrue(isinstance(item[\"test_dict\"][\"filename_or_obj\"], str))\n\n                # check the case that different items use different interpolation mode to invert transforms\n                d = item[\"image_inverted1\"]\n                # if the interpolation mode is nearest, accumulated diff should be smaller than 1\n                self.assertLess(torch.sum(d.to(torch.float) - d.to(torch.uint8).to(torch.float)).item(), 1.0)\n                self.assertTupleEqual(d.shape, (1, 100, 101, 107))\n\n                d = item[\"label_inverted1\"]\n                # if the interpolation mode is not nearest, accumulated diff should be greater than 10000\n                self.assertGreater(torch.sum(d.to(torch.float) - d.to(torch.uint8).to(torch.float)).item(), 10000.0)\n                self.assertTupleEqual(d.shape, (1, 100, 101, 107))\n\n        # check labels match\n        reverted = item[\"label_inverted\"].detach().cpu().numpy().astype(np.int32)\n        original = LoadImaged(KEYS)(data[-1])[\"label\"]\n        n_good = np.sum(np.isclose(reverted, original, atol=1e-3))\n        reverted_name = item[\"label_inverted_meta_dict\"][\"filename_or_obj\"]\n        original_name = data[-1][\"label\"]\n        self.assertEqual(reverted_name, original_name)\n        print(\"invert diff\", reverted.size - n_good)\n        # 25300: 2 workers (cpu, non-macos)\n        # 1812: 0 workers (gpu or macos)\n        # 1821: windows torch 1.10.0\n        self.assertTrue((reverted.size - n_good) in (34007, 1812, 1821), f\"diff.  {reverted.size - n_good}\")\n\n        set_determinism(seed=None)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_k_space_spike_noise.py_unittest_for_shape_in_128_64_.for_p_in_TEST_NDARRAYS_.TESTS_append_shape_p_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_k_space_spike_noise.py_unittest_for_shape_in_128_64_.for_p_in_TEST_NDARRAYS_.TESTS_append_shape_p_", "embedding": null, "metadata": {"file_path": "tests/test_k_space_spike_noise.py", "file_name": "test_k_space_spike_noise.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 28, "span_ids": ["docstring"], "tokens": 126}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom copy import deepcopy\n\nimport numpy as np\nimport torch\nfrom numpy.fft import fftn, fftshift\nfrom parameterized import parameterized\n\nfrom monai.data.synthetic import create_test_image_2d, create_test_image_3d\nfrom monai.transforms import KSpaceSpikeNoise\nfrom monai.utils.misc import set_determinism\nfrom tests.utils import TEST_NDARRAYS\n\nTESTS = []\nfor shape in ((128, 64), (64, 48, 80)):\n    for p in TEST_NDARRAYS:\n        TESTS.append((shape, p))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_k_space_spike_noise.py_TestKSpaceSpikeNoise_TestKSpaceSpikeNoise.test_same_result.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_k_space_spike_noise.py_TestKSpaceSpikeNoise_TestKSpaceSpikeNoise.test_same_result.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_k_space_spike_noise.py", "file_name": "test_k_space_spike_noise.py", "file_type": "text/x-python", "category": "test", "start_line": 31, "end_line": 62, "span_ids": ["TestKSpaceSpikeNoise.test_same_result", "TestKSpaceSpikeNoise.setUp", "TestKSpaceSpikeNoise.tearDown", "TestKSpaceSpikeNoise.get_data", "TestKSpaceSpikeNoise"], "tokens": 296}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestKSpaceSpikeNoise(unittest.TestCase):\n    def setUp(self):\n        set_determinism(0)\n        super().setUp()\n\n    def tearDown(self):\n        set_determinism(None)\n\n    @staticmethod\n    def get_data(im_shape, im_type):\n        create_test_image = create_test_image_2d if len(im_shape) == 2 else create_test_image_3d\n        im, _ = create_test_image(*im_shape, rad_max=20, noise_max=0.0, num_seg_classes=5)\n        return im_type(im[None])\n\n    @parameterized.expand(TESTS)\n    def test_same_result(self, im_shape, im_type):\n\n        im = self.get_data(im_shape, im_type)\n        loc = [0, int(im.shape[1] / 2), 0] if len(im_shape) == 2 else [0, int(im.shape[1] / 2), 0, 0]\n        k_intensity = 10\n        t = KSpaceSpikeNoise(loc, k_intensity)\n\n        out1 = t(deepcopy(im))\n        out2 = t(deepcopy(im))\n\n        self.assertEqual(type(im), type(out1))\n        if isinstance(out1, torch.Tensor):\n            self.assertEqual(im.device, out1.device)\n            out1 = out1.cpu()\n            out2 = out2.cpu()\n\n        np.testing.assert_allclose(out1, out2)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_k_space_spike_noised.py_TestKSpaceSpikeNoised.test_same_result_TestKSpaceSpikeNoised.test_same_result.for_k_in_KEYS_.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_k_space_spike_noised.py_TestKSpaceSpikeNoised.test_same_result_TestKSpaceSpikeNoised.test_same_result.for_k_in_KEYS_.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_k_space_spike_noised.py", "file_name": "test_k_space_spike_noised.py", "file_type": "text/x-python", "category": "test", "start_line": 48, "end_line": 65, "span_ids": ["TestKSpaceSpikeNoised.test_same_result"], "tokens": 194}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestKSpaceSpikeNoised(unittest.TestCase):\n\n    @parameterized.expand(TESTS)\n    def test_same_result(self, im_shape, im_type):\n\n        data = self.get_data(im_shape, im_type)\n        loc = [0] + [int(im_shape[i] / 2) for i in range(len(im_shape))]\n        k_intensity = 10\n\n        t = KSpaceSpikeNoised(KEYS, loc, k_intensity)\n        out1 = t(deepcopy(data))\n        out2 = t(deepcopy(data))\n\n        for k in KEYS:\n            self.assertEqual(type(out1[k]), type(data[k]))\n            if isinstance(out1[k], torch.Tensor):\n                self.assertEqual(out1[k].device, data[k].device)\n                out1[k] = out1[k].cpu()\n                out2[k] = out2[k].cpu()\n            np.testing.assert_allclose(out1[k], out2[k])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_keep_largest_connected_componentd.py_unittest_grid_3._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_keep_largest_connected_componentd.py_unittest_grid_3._", "embedding": null, "metadata": {"file_path": "tests/test_keep_largest_connected_componentd.py", "file_name": "test_keep_largest_connected_componentd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 44, "span_ids": ["docstring"], "tokens": 607}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import KeepLargestConnectedComponentd\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\ngrid_1 = [[[0, 0, 1, 0, 0], [0, 2, 1, 1, 1], [1, 2, 1, 0, 0], [1, 2, 0, 1, 0], [2, 2, 0, 0, 2]]]\ngrid_2 = [[[0, 0, 0, 0, 1], [0, 0, 1, 1, 1], [1, 0, 1, 1, 2], [1, 0, 1, 2, 2], [0, 0, 0, 0, 1]]]\ngrid_3 = [\n    [\n        [1.0, 1.0, 0.0, 1.0, 1.0],\n        [1.0, 0.0, 0.0, 0.0, 0.0],\n        [0.0, 0.0, 0.0, 1.0, 1.0],\n        [0.0, 0.0, 1.0, 0.0, 1.0],\n        [0.0, 0.0, 1.0, 1.0, 0.0],\n    ],\n    [\n        [0.0, 0.0, 1.0, 0.0, 0.0],\n        [0.0, 0.0, 1.0, 1.0, 1.0],\n        [1.0, 0.0, 1.0, 0.0, 0.0],\n        [1.0, 0.0, 0.0, 1.0, 0.0],\n        [0.0, 0.0, 0.0, 0.0, 0.0],\n    ],\n    [\n        [0.0, 0.0, 0.0, 0.0, 0.0],\n        [0.0, 1.0, 0.0, 0.0, 0.0],\n        [0.0, 1.0, 0.0, 0.0, 0.0],\n        [0.0, 1.0, 0.0, 0.0, 0.0],\n        [1.0, 1.0, 0.0, 0.0, 1.0],\n    ],\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_label_filter.py_unittest_INVALID_CASES._ITEST_CASE_1_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_label_filter.py_unittest_INVALID_CASES._ITEST_CASE_1_", "embedding": null, "metadata": {"file_path": "tests/test_label_filter.py", "file_name": "test_label_filter.py", "file_type": "text/x-python", "category": "test", "start_line": 13, "end_line": 57, "span_ids": ["docstring"], "tokens": 387}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import LabelFilter\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\ngrid_1 = torch.tensor([[[[1, 2, 3], [4, 5, 6], [7, 8, 9]]]])\n\nVALID_TESTS = []\nfor p in TEST_NDARRAYS:\n    VALID_TESTS.append(\n        [\n            \"filter_single_label\",\n            {\"applied_labels\": 3},\n            p(grid_1),\n            p(torch.tensor([[[[0, 0, 3], [0, 0, 0], [0, 0, 0]]]])),\n        ]\n    )\n\n    VALID_TESTS.append(\n        [\n            \"filter_single_label_list\",\n            {\"applied_labels\": [3]},\n            p(grid_1),\n            p(torch.tensor([[[[0, 0, 3], [0, 0, 0], [0, 0, 0]]]])),\n        ]\n    )\n\n    VALID_TESTS.append(\n        [\n            \"filter_multi_label\",\n            {\"applied_labels\": [3, 5, 8]},\n            p(grid_1),\n            p(torch.tensor([[[[0, 0, 3], [0, 5, 0], [0, 8, 0]]]])),\n        ]\n    )\n\n    VALID_TESTS.append([\"filter_all\", {\"applied_labels\": [1, 2, 3, 4, 5, 6, 7, 8, 9]}, p(grid_1), p(grid_1)])\n\n\nITEST_CASE_1 = [\"invalid_image_data_type\", {\"applied_labels\": 1}, [[[[1, 1, 1]]]], NotImplementedError]\n\nINVALID_CASES = [ITEST_CASE_1]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_label_filter.py_TestLabelFilter_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_label_filter.py_TestLabelFilter_", "embedding": null, "metadata": {"file_path": "tests/test_label_filter.py", "file_name": "test_label_filter.py", "file_type": "text/x-python", "category": "test", "start_line": 60, "end_line": 79, "span_ids": ["TestLabelFilter.test_raise_exception", "TestLabelFilter.test_correct_results", "TestLabelFilter", "impl:14"], "tokens": 140}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestLabelFilter(unittest.TestCase):\n    @parameterized.expand(VALID_TESTS)\n    def test_correct_results(self, _, args, input_image, expected):\n        converter = LabelFilter(**args)\n        result = converter(input_image)\n        assert_allclose(result, expected)\n\n    @parameterized.expand(INVALID_CASES)\n    def test_raise_exception(self, _, args, input_image, expected_error):\n        with self.assertRaises(expected_error):\n            converter = LabelFilter(**args)\n            if isinstance(input_image, torch.Tensor) and torch.cuda.is_available():\n                _ = converter(input_image.cuda())\n            else:\n                _ = converter(input_image)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_label_to_maskd.py_unittest_for_p_in_TEST_NDARRAYS_.None_4": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_label_to_maskd.py_unittest_for_p_in_TEST_NDARRAYS_.None_4", "embedding": null, "metadata": {"file_path": "tests/test_label_to_maskd.py", "file_name": "test_label_to_maskd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 57, "span_ids": ["docstring"], "tokens": 744}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import LabelToMaskd\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    TESTS.append(\n        [\n            {\"keys\": \"img\", \"select_labels\": [2, 3], \"merge_channels\": False},\n            {\"img\": p(np.array([[[1, 1, 1], [2, 2, 2], [3, 3, 3], [4, 4, 4], [5, 5, 5], [6, 6, 6]]]))},\n            np.array([[[0, 0, 0], [1, 1, 1], [1, 1, 1], [0, 0, 0], [0, 0, 0], [0, 0, 0]]]),\n        ]\n    )\n    TESTS.append(\n        [\n            {\"keys\": \"img\", \"select_labels\": 2, \"merge_channels\": False},\n            {\"img\": p(np.array([[[1, 1, 1], [2, 2, 2], [3, 3, 3], [4, 4, 4], [5, 5, 5], [6, 6, 6]]]))},\n            np.array([[[0, 0, 0], [1, 1, 1], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]]]),\n        ]\n    )\n    TESTS.append(\n        [\n            {\"keys\": \"img\", \"select_labels\": [1, 2], \"merge_channels\": False},\n            {\"img\": p(np.array([[[0, 0, 1], [0, 1, 0]], [[1, 0, 0], [0, 1, 1]], [[1, 0, 1], [1, 1, 0]]]))},\n            np.array([[[1, 0, 0], [0, 1, 1]], [[1, 0, 1], [1, 1, 0]]]),\n        ]\n    )\n    TESTS.append(\n        [\n            {\"keys\": \"img\", \"select_labels\": 2, \"merge_channels\": False},\n            {\"img\": p(np.array([[[0, 0, 1], [0, 1, 0]], [[1, 0, 0], [0, 1, 1]], [[1, 0, 1], [1, 1, 0]]]))},\n            np.array([[[1, 0, 1], [1, 1, 0]]]),\n        ]\n    )\n    TESTS.append(\n        [\n            {\"keys\": \"img\", \"select_labels\": [1, 2], \"merge_channels\": True},\n            {\"img\": p(np.array([[[0, 0, 1], [0, 1, 0]], [[1, 0, 0], [0, 1, 1]], [[1, 0, 1], [1, 1, 0]]]))},\n            np.array([[[1, 0, 1], [1, 1, 1]]]),\n        ]\n    )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_label_to_maskd.py_TestLabelToMaskd_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_label_to_maskd.py_TestLabelToMaskd_", "embedding": null, "metadata": {"file_path": "tests/test_label_to_maskd.py", "file_name": "test_label_to_maskd.py", "file_type": "text/x-python", "category": "test", "start_line": 60, "end_line": 73, "span_ids": ["TestLabelToMaskd.test_value", "impl:9", "TestLabelToMaskd"], "tokens": 111}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestLabelToMaskd(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_value(self, argments, input_data, expected_data):\n        result = LabelToMaskd(**argments)(input_data)\n        r, i = result[\"img\"], input_data[\"img\"]\n        self.assertEqual(type(r), type(i))\n        if isinstance(r, torch.Tensor):\n            self.assertEqual(r.device, i.device)\n        assert_allclose(r, expected_data, type_test=False)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_lmdbdataset.py_TestLMDBDataset.test_shape_TestLMDBDataset.test_shape.with_tempfile_TemporaryDi.None_1.else_.with_self_assertRaises_Ru._filename_list_updated_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_lmdbdataset.py_TestLMDBDataset.test_shape_TestLMDBDataset.test_shape.with_tempfile_TemporaryDi.None_1.else_.with_self_assertRaises_Ru._filename_list_updated_", "embedding": null, "metadata": {"file_path": "tests/test_lmdbdataset.py", "file_name": "test_lmdbdataset.py", "file_type": "text/x-python", "category": "test", "start_line": 124, "end_line": 202, "span_ids": ["TestLMDBDataset.test_shape"], "tokens": 1049}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_windows\nclass TestLMDBDataset(unittest.TestCase):\n\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5, TEST_CASE_6, TEST_CASE_7])\n    def test_shape(self, transform, expected_shape, kwargs=None):\n        kwargs = kwargs or {}\n        test_image = nib.Nifti1Image(np.random.randint(0, 2, size=[128, 128, 128]), np.eye(4))\n        with tempfile.TemporaryDirectory() as tempdir:\n            nib.save(test_image, os.path.join(tempdir, \"test_image1.nii.gz\"))\n            nib.save(test_image, os.path.join(tempdir, \"test_label1.nii.gz\"))\n            nib.save(test_image, os.path.join(tempdir, \"test_extra1.nii.gz\"))\n            nib.save(test_image, os.path.join(tempdir, \"test_image2.nii.gz\"))\n            nib.save(test_image, os.path.join(tempdir, \"test_label2.nii.gz\"))\n            nib.save(test_image, os.path.join(tempdir, \"test_extra2.nii.gz\"))\n            test_data = [\n                {\n                    \"image\": os.path.join(tempdir, \"test_image1.nii.gz\"),\n                    \"label\": os.path.join(tempdir, \"test_label1.nii.gz\"),\n                    \"extra\": os.path.join(tempdir, \"test_extra1.nii.gz\"),\n                },\n                {\n                    \"image\": os.path.join(tempdir, \"test_image2.nii.gz\"),\n                    \"label\": os.path.join(tempdir, \"test_label2.nii.gz\"),\n                    \"extra\": os.path.join(tempdir, \"test_extra2.nii.gz\"),\n                },\n            ]\n\n            cache_dir = os.path.join(os.path.join(tempdir, \"cache\"), \"data\")\n            dataset_precached = LMDBDataset(\n                data=test_data, transform=transform, progress=False, cache_dir=cache_dir, **kwargs\n            )\n            data1_precached = dataset_precached[0]\n            data2_precached = dataset_precached[1]\n\n            dataset_postcached = LMDBDataset(\n                data=test_data, transform=transform, progress=False, cache_dir=cache_dir, **kwargs\n            )\n            data1_postcached = dataset_postcached[0]\n            data2_postcached = dataset_postcached[1]\n\n            if transform is None:\n                self.assertEqual(data1_precached[\"image\"], os.path.join(tempdir, \"test_image1.nii.gz\"))\n                self.assertEqual(data2_precached[\"label\"], os.path.join(tempdir, \"test_label2.nii.gz\"))\n                self.assertEqual(data1_postcached[\"image\"], os.path.join(tempdir, \"test_image1.nii.gz\"))\n                self.assertEqual(data2_postcached[\"extra\"], os.path.join(tempdir, \"test_extra2.nii.gz\"))\n            else:\n                self.assertTupleEqual(data1_precached[\"image\"].shape, expected_shape)\n                self.assertTupleEqual(data1_precached[\"label\"].shape, expected_shape)\n                self.assertTupleEqual(data1_precached[\"extra\"].shape, expected_shape)\n                self.assertTupleEqual(data2_precached[\"image\"].shape, expected_shape)\n                self.assertTupleEqual(data2_precached[\"label\"].shape, expected_shape)\n                self.assertTupleEqual(data2_precached[\"extra\"].shape, expected_shape)\n\n                self.assertTupleEqual(data1_postcached[\"image\"].shape, expected_shape)\n                self.assertTupleEqual(data1_postcached[\"label\"].shape, expected_shape)\n                self.assertTupleEqual(data1_postcached[\"extra\"].shape, expected_shape)\n                self.assertTupleEqual(data2_postcached[\"image\"].shape, expected_shape)\n                self.assertTupleEqual(data2_postcached[\"label\"].shape, expected_shape)\n                self.assertTupleEqual(data2_postcached[\"extra\"].shape, expected_shape)\n\n            # update the data to cache\n            test_data_new = [\n                {\n                    \"image\": os.path.join(tempdir, \"test_image1_new.nii.gz\"),\n                    \"label\": os.path.join(tempdir, \"test_label1_new.nii.gz\"),\n                    \"extra\": os.path.join(tempdir, \"test_extra1_new.nii.gz\"),\n                },\n                {\n                    \"image\": os.path.join(tempdir, \"test_image2_new.nii.gz\"),\n                    \"label\": os.path.join(tempdir, \"test_label2_new.nii.gz\"),\n                    \"extra\": os.path.join(tempdir, \"test_extra2_new.nii.gz\"),\n                },\n            ]\n            # test new exchanged cache content\n            if transform is None:\n                dataset_postcached.set_data(data=test_data_new)\n                self.assertEqual(dataset_postcached[0][\"image\"], os.path.join(tempdir, \"test_image1_new.nii.gz\"))\n                self.assertEqual(dataset_postcached[0][\"label\"], os.path.join(tempdir, \"test_label1_new.nii.gz\"))\n                self.assertEqual(dataset_postcached[1][\"extra\"], os.path.join(tempdir, \"test_extra2_new.nii.gz\"))\n            else:\n                with self.assertRaises(RuntimeError):\n                    dataset_postcached.set_data(data=test_data_new)  # filename list updated, files do not exist", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_image.py_TestLoadImage.test_kwargs_TestLoadImage.test_kwargs.with_tempfile_TemporaryDi.self_assertTupleEqual_res": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_image.py_TestLoadImage.test_kwargs_TestLoadImage.test_kwargs.with_tempfile_TemporaryDi.self_assertTupleEqual_res", "embedding": null, "metadata": {"file_path": "tests/test_load_image.py", "file_name": "test_load_image.py", "file_type": "text/x-python", "category": "test", "start_line": 213, "end_line": 230, "span_ids": ["TestLoadImage.test_kwargs"], "tokens": 181}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestLoadImage(unittest.TestCase):\n\n    def test_kwargs(self):\n        spatial_size = (32, 64, 128)\n        test_image = np.random.rand(*spatial_size)\n        with tempfile.TemporaryDirectory() as tempdir:\n            filename = os.path.join(tempdir, \"test_image.nii.gz\")\n            itk_np_view = itk.image_view_from_array(test_image)\n            itk.imwrite(itk_np_view, filename)\n\n            loader = LoadImage(image_only=False)\n            reader = ITKReader(fallback_only=False)\n            loader.register(reader)\n            result, header = loader(filename)\n\n            reader = ITKReader()\n            img = reader.read(filename, fallback_only=False)\n            result_raw, header_raw = reader.get_data(img)\n            np.testing.assert_allclose(header[\"spatial_shape\"], header_raw[\"spatial_shape\"])\n            self.assertTupleEqual(result.shape, result_raw.shape)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_image.py_TestLoadImage.test_my_reader_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_image.py_TestLoadImage.test_my_reader_", "embedding": null, "metadata": {"file_path": "tests/test_load_image.py", "file_name": "test_load_image.py", "file_type": "text/x-python", "category": "test", "start_line": 245, "end_line": 260, "span_ids": ["impl:29", "TestLoadImage.test_my_reader"], "tokens": 163}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestLoadImage(unittest.TestCase):\n\n    def test_my_reader(self):\n        \"\"\"test customised readers\"\"\"\n        out = LoadImage(reader=_MiniReader, is_compatible=True)(\"test\")\n        self.assertEqual(out[1][\"name\"], \"my test\")\n        out = LoadImage(reader=_MiniReader, is_compatible=False)(\"test\")\n        self.assertEqual(out[1][\"name\"], \"my test\")\n        for item in (_MiniReader, _MiniReader(is_compatible=False)):\n            out = LoadImage(reader=item)(\"test\")\n            self.assertEqual(out[1][\"name\"], \"my test\")\n        out = LoadImage()(\"test\", reader=_MiniReader(is_compatible=False))\n        self.assertEqual(out[1][\"name\"], \"my test\")\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_imaged.py_TestLoadImaged.test_register_TestLoadImaged.test_register.with_tempfile_TemporaryDi.self_assertTupleEqual_res": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_imaged.py_TestLoadImaged.test_register_TestLoadImaged.test_register.with_tempfile_TemporaryDi.self_assertTupleEqual_res", "embedding": null, "metadata": {"file_path": "tests/test_load_imaged.py", "file_name": "test_load_imaged.py", "file_type": "text/x-python", "category": "test", "start_line": 46, "end_line": 58, "span_ids": ["TestLoadImaged.test_register"], "tokens": 152}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestLoadImaged(unittest.TestCase):\n\n    def test_register(self):\n        spatial_size = (32, 64, 128)\n        test_image = np.random.rand(*spatial_size)\n        with tempfile.TemporaryDirectory() as tempdir:\n            filename = os.path.join(tempdir, \"test_image.nii.gz\")\n            itk_np_view = itk.image_view_from_array(test_image)\n            itk.imwrite(itk_np_view, filename)\n\n            loader = LoadImaged(keys=\"img\")\n            loader.register(ITKReader())\n            result = loader({\"img\": Path(filename)})\n            self.assertTupleEqual(tuple(result[\"img_meta_dict\"][\"spatial_shape\"]), spatial_size[::-1])\n            self.assertTupleEqual(result[\"img\"].shape, spatial_size[::-1])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_imaged.py_TestLoadImaged.test_channel_dim_TestLoadImaged.test_no_file.None_1.LoadImaged_keys_img_re": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_imaged.py_TestLoadImaged.test_channel_dim_TestLoadImaged.test_no_file.None_1.LoadImaged_keys_img_re", "embedding": null, "metadata": {"file_path": "tests/test_load_imaged.py", "file_name": "test_load_imaged.py", "file_type": "text/x-python", "category": "test", "start_line": 60, "end_line": 77, "span_ids": ["TestLoadImaged.test_no_file", "TestLoadImaged.test_channel_dim"], "tokens": 231}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestLoadImaged(unittest.TestCase):\n\n    def test_channel_dim(self):\n        spatial_size = (32, 64, 3, 128)\n        test_image = np.random.rand(*spatial_size)\n        with tempfile.TemporaryDirectory() as tempdir:\n            filename = os.path.join(tempdir, \"test_image.nii.gz\")\n            nib.save(nib.Nifti1Image(test_image, affine=np.eye(4)), filename)\n\n            loader = LoadImaged(keys=\"img\")\n            loader.register(ITKReader(channel_dim=2))\n            result = EnsureChannelFirstD(\"img\")(loader({\"img\": filename}))\n            self.assertTupleEqual(tuple(result[\"img_meta_dict\"][\"spatial_shape\"]), (32, 64, 128))\n            self.assertTupleEqual(result[\"img\"].shape, (3, 32, 64, 128))\n\n    def test_no_file(self):\n        with self.assertRaises(RuntimeError):\n            LoadImaged(keys=\"img\")({\"img\": \"unknown\"})\n        with self.assertRaises(RuntimeError):\n            LoadImaged(keys=\"img\", reader=\"nibabelreader\")({\"img\": \"unknown\"})", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_imaged.py_TestConsistency_TestConsistency._cmp.with_tempfile_TemporaryDi.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_imaged.py_TestConsistency_TestConsistency._cmp.with_tempfile_TemporaryDi.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_load_imaged.py", "file_name": "test_load_imaged.py", "file_type": "text/x-python", "category": "test", "start_line": 80, "end_line": 103, "span_ids": ["TestConsistency._cmp", "TestConsistency"], "tokens": 356}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestConsistency(unittest.TestCase):\n    def _cmp(self, filename, shape, ch_shape, reader_1, reader_2, outname, ext):\n        data_dict = {\"img\": filename}\n        keys = data_dict.keys()\n        xforms = Compose([LoadImaged(keys, reader=reader_1), EnsureChannelFirstD(keys)])\n        img_dict = xforms(data_dict)  # load dicom with itk\n        self.assertTupleEqual(img_dict[\"img\"].shape, ch_shape)\n        self.assertTupleEqual(tuple(img_dict[\"img_meta_dict\"][\"spatial_shape\"]), shape)\n\n        with tempfile.TemporaryDirectory() as tempdir:\n            save_xform = SaveImageD(\n                keys, meta_keys=\"img_meta_dict\", output_dir=tempdir, squeeze_end_dims=False, output_ext=ext\n            )\n            save_xform(img_dict)  # save to nifti\n\n            new_xforms = Compose([LoadImaged(keys, reader=reader_2), EnsureChannelFirstD(keys)])\n            out = new_xforms({\"img\": os.path.join(tempdir, outname)})  # load nifti with itk\n            self.assertTupleEqual(out[\"img\"].shape, ch_shape)\n            self.assertTupleEqual(tuple(out[\"img_meta_dict\"][\"spatial_shape\"]), shape)\n            if \"affine\" in img_dict[\"img_meta_dict\"] and \"affine\" in out[\"img_meta_dict\"]:\n                np.testing.assert_allclose(\n                    img_dict[\"img_meta_dict\"][\"affine\"], out[\"img_meta_dict\"][\"affine\"], rtol=1e-3\n                )\n            np.testing.assert_allclose(out[\"img\"], img_dict[\"img\"], rtol=1e-3)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_imaged.py_TestConsistency.test_dicom_TestConsistency.test_dicom.None_2": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_imaged.py_TestConsistency.test_dicom_TestConsistency.test_dicom.None_2", "embedding": null, "metadata": {"file_path": "tests/test_load_imaged.py", "file_name": "test_load_imaged.py", "file_type": "text/x-python", "category": "test", "start_line": 105, "end_line": 112, "span_ids": ["TestConsistency.test_dicom"], "tokens": 201}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestConsistency(unittest.TestCase):\n\n    def test_dicom(self):\n        img_dir = \"tests/testing_data/CT_DICOM\"\n        self._cmp(\n            img_dir, (16, 16, 4), (1, 16, 16, 4), \"itkreader\", \"itkreader\", \"CT_DICOM/CT_DICOM_trans.nii.gz\", \".nii.gz\"\n        )\n        output_name = \"CT_DICOM/CT_DICOM_trans.nii.gz\"\n        self._cmp(img_dir, (16, 16, 4), (1, 16, 16, 4), \"nibabelreader\", \"itkreader\", output_name, \".nii.gz\")\n        self._cmp(img_dir, (16, 16, 4), (1, 16, 16, 4), \"itkreader\", \"nibabelreader\", output_name, \".nii.gz\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_imaged.py_TestConsistency.test_multi_dicom_TestConsistency.test_multi_dicom.None_2": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_imaged.py_TestConsistency.test_multi_dicom_TestConsistency.test_multi_dicom.None_2", "embedding": null, "metadata": {"file_path": "tests/test_load_imaged.py", "file_name": "test_load_imaged.py", "file_type": "text/x-python", "category": "test", "start_line": 114, "end_line": 123, "span_ids": ["TestConsistency.test_multi_dicom"], "tokens": 235}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestConsistency(unittest.TestCase):\n\n    def test_multi_dicom(self):\n        \"\"\"multichannel dicom reading, saving to nifti, then load with itk or nibabel\"\"\"\n\n        img_dir = [\"tests/testing_data/CT_DICOM\", \"tests/testing_data/CT_DICOM\"]\n        self._cmp(\n            img_dir, (16, 16, 4), (2, 16, 16, 4), \"itkreader\", \"itkreader\", \"CT_DICOM/CT_DICOM_trans.nii.gz\", \".nii.gz\"\n        )\n        output_name = \"CT_DICOM/CT_DICOM_trans.nii.gz\"\n        self._cmp(img_dir, (16, 16, 4), (2, 16, 16, 4), \"nibabelreader\", \"itkreader\", output_name, \".nii.gz\")\n        self._cmp(img_dir, (16, 16, 4), (2, 16, 16, 4), \"itkreader\", \"nibabelreader\", output_name, \".nii.gz\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_imaged.py_TestConsistency.test_png_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_imaged.py_TestConsistency.test_png_", "embedding": null, "metadata": {"file_path": "tests/test_load_imaged.py", "file_name": "test_load_imaged.py", "file_type": "text/x-python", "category": "test", "start_line": 125, "end_line": 141, "span_ids": ["impl:7", "TestConsistency.test_png"], "tokens": 252}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestConsistency(unittest.TestCase):\n\n    def test_png(self):\n        \"\"\"png reading with itk, saving to nifti, then load with itk or nibabel or PIL\"\"\"\n\n        test_image = np.random.randint(0, 256, size=(256, 224, 3)).astype(\"uint8\")\n        with tempfile.TemporaryDirectory() as tempdir:\n            filename = os.path.join(tempdir, \"test_image.png\")\n            itk_np_view = itk.image_view_from_array(test_image, is_vector=True)\n            itk.imwrite(itk_np_view, filename)\n            output_name = \"test_image/test_image_trans.png\"\n            self._cmp(filename, (224, 256), (3, 224, 256), \"itkreader\", \"itkreader\", output_name, \".png\")\n            self._cmp(filename, (224, 256), (3, 224, 256), \"itkreader\", \"PILReader\", output_name, \".png\")\n            self._cmp(filename, (224, 256), (3, 224, 256), \"itkreader\", \"nibabelreader\", output_name, \".png\")\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_loader_semaphore.py_mp_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_loader_semaphore.py_mp_", "embedding": null, "metadata": {"file_path": "tests/test_loader_semaphore.py", "file_name": "test_loader_semaphore.py", "file_type": "text/x-python", "category": "test", "start_line": 14, "end_line": 47, "span_ids": ["_main", "impl", "TestImportLock", "_run_test", "TestImportLock.test_start", "docstring", "w"], "tokens": 114}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import multiprocessing as mp\nimport unittest\n\nimport monai  # noqa\n\n\ndef w():\n    pass\n\n\ndef _main():\n    ps = mp.Process(target=w)\n    ps.start()\n    ps.join()\n\n\ndef _run_test():\n    try:\n        tmp = mp.get_context(\"spawn\")\n    except RuntimeError:\n        tmp = mp\n    p = tmp.Process(target=_main)\n    p.start()\n    p.join()\n\n\nclass TestImportLock(unittest.TestCase):\n    def test_start(self):\n        _run_test()\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_look_up_option.py_unittest_TEST_CASES._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_look_up_option.py_unittest_TEST_CASES._", "embedding": null, "metadata": {"file_path": "tests/test_look_up_option.py", "file_name": "test_look_up_option.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 36, "span_ids": ["impl", "_CaseEnum", "_CaseEnum1", "docstring"], "tokens": 144}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom enum import Enum\n\nfrom parameterized import parameterized\n\nfrom monai.utils import look_up_option\n\n\nclass _CaseEnum(Enum):\n    CONST = \"constant\"\n    EMPTY = \"empty\"\n\n\nclass _CaseEnum1(Enum):\n    CONST = \"constant\"\n    EMPTY = \"empty\"\n\n\nTEST_CASES = (\n    (\"test\", (\"test\", \"test1\"), \"test\"),\n    (\"test1\", {\"test1\", \"test\"}, \"test1\"),\n    (2, {1: \"test\", 2: \"valid\"}, \"valid\"),\n    (_CaseEnum.EMPTY, _CaseEnum, _CaseEnum.EMPTY),\n    (\"empty\", _CaseEnum, _CaseEnum.EMPTY),\n)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_look_up_option.py_TestLookUpOption_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_look_up_option.py_TestLookUpOption_", "embedding": null, "metadata": {"file_path": "tests/test_look_up_option.py", "file_name": "test_look_up_option.py", "file_type": "text/x-python", "category": "test", "start_line": 39, "end_line": 72, "span_ids": ["impl:3", "TestLookUpOption.test_no_found", "TestLookUpOption.test_default", "TestLookUpOption", "TestLookUpOption.test_look_up"], "tokens": 320}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestLookUpOption(unittest.TestCase):\n    @parameterized.expand(TEST_CASES)\n    def test_look_up(self, input_str, supported, expected):\n        output = look_up_option(input_str, supported)\n        self.assertEqual(output, expected)\n\n    def test_default(self):\n        output = look_up_option(\"not here\", {\"a\", \"b\"}, default=None)\n        self.assertEqual(output, None)\n\n    def test_no_found(self):\n        with self.assertRaisesRegex(ValueError, \"Unsupported\"):\n            look_up_option(\"not here\", {\"a\", \"b\"})\n        with self.assertRaisesRegex(ValueError, \"Unsupported\"):\n            look_up_option(\"not here\", [\"a\", \"b\"])\n        with self.assertRaisesRegex(ValueError, \"Unsupported\"):\n            look_up_option(\"not here\", {\"a\": 1, \"b\": 2})\n        with self.assertRaisesRegex(ValueError, \"did you mean\"):\n            look_up_option(3, {1: \"a\", 2: \"b\", \"c\": 3})\n        with self.assertRaisesRegex(ValueError, \"did.*empty\"):\n            look_up_option(\"empy\", _CaseEnum)\n        with self.assertRaisesRegex(ValueError, \"Unsupported\"):\n            look_up_option(_CaseEnum1.EMPTY, _CaseEnum)\n        with self.assertRaisesRegex(ValueError, \"Unsupported\"):\n            look_up_option(None, _CaseEnum)\n        with self.assertRaisesRegex(ValueError, \"No\"):\n            look_up_option(None, None)\n        with self.assertRaisesRegex(ValueError, \"No\"):\n            look_up_option(\"test\", None)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_lr_scheduler.py_unittest_TEST_CASE_LRSCHEDULER._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_lr_scheduler.py_unittest_TEST_CASE_LRSCHEDULER._", "embedding": null, "metadata": {"file_path": "tests/test_lr_scheduler.py", "file_name": "test_lr_scheduler.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 32, "span_ids": ["SchedulerTestNet", "impl", "docstring", "SchedulerTestNet.forward", "SchedulerTestNet.__init__"], "tokens": 181}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.optimizers.lr_scheduler import WarmupCosineSchedule\n\n\nclass SchedulerTestNet(torch.nn.Module):\n    def __init__(self):\n        super().__init__()\n        self.conv1 = torch.nn.Conv2d(1, 1, 1)\n        self.conv2 = torch.nn.Conv2d(1, 1, 1)\n\n    def forward(self, x):\n        return self.conv2(torch.nn.functional.relu(self.conv1(x)))\n\n\nTEST_CASE_LRSCHEDULER = [\n    [{\"warmup_steps\": 2, \"t_total\": 10}, [0.000, 0.500, 1.00, 0.962, 0.854, 0.691, 0.500, 0.309, 0.146, 0.038]]\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_lr_scheduler.py_TestLRSCHEDULER_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_lr_scheduler.py_TestLRSCHEDULER_", "embedding": null, "metadata": {"file_path": "tests/test_lr_scheduler.py", "file_name": "test_lr_scheduler.py", "file_type": "text/x-python", "category": "test", "start_line": 35, "end_line": 53, "span_ids": ["TestLRSCHEDULER", "impl:3", "TestLRSCHEDULER.test_shape"], "tokens": 183}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestLRSCHEDULER(unittest.TestCase):\n    @parameterized.expand(TEST_CASE_LRSCHEDULER)\n    def test_shape(self, input_param, expected_lr):\n        net = SchedulerTestNet()\n        optimizer = torch.optim.Adam(net.parameters(), lr=1.0)\n        scheduler = WarmupCosineSchedule(optimizer, **input_param)\n        self.assertEqual(len([scheduler.get_last_lr()[0]]), 1)\n        lrs_1 = []\n        for _ in range(input_param[\"t_total\"]):\n            lrs_1.append(float(f\"{scheduler.get_last_lr()[0]:.3f}\"))\n            optimizer.step()\n            scheduler.step()\n        for a, b in zip(lrs_1, expected_lr):\n            self.assertEqual(a, b, msg=f\"LR is wrong ! expected {b}, got {a}\")\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_map_classes_to_indices.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_map_classes_to_indices.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_map_classes_to_indices.py", "file_name": "test_map_classes_to_indices.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 133, "span_ids": ["impl:10", "TestMapClassesToIndices", "TestMapClassesToIndices.test_value", "docstring"], "tokens": 1112}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import map_classes_to_indices\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    TESTS.append(\n        [\n            # test Argmax data\n            {\n                \"label\": p(np.array([[[0, 1, 2], [2, 0, 1], [1, 2, 0]]])),\n                \"num_classes\": 3,\n                \"image\": None,\n                \"image_threshold\": 0.0,\n            },\n            [np.array([0, 4, 8]), np.array([1, 5, 6]), np.array([2, 3, 7])],\n        ]\n    )\n\n    TESTS.append(\n        [\n            {\n                \"label\": p(np.array([[[0, 1, 2], [2, 0, 1], [1, 2, 0]]])),\n                \"num_classes\": 3,\n                \"image\": p(np.array([[[132, 1434, 51], [61, 0, 133], [523, 44, 232]]])),\n                \"image_threshold\": 60,\n            },\n            [np.array([0, 8]), np.array([1, 5, 6]), np.array([3])],\n        ]\n    )\n\n    TESTS.append(\n        [\n            # test One-Hot data\n            {\n                \"label\": p(\n                    np.array(\n                        [\n                            [[1, 0, 0], [0, 1, 0], [0, 0, 1]],\n                            [[0, 1, 0], [0, 0, 1], [1, 0, 0]],\n                            [[0, 0, 1], [1, 0, 0], [0, 1, 0]],\n                        ]\n                    )\n                ),\n                \"image\": None,\n                \"image_threshold\": 0.0,\n            },\n            [np.array([0, 4, 8]), np.array([1, 5, 6]), np.array([2, 3, 7])],\n        ]\n    )\n\n    TESTS.append(\n        [\n            {\n                \"label\": p(\n                    np.array(\n                        [\n                            [[1, 0, 0], [0, 1, 0], [0, 0, 1]],\n                            [[0, 1, 0], [0, 0, 1], [1, 0, 0]],\n                            [[0, 0, 1], [1, 0, 0], [0, 1, 0]],\n                        ]\n                    )\n                ),\n                \"num_classes\": None,\n                \"image\": p(np.array([[[132, 1434, 51], [61, 0, 133], [523, 44, 232]]])),\n                \"image_threshold\": 60,\n            },\n            [np.array([0, 8]), np.array([1, 5, 6]), np.array([3])],\n        ]\n    )\n\n    TESTS.append(\n        [\n            # test empty class\n            {\n                \"label\": p(np.array([[[0, 1, 2], [2, 0, 1], [1, 2, 0]]])),\n                \"num_classes\": 5,\n                \"image\": None,\n                \"image_threshold\": 0.0,\n            },\n            [np.array([0, 4, 8]), np.array([1, 5, 6]), np.array([2, 3, 7]), np.array([]), np.array([])],\n        ]\n    )\n\n    TESTS.append(\n        [\n            # test empty class\n            {\n                \"label\": p(\n                    np.array(\n                        [\n                            [[1, 0, 0], [0, 1, 0], [0, 0, 1]],\n                            [[0, 1, 0], [0, 0, 1], [1, 0, 0]],\n                            [[0, 0, 1], [1, 0, 0], [0, 1, 0]],\n                            [[0, 0, 0], [0, 0, 0], [0, 0, 0]],\n                            [[0, 0, 0], [0, 0, 0], [0, 0, 0]],\n                        ]\n                    )\n                ),\n                \"image\": None,\n                \"image_threshold\": 0.0,\n            },\n            [np.array([0, 4, 8]), np.array([1, 5, 6]), np.array([2, 3, 7]), np.array([]), np.array([])],\n        ]\n    )\n\n\nclass TestMapClassesToIndices(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_value(self, input_data, expected_indices):\n        indices = map_classes_to_indices(**input_data)\n        for i, e in zip(indices, expected_indices):\n            assert_allclose(i, e, type_test=False)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_map_label_value.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_map_label_value.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_map_label_value.py", "file_name": "test_map_label_value.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 81, "span_ids": ["TestMapLabelValue", "TestMapLabelValue.test_shape", "impl:7", "docstring", "impl:6"], "tokens": 795}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import MapLabelValue\nfrom tests.utils import TEST_NDARRAYS\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    TESTS.extend(\n        [\n            [{\"orig_labels\": [3, 2, 1], \"target_labels\": [0, 1, 2]}, p([[3, 1], [1, 2]]), p([[0.0, 2.0], [2.0, 1.0]])],\n            [\n                {\"orig_labels\": [3, 5, 8], \"target_labels\": [0, 1, 2]},\n                p([[[3], [5], [5], [8]]]),\n                p([[[0.0], [1.0], [1.0], [2.0]]]),\n            ],\n            [{\"orig_labels\": [1, 2, 3], \"target_labels\": [0, 1, 2]}, p([3, 1, 1, 2]), p([2.0, 0.0, 0.0, 1.0])],\n            [{\"orig_labels\": [1, 2, 3], \"target_labels\": [0.5, 1.5, 2.5]}, p([3, 1, 1, 2]), p([2.5, 0.5, 0.5, 1.5])],\n        ]\n    )\n    # note: PyTorch 1.5.1 doesn't support rich dtypes\n    TESTS.append(\n        [\n            {\"orig_labels\": [1.5, 2.5, 3.5], \"target_labels\": [0, 1, 2], \"dtype\": np.int8},\n            p([3.5, 1.5, 1.5, 2.5]),\n            p([2, 0, 0, 1]),\n        ]\n    )\nTESTS.extend(\n    [\n        [\n            {\"orig_labels\": [\"label3\", \"label2\", \"label1\"], \"target_labels\": [0, 1, 2]},\n            np.array([[\"label3\", \"label1\"], [\"label1\", \"label2\"]]),\n            np.array([[0, 2], [2, 1]]),\n        ],\n        [\n            {\"orig_labels\": [3.5, 2.5, 1.5], \"target_labels\": [\"label0\", \"label1\", \"label2\"], \"dtype\": \"str\"},\n            np.array([[3.5, 1.5], [1.5, 2.5]]),\n            np.array([[\"label0\", \"label2\"], [\"label2\", \"label1\"]]),\n        ],\n        [\n            {\n                \"orig_labels\": [\"label3\", \"label2\", \"label1\"],\n                \"target_labels\": [\"label1\", \"label2\", \"label3\"],\n                \"dtype\": \"str\",\n            },\n            np.array([[\"label3\", \"label1\"], [\"label1\", \"label2\"]]),\n            np.array([[\"label1\", \"label3\"], [\"label3\", \"label2\"]]),\n        ],\n    ]\n)\n\n\nclass TestMapLabelValue(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_shape(self, input_param, input_data, expected_value):\n        result = MapLabelValue(**input_param)(input_data)\n        if isinstance(expected_value, torch.Tensor):\n            torch.testing.assert_allclose(result, expected_value)\n        else:\n            np.testing.assert_equal(result, expected_value)\n        self.assertTupleEqual(result.shape, expected_value.shape)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_mask_intensity.py_unittest_TEST_CASE_4._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_mask_intensity.py_unittest_TEST_CASE_4._", "embedding": null, "metadata": {"file_path": "tests/test_mask_intensity.py", "file_name": "test_mask_intensity.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 45, "span_ids": ["impl:7", "docstring"], "tokens": 731}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import MaskIntensity\n\nTEST_CASE_1 = [\n    {\"mask_data\": np.array([[[0, 0, 0], [0, 1, 0], [0, 0, 0]]])},\n    np.array([[[1, 1, 1], [2, 2, 2], [3, 3, 3]], [[4, 4, 4], [5, 5, 5], [6, 6, 6]]]),\n    np.array([[[0, 0, 0], [0, 2, 0], [0, 0, 0]], [[0, 0, 0], [0, 5, 0], [0, 0, 0]]]),\n]\n\nTEST_CASE_2 = [\n    {\"mask_data\": np.array([[[0, 0, 0], [0, 5, 0], [0, 0, 0]]])},\n    np.array([[[1, 1, 1], [2, 2, 2], [3, 3, 3]], [[4, 4, 4], [5, 5, 5], [6, 6, 6]]]),\n    np.array([[[0, 0, 0], [0, 2, 0], [0, 0, 0]], [[0, 0, 0], [0, 5, 0], [0, 0, 0]]]),\n]\n\nTEST_CASE_3 = [\n    {\"mask_data\": np.array([[[0, 0, 0], [0, 1, 0], [0, 0, 0]], [[0, 1, 0], [0, 1, 0], [0, 1, 0]]])},\n    np.array([[[1, 1, 1], [2, 2, 2], [3, 3, 3]], [[4, 4, 4], [5, 5, 5], [6, 6, 6]]]),\n    np.array([[[0, 0, 0], [0, 2, 0], [0, 0, 0]], [[0, 4, 0], [0, 5, 0], [0, 6, 0]]]),\n]\n\nTEST_CASE_4 = [\n    {\n        \"mask_data\": np.array([[[1, 2, 3], [4, 5, 6], [7, 8, 9]]]),\n        \"select_fn\": lambda x: np.where((x > 3) & (x < 7), True, False),\n    },\n    np.array([[[1, 1, 1], [2, 2, 2], [3, 3, 3]], [[4, 4, 4], [5, 5, 5], [6, 6, 6]]]),\n    np.array([[[0, 0, 0], [2, 2, 2], [0, 0, 0]], [[0, 0, 0], [5, 5, 5], [0, 0, 0]]]),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_mask_intensity.py_TEST_CASE_5_TEST_CASE_5._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_mask_intensity.py_TEST_CASE_5_TEST_CASE_5._", "embedding": null, "metadata": {"file_path": "tests/test_mask_intensity.py", "file_name": "test_mask_intensity.py", "file_type": "text/x-python", "category": "test", "start_line": 47, "end_line": 51, "span_ids": ["impl:7"], "tokens": 191}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "TEST_CASE_5 = [\n    {\"mask_data\": np.array([[[0, 0, 0], [0, 1, 0], [0, 0, 0]], [[0, 1, 0], [0, 1, 0], [0, 1, 0]]])},\n    torch.as_tensor([[[1, 1, 1], [2, 2, 2], [3, 3, 3]], [[4, 4, 4], [5, 5, 5], [6, 6, 6]]]),\n    torch.as_tensor([[[0, 0, 0], [0, 2, 0], [0, 0, 0]], [[0, 4, 0], [0, 5, 0], [0, 6, 0]]]),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_mask_intensity.py_TestMaskIntensity_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_mask_intensity.py_TestMaskIntensity_", "embedding": null, "metadata": {"file_path": "tests/test_mask_intensity.py", "file_name": "test_mask_intensity.py", "file_type": "text/x-python", "category": "test", "start_line": 54, "end_line": 71, "span_ids": ["impl:11", "TestMaskIntensity", "TestMaskIntensity.test_runtime_mask", "TestMaskIntensity.test_value"], "tokens": 275}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestMaskIntensity(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5])\n    def test_value(self, argments, image, expected_data):\n        result = MaskIntensity(**argments)(image)\n        np.testing.assert_allclose(result, expected_data)\n\n    def test_runtime_mask(self):\n        mask_data = np.array([[[0, 0, 0], [0, 1, 0], [0, 0, 0]]])\n        img = np.array([[[1, 1, 1], [2, 2, 2], [3, 3, 3]], [[4, 4, 4], [5, 5, 5], [6, 6, 6]]])\n        expected = np.array([[[0, 0, 0], [0, 2, 0], [0, 0, 0]], [[0, 0, 0], [0, 5, 0], [0, 0, 0]]])\n\n        result = MaskIntensity()(img=img, mask_data=mask_data)\n        np.testing.assert_allclose(result, expected)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_masked_inference_wsi_dataset.py_TEST_CASE_2_TEST_CASE_OPENSLIDE_1._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_masked_inference_wsi_dataset.py_TEST_CASE_2_TEST_CASE_OPENSLIDE_1._", "embedding": null, "metadata": {"file_path": "tests/test_masked_inference_wsi_dataset.py", "file_name": "test_masked_inference_wsi_dataset.py", "file_type": "text/x-python", "category": "test", "start_line": 73, "end_line": 157, "span_ids": ["impl:20", "impl:28"], "tokens": 747}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "TEST_CASE_2 = [\n    {\"data\": [{\"image\": FILE_PATH, \"mask\": MASK4}], \"patch_size\": 1, \"image_reader_name\": \"cuCIM\"},\n    [\n        {\n            \"image\": np.array([[[243]], [[243]], [[243]]], dtype=np.uint8),\n            \"name\": FILE_NAME,\n            \"mask_location\": [100, 100],\n        },\n        {\n            \"image\": np.array([[[243]], [[243]], [[243]]], dtype=np.uint8),\n            \"name\": FILE_NAME,\n            \"mask_location\": [100, 101],\n        },\n        {\n            \"image\": np.array([[[243]], [[243]], [[243]]], dtype=np.uint8),\n            \"name\": FILE_NAME,\n            \"mask_location\": [101, 100],\n        },\n        {\n            \"image\": np.array([[[243]], [[243]], [[243]]], dtype=np.uint8),\n            \"name\": FILE_NAME,\n            \"mask_location\": [101, 101],\n        },\n    ],\n]\n\nTEST_CASE_3 = [\n    {\"data\": [{\"image\": FILE_PATH, \"mask\": MASK1}], \"patch_size\": 2, \"image_reader_name\": \"cuCIM\"},\n    [\n        {\n            \"image\": np.array(\n                [[[243, 243], [243, 243]], [[243, 243], [243, 243]], [[243, 243], [243, 243]]], dtype=np.uint8\n            ),\n            \"name\": FILE_NAME,\n            \"mask_location\": [100, 100],\n        }\n    ],\n]\n\nTEST_CASE_4 = [\n    {\n        \"data\": [{\"image\": FILE_PATH, \"mask\": MASK1}, {\"image\": FILE_PATH, \"mask\": MASK2}],\n        \"patch_size\": 1,\n        \"image_reader_name\": \"cuCIM\",\n    },\n    [\n        {\n            \"image\": np.array([[[243]], [[243]], [[243]]], dtype=np.uint8),\n            \"name\": FILE_NAME,\n            \"mask_location\": [100, 100],\n        },\n        {\n            \"image\": np.array([[[243]], [[243]], [[243]]], dtype=np.uint8),\n            \"name\": FILE_NAME,\n            \"mask_location\": [100, 100],\n        },\n        {\n            \"image\": np.array([[[243]], [[243]], [[243]]], dtype=np.uint8),\n            \"name\": FILE_NAME,\n            \"mask_location\": [100, 101],\n        },\n    ],\n]\n\n\nTEST_CASE_OPENSLIDE_0 = [\n    {\"data\": [{\"image\": FILE_PATH, \"mask\": MASK1}], \"patch_size\": 1, \"image_reader_name\": \"OpenSlide\"},\n    [{\"image\": np.array([[[243]], [[243]], [[243]]], dtype=np.uint8), \"name\": FILE_NAME, \"mask_location\": [100, 100]}],\n]\n\nTEST_CASE_OPENSLIDE_1 = [\n    {\"data\": [{\"image\": FILE_PATH, \"mask\": MASK2}], \"patch_size\": 1, \"image_reader_name\": \"OpenSlide\"},\n    [\n        {\n            \"image\": np.array([[[243]], [[243]], [[243]]], dtype=np.uint8),\n            \"name\": FILE_NAME,\n            \"mask_location\": [100, 100],\n        },\n        {\n            \"image\": np.array([[[243]], [[243]], [[243]]], dtype=np.uint8),\n            \"name\": FILE_NAME,\n            \"mask_location\": [100, 101],\n        },\n    ],\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_masked_loss.py_TestMaskedLoss_TestMaskedLoss.test_shape.None_1": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_masked_loss.py_TestMaskedLoss_TestMaskedLoss.test_shape.None_1", "embedding": null, "metadata": {"file_path": "tests/test_masked_loss.py", "file_name": "test_masked_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 40, "end_line": 62, "span_ids": ["TestMaskedLoss", "TestMaskedLoss.setUp", "TestMaskedLoss.tearDown", "TestMaskedLoss.test_shape"], "tokens": 244}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestMaskedLoss(unittest.TestCase):\n    def setUp(self):\n        set_determinism(0)\n\n    def tearDown(self):\n        set_determinism(None)\n\n    @parameterized.expand(TEST_CASES)\n    def test_shape(self, input_param, expected_val):\n        size = [3, 3, 5, 5]\n        label = torch.randint(low=0, high=2, size=size)\n        label = torch.argmax(label, dim=1, keepdim=True)\n        pred = torch.randn(size)\n        result = MaskedLoss(**input_param)(pred, label, None)\n        out = result.detach().cpu().numpy()\n        checked = np.allclose(out, expected_val[0][0]) or np.allclose(out, expected_val[0][1])\n        self.assertTrue(checked)\n\n        mask = torch.randint(low=0, high=2, size=label.shape)\n        result = MaskedLoss(**input_param)(pred, label, mask)\n        out = result.detach().cpu().numpy()\n        checked = np.allclose(out, expected_val[1][0]) or np.allclose(out, expected_val[1][1])\n        self.assertTrue(checked)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_mean_ensemble.py_unittest_for_p_in_TEST_NDARRAYS_.None_5": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_mean_ensemble.py_unittest_for_p_in_TEST_NDARRAYS_.None_5", "embedding": null, "metadata": {"file_path": "tests/test_mean_ensemble.py", "file_name": "test_mean_ensemble.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 55, "span_ids": ["docstring"], "tokens": 532}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import MeanEnsemble\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    TESTS.append([{\"weights\": None}, [p(torch.ones(2, 2, 2)), p(torch.ones(2, 2, 2)) + 2], p(torch.ones(2, 2, 2)) + 1])\n\n    TESTS.append(\n        [{\"weights\": None}, p(torch.stack([torch.ones(2, 2, 2), torch.ones(2, 2, 2) + 2])), p(torch.ones(2, 2, 2)) + 1]\n    )\n\n    TESTS.append(\n        [{\"weights\": [1, 3]}, [p(torch.ones(2, 2, 2)), p(torch.ones(2, 2, 2)) + 2], p(torch.ones(2, 2, 2)) * 2.5]\n    )\n\n    TESTS.append(\n        [\n            {\"weights\": [[1, 3], [3, 1]]},\n            [p(torch.ones(2, 2, 2)), p(torch.ones(2, 2, 2)) + 2],\n            p(torch.ones(2, 2, 2) * torch.tensor([2.5, 1.5]).reshape(2, 1, 1)),\n        ]\n    )\n\n    TESTS.append(\n        [\n            {\"weights\": np.array([[1, 3], [3, 1]])},\n            [p(torch.ones(2, 2, 2)), p(torch.ones(2, 2, 2)) + 2],\n            p(torch.ones(2, 2, 2) * torch.tensor([2.5, 1.5]).reshape(2, 1, 1)),\n        ]\n    )\n\n    TESTS.append(\n        [\n            {\"weights\": torch.tensor([[[1, 3]], [[3, 1]]])},\n            [p(torch.ones(2, 2, 2, 2)), p(torch.ones(2, 2, 2, 2)) + 2],\n            p(torch.ones(2, 2, 2, 2) * torch.tensor([2.5, 1.5]).reshape(1, 2, 1, 1)),\n        ]\n    )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_mean_ensembled.py_unittest_for_p_in_TEST_NDARRAYS_.None_5": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_mean_ensembled.py_unittest_for_p_in_TEST_NDARRAYS_.None_5", "embedding": null, "metadata": {"file_path": "tests/test_mean_ensembled.py", "file_name": "test_mean_ensembled.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 69, "span_ids": ["docstring"], "tokens": 711}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import MeanEnsembled\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    TESTS.append(\n        [\n            {\"keys\": [\"pred0\", \"pred1\"], \"output_key\": \"output\", \"weights\": None},\n            {\"pred0\": p(torch.ones(2, 2, 2)), \"pred1\": p(torch.ones(2, 2, 2)) + 2},\n            p(torch.ones(2, 2, 2)) + 1,\n        ]\n    )\n\n    TESTS.append(\n        [\n            {\"keys\": \"output\", \"weights\": None},\n            {\"output\": p(torch.stack([torch.ones(2, 2, 2), torch.ones(2, 2, 2) + 2]))},\n            p(torch.ones(2, 2, 2)) + 1,\n        ]\n    )\n\n    TESTS.append(\n        [\n            {\"keys\": [\"pred0\", \"pred1\"], \"output_key\": \"output\", \"weights\": [1, 3]},\n            {\"pred0\": p(torch.ones(2, 2, 2, 2)), \"pred1\": p(torch.ones(2, 2, 2, 2)) + 2},\n            p(torch.ones(2, 2, 2, 2)) * 2.5,\n        ]\n    )\n\n    TESTS.append(\n        [\n            {\"keys\": [\"pred0\", \"pred1\"], \"output_key\": \"output\", \"weights\": [[1, 3], [3, 1]]},\n            {\"pred0\": p(torch.ones(2, 2, 2)), \"pred1\": p(torch.ones(2, 2, 2)) + 2},\n            p(torch.ones(2, 2, 2) * torch.tensor([2.5, 1.5]).reshape(2, 1, 1)),\n        ]\n    )\n\n    TESTS.append(\n        [\n            {\"keys\": [\"pred0\", \"pred1\"], \"output_key\": \"output\", \"weights\": np.array([[[1, 3]], [[3, 1]]])},\n            {\"pred0\": p(torch.ones(2, 2, 2, 2)), \"pred1\": p(torch.ones(2, 2, 2, 2)) + 2},\n            p(torch.ones(2, 2, 2, 2) * torch.tensor([2.5, 1.5]).reshape(1, 2, 1, 1)),\n        ]\n    )\n\n    TESTS.append(\n        [\n            {\"keys\": [\"pred0\", \"pred1\"], \"output_key\": \"output\", \"weights\": torch.tensor([[[1, 3]], [[3, 1]]])},\n            {\"pred0\": p(torch.ones(2, 2, 2, 2)), \"pred1\": p(torch.ones(2, 2, 2, 2)) + 2},\n            p(torch.ones(2, 2, 2, 2) * torch.tensor([2.5, 1.5]).reshape(1, 2, 1, 1)),\n        ]\n    )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_mlp.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_mlp.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_mlp.py", "file_name": "test_mlp.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 49, "span_ids": ["TestMLPBlock.test_shape", "TestMLPBlock.test_ill_arg", "docstring", "impl:9", "TestMLPBlock"], "tokens": 271}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks import eval_mode\nfrom monai.networks.blocks.mlp import MLPBlock\n\nTEST_CASE_MLP = []\nfor dropout_rate in np.linspace(0, 1, 4):\n    for hidden_size in [128, 256, 512, 768]:\n        for mlp_dim in [512, 1028, 2048, 3072]:\n\n            test_case = [\n                {\"hidden_size\": hidden_size, \"mlp_dim\": mlp_dim, \"dropout_rate\": dropout_rate},\n                (2, 512, hidden_size),\n                (2, 512, hidden_size),\n            ]\n            TEST_CASE_MLP.append(test_case)\n\n\nclass TestMLPBlock(unittest.TestCase):\n    @parameterized.expand(TEST_CASE_MLP)\n    def test_shape(self, input_param, input_shape, expected_shape):\n        net = MLPBlock(**input_param)\n        with eval_mode(net):\n            result = net(torch.randn(input_shape))\n            self.assertEqual(result.shape, expected_shape)\n\n    def test_ill_arg(self):\n        with self.assertRaises(ValueError):\n            MLPBlock(hidden_size=128, mlp_dim=512, dropout_rate=5.0)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nifti_rw.py_os_for_p_in_TEST_NDARRAYS_.for_q_in_TEST_NDARRAYS_.None_4": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nifti_rw.py_os_for_p_in_TEST_NDARRAYS_.for_q_in_TEST_NDARRAYS_.None_4", "embedding": null, "metadata": {"file_path": "tests/test_nifti_rw.py", "file_name": "test_nifti_rw.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 77, "span_ids": ["docstring"], "tokens": 604}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport tempfile\nimport unittest\n\nimport nibabel as nib\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.data import write_nifti\nfrom monai.transforms import LoadImage, Orientation, Spacing\nfrom tests.utils import TEST_NDARRAYS, assert_allclose, make_nifti_image\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    for q in TEST_NDARRAYS:\n        TEST_IMAGE = p(np.arange(24).reshape((2, 4, 3)))\n        TEST_AFFINE = q(\n            np.array(\n                [[-5.3, 0.0, 0.0, 102.01], [0.0, 0.52, 2.17, -7.50], [-0.0, 1.98, -0.26, -23.12], [0.0, 0.0, 0.0, 1.0]]\n            )\n        )\n        TESTS.append(\n            [\n                TEST_IMAGE,\n                TEST_AFFINE,\n                dict(reader=\"NibabelReader\", image_only=False, as_closest_canonical=True),\n                np.arange(24).reshape((2, 4, 3)),\n            ]\n        )\n        TESTS.append(\n            [\n                TEST_IMAGE,\n                TEST_AFFINE,\n                dict(reader=\"NibabelReader\", image_only=True, as_closest_canonical=True),\n                np.array(\n                    [\n                        [[12.0, 15.0, 18.0, 21.0], [13.0, 16.0, 19.0, 22.0], [14.0, 17.0, 20.0, 23.0]],\n                        [[0.0, 3.0, 6.0, 9.0], [1.0, 4.0, 7.0, 10.0], [2.0, 5.0, 8.0, 11.0]],\n                    ]\n                ),\n            ]\n        )\n        TESTS.append(\n            [\n                TEST_IMAGE,\n                TEST_AFFINE,\n                dict(reader=\"NibabelReader\", image_only=True, as_closest_canonical=False),\n                np.arange(24).reshape((2, 4, 3)),\n            ]\n        )\n        TESTS.append(\n            [\n                TEST_IMAGE,\n                TEST_AFFINE,\n                dict(reader=\"NibabelReader\", image_only=False, as_closest_canonical=False),\n                np.arange(24).reshape((2, 4, 3)),\n            ]\n        )\n        TESTS.append(\n            [\n                TEST_IMAGE,\n                None,\n                dict(reader=\"NibabelReader\", image_only=False, as_closest_canonical=False),\n                np.arange(24).reshape((2, 4, 3)),\n            ]\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nifti_rw.py_TestNiftiLoadRead_TestNiftiLoadRead.test_orientation.assert_allclose_saved_dat": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nifti_rw.py_TestNiftiLoadRead_TestNiftiLoadRead.test_orientation.assert_allclose_saved_dat", "embedding": null, "metadata": {"file_path": "tests/test_nifti_rw.py", "file_name": "test_nifti_rw.py", "file_type": "text/x-python", "category": "test", "start_line": 80, "end_line": 109, "span_ids": ["TestNiftiLoadRead", "TestNiftiLoadRead.test_orientation"], "tokens": 248}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestNiftiLoadRead(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_orientation(self, array, affine, reader_param, expected):\n        test_image = make_nifti_image(array, affine)\n\n        # read test cases\n        loader = LoadImage(**reader_param)\n        load_result = loader(test_image)\n        if isinstance(load_result, tuple):\n            data_array, header = load_result\n        else:\n            data_array = load_result\n            header = None\n        if os.path.exists(test_image):\n            os.remove(test_image)\n\n        # write test cases\n        if header is not None:\n            write_nifti(data_array, test_image, header[\"affine\"], header.get(\"original_affine\", None))\n        elif affine is not None:\n            write_nifti(data_array, test_image, affine)\n        saved = nib.load(test_image)\n        saved_affine = saved.affine\n        saved_data = saved.get_fdata()\n        if os.path.exists(test_image):\n            os.remove(test_image)\n\n        if affine is not None:\n            assert_allclose(saved_affine, affine, type_test=False)\n        assert_allclose(saved_data, expected, type_test=False)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nifti_rw.py_TestNiftiLoadRead.test_write_2d_TestNiftiLoadRead.test_write_2d.with_tempfile_TemporaryDi.for_p_in_TEST_NDARRAYS_.None_5": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nifti_rw.py_TestNiftiLoadRead.test_write_2d_TestNiftiLoadRead.test_write_2d.with_tempfile_TemporaryDi.for_p_in_TEST_NDARRAYS_.None_5", "embedding": null, "metadata": {"file_path": "tests/test_nifti_rw.py", "file_name": "test_nifti_rw.py", "file_type": "text/x-python", "category": "test", "start_line": 152, "end_line": 169, "span_ids": ["TestNiftiLoadRead.test_write_2d"], "tokens": 305}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestNiftiLoadRead(unittest.TestCase):\n\n    def test_write_2d(self):\n        with tempfile.TemporaryDirectory() as out_dir:\n            image_name = os.path.join(out_dir, \"test.nii.gz\")\n            for p in TEST_NDARRAYS:\n                img = p(np.arange(6).reshape((2, 3)))\n                write_nifti(img, image_name, affine=np.diag([1]), target_affine=np.diag([1.4]))\n                out = nib.load(image_name)\n                np.testing.assert_allclose(out.get_fdata(), [[0, 1, 2], [3.0, 4, 5]])\n                np.testing.assert_allclose(out.affine, np.diag([1.4, 1, 1, 1]))\n\n                image_name = os.path.join(out_dir, \"test1.nii.gz\")\n                img = np.arange(5).reshape((1, 5))\n                write_nifti(\n                    img, image_name, affine=np.diag([1, 1, 1, 3, 3]), target_affine=np.diag([1.4, 2.0, 1, 3, 5])\n                )\n                out = nib.load(image_name)\n                np.testing.assert_allclose(out.get_fdata(), [[0, 2, 4]])\n                np.testing.assert_allclose(out.affine, np.diag([1.4, 2, 1, 1]))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nifti_rw.py_TestNiftiLoadRead.test_write_3d_TestNiftiLoadRead.test_write_3d.with_tempfile_TemporaryDi.for_p_in_TEST_NDARRAYS_.None_5": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nifti_rw.py_TestNiftiLoadRead.test_write_3d_TestNiftiLoadRead.test_write_3d.with_tempfile_TemporaryDi.for_p_in_TEST_NDARRAYS_.None_5", "embedding": null, "metadata": {"file_path": "tests/test_nifti_rw.py", "file_name": "test_nifti_rw.py", "file_type": "text/x-python", "category": "test", "start_line": 171, "end_line": 188, "span_ids": ["TestNiftiLoadRead.test_write_3d"], "tokens": 312}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestNiftiLoadRead(unittest.TestCase):\n\n    def test_write_3d(self):\n        with tempfile.TemporaryDirectory() as out_dir:\n            image_name = os.path.join(out_dir, \"test.nii.gz\")\n            for p in TEST_NDARRAYS:\n                img = p(np.arange(6).reshape((1, 2, 3)))\n                write_nifti(img, image_name, affine=np.diag([1]), target_affine=np.diag([1.4]))\n                out = nib.load(image_name)\n                np.testing.assert_allclose(out.get_fdata(), [[[0, 1, 2], [3, 4, 5]]])\n                np.testing.assert_allclose(out.affine, np.diag([1.4, 1, 1, 1]))\n\n                image_name = os.path.join(out_dir, \"test1.nii.gz\")\n                img = p(np.arange(5).reshape((1, 1, 5)))\n                write_nifti(\n                    img, image_name, affine=np.diag([1, 1, 1, 3, 3]), target_affine=np.diag([1.4, 2.0, 2, 3, 5])\n                )\n                out = nib.load(image_name)\n                np.testing.assert_allclose(out.get_fdata(), [[[0, 2, 4]]])\n                np.testing.assert_allclose(out.affine, np.diag([1.4, 2, 2, 1]))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nifti_rw.py_TestNiftiLoadRead.test_write_4d_TestNiftiLoadRead.test_write_4d.with_tempfile_TemporaryDi.for_p_in_TEST_NDARRAYS_.None_5": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nifti_rw.py_TestNiftiLoadRead.test_write_4d_TestNiftiLoadRead.test_write_4d.with_tempfile_TemporaryDi.for_p_in_TEST_NDARRAYS_.None_5", "embedding": null, "metadata": {"file_path": "tests/test_nifti_rw.py", "file_name": "test_nifti_rw.py", "file_type": "text/x-python", "category": "test", "start_line": 190, "end_line": 207, "span_ids": ["TestNiftiLoadRead.test_write_4d"], "tokens": 331}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestNiftiLoadRead(unittest.TestCase):\n\n    def test_write_4d(self):\n        with tempfile.TemporaryDirectory() as out_dir:\n            image_name = os.path.join(out_dir, \"test.nii.gz\")\n            for p in TEST_NDARRAYS:\n                img = p(np.arange(6).reshape((1, 1, 3, 2)))\n                write_nifti(img, image_name, affine=np.diag([1.4, 1]), target_affine=np.diag([1, 1.4, 1]))\n                out = nib.load(image_name)\n                np.testing.assert_allclose(out.get_fdata(), [[[[0, 1], [2, 3], [4, 5]]]])\n                np.testing.assert_allclose(out.affine, np.diag([1, 1.4, 1, 1]))\n\n                image_name = os.path.join(out_dir, \"test1.nii.gz\")\n                img = p(np.arange(5).reshape((1, 1, 5, 1)))\n                write_nifti(\n                    img, image_name, affine=np.diag([1, 1, 1, 3, 3]), target_affine=np.diag([1.4, 2.0, 2, 3, 5])\n                )\n                out = nib.load(image_name)\n                np.testing.assert_allclose(out.get_fdata(), [[[[0], [2], [4]]]])\n                np.testing.assert_allclose(out.affine, np.diag([1.4, 2, 2, 1]))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_normalize_intensity.py_unittest_for_p_in_TEST_NDARRAYS_.None_8": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_normalize_intensity.py_unittest_for_p_in_TEST_NDARRAYS_.None_8", "embedding": null, "metadata": {"file_path": "tests/test_normalize_intensity.py", "file_name": "test_normalize_intensity.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 80, "span_ids": ["docstring"], "tokens": 985}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import NormalizeIntensity\nfrom tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, assert_allclose\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    TESTS.append([p, {\"nonzero\": True}, np.array([0.0, 3.0, 0.0, 4.0]), np.array([0.0, -1.0, 0.0, 1.0])])\n    for q in TEST_NDARRAYS:\n        for u in TEST_NDARRAYS:\n            TESTS.append(\n                [\n                    p,\n                    {\n                        \"subtrahend\": q(np.array([3.5, 3.5, 3.5, 3.5])),\n                        \"divisor\": u(np.array([0.5, 0.5, 0.5, 0.5])),\n                        \"nonzero\": True,\n                    },\n                    p(np.array([0.0, 3.0, 0.0, 4.0])),\n                    p(np.array([0.0, -1.0, 0.0, 1.0])),\n                ]\n            )\n    TESTS.append([p, {\"nonzero\": True}, p(np.array([0.0, 0.0, 0.0, 0.0])), p(np.array([0.0, 0.0, 0.0, 0.0]))])\n    TESTS.append([p, {\"nonzero\": False}, p(np.array([0.0, 0.0, 0.0, 0.0])), p(np.array([0.0, 0.0, 0.0, 0.0]))])\n    TESTS.append([p, {\"nonzero\": False}, p(np.array([1, 1, 1, 1])), p(np.array([0.0, 0.0, 0.0, 0.0]))])\n    TESTS.append(\n        [\n            p,\n            {\"nonzero\": False, \"channel_wise\": True, \"subtrahend\": [1, 2, 3], \"dtype\": np.float32},\n            p(np.ones((3, 2, 2))),\n            p(np.array([[[0.0, 0.0], [0.0, 0.0]], [[-1.0, -1.0], [-1.0, -1.0]], [[-2.0, -2.0], [-2.0, -2.0]]])),\n        ]\n    )\n    TESTS.append(\n        [\n            p,\n            {\"nonzero\": True, \"channel_wise\": True, \"subtrahend\": [1, 2, 3], \"divisor\": [0, 0, 2], \"dtype\": \"float32\"},\n            p(np.ones((3, 2, 2))),\n            p(np.array([[[0.0, 0.0], [0.0, 0.0]], [[-1.0, -1.0], [-1.0, -1.0]], [[-1.0, -1.0], [-1.0, -1.0]]])),\n        ]\n    )\n    TESTS.append(\n        [\n            p,\n            {\"nonzero\": True, \"channel_wise\": False, \"subtrahend\": 2, \"divisor\": 0, \"dtype\": torch.float32},\n            p(np.ones((3, 2, 2))),\n            p(np.ones((3, 2, 2)) * -1.0),\n        ]\n    )\n    TESTS.append(\n        [\n            p,\n            {\"nonzero\": True, \"channel_wise\": False, \"subtrahend\": np.ones((3, 2, 2)) * 0.5, \"divisor\": 0},\n            p(np.ones((3, 2, 2))),\n            p(np.ones((3, 2, 2)) * 0.5),\n        ]\n    )\n    TESTS.append(\n        [\n            p,\n            {\"nonzero\": True, \"channel_wise\": True, \"subtrahend\": np.ones((3, 2, 2)) * 0.5, \"divisor\": [0, 1, 0]},\n            p(np.ones((3, 2, 2))),\n            p(np.ones((3, 2, 2)) * 0.5),\n        ]\n    )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_normalize_intensity.py_TestNormalizeIntensity_TestNormalizeIntensity.test_nonzero.assert_allclose_normalize": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_normalize_intensity.py_TestNormalizeIntensity_TestNormalizeIntensity.test_nonzero.assert_allclose_normalize", "embedding": null, "metadata": {"file_path": "tests/test_normalize_intensity.py", "file_name": "test_normalize_intensity.py", "file_type": "text/x-python", "category": "test", "start_line": 83, "end_line": 101, "span_ids": ["TestNormalizeIntensity.test_default", "TestNormalizeIntensity", "TestNormalizeIntensity.test_nonzero"], "tokens": 211}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestNormalizeIntensity(NumpyImageTestCase2D):\n    @parameterized.expand([[p] for p in TEST_NDARRAYS])\n    def test_default(self, im_type):\n        im = im_type(self.imt.copy())\n        normalizer = NormalizeIntensity()\n        normalized = normalizer(im)\n        self.assertEqual(type(im), type(normalized))\n        if isinstance(normalized, torch.Tensor):\n            self.assertEqual(im.device, normalized.device)\n        self.assertTrue(normalized.dtype in (np.float32, torch.float32))\n        expected = (self.imt - np.mean(self.imt)) / np.std(self.imt)\n        assert_allclose(normalized, expected, type_test=False, rtol=1e-3)\n\n    @parameterized.expand(TESTS)\n    def test_nonzero(self, in_type, input_param, input_data, expected_data):\n        normalizer = NormalizeIntensity(**input_param)\n        im = in_type(input_data)\n        normalized = normalizer(im)\n        assert_allclose(normalized, in_type(expected_data))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_normalize_intensity.py_TestNormalizeIntensity.test_channel_wise_TestNormalizeIntensity.test_channel_wise.assert_allclose_normalize": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_normalize_intensity.py_TestNormalizeIntensity.test_channel_wise_TestNormalizeIntensity.test_channel_wise.assert_allclose_normalize", "embedding": null, "metadata": {"file_path": "tests/test_normalize_intensity.py", "file_name": "test_normalize_intensity.py", "file_type": "text/x-python", "category": "test", "start_line": 103, "end_line": 109, "span_ids": ["TestNormalizeIntensity.test_channel_wise"], "tokens": 167}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestNormalizeIntensity(NumpyImageTestCase2D):\n\n    @parameterized.expand([[p] for p in TEST_NDARRAYS])\n    def test_channel_wise(self, im_type):\n        normalizer = NormalizeIntensity(nonzero=True, channel_wise=True)\n        input_data = im_type(np.array([[0.0, 3.0, 0.0, 4.0], [0.0, 4.0, 0.0, 5.0]]))\n        expected = np.array([[0.0, -1.0, 0.0, 1.0], [0.0, -1.0, 0.0, 1.0]])\n        normalized = normalizer(input_data)\n        assert_allclose(normalized, im_type(expected))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_normalize_intensityd.py_unittest_for_p_in_TEST_NDARRAYS_.for_q_in_TEST_NDARRAYS_.None_2": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_normalize_intensityd.py_unittest_for_p_in_TEST_NDARRAYS_.for_q_in_TEST_NDARRAYS_.None_2", "embedding": null, "metadata": {"file_path": "tests/test_normalize_intensityd.py", "file_name": "test_normalize_intensityd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 49, "span_ids": ["docstring"], "tokens": 362}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import NormalizeIntensityd\nfrom tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, assert_allclose\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    for q in TEST_NDARRAYS:\n        TESTS.append(\n            [\n                {\"keys\": [\"img\"], \"nonzero\": True},\n                {\"img\": p(np.array([0.0, 3.0, 0.0, 4.0]))},\n                p(np.array([0.0, -1.0, 0.0, 1.0])),\n            ]\n        )\n        TESTS.append(\n            [\n                {\n                    \"keys\": [\"img\"],\n                    \"subtrahend\": q(np.array([3.5, 3.5, 3.5, 3.5])),\n                    \"divisor\": q(np.array([0.5, 0.5, 0.5, 0.5])),\n                    \"nonzero\": True,\n                },\n                {\"img\": p(np.array([0.0, 3.0, 0.0, 4.0]))},\n                p(np.array([0.0, -1.0, 0.0, 1.0])),\n            ]\n        )\n        TESTS.append(\n            [\n                {\"keys\": [\"img\"], \"nonzero\": True},\n                {\"img\": p(np.array([0.0, 0.0, 0.0, 0.0]))},\n                p(np.array([0.0, 0.0, 0.0, 0.0])),\n            ]\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_normalize_intensityd.py_TestNormalizeIntensityd_TestNormalizeIntensityd.test_nonzero.assert_allclose_normalize": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_normalize_intensityd.py_TestNormalizeIntensityd_TestNormalizeIntensityd.test_nonzero.assert_allclose_normalize", "embedding": null, "metadata": {"file_path": "tests/test_normalize_intensityd.py", "file_name": "test_normalize_intensityd.py", "file_type": "text/x-python", "category": "test", "start_line": 52, "end_line": 73, "span_ids": ["TestNormalizeIntensityd.test_nonzero", "TestNormalizeIntensityd", "TestNormalizeIntensityd.test_image_normalize_intensityd"], "tokens": 242}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestNormalizeIntensityd(NumpyImageTestCase2D):\n    @parameterized.expand([[p] for p in TEST_NDARRAYS])\n    def test_image_normalize_intensityd(self, im_type):\n        key = \"img\"\n        im = im_type(self.imt)\n        normalizer = NormalizeIntensityd(keys=[key])\n        normalized = normalizer({key: im})[key]\n        expected = (self.imt - np.mean(self.imt)) / np.std(self.imt)\n        self.assertEqual(type(im), type(normalized))\n        if isinstance(normalized, torch.Tensor):\n            self.assertEqual(im.device, normalized.device)\n        assert_allclose(normalized, im_type(expected), rtol=1e-3)\n\n    @parameterized.expand(TESTS)\n    def test_nonzero(self, input_param, input_data, expected_data):\n        key = \"img\"\n        normalizer = NormalizeIntensityd(**input_param)\n        normalized = normalizer(input_data)[key]\n        self.assertEqual(type(input_data[key]), type(normalized))\n        if isinstance(normalized, torch.Tensor):\n            self.assertEqual(input_data[key].device, normalized.device)\n        assert_allclose(normalized, expected_data)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nvtx_decorator.py_TestNVTXRangeDecorator_TestNVTXRangeDecorator.test_tranform_array.None_6": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nvtx_decorator.py_TestNVTXRangeDecorator_TestNVTXRangeDecorator.test_tranform_array.None_6", "embedding": null, "metadata": {"file_path": "tests/test_nvtx_decorator.py", "file_name": "test_nvtx_decorator.py", "file_type": "text/x-python", "category": "test", "start_line": 53, "end_line": 78, "span_ids": ["TestNVTXRangeDecorator.test_tranform_array", "TestNVTXRangeDecorator"], "tokens": 242}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@unittest.skipUnless(has_nvtx, \"CUDA is required for NVTX Range!\")\nclass TestNVTXRangeDecorator(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_ARRAY_0, TEST_CASE_ARRAY_1])\n    def test_tranform_array(self, input):\n        transforms = Compose([Range(\"random flip\")(Flip()), Range()(ToTensor())])\n        # Apply transforms\n        output = transforms(input)\n\n        # Decorate with NVTX Range\n        transforms1 = Range()(transforms)\n        transforms2 = Range(\"Transforms2\")(transforms)\n        transforms3 = Range(name=\"Transforms3\", methods=\"__call__\")(transforms)\n\n        # Apply transforms with Range\n        output1 = transforms1(input)\n        output2 = transforms2(input)\n        output3 = transforms3(input)\n\n        # Check the outputs\n        self.assertIsInstance(output, torch.Tensor)\n        self.assertIsInstance(output1, torch.Tensor)\n        self.assertIsInstance(output2, torch.Tensor)\n        self.assertIsInstance(output3, torch.Tensor)\n        np.testing.assert_equal(output.numpy(), output1.numpy())\n        np.testing.assert_equal(output.numpy(), output2.numpy())\n        np.testing.assert_equal(output.numpy(), output3.numpy())", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nvtx_decorator.py_TestNVTXRangeDecorator.test_tranform_dict_TestNVTXRangeDecorator.test_tranform_dict.None_6": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nvtx_decorator.py_TestNVTXRangeDecorator.test_tranform_dict_TestNVTXRangeDecorator.test_tranform_dict.None_6", "embedding": null, "metadata": {"file_path": "tests/test_nvtx_decorator.py", "file_name": "test_nvtx_decorator.py", "file_type": "text/x-python", "category": "test", "start_line": 80, "end_line": 103, "span_ids": ["TestNVTXRangeDecorator.test_tranform_dict"], "tokens": 258}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@unittest.skipUnless(has_nvtx, \"CUDA is required for NVTX Range!\")\nclass TestNVTXRangeDecorator(unittest.TestCase):\n\n    @parameterized.expand([TEST_CASE_DICT_0, TEST_CASE_DICT_1])\n    def test_tranform_dict(self, input):\n        transforms = Compose([Range(\"random flip dict\")(FlipD(keys=\"image\")), Range()(ToTensorD(\"image\"))])\n        # Apply transforms\n        output = transforms(input)[\"image\"]\n\n        # Decorate with NVTX Range\n        transforms1 = Range()(transforms)\n        transforms2 = Range(\"Transforms2\")(transforms)\n        transforms3 = Range(name=\"Transforms3\", methods=\"__call__\")(transforms)\n\n        # Apply transforms with Range\n        output1 = transforms1(input)[\"image\"]\n        output2 = transforms2(input)[\"image\"]\n        output3 = transforms3(input)[\"image\"]\n\n        # Check the outputs\n        self.assertIsInstance(output, torch.Tensor)\n        self.assertIsInstance(output1, torch.Tensor)\n        self.assertIsInstance(output2, torch.Tensor)\n        self.assertIsInstance(output3, torch.Tensor)\n        np.testing.assert_equal(output.numpy(), output1.numpy())\n        np.testing.assert_equal(output.numpy(), output2.numpy())\n        np.testing.assert_equal(output.numpy(), output3.numpy())", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nvtx_decorator.py_TestNVTXRangeDecorator.test_tranform_randomized_TestNVTXRangeDecorator.test_tranform_randomized.for_tran_in_transforms_tr.if_isinstance_tran_Rando.break": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nvtx_decorator.py_TestNVTXRangeDecorator.test_tranform_randomized_TestNVTXRangeDecorator.test_tranform_randomized.for_tran_in_transforms_tr.if_isinstance_tran_Rando.break", "embedding": null, "metadata": {"file_path": "tests/test_nvtx_decorator.py", "file_name": "test_nvtx_decorator.py", "file_type": "text/x-python", "category": "test", "start_line": 130, "end_line": 168, "span_ids": ["TestNVTXRangeDecorator.test_tranform_randomized"], "tokens": 326}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@unittest.skipUnless(has_nvtx, \"CUDA is required for NVTX Range!\")\nclass TestNVTXRangeDecorator(unittest.TestCase):\n\n    @parameterized.expand([TEST_CASE_ARRAY_1])\n    def test_tranform_randomized(self, input):\n        # Compose deterministic and randomized transforms\n        transforms = Compose(\n            [\n                Range(\"flip\")(Flip()),\n                Rotate90(),\n                Range()(RandAdjustContrast(prob=0.0)),\n                Range(\"random flip\")(RandFlip(prob=1.0)),\n                ToTensor(),\n            ]\n        )\n        # Apply transforms\n        output = transforms(input)\n\n        # Decorate with NVTX Range\n        transforms1 = Range()(transforms)\n        transforms2 = Range(\"Transforms2\")(transforms)\n        transforms3 = Range(name=\"Transforms3\", methods=\"__call__\")(transforms)\n\n        # Apply transforms with Range\n        output1 = transforms1(input)\n        output2 = transforms2(input)\n        output3 = transforms3(input)\n\n        # Check if the outputs are equal\n        self.assertIsInstance(output, torch.Tensor)\n        self.assertIsInstance(output1, torch.Tensor)\n        self.assertIsInstance(output2, torch.Tensor)\n        self.assertIsInstance(output3, torch.Tensor)\n        np.testing.assert_equal(output.numpy(), output1.numpy())\n        np.testing.assert_equal(output.numpy(), output2.numpy())\n        np.testing.assert_equal(output.numpy(), output3.numpy())\n\n        # Check if the first randomized is RandAdjustContrast\n        for tran in transforms.transforms:\n            if isinstance(tran, Randomizable):\n                self.assertIsInstance(tran, RandAdjustContrast)\n                break", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nvtx_decorator.py_TestNVTXRangeDecorator.test_network_TestNVTXRangeDecorator.test_network.None_6": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nvtx_decorator.py_TestNVTXRangeDecorator.test_network_TestNVTXRangeDecorator.test_network.None_6", "embedding": null, "metadata": {"file_path": "tests/test_nvtx_decorator.py", "file_name": "test_nvtx_decorator.py", "file_type": "text/x-python", "category": "test", "start_line": 170, "end_line": 195, "span_ids": ["TestNVTXRangeDecorator.test_network"], "tokens": 241}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@unittest.skipUnless(has_nvtx, \"CUDA is required for NVTX Range!\")\nclass TestNVTXRangeDecorator(unittest.TestCase):\n\n    @parameterized.expand([TEST_CASE_TORCH_0, TEST_CASE_TORCH_1])\n    def test_network(self, input):\n        # Create a network\n        model = torch.nn.Sequential(torch.nn.ReLU(), torch.nn.Sigmoid())\n\n        # Forward\n        output = model(input)\n\n        # Decorate with NVTX Range\n        model1 = Range()(model)\n        model2 = Range(\"Model2\")(model)\n        model3 = Range(name=\"Model3\", methods=\"forward\")(model)\n\n        # Forward with Range\n        output1 = model1(input)\n        output2 = model2(input)\n        output3 = model3(input)\n\n        # Check the outputs\n        self.assertIsInstance(output, torch.Tensor)\n        self.assertIsInstance(output1, torch.Tensor)\n        self.assertIsInstance(output2, torch.Tensor)\n        self.assertIsInstance(output3, torch.Tensor)\n        np.testing.assert_equal(output.numpy(), output1.numpy())\n        np.testing.assert_equal(output.numpy(), output2.numpy())\n        np.testing.assert_equal(output.numpy(), output3.numpy())", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nvtx_decorator.py_TestNVTXRangeDecorator.test_loss_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nvtx_decorator.py_TestNVTXRangeDecorator.test_loss_", "embedding": null, "metadata": {"file_path": "tests/test_nvtx_decorator.py", "file_name": "test_nvtx_decorator.py", "file_type": "text/x-python", "category": "test", "start_line": 197, "end_line": 242, "span_ids": ["TestNVTXRangeDecorator.test_context_manager", "TestNVTXRangeDecorator.test_loss", "impl:18"], "tokens": 356}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@unittest.skipUnless(has_nvtx, \"CUDA is required for NVTX Range!\")\nclass TestNVTXRangeDecorator(unittest.TestCase):\n\n    @parameterized.expand([TEST_CASE_TORCH_0, TEST_CASE_TORCH_1])\n    def test_loss(self, input):\n        # Create a network and loss\n        model = torch.nn.Sigmoid()\n        loss = torch.nn.BCELoss()\n        pred = model(input)\n        target = torch.empty_like(input).random_(2)\n\n        # Loss evaluation\n        output = loss(pred, target)\n\n        # Decorate with NVTX Range\n        loss1 = Range()(loss)\n        loss2 = Range(\"Loss2\")(loss)\n        loss3 = Range(name=\"Loss3\", methods=\"forward\")(loss)\n\n        # Loss evaluation with Range\n        output1 = loss1(pred, target)\n        output2 = loss2(pred, target)\n        output3 = loss3(pred, target)\n\n        # Check the outputs\n        self.assertIsInstance(output, torch.Tensor)\n        self.assertIsInstance(output1, torch.Tensor)\n        self.assertIsInstance(output2, torch.Tensor)\n        self.assertIsInstance(output3, torch.Tensor)\n        np.testing.assert_equal(output.numpy(), output1.numpy())\n        np.testing.assert_equal(output.numpy(), output2.numpy())\n        np.testing.assert_equal(output.numpy(), output3.numpy())\n\n    def test_context_manager(self):\n        model = torch.nn.Sigmoid()\n        loss = torch.nn.BCELoss()\n\n        with Range():\n            input = torch.randn(3, requires_grad=True)\n            target = torch.empty(3).random_(2)\n\n        with Range(\"Model\"):\n            output = loss(model(input), target)\n            output.backward()\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nvtx_transform.py_unittest_TEST_CASE_DICT_1._image_np_random_rand": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nvtx_transform.py_unittest_TEST_CASE_DICT_1._image_np_random_rand", "embedding": null, "metadata": {"file_path": "tests/test_nvtx_transform.py", "file_name": "test_nvtx_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 41, "span_ids": ["docstring"], "tokens": 229}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import Compose, Flip, RandFlip, RandFlipD, Randomizable, ToTensor, ToTensorD\nfrom monai.transforms.nvtx import (\n    Mark,\n    MarkD,\n    RandMark,\n    RandMarkD,\n    RandRangePop,\n    RandRangePopD,\n    RandRangePush,\n    RandRangePushD,\n    RangePop,\n    RangePopD,\n    RangePush,\n    RangePushD,\n)\nfrom monai.utils import optional_import\n\n_, has_nvtx = optional_import(\"torch._C._nvtx\", descriptor=\"NVTX is not installed. Are you sure you have a CUDA build?\")\n\n\nTEST_CASE_ARRAY_0 = [np.random.randn(3, 3)]\nTEST_CASE_ARRAY_1 = [np.random.randn(3, 10, 10)]\nTEST_CASE_DICT_0 = [{\"image\": np.random.randn(3, 3)}]\nTEST_CASE_DICT_1 = [{\"image\": np.random.randn(3, 10, 10)}]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nvtx_transform.py_TestNVTXTransforms_TestNVTXTransforms.test_nvtx_transfroms_alone.for_tran_in_transforms_tr.if_isinstance_tran_Rando.break": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nvtx_transform.py_TestNVTXTransforms_TestNVTXTransforms.test_nvtx_transfroms_alone.for_tran_in_transforms_tr.if_isinstance_tran_Rando.break", "embedding": null, "metadata": {"file_path": "tests/test_nvtx_transform.py", "file_name": "test_nvtx_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 44, "end_line": 65, "span_ids": ["TestNVTXTransforms", "TestNVTXTransforms.test_nvtx_transfroms_alone"], "tokens": 193}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestNVTXTransforms(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_ARRAY_0, TEST_CASE_ARRAY_1, TEST_CASE_DICT_0, TEST_CASE_DICT_1])\n    @unittest.skipUnless(has_nvtx, \"CUDA is required for NVTX!\")\n    def test_nvtx_transfroms_alone(self, input):\n        transforms = Compose(\n            [\n                Mark(\"Mark: Transforms Start!\"),\n                RangePush(\"Range: RandFlipD\"),\n                RangePop(),\n                RandRangePush(\"Range: ToTensorD\"),\n                RandRangePop(),\n                RandMark(\"Mark: Transforms End!\"),\n            ]\n        )\n        output = transforms(input)\n        self.assertEqual(id(input), id(output))\n\n        # Check if chain of randomizable/non-randomizable transforms is not broken\n        for tran in transforms.transforms:\n            if isinstance(tran, Randomizable):\n                self.assertIsInstance(tran, RangePush)\n                break", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nvtx_transform.py_TestNVTXTransforms.test_nvtx_transfroms_array_TestNVTXTransforms.test_nvtx_transfroms_array.None_3": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nvtx_transform.py_TestNVTXTransforms.test_nvtx_transfroms_array_TestNVTXTransforms.test_nvtx_transfroms_array.None_3", "embedding": null, "metadata": {"file_path": "tests/test_nvtx_transform.py", "file_name": "test_nvtx_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 67, "end_line": 101, "span_ids": ["TestNVTXTransforms.test_nvtx_transfroms_array"], "tokens": 278}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestNVTXTransforms(unittest.TestCase):\n\n    @parameterized.expand([TEST_CASE_ARRAY_0, TEST_CASE_ARRAY_1])\n    @unittest.skipUnless(has_nvtx, \"CUDA is required for NVTX!\")\n    def test_nvtx_transfroms_array(self, input):\n        # with prob == 0.0\n        transforms = Compose(\n            [\n                RandMark(\"Mark: Transforms Start!\"),\n                RandRangePush(\"Range: RandFlip\"),\n                RandFlip(prob=0.0),\n                RandRangePop(),\n                RangePush(\"Range: ToTensor\"),\n                ToTensor(),\n                RangePop(),\n                Mark(\"Mark: Transforms End!\"),\n            ]\n        )\n        output = transforms(input)\n        self.assertIsInstance(output, torch.Tensor)\n        np.testing.assert_array_equal(input, output)\n        # with prob == 1.0\n        transforms = Compose(\n            [\n                RandMark(\"Mark: Transforms Start!\"),\n                RandRangePush(\"Range: RandFlip\"),\n                RandFlip(prob=1.0),\n                RandRangePop(),\n                RangePush(\"Range: ToTensor\"),\n                ToTensor(),\n                RangePop(),\n                Mark(\"Mark: Transforms End!\"),\n            ]\n        )\n        output = transforms(input)\n        self.assertIsInstance(output, torch.Tensor)\n        np.testing.assert_array_equal(input, Flip()(output.numpy()))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nvtx_transform.py_TestNVTXTransforms.test_nvtx_transfroms_dict_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nvtx_transform.py_TestNVTXTransforms.test_nvtx_transfroms_dict_", "embedding": null, "metadata": {"file_path": "tests/test_nvtx_transform.py", "file_name": "test_nvtx_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 103, "end_line": 142, "span_ids": ["impl:10", "TestNVTXTransforms.test_nvtx_transfroms_dict"], "tokens": 358}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestNVTXTransforms(unittest.TestCase):\n\n    @parameterized.expand([TEST_CASE_DICT_0, TEST_CASE_DICT_1])\n    @unittest.skipUnless(has_nvtx, \"CUDA is required for NVTX!\")\n    def test_nvtx_transfroms_dict(self, input):\n        # with prob == 0.0\n        transforms = Compose(\n            [\n                RandMarkD(\"Mark: Transforms (p=0) Start!\"),\n                RandRangePushD(\"Range: RandFlipD\"),\n                RandFlipD(keys=\"image\", prob=0.0),\n                RandRangePopD(),\n                RangePushD(\"Range: ToTensorD\"),\n                ToTensorD(keys=(\"image\")),\n                RangePopD(),\n                MarkD(\"Mark: Transforms (p=0) End!\"),\n            ]\n        )\n        output = transforms(input)\n        self.assertIsInstance(output[\"image\"], torch.Tensor)\n        np.testing.assert_array_equal(input[\"image\"], output[\"image\"])\n        # with prob == 1.0\n        transforms = Compose(\n            [\n                RandMarkD(\"Mark: Transforms (p=1) Start!\"),\n                RandRangePushD(\"Range: RandFlipD\"),\n                RandFlipD(keys=\"image\", prob=1.0),\n                RandRangePopD(),\n                RangePushD(\"Range: ToTensorD\"),\n                ToTensorD(keys=(\"image\")),\n                RangePopD(),\n                MarkD(\"Mark: Transforms (p=1) End!\"),\n            ]\n        )\n        output = transforms(input)\n        self.assertIsInstance(output[\"image\"], torch.Tensor)\n        np.testing.assert_array_equal(input[\"image\"], Flip()(output[\"image\"].numpy()))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_one_of.py_unittest_TEST_INVERSES._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_one_of.py_unittest_TEST_INVERSES._", "embedding": null, "metadata": {"file_path": "tests/test_one_of.py", "file_name": "test_one_of.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 113, "span_ids": ["impl", "MapBase.__init__", "C", "docstring", "X", "A.__call__", "MapBase", "MapBase.__call__", "C.__call__", "A", "Y.__call__", "B", "NonInv", "Inv", "Inv.__call__", "InvB.__init__", "NonInv.__init__", "Inv.inverse", "InvB", "X.__call__", "Y", "B.__call__", "InvA", "InvA.__init__"], "tokens": 651}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom copy import deepcopy\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import (\n    InvertibleTransform,\n    OneOf,\n    RandScaleIntensityd,\n    RandShiftIntensityd,\n    Resized,\n    TraceableTransform,\n    Transform,\n)\nfrom monai.transforms.compose import Compose\nfrom monai.transforms.transform import MapTransform\nfrom monai.utils.enums import TraceKeys\n\n\nclass X(Transform):\n    def __call__(self, x):\n        return x\n\n\nclass Y(Transform):\n    def __call__(self, x):\n        return x\n\n\nclass A(Transform):\n    def __call__(self, x):\n        return x + 1\n\n\nclass B(Transform):\n    def __call__(self, x):\n        return x + 2\n\n\nclass C(Transform):\n    def __call__(self, x):\n        return x + 3\n\n\nclass MapBase(MapTransform):\n    def __init__(self, keys):\n        super().__init__(keys)\n        self.fwd_fn, self.inv_fn = None, None\n\n    def __call__(self, data):\n        d = deepcopy(dict(data))\n        for key in self.key_iterator(d):\n            d[key] = self.fwd_fn(d[key])\n        return d\n\n\nclass NonInv(MapBase):\n    def __init__(self, keys):\n        super().__init__(keys)\n        self.fwd_fn = lambda x: x * 2\n\n\nclass Inv(MapBase, InvertibleTransform):\n    def __call__(self, data):\n        d = deepcopy(dict(data))\n        for key in self.key_iterator(d):\n            d[key] = self.fwd_fn(d[key])\n            self.push_transform(d, key)\n        return d\n\n    def inverse(self, data):\n        d = deepcopy(dict(data))\n        for key in self.key_iterator(d):\n            d[key] = self.inv_fn(d[key])\n            self.pop_transform(d, key)\n        return d\n\n\nclass InvA(Inv):\n    def __init__(self, keys):\n        super().__init__(keys)\n        self.fwd_fn = lambda x: x + 1\n        self.inv_fn = lambda x: x - 1\n\n\nclass InvB(Inv):\n    def __init__(self, keys):\n        super().__init__(keys)\n        self.fwd_fn = lambda x: x + 100\n        self.inv_fn = lambda x: x - 100\n\n\nTESTS = [((X(), Y(), X()), (1, 2, 1), (0.25, 0.5, 0.25))]\n\nKEYS = [\"x\", \"y\"]\nTEST_INVERSES = [\n    (OneOf((InvA(KEYS), InvB(KEYS))), True),\n    (OneOf((OneOf((InvA(KEYS), InvB(KEYS))), OneOf((InvB(KEYS), InvA(KEYS))))), True),\n    (OneOf((Compose((InvA(KEYS), InvB(KEYS))), Compose((InvB(KEYS), InvA(KEYS))))), True),\n    (OneOf((NonInv(KEYS), NonInv(KEYS))), False),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_one_of.py_TestOneOf_TestOneOf.test_len_and_flatten.self_assertTupleEqual_p_f": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_one_of.py_TestOneOf_TestOneOf.test_len_and_flatten.self_assertTupleEqual_p_f", "embedding": null, "metadata": {"file_path": "tests/test_one_of.py", "file_name": "test_one_of.py", "file_type": "text/x-python", "category": "test", "start_line": 107, "end_line": 125, "span_ids": ["TestOneOf", "TestOneOf.test_normalize_weights", "TestOneOf.test_no_weights_arg", "TestOneOf.test_len_and_flatten"], "tokens": 292}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestOneOf(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_normalize_weights(self, transforms, input_weights, expected_weights):\n        tr = OneOf(transforms, input_weights)\n        self.assertTupleEqual(tr.weights, expected_weights)\n\n    def test_no_weights_arg(self):\n        p = OneOf((X(), Y(), X(), Y()))\n        expected_weights = (0.25,) * 4\n        self.assertTupleEqual(p.weights, expected_weights)\n\n    def test_len_and_flatten(self):\n        p1 = OneOf((X(), Y()), (1, 3))  # 0.25, 0.75\n        p2 = OneOf((Y(), Y()), (2, 2))  # 0.5. 0.5\n        p = OneOf((p1, p2, X()), (1, 2, 1))  # 0.25, 0.5, 0.25\n        expected_order = (X, Y, Y, Y, X)\n        expected_weights = (0.25 * 0.25, 0.25 * 0.75, 0.5 * 0.5, 0.5 * 0.5, 0.25)\n        self.assertEqual(len(p), len(expected_order))\n        self.assertTupleEqual(p.flatten().weights, expected_weights)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_one_of.py_TestOneOf.test_compose_flatten_does_not_affect_one_of_TestOneOf.test_compose_flatten_does_not_affect_one_of._match_p_f_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_one_of.py_TestOneOf.test_compose_flatten_does_not_affect_one_of_TestOneOf.test_compose_flatten_does_not_affect_one_of._match_p_f_", "embedding": null, "metadata": {"file_path": "tests/test_one_of.py", "file_name": "test_one_of.py", "file_type": "text/x-python", "category": "test", "start_line": 127, "end_line": 139, "span_ids": ["TestOneOf.test_compose_flatten_does_not_affect_one_of"], "tokens": 137}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestOneOf(unittest.TestCase):\n\n    def test_compose_flatten_does_not_affect_one_of(self):\n        p = Compose([A(), B(), OneOf([C(), Inv(KEYS), Compose([X(), Y()])])])\n        f = p.flatten()\n        # in this case the flattened transform should be the same.\n\n        def _match(a, b):\n            self.assertEqual(type(a), type(b))\n            for a_, b_ in zip(a.transforms, b.transforms):\n                self.assertEqual(type(a_), type(b_))\n                if isinstance(a_, (Compose, OneOf)):\n                    _match(a_, b_)\n\n        _match(p, f)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_one_of.py_TestOneOf.test_one_of_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_one_of.py_TestOneOf.test_one_of_", "embedding": null, "metadata": {"file_path": "tests/test_one_of.py", "file_name": "test_one_of.py", "file_type": "text/x-python", "category": "test", "start_line": 167, "end_line": 180, "span_ids": ["TestOneOf.test_one_of", "impl:7"], "tokens": 160}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestOneOf(unittest.TestCase):\n\n    def test_one_of(self):\n        p = OneOf((A(), B(), C()), (1, 2, 1))\n        counts = [0] * 3\n        for _i in range(10000):\n            out = p(1.0)\n            counts[int(out - 2)] += 1\n        self.assertAlmostEqual(counts[0] / 10000, 0.25, delta=1.0)\n        self.assertAlmostEqual(counts[1] / 10000, 0.50, delta=1.0)\n        self.assertAlmostEqual(counts[2] / 10000, 0.25, delta=1.0)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_partition_dataset.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_partition_dataset.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_partition_dataset.py", "file_name": "test_partition_dataset.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 128, "span_ids": ["TestPartitionDataset.test_value", "impl:11", "impl:17", "docstring", "TestPartitionDataset"], "tokens": 786}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nfrom parameterized import parameterized\n\nfrom monai.data import partition_dataset\n\nTEST_CASE_1 = [\n    {\n        \"data\": [1, 2, 3, 4],\n        \"num_partitions\": 2,\n        \"shuffle\": False,\n        \"seed\": 0,\n        \"drop_last\": False,\n        \"even_divisible\": False,\n    },\n    [[1, 3], [2, 4]],\n]\n\nTEST_CASE_2 = [\n    {\n        \"data\": [1, 2, 3, 4],\n        \"num_partitions\": 2,\n        \"shuffle\": True,\n        \"seed\": 123,\n        \"drop_last\": False,\n        \"even_divisible\": False,\n    },\n    [[4, 2], [1, 3]],\n]\n\nTEST_CASE_3 = [\n    {\n        \"data\": [1, 2, 3, 4, 5],\n        \"num_partitions\": 2,\n        \"shuffle\": False,\n        \"seed\": 0,\n        \"drop_last\": False,\n        \"even_divisible\": False,\n    },\n    [[1, 3, 5], [2, 4]],\n]\n\nTEST_CASE_4 = [\n    {\n        \"data\": [1, 2, 3, 4, 5],\n        \"num_partitions\": 2,\n        \"shuffle\": False,\n        \"seed\": 0,\n        \"drop_last\": False,\n        \"even_divisible\": True,\n    },\n    [[1, 3, 5], [2, 4, 1]],\n]\n\nTEST_CASE_5 = [\n    {\n        \"data\": [1, 2, 3, 4, 5],\n        \"num_partitions\": 2,\n        \"shuffle\": False,\n        \"seed\": 0,\n        \"drop_last\": True,\n        \"even_divisible\": True,\n    },\n    [[1, 3], [2, 4]],\n]\n\nTEST_CASE_6 = [\n    {\n        \"data\": [1, 2, 3, 4, 5],\n        \"ratios\": [3, 2],\n        \"num_partitions\": None,\n        \"shuffle\": False,\n        \"seed\": 0,\n        \"drop_last\": True,\n        \"even_divisible\": True,\n    },\n    [[1, 2, 3], [4, 5]],\n]\n\nTEST_CASE_7 = [\n    {\n        \"data\": [1, 2, 3, 4, 5],\n        \"ratios\": [2, 1],\n        \"num_partitions\": None,\n        \"shuffle\": False,\n        \"seed\": 0,\n        \"drop_last\": True,\n        \"even_divisible\": True,\n    },\n    [[1, 2, 3], [4, 5]],\n]\n\nTEST_CASE_8 = [\n    {\n        \"data\": [1, 2, 3, 4, 5],\n        \"ratios\": [2, 1],\n        \"num_partitions\": None,\n        \"shuffle\": True,\n        \"seed\": 123,\n        \"drop_last\": True,\n        \"even_divisible\": True,\n    },\n    [[2, 4, 5], [1, 3]],\n]\n\n\nclass TestPartitionDataset(unittest.TestCase):\n    @parameterized.expand(\n        [TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5, TEST_CASE_6, TEST_CASE_7, TEST_CASE_8]\n    )\n    def test_value(self, input_param, result):\n        self.assertListEqual(partition_dataset(**input_param), result)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_patchembedding.py_unittest_for_dropout_rate_in_0_5_.for_in_channels_in_1_4_.for_hidden_size_in_360_.for_img_size_in_96_128_.for_patch_size_in_8_16_.for_num_heads_in_8_12_.for_pos_embed_in_conv_.for_nd_in_2_3_.TEST_CASE_PATCHEMBEDDINGB": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_patchembedding.py_unittest_for_dropout_rate_in_0_5_.for_in_channels_in_1_4_.for_hidden_size_in_360_.for_img_size_in_96_128_.for_patch_size_in_8_16_.for_num_heads_in_8_12_.for_pos_embed_in_conv_.for_nd_in_2_3_.TEST_CASE_PATCHEMBEDDINGB", "embedding": null, "metadata": {"file_path": "tests/test_patchembedding.py", "file_name": "test_patchembedding.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 49, "span_ids": ["docstring"], "tokens": 328}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom unittest import skipUnless\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks import eval_mode\nfrom monai.networks.blocks.patchembedding import PatchEmbeddingBlock\nfrom monai.utils import optional_import\n\neinops, has_einops = optional_import(\"einops\")\n\nTEST_CASE_PATCHEMBEDDINGBLOCK = []\nfor dropout_rate in (0.5,):\n    for in_channels in [1, 4]:\n        for hidden_size in [360, 768]:\n            for img_size in [96, 128]:\n                for patch_size in [8, 16]:\n                    for num_heads in [8, 12]:\n                        for pos_embed in [\"conv\", \"perceptron\"]:\n                            # for classification in (False, True):  # TODO: add classification tests\n                            for nd in (2, 3):\n                                test_case = [\n                                    {\n                                        \"in_channels\": in_channels,\n                                        \"img_size\": (img_size,) * nd,\n                                        \"patch_size\": (patch_size,) * nd,\n                                        \"hidden_size\": hidden_size,\n                                        \"num_heads\": num_heads,\n                                        \"pos_embed\": pos_embed,\n                                        \"dropout_rate\": dropout_rate,\n                                    },\n                                    (2, in_channels, *([img_size] * nd)),\n                                    (2, (img_size // patch_size) ** nd, hidden_size),\n                                ]\n                                if nd == 2:\n                                    test_case[0][\"spatial_dims\"] = 2  # type: ignore\n                                TEST_CASE_PATCHEMBEDDINGBLOCK.append(test_case)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_patchembedding.py_TestPatchEmbeddingBlock_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_patchembedding.py_TestPatchEmbeddingBlock_", "embedding": null, "metadata": {"file_path": "tests/test_patchembedding.py", "file_name": "test_patchembedding.py", "file_type": "text/x-python", "category": "test", "start_line": 52, "end_line": 120, "span_ids": ["TestPatchEmbeddingBlock", "TestPatchEmbeddingBlock.test_shape", "TestPatchEmbeddingBlock.test_ill_arg", "impl:17"], "tokens": 462}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestPatchEmbeddingBlock(unittest.TestCase):\n    @parameterized.expand(TEST_CASE_PATCHEMBEDDINGBLOCK)\n    @skipUnless(has_einops, \"Requires einops\")\n    def test_shape(self, input_param, input_shape, expected_shape):\n        net = PatchEmbeddingBlock(**input_param)\n        with eval_mode(net):\n            result = net(torch.randn(input_shape))\n            self.assertEqual(result.shape, expected_shape)\n\n    def test_ill_arg(self):\n        with self.assertRaises(ValueError):\n            PatchEmbeddingBlock(\n                in_channels=1,\n                img_size=(128, 128, 128),\n                patch_size=(16, 16, 16),\n                hidden_size=128,\n                num_heads=12,\n                pos_embed=\"conv\",\n                dropout_rate=5.0,\n            )\n\n        with self.assertRaises(ValueError):\n            PatchEmbeddingBlock(\n                in_channels=1,\n                img_size=(32, 32, 32),\n                patch_size=(64, 64, 64),\n                hidden_size=512,\n                num_heads=8,\n                pos_embed=\"perceptron\",\n                dropout_rate=0.3,\n            )\n\n        with self.assertRaises(ValueError):\n            PatchEmbeddingBlock(\n                in_channels=1,\n                img_size=(96, 96, 96),\n                patch_size=(8, 8, 8),\n                hidden_size=512,\n                num_heads=14,\n                pos_embed=\"conv\",\n                dropout_rate=0.3,\n            )\n\n        with self.assertRaises(ValueError):\n            PatchEmbeddingBlock(\n                in_channels=1,\n                img_size=(97, 97, 97),\n                patch_size=(4, 4, 4),\n                hidden_size=768,\n                num_heads=8,\n                pos_embed=\"perceptron\",\n                dropout_rate=0.3,\n            )\n\n        with self.assertRaises(ValueError):\n            PatchEmbeddingBlock(\n                in_channels=4,\n                img_size=(96, 96, 96),\n                patch_size=(16, 16, 16),\n                hidden_size=768,\n                num_heads=12,\n                pos_embed=\"perc\",\n                dropout_rate=0.3,\n            )\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_pathology_he_stain.py_unittest_None_21": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_pathology_he_stain.py_unittest_None_21", "embedding": null, "metadata": {"file_path": "tests/test_pathology_he_stain.py", "file_name": "test_pathology_he_stain.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 66, "span_ids": ["docstring:19", "docstring"], "tokens": 675}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.apps.pathology.transforms import ExtractHEStains, NormalizeHEStains\n\n# None inputs\nEXTRACT_STAINS_TEST_CASE_0 = (None,)\nEXTRACT_STAINS_TEST_CASE_00 = (None, None)\nNORMALIZE_STAINS_TEST_CASE_0 = (None,)\nNORMALIZE_STAINS_TEST_CASE_00: tuple = ({}, None, None)\n\n# input pixels with negative values\nNEGATIVE_VALUE_TEST_CASE = [np.full((3, 2, 3), -1)]\n\n# input pixels with greater than 255 values\nINVALID_VALUE_TEST_CASE = [np.full((3, 2, 3), 256)]\n\n# input pixels all transparent and below the beta absorbance threshold\nEXTRACT_STAINS_TEST_CASE_1 = [np.full((3, 2, 3), 240)]\n\n# input pixels uniformly filled, but above beta absorbance threshold\nEXTRACT_STAINS_TEST_CASE_2 = [np.full((3, 2, 3), 100)]\n\n# input pixels uniformly filled (different value), but above beta absorbance threshold\nEXTRACT_STAINS_TEST_CASE_3 = [np.full((3, 2, 3), 150)]\n\n# input pixels uniformly filled with zeros, leading to two identical stains extracted\nEXTRACT_STAINS_TEST_CASE_4 = [\n    np.zeros((3, 2, 3)),\n    np.array([[0.0, 0.0], [0.70710678, 0.70710678], [0.70710678, 0.70710678]]),\n]\n\n# input pixels not uniformly filled, leading to two different stains extracted\nEXTRACT_STAINS_TEST_CASE_5 = [\n    np.array([[[100, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0]]]),\n    np.array([[0.70710677, 0.18696113], [0.0, 0.0], [0.70710677, 0.98236734]]),\n]\n\n\n# input pixels all transparent and below the beta absorbance threshold\nNORMALIZE_STAINS_TEST_CASE_1 = [np.full((3, 2, 3), 240)]\n\n# input pixels uniformly filled with zeros, and target stain matrix provided\nNORMALIZE_STAINS_TEST_CASE_2 = [{\"target_he\": np.full((3, 2), 1)}, np.zeros((3, 2, 3)), np.full((3, 2, 3), 11)]\n\n# input pixels uniformly filled with zeros, and target stain matrix not provided\nNORMALIZE_STAINS_TEST_CASE_3 = [\n    {},\n    np.zeros((3, 2, 3)),\n    np.array([[[63, 25, 60], [63, 25, 60]], [[63, 25, 60], [63, 25, 60]], [[63, 25, 60], [63, 25, 60]]]),\n]\n\n# input pixels not uniformly filled", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_pathology_he_stain.py_NORMALIZE_STAINS_TEST_CASE_4_NORMALIZE_STAINS_TEST_CASE_4._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_pathology_he_stain.py_NORMALIZE_STAINS_TEST_CASE_4_NORMALIZE_STAINS_TEST_CASE_4._", "embedding": null, "metadata": {"file_path": "tests/test_pathology_he_stain.py", "file_name": "test_pathology_he_stain.py", "file_type": "text/x-python", "category": "test", "start_line": 67, "end_line": 71, "span_ids": ["docstring:19"], "tokens": 145}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "NORMALIZE_STAINS_TEST_CASE_4 = [\n    {\"target_he\": np.full((3, 2), 1)},\n    np.array([[[100, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0]]]),\n    np.array([[[87, 87, 87], [33, 33, 33]], [[33, 33, 33], [33, 33, 33]], [[33, 33, 33], [33, 33, 33]]]),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_pathology_he_stain.py_TestExtractHEStains_TestExtractHEStains.test_transparent_image.if_image_is_None_.else_.with_self_assertRaises_Va.ExtractHEStains_image_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_pathology_he_stain.py_TestExtractHEStains_TestExtractHEStains.test_transparent_image.if_image_is_None_.else_.with_self_assertRaises_Va.ExtractHEStains_image_", "embedding": null, "metadata": {"file_path": "tests/test_pathology_he_stain.py", "file_name": "test_pathology_he_stain.py", "file_type": "text/x-python", "category": "test", "start_line": 74, "end_line": 91, "span_ids": ["TestExtractHEStains", "TestExtractHEStains.test_transparent_image"], "tokens": 156}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestExtractHEStains(unittest.TestCase):\n    @parameterized.expand(\n        [NEGATIVE_VALUE_TEST_CASE, INVALID_VALUE_TEST_CASE, EXTRACT_STAINS_TEST_CASE_0, EXTRACT_STAINS_TEST_CASE_1]\n    )\n    def test_transparent_image(self, image):\n        \"\"\"\n        Test HE stain extraction on an image that comprises\n        only transparent pixels - pixels with absorbance below the\n        beta absorbance threshold. A ValueError should be raised,\n        since once the transparent pixels are removed, there are no\n        remaining pixels to compute eigenvectors.\n        \"\"\"\n        if image is None:\n            with self.assertRaises(TypeError):\n                ExtractHEStains()(image)\n        else:\n            with self.assertRaises(ValueError):\n                ExtractHEStains()(image)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_pathology_he_stain.py_TestExtractHEStains.test_identical_result_vectors_TestExtractHEStains.test_identical_result_vectors.if_image_is_None_.else_.np_testing_assert_array_e": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_pathology_he_stain.py_TestExtractHEStains.test_identical_result_vectors_TestExtractHEStains.test_identical_result_vectors.if_image_is_None_.else_.np_testing_assert_array_e", "embedding": null, "metadata": {"file_path": "tests/test_pathology_he_stain.py", "file_name": "test_pathology_he_stain.py", "file_type": "text/x-python", "category": "test", "start_line": 93, "end_line": 108, "span_ids": ["TestExtractHEStains.test_identical_result_vectors"], "tokens": 177}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestExtractHEStains(unittest.TestCase):\n\n    @parameterized.expand([EXTRACT_STAINS_TEST_CASE_0, EXTRACT_STAINS_TEST_CASE_2, EXTRACT_STAINS_TEST_CASE_3])\n    def test_identical_result_vectors(self, image):\n        \"\"\"\n        Test HE stain extraction on input images that are\n        uniformly filled with pixels that have absorbance above the\n        beta absorbance threshold. Since input image is uniformly filled,\n        the two extracted stains should have the same RGB values. So,\n        we assert that the first column is equal to the second column\n        of the returned stain matrix.\n        \"\"\"\n        if image is None:\n            with self.assertRaises(TypeError):\n                ExtractHEStains()(image)\n        else:\n            result = ExtractHEStains()(image)\n            np.testing.assert_array_equal(result[:, 0], result[:, 1])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_pathology_he_stain.py_TestExtractHEStains.test_result_value_TestExtractHEStains.test_result_value.if_image_is_None_.else_.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_pathology_he_stain.py_TestExtractHEStains.test_result_value_TestExtractHEStains.test_result_value.if_image_is_None_.else_.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_pathology_he_stain.py", "file_name": "test_pathology_he_stain.py", "file_type": "text/x-python", "category": "test", "start_line": 110, "end_line": 143, "span_ids": ["TestExtractHEStains.test_result_value"], "tokens": 432}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestExtractHEStains(unittest.TestCase):\n\n    @parameterized.expand([EXTRACT_STAINS_TEST_CASE_00, EXTRACT_STAINS_TEST_CASE_4, EXTRACT_STAINS_TEST_CASE_5])\n    def test_result_value(self, image, expected_data):\n        \"\"\"\n        Test that an input image returns an expected stain matrix.\n\n        For test case 4:\n        - a uniformly filled input image should result in\n          eigenvectors [[1,0,0],[0,1,0],[0,0,1]]\n        - phi should be an array containing only values of\n          arctan(1) since the ratio between the eigenvectors\n          corresponding to the two largest eigenvalues is 1\n        - maximum phi and minimum phi should thus be arctan(1)\n        - thus, maximum vector and minimum vector should be\n          [[0],[0.70710677],[0.70710677]]\n        - the resulting extracted stain should be\n          [[0,0],[0.70710678,0.70710678],[0.70710678,0.70710678]]\n\n        For test case 5:\n        - the non-uniformly filled input image should result in\n          eigenvectors [[0,0,1],[1,0,0],[0,1,0]]\n        - maximum phi and minimum phi should thus be 0.785 and\n          0.188 respectively\n        - thus, maximum vector and minimum vector should be\n          [[0.18696113],[0],[0.98236734]] and\n          [[0.70710677],[0],[0.70710677]] respectively\n        - the resulting extracted stain should be\n          [[0.70710677,0.18696113],[0,0],[0.70710677,0.98236734]]\n        \"\"\"\n        if image is None:\n            with self.assertRaises(TypeError):\n                ExtractHEStains()(image)\n        else:\n            result = ExtractHEStains()(image)\n            np.testing.assert_allclose(result, expected_data)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_pathology_he_stain.py_TestNormalizeHEStains_TestNormalizeHEStains.test_transparent_image.if_image_is_None_.else_.with_self_assertRaises_Va.NormalizeHEStains_image": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_pathology_he_stain.py_TestNormalizeHEStains_TestNormalizeHEStains.test_transparent_image.if_image_is_None_.else_.with_self_assertRaises_Va.NormalizeHEStains_image", "embedding": null, "metadata": {"file_path": "tests/test_pathology_he_stain.py", "file_name": "test_pathology_he_stain.py", "file_type": "text/x-python", "category": "test", "start_line": 146, "end_line": 163, "span_ids": ["TestNormalizeHEStains", "TestNormalizeHEStains.test_transparent_image"], "tokens": 156}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestNormalizeHEStains(unittest.TestCase):\n    @parameterized.expand(\n        [NEGATIVE_VALUE_TEST_CASE, INVALID_VALUE_TEST_CASE, NORMALIZE_STAINS_TEST_CASE_0, NORMALIZE_STAINS_TEST_CASE_1]\n    )\n    def test_transparent_image(self, image):\n        \"\"\"\n        Test HE stain normalization on an image that comprises\n        only transparent pixels - pixels with absorbance below the\n        beta absorbance threshold. A ValueError should be raised,\n        since once the transparent pixels are removed, there are no\n        remaining pixels to compute eigenvectors.\n        \"\"\"\n        if image is None:\n            with self.assertRaises(TypeError):\n                NormalizeHEStains()(image)\n        else:\n            with self.assertRaises(ValueError):\n                NormalizeHEStains()(image)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_pathology_he_stain.py_TestNormalizeHEStains.test_result_value_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_pathology_he_stain.py_TestNormalizeHEStains.test_result_value_", "embedding": null, "metadata": {"file_path": "tests/test_pathology_he_stain.py", "file_name": "test_pathology_he_stain.py", "file_type": "text/x-python", "category": "test", "start_line": 165, "end_line": 228, "span_ids": ["TestNormalizeHEStains.test_result_value", "impl:31"], "tokens": 842}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestNormalizeHEStains(unittest.TestCase):\n\n    @parameterized.expand(\n        [\n            NORMALIZE_STAINS_TEST_CASE_00,\n            NORMALIZE_STAINS_TEST_CASE_2,\n            NORMALIZE_STAINS_TEST_CASE_3,\n            NORMALIZE_STAINS_TEST_CASE_4,\n        ]\n    )\n    def test_result_value(self, argments, image, expected_data):\n        \"\"\"\n        Test that an input image returns an expected normalized image.\n\n        For test case 2:\n        - This case tests calling the stain normalizer, after the\n          _deconvolution_extract_conc function. This is because the normalized\n          concentration returned for each pixel is the same as the reference\n          maximum stain concentrations in the case that the image is uniformly\n          filled, as in this test case. This is because the maximum concentration\n          for each stain is the same as each pixel's concentration.\n        - Thus, the normalized concentration matrix should be a (2, 6) matrix\n          with the first row having all values of 1.9705, second row all 1.0308.\n        - Taking the matrix product of the target stain matrix and the concentration\n          matrix, then using the inverse Beer-Lambert transform to obtain the RGB\n          image from the absorbance image, and finally converting to uint8,\n          we get that the stain normalized image should be a matrix of\n          dims (3, 2, 3), with all values 11.\n\n        For test case 3:\n        - This case also tests calling the stain normalizer, after the\n          _deconvolution_extract_conc function returns the image concentration\n          matrix.\n        - As in test case 2, the normalized concentration matrix should be a (2, 6) matrix\n          with the first row having all values of 1.9705, second row all 1.0308.\n        - Taking the matrix product of the target default stain matrix and the concentration\n          matrix, then using the inverse Beer-Lambert transform to obtain the RGB\n          image from the absorbance image, and finally converting to uint8,\n          we get that the stain normalized image should be [[[63, 25, 60], [63, 25, 60]],\n          [[63, 25, 60], [63, 25, 60]], [[63, 25, 60], [63, 25, 60]]]\n\n        For test case 4:\n        - For this non-uniformly filled image, the stain extracted should be\n          [[0.70710677,0.18696113],[0,0],[0.70710677,0.98236734]], as validated for the\n          ExtractHEStains class. Solving the linear least squares problem (since\n          absorbance matrix = stain matrix * concentration matrix), we obtain the concentration\n          matrix that should be [[-0.3101, 7.7508, 7.7508, 7.7508, 7.7508, 7.7508],\n          [5.8022, 0, 0, 0, 0, 0]]\n        - Normalizing the concentration matrix, taking the matrix product of the\n          target stain matrix and the concentration matrix, using the inverse\n          Beer-Lambert transform to obtain the RGB image from the absorbance\n          image, and finally converting to uint8, we get that the stain normalized\n          image should be [[[87, 87, 87], [33, 33, 33]], [[33, 33, 33], [33, 33, 33]],\n          [[33, 33, 33], [33, 33, 33]]]\n        \"\"\"\n        if image is None:\n            with self.assertRaises(TypeError):\n                NormalizeHEStains()(image)\n        else:\n            result = NormalizeHEStains(**argments)(image)\n            np.testing.assert_allclose(result, expected_data)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_pathology_he_stain_dict.py_unittest_NORMALIZE_STAINS_TEST_CASE_4._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_pathology_he_stain_dict.py_unittest_NORMALIZE_STAINS_TEST_CASE_4._", "embedding": null, "metadata": {"file_path": "tests/test_pathology_he_stain_dict.py", "file_name": "test_pathology_he_stain_dict.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 64, "span_ids": ["docstring:17", "docstring"], "tokens": 765}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.apps.pathology.transforms import ExtractHEStainsD, NormalizeHEStainsD\n\n# None inputs\nEXTRACT_STAINS_TEST_CASE_0 = (None,)\nEXTRACT_STAINS_TEST_CASE_00 = (None, None)\nNORMALIZE_STAINS_TEST_CASE_0 = (None,)\nNORMALIZE_STAINS_TEST_CASE_00: tuple = ({}, None, None)\n\n# input pixels all transparent and below the beta absorbance threshold\nEXTRACT_STAINS_TEST_CASE_1 = [np.full((3, 2, 3), 240)]\n\n# input pixels uniformly filled, but above beta absorbance threshold\nEXTRACT_STAINS_TEST_CASE_2 = [np.full((3, 2, 3), 100)]\n\n# input pixels uniformly filled (different value), but above beta absorbance threshold\nEXTRACT_STAINS_TEST_CASE_3 = [np.full((3, 2, 3), 150)]\n\n# input pixels uniformly filled with zeros, leading to two identical stains extracted\nEXTRACT_STAINS_TEST_CASE_4 = [\n    np.zeros((3, 2, 3)),\n    np.array([[0.0, 0.0], [0.70710678, 0.70710678], [0.70710678, 0.70710678]]),\n]\n\n# input pixels not uniformly filled, leading to two different stains extracted\nEXTRACT_STAINS_TEST_CASE_5 = [\n    np.array([[[100, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0]]]),\n    np.array([[0.70710677, 0.18696113], [0.0, 0.0], [0.70710677, 0.98236734]]),\n]\n\n# input pixels all transparent and below the beta absorbance threshold\nNORMALIZE_STAINS_TEST_CASE_1 = [np.full((3, 2, 3), 240)]\n\n# input pixels uniformly filled with zeros, and target stain matrix provided\nNORMALIZE_STAINS_TEST_CASE_2 = [{\"target_he\": np.full((3, 2), 1)}, np.zeros((3, 2, 3)), np.full((3, 2, 3), 11)]\n\n# input pixels uniformly filled with zeros, and target stain matrix not provided\nNORMALIZE_STAINS_TEST_CASE_3 = [\n    {},\n    np.zeros((3, 2, 3)),\n    np.array([[[63, 25, 60], [63, 25, 60]], [[63, 25, 60], [63, 25, 60]], [[63, 25, 60], [63, 25, 60]]]),\n]\n\n# input pixels not uniformly filled\nNORMALIZE_STAINS_TEST_CASE_4 = [\n    {\"target_he\": np.full((3, 2), 1)},\n    np.array([[[100, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0]]]),\n    np.array([[[87, 87, 87], [33, 33, 33]], [[33, 33, 33], [33, 33, 33]], [[33, 33, 33], [33, 33, 33]]]),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_pathology_he_stain_dict.py_TestExtractHEStainsD_TestExtractHEStainsD.test_transparent_image.if_image_is_None_.else_.with_self_assertRaises_Va.ExtractHEStainsD_key_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_pathology_he_stain_dict.py_TestExtractHEStainsD_TestExtractHEStainsD.test_transparent_image.if_image_is_None_.else_.with_self_assertRaises_Va.ExtractHEStainsD_key_", "embedding": null, "metadata": {"file_path": "tests/test_pathology_he_stain_dict.py", "file_name": "test_pathology_he_stain_dict.py", "file_type": "text/x-python", "category": "test", "start_line": 67, "end_line": 83, "span_ids": ["TestExtractHEStainsD", "TestExtractHEStainsD.test_transparent_image"], "tokens": 160}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestExtractHEStainsD(unittest.TestCase):\n    @parameterized.expand([EXTRACT_STAINS_TEST_CASE_0, EXTRACT_STAINS_TEST_CASE_1])\n    def test_transparent_image(self, image):\n        \"\"\"\n        Test HE stain extraction on an image that comprises\n        only transparent pixels - pixels with absorbance below the\n        beta absorbance threshold. A ValueError should be raised,\n        since once the transparent pixels are removed, there are no\n        remaining pixels to compute eigenvectors.\n        \"\"\"\n        key = \"image\"\n        if image is None:\n            with self.assertRaises(TypeError):\n                ExtractHEStainsD([key])({key: image})\n        else:\n            with self.assertRaises(ValueError):\n                ExtractHEStainsD([key])({key: image})", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_pathology_he_stain_dict.py_TestExtractHEStainsD.test_identical_result_vectors_TestExtractHEStainsD.test_identical_result_vectors.if_image_is_None_.else_.np_testing_assert_array_e": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_pathology_he_stain_dict.py_TestExtractHEStainsD.test_identical_result_vectors_TestExtractHEStainsD.test_identical_result_vectors.if_image_is_None_.else_.np_testing_assert_array_e", "embedding": null, "metadata": {"file_path": "tests/test_pathology_he_stain_dict.py", "file_name": "test_pathology_he_stain_dict.py", "file_type": "text/x-python", "category": "test", "start_line": 85, "end_line": 101, "span_ids": ["TestExtractHEStainsD.test_identical_result_vectors"], "tokens": 200}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestExtractHEStainsD(unittest.TestCase):\n\n    @parameterized.expand([EXTRACT_STAINS_TEST_CASE_0, EXTRACT_STAINS_TEST_CASE_2, EXTRACT_STAINS_TEST_CASE_3])\n    def test_identical_result_vectors(self, image):\n        \"\"\"\n        Test HE stain extraction on input images that are\n        uniformly filled with pixels that have absorbance above the\n        beta absorbance threshold. Since input image is uniformly filled,\n        the two extracted stains should have the same RGB values. So,\n        we assert that the first column is equal to the second column\n        of the returned stain matrix.\n        \"\"\"\n        key = \"image\"\n        if image is None:\n            with self.assertRaises(TypeError):\n                ExtractHEStainsD([key])({key: image})\n        else:\n            result = ExtractHEStainsD([key])({key: image})\n            np.testing.assert_array_equal(result[key][:, 0], result[key][:, 1])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_pathology_he_stain_dict.py_TestExtractHEStainsD.test_result_value_TestExtractHEStainsD.test_result_value.if_image_is_None_.else_.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_pathology_he_stain_dict.py_TestExtractHEStainsD.test_result_value_TestExtractHEStainsD.test_result_value.if_image_is_None_.else_.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_pathology_he_stain_dict.py", "file_name": "test_pathology_he_stain_dict.py", "file_type": "text/x-python", "category": "test", "start_line": 103, "end_line": 137, "span_ids": ["TestExtractHEStainsD.test_result_value"], "tokens": 452}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestExtractHEStainsD(unittest.TestCase):\n\n    @parameterized.expand([EXTRACT_STAINS_TEST_CASE_00, EXTRACT_STAINS_TEST_CASE_4, EXTRACT_STAINS_TEST_CASE_5])\n    def test_result_value(self, image, expected_data):\n        \"\"\"\n        Test that an input image returns an expected stain matrix.\n\n        For test case 4:\n        - a uniformly filled input image should result in\n          eigenvectors [[1,0,0],[0,1,0],[0,0,1]]\n        - phi should be an array containing only values of\n          arctan(1) since the ratio between the eigenvectors\n          corresponding to the two largest eigenvalues is 1\n        - maximum phi and minimum phi should thus be arctan(1)\n        - thus, maximum vector and minimum vector should be\n          [[0],[0.70710677],[0.70710677]]\n        - the resulting extracted stain should be\n          [[0,0],[0.70710678,0.70710678],[0.70710678,0.70710678]]\n\n        For test case 5:\n        - the non-uniformly filled input image should result in\n          eigenvectors [[0,0,1],[1,0,0],[0,1,0]]\n        - maximum phi and minimum phi should thus be 0.785 and\n          0.188 respectively\n        - thus, maximum vector and minimum vector should be\n          [[0.18696113],[0],[0.98236734]] and\n          [[0.70710677],[0],[0.70710677]] respectively\n        - the resulting extracted stain should be\n          [[0.70710677,0.18696113],[0,0],[0.70710677,0.98236734]]\n        \"\"\"\n        key = \"image\"\n        if image is None:\n            with self.assertRaises(TypeError):\n                ExtractHEStainsD([key])({key: image})\n        else:\n            result = ExtractHEStainsD([key])({key: image})\n            np.testing.assert_allclose(result[key], expected_data)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_pathology_he_stain_dict.py_TestNormalizeHEStainsD_TestNormalizeHEStainsD.test_transparent_image.if_image_is_None_.else_.with_self_assertRaises_Va.NormalizeHEStainsD_key_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_pathology_he_stain_dict.py_TestNormalizeHEStainsD_TestNormalizeHEStainsD.test_transparent_image.if_image_is_None_.else_.with_self_assertRaises_Va.NormalizeHEStainsD_key_", "embedding": null, "metadata": {"file_path": "tests/test_pathology_he_stain_dict.py", "file_name": "test_pathology_he_stain_dict.py", "file_type": "text/x-python", "category": "test", "start_line": 140, "end_line": 156, "span_ids": ["TestNormalizeHEStainsD", "TestNormalizeHEStainsD.test_transparent_image"], "tokens": 160}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestNormalizeHEStainsD(unittest.TestCase):\n    @parameterized.expand([NORMALIZE_STAINS_TEST_CASE_0, NORMALIZE_STAINS_TEST_CASE_1])\n    def test_transparent_image(self, image):\n        \"\"\"\n        Test HE stain normalization on an image that comprises\n        only transparent pixels - pixels with absorbance below the\n        beta absorbance threshold. A ValueError should be raised,\n        since once the transparent pixels are removed, there are no\n        remaining pixels to compute eigenvectors.\n        \"\"\"\n        key = \"image\"\n        if image is None:\n            with self.assertRaises(TypeError):\n                NormalizeHEStainsD([key])({key: image})\n        else:\n            with self.assertRaises(ValueError):\n                NormalizeHEStainsD([key])({key: image})", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_pathology_he_stain_dict.py_TestNormalizeHEStainsD.test_result_value_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_pathology_he_stain_dict.py_TestNormalizeHEStainsD.test_result_value_", "embedding": null, "metadata": {"file_path": "tests/test_pathology_he_stain_dict.py", "file_name": "test_pathology_he_stain_dict.py", "file_type": "text/x-python", "category": "test", "start_line": 158, "end_line": 222, "span_ids": ["impl:27", "TestNormalizeHEStainsD.test_result_value"], "tokens": 863}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestNormalizeHEStainsD(unittest.TestCase):\n\n    @parameterized.expand(\n        [\n            NORMALIZE_STAINS_TEST_CASE_00,\n            NORMALIZE_STAINS_TEST_CASE_2,\n            NORMALIZE_STAINS_TEST_CASE_3,\n            NORMALIZE_STAINS_TEST_CASE_4,\n        ]\n    )\n    def test_result_value(self, argments, image, expected_data):\n        \"\"\"\n        Test that an input image returns an expected normalized image.\n\n        For test case 2:\n        - This case tests calling the stain normalizer, after the\n          _deconvolution_extract_conc function. This is because the normalized\n          concentration returned for each pixel is the same as the reference\n          maximum stain concentrations in the case that the image is uniformly\n          filled, as in this test case. This is because the maximum concentration\n          for each stain is the same as each pixel's concentration.\n        - Thus, the normalized concentration matrix should be a (2, 6) matrix\n          with the first row having all values of 1.9705, second row all 1.0308.\n        - Taking the matrix product of the target stain matrix and the concentration\n          matrix, then using the inverse Beer-Lambert transform to obtain the RGB\n          image from the absorbance image, and finally converting to uint8,\n          we get that the stain normalized image should be a matrix of\n          dims (3, 2, 3), with all values 11.\n\n        For test case 3:\n        - This case also tests calling the stain normalizer, after the\n          _deconvolution_extract_conc function returns the image concentration\n          matrix.\n        - As in test case 2, the normalized concentration matrix should be a (2, 6) matrix\n          with the first row having all values of 1.9705, second row all 1.0308.\n        - Taking the matrix product of the target default stain matrix and the concentration\n          matrix, then using the inverse Beer-Lambert transform to obtain the RGB\n          image from the absorbance image, and finally converting to uint8,\n          we get that the stain normalized image should be [[[63, 25, 60], [63, 25, 60]],\n          [[63, 25, 60], [63, 25, 60]], [[63, 25, 60], [63, 25, 60]]]\n\n        For test case 4:\n        - For this non-uniformly filled image, the stain extracted should be\n          [[0.70710677,0.18696113],[0,0],[0.70710677,0.98236734]], as validated for the\n          ExtractHEStains class. Solving the linear least squares problem (since\n          absorbance matrix = stain matrix * concentration matrix), we obtain the concentration\n          matrix that should be [[-0.3101, 7.7508, 7.7508, 7.7508, 7.7508, 7.7508],\n          [5.8022, 0, 0, 0, 0, 0]]\n        - Normalizing the concentration matrix, taking the matrix product of the\n          target stain matrix and the concentration matrix, using the inverse\n          Beer-Lambert transform to obtain the RGB image from the absorbance\n          image, and finally converting to uint8, we get that the stain normalized\n          image should be [[[87, 87, 87], [33, 33, 33]], [[33, 33, 33], [33, 33, 33]],\n          [[33, 33, 33], [33, 33, 33]]]\n        \"\"\"\n        key = \"image\"\n        if image is None:\n            with self.assertRaises(TypeError):\n                NormalizeHEStainsD([key])({key: image})\n        else:\n            result = NormalizeHEStainsD([key], **argments)({key: image})\n            np.testing.assert_allclose(result[key], expected_data)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_png_rw.py_os_TestPngWrite.test_write_gray_1height.with_tempfile_TemporaryDi.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_png_rw.py_os_TestPngWrite.test_write_gray_1height.with_tempfile_TemporaryDi.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_png_rw.py", "file_name": "test_png_rw.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 41, "span_ids": ["TestPngWrite.test_write_gray", "TestPngWrite", "TestPngWrite.test_write_gray_1height", "docstring"], "tokens": 254}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport tempfile\nimport unittest\n\nimport numpy as np\nfrom PIL import Image\n\nfrom monai.data import write_png\n\n\nclass TestPngWrite(unittest.TestCase):\n    def test_write_gray(self):\n        with tempfile.TemporaryDirectory() as out_dir:\n            image_name = os.path.join(out_dir, \"test.png\")\n            img = np.random.rand(2, 3)\n            img_save_val = (255 * img).astype(np.uint8)\n            write_png(img, image_name, scale=255)\n            out = np.asarray(Image.open(image_name))\n            out = np.moveaxis(out, 0, 1)\n            np.testing.assert_allclose(out, img_save_val)\n\n    def test_write_gray_1height(self):\n        with tempfile.TemporaryDirectory() as out_dir:\n            image_name = os.path.join(out_dir, \"test.png\")\n            img = np.random.rand(1, 3)\n            img_save_val = (65535 * img).astype(np.uint16)\n            write_png(img, image_name, scale=65535)\n            out = np.asarray(Image.open(image_name))\n            out = np.moveaxis(out, 0, 1)\n            np.testing.assert_allclose(out, img_save_val)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_png_rw.py_TestPngWrite.test_write_gray_1channel_TestPngWrite.test_write_gray_1channel.with_tempfile_TemporaryDi.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_png_rw.py_TestPngWrite.test_write_gray_1channel_TestPngWrite.test_write_gray_1channel.with_tempfile_TemporaryDi.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_png_rw.py", "file_name": "test_png_rw.py", "file_type": "text/x-python", "category": "test", "start_line": 43, "end_line": 51, "span_ids": ["TestPngWrite.test_write_gray_1channel"], "tokens": 125}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestPngWrite(unittest.TestCase):\n\n    def test_write_gray_1channel(self):\n        with tempfile.TemporaryDirectory() as out_dir:\n            image_name = os.path.join(out_dir, \"test.png\")\n            img = np.random.rand(2, 3, 1)\n            img_save_val = (255 * img).astype(np.uint8).squeeze(2)\n            write_png(img, image_name, scale=255)\n            out = np.asarray(Image.open(image_name))\n            out = np.moveaxis(out, 0, 1)\n            np.testing.assert_allclose(out, img_save_val)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_png_rw.py_TestPngWrite.test_write_rgb_TestPngWrite.test_write_rgb.with_tempfile_TemporaryDi.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_png_rw.py_TestPngWrite.test_write_rgb_TestPngWrite.test_write_rgb.with_tempfile_TemporaryDi.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_png_rw.py", "file_name": "test_png_rw.py", "file_type": "text/x-python", "category": "test", "start_line": 53, "end_line": 61, "span_ids": ["TestPngWrite.test_write_rgb"], "tokens": 118}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestPngWrite(unittest.TestCase):\n\n    def test_write_rgb(self):\n        with tempfile.TemporaryDirectory() as out_dir:\n            image_name = os.path.join(out_dir, \"test.png\")\n            img = np.random.rand(2, 3, 3)\n            img_save_val = (255 * img).astype(np.uint8)\n            write_png(img, image_name, scale=255)\n            out = np.asarray(Image.open(image_name))\n            out = np.moveaxis(out, 0, 1)\n            np.testing.assert_allclose(out, img_save_val)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_png_rw.py_TestPngWrite.test_write_2channels_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_png_rw.py_TestPngWrite.test_write_2channels_", "embedding": null, "metadata": {"file_path": "tests/test_png_rw.py", "file_name": "test_png_rw.py", "file_type": "text/x-python", "category": "test", "start_line": 63, "end_line": 84, "span_ids": ["TestPngWrite.test_write_2channels", "TestPngWrite.test_write_output_shape", "impl"], "tokens": 226}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestPngWrite(unittest.TestCase):\n\n    def test_write_2channels(self):\n        with tempfile.TemporaryDirectory() as out_dir:\n            image_name = os.path.join(out_dir, \"test.png\")\n            img = np.random.rand(2, 3, 2)\n            img_save_val = (255 * img).astype(np.uint8)\n            write_png(img, image_name, scale=255)\n            out = np.asarray(Image.open(image_name))\n            out = np.moveaxis(out, 0, 1)\n            np.testing.assert_allclose(out, img_save_val)\n\n    def test_write_output_shape(self):\n        with tempfile.TemporaryDirectory() as out_dir:\n            image_name = os.path.join(out_dir, \"test.png\")\n            img = np.random.rand(2, 2, 3)\n            write_png(img, image_name, (4, 4), scale=255)\n            out = np.asarray(Image.open(image_name))\n            np.testing.assert_allclose(out.shape, (4, 4, 3))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_prepare_batch_default.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_prepare_batch_default.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_prepare_batch_default.py", "file_name": "test_prepare_batch_default.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 55, "span_ids": ["impl", "TestNet", "docstring", "TestPrepareBatchDefault.test_content", "TestNet.forward", "TestPrepareBatchDefault"], "tokens": 264}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\n\nfrom monai.engines import PrepareBatchDefault, SupervisedEvaluator\nfrom tests.utils import assert_allclose\n\n\nclass TestNet(torch.nn.Module):\n    def forward(self, x: torch.Tensor):\n        return x\n\n\nclass TestPrepareBatchDefault(unittest.TestCase):\n    def test_content(self):\n        device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n        dataloader = [\n            {\n                \"image\": torch.tensor([1, 2]),\n                \"label\": torch.tensor([3, 4]),\n                \"extra1\": torch.tensor([5, 6]),\n                \"extra2\": 16,\n                \"extra3\": \"test\",\n            }\n        ]\n        # set up engine\n        evaluator = SupervisedEvaluator(\n            device=device,\n            val_data_loader=dataloader,\n            epoch_length=1,\n            network=TestNet(),\n            non_blocking=False,\n            prepare_batch=PrepareBatchDefault(),\n            decollate=False,\n        )\n        evaluator.run()\n        output = evaluator.state.output\n        assert_allclose(output[\"image\"], torch.tensor([1, 2], device=device))\n        assert_allclose(output[\"label\"], torch.tensor([3, 4], device=device))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_prepare_batch_extra_input.py_unittest_TestNet.forward.return._x_x_t1_t1_t2_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_prepare_batch_extra_input.py_unittest_TestNet.forward.return._x_x_t1_t1_t2_", "embedding": null, "metadata": {"file_path": "tests/test_prepare_batch_extra_input.py", "file_name": "test_prepare_batch_extra_input.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 38, "span_ids": ["TestNet", "TestNet.forward", "docstring"], "tokens": 278}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.engines import PrepareBatchExtraInput, SupervisedEvaluator\nfrom tests.utils import assert_allclose\n\nTEST_CASE_0 = [\n    {\"extra_keys\": \"extra1\"},\n    {\"x\": torch.tensor([1, 2]), \"t1\": torch.tensor([5, 6]), \"t2\": None, \"t3\": None},\n]\n\nTEST_CASE_1 = [\n    {\"extra_keys\": [\"extra1\", \"extra3\"]},\n    {\"x\": torch.tensor([1, 2]), \"t1\": torch.tensor([5, 6]), \"t2\": \"test\", \"t3\": None},\n]\n\nTEST_CASE_2 = [\n    {\"extra_keys\": {\"t1\": \"extra2\", \"t2\": \"extra3\", \"t3\": \"extra1\"}},\n    {\"x\": torch.tensor([1, 2]), \"t1\": 16, \"t2\": \"test\", \"t3\": torch.tensor([5, 6])},\n]\n\n\nclass TestNet(torch.nn.Module):\n    def forward(self, x: torch.Tensor, t1=None, t2=None, t3=None):\n        return {\"x\": x, \"t1\": t1, \"t2\": t2, \"t3\": t3}", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_prepare_batch_extra_input.py_TestPrepareBatchExtraInput_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_prepare_batch_extra_input.py_TestPrepareBatchExtraInput_", "embedding": null, "metadata": {"file_path": "tests/test_prepare_batch_extra_input.py", "file_name": "test_prepare_batch_extra_input.py", "file_type": "text/x-python", "category": "test", "start_line": 41, "end_line": 77, "span_ids": ["TestPrepareBatchExtraInput.test_content", "impl:7", "TestPrepareBatchExtraInput"], "tokens": 292}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestPrepareBatchExtraInput(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_0, TEST_CASE_1, TEST_CASE_2])\n    def test_content(self, input_args, expected_value):\n        device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n        dataloader = [\n            {\n                \"image\": torch.tensor([1, 2]),\n                \"label\": torch.tensor([3, 4]),\n                \"extra1\": torch.tensor([5, 6]),\n                \"extra2\": 16,\n                \"extra3\": \"test\",\n            }\n        ]\n        # set up engine\n        evaluator = SupervisedEvaluator(\n            device=device,\n            val_data_loader=dataloader,\n            epoch_length=1,\n            network=TestNet(),\n            non_blocking=True,\n            prepare_batch=PrepareBatchExtraInput(**input_args),\n            decollate=False,\n        )\n        evaluator.run()\n        output = evaluator.state.output\n        assert_allclose(output[\"image\"], torch.tensor([1, 2], device=device))\n        assert_allclose(output[\"label\"], torch.tensor([3, 4], device=device))\n        for k, v in output[\"pred\"].items():\n            if isinstance(v, torch.Tensor):\n                assert_allclose(v, expected_value[k].to(device))\n            else:\n                self.assertEqual(v, expected_value[k])\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_print_transform_backends.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_print_transform_backends.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_print_transform_backends.py", "file_name": "test_print_transform_backends.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 28, "span_ids": ["TestPrintTransformBackends", "TestPrintTransformBackends.test_get_number_of_conversions", "impl", "docstring"], "tokens": 101}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nfrom monai.transforms.utils import get_transform_backends, print_transform_backends\n\n\nclass TestPrintTransformBackends(unittest.TestCase):\n    def test_get_number_of_conversions(self):\n        tr_t_or_np, *_ = get_transform_backends()\n        self.assertGreater(len(tr_t_or_np), 0)\n        print_transform_backends()\n\n\nif __name__ == \"__main__\":\n    # unittest.main()\n    a = TestPrintTransformBackends()\n    a.test_get_number_of_conversions()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_affine.py_unittest_for_p_in_TEST_NDARRAYS_.for_device_in_None_cpu.None_8": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_affine.py_unittest_for_p_in_TEST_NDARRAYS_.for_device_in_None_cpu.None_8", "embedding": null, "metadata": {"file_path": "tests/test_rand_affine.py", "file_name": "test_rand_affine.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 126, "span_ids": ["docstring"], "tokens": 1122}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import RandAffine\nfrom monai.utils.type_conversion import convert_data_type\nfrom tests.utils import TEST_NDARRAYS, assert_allclose, is_tf32_env\n\n_rtol = 1e-3 if is_tf32_env() else 1e-4\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    for device in [None, \"cpu\", \"cuda\"] if torch.cuda.is_available() else [None, \"cpu\"]:\n        TESTS.append(\n            [dict(device=device), {\"img\": p(torch.arange(27).reshape((3, 3, 3)))}, p(np.arange(27).reshape((3, 3, 3)))]\n        )\n        TESTS.append(\n            [\n                dict(device=device, spatial_size=-1),\n                {\"img\": p(torch.arange(27).reshape((3, 3, 3)))},\n                p(np.arange(27).reshape((3, 3, 3))),\n            ]\n        )\n        TESTS.append(\n            [\n                dict(device=device),\n                {\"img\": p(torch.arange(27).reshape((3, 3, 3))), \"spatial_size\": (2, 2)},\n                p(np.array([[[2.0, 3.0], [5.0, 6.0]], [[11.0, 12.0], [14.0, 15.0]], [[20.0, 21.0], [23.0, 24.0]]])),\n            ]\n        )\n        TESTS.append(\n            [\n                dict(device=device),\n                {\"img\": p(torch.ones((1, 3, 3, 3))), \"spatial_size\": (2, 2, 2)},\n                p(torch.ones((1, 2, 2, 2))),\n            ]\n        )\n        TESTS.append(\n            [\n                dict(device=device, spatial_size=(2, 2, 2), cache_grid=True),\n                {\"img\": p(torch.ones((1, 3, 3, 3)))},\n                p(torch.ones((1, 2, 2, 2))),\n            ]\n        )\n        TESTS.append(\n            [\n                dict(\n                    prob=0.9,\n                    rotate_range=(np.pi / 2,),\n                    shear_range=[1, 2],\n                    translate_range=[2, 1],\n                    padding_mode=\"zeros\",\n                    spatial_size=(2, 2, 2),\n                    device=device,\n                ),\n                {\"img\": p(torch.ones((1, 3, 3, 3))), \"mode\": \"bilinear\"},\n                p(torch.tensor([[[[0.3658, 1.0000], [1.0000, 1.0000]], [[1.0000, 1.0000], [1.0000, 0.9333]]]])),\n            ]\n        )\n        TESTS.append(\n            [\n                dict(\n                    prob=0.9,\n                    rotate_range=(np.pi / 2,),\n                    shear_range=[1, 2],\n                    translate_range=[2, 1],\n                    padding_mode=\"zeros\",\n                    spatial_size=(2, 2, 2),\n                    cache_grid=True,\n                    device=device,\n                ),\n                {\"img\": p(torch.ones((1, 3, 3, 3))), \"mode\": \"bilinear\"},\n                p(torch.tensor([[[[0.3658, 1.0000], [1.0000, 1.0000]], [[1.0000, 1.0000], [1.0000, 0.9333]]]])),\n            ]\n        )\n        TESTS.append(\n            [\n                dict(\n                    prob=0.9,\n                    rotate_range=(np.pi / 2,),\n                    shear_range=[1, 2],\n                    translate_range=[2, 1],\n                    scale_range=[0.1, 0.2],\n                    device=device,\n                ),\n                {\"img\": p(torch.arange(64).reshape((1, 8, 8))), \"spatial_size\": (3, 3)},\n                p(\n                    torch.tensor(\n                        [[[18.7362, 15.5820, 12.4278], [27.3988, 24.2446, 21.0904], [36.0614, 32.9072, 29.7530]]]\n                    )\n                ),\n            ]\n        )\n        TESTS.append(\n            [\n                dict(\n                    prob=0.9,\n                    rotate_range=(np.pi / 2,),\n                    shear_range=[1, 2],\n                    translate_range=[2, 1],\n                    scale_range=[0.1, 0.2],\n                    spatial_size=(3, 3),\n                    cache_grid=True,\n                    device=device,\n                ),\n                {\"img\": p(torch.arange(64).reshape((1, 8, 8)))},\n                p(\n                    torch.tensor(\n                        [[[18.7362, 15.5820, 12.4278], [27.3988, 24.2446, 21.0904], [36.0614, 32.9072, 29.7530]]]\n                    )\n                ),\n            ]\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_affine.py_TEST_CASES_SKIPPED_CONSISTENCY_TestRandAffine.test_ill_cache.None_1.RandAffine_cache_grid_Tru": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_affine.py_TEST_CASES_SKIPPED_CONSISTENCY_TestRandAffine.test_ill_cache.None_1.RandAffine_cache_grid_Tru", "embedding": null, "metadata": {"file_path": "tests/test_rand_affine.py", "file_name": "test_rand_affine.py", "file_type": "text/x-python", "category": "test", "start_line": 128, "end_line": 148, "span_ids": ["TestRandAffine", "impl:16", "TestRandAffine.test_rand_affine", "TestRandAffine.test_ill_cache"], "tokens": 226}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "TEST_CASES_SKIPPED_CONSISTENCY = []\nfor p in TEST_NDARRAYS:\n    for in_dtype in (np.int32, np.float32):\n        TEST_CASES_SKIPPED_CONSISTENCY.append((p(np.arange(9 * 10).reshape(1, 9, 10)), in_dtype))\n\n\nclass TestRandAffine(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_rand_affine(self, input_param, input_data, expected_val):\n        g = RandAffine(**input_param)\n        g.set_random_state(123)\n        result = g(**input_data)\n        if input_param.get(\"cache_grid\", False):\n            self.assertTrue(g._cached_grid is not None)\n        assert_allclose(result, expected_val, rtol=_rtol, atol=1e-4)\n\n    def test_ill_cache(self):\n        with self.assertWarns(UserWarning):\n            RandAffine(cache_grid=True)\n        with self.assertWarns(UserWarning):\n            RandAffine(cache_grid=True, spatial_size=(1, 1, -1))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_affine_grid.py_unittest_for_p_in_TEST_NDARRAYS_.for_device_in_None_cpu.None_2": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_affine_grid.py_unittest_for_p_in_TEST_NDARRAYS_.for_device_in_None_cpu.None_2", "embedding": null, "metadata": {"file_path": "tests/test_rand_affine_grid.py", "file_name": "test_rand_affine_grid.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 115, "span_ids": ["docstring"], "tokens": 1217}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import RandAffineGrid\nfrom tests.utils import TEST_NDARRAYS, assert_allclose, is_tf32_env\n\n_rtol = 1e-1 if is_tf32_env else 1e-4\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    for device in [None, \"cpu\", \"cuda\"] if torch.cuda.is_available() else [None, \"cpu\"]:\n        TESTS.append([{\"device\": device}, {\"grid\": p(torch.ones((3, 3, 3)))}, p(np.ones((3, 3, 3)))])\n        TESTS.append(\n            [\n                {\"rotate_range\": (1, 2), \"translate_range\": (3, 3, 3)},\n                {\"grid\": p(torch.arange(0, 27).reshape((3, 3, 3)))},\n                p(\n                    np.array(\n                        [\n                            [\n                                [-32.81998, -33.910976, -35.001972],\n                                [-36.092968, -37.183964, -38.27496],\n                                [-39.36596, -40.456955, -41.54795],\n                            ],\n                            [\n                                [2.1380205, 3.1015975, 4.0651755],\n                                [5.028752, 5.9923296, 6.955907],\n                                [7.919484, 8.883063, 9.84664],\n                            ],\n                            [[18.0, 19.0, 20.0], [21.0, 22.0, 23.0], [24.0, 25.0, 26.0]],\n                        ]\n                    )\n                ),\n            ]\n        )\n        TESTS.append(\n            [\n                {\"translate_range\": (3, 3, 3), \"device\": device},\n                {\"spatial_size\": (3, 3, 3)},\n                np.array(\n                    [\n                        [\n                            [\n                                [0.17881513, 0.17881513, 0.17881513],\n                                [0.17881513, 0.17881513, 0.17881513],\n                                [0.17881513, 0.17881513, 0.17881513],\n                            ],\n                            [\n                                [1.1788151, 1.1788151, 1.1788151],\n                                [1.1788151, 1.1788151, 1.1788151],\n                                [1.1788151, 1.1788151, 1.1788151],\n                            ],\n                            [\n                                [2.1788151, 2.1788151, 2.1788151],\n                                [2.1788151, 2.1788151, 2.1788151],\n                                [2.1788151, 2.1788151, 2.1788151],\n                            ],\n                        ],\n                        [\n                            [\n                                [-2.283164, -2.283164, -2.283164],\n                                [-1.283164, -1.283164, -1.283164],\n                                [-0.28316402, -0.28316402, -0.28316402],\n                            ],\n                            [\n                                [-2.283164, -2.283164, -2.283164],\n                                [-1.283164, -1.283164, -1.283164],\n                                [-0.28316402, -0.28316402, -0.28316402],\n                            ],\n                            [\n                                [-2.283164, -2.283164, -2.283164],\n                                [-1.283164, -1.283164, -1.283164],\n                                [-0.28316402, -0.28316402, -0.28316402],\n                            ],\n                        ],\n                        [\n                            [\n                                [-2.6388912, -1.6388912, -0.6388912],\n                                [-2.6388912, -1.6388912, -0.6388912],\n                                [-2.6388912, -1.6388912, -0.6388912],\n                            ],\n                            [\n                                [-2.6388912, -1.6388912, -0.6388912],\n                                [-2.6388912, -1.6388912, -0.6388912],\n                                [-2.6388912, -1.6388912, -0.6388912],\n                            ],\n                            [\n                                [-2.6388912, -1.6388912, -0.6388912],\n                                [-2.6388912, -1.6388912, -0.6388912],\n                                [-2.6388912, -1.6388912, -0.6388912],\n                            ],\n                        ],\n                        [\n                            [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],\n                            [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],\n                            [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],\n                        ],\n                    ]\n                ),\n            ]\n        )\n        # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_affine_grid.py_for_p_in_TEST_NDARRAYS_.for_device_in_None_cpu.None_3_for_p_in_TEST_NDARRAYS_.for_device_in_None_cpu.None_3": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_affine_grid.py_for_p_in_TEST_NDARRAYS_.for_device_in_None_cpu.None_3_for_p_in_TEST_NDARRAYS_.for_device_in_None_cpu.None_3", "embedding": null, "metadata": {"file_path": "tests/test_rand_affine_grid.py", "file_name": "test_rand_affine_grid.py", "file_type": "text/x-python", "category": "test", "start_line": 116, "end_line": 195, "span_ids": ["docstring"], "tokens": 1107}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "for p in TEST_NDARRAYS:\n    for device in [None, \"cpu\", \"cuda\"] if torch.cuda.is_available() else [None, \"cpu\"]:\n        # ... other code\n        TESTS.append(\n            [\n                {\"device\": device, \"rotate_range\": (1.0, 1.0, 1.0), \"shear_range\": (0.1,), \"scale_range\": (1.2,)},\n                {\"grid\": p(torch.arange(0, 108).reshape((4, 3, 3, 3)))},\n                p(\n                    np.array(\n                        [\n                            [\n                                [\n                                    [-9.4201e00, -8.1672e00, -6.9143e00],\n                                    [-5.6614e00, -4.4085e00, -3.1556e00],\n                                    [-1.9027e00, -6.4980e-01, 6.0310e-01],\n                                ],\n                                [\n                                    [1.8560e00, 3.1089e00, 4.3618e00],\n                                    [5.6147e00, 6.8676e00, 8.1205e00],\n                                    [9.3734e00, 1.0626e01, 1.1879e01],\n                                ],\n                                [\n                                    [1.3132e01, 1.4385e01, 1.5638e01],\n                                    [1.6891e01, 1.8144e01, 1.9397e01],\n                                    [2.0650e01, 2.1902e01, 2.3155e01],\n                                ],\n                            ],\n                            [\n                                [\n                                    [9.9383e-02, -4.8845e-01, -1.0763e00],\n                                    [-1.6641e00, -2.2519e00, -2.8398e00],\n                                    [-3.4276e00, -4.0154e00, -4.6032e00],\n                                ],\n                                [\n                                    [-5.1911e00, -5.7789e00, -6.3667e00],\n                                    [-6.9546e00, -7.5424e00, -8.1302e00],\n                                    [-8.7180e00, -9.3059e00, -9.8937e00],\n                                ],\n                                [\n                                    [-1.0482e01, -1.1069e01, -1.1657e01],\n                                    [-1.2245e01, -1.2833e01, -1.3421e01],\n                                    [-1.4009e01, -1.4596e01, -1.5184e01],\n                                ],\n                            ],\n                            [\n                                [\n                                    [5.9635e01, 6.1199e01, 6.2764e01],\n                                    [6.4328e01, 6.5892e01, 6.7456e01],\n                                    [6.9021e01, 7.0585e01, 7.2149e01],\n                                ],\n                                [\n                                    [7.3714e01, 7.5278e01, 7.6842e01],\n                                    [7.8407e01, 7.9971e01, 8.1535e01],\n                                    [8.3099e01, 8.4664e01, 8.6228e01],\n                                ],\n                                [\n                                    [8.7792e01, 8.9357e01, 9.0921e01],\n                                    [9.2485e01, 9.4049e01, 9.5614e01],\n                                    [9.7178e01, 9.8742e01, 1.0031e02],\n                                ],\n                            ],\n                            [\n                                [\n                                    [8.1000e01, 8.2000e01, 8.3000e01],\n                                    [8.4000e01, 8.5000e01, 8.6000e01],\n                                    [8.7000e01, 8.8000e01, 8.9000e01],\n                                ],\n                                [\n                                    [9.0000e01, 9.1000e01, 9.2000e01],\n                                    [9.3000e01, 9.4000e01, 9.5000e01],\n                                    [9.6000e01, 9.7000e01, 9.8000e01],\n                                ],\n                                [\n                                    [9.9000e01, 1.0000e02, 1.0100e02],\n                                    [1.0200e02, 1.0300e02, 1.0400e02],\n                                    [1.0500e02, 1.0600e02, 1.0700e02],\n                                ],\n                            ],\n                        ]\n                    )\n                ),\n            ]\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_affined.py_unittest_for_p_in_TEST_NDARRAYS_.for_device_in_None_cpu.None_8": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_affined.py_unittest_for_p_in_TEST_NDARRAYS_.for_device_in_None_cpu.None_8", "embedding": null, "metadata": {"file_path": "tests/test_rand_affined.py", "file_name": "test_rand_affined.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 168, "span_ids": ["docstring"], "tokens": 1558}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import RandAffined\nfrom monai.utils import GridSampleMode\nfrom tests.utils import TEST_NDARRAYS, assert_allclose, is_tf32_env\n\n_rtol = 1e-3 if is_tf32_env() else 1e-4\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    for device in [None, \"cpu\", \"cuda\"] if torch.cuda.is_available() else [None, \"cpu\"]:\n        TESTS.append(\n            [\n                dict(device=device, spatial_size=None, keys=(\"img\", \"seg\")),\n                {\"img\": p(torch.arange(27).reshape((3, 3, 3))), \"seg\": p(torch.arange(27).reshape((3, 3, 3)))},\n                p(np.arange(27).reshape((3, 3, 3))),\n            ]\n        )\n        TESTS.append(\n            [\n                dict(device=device, spatial_size=(2, 2), keys=(\"img\", \"seg\")),\n                {\"img\": p(torch.ones((3, 3, 3))), \"seg\": p(torch.ones((3, 3, 3)))},\n                p(np.ones((3, 2, 2))),\n            ]\n        )\n        TESTS.append(\n            [\n                dict(device=device, spatial_size=(2, 2), cache_grid=True, keys=(\"img\", \"seg\")),\n                {\"img\": p(torch.ones((3, 3, 3))), \"seg\": p(torch.ones((3, 3, 3)))},\n                p(np.ones((3, 2, 2))),\n            ]\n        )\n        TESTS.append(\n            [\n                dict(device=device, spatial_size=(2, 2, 2), keys=(\"img\", \"seg\")),\n                {\"img\": p(torch.ones((1, 3, 3, 3))), \"seg\": p(torch.ones((1, 3, 3, 3)))},\n                p(torch.ones((1, 2, 2, 2))),\n            ]\n        )\n        TESTS.append(\n            [\n                dict(\n                    prob=0.9,\n                    rotate_range=(np.pi / 2,),\n                    shear_range=[1, 2],\n                    translate_range=[2, 1],\n                    spatial_size=(2, 2, 2),\n                    padding_mode=\"zeros\",\n                    device=device,\n                    keys=(\"img\", \"seg\"),\n                    mode=\"bilinear\",\n                ),\n                {\"img\": p(torch.ones((1, 3, 3, 3))), \"seg\": p(torch.ones((1, 3, 3, 3)))},\n                p(torch.tensor([[[[0.3658, 1.0000], [1.0000, 1.0000]], [[1.0000, 1.0000], [1.0000, 0.9333]]]])),\n            ]\n        )\n        TESTS.append(\n            [\n                dict(\n                    prob=0.9,\n                    rotate_range=(np.pi / 2,),\n                    shear_range=[1, 2],\n                    translate_range=[2, 1],\n                    scale_range=[0.1, 0.2],\n                    spatial_size=(3, 3),\n                    keys=(\"img\", \"seg\"),\n                    device=device,\n                ),\n                {\"img\": p(torch.arange(64).reshape((1, 8, 8))), \"seg\": p(torch.arange(64).reshape((1, 8, 8)))},\n                p(\n                    torch.tensor(\n                        [[[18.7362, 15.5820, 12.4278], [27.3988, 24.2446, 21.0904], [36.0614, 32.9072, 29.7530]]]\n                    )\n                ),\n            ]\n        )\n        TESTS.append(\n            [\n                dict(\n                    prob=0.9,\n                    mode=(\"bilinear\", \"nearest\"),\n                    rotate_range=(np.pi / 2,),\n                    shear_range=[1, 2],\n                    translate_range=[2, 1],\n                    scale_range=[0.1, 0.2],\n                    spatial_size=(3, 3),\n                    keys=(\"img\", \"seg\"),\n                    device=device,\n                ),\n                {\"img\": p(torch.arange(64).reshape((1, 8, 8))), \"seg\": p(torch.arange(64).reshape((1, 8, 8)))},\n                {\n                    \"img\": p(\n                        np.array(\n                            [\n                                [\n                                    [18.736153, 15.581954, 12.4277525],\n                                    [27.398798, 24.244598, 21.090399],\n                                    [36.061443, 32.90724, 29.753046],\n                                ]\n                            ]\n                        )\n                    ),\n                    \"seg\": p(np.array([[[19.0, 20.0, 12.0], [27.0, 28.0, 20.0], [35.0, 36.0, 29.0]]])),\n                },\n            ]\n        )\n        TESTS.append(\n            [\n                dict(\n                    prob=0.9,\n                    rotate_range=(np.pi / 2,),\n                    shear_range=[1, 2],\n                    translate_range=[2, 1],\n                    spatial_size=(2, 2, 2),\n                    padding_mode=\"zeros\",\n                    device=device,\n                    keys=(\"img\", \"seg\"),\n                    mode=GridSampleMode.BILINEAR,\n                ),\n                {\"img\": p(torch.ones((1, 3, 3, 3))), \"seg\": p(torch.ones((1, 3, 3, 3)))},\n                p(torch.tensor([[[[0.3658, 1.0000], [1.0000, 1.0000]], [[1.0000, 1.0000], [1.0000, 0.9333]]]])),\n            ]\n        )\n        TESTS.append(\n            [\n                dict(\n                    prob=0.9,\n                    mode=(GridSampleMode.BILINEAR, GridSampleMode.NEAREST),\n                    rotate_range=(np.pi / 2,),\n                    shear_range=[1, 2],\n                    translate_range=[2, 1],\n                    scale_range=[0.1, 0.2],\n                    spatial_size=(3, 3),\n                    keys=(\"img\", \"seg\"),\n                    device=device,\n                ),\n                {\"img\": p(torch.arange(64).reshape((1, 8, 8))), \"seg\": p(torch.arange(64).reshape((1, 8, 8)))},\n                {\n                    \"img\": p(\n                        np.array(\n                            [\n                                [\n                                    [18.736153, 15.581954, 12.4277525],\n                                    [27.398798, 24.244598, 21.090399],\n                                    [36.061443, 32.90724, 29.753046],\n                                ]\n                            ]\n                        )\n                    ),\n                    \"seg\": p(np.array([[[19.0, 20.0, 12.0], [27.0, 28.0, 20.0], [35.0, 36.0, 29.0]]])),\n                },\n            ]\n        )\n        # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_affined.py_for_p_in_TEST_NDARRAYS_.for_device_in_None_cpu.None_9_for_p_in_TEST_NDARRAYS_.for_device_in_None_cpu.None_9": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_affined.py_for_p_in_TEST_NDARRAYS_.for_device_in_None_cpu.None_9_for_p_in_TEST_NDARRAYS_.for_device_in_None_cpu.None_9", "embedding": null, "metadata": {"file_path": "tests/test_rand_affined.py", "file_name": "test_rand_affined.py", "file_type": "text/x-python", "category": "test", "start_line": 169, "end_line": 199, "span_ids": ["docstring"], "tokens": 326}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "for p in TEST_NDARRAYS:\n    for device in [None, \"cpu\", \"cuda\"] if torch.cuda.is_available() else [None, \"cpu\"]:\n        # ... other code\n        TESTS.append(\n            [\n                dict(\n                    prob=0.9,\n                    mode=(GridSampleMode.BILINEAR, GridSampleMode.NEAREST),\n                    rotate_range=(np.pi / 2,),\n                    shear_range=[1, 2],\n                    translate_range=[2, 1],\n                    scale_range=[0.1, 0.2],\n                    spatial_size=(3, 3),\n                    cache_grid=True,\n                    keys=(\"img\", \"seg\"),\n                    device=device,\n                ),\n                {\"img\": p(torch.arange(64).reshape((1, 8, 8))), \"seg\": p(torch.arange(64).reshape((1, 8, 8)))},\n                {\n                    \"img\": p(\n                        np.array(\n                            [\n                                [\n                                    [18.736153, 15.581954, 12.4277525],\n                                    [27.398798, 24.244598, 21.090399],\n                                    [36.061443, 32.90724, 29.753046],\n                                ]\n                            ]\n                        )\n                    ),\n                    \"seg\": p(np.array([[[19.0, 20.0, 12.0], [27.0, 28.0, 20.0], [35.0, 36.0, 29.0]]])),\n                },\n            ]\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_affined.py_TestRandAffined_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_affined.py_TestRandAffined_", "embedding": null, "metadata": {"file_path": "tests/test_rand_affined.py", "file_name": "test_rand_affined.py", "file_type": "text/x-python", "category": "test", "start_line": 202, "end_line": 232, "span_ids": ["TestRandAffined", "TestRandAffined.test_rand_affined", "TestRandAffined.test_ill_cache", "impl:17"], "tokens": 308}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandAffined(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_rand_affined(self, input_param, input_data, expected_val):\n        g = RandAffined(**input_param).set_random_state(123)\n        res = g(input_data)\n        if input_param.get(\"cache_grid\", False):\n            self.assertTrue(g.rand_affine._cached_grid is not None)\n        for key in res:\n            result = res[key]\n            if \"_transforms\" in key:\n                continue\n            expected = expected_val[key] if isinstance(expected_val, dict) else expected_val\n            assert_allclose(result, expected, rtol=_rtol, atol=1e-3)\n\n        g.set_random_state(4)\n        res = g(input_data)\n        # affine should be tensor because the resampler only supports pytorch backend\n        self.assertTrue(isinstance(res[\"img_transforms\"][0][\"extra_info\"][\"affine\"], torch.Tensor))\n\n    def test_ill_cache(self):\n        with self.assertWarns(UserWarning):\n            # spatial size is None\n            RandAffined(device=device, spatial_size=None, prob=1.0, cache_grid=True, keys=(\"img\", \"seg\"))\n        with self.assertWarns(UserWarning):\n            # spatial size is dynamic\n            RandAffined(device=device, spatial_size=(2, -1), prob=1.0, cache_grid=True, keys=(\"img\", \"seg\"))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_bias_field.py_unittest_TEST_CASES_2D_ONES._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_bias_field.py_unittest_TEST_CASES_2D_ONES._", "embedding": null, "metadata": {"file_path": "tests/test_rand_bias_field.py", "file_name": "test_rand_bias_field.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 26, "span_ids": ["docstring"], "tokens": 182}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import RandBiasField\n\nTEST_CASES_2D = [{\"prob\": 1.0}, (3, 32, 32)]\nTEST_CASES_3D = [{\"prob\": 1.0}, (3, 32, 32, 32)]\nTEST_CASES_2D_ZERO_RANGE = [{\"prob\": 1.0, \"coeff_range\": (0.0, 0.0)}, (2, 3, 3)]\nTEST_CASES_2D_ONES = [\n    {\"prob\": 1.0, \"coeff_range\": (1.0, 1.0)},\n    np.asarray([[[7.389056, 0.1353353], [7.389056, 22026.46]]]),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_bias_field.py_TestRandBiasField_TestRandBiasField.test_output_shape.for_fn_in_np_random_tor.for_degree_in_1_2_3_.None_2": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_bias_field.py_TestRandBiasField_TestRandBiasField.test_output_shape.for_fn_in_np_random_tor.for_degree_in_1_2_3_.None_2", "embedding": null, "metadata": {"file_path": "tests/test_rand_bias_field.py", "file_name": "test_rand_bias_field.py", "file_type": "text/x-python", "category": "test", "start_line": 29, "end_line": 42, "span_ids": ["TestRandBiasField.test_output_shape", "TestRandBiasField"], "tokens": 153}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandBiasField(unittest.TestCase):\n    @parameterized.expand([TEST_CASES_2D, TEST_CASES_3D])\n    def test_output_shape(self, class_args, img_shape):\n        for fn in (np.random, torch):\n            for degree in [1, 2, 3]:\n                bias_field = RandBiasField(degree=degree, **class_args)\n                img = fn.rand(*img_shape)\n                output = bias_field(img)\n                np.testing.assert_equal(output.shape, img_shape)\n                self.assertTrue(output.dtype in (np.float32, torch.float32))\n\n                img_zero = np.zeros([*img_shape])\n                output_zero = bias_field(img_zero)\n                np.testing.assert_equal(output_zero, img_zero)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_bias_field.py_TestRandBiasField.test_zero_range_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_bias_field.py_TestRandBiasField.test_zero_range_", "embedding": null, "metadata": {"file_path": "tests/test_rand_bias_field.py", "file_name": "test_rand_bias_field.py", "file_type": "text/x-python", "category": "test", "start_line": 44, "end_line": 67, "span_ids": ["TestRandBiasField.test_zero_prob", "impl:9", "TestRandBiasField.test_one_range_input", "TestRandBiasField.test_zero_range"], "tokens": 229}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandBiasField(unittest.TestCase):\n\n    @parameterized.expand([TEST_CASES_2D_ZERO_RANGE])\n    def test_zero_range(self, class_args, img_shape):\n        bias_field = RandBiasField(**class_args)\n        img = np.ones(img_shape)\n        output = bias_field(img)\n        np.testing.assert_allclose(output, np.ones(img_shape), rtol=1e-3)\n\n    @parameterized.expand([TEST_CASES_2D_ONES])\n    def test_one_range_input(self, class_args, expected):\n        bias_field = RandBiasField(**class_args)\n        img = np.ones([1, 2, 2])\n        output = bias_field(img)\n        np.testing.assert_allclose(output, expected.astype(bias_field.dtype), rtol=1e-3)\n\n    def test_zero_prob(self):\n        bias_field = RandBiasField(prob=0.0)\n        img = np.random.rand(3, 32, 32)\n        output = bias_field(img)\n        np.testing.assert_equal(output, img)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_bias_fieldd.py_unittest_TEST_CASES_2D_ONES._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_bias_fieldd.py_unittest_TEST_CASES_2D_ONES._", "embedding": null, "metadata": {"file_path": "tests/test_rand_bias_fieldd.py", "file_name": "test_rand_bias_fieldd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 25, "span_ids": ["docstring"], "tokens": 193}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import RandBiasFieldd\n\nTEST_CASES_2D = [{\"prob\": 1.0}, (3, 32, 32)]\nTEST_CASES_3D = [{\"prob\": 1.0}, (3, 32, 32, 32)]\nTEST_CASES_2D_ZERO_RANGE = [{\"prob\": 1.0, \"coeff_range\": (0.0, 0.0)}, (3, 32, 32)]\nTEST_CASES_2D_ONES = [\n    {\"prob\": 1.0, \"coeff_range\": (1.0, 1.0)},\n    np.asarray([[[7.3890562e00, 1.3533528e-01], [7.3890562e00, 2.2026465e04]]]),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_bias_fieldd.py_TestRandBiasFieldd_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_bias_fieldd.py_TestRandBiasFieldd_", "embedding": null, "metadata": {"file_path": "tests/test_rand_bias_fieldd.py", "file_name": "test_rand_bias_fieldd.py", "file_type": "text/x-python", "category": "test", "start_line": 28, "end_line": 64, "span_ids": ["TestRandBiasFieldd.test_zero_prob", "TestRandBiasFieldd.test_output_shape", "TestRandBiasFieldd.test_one_range_input", "TestRandBiasFieldd", "TestRandBiasFieldd.test_zero_range", "impl:9"], "tokens": 380}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandBiasFieldd(unittest.TestCase):\n    @parameterized.expand([TEST_CASES_2D, TEST_CASES_3D])\n    def test_output_shape(self, class_args, img_shape):\n        key = \"img\"\n        bias_field = RandBiasFieldd(keys=[key], **class_args)\n        img = np.random.rand(*img_shape)\n        output = bias_field({key: img})\n        np.testing.assert_equal(output[key].shape, img_shape)\n        np.testing.assert_equal(output[key].dtype, bias_field.rand_bias_field.dtype)\n\n    @parameterized.expand([TEST_CASES_2D_ZERO_RANGE])\n    def test_zero_range(self, class_args, img_shape):\n        key = \"img\"\n        bias_field = RandBiasFieldd(keys=[key], **class_args)\n        img = np.ones(img_shape)\n        output = bias_field({key: img})\n        np.testing.assert_allclose(output[key], np.ones(img_shape))\n\n    @parameterized.expand([TEST_CASES_2D_ONES])\n    def test_one_range_input(self, class_args, expected):\n        key = \"img\"\n        bias_field = RandBiasFieldd(keys=[key], **class_args)\n        img = np.ones([1, 2, 2])\n        output = bias_field({key: img})\n        np.testing.assert_allclose(output[key], expected.astype(bias_field.rand_bias_field.dtype), rtol=1e-3)\n\n    def test_zero_prob(self):\n        key = \"img\"\n        bias_field = RandBiasFieldd(keys=[key], prob=0.0)\n        img = np.random.rand(3, 32, 32)\n        output = bias_field({key: img})\n        np.testing.assert_equal(output[key], img)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_coarse_dropout.py_unittest_TEST_CASE_7._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_coarse_dropout.py_unittest_TEST_CASE_7._", "embedding": null, "metadata": {"file_path": "tests/test_rand_coarse_dropout.py", "file_name": "test_rand_coarse_dropout.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 59, "span_ids": ["impl:11", "docstring"], "tokens": 625}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import RandCoarseDropout\nfrom monai.utils import fall_back_tuple\n\nTEST_CASE_0 = [\n    {\"holes\": 2, \"spatial_size\": [2, 2, 2], \"fill_value\": 5, \"prob\": 1.0},\n    np.random.randint(0, 2, size=[3, 3, 3, 4]),\n]\n\nTEST_CASE_1 = [\n    {\"holes\": 1, \"spatial_size\": [1, 2, 3], \"fill_value\": 5, \"max_holes\": 5, \"prob\": 1.0},\n    np.random.randint(0, 2, size=[3, 3, 3, 4]),\n]\n\nTEST_CASE_2 = [\n    {\"holes\": 2, \"spatial_size\": [2, 2, 2], \"fill_value\": 5, \"max_spatial_size\": [4, 4, 3], \"prob\": 1.0},\n    np.random.randint(0, 2, size=[3, 3, 3, 4]),\n]\n\nTEST_CASE_3 = [\n    {\"holes\": 2, \"spatial_size\": [2, -1, 2], \"fill_value\": 5, \"max_spatial_size\": [4, 4, -1], \"prob\": 1.0},\n    np.random.randint(0, 2, size=[3, 3, 3, 4]),\n]\n\nTEST_CASE_4 = [\n    {\"holes\": 2, \"spatial_size\": [2, 2, 2], \"fill_value\": (3, 6), \"prob\": 1.0},\n    np.random.randint(0, 2, size=[3, 3, 3, 4]),\n]\n\nTEST_CASE_5 = [\n    {\"holes\": 2, \"spatial_size\": [2, 2, 2], \"fill_value\": None, \"prob\": 1.0},\n    np.random.randint(0, 2, size=[3, 3, 3, 4]),\n]\n\nTEST_CASE_6 = [\n    {\"holes\": 2, \"spatial_size\": [2, 2, 2], \"dropout_holes\": False, \"fill_value\": (3, 6), \"prob\": 1.0},\n    np.random.randint(0, 2, size=[3, 3, 3, 4]),\n]\n\nTEST_CASE_7 = [\n    {\"holes\": 2, \"spatial_size\": [2, 2, 2], \"dropout_holes\": False, \"fill_value\": (3, 6), \"prob\": 1.0},\n    torch.randint(0, 2, size=[3, 3, 3, 4]),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_coarse_dropout.py_TestRandCoarseDropout_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_coarse_dropout.py_TestRandCoarseDropout_", "embedding": null, "metadata": {"file_path": "tests/test_rand_coarse_dropout.py", "file_name": "test_rand_coarse_dropout.py", "file_type": "text/x-python", "category": "test", "start_line": 62, "end_line": 107, "span_ids": ["TestRandCoarseDropout.test_value", "TestRandCoarseDropout", "impl:17"], "tokens": 442}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandCoarseDropout(unittest.TestCase):\n    @parameterized.expand(\n        [TEST_CASE_0, TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5, TEST_CASE_6, TEST_CASE_7]\n    )\n    def test_value(self, input_param, input_data):\n        dropout = RandCoarseDropout(**input_param)\n        result = dropout(input_data)\n        self.assertEqual(type(result), type(input_data))\n        holes = input_param.get(\"holes\")\n        max_holes = input_param.get(\"max_holes\")\n        spatial_size = fall_back_tuple(input_param.get(\"spatial_size\"), input_data.shape[1:])\n        max_spatial_size = fall_back_tuple(input_param.get(\"max_spatial_size\"), input_data.shape[1:])\n\n        if max_holes is None:\n            self.assertEqual(len(dropout.hole_coords), holes)\n        else:\n            self.assertGreaterEqual(len(dropout.hole_coords), holes)\n            self.assertLessEqual(len(dropout.hole_coords), max_holes)\n\n        for h in dropout.hole_coords:\n            data = result[h]\n            # test hole value\n            if input_param.get(\"dropout_holes\", True):\n                fill_value = input_param.get(\"fill_value\", None)\n                if isinstance(fill_value, (int, float)):\n                    np.testing.assert_allclose(data, fill_value)\n                elif fill_value is not None:\n                    min_value = data.min()\n                    max_value = data.max()\n                    self.assertGreaterEqual(max_value, min_value)\n                    self.assertGreaterEqual(min_value, fill_value[0])\n                    self.assertLess(max_value, fill_value[1])\n            else:\n                np.testing.assert_allclose(data, input_data[h])\n\n            if max_spatial_size is None:\n                self.assertTupleEqual(data.shape[1:], tuple(spatial_size))\n            else:\n                for d, s, m in zip(data.shape[1:], spatial_size, max_spatial_size):\n                    self.assertGreaterEqual(d, s)\n                    self.assertLessEqual(d, m)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_coarse_dropoutd.py_unittest_TEST_CASE_6._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_coarse_dropoutd.py_unittest_TEST_CASE_6._", "embedding": null, "metadata": {"file_path": "tests/test_rand_coarse_dropoutd.py", "file_name": "test_rand_coarse_dropoutd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 60, "span_ids": ["impl:9", "docstring"], "tokens": 606}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import RandCoarseDropoutd\nfrom monai.utils import fall_back_tuple\n\nTEST_CASE_0 = [\n    {\"keys\": \"img\", \"holes\": 2, \"spatial_size\": [2, 2, 2], \"fill_value\": 5, \"prob\": 1.0},\n    {\"img\": np.random.randint(0, 2, size=[3, 3, 3, 4])},\n]\n\nTEST_CASE_1 = [\n    {\"keys\": \"img\", \"holes\": 1, \"spatial_size\": [1, 2, 3], \"fill_value\": 5, \"max_holes\": 5, \"prob\": 1.0},\n    {\"img\": np.random.randint(0, 2, size=[3, 3, 3, 4])},\n]\n\nTEST_CASE_2 = [\n    {\"keys\": \"img\", \"holes\": 2, \"spatial_size\": [2, 2, 2], \"fill_value\": 5, \"max_spatial_size\": [4, 4, 3], \"prob\": 1.0},\n    {\"img\": np.random.randint(0, 2, size=[3, 3, 3, 4])},\n]\n\nTEST_CASE_3 = [\n    {\n        \"keys\": \"img\",\n        \"holes\": 2,\n        \"spatial_size\": [2, -1, 2],\n        \"fill_value\": 5,\n        \"max_spatial_size\": [4, 4, -1],\n        \"prob\": 1.0,\n    },\n    {\"img\": np.random.randint(0, 2, size=[3, 3, 3, 4])},\n]\n\nTEST_CASE_4 = [\n    {\"keys\": \"img\", \"holes\": 2, \"spatial_size\": [2, 2, 2], \"fill_value\": (0.2, 0.6), \"prob\": 1.0},\n    {\"img\": np.random.rand(3, 3, 3, 4)},\n]\n\nTEST_CASE_5 = [\n    {\"keys\": \"img\", \"holes\": 2, \"spatial_size\": [2, 2, 2], \"fill_value\": None, \"prob\": 1.0},\n    {\"img\": np.random.rand(3, 3, 3, 4)},\n]\n\nTEST_CASE_6 = [\n    {\"keys\": \"img\", \"holes\": 2, \"spatial_size\": [2, 2, 2], \"dropout_holes\": False, \"fill_value\": 0.5, \"prob\": 1.0},\n    {\"img\": np.random.rand(3, 3, 3, 4)},\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_coarse_dropoutd.py_TestRandCoarseDropoutd_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_coarse_dropoutd.py_TestRandCoarseDropoutd_", "embedding": null, "metadata": {"file_path": "tests/test_rand_coarse_dropoutd.py", "file_name": "test_rand_coarse_dropoutd.py", "file_type": "text/x-python", "category": "test", "start_line": 63, "end_line": 105, "span_ids": ["TestRandCoarseDropoutd.test_value", "TestRandCoarseDropoutd", "impl:15"], "tokens": 449}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandCoarseDropoutd(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_0, TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5, TEST_CASE_6])\n    def test_value(self, input_param, input_data):\n        dropout = RandCoarseDropoutd(**input_param)\n        result = dropout(input_data)[\"img\"]\n        holes = input_param.get(\"holes\")\n        max_holes = input_param.get(\"max_holes\")\n        spatial_size = fall_back_tuple(input_param.get(\"spatial_size\"), input_data[\"img\"].shape[1:])\n        max_spatial_size = fall_back_tuple(input_param.get(\"max_spatial_size\"), input_data[\"img\"].shape[1:])\n\n        if max_holes is None:\n            self.assertEqual(len(dropout.dropper.hole_coords), holes)\n        else:\n            self.assertGreaterEqual(len(dropout.dropper.hole_coords), holes)\n            self.assertLessEqual(len(dropout.dropper.hole_coords), max_holes)\n\n        for h in dropout.dropper.hole_coords:\n            data = result[h]\n            # test hole value\n            if input_param.get(\"dropout_holes\", True):\n                fill_value = input_param.get(\"fill_value\", 0)\n                if isinstance(fill_value, (int, float)):\n                    np.testing.assert_allclose(data, fill_value)\n                elif fill_value is not None:\n                    min_value = data.min()\n                    max_value = data.max()\n                    self.assertGreaterEqual(max_value, min_value)\n                    self.assertGreaterEqual(min_value, fill_value[0])\n                    self.assertLess(max_value, fill_value[1])\n            else:\n                np.testing.assert_allclose(data, input_data[\"img\"][h])\n\n            if max_spatial_size is None:\n                self.assertTupleEqual(data.shape[1:], tuple(spatial_size))\n            else:\n                for d, s, m in zip(data.shape[1:], spatial_size, max_spatial_size):\n                    self.assertGreaterEqual(d, s)\n                    self.assertLessEqual(d, m)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_coarse_shuffle.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_coarse_shuffle.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_rand_coarse_shuffle.py", "file_name": "test_rand_coarse_shuffle.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 63, "span_ids": ["impl:3", "TestRandCoarseShuffle.test_shuffle", "TestRandCoarseShuffle", "docstring"], "tokens": 602}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import RandCoarseShuffle\n\nTEST_CASES = [\n    [\n        {\"holes\": 5, \"spatial_size\": 1, \"max_spatial_size\": -1, \"prob\": 0.0},\n        {\"img\": np.arange(8).reshape((1, 2, 2, 2))},\n        np.arange(8).reshape((1, 2, 2, 2)),\n    ],\n    [\n        {\"holes\": 10, \"spatial_size\": 1, \"max_spatial_size\": -1, \"prob\": 1.0},\n        {\"img\": np.arange(27).reshape((1, 3, 3, 3))},\n        np.asarray(\n            [\n                [\n                    [[8, 19, 26], [24, 6, 15], [0, 13, 25]],\n                    [[17, 3, 5], [10, 1, 12], [22, 4, 11]],\n                    [[21, 20, 23], [14, 2, 16], [18, 9, 7]],\n                ]\n            ]\n        ),\n    ],\n    [\n        {\"holes\": 2, \"spatial_size\": 1, \"max_spatial_size\": -1, \"prob\": 1.0},\n        {\"img\": np.arange(16).reshape((2, 2, 2, 2))},\n        np.asarray([[[[6, 1], [4, 3]], [[0, 2], [7, 5]]], [[[14, 10], [9, 8]], [[12, 15], [13, 11]]]]),\n    ],\n    [\n        {\"holes\": 2, \"spatial_size\": 1, \"max_spatial_size\": -1, \"prob\": 1.0},\n        {\"img\": torch.arange(16).reshape((2, 2, 2, 2))},\n        torch.as_tensor([[[[6, 1], [4, 3]], [[0, 2], [7, 5]]], [[[14, 10], [9, 8]], [[12, 15], [13, 11]]]]),\n    ],\n]\n\n\nclass TestRandCoarseShuffle(unittest.TestCase):\n    @parameterized.expand(TEST_CASES)\n    def test_shuffle(self, input_param, input_data, expected_val):\n        g = RandCoarseShuffle(**input_param)\n        g.set_random_state(seed=12)\n        result = g(**input_data)\n        np.testing.assert_allclose(result, expected_val, rtol=1e-4, atol=1e-4)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_coarse_shuffled.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_coarse_shuffled.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_rand_coarse_shuffled.py", "file_name": "test_rand_coarse_shuffled.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 57, "span_ids": ["TestRandCoarseShuffled.test_shuffle", "impl:3", "TestRandCoarseShuffled", "docstring"], "tokens": 502}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import RandCoarseShuffled\n\nTEST_CASES = [\n    [\n        {\"keys\": \"img\", \"holes\": 5, \"spatial_size\": 1, \"max_spatial_size\": -1, \"prob\": 0.0},\n        {\"img\": np.arange(8).reshape((1, 2, 2, 2))},\n        np.arange(8).reshape((1, 2, 2, 2)),\n    ],\n    [\n        {\"keys\": \"img\", \"holes\": 10, \"spatial_size\": 1, \"max_spatial_size\": -1, \"prob\": 1.0},\n        {\"img\": np.arange(27).reshape((1, 3, 3, 3))},\n        np.asarray(\n            [\n                [\n                    [[8, 19, 26], [24, 6, 15], [0, 13, 25]],\n                    [[17, 3, 5], [10, 1, 12], [22, 4, 11]],\n                    [[21, 20, 23], [14, 2, 16], [18, 9, 7]],\n                ]\n            ]\n        ),\n    ],\n    [\n        {\"keys\": \"img\", \"holes\": 2, \"spatial_size\": 1, \"max_spatial_size\": -1, \"prob\": 1.0},\n        {\"img\": np.arange(16).reshape((2, 2, 2, 2))},\n        np.asarray([[[[6, 1], [4, 3]], [[0, 2], [7, 5]]], [[[14, 10], [9, 8]], [[12, 15], [13, 11]]]]),\n    ],\n]\n\n\nclass TestRandCoarseShuffled(unittest.TestCase):\n    @parameterized.expand(TEST_CASES)\n    def test_shuffle(self, input_param, input_data, expected_val):\n        g = RandCoarseShuffled(**input_param)\n        g.set_random_state(seed=12)\n        result = g(input_data)\n        np.testing.assert_allclose(result[\"img\"], expected_val, rtol=1e-4, atol=1e-4)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_crop_by_label_classes.py_unittest_for_p_in_TEST_NDARRAYS_.None_4": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_crop_by_label_classes.py_unittest_for_p_in_TEST_NDARRAYS_.None_4", "embedding": null, "metadata": {"file_path": "tests/test_rand_crop_by_label_classes.py", "file_name": "test_rand_crop_by_label_classes.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 122, "span_ids": ["docstring"], "tokens": 1042}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import ClassesToIndices, RandCropByLabelClasses\nfrom tests.utils import TEST_NDARRAYS\n\nTESTS_INDICES, TESTS_SHAPE = [], []\nfor p in TEST_NDARRAYS:\n    # One-Hot label\n    TESTS_INDICES.append(\n        [\n            {\n                \"label\": p(np.random.randint(0, 2, size=[3, 3, 3, 3])),\n                \"num_classes\": None,\n                \"spatial_size\": [2, 2, -1],\n                \"ratios\": [1, 1, 1],\n                \"num_samples\": 2,\n                \"image\": p(np.random.randint(0, 2, size=[3, 3, 3, 3])),\n                \"image_threshold\": 0,\n            },\n            {\"img\": p(np.random.randint(0, 2, size=[3, 3, 3, 3]))},\n            list,\n            (3, 2, 2, 3),\n        ]\n    )\n\n    TESTS_INDICES.append(\n        [\n            # Argmax label\n            {\n                \"label\": p(np.random.randint(0, 2, size=[1, 3, 3, 3])),\n                \"num_classes\": 2,\n                \"spatial_size\": [2, 2, 2],\n                \"ratios\": [1, 1],\n                \"num_samples\": 2,\n                \"image\": p(np.random.randint(0, 2, size=[3, 3, 3, 3])),\n                \"image_threshold\": 0,\n            },\n            {\"img\": p(np.random.randint(0, 2, size=[3, 3, 3, 3]))},\n            list,\n            (3, 2, 2, 2),\n        ]\n    )\n\n    TESTS_SHAPE.append(\n        [\n            # provide label at runtime\n            {\n                \"label\": None,\n                \"num_classes\": 2,\n                \"spatial_size\": [2, 2, 2],\n                \"ratios\": [1, 1],\n                \"num_samples\": 2,\n                \"image\": p(np.random.randint(0, 2, size=[3, 3, 3, 3])),\n                \"image_threshold\": 0,\n            },\n            {\n                \"img\": p(np.random.randint(0, 2, size=[3, 3, 3, 3])),\n                \"label\": p(np.random.randint(0, 2, size=[1, 3, 3, 3])),\n                \"image\": p(np.random.randint(0, 2, size=[3, 3, 3, 3])),\n            },\n            list,\n            (3, 2, 2, 2),\n        ]\n    )\n    TESTS_SHAPE.append(\n        [\n            # provide label at runtime\n            {\n                \"label\": None,\n                \"num_classes\": 2,\n                \"spatial_size\": [4, 4, 2],\n                \"ratios\": [1, 1],\n                \"num_samples\": 2,\n                \"image\": p(np.random.randint(0, 2, size=[3, 3, 3, 3])),\n                \"image_threshold\": 0,\n                \"allow_smaller\": True,\n            },\n            {\n                \"img\": p(np.random.randint(0, 2, size=[3, 3, 3, 3])),\n                \"label\": p(np.random.randint(0, 2, size=[1, 3, 3, 3])),\n                \"image\": p(np.random.randint(0, 2, size=[3, 3, 3, 3])),\n            },\n            list,\n            (3, 3, 3, 2),\n        ]\n    )\n    TESTS_SHAPE.append(\n        [\n            # provide label at runtime\n            {\n                \"label\": None,\n                \"num_classes\": 2,\n                \"spatial_size\": [4, 4, 4],\n                \"ratios\": [1, 1],\n                \"num_samples\": 2,\n                \"image\": p(np.random.randint(0, 2, size=[3, 3, 3, 3])),\n                \"image_threshold\": 0,\n                \"allow_smaller\": True,\n            },\n            {\n                \"img\": p(np.random.randint(0, 2, size=[3, 3, 3, 3])),\n                \"label\": p(np.random.randint(0, 2, size=[1, 3, 3, 3])),\n                \"image\": p(np.random.randint(0, 2, size=[3, 3, 3, 3])),\n            },\n            list,\n            (3, 3, 3, 3),\n        ]\n    )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_crop_by_label_classes.py_TestRandCropByLabelClasses_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_crop_by_label_classes.py_TestRandCropByLabelClasses_", "embedding": null, "metadata": {"file_path": "tests/test_rand_crop_by_label_classes.py", "file_name": "test_rand_crop_by_label_classes.py", "file_type": "text/x-python", "category": "test", "start_line": 125, "end_line": 147, "span_ids": ["TestRandCropByLabelClasses", "TestRandCropByLabelClasses.test_indices", "TestRandCropByLabelClasses.test_type_shape", "impl:8"], "tokens": 241}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandCropByLabelClasses(unittest.TestCase):\n    @parameterized.expand(TESTS_INDICES + TESTS_SHAPE)\n    def test_type_shape(self, input_param, input_data, expected_type, expected_shape):\n        result = RandCropByLabelClasses(**input_param)(**input_data)\n        self.assertIsInstance(result, expected_type)\n        self.assertTupleEqual(result[0].shape, expected_shape)\n\n    @parameterized.expand(TESTS_INDICES)\n    def test_indices(self, input_param, input_data, expected_type, expected_shape):\n        input_param[\"indices\"] = ClassesToIndices(num_classes=input_param[\"num_classes\"])(input_param[\"label\"])\n        result = RandCropByLabelClasses(**input_param)(**input_data)\n        self.assertIsInstance(result, expected_type)\n        self.assertTupleEqual(result[0].shape, expected_shape)\n        # test set indices at runtime\n        input_data[\"indices\"] = input_param[\"indices\"]\n        result = RandCropByLabelClasses(**input_param)(**input_data)\n        self.assertIsInstance(result, expected_type)\n        self.assertTupleEqual(result[0].shape, expected_shape)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_crop_by_label_classesd.py_unittest_for_p_in_TEST_NDARRAYS_.None_3": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_crop_by_label_classesd.py_unittest_for_p_in_TEST_NDARRAYS_.None_3", "embedding": null, "metadata": {"file_path": "tests/test_rand_crop_by_label_classesd.py", "file_name": "test_rand_crop_by_label_classesd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 114, "span_ids": ["docstring"], "tokens": 854}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import ClassesToIndicesd, RandCropByLabelClassesd\nfrom tests.utils import TEST_NDARRAYS\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    TESTS.append(\n        [\n            # One-Hot label\n            {\n                \"keys\": \"img\",\n                \"label_key\": \"label\",\n                \"num_classes\": None,\n                \"spatial_size\": [2, 2, -1],\n                \"ratios\": [1, 1, 1],\n                \"num_samples\": 2,\n                \"image_key\": \"image\",\n                \"image_threshold\": 0,\n            },\n            {\n                \"img\": p(np.random.randint(0, 2, size=[3, 3, 3, 3])),\n                \"image\": p(np.random.randint(0, 2, size=[3, 3, 3, 3])),\n                \"label\": p(np.random.randint(0, 2, size=[3, 3, 3, 3])),\n            },\n            list,\n            (3, 2, 2, 3),\n        ]\n    )\n\n    TESTS.append(\n        [\n            # Argmax label\n            {\n                \"keys\": \"img\",\n                \"label_key\": \"label\",\n                \"num_classes\": 2,\n                \"spatial_size\": [2, 2, 2],\n                \"ratios\": [1, 1],\n                \"num_samples\": 2,\n                \"image_key\": \"image\",\n                \"image_threshold\": 0,\n            },\n            {\n                \"img\": p(np.random.randint(0, 2, size=[3, 3, 3, 3])),\n                \"image\": p(np.random.randint(0, 2, size=[3, 3, 3, 3])),\n                \"label\": p(np.random.randint(0, 2, size=[1, 3, 3, 3])),\n            },\n            list,\n            (3, 2, 2, 2),\n        ]\n    )\n\n    TESTS.append(\n        [\n            # Argmax label\n            {\n                \"keys\": \"img\",\n                \"label_key\": \"label\",\n                \"num_classes\": 2,\n                \"spatial_size\": [4, 4, 2],\n                \"ratios\": [1, 1],\n                \"num_samples\": 2,\n                \"image_key\": \"image\",\n                \"image_threshold\": 0,\n                \"allow_smaller\": True,\n            },\n            {\n                \"img\": p(np.random.randint(0, 2, size=[3, 3, 3, 3])),\n                \"image\": p(np.random.randint(0, 2, size=[3, 3, 3, 3])),\n                \"label\": p(np.random.randint(0, 2, size=[1, 3, 3, 3])),\n            },\n            list,\n            (3, 3, 3, 2),\n        ]\n    )\n\n    TESTS.append(\n        [\n            # Argmax label\n            {\n                \"keys\": \"img\",\n                \"label_key\": \"label\",\n                \"num_classes\": 2,\n                \"spatial_size\": [4, 4, 4],\n                \"ratios\": [1, 1],\n                \"num_samples\": 2,\n                \"image_key\": \"image\",\n                \"image_threshold\": 0,\n                \"allow_smaller\": True,\n            },\n            {\n                \"img\": p(np.random.randint(0, 2, size=[3, 3, 3, 3])),\n                \"image\": p(np.random.randint(0, 2, size=[3, 3, 3, 3])),\n                \"label\": p(np.random.randint(0, 2, size=[1, 3, 3, 3])),\n            },\n            list,\n            (3, 3, 3, 3),\n        ]\n    )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_crop_by_label_classesd.py_TestRandCropByLabelClassesd_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_crop_by_label_classesd.py_TestRandCropByLabelClassesd_", "embedding": null, "metadata": {"file_path": "tests/test_rand_crop_by_label_classesd.py", "file_name": "test_rand_crop_by_label_classesd.py", "file_type": "text/x-python", "category": "test", "start_line": 117, "end_line": 133, "span_ids": ["TestRandCropByLabelClassesd", "TestRandCropByLabelClassesd.test_type_shape", "impl:8"], "tokens": 176}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandCropByLabelClassesd(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_type_shape(self, input_param, input_data, expected_type, expected_shape):\n        result = RandCropByLabelClassesd(**input_param)(input_data)\n        self.assertIsInstance(result, expected_type)\n        self.assertTupleEqual(result[0][\"img\"].shape, expected_shape)\n        # test with pre-computed indices\n        input_data = ClassesToIndicesd(keys=\"label\", num_classes=input_param[\"num_classes\"])(input_data)\n        input_param[\"indices_key\"] = \"label_cls_indices\"\n        result = RandCropByLabelClassesd(**input_param)(input_data)\n        self.assertIsInstance(result, expected_type)\n        self.assertTupleEqual(result[0][\"img\"].shape, expected_shape)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_crop_by_pos_neg_label.py_unittest_None_3": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_crop_by_pos_neg_label.py_unittest_None_3", "embedding": null, "metadata": {"file_path": "tests/test_rand_crop_by_pos_neg_label.py", "file_name": "test_rand_crop_by_pos_neg_label.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 85, "span_ids": ["impl:5", "docstring"], "tokens": 681}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom copy import deepcopy\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import RandCropByPosNegLabel\nfrom tests.utils import TEST_NDARRAYS\n\nTESTS = []\nTESTS.append(\n    [\n        {\n            \"label\": np.random.randint(0, 2, size=[3, 3, 3, 3]),\n            \"spatial_size\": [2, 2, -1],\n            \"pos\": 1,\n            \"neg\": 1,\n            \"num_samples\": 2,\n            \"image\": np.random.randint(0, 2, size=[3, 3, 3, 3]),\n            \"image_threshold\": 0,\n        },\n        {\"img\": np.random.randint(0, 2, size=[3, 3, 3, 3])},\n        (3, 2, 2, 3),\n    ]\n)\nTESTS.append(\n    [\n        {\n            \"label\": np.random.randint(0, 2, size=[3, 3, 3, 3]),\n            \"spatial_size\": [2, 2, 2],\n            \"pos\": 1,\n            \"neg\": 1,\n            \"num_samples\": 2,\n            \"image\": np.random.randint(0, 2, size=[3, 3, 3, 3]),\n            \"image_threshold\": 0,\n        },\n        {\"img\": np.random.randint(0, 2, size=[3, 3, 3, 3])},\n        (3, 2, 2, 2),\n    ]\n)\nTESTS.append(\n    [\n        {\n            \"label\": None,\n            \"spatial_size\": [2, 2, 2],\n            \"pos\": 1,\n            \"neg\": 1,\n            \"num_samples\": 2,\n            \"image\": np.random.randint(0, 2, size=[3, 3, 3, 3]),\n            \"image_threshold\": 0,\n        },\n        {\n            \"img\": np.random.randint(0, 2, size=[3, 3, 3, 3]),\n            \"label\": np.random.randint(0, 2, size=[3, 3, 3, 3]),\n            \"image\": np.random.randint(0, 2, size=[3, 3, 3, 3]),\n        },\n        (3, 2, 2, 2),\n    ]\n)\nTESTS.append(\n    [\n        {\n            \"label\": np.random.randint(0, 2, size=[3, 3, 3, 3]),\n            \"spatial_size\": [4, 4, 2],\n            \"pos\": 1,\n            \"neg\": 1,\n            \"num_samples\": 2,\n            \"image\": np.random.randint(0, 2, size=[3, 3, 3, 3]),\n            \"allow_smaller\": True,\n        },\n        {\"img\": np.random.randint(0, 2, size=[3, 3, 3, 3])},\n        (3, 3, 3, 2),\n    ]\n)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_crop_by_pos_neg_label.py_None_4_None_4": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_crop_by_pos_neg_label.py_None_4_None_4", "embedding": null, "metadata": {"file_path": "tests/test_rand_crop_by_pos_neg_label.py", "file_name": "test_rand_crop_by_pos_neg_label.py", "file_type": "text/x-python", "category": "test", "start_line": 86, "end_line": 100, "span_ids": ["impl:5"], "tokens": 150}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "TESTS.append(\n    [\n        {\n            \"label\": np.random.randint(0, 2, size=[3, 3, 3, 3]),\n            \"spatial_size\": [4, 4, 4],\n            \"pos\": 1,\n            \"neg\": 1,\n            \"num_samples\": 2,\n            \"image\": np.random.randint(0, 2, size=[3, 3, 3, 3]),\n            \"allow_smaller\": True,\n        },\n        {\"img\": np.random.randint(0, 2, size=[3, 3, 3, 3])},\n        (3, 3, 3, 3),\n    ]\n)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_crop_by_pos_neg_label.py_TestRandCropByPosNegLabel_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_crop_by_pos_neg_label.py_TestRandCropByPosNegLabel_", "embedding": null, "metadata": {"file_path": "tests/test_rand_crop_by_pos_neg_label.py", "file_name": "test_rand_crop_by_pos_neg_label.py", "file_type": "text/x-python", "category": "test", "start_line": 103, "end_line": 134, "span_ids": ["TestRandCropByPosNegLabel", "TestRandCropByPosNegLabel.convert_data_type", "impl:8", "TestRandCropByPosNegLabel.test_type_shape"], "tokens": 280}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandCropByPosNegLabel(unittest.TestCase):\n    @staticmethod\n    def convert_data_type(im_type, d, keys=(\"img\", \"image\", \"label\")):\n        out = deepcopy(d)\n        for k, v in out.items():\n            if k in keys and isinstance(v, np.ndarray):\n                out[k] = im_type(v)\n        return out\n\n    @parameterized.expand(TESTS)\n    def test_type_shape(self, input_param, input_data, expected_shape):\n        results = []\n        for p in TEST_NDARRAYS:\n            input_param_mod = self.convert_data_type(p, input_param)\n            input_data_mod = self.convert_data_type(p, input_data)\n            cropper = RandCropByPosNegLabel(**input_param_mod)\n            cropper.set_random_state(0)\n            result = cropper(**input_data_mod)\n\n            self.assertIsInstance(result, list)\n            self.assertTupleEqual(result[0].shape, expected_shape)\n\n            # check for same results across numpy, torch.Tensor and torch.cuda.Tensor\n            result = np.asarray([i if isinstance(i, np.ndarray) else i.cpu().numpy() for i in result])\n            results.append(np.asarray(result))\n            if len(results) > 1:\n                np.testing.assert_allclose(results[0], results[-1])\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_crop_by_pos_neg_labeld.py_unittest_TESTS._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_crop_by_pos_neg_labeld.py_unittest_TESTS._", "embedding": null, "metadata": {"file_path": "tests/test_rand_crop_by_pos_neg_labeld.py", "file_name": "test_rand_crop_by_pos_neg_labeld.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 119, "span_ids": ["docstring"], "tokens": 994}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom copy import deepcopy\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import RandCropByPosNegLabeld\nfrom tests.utils import TEST_NDARRAYS\n\nTESTS = [\n    [\n        {\n            \"keys\": [\"image\", \"extra\", \"label\"],\n            \"label_key\": \"label\",\n            \"spatial_size\": [-1, 2, 2],\n            \"pos\": 1,\n            \"neg\": 1,\n            \"num_samples\": 2,\n            \"image_key\": None,\n            \"image_threshold\": 0,\n        },\n        {\n            \"image\": np.random.randint(0, 2, size=[3, 3, 3, 3]),\n            \"extra\": np.random.randint(0, 2, size=[3, 3, 3, 3]),\n            \"label\": np.random.randint(0, 2, size=[3, 3, 3, 3]),\n            \"image_meta_dict\": {\"affine\": np.eye(3), \"shape\": \"CHWD\"},\n        },\n        (3, 3, 2, 2),\n    ],\n    [\n        {\n            \"keys\": [\"image\", \"extra\", \"label\"],\n            \"label_key\": \"label\",\n            \"spatial_size\": [2, 2, 2],\n            \"pos\": 1,\n            \"neg\": 1,\n            \"num_samples\": 2,\n            \"image_key\": None,\n            \"image_threshold\": 0,\n        },\n        {\n            \"image\": np.random.randint(0, 2, size=[3, 3, 3, 3]),\n            \"extra\": np.random.randint(0, 2, size=[3, 3, 3, 3]),\n            \"label\": np.random.randint(0, 2, size=[3, 3, 3, 3]),\n            \"label_meta_dict\": {\"affine\": np.eye(3), \"shape\": \"CHWD\"},\n        },\n        (3, 2, 2, 2),\n    ],\n    [\n        {\n            \"keys\": [\"image\", \"extra\", \"label\"],\n            \"label_key\": \"label\",\n            \"spatial_size\": [2, 2, 2],\n            \"pos\": 1,\n            \"neg\": 1,\n            \"num_samples\": 2,\n            \"image_key\": None,\n            \"image_threshold\": 0,\n        },\n        {\n            \"image\": np.zeros([3, 3, 3, 3]) - 1,\n            \"extra\": np.zeros([3, 3, 3, 3]),\n            \"label\": np.ones([3, 3, 3, 3]),\n            \"extra_meta_dict\": {\"affine\": np.eye(3), \"shape\": \"CHWD\"},\n        },\n        (3, 2, 2, 2),\n    ],\n    [\n        {\n            \"keys\": [\"image\", \"extra\", \"label\"],\n            \"label_key\": \"label\",\n            \"spatial_size\": [4, 4, 2],\n            \"pos\": 1,\n            \"neg\": 1,\n            \"num_samples\": 2,\n            \"image_key\": None,\n            \"image_threshold\": 0,\n            \"allow_smaller\": True,\n        },\n        {\n            \"image\": np.zeros([3, 3, 3, 3]) - 1,\n            \"extra\": np.zeros([3, 3, 3, 3]),\n            \"label\": np.ones([3, 3, 3, 3]),\n            \"extra_meta_dict\": {\"affine\": np.eye(3), \"shape\": \"CHWD\"},\n        },\n        (3, 3, 3, 2),\n    ],\n    [\n        {\n            \"keys\": [\"image\", \"extra\", \"label\"],\n            \"label_key\": \"label\",\n            \"spatial_size\": [4, 4, 4],\n            \"pos\": 1,\n            \"neg\": 1,\n            \"num_samples\": 2,\n            \"image_key\": None,\n            \"image_threshold\": 0,\n            \"allow_smaller\": True,\n        },\n        {\n            \"image\": np.zeros([3, 3, 3, 3]) - 1,\n            \"extra\": np.zeros([3, 3, 3, 3]),\n            \"label\": np.ones([3, 3, 3, 3]),\n            \"extra_meta_dict\": {\"affine\": np.eye(3), \"shape\": \"CHWD\"},\n        },\n        (3, 3, 3, 3),\n    ],\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_cucim_dict_transform.py_unittest_TEST_CASE_RAND_ROTATE_1._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_cucim_dict_transform.py_unittest_TEST_CASE_RAND_ROTATE_1._", "embedding": null, "metadata": {"file_path": "tests/test_rand_cucim_dict_transform.py", "file_name": "test_rand_cucim_dict_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 42, "span_ids": ["impl:8", "docstring"], "tokens": 584}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import RandCuCIMd\nfrom monai.utils import optional_import, set_determinism\nfrom tests.utils import HAS_CUPY, skip_if_no_cuda\n\n_, has_cut = optional_import(\"cucim.core.operations.expose.transform\")\ncp, _ = optional_import(\"cupy\")\n\nset_determinism(seed=0)\n\nTEST_CASE_COLOR_JITTER_1 = [\n    {\"name\": \"color_jitter\", \"brightness\": 0.0, \"contrast\": 0.0, \"saturation\": 0.0, \"hue\": 0.0},\n    np.array([[[0, 1], [2, 3]], [[0, 10], [20, 30]], [[0, 50], [100, 150]]], dtype=np.uint8),\n    np.array([[[0, 1], [2, 3]], [[0, 10], [20, 30]], [[0, 50], [100, 150]]], dtype=np.uint8),\n]\n\nTEST_CASE_FLIP_1 = [\n    {\"name\": \"image_flip\", \"spatial_axis\": -1},\n    np.array([[[0.0, 1.0], [2.0, 3.0]], [[0.0, 1.0], [2.0, 3.0]], [[0.0, 1.0], [2.0, 3.0]]], dtype=np.float32),\n    np.array([[[1.0, 0.0], [3.0, 2.0]], [[1.0, 0.0], [3.0, 2.0]], [[1.0, 0.0], [3.0, 2.0]]], dtype=np.float32),\n]\n\nTEST_CASE_RAND_ROTATE_1 = [\n    {\"name\": \"rand_image_rotate_90\", \"prob\": 1.0, \"max_k\": 1, \"spatial_axis\": (-2, -1)},\n    np.array([[[0.0, 1.0], [2.0, 3.0]], [[0.0, 1.0], [2.0, 3.0]], [[0.0, 1.0], [2.0, 3.0]]], dtype=np.float32),\n    np.array([[[1.0, 3.0], [0.0, 2.0]], [[1.0, 3.0], [0.0, 2.0]], [[1.0, 3.0], [0.0, 2.0]]], dtype=np.float32),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_cucim_dict_transform.py_TEST_CASE_RAND_ROTATE_2_TEST_CASE_RAND_ZOOM_2._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_cucim_dict_transform.py_TEST_CASE_RAND_ROTATE_2_TEST_CASE_RAND_ZOOM_2._", "embedding": null, "metadata": {"file_path": "tests/test_rand_cucim_dict_transform.py", "file_name": "test_rand_cucim_dict_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 45, "end_line": 73, "span_ids": ["impl:14", "impl:8"], "tokens": 676}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "TEST_CASE_RAND_ROTATE_2 = [\n    {\"name\": \"rand_image_rotate_90\", \"prob\": 0.0, \"max_k\": 1, \"spatial_axis\": (-2, -1)},\n    np.array([[[0.0, 1.0], [2.0, 3.0]], [[0.0, 1.0], [2.0, 3.0]], [[0.0, 1.0], [2.0, 3.0]]], dtype=np.float32),\n    np.array([[[0.0, 1.0], [2.0, 3.0]], [[0.0, 1.0], [2.0, 3.0]], [[0.0, 1.0], [2.0, 3.0]]], dtype=np.float32),\n]\n\nTEST_CASE_SCALE_INTENSITY_1 = [\n    {\"name\": \"scale_intensity_range\", \"a_min\": 0.0, \"a_max\": 4.0, \"b_min\": 0.0, \"b_max\": 1.0, \"clip\": False},\n    np.array([[[0.0, 1.0], [2.0, 3.0]], [[0.0, 1.0], [2.0, 3.0]], [[0.0, 1.0], [2.0, 3.0]]], dtype=np.float32),\n    np.array([[[0.0, 0.25], [0.5, 0.75]], [[0.0, 0.25], [0.5, 0.75]], [[0.0, 0.25], [0.5, 0.75]]], dtype=np.float32),\n]\n\nTEST_CASE_ZOOM_1 = [\n    {\"name\": \"zoom\", \"zoom_factor\": (0.5, 0.5)},\n    np.mgrid[:3, 1:4].astype(dtype=np.float32),\n    np.concatenate([np.ones((1, 3, 3), dtype=np.float32) * 1.0, np.ones((1, 3, 3), dtype=np.float32) * 2.0]),\n]\n\nTEST_CASE_RAND_ZOOM_1 = [\n    {\"name\": \"rand_zoom\", \"prob\": 1.0, \"min_zoom\": 0.5, \"max_zoom\": 0.5},\n    np.mgrid[:3, 1:4].astype(dtype=np.float32),\n    np.concatenate([np.ones((1, 3, 3), dtype=np.float32) * 1.0, np.ones((1, 3, 3), dtype=np.float32) * 2.0]),\n]\n\nTEST_CASE_RAND_ZOOM_2 = [\n    {\"name\": \"rand_zoom\", \"prob\": 0.0, \"min_zoom\": 0.5, \"max_zoom\": 0.5},\n    np.mgrid[:3, 1:4].astype(dtype=np.float32),\n    np.mgrid[:3, 1:4].astype(dtype=np.float32),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_cucim_dict_transform.py_TestRandCuCIMDict_TestRandCuCIMDict.test_tramsforms_numpy_single.None_5": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_cucim_dict_transform.py_TestRandCuCIMDict_TestRandCuCIMDict.test_tramsforms_numpy_single.None_5", "embedding": null, "metadata": {"file_path": "tests/test_rand_cucim_dict_transform.py", "file_name": "test_rand_cucim_dict_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 76, "end_line": 103, "span_ids": ["TestRandCuCIMDict.test_tramsforms_numpy_single", "TestRandCuCIMDict"], "tokens": 288}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_no_cuda\n@unittest.skipUnless(HAS_CUPY, \"CuPy is required.\")\n@unittest.skipUnless(has_cut, \"cuCIM transforms are required.\")\nclass TestRandCuCIMDict(unittest.TestCase):\n    @parameterized.expand(\n        [\n            TEST_CASE_COLOR_JITTER_1,\n            TEST_CASE_FLIP_1,\n            TEST_CASE_RAND_ROTATE_1,\n            TEST_CASE_RAND_ROTATE_2,\n            TEST_CASE_SCALE_INTENSITY_1,\n            TEST_CASE_ZOOM_1,\n            TEST_CASE_RAND_ZOOM_1,\n            TEST_CASE_RAND_ZOOM_2,\n        ]\n    )\n    def test_tramsforms_numpy_single(self, params, input, expected):\n        input = {\"image\": input}\n        # apply_prob=1.0\n        output = RandCuCIMd(keys=\"image\", apply_prob=1.0, **params)(input)[\"image\"]\n        self.assertTrue(output.dtype == expected.dtype)\n        self.assertTrue(isinstance(output, np.ndarray))\n        cp.testing.assert_allclose(output, expected)\n        # apply_prob=0.0\n        output = RandCuCIMd(keys=\"image\", apply_prob=0.0, **params)(input)[\"image\"]\n        self.assertTrue(output.dtype == input[\"image\"].dtype)\n        self.assertTrue(isinstance(output, np.ndarray))\n        cp.testing.assert_allclose(output, input[\"image\"])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_cucim_dict_transform.py_TestRandCuCIMDict.test_tramsforms_numpy_batch_TestRandCuCIMDict.test_tramsforms_numpy_batch.None_5": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_cucim_dict_transform.py_TestRandCuCIMDict.test_tramsforms_numpy_batch_TestRandCuCIMDict.test_tramsforms_numpy_batch.None_5", "embedding": null, "metadata": {"file_path": "tests/test_rand_cucim_dict_transform.py", "file_name": "test_rand_cucim_dict_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 105, "end_line": 129, "span_ids": ["TestRandCuCIMDict.test_tramsforms_numpy_batch"], "tokens": 303}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_no_cuda\n@unittest.skipUnless(HAS_CUPY, \"CuPy is required.\")\n@unittest.skipUnless(has_cut, \"cuCIM transforms are required.\")\nclass TestRandCuCIMDict(unittest.TestCase):\n\n    @parameterized.expand(\n        [\n            TEST_CASE_COLOR_JITTER_1,\n            TEST_CASE_FLIP_1,\n            TEST_CASE_RAND_ROTATE_1,\n            TEST_CASE_RAND_ROTATE_2,\n            TEST_CASE_SCALE_INTENSITY_1,\n            TEST_CASE_ZOOM_1,\n            TEST_CASE_RAND_ZOOM_1,\n            TEST_CASE_RAND_ZOOM_2,\n        ]\n    )\n    def test_tramsforms_numpy_batch(self, params, input, expected):\n        input = {\"image\": input[cp.newaxis, ...]}\n        expected = expected[cp.newaxis, ...]\n        # apply_prob=1.0\n        output = RandCuCIMd(keys=\"image\", apply_prob=1.0, **params)(input)[\"image\"]\n        self.assertTrue(output.dtype == expected.dtype)\n        self.assertTrue(isinstance(output, np.ndarray))\n        cp.testing.assert_allclose(output, expected)\n        # apply_prob=0.0\n        output = RandCuCIMd(keys=\"image\", apply_prob=0.0, **params)(input)[\"image\"]\n        self.assertTrue(output.dtype == input[\"image\"].dtype)\n        self.assertTrue(isinstance(output, np.ndarray))\n        cp.testing.assert_allclose(output, input[\"image\"])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_cucim_dict_transform.py_TestRandCuCIMDict.test_tramsforms_cupy_single_TestRandCuCIMDict.test_tramsforms_cupy_single.None_5": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_cucim_dict_transform.py_TestRandCuCIMDict.test_tramsforms_cupy_single_TestRandCuCIMDict.test_tramsforms_cupy_single.None_5", "embedding": null, "metadata": {"file_path": "tests/test_rand_cucim_dict_transform.py", "file_name": "test_rand_cucim_dict_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 131, "end_line": 155, "span_ids": ["TestRandCuCIMDict.test_tramsforms_cupy_single"], "tokens": 298}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_no_cuda\n@unittest.skipUnless(HAS_CUPY, \"CuPy is required.\")\n@unittest.skipUnless(has_cut, \"cuCIM transforms are required.\")\nclass TestRandCuCIMDict(unittest.TestCase):\n\n    @parameterized.expand(\n        [\n            TEST_CASE_COLOR_JITTER_1,\n            TEST_CASE_FLIP_1,\n            TEST_CASE_RAND_ROTATE_1,\n            TEST_CASE_RAND_ROTATE_2,\n            TEST_CASE_SCALE_INTENSITY_1,\n            TEST_CASE_ZOOM_1,\n            TEST_CASE_RAND_ZOOM_1,\n            TEST_CASE_RAND_ZOOM_2,\n        ]\n    )\n    def test_tramsforms_cupy_single(self, params, input, expected):\n        input = {\"image\": cp.asarray(input)}\n        expected = cp.asarray(expected)\n        # apply_prob=1.0\n        output = RandCuCIMd(keys=\"image\", apply_prob=1.0, **params)(input)[\"image\"]\n        self.assertTrue(output.dtype == expected.dtype)\n        self.assertTrue(isinstance(output, cp.ndarray))\n        cp.testing.assert_allclose(output, expected)\n        # apply_prob=0.0\n        output = RandCuCIMd(keys=\"image\", apply_prob=0.0, **params)(input)[\"image\"]\n        self.assertTrue(output.dtype == input[\"image\"].dtype)\n        self.assertTrue(isinstance(output, cp.ndarray))\n        cp.testing.assert_allclose(output, input[\"image\"])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_cucim_dict_transform.py_TestRandCuCIMDict.test_tramsforms_cupy_batch_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_cucim_dict_transform.py_TestRandCuCIMDict.test_tramsforms_cupy_batch_", "embedding": null, "metadata": {"file_path": "tests/test_rand_cucim_dict_transform.py", "file_name": "test_rand_cucim_dict_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 157, "end_line": 186, "span_ids": ["impl:20", "TestRandCuCIMDict.test_tramsforms_cupy_batch"], "tokens": 314}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_no_cuda\n@unittest.skipUnless(HAS_CUPY, \"CuPy is required.\")\n@unittest.skipUnless(has_cut, \"cuCIM transforms are required.\")\nclass TestRandCuCIMDict(unittest.TestCase):\n\n    @parameterized.expand(\n        [\n            TEST_CASE_COLOR_JITTER_1,\n            TEST_CASE_FLIP_1,\n            TEST_CASE_RAND_ROTATE_1,\n            TEST_CASE_RAND_ROTATE_2,\n            TEST_CASE_SCALE_INTENSITY_1,\n            TEST_CASE_ZOOM_1,\n            TEST_CASE_RAND_ZOOM_1,\n            TEST_CASE_RAND_ZOOM_2,\n        ]\n    )\n    def test_tramsforms_cupy_batch(self, params, input, expected):\n        input = {\"image\": cp.asarray(input)[cp.newaxis, ...]}\n        expected = cp.asarray(expected)[cp.newaxis, ...]\n        # apply_prob=1.0\n        output = RandCuCIMd(keys=\"image\", **params)(input)[\"image\"]\n        self.assertTrue(output.dtype == expected.dtype)\n        self.assertTrue(isinstance(output, cp.ndarray))\n        cp.testing.assert_allclose(output, expected)\n        # apply_prob=0.0\n        output = RandCuCIMd(keys=\"image\", apply_prob=0.0, **params)(input)[\"image\"]\n        self.assertTrue(output.dtype == input[\"image\"].dtype)\n        self.assertTrue(isinstance(output, cp.ndarray))\n        cp.testing.assert_allclose(output, input[\"image\"])\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_cucim_transform.py_unittest_TEST_CASE_RAND_ROTATE_1._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_cucim_transform.py_unittest_TEST_CASE_RAND_ROTATE_1._", "embedding": null, "metadata": {"file_path": "tests/test_rand_cucim_transform.py", "file_name": "test_rand_cucim_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 42, "span_ids": ["impl:8", "docstring"], "tokens": 583}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import RandCuCIM\nfrom monai.utils import optional_import, set_determinism\nfrom tests.utils import HAS_CUPY, skip_if_no_cuda\n\n_, has_cut = optional_import(\"cucim.core.operations.expose.transform\")\ncp, _ = optional_import(\"cupy\")\n\nset_determinism(seed=0)\n\nTEST_CASE_COLOR_JITTER_1 = [\n    {\"name\": \"color_jitter\", \"brightness\": 0.0, \"contrast\": 0.0, \"saturation\": 0.0, \"hue\": 0.0},\n    np.array([[[0, 1], [2, 3]], [[0, 10], [20, 30]], [[0, 50], [100, 150]]], dtype=np.uint8),\n    np.array([[[0, 1], [2, 3]], [[0, 10], [20, 30]], [[0, 50], [100, 150]]], dtype=np.uint8),\n]\n\nTEST_CASE_FLIP_1 = [\n    {\"name\": \"image_flip\", \"spatial_axis\": -1},\n    np.array([[[0.0, 1.0], [2.0, 3.0]], [[0.0, 1.0], [2.0, 3.0]], [[0.0, 1.0], [2.0, 3.0]]], dtype=np.float32),\n    np.array([[[1.0, 0.0], [3.0, 2.0]], [[1.0, 0.0], [3.0, 2.0]], [[1.0, 0.0], [3.0, 2.0]]], dtype=np.float32),\n]\n\nTEST_CASE_RAND_ROTATE_1 = [\n    {\"name\": \"rand_image_rotate_90\", \"prob\": 1.0, \"max_k\": 1, \"spatial_axis\": (-2, -1)},\n    np.array([[[0.0, 1.0], [2.0, 3.0]], [[0.0, 1.0], [2.0, 3.0]], [[0.0, 1.0], [2.0, 3.0]]], dtype=np.float32),\n    np.array([[[1.0, 3.0], [0.0, 2.0]], [[1.0, 3.0], [0.0, 2.0]], [[1.0, 3.0], [0.0, 2.0]]], dtype=np.float32),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_cucim_transform.py_TEST_CASE_RAND_ROTATE_2_TEST_CASE_RAND_ZOOM_2._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_cucim_transform.py_TEST_CASE_RAND_ROTATE_2_TEST_CASE_RAND_ZOOM_2._", "embedding": null, "metadata": {"file_path": "tests/test_rand_cucim_transform.py", "file_name": "test_rand_cucim_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 45, "end_line": 73, "span_ids": ["impl:14", "impl:8"], "tokens": 676}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "TEST_CASE_RAND_ROTATE_2 = [\n    {\"name\": \"rand_image_rotate_90\", \"prob\": 0.0, \"max_k\": 1, \"spatial_axis\": (-2, -1)},\n    np.array([[[0.0, 1.0], [2.0, 3.0]], [[0.0, 1.0], [2.0, 3.0]], [[0.0, 1.0], [2.0, 3.0]]], dtype=np.float32),\n    np.array([[[0.0, 1.0], [2.0, 3.0]], [[0.0, 1.0], [2.0, 3.0]], [[0.0, 1.0], [2.0, 3.0]]], dtype=np.float32),\n]\n\nTEST_CASE_SCALE_INTENSITY_1 = [\n    {\"name\": \"scale_intensity_range\", \"a_min\": 0.0, \"a_max\": 4.0, \"b_min\": 0.0, \"b_max\": 1.0, \"clip\": False},\n    np.array([[[0.0, 1.0], [2.0, 3.0]], [[0.0, 1.0], [2.0, 3.0]], [[0.0, 1.0], [2.0, 3.0]]], dtype=np.float32),\n    np.array([[[0.0, 0.25], [0.5, 0.75]], [[0.0, 0.25], [0.5, 0.75]], [[0.0, 0.25], [0.5, 0.75]]], dtype=np.float32),\n]\n\nTEST_CASE_ZOOM_1 = [\n    {\"name\": \"zoom\", \"zoom_factor\": (0.5, 0.5)},\n    np.mgrid[:3, 1:4].astype(dtype=np.float32),\n    np.concatenate([np.ones((1, 3, 3), dtype=np.float32) * 1.0, np.ones((1, 3, 3), dtype=np.float32) * 2.0]),\n]\n\nTEST_CASE_RAND_ZOOM_1 = [\n    {\"name\": \"rand_zoom\", \"prob\": 1.0, \"min_zoom\": 0.5, \"max_zoom\": 0.5},\n    np.mgrid[:3, 1:4].astype(dtype=np.float32),\n    np.concatenate([np.ones((1, 3, 3), dtype=np.float32) * 1.0, np.ones((1, 3, 3), dtype=np.float32) * 2.0]),\n]\n\nTEST_CASE_RAND_ZOOM_2 = [\n    {\"name\": \"rand_zoom\", \"prob\": 0.0, \"min_zoom\": 0.5, \"max_zoom\": 0.5},\n    np.mgrid[:3, 1:4].astype(dtype=np.float32),\n    np.mgrid[:3, 1:4].astype(dtype=np.float32),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_cucim_transform.py_TestRandCuCIM_TestRandCuCIM.test_tramsforms_numpy_single.None_5": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_cucim_transform.py_TestRandCuCIM_TestRandCuCIM.test_tramsforms_numpy_single.None_5", "embedding": null, "metadata": {"file_path": "tests/test_rand_cucim_transform.py", "file_name": "test_rand_cucim_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 76, "end_line": 102, "span_ids": ["TestRandCuCIM", "TestRandCuCIM.test_tramsforms_numpy_single"], "tokens": 262}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_no_cuda\n@unittest.skipUnless(HAS_CUPY, \"CuPy is required.\")\n@unittest.skipUnless(has_cut, \"cuCIM transforms are required.\")\nclass TestRandCuCIM(unittest.TestCase):\n    @parameterized.expand(\n        [\n            TEST_CASE_COLOR_JITTER_1,\n            TEST_CASE_FLIP_1,\n            TEST_CASE_RAND_ROTATE_1,\n            TEST_CASE_RAND_ROTATE_2,\n            TEST_CASE_SCALE_INTENSITY_1,\n            TEST_CASE_ZOOM_1,\n            TEST_CASE_RAND_ZOOM_1,\n            TEST_CASE_RAND_ZOOM_2,\n        ]\n    )\n    def test_tramsforms_numpy_single(self, params, input, expected):\n        # apply_prob=1.0\n        output = RandCuCIM(apply_prob=1.0, **params)(input)\n        self.assertTrue(output.dtype == expected.dtype)\n        self.assertTrue(isinstance(output, np.ndarray))\n        cp.testing.assert_allclose(output, expected)\n        # apply_prob=0.0\n        output = RandCuCIM(apply_prob=0.0, **params)(input)\n        self.assertTrue(output.dtype == input.dtype)\n        self.assertTrue(isinstance(output, np.ndarray))\n        cp.testing.assert_allclose(output, input)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_cucim_transform.py_TestRandCuCIM.test_tramsforms_numpy_batch_TestRandCuCIM.test_tramsforms_numpy_batch.None_5": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_cucim_transform.py_TestRandCuCIM.test_tramsforms_numpy_batch_TestRandCuCIM.test_tramsforms_numpy_batch.None_5", "embedding": null, "metadata": {"file_path": "tests/test_rand_cucim_transform.py", "file_name": "test_rand_cucim_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 104, "end_line": 128, "span_ids": ["TestRandCuCIM.test_tramsforms_numpy_batch"], "tokens": 282}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_no_cuda\n@unittest.skipUnless(HAS_CUPY, \"CuPy is required.\")\n@unittest.skipUnless(has_cut, \"cuCIM transforms are required.\")\nclass TestRandCuCIM(unittest.TestCase):\n\n    @parameterized.expand(\n        [\n            TEST_CASE_COLOR_JITTER_1,\n            TEST_CASE_FLIP_1,\n            TEST_CASE_RAND_ROTATE_1,\n            TEST_CASE_RAND_ROTATE_2,\n            TEST_CASE_SCALE_INTENSITY_1,\n            TEST_CASE_ZOOM_1,\n            TEST_CASE_RAND_ZOOM_1,\n            TEST_CASE_RAND_ZOOM_2,\n        ]\n    )\n    def test_tramsforms_numpy_batch(self, params, input, expected):\n        input = input[cp.newaxis, ...]\n        expected = expected[cp.newaxis, ...]\n        # apply_prob=1.0\n        output = RandCuCIM(apply_prob=1.0, **params)(input)\n        self.assertTrue(output.dtype == expected.dtype)\n        self.assertTrue(isinstance(output, np.ndarray))\n        cp.testing.assert_allclose(output, expected)\n        # apply_prob=0.0\n        output = RandCuCIM(apply_prob=0.0, **params)(input)\n        self.assertTrue(output.dtype == input.dtype)\n        self.assertTrue(isinstance(output, np.ndarray))\n        cp.testing.assert_allclose(output, input)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_cucim_transform.py_TestRandCuCIM.test_tramsforms_cupy_single_TestRandCuCIM.test_tramsforms_cupy_single.None_5": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_cucim_transform.py_TestRandCuCIM.test_tramsforms_cupy_single_TestRandCuCIM.test_tramsforms_cupy_single.None_5", "embedding": null, "metadata": {"file_path": "tests/test_rand_cucim_transform.py", "file_name": "test_rand_cucim_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 130, "end_line": 154, "span_ids": ["TestRandCuCIM.test_tramsforms_cupy_single"], "tokens": 277}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_no_cuda\n@unittest.skipUnless(HAS_CUPY, \"CuPy is required.\")\n@unittest.skipUnless(has_cut, \"cuCIM transforms are required.\")\nclass TestRandCuCIM(unittest.TestCase):\n\n    @parameterized.expand(\n        [\n            TEST_CASE_COLOR_JITTER_1,\n            TEST_CASE_FLIP_1,\n            TEST_CASE_RAND_ROTATE_1,\n            TEST_CASE_RAND_ROTATE_2,\n            TEST_CASE_SCALE_INTENSITY_1,\n            TEST_CASE_ZOOM_1,\n            TEST_CASE_RAND_ZOOM_1,\n            TEST_CASE_RAND_ZOOM_2,\n        ]\n    )\n    def test_tramsforms_cupy_single(self, params, input, expected):\n        input = cp.asarray(input)\n        expected = cp.asarray(expected)\n        # apply_prob=1.0\n        output = RandCuCIM(apply_prob=1.0, **params)(input)\n        self.assertTrue(output.dtype == expected.dtype)\n        self.assertTrue(isinstance(output, cp.ndarray))\n        cp.testing.assert_allclose(output, expected)\n        # apply_prob=0.0\n        output = RandCuCIM(apply_prob=0.0, **params)(input)\n        self.assertTrue(output.dtype == input.dtype)\n        self.assertTrue(isinstance(output, cp.ndarray))\n        cp.testing.assert_allclose(output, input)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_cucim_transform.py_TestRandCuCIM.test_tramsforms_cupy_batch_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_cucim_transform.py_TestRandCuCIM.test_tramsforms_cupy_batch_", "embedding": null, "metadata": {"file_path": "tests/test_rand_cucim_transform.py", "file_name": "test_rand_cucim_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 156, "end_line": 185, "span_ids": ["impl:20", "TestRandCuCIM.test_tramsforms_cupy_batch"], "tokens": 291}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_no_cuda\n@unittest.skipUnless(HAS_CUPY, \"CuPy is required.\")\n@unittest.skipUnless(has_cut, \"cuCIM transforms are required.\")\nclass TestRandCuCIM(unittest.TestCase):\n\n    @parameterized.expand(\n        [\n            TEST_CASE_COLOR_JITTER_1,\n            TEST_CASE_FLIP_1,\n            TEST_CASE_RAND_ROTATE_1,\n            TEST_CASE_RAND_ROTATE_2,\n            TEST_CASE_SCALE_INTENSITY_1,\n            TEST_CASE_ZOOM_1,\n            TEST_CASE_RAND_ZOOM_1,\n            TEST_CASE_RAND_ZOOM_2,\n        ]\n    )\n    def test_tramsforms_cupy_batch(self, params, input, expected):\n        input = cp.asarray(input)[cp.newaxis, ...]\n        expected = cp.asarray(expected)[cp.newaxis, ...]\n        # apply_prob=1.0\n        output = RandCuCIM(**params)(input)\n        self.assertTrue(output.dtype == expected.dtype)\n        self.assertTrue(isinstance(output, cp.ndarray))\n        cp.testing.assert_allclose(output, expected)\n        # apply_prob=0.0\n        output = RandCuCIM(apply_prob=0.0, **params)(input)\n        self.assertTrue(output.dtype == input.dtype)\n        self.assertTrue(isinstance(output, cp.ndarray))\n        cp.testing.assert_allclose(output, input)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_deform_grid.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_deform_grid.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_rand_deform_grid.py", "file_name": "test_rand_deform_grid.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 137, "span_ids": ["TestRandDeformGrid.test_rand_deform_grid", "impl:3", "TestRandDeformGrid", "docstring"], "tokens": 145}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import RandDeformGrid\nfrom tests.utils import assert_allclose\n\nTEST_CASES =\n # ... other code\n\n\nclass TestRandDeformGrid(unittest.TestCase):\n    @parameterized.expand(TEST_CASES)\n    def test_rand_deform_grid(self, input_param, input_data, expected_val):\n        g = RandDeformGrid(**input_param)\n        g.set_random_state(123)\n        result = g(**input_data)\n        assert_allclose(result, expected_val, type_test=False, rtol=1e-3, atol=1e-3)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_elastic_2d.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_elastic_2d.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_rand_elastic_2d.py", "file_name": "test_rand_elastic_2d.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 120, "span_ids": ["TestRand2DElastic.test_rand_2d_elastic", "impl:12", "TestRand2DElastic", "docstring"], "tokens": 997}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import Rand2DElastic\nfrom tests.utils import TEST_NDARRAYS, assert_allclose, is_tf32_env\n\n_rtol = 5e-3 if is_tf32_env() else 1e-4\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    for device in [None, \"cpu\", \"cuda\"] if torch.cuda.is_available() else [None, \"cpu\"]:\n        TESTS.append(\n            [\n                {\"spacing\": (0.3, 0.3), \"magnitude_range\": (1.0, 2.0), \"prob\": 0.0, \"device\": device},\n                {\"img\": p(torch.ones((3, 3, 3))), \"spatial_size\": (2, 2)},\n                p(np.ones((3, 2, 2))),\n            ]\n        )\n        TESTS.append(\n            [\n                {\"spacing\": (0.3, 0.3), \"magnitude_range\": (1.0, 2.0), \"prob\": 0.0, \"device\": device},\n                {\"img\": p(torch.arange(27).reshape((3, 3, 3)))},\n                p(np.arange(27).reshape((3, 3, 3))),\n            ]\n        )\n        TESTS.append(\n            [\n                {\n                    \"spacing\": (0.3, 0.3),\n                    \"magnitude_range\": (1.0, 2.0),\n                    \"prob\": 0.9,\n                    \"device\": device,\n                    \"padding_mode\": \"zeros\",\n                },\n                {\"img\": p(torch.ones((3, 3, 3))), \"spatial_size\": (2, 2), \"mode\": \"bilinear\"},\n                p(\n                    np.array(\n                        [\n                            [[0.45531988, 0.0], [0.0, 0.71558857]],\n                            [[0.45531988, 0.0], [0.0, 0.71558857]],\n                            [[0.45531988, 0.0], [0.0, 0.71558857]],\n                        ]\n                    )\n                ),\n            ]\n        )\n        TESTS.append(\n            [\n                {\n                    \"spacing\": (1.0, 1.0),\n                    \"magnitude_range\": (1.0, 1.0),\n                    \"scale_range\": [1.2, 2.2],\n                    \"prob\": 0.9,\n                    \"padding_mode\": \"border\",\n                    \"device\": device,\n                    \"spatial_size\": (2, 2),\n                },\n                {\"img\": p(torch.arange(27).reshape((3, 3, 3)))},\n                p(\n                    torch.tensor(\n                        [\n                            [[3.0793, 2.6141], [4.0568, 5.9978]],\n                            [[12.0793, 11.6141], [13.0568, 14.9978]],\n                            [[21.0793, 20.6141], [22.0568, 23.9978]],\n                        ]\n                    )\n                ),\n            ]\n        )\n        TESTS.append(\n            [\n                {\n                    \"spacing\": (0.3, 0.3),\n                    \"magnitude_range\": (0.1, 0.2),\n                    \"translate_range\": [-0.01, 0.01],\n                    \"scale_range\": [0.01, 0.02],\n                    \"prob\": 0.9,\n                    \"device\": device,\n                    \"spatial_size\": (2, 2),\n                },\n                {\"img\": p(torch.arange(27).reshape((3, 3, 3)))},\n                p(\n                    np.array(\n                        [\n                            [[1.3584113, 1.9251312], [5.626623, 6.642721]],\n                            [[10.358411, 10.925131], [14.626623, 15.642721]],\n                            [[19.358412, 19.92513], [23.626623, 24.642721]],\n                        ]\n                    )\n                ),\n            ]\n        )\n\n\nclass TestRand2DElastic(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_rand_2d_elastic(self, input_param, input_data, expected_val):\n        g = Rand2DElastic(**input_param)\n        g.set_random_state(123)\n        result = g(**input_data)\n        assert_allclose(result, expected_val, rtol=_rtol, atol=1e-4)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_elastic_3d.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_elastic_3d.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_rand_elastic_3d.py", "file_name": "test_rand_elastic_3d.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 96, "span_ids": ["impl:9", "TestRand3DElastic.test_rand_3d_elastic", "TestRand3DElastic", "docstring"], "tokens": 745}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import Rand3DElastic\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    for device in [None, \"cpu\", \"cuda\"] if torch.cuda.is_available() else [None, \"cpu\"]:\n        TESTS.append(\n            [\n                {\n                    \"magnitude_range\": (0.3, 2.3),\n                    \"sigma_range\": (1.0, 20.0),\n                    \"prob\": 0.0,\n                    \"device\": device,\n                    \"spatial_size\": -1,\n                },\n                {\"img\": p(torch.arange(72).reshape((2, 3, 3, 4)))},\n                p(np.arange(72).reshape((2, 3, 3, 4))),\n            ]\n        )\n        TESTS.append(\n            [\n                {\"magnitude_range\": (0.3, 2.3), \"sigma_range\": (1.0, 20.0), \"prob\": 0.0, \"device\": device},\n                {\"img\": p(torch.ones((2, 3, 3, 3))), \"spatial_size\": (2, 2, 2)},\n                p(np.ones((2, 2, 2, 2))),\n            ]\n        )\n        TESTS.append(\n            [\n                {\"magnitude_range\": (0.3, 0.3), \"sigma_range\": (1.0, 2.0), \"prob\": 0.9, \"device\": device},\n                {\"img\": p(torch.arange(27).reshape((1, 3, 3, 3))), \"spatial_size\": (2, 2, 2)},\n                p(\n                    np.array(\n                        [\n                            [\n                                [[6.4939356, 7.50289], [9.518351, 10.522849]],\n                                [[15.512375, 16.523542], [18.531467, 19.53646]],\n                            ]\n                        ]\n                    )\n                ),\n            ]\n        )\n        TESTS.append(\n            [\n                {\n                    \"magnitude_range\": (0.3, 0.3),\n                    \"sigma_range\": (1.0, 2.0),\n                    \"prob\": 0.9,\n                    \"rotate_range\": [1, 1, 1],\n                    \"device\": device,\n                    \"spatial_size\": (2, 2, 2),\n                },\n                {\"img\": p(torch.arange(27).reshape((1, 3, 3, 3))), \"mode\": \"bilinear\"},\n                p(\n                    np.array(\n                        [\n                            [\n                                [[5.0069294, 9.463932], [9.287769, 13.739735]],\n                                [[12.319424, 16.777205], [16.594296, 21.045748]],\n                            ]\n                        ]\n                    )\n                ),\n            ]\n        )\n\n\nclass TestRand3DElastic(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_rand_3d_elastic(self, input_param, input_data, expected_val):\n        g = Rand3DElastic(**input_param)\n        g.set_random_state(123)\n        result = g(**input_data)\n        assert_allclose(result, expected_val, rtol=1e-4, atol=1e-4)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_elasticd_2d.py_unittest_for_p_in_TEST_NDARRAYS_.for_device_in_None_cpu.None_5": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_elasticd_2d.py_unittest_for_p_in_TEST_NDARRAYS_.for_device_in_None_cpu.None_5", "embedding": null, "metadata": {"file_path": "tests/test_rand_elasticd_2d.py", "file_name": "test_rand_elasticd_2d.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 157, "span_ids": ["docstring"], "tokens": 1400}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import Rand2DElasticd\nfrom tests.utils import TEST_NDARRAYS, assert_allclose, is_tf32_env\n\n_rtol = 5e-3 if is_tf32_env() else 1e-4\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    for device in [None, \"cpu\", \"cuda\"] if torch.cuda.is_available() else [None, \"cpu\"]:\n        TESTS.append(\n            [\n                {\n                    \"keys\": (\"img\", \"seg\"),\n                    \"spacing\": (0.3, 0.3),\n                    \"magnitude_range\": (1.0, 2.0),\n                    \"prob\": 0.0,\n                    \"device\": device,\n                    \"spatial_size\": (2, 2),\n                },\n                {\"img\": p(torch.ones((3, 3, 3))), \"seg\": p(torch.ones((3, 3, 3)))},\n                p(np.ones((3, 2, 2))),\n            ]\n        )\n        TESTS.append(\n            [\n                {\n                    \"keys\": (\"img\", \"seg\"),\n                    \"spacing\": (0.3, 0.3),\n                    \"magnitude_range\": (0.3, 0.3),\n                    \"prob\": 0.0,\n                    \"device\": device,\n                    \"spatial_size\": -1,\n                },\n                {\"img\": p(torch.arange(4).reshape((1, 2, 2))), \"seg\": p(torch.arange(4).reshape((1, 2, 2)))},\n                p(np.arange(4).reshape((1, 2, 2))),\n            ]\n        )\n        TESTS.append(\n            [\n                {\n                    \"keys\": (\"img\", \"seg\"),\n                    \"spacing\": (0.3, 0.3),\n                    \"magnitude_range\": (1.0, 2.0),\n                    \"prob\": 0.9,\n                    \"padding_mode\": \"zeros\",\n                    \"device\": device,\n                    \"spatial_size\": (2, 2),\n                    \"mode\": \"bilinear\",\n                },\n                {\"img\": p(torch.ones((3, 3, 3))), \"seg\": p(torch.ones((3, 3, 3)))},\n                p(\n                    np.array(\n                        [\n                            [[0.45531988, 0.0], [0.0, 0.71558857]],\n                            [[0.45531988, 0.0], [0.0, 0.71558857]],\n                            [[0.45531988, 0.0], [0.0, 0.71558857]],\n                        ]\n                    )\n                ),\n            ]\n        )\n        TESTS.append(\n            [\n                {\n                    \"keys\": (\"img\", \"seg\"),\n                    \"spacing\": (1.0, 1.0),\n                    \"magnitude_range\": (1.0, 1.0),\n                    \"scale_range\": [1.2, 2.2],\n                    \"prob\": 0.9,\n                    \"padding_mode\": \"border\",\n                    \"device\": device,\n                    \"spatial_size\": (2, 2),\n                },\n                {\"img\": p(torch.arange(27).reshape((3, 3, 3))), \"seg\": p(torch.arange(27).reshape((3, 3, 3)))},\n                p(\n                    torch.tensor(\n                        [\n                            [[3.0793, 2.6141], [4.0568, 5.9978]],\n                            [[12.0793, 11.6141], [13.0568, 14.9978]],\n                            [[21.0793, 20.6141], [22.0568, 23.9978]],\n                        ]\n                    )\n                ),\n            ]\n        )\n        TESTS.append(\n            [\n                {\n                    \"keys\": (\"img\", \"seg\"),\n                    \"spacing\": (0.3, 0.3),\n                    \"magnitude_range\": (0.1, 0.2),\n                    \"translate_range\": [-0.01, 0.01],\n                    \"scale_range\": [0.01, 0.02],\n                    \"prob\": 0.9,\n                    \"device\": device,\n                    \"spatial_size\": (2, 2),\n                },\n                {\"img\": p(torch.arange(27).reshape((3, 3, 3))), \"seg\": p(torch.arange(27).reshape((3, 3, 3)))},\n                p(\n                    np.array(\n                        [\n                            [[1.3584113, 1.9251312], [5.626623, 6.642721]],\n                            [[10.358411, 10.925131], [14.626623, 15.642721]],\n                            [[19.358412, 19.92513], [23.626623, 24.642721]],\n                        ]\n                    )\n                ),\n            ]\n        )\n        TESTS.append(\n            [\n                {\n                    \"keys\": (\"img\", \"seg\"),\n                    \"mode\": (\"bilinear\", \"nearest\"),\n                    \"spacing\": (0.3, 0.3),\n                    \"magnitude_range\": (0.1, 0.2),\n                    \"translate_range\": [-0.01, 0.01],\n                    \"scale_range\": [0.01, 0.02],\n                    \"prob\": 0.9,\n                    \"device\": device,\n                    \"spatial_size\": (2, 2),\n                },\n                {\"img\": p(torch.arange(27).reshape((3, 3, 3))), \"seg\": p(torch.arange(27).reshape((3, 3, 3)))},\n                {\n                    \"img\": p(\n                        torch.tensor(\n                            [\n                                [[1.3584, 1.9251], [5.6266, 6.6427]],\n                                [[10.3584, 10.9251], [14.6266, 15.6427]],\n                                [[19.3584, 19.9251], [23.6266, 24.6427]],\n                            ]\n                        )\n                    ),\n                    \"seg\": p(\n                        torch.tensor(\n                            [[[0.0, 2.0], [6.0, 8.0]], [[9.0, 11.0], [15.0, 17.0]], [[18.0, 20.0], [24.0, 26.0]]]\n                        )\n                    ),\n                },\n            ]\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_elasticd_3d.py_unittest_for_p_in_TEST_NDARRAYS_.for_device_in_None_cpu.None_5": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_elasticd_3d.py_unittest_for_p_in_TEST_NDARRAYS_.for_device_in_None_cpu.None_5", "embedding": null, "metadata": {"file_path": "tests/test_rand_elasticd_3d.py", "file_name": "test_rand_elasticd_3d.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 136, "span_ids": ["docstring"], "tokens": 1205}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import Rand3DElasticd\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    for device in [None, \"cpu\", \"cuda\"] if torch.cuda.is_available() else [None, \"cpu\"]:\n        TESTS.append(\n            [\n                {\n                    \"keys\": (\"img\", \"seg\"),\n                    \"magnitude_range\": (0.3, 2.3),\n                    \"sigma_range\": (1.0, 20.0),\n                    \"prob\": 0.0,\n                    \"device\": device,\n                    \"spatial_size\": (2, 2, 2),\n                },\n                {\"img\": p(torch.ones((2, 3, 3, 3))), \"seg\": p(torch.ones((2, 3, 3, 3)))},\n                p(np.ones((2, 2, 2, 2))),\n            ]\n        )\n        TESTS.append(\n            [\n                {\n                    \"keys\": (\"img\", \"seg\"),\n                    \"magnitude_range\": (0.3, 2.3),\n                    \"sigma_range\": (1.0, 20.0),\n                    \"prob\": 0.0,\n                    \"device\": device,\n                    \"spatial_size\": (2, -1, -1),\n                },\n                {\"img\": p(torch.ones((2, 3, 3, 3))), \"seg\": p(torch.ones((2, 3, 3, 3)))},\n                p(np.ones((2, 2, 3, 3))),\n            ]\n        )\n        TESTS.append(\n            [\n                {\n                    \"keys\": (\"img\", \"seg\"),\n                    \"magnitude_range\": (0.3, 2.3),\n                    \"sigma_range\": (1.0, 20.0),\n                    \"prob\": 0.0,\n                    \"device\": device,\n                    \"spatial_size\": -1,\n                },\n                {\"img\": p(torch.arange(8).reshape((1, 2, 2, 2))), \"seg\": p(torch.arange(8).reshape((1, 2, 2, 2)))},\n                p(np.arange(8).reshape((1, 2, 2, 2))),\n            ]\n        )\n        TESTS.append(\n            [\n                {\n                    \"keys\": (\"img\", \"seg\"),\n                    \"magnitude_range\": (0.3, 0.3),\n                    \"sigma_range\": (1.0, 2.0),\n                    \"prob\": 0.9,\n                    \"device\": device,\n                    \"spatial_size\": (2, 2, 2),\n                },\n                {\"img\": p(torch.arange(27).reshape((1, 3, 3, 3))), \"seg\": p(torch.arange(27).reshape((1, 3, 3, 3)))},\n                p(\n                    np.array(\n                        [\n                            [\n                                [[6.4939356, 7.50289], [9.518351, 10.522849]],\n                                [[15.512375, 16.523542], [18.531467, 19.53646]],\n                            ]\n                        ]\n                    )\n                ),\n            ]\n        )\n        TESTS.append(\n            [\n                {\n                    \"keys\": (\"img\", \"seg\"),\n                    \"magnitude_range\": (0.3, 0.3),\n                    \"sigma_range\": (1.0, 2.0),\n                    \"prob\": 0.9,\n                    \"rotate_range\": [1, 1, 1],\n                    \"device\": device,\n                    \"spatial_size\": (2, 2, 2),\n                    \"mode\": \"bilinear\",\n                },\n                {\"img\": p(torch.arange(27).reshape((1, 3, 3, 3))), \"seg\": p(torch.arange(27).reshape((1, 3, 3, 3)))},\n                p(\n                    np.array(\n                        [\n                            [\n                                [[5.0069294, 9.463932], [9.287769, 13.739735]],\n                                [[12.319424, 16.777205], [16.594296, 21.045748]],\n                            ]\n                        ]\n                    )\n                ),\n            ]\n        )\n        TESTS.append(\n            [\n                {\n                    \"keys\": (\"img\", \"seg\"),\n                    \"mode\": (\"bilinear\", \"nearest\"),\n                    \"magnitude_range\": (0.3, 0.3),\n                    \"sigma_range\": (1.0, 2.0),\n                    \"prob\": 0.9,\n                    \"rotate_range\": [1, 1, 1],\n                    \"device\": device,\n                    \"spatial_size\": (2, 2, 2),\n                },\n                {\"img\": p(torch.arange(27).reshape((1, 3, 3, 3))), \"seg\": p(torch.arange(27).reshape((1, 3, 3, 3)))},\n                {\n                    \"img\": p(\n                        torch.tensor(\n                            [[[[5.0069, 9.4639], [9.2878, 13.7397]], [[12.3194, 16.7772], [16.5943, 21.0457]]]]\n                        )\n                    ),\n                    \"seg\": p(torch.tensor([[[[4.0, 14.0], [7.0, 14.0]], [[9.0, 19.0], [12.0, 22.0]]]])),\n                },\n            ]\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_gaussian_noise.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_gaussian_noise.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_rand_gaussian_noise.py", "file_name": "test_rand_gaussian_noise.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 46, "span_ids": ["TestRandGaussianNoise.test_correct_results", "impl:6", "TestRandGaussianNoise", "docstring"], "tokens": 280}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import RandGaussianNoise\nfrom tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    TESTS.append((\"test_zero_mean\", p, 0, 0.1))\n    TESTS.append((\"test_non_zero_mean\", p, 1, 0.5))\n\n\nclass TestRandGaussianNoise(NumpyImageTestCase2D):\n    @parameterized.expand(TESTS)\n    def test_correct_results(self, _, im_type, mean, std):\n        seed = 0\n        gaussian_fn = RandGaussianNoise(prob=1.0, mean=mean, std=std)\n        gaussian_fn.set_random_state(seed)\n        im = im_type(self.imt)\n        noised = gaussian_fn(im)\n        np.random.seed(seed)\n        np.random.random()\n        expected = self.imt + np.random.normal(mean, np.random.uniform(0, std), size=self.imt.shape)\n        self.assertEqual(type(im), type(noised))\n        if isinstance(noised, torch.Tensor):\n            noised = noised.cpu()\n        np.testing.assert_allclose(expected, noised, atol=1e-5)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_gaussian_noised.py_TestRandGaussianNoised_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_gaussian_noised.py_TestRandGaussianNoised_", "embedding": null, "metadata": {"file_path": "tests/test_rand_gaussian_noised.py", "file_name": "test_rand_gaussian_noised.py", "file_type": "text/x-python", "category": "test", "start_line": 29, "end_line": 50, "span_ids": ["impl:8", "TestRandGaussianNoised", "TestRandGaussianNoised.test_correct_results"], "tokens": 233}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandGaussianNoised(NumpyImageTestCase2D):\n    @parameterized.expand(TESTS)\n    def test_correct_results(self, _, im_type, keys, mean, std):\n        gaussian_fn = RandGaussianNoised(keys=keys, prob=1.0, mean=mean, std=std, dtype=np.float64)\n        gaussian_fn.set_random_state(seed)\n        im = im_type(self.imt)\n        noised = gaussian_fn({k: im for k in keys})\n        np.random.seed(seed)\n        # simulate the randomize() of transform\n        np.random.random()\n        noise = np.random.normal(mean, np.random.uniform(0, std), size=self.imt.shape)\n        for k in keys:\n            expected = self.imt + noise\n            self.assertEqual(type(im), type(noised[k]))\n            if isinstance(noised[k], torch.Tensor):\n                noised[k] = noised[k].cpu()\n            np.testing.assert_allclose(expected, noised[k], atol=1e-5, rtol=1e-5)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_gaussian_sharpen.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_gaussian_sharpen.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_rand_gaussian_sharpen.py", "file_name": "test_rand_gaussian_sharpen.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 139, "span_ids": ["impl:8", "TestRandGaussianSharpen", "TestRandGaussianSharpen.test_value", "docstring"], "tokens": 1276}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nfrom parameterized import parameterized\n\nfrom monai.transforms import RandGaussianSharpen\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTESTS = []\n\nfor p in TEST_NDARRAYS:\n    TESTS.append(\n        [\n            {\"prob\": 1.0},\n            p([[[1, 1, 1], [2, 2, 2], [3, 3, 3]], [[4, 4, 4], [5, 5, 5], [6, 6, 6]]]),\n            p(\n                [\n                    [\n                        [5.2919216, 5.5854445, 5.29192],\n                        [11.3982, 12.62332, 11.398202],\n                        [14.870525, 17.323769, 14.870527],\n                    ],\n                    [\n                        [20.413757, 22.767355, 20.413757],\n                        [28.495504, 31.558315, 28.495499],\n                        [29.99236, 34.505676, 29.992361],\n                    ],\n                ]\n            ),\n        ]\n    )\n\n    TESTS.append(\n        [\n            {\n                \"sigma1_x\": (0.5, 0.75),\n                \"sigma1_y\": (0.5, 0.75),\n                \"sigma1_z\": (0.5, 0.75),\n                \"sigma2_x\": 0.4,\n                \"sigma2_y\": 0.4,\n                \"sigma2_z\": 0.4,\n                \"prob\": 1.0,\n            },\n            p([[[1, 1, 1], [2, 2, 2], [3, 3, 3]], [[4, 4, 4], [5, 5, 5], [6, 6, 6]]]),\n            p(\n                [\n                    [\n                        [4.1071496, 3.597953, 4.1071477],\n                        [10.062014, 9.825114, 10.0620165],\n                        [14.698058, 15.818766, 14.698058],\n                    ],\n                    [\n                        [18.211048, 18.16049, 18.211048],\n                        [25.155039, 24.56279, 25.155039],\n                        [28.801964, 30.381308, 28.801964],\n                    ],\n                ]\n            ),\n        ]\n    )\n\n    TESTS.append(\n        [\n            {\n                \"sigma1_x\": (0.5, 0.75),\n                \"sigma1_y\": (0.5, 0.75),\n                \"sigma1_z\": (0.5, 0.75),\n                \"sigma2_x\": (0.5, 0.75),\n                \"sigma2_y\": (0.5, 0.75),\n                \"sigma2_z\": (0.5, 0.75),\n                \"prob\": 1.0,\n            },\n            p([[[1, 1, 1], [2, 2, 2], [3, 3, 3]], [[4, 4, 4], [5, 5, 5], [6, 6, 6]]]),\n            p(\n                [\n                    [\n                        [4.81077, 4.4237204, 4.81077],\n                        [12.061236, 12.298177, 12.061236],\n                        [17.362553, 19.201174, 17.362553],\n                    ],\n                    [\n                        [21.440754, 22.142393, 21.440754],\n                        [30.15308, 30.745445, 30.153086],\n                        [33.99255, 36.919838, 33.99255],\n                    ],\n                ]\n            ),\n        ]\n    )\n\n    TESTS.append(\n        [\n            {\n                \"sigma1_x\": (0.5, 0.75),\n                \"sigma1_y\": (0.5, 0.75),\n                \"sigma1_z\": (0.5, 0.75),\n                \"sigma2_x\": (0.5, 0.75),\n                \"sigma2_y\": (0.5, 0.75),\n                \"sigma2_z\": (0.5, 0.75),\n                \"approx\": \"scalespace\",\n                \"prob\": 1.0,\n            },\n            p([[[1, 1, 1], [2, 2, 2], [3, 3, 3]], [[4, 4, 4], [5, 5, 5], [6, 6, 6]]]),\n            p(\n                [\n                    [\n                        [4.430213, 3.2278745, 4.4302144],\n                        [10.325399, 8.507457, 10.325399],\n                        [17.494898, 16.5609, 17.494894],\n                    ],\n                    [[20.87405, 18.06946, 20.87405], [25.813503, 21.268656, 25.8135], [33.93874, 31.402481, 33.938725]],\n                ]\n            ),\n        ]\n    )\n\n\nclass TestRandGaussianSharpen(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_value(self, argments, image, expected_data):\n        converter = RandGaussianSharpen(**argments)\n        converter.set_random_state(seed=0)\n        result = converter(image)\n        assert_allclose(result, expected_data, atol=0, rtol=1e-4, type_test=False)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_gaussian_sharpend.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_gaussian_sharpend.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_rand_gaussian_sharpend.py", "file_name": "test_rand_gaussian_sharpend.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 142, "span_ids": ["impl:8", "TestRandGaussianSharpend.test_value", "TestRandGaussianSharpend", "docstring"], "tokens": 1334}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import RandGaussianSharpend\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    TESTS.append(\n        [\n            {\"keys\": \"img\", \"prob\": 1.0},\n            {\"img\": p(np.array([[[1, 1, 1], [2, 2, 2], [3, 3, 3]], [[4, 4, 4], [5, 5, 5], [6, 6, 6]]]))},\n            np.array(\n                [\n                    [\n                        [5.2919216, 5.5854445, 5.29192],\n                        [11.3982, 12.62332, 11.398202],\n                        [14.870525, 17.323769, 14.870527],\n                    ],\n                    [\n                        [20.413757, 22.767355, 20.413757],\n                        [28.495504, 31.558315, 28.495499],\n                        [29.99236, 34.505676, 29.992361],\n                    ],\n                ]\n            ),\n        ]\n    )\n\n    TESTS.append(\n        [\n            {\n                \"keys\": \"img\",\n                \"sigma1_x\": (0.5, 0.75),\n                \"sigma1_y\": (0.5, 0.75),\n                \"sigma1_z\": (0.5, 0.75),\n                \"sigma2_x\": 0.4,\n                \"sigma2_y\": 0.4,\n                \"sigma2_z\": 0.4,\n                \"prob\": 1.0,\n            },\n            {\"img\": p(np.array([[[1, 1, 1], [2, 2, 2], [3, 3, 3]], [[4, 4, 4], [5, 5, 5], [6, 6, 6]]]))},\n            np.array(\n                [\n                    [\n                        [4.1071496, 3.597953, 4.1071477],\n                        [10.062014, 9.825114, 10.0620165],\n                        [14.698058, 15.818766, 14.698058],\n                    ],\n                    [\n                        [18.211048, 18.16049, 18.211048],\n                        [25.155039, 24.56279, 25.155039],\n                        [28.801964, 30.381308, 28.801964],\n                    ],\n                ]\n            ),\n        ]\n    )\n\n    TESTS.append(\n        [\n            {\n                \"keys\": \"img\",\n                \"sigma1_x\": (0.5, 0.75),\n                \"sigma1_y\": (0.5, 0.75),\n                \"sigma1_z\": (0.5, 0.75),\n                \"sigma2_x\": (0.5, 0.75),\n                \"sigma2_y\": (0.5, 0.75),\n                \"sigma2_z\": (0.5, 0.75),\n                \"prob\": 1.0,\n            },\n            {\"img\": p(np.array([[[1, 1, 1], [2, 2, 2], [3, 3, 3]], [[4, 4, 4], [5, 5, 5], [6, 6, 6]]]))},\n            np.array(\n                [\n                    [\n                        [4.81077, 4.4237204, 4.81077],\n                        [12.061236, 12.298177, 12.061236],\n                        [17.362553, 19.201174, 17.362553],\n                    ],\n                    [\n                        [21.440754, 22.142393, 21.440754],\n                        [30.15308, 30.745445, 30.153086],\n                        [33.99255, 36.919838, 33.99255],\n                    ],\n                ]\n            ),\n        ]\n    )\n\n    TESTS.append(\n        [\n            {\n                \"keys\": \"img\",\n                \"sigma1_x\": (0.5, 0.75),\n                \"sigma1_y\": (0.5, 0.75),\n                \"sigma1_z\": (0.5, 0.75),\n                \"sigma2_x\": (0.5, 0.75),\n                \"sigma2_y\": (0.5, 0.75),\n                \"sigma2_z\": (0.5, 0.75),\n                \"approx\": \"scalespace\",\n                \"prob\": 1.0,\n            },\n            {\"img\": p(np.array([[[1, 1, 1], [2, 2, 2], [3, 3, 3]], [[4, 4, 4], [5, 5, 5], [6, 6, 6]]]))},\n            np.array(\n                [\n                    [\n                        [4.430213, 3.2278745, 4.4302144],\n                        [10.325399, 8.507457, 10.325399],\n                        [17.494898, 16.5609, 17.494894],\n                    ],\n                    [[20.87405, 18.06946, 20.87405], [25.813503, 21.268656, 25.8135], [33.93874, 31.402481, 33.938725]],\n                ]\n            ),\n        ]\n    )\n\n\nclass TestRandGaussianSharpend(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_value(self, argments, image, expected_data):\n        converter = RandGaussianSharpend(**argments)\n        converter.set_random_state(seed=0)\n        result = converter(image)\n        assert_allclose(result[\"img\"], expected_data, rtol=1e-4, type_test=False)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_gaussian_smoothd.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_gaussian_smoothd.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_rand_gaussian_smoothd.py", "file_name": "test_rand_gaussian_smoothd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 97, "span_ids": ["impl:7", "TestRandGaussianSmoothd.test_value", "TestRandGaussianSmoothd", "docstring"], "tokens": 942}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import RandGaussianSmoothd\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    TESTS.append(\n        [\n            {\"keys\": \"img\", \"sigma_x\": (0.5, 1.5), \"prob\": 1.0},\n            {\"img\": p(np.array([[[1, 1, 1], [2, 2, 2], [3, 3, 3]], [[4, 4, 4], [5, 5, 5], [6, 6, 6]]]))},\n            np.array(\n                [\n                    [\n                        [0.71806467, 0.9074683, 0.71806467],\n                        [1.0718315, 1.3545481, 1.0718315],\n                        [1.0337002, 1.306359, 1.0337002],\n                    ],\n                    [\n                        [2.0318885, 2.5678391, 2.0318885],\n                        [2.6795788, 3.3863702, 2.6795788],\n                        [2.3475242, 2.9667296, 2.3475242],\n                    ],\n                ]\n            ),\n        ]\n    )\n\n    TESTS.append(\n        [\n            {\"keys\": \"img\", \"sigma_x\": (0.5, 1.5), \"sigma_y\": (0.5, 1.0), \"prob\": 1.0},\n            {\"img\": p(np.array([[[1, 1, 1], [2, 2, 2], [3, 3, 3]], [[4, 4, 4], [5, 5, 5], [6, 6, 6]]]))},\n            np.array(\n                [\n                    [\n                        [0.7686928, 0.9848021, 0.7686928],\n                        [1.1474025, 1.4699818, 1.1474024],\n                        [1.1065826, 1.4176859, 1.1065826],\n                    ],\n                    [\n                        [2.1751494, 2.7866683, 2.1751497],\n                        [2.8685062, 3.6749542, 2.8685062],\n                        [2.5130394, 3.219552, 2.5130394],\n                    ],\n                ]\n            ),\n        ]\n    )\n\n    TESTS.append(\n        [\n            {\"keys\": \"img\", \"sigma_x\": (0.5, 1.5), \"sigma_y\": (0.5, 1.0), \"approx\": \"scalespace\", \"prob\": 1.0},\n            {\"img\": p(np.array([[[1, 1, 1], [2, 2, 2], [3, 3, 3]], [[4, 4, 4], [5, 5, 5], [6, 6, 6]]]))},\n            np.array(\n                [\n                    [\n                        [0.8128456, 0.96736777, 0.8128456],\n                        [1.2742369, 1.5164697, 1.2742369],\n                        [1.2800367, 1.5233722, 1.2800368],\n                    ],\n                    [\n                        [2.3825073, 2.8354228, 2.3825073],\n                        [3.1855922, 3.7911744, 3.1855922],\n                        [2.8496985, 3.391427, 2.8496985],\n                    ],\n                ]\n            ),\n        ]\n    )\n\n\nclass TestRandGaussianSmoothd(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_value(self, argments, image, expected_data):\n        converter = RandGaussianSmoothd(**argments)\n        converter.set_random_state(seed=0)\n        result = converter(image)\n        assert_allclose(result[\"img\"], expected_data, rtol=1e-4, type_test=False)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_gibbs_noise.py_unittest_for_shape_in_128_64_.for_input_type_in_TEST_ND.TEST_CASES_append_shape_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_gibbs_noise.py_unittest_for_shape_in_128_64_.for_input_type_in_TEST_ND.TEST_CASES_append_shape_", "embedding": null, "metadata": {"file_path": "tests/test_rand_gibbs_noise.py", "file_name": "test_rand_gibbs_noise.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 30, "span_ids": ["docstring"], "tokens": 155}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom copy import deepcopy\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.data.synthetic import create_test_image_2d, create_test_image_3d\nfrom monai.transforms import RandGibbsNoise\nfrom monai.utils.misc import set_determinism\nfrom monai.utils.module import optional_import\nfrom tests.utils import TEST_NDARRAYS\n\n_, has_torch_fft = optional_import(\"torch.fft\", name=\"fftshift\")\n\nTEST_CASES = []\nfor shape in ((128, 64), (64, 48, 80)):\n    for input_type in TEST_NDARRAYS if has_torch_fft else [np.array]:\n        TEST_CASES.append((shape, input_type))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_gibbs_noise.py_TestRandGibbsNoise_TestRandGibbsNoise.test_0_prob.torch_testing_assert_allc": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_gibbs_noise.py_TestRandGibbsNoise_TestRandGibbsNoise.test_0_prob.torch_testing_assert_allc", "embedding": null, "metadata": {"file_path": "tests/test_rand_gibbs_noise.py", "file_name": "test_rand_gibbs_noise.py", "file_type": "text/x-python", "category": "test", "start_line": 33, "end_line": 53, "span_ids": ["TestRandGibbsNoise.setUp", "TestRandGibbsNoise.get_data", "TestRandGibbsNoise.test_0_prob", "TestRandGibbsNoise.tearDown", "TestRandGibbsNoise"], "tokens": 210}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandGibbsNoise(unittest.TestCase):\n    def setUp(self):\n        set_determinism(0)\n        super().setUp()\n\n    def tearDown(self):\n        set_determinism(None)\n\n    @staticmethod\n    def get_data(im_shape, input_type):\n        create_test_image = create_test_image_2d if len(im_shape) == 2 else create_test_image_3d\n        im = create_test_image(*im_shape, rad_max=20, noise_max=0.0, num_seg_classes=5)[0][None]\n        return input_type(im)\n\n    @parameterized.expand(TEST_CASES)\n    def test_0_prob(self, im_shape, input_type):\n        im = self.get_data(im_shape, input_type)\n        alpha = [0.5, 1.0]\n        t = RandGibbsNoise(0.0, alpha)\n        out = t(im)\n        torch.testing.assert_allclose(im, out, rtol=1e-7, atol=0)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_gibbs_noised.py_TestRandGibbsNoised_TestRandGibbsNoised.test_0_prob.for_k_in_KEYS_.torch_testing_assert_allc": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_gibbs_noised.py_TestRandGibbsNoised_TestRandGibbsNoised.test_0_prob.for_k_in_KEYS_.torch_testing_assert_allc", "embedding": null, "metadata": {"file_path": "tests/test_rand_gibbs_noised.py", "file_name": "test_rand_gibbs_noised.py", "file_type": "text/x-python", "category": "test", "start_line": 35, "end_line": 56, "span_ids": ["TestRandGibbsNoised", "TestRandGibbsNoised.test_0_prob", "TestRandGibbsNoised.setUp", "TestRandGibbsNoised.tearDown", "TestRandGibbsNoised.get_data"], "tokens": 234}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandGibbsNoised(unittest.TestCase):\n    def setUp(self):\n        set_determinism(0)\n        super().setUp()\n\n    def tearDown(self):\n        set_determinism(None)\n\n    @staticmethod\n    def get_data(im_shape, input_type):\n        create_test_image = create_test_image_2d if len(im_shape) == 2 else create_test_image_3d\n        ims = create_test_image(*im_shape, rad_max=20, noise_max=0.0, num_seg_classes=5)\n        return {k: input_type(v) for k, v in zip(KEYS, ims)}\n\n    @parameterized.expand(TEST_CASES)\n    def test_0_prob(self, im_shape, input_type):\n        data = self.get_data(im_shape, input_type)\n        alpha = [0.5, 1.0]\n        t = RandGibbsNoised(KEYS, 0.0, alpha)\n        out = t(data)\n        for k in KEYS:\n            torch.testing.assert_allclose(data[k], out[k], rtol=1e-7, atol=0)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_gibbs_noised.py_TestRandGibbsNoised.test_identity_TestRandGibbsNoised.test_identity.for_k_in_KEYS_.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_gibbs_noised.py_TestRandGibbsNoised.test_identity_TestRandGibbsNoised.test_identity.for_k_in_KEYS_.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_rand_gibbs_noised.py", "file_name": "test_rand_gibbs_noised.py", "file_type": "text/x-python", "category": "test", "start_line": 71, "end_line": 82, "span_ids": ["TestRandGibbsNoised.test_identity"], "tokens": 162}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandGibbsNoised(unittest.TestCase):\n\n    @parameterized.expand(TEST_CASES)\n    def test_identity(self, im_shape, input_type):\n        data = self.get_data(im_shape, input_type)\n        alpha = [0.0, 0.0]\n        t = RandGibbsNoised(KEYS, 1.0, alpha)\n        out = t(deepcopy(data))\n        for k in KEYS:\n            self.assertEqual(type(out[k]), type(data[k]))\n            if isinstance(out[k], torch.Tensor):\n                self.assertEqual(out[k].device, data[k].device)\n                out[k], data[k] = out[k].cpu(), data[k].cpu()\n            np.testing.assert_allclose(data[k], out[k], atol=1e-2)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_gibbs_noised.py_TestRandGibbsNoised.test_alpha_1_TestRandGibbsNoised.test_alpha_1.for_k_in_KEYS_.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_gibbs_noised.py_TestRandGibbsNoised.test_alpha_1_TestRandGibbsNoised.test_alpha_1.for_k_in_KEYS_.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_rand_gibbs_noised.py", "file_name": "test_rand_gibbs_noised.py", "file_type": "text/x-python", "category": "test", "start_line": 84, "end_line": 95, "span_ids": ["TestRandGibbsNoised.test_alpha_1"], "tokens": 169}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandGibbsNoised(unittest.TestCase):\n\n    @parameterized.expand(TEST_CASES)\n    def test_alpha_1(self, im_shape, input_type):\n        data = self.get_data(im_shape, input_type)\n        alpha = [1.0, 1.0]\n        t = RandGibbsNoised(KEYS, 1.0, alpha)\n        out = t(deepcopy(data))\n        for k in KEYS:\n            self.assertEqual(type(out[k]), type(data[k]))\n            if isinstance(out[k], torch.Tensor):\n                self.assertEqual(out[k].device, data[k].device)\n                out[k], data[k] = out[k].cpu(), data[k].cpu()\n            np.testing.assert_allclose(0.0 * data[k], out[k], atol=1e-2)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_gibbs_noised.py_TestRandGibbsNoised.test_dict_matches_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_gibbs_noised.py_TestRandGibbsNoised.test_dict_matches_", "embedding": null, "metadata": {"file_path": "tests/test_rand_gibbs_noised.py", "file_name": "test_rand_gibbs_noised.py", "file_type": "text/x-python", "category": "test", "start_line": 97, "end_line": 118, "span_ids": ["impl:9", "TestRandGibbsNoised.test_dict_matches", "TestRandGibbsNoised.test_alpha"], "tokens": 277}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandGibbsNoised(unittest.TestCase):\n\n    @parameterized.expand(TEST_CASES)\n    def test_dict_matches(self, im_shape, input_type):\n        data = self.get_data(im_shape, input_type)\n        # use same image for both dictionary entries to check same trans is applied to them\n        data = {KEYS[0]: deepcopy(data[KEYS[0]]), KEYS[1]: deepcopy(data[KEYS[0]])}\n        alpha = [0.5, 1.0]\n        t = RandGibbsNoised(KEYS, 1.0, alpha)\n        out = t(deepcopy(data))\n        torch.testing.assert_allclose(out[KEYS[0]], out[KEYS[1]], rtol=1e-7, atol=0)\n\n    @parameterized.expand(TEST_CASES)\n    def test_alpha(self, im_shape, input_type):\n        data = self.get_data(im_shape, input_type)\n        alpha = [0.5, 0.51]\n        t = RandGibbsNoised(KEYS, 1.0, alpha)\n        _ = t(deepcopy(data))\n        self.assertTrue(0.5 <= t.rand_gibbs_noise.sampled_alpha <= 0.51)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_grid_distortion.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_grid_distortion.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_rand_grid_distortion.py", "file_name": "test_rand_grid_distortion.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 95, "span_ids": ["TestRandGridDistortion", "impl:10", "TestRandGridDistortion.test_rand_grid_distortion", "docstring"], "tokens": 1179}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import RandGridDistortion\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    seed = 0\n    TESTS.append(\n        [\n            dict(num_cells=2, prob=1.0, distort_limit=0.5, mode=\"nearest\", padding_mode=\"zeros\"),\n            seed,\n            p(np.indices([6, 6]).astype(np.float32)),\n            p(\n                np.array(\n                    [\n                        [\n                            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n                            [2.0, 2.0, 2.0, 2.0, 2.0, 0.0],\n                            [4.0, 4.0, 4.0, 4.0, 4.0, 0.0],\n                            [4.0, 4.0, 4.0, 4.0, 4.0, 0.0],\n                            [5.0, 5.0, 5.0, 5.0, 5.0, 0.0],\n                            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n                        ],\n                        [\n                            [0.0, 1.0, 3.0, 3.0, 4.0, 0.0],\n                            [0.0, 1.0, 3.0, 3.0, 4.0, 0.0],\n                            [0.0, 1.0, 3.0, 3.0, 4.0, 0.0],\n                            [0.0, 1.0, 3.0, 3.0, 4.0, 0.0],\n                            [0.0, 1.0, 3.0, 3.0, 4.0, 0.0],\n                            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n                        ],\n                    ]\n                ).astype(np.float32)\n            ),\n        ]\n    )\n    seed = 1\n    TESTS.append(\n        [\n            dict(num_cells=(2, 2), prob=1.0, distort_limit=0.1, mode=\"bilinear\", padding_mode=\"reflection\"),\n            seed,\n            p(np.indices([6, 6]).astype(np.float32)),\n            p(\n                np.array(\n                    [\n                        [\n                            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n                            [1.5660975, 1.5660975, 1.5660975, 1.5660975, 1.5660974, 1.5660975],\n                            [3.132195, 3.132195, 3.132195, 3.132195, 3.132195, 3.132195],\n                            [3.132195, 3.132195, 3.132195, 3.132195, 3.132195, 3.132195],\n                            [4.482229, 4.482229, 4.482229, 4.482229, 4.482229, 4.482229],\n                            [4.167737, 4.167737, 4.167737, 4.167737, 4.167737, 4.167737],\n                        ],\n                        [\n                            [0.0, 1.3940268, 2.7880535, 2.7880535, 4.1657553, 4.4565434],\n                            [0.0, 1.3940268, 2.7880535, 2.7880535, 4.1657553, 4.4565434],\n                            [0.0, 1.3940268, 2.7880535, 2.7880535, 4.1657553, 4.4565434],\n                            [0.0, 1.3940268, 2.7880535, 2.7880535, 4.1657553, 4.4565434],\n                            [0.0, 1.3940268, 2.7880535, 2.7880535, 4.1657553, 4.4565434],\n                            [0.0, 1.3940266, 2.7880538, 2.7880538, 4.1657557, 4.456543],\n                        ],\n                    ]\n                ).astype(np.float32)\n            ),\n        ]\n    )\n\n\nclass TestRandGridDistortion(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_rand_grid_distortion(self, input_param, seed, input_data, expected_val):\n        g = RandGridDistortion(**input_param)\n        g.set_random_state(seed=seed)\n        result = g(input_data)\n        assert_allclose(result, expected_val, rtol=1e-4, atol=1e-4)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_grid_distortiond.py_unittest_for_p_in_TEST_NDARRAYS_.TESTS_append_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_grid_distortiond.py_unittest_for_p_in_TEST_NDARRAYS_.TESTS_append_", "embedding": null, "metadata": {"file_path": "tests/test_rand_grid_distortiond.py", "file_name": "test_rand_grid_distortiond.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 74, "span_ids": ["docstring"], "tokens": 847}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import RandGridDistortiond\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTESTS = []\nnum_cells = 2\nseed = 0\nfor p in TEST_NDARRAYS:\n    img = np.indices([6, 6]).astype(np.float32)\n    TESTS.append(\n        [\n            dict(\n                keys=[\"img\", \"mask\"],\n                num_cells=num_cells,\n                prob=1.0,\n                distort_limit=(-0.1, 0.1),\n                mode=[\"bilinear\", \"nearest\"],\n                padding_mode=\"zeros\",\n            ),\n            seed,\n            {\"img\": p(img), \"mask\": p(np.ones_like(img[:1]))},\n            p(\n                np.array(\n                    [\n                        [\n                            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n                            [1.5645568, 1.5645568, 1.5645568, 1.5645568, 1.5645568, 0.0],\n                            [3.1291137, 3.1291137, 3.1291137, 3.1291137, 3.1291137, 0.0],\n                            [3.1291137, 3.1291137, 3.1291137, 3.1291137, 3.1291137, 0.0],\n                            [4.6599426, 4.6599426, 4.6599426, 4.6599426, 4.6599426, 0.0],\n                            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n                        ],\n                        [\n                            [0.0, 1.4770963, 2.9541926, 2.9541926, 4.497961, 0.0],\n                            [0.0, 1.4770963, 2.9541926, 2.9541926, 4.497961, 0.0],\n                            [0.0, 1.4770963, 2.9541926, 2.9541926, 4.497961, 0.0],\n                            [0.0, 1.4770963, 2.9541926, 2.9541926, 4.497961, 0.0],\n                            [0.0, 1.4770963, 2.9541926, 2.9541926, 4.497961, 0.0],\n                            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n                        ],\n                    ]\n                ).astype(np.float32)\n            ),\n            p(\n                np.array(\n                    [\n                        [\n                            [1.0, 1.0, 1.0, 1.0, 1.0, 0.0],\n                            [1.0, 1.0, 1.0, 1.0, 1.0, 0.0],\n                            [1.0, 1.0, 1.0, 1.0, 1.0, 0.0],\n                            [1.0, 1.0, 1.0, 1.0, 1.0, 0.0],\n                            [1.0, 1.0, 1.0, 1.0, 1.0, 0.0],\n                            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n                        ]\n                    ]\n                )\n            ),\n        ]\n    )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_grid_distortiond.py_TestRandGridDistortiond_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_grid_distortiond.py_TestRandGridDistortiond_", "embedding": null, "metadata": {"file_path": "tests/test_rand_grid_distortiond.py", "file_name": "test_rand_grid_distortiond.py", "file_type": "text/x-python", "category": "test", "start_line": 77, "end_line": 89, "span_ids": ["impl:11", "TestRandGridDistortiond", "TestRandGridDistortiond.test_rand_grid_distortiond"], "tokens": 138}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandGridDistortiond(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_rand_grid_distortiond(self, input_param, seed, input_data, expected_val_img, expected_val_mask):\n        g = RandGridDistortiond(**input_param)\n        g.set_random_state(seed=seed)\n        result = g(input_data)\n        assert_allclose(result[\"img\"], expected_val_img, rtol=1e-4, atol=1e-4)\n        assert_allclose(result[\"mask\"], expected_val_mask, rtol=1e-4, atol=1e-4)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_histogram_shiftd.py_unittest_for_p_in_TEST_NDARRAYS_.None_2": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_histogram_shiftd.py_unittest_for_p_in_TEST_NDARRAYS_.None_2", "embedding": null, "metadata": {"file_path": "tests/test_rand_histogram_shiftd.py", "file_name": "test_rand_histogram_shiftd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 58, "span_ids": ["docstring"], "tokens": 544}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms.intensity.dictionary import RandHistogramShiftd\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    TESTS.append(\n        [\n            {\"keys\": (\"img\",), \"num_control_points\": 5, \"prob\": 0.0},\n            {\"img\": p(np.arange(8).reshape((1, 2, 2, 2))), \"seg\": p(np.ones(8).reshape((1, 2, 2, 2)))},\n            {\"img\": np.arange(8).reshape((1, 2, 2, 2)), \"seg\": np.ones(8).reshape((1, 2, 2, 2))},\n        ]\n    )\n    TESTS.append(\n        [\n            {\"keys\": (\"img\",), \"num_control_points\": 5, \"prob\": 0.9},\n            {\n                \"img\": p(np.arange(8).reshape((1, 2, 2, 2)).astype(np.float32)),\n                \"seg\": p(np.ones(8).reshape((1, 2, 2, 2))),\n            },\n            {\n                \"img\": np.array(\n                    [[[[0.0, 0.57227867], [1.1391707, 1.68990281]], [[2.75833219, 4.34445884], [5.70913743, 7.0]]]]\n                ),\n                \"seg\": np.ones(8).reshape((1, 2, 2, 2)),\n            },\n        ]\n    )\n    TESTS.append(\n        [\n            {\"keys\": (\"img\",), \"num_control_points\": (5, 20), \"prob\": 0.9},\n            {\n                \"img\": p(np.arange(8).reshape((1, 2, 2, 2)).astype(np.float32)),\n                \"seg\": p(np.ones(8).reshape((1, 2, 2, 2))),\n            },\n            {\n                \"img\": np.array(\n                    [[[[0.0, 1.17472492], [2.21553091, 2.88292011]], [[3.98407301, 5.01302123], [6.09275004, 7.0]]]]\n                ),\n                \"seg\": np.ones(8).reshape((1, 2, 2, 2)),\n            },\n        ]\n    )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_k_space_spike_noise.py_unittest_for_shape_in_128_64_.for_p_in_TEST_NDARRAYS_.for_channel_wise_in_True.TESTS_append_shape_p_c": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_k_space_spike_noise.py_unittest_for_shape_in_128_64_.for_p_in_TEST_NDARRAYS_.for_channel_wise_in_True.TESTS_append_shape_p_c", "embedding": null, "metadata": {"file_path": "tests/test_rand_k_space_spike_noise.py", "file_name": "test_rand_k_space_spike_noise.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 28, "span_ids": ["docstring"], "tokens": 138}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom copy import deepcopy\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.data.synthetic import create_test_image_2d, create_test_image_3d\nfrom monai.transforms import KSpaceSpikeNoise, RandKSpaceSpikeNoise\nfrom monai.utils.misc import set_determinism\nfrom tests.utils import TEST_NDARRAYS\n\nTESTS = []\nfor shape in ((128, 64), (64, 48, 80)):\n    for p in TEST_NDARRAYS:\n        for channel_wise in (True, False):\n            TESTS.append((shape, p, channel_wise))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_k_space_spike_noise.py_TestRandKSpaceSpikeNoise.test_same_result_TestRandKSpaceSpikeNoise.test_same_result.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_k_space_spike_noise.py_TestRandKSpaceSpikeNoise.test_same_result_TestRandKSpaceSpikeNoise.test_same_result.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_rand_k_space_spike_noise.py", "file_name": "test_rand_k_space_spike_noise.py", "file_type": "text/x-python", "category": "test", "start_line": 71, "end_line": 84, "span_ids": ["TestRandKSpaceSpikeNoise.test_same_result"], "tokens": 171}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandKSpaceSpikeNoise(unittest.TestCase):\n\n    @parameterized.expand(TESTS)\n    def test_same_result(self, im_shape, im_type, channel_wise):\n        im = self.get_data(im_shape, im_type)\n        intensity_range = [14, 15]\n        t = RandKSpaceSpikeNoise(0.0, intensity_range, channel_wise)\n        t.set_random_state(42)\n        out1 = t(deepcopy(im))\n        t.set_random_state(42)\n        out2 = t(deepcopy(im))\n        self.assertEqual(type(im), type(out1))\n        if isinstance(out1, torch.Tensor):\n            self.assertEqual(out1.device, im.device)\n            out1, out2 = out1.cpu(), out2.cpu()\n        np.testing.assert_allclose(out1, out2)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_k_space_spike_noised.py_TestKSpaceSpikeNoised.test_same_result_TestKSpaceSpikeNoised.test_same_result.for_k_in_KEYS_.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_k_space_spike_noised.py_TestKSpaceSpikeNoised.test_same_result_TestKSpaceSpikeNoised.test_same_result.for_k_in_KEYS_.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_rand_k_space_spike_noised.py", "file_name": "test_rand_k_space_spike_noised.py", "file_type": "text/x-python", "category": "test", "start_line": 47, "end_line": 65, "span_ids": ["TestKSpaceSpikeNoised.test_same_result"], "tokens": 200}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestKSpaceSpikeNoised(unittest.TestCase):\n\n    @parameterized.expand(TESTS)\n    def test_same_result(self, im_shape, im_type):\n\n        data = self.get_data(im_shape, im_type)\n\n        t = RandKSpaceSpikeNoised(KEYS, prob=1.0, intensity_range=(13, 15), channel_wise=True)\n        t.set_random_state(42)\n        out1 = t(deepcopy(data))\n\n        t.set_random_state(42)\n        out2 = t(deepcopy(data))\n\n        for k in KEYS:\n            self.assertEqual(type(out1[k]), type(data[k]))\n            if isinstance(out1[k], torch.Tensor):\n                self.assertEqual(out1[k].device, data[k].device)\n                out1[k] = out1[k].cpu()\n                out2[k] = out2[k].cpu()\n            np.testing.assert_allclose(out1[k], out2[k], atol=1e-10)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_k_space_spike_noised.py_TestKSpaceSpikeNoised.test_0_prob_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_k_space_spike_noised.py_TestKSpaceSpikeNoised.test_0_prob_", "embedding": null, "metadata": {"file_path": "tests/test_rand_k_space_spike_noised.py", "file_name": "test_rand_k_space_spike_noised.py", "file_type": "text/x-python", "category": "test", "start_line": 67, "end_line": 91, "span_ids": ["TestKSpaceSpikeNoised.test_0_prob", "impl:8"], "tokens": 242}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestKSpaceSpikeNoised(unittest.TestCase):\n\n    @parameterized.expand(TESTS)\n    def test_0_prob(self, im_shape, im_type):\n        data = self.get_data(im_shape, im_type)\n\n        t1 = RandKSpaceSpikeNoised(KEYS, prob=0.0, intensity_range=(13, 15), channel_wise=True)\n\n        t2 = RandKSpaceSpikeNoised(KEYS, prob=0.0, intensity_range=(13, 15), channel_wise=True)\n        out1 = t1(data)\n        out2 = t2(data)\n\n        for k in KEYS:\n            self.assertEqual(type(out1[k]), type(data[k]))\n            if isinstance(out1[k], torch.Tensor):\n                self.assertEqual(out1[k].device, data[k].device)\n                out1[k] = out1[k].cpu()\n                out2[k] = out2[k].cpu()\n                data[k] = data[k].cpu()\n\n            np.testing.assert_allclose(data[k], out1[k])\n            np.testing.assert_allclose(data[k], out2[k])\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_lambda.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_lambda.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_rand_lambda.py", "file_name": "test_rand_lambda.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 54, "span_ids": ["impl", "RandTest.randomize", "TestRandLambda.test_rand_lambdad_identity", "docstring", "RandTest", "TestRandLambda", "RandTest.__call__"], "tokens": 246}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\n\nfrom monai.transforms.transform import Randomizable\nfrom monai.transforms.utility.array import RandLambda\n\n\nclass RandTest(Randomizable):\n    \"\"\"\n    randomisable transform for testing.\n    \"\"\"\n\n    def randomize(self, data=None):\n        self._a = self.R.random()\n\n    def __call__(self, data):\n        self.randomize()\n        return data + self._a\n\n\nclass TestRandLambda(unittest.TestCase):\n    def test_rand_lambdad_identity(self):\n        img = np.zeros((10, 10))\n\n        test_func = RandTest()\n        test_func.set_random_state(seed=134)\n        expected = test_func(img)\n        test_func.set_random_state(seed=134)\n        ret = RandLambda(func=test_func)(img)\n        np.testing.assert_allclose(expected, ret)\n        ret = RandLambda(func=test_func, prob=0.0)(img)\n        np.testing.assert_allclose(img, ret)\n\n        trans = RandLambda(func=test_func, prob=0.5)\n        trans.set_random_state(seed=123)\n        ret = trans(img)\n        np.testing.assert_allclose(img, ret)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_rician_noise.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_rician_noise.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_rand_rician_noise.py", "file_name": "test_rand_rician_noise.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 48, "span_ids": ["TestRandRicianNoise.test_correct_results", "TestRandRicianNoise", "impl:6", "docstring"], "tokens": 306}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import RandRicianNoise\nfrom tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    TESTS.append((\"test_zero_mean\", p, 0, 0.1))\n    TESTS.append((\"test_non_zero_mean\", p, 1, 0.5))\n\n\nclass TestRandRicianNoise(NumpyImageTestCase2D):\n    @parameterized.expand(TESTS)\n    def test_correct_results(self, _, in_type, mean, std):\n        seed = 0\n        rician_fn = RandRicianNoise(prob=1.0, mean=mean, std=std)\n        rician_fn.set_random_state(seed)\n        noised = rician_fn(in_type(self.imt))\n        np.random.seed(seed)\n        np.random.random()\n        _std = np.random.uniform(0, std)\n        expected = np.sqrt(\n            (self.imt + np.random.normal(mean, _std, size=self.imt.shape)) ** 2\n            + np.random.normal(mean, _std, size=self.imt.shape) ** 2\n        )\n        if isinstance(noised, torch.Tensor):\n            noised = noised.cpu()\n        np.testing.assert_allclose(expected, noised, atol=1e-5)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_rician_noised.py_TestRandRicianNoisedNumpy_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_rician_noised.py_TestRandRicianNoisedNumpy_", "embedding": null, "metadata": {"file_path": "tests/test_rand_rician_noised.py", "file_name": "test_rand_rician_noised.py", "file_type": "text/x-python", "category": "test", "start_line": 29, "end_line": 51, "span_ids": ["TestRandRicianNoisedNumpy", "TestRandRicianNoisedNumpy.test_correct_results", "impl:8"], "tokens": 258}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandRicianNoisedNumpy(NumpyImageTestCase2D):\n    @parameterized.expand(TESTS)\n    def test_correct_results(self, _, in_type, keys, mean, std):\n        rician_fn = RandRicianNoised(keys=keys, prob=1.0, mean=mean, std=std, dtype=np.float64)\n        rician_fn.set_random_state(seed)\n        noised = rician_fn({k: in_type(self.imt) for k in keys})\n        np.random.seed(seed)\n        for k in keys:\n            # simulate the `randomize` function of transform\n            np.random.random()\n            _std = np.random.uniform(0, std)\n            expected = np.sqrt(\n                (self.imt + np.random.normal(mean, _std, size=self.imt.shape)) ** 2\n                + np.random.normal(mean, _std, size=self.imt.shape) ** 2\n            )\n            if isinstance(noised[k], torch.Tensor):\n                noised[k] = noised[k].cpu()\n            np.testing.assert_allclose(expected, noised[k], atol=1e-5, rtol=1e-5)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_rotate.py_unittest_None_1.TEST_CASES_3D_append_p_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_rotate.py_unittest_None_1.TEST_CASES_3D_append_p_", "embedding": null, "metadata": {"file_path": "tests/test_rand_rotate.py", "file_name": "test_rand_rotate.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 61, "span_ids": ["docstring"], "tokens": 486}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom typing import List, Tuple\n\nimport numpy as np\nimport scipy.ndimage\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import RandRotate\nfrom tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, NumpyImageTestCase3D\n\nTEST_CASES_2D: List[Tuple] = []\nfor p in TEST_NDARRAYS:\n    TEST_CASES_2D.append((p, np.pi / 2, True, \"bilinear\", \"border\", False))\n    TEST_CASES_2D.append((p, np.pi / 4, True, \"nearest\", \"border\", False))\n    TEST_CASES_2D.append((p, np.pi, False, \"nearest\", \"zeros\", True))\n    TEST_CASES_2D.append((p, (-np.pi / 4, 0), False, \"nearest\", \"zeros\", True))\n\nTEST_CASES_3D: List[Tuple] = []\nfor p in TEST_NDARRAYS:\n    TEST_CASES_3D.append(\n        (p, np.pi / 2, -np.pi / 6, (0.0, np.pi), False, \"bilinear\", \"border\", False, (1, 87, 104, 109))\n    )\n    TEST_CASES_3D.append(\n        (\n            p,\n            np.pi / 4,\n            (-np.pi / 9, np.pi / 4.5),\n            (np.pi / 9, np.pi / 6),\n            False,\n            \"nearest\",\n            \"border\",\n            True,\n            (1, 89, 105, 104),\n        )\n    )\n    TEST_CASES_3D.append(\n        (\n            p,\n            0.0,\n            (2 * np.pi, 2.06 * np.pi),\n            (-np.pi / 180, np.pi / 180),\n            True,\n            \"nearest\",\n            \"zeros\",\n            True,\n            (1, 48, 64, 80),\n        )\n    )\n    TEST_CASES_3D.append((p, (-np.pi / 4, 0), 0, 0, False, \"nearest\", \"zeros\", False, (1, 48, 77, 90)))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_rotate.py_TestRandRotate2D_TestRandRotate2D.test_correct_results.self_assertLessEqual_np_a": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_rotate.py_TestRandRotate2D_TestRandRotate2D.test_correct_results.self_assertLessEqual_np_a", "embedding": null, "metadata": {"file_path": "tests/test_rand_rotate.py", "file_name": "test_rand_rotate.py", "file_type": "text/x-python", "category": "test", "start_line": 64, "end_line": 92, "span_ids": ["TestRandRotate2D", "TestRandRotate2D.test_correct_results"], "tokens": 302}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandRotate2D(NumpyImageTestCase2D):\n    @parameterized.expand(TEST_CASES_2D)\n    def test_correct_results(self, im_type, degrees, keep_size, mode, padding_mode, align_corners):\n        rotate_fn = RandRotate(\n            range_x=degrees,\n            prob=1.0,\n            keep_size=keep_size,\n            mode=mode,\n            padding_mode=padding_mode,\n            align_corners=align_corners,\n        )\n        rotate_fn.set_random_state(243)\n        rotated = rotate_fn(im_type(self.imt[0]))\n\n        _order = 0 if mode == \"nearest\" else 1\n        if mode == \"border\":\n            _mode = \"nearest\"\n        elif mode == \"reflection\":\n            _mode = \"reflect\"\n        else:\n            _mode = \"constant\"\n        angle = rotate_fn.x\n        expected = scipy.ndimage.rotate(\n            self.imt[0, 0], -np.rad2deg(angle), (0, 1), not keep_size, order=_order, mode=_mode, prefilter=False\n        )\n        expected = np.stack(expected).astype(np.float32)\n        rotated = rotated.cpu() if isinstance(rotated, torch.Tensor) else rotated\n        good = np.sum(np.isclose(expected, rotated[0], atol=1e-3))\n        self.assertLessEqual(np.abs(good - expected.size), 5, \"diff at most 5 pixels\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_rotate90.py_unittest_TestRandRotate90.test_k.for_p_in_TEST_NDARRAYS_.assert_allclose_rotated_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_rotate90.py_unittest_TestRandRotate90.test_k.for_p_in_TEST_NDARRAYS_.assert_allclose_rotated_", "embedding": null, "metadata": {"file_path": "tests/test_rand_rotate90.py", "file_name": "test_rand_rotate90.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 37, "span_ids": ["TestRandRotate90", "TestRandRotate90.test_default", "TestRandRotate90.test_k", "docstring"], "tokens": 264}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\n\nfrom monai.transforms import RandRotate90\nfrom tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, assert_allclose\n\n\nclass TestRandRotate90(NumpyImageTestCase2D):\n    def test_default(self):\n        rotate = RandRotate90()\n        for p in TEST_NDARRAYS:\n            rotate.set_random_state(123)\n            rotated = rotate(p(self.imt[0]))\n            expected = [np.rot90(channel, 0, (0, 1)) for channel in self.imt[0]]\n            expected = np.stack(expected)\n            assert_allclose(rotated, p(expected), rtol=1.0e-5, atol=1.0e-8)\n\n    def test_k(self):\n        rotate = RandRotate90(max_k=2)\n        for p in TEST_NDARRAYS:\n            rotate.set_random_state(123)\n            rotated = rotate(p(self.imt[0]))\n            expected = [np.rot90(channel, 0, (0, 1)) for channel in self.imt[0]]\n            expected = np.stack(expected)\n            assert_allclose(rotated, p(expected), rtol=1.0e-5, atol=1.0e-8)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_rotate90.py_TestRandRotate90.test_spatial_axes_TestRandRotate90.test_spatial_axes.for_p_in_TEST_NDARRAYS_.assert_allclose_rotated_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_rotate90.py_TestRandRotate90.test_spatial_axes_TestRandRotate90.test_spatial_axes.for_p_in_TEST_NDARRAYS_.assert_allclose_rotated_", "embedding": null, "metadata": {"file_path": "tests/test_rand_rotate90.py", "file_name": "test_rand_rotate90.py", "file_type": "text/x-python", "category": "test", "start_line": 39, "end_line": 46, "span_ids": ["TestRandRotate90.test_spatial_axes"], "tokens": 127}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandRotate90(NumpyImageTestCase2D):\n\n    def test_spatial_axes(self):\n        rotate = RandRotate90(spatial_axes=(0, 1))\n        for p in TEST_NDARRAYS:\n            rotate.set_random_state(123)\n            rotated = rotate(p(self.imt[0]))\n            expected = [np.rot90(channel, 0, (0, 1)) for channel in self.imt[0]]\n            expected = np.stack(expected)\n            assert_allclose(rotated, p(expected), rtol=1.0e-5, atol=1.0e-8)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_rotate90.py_TestRandRotate90.test_prob_k_spatial_axes_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_rotate90.py_TestRandRotate90.test_prob_k_spatial_axes_", "embedding": null, "metadata": {"file_path": "tests/test_rand_rotate90.py", "file_name": "test_rand_rotate90.py", "file_type": "text/x-python", "category": "test", "start_line": 48, "end_line": 60, "span_ids": ["impl", "TestRandRotate90.test_prob_k_spatial_axes"], "tokens": 151}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandRotate90(NumpyImageTestCase2D):\n\n    def test_prob_k_spatial_axes(self):\n        rotate = RandRotate90(prob=1.0, max_k=2, spatial_axes=(0, 1))\n        for p in TEST_NDARRAYS:\n            rotate.set_random_state(234)\n            rotated = rotate(p(self.imt[0]))\n            expected = [np.rot90(channel, 1, (0, 1)) for channel in self.imt[0]]\n            expected = np.stack(expected)\n            assert_allclose(rotated, p(expected), rtol=1.0e-5, atol=1.0e-8)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_rotate90d.py_unittest_TestRandRotate90d.test_k.for_p_in_TEST_NDARRAYS_.assert_allclose_rotated_k": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_rotate90d.py_unittest_TestRandRotate90d.test_k.for_p_in_TEST_NDARRAYS_.assert_allclose_rotated_k", "embedding": null, "metadata": {"file_path": "tests/test_rand_rotate90d.py", "file_name": "test_rand_rotate90d.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 39, "span_ids": ["TestRandRotate90d.test_k", "TestRandRotate90d", "TestRandRotate90d.test_default", "docstring"], "tokens": 256}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\n\nfrom monai.transforms import RandRotate90d\nfrom tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, assert_allclose\n\n\nclass TestRandRotate90d(NumpyImageTestCase2D):\n    def test_default(self):\n        key = None\n        rotate = RandRotate90d(keys=key)\n        for p in TEST_NDARRAYS:\n            rotate.set_random_state(123)\n            rotated = rotate({key: p(self.imt[0])})\n            expected = [np.rot90(channel, 0, (0, 1)) for channel in self.imt[0]]\n            expected = np.stack(expected)\n            assert_allclose(rotated[key], p(expected))\n\n    def test_k(self):\n        key = \"test\"\n        rotate = RandRotate90d(keys=key, max_k=2)\n        for p in TEST_NDARRAYS:\n            rotate.set_random_state(234)\n            rotated = rotate({key: p(self.imt[0])})\n            expected = [np.rot90(channel, 0, (0, 1)) for channel in self.imt[0]]\n            expected = np.stack(expected)\n            assert_allclose(rotated[key], p(expected))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_rotate90d.py_TestRandRotate90d.test_spatial_axes_TestRandRotate90d.test_spatial_axes.for_p_in_TEST_NDARRAYS_.assert_allclose_rotated_k": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_rotate90d.py_TestRandRotate90d.test_spatial_axes_TestRandRotate90d.test_spatial_axes.for_p_in_TEST_NDARRAYS_.assert_allclose_rotated_k", "embedding": null, "metadata": {"file_path": "tests/test_rand_rotate90d.py", "file_name": "test_rand_rotate90d.py", "file_type": "text/x-python", "category": "test", "start_line": 41, "end_line": 49, "span_ids": ["TestRandRotate90d.test_spatial_axes"], "tokens": 123}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandRotate90d(NumpyImageTestCase2D):\n\n    def test_spatial_axes(self):\n        key = \"test\"\n        rotate = RandRotate90d(keys=key, spatial_axes=(0, 1))\n        for p in TEST_NDARRAYS:\n            rotate.set_random_state(234)\n            rotated = rotate({key: p(self.imt[0])})\n            expected = [np.rot90(channel, 0, (0, 1)) for channel in self.imt[0]]\n            expected = np.stack(expected)\n            assert_allclose(rotated[key], p(expected))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_rotated.py_unittest_TEST_CASES_3D._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_rotated.py_unittest_TEST_CASES_3D._", "embedding": null, "metadata": {"file_path": "tests/test_rand_rotated.py", "file_name": "test_rand_rotated.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 32, "span_ids": ["docstring"], "tokens": 222}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom typing import List, Tuple\n\nimport numpy as np\nimport scipy.ndimage\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import RandRotated\nfrom monai.utils import GridSampleMode, GridSamplePadMode\nfrom tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, NumpyImageTestCase3D\n\nTEST_CASES_2D: List[Tuple] = []\nfor p in TEST_NDARRAYS:\n    TEST_CASES_2D.append((p, np.pi / 2, True, \"bilinear\", \"border\", False))\n    TEST_CASES_2D.append((p, np.pi / 4, True, \"nearest\", \"border\", False))\n    TEST_CASES_2D.append((p, np.pi, False, \"nearest\", \"zeros\", True))\n    TEST_CASES_2D.append((p, (-np.pi / 4, 0), False, \"nearest\", \"zeros\", True))\n\n\nTEST_CASES_3D: List[Tuple] = []", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_rotated.py_None_1_None_1.None_7": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_rotated.py_None_1_None_1.None_7", "embedding": null, "metadata": {"file_path": "tests/test_rand_rotated.py", "file_name": "test_rand_rotated.py", "file_type": "text/x-python", "category": "test", "start_line": 33, "end_line": 105, "span_ids": ["docstring"], "tokens": 597}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "for p in TEST_NDARRAYS:\n    TEST_CASES_3D.append(\n        (p, np.pi / 2, -np.pi / 6, (0.0, np.pi), False, \"bilinear\", \"border\", False, (1, 87, 104, 109))\n    )\n    TEST_CASES_3D.append(\n        (\n            p,\n            np.pi / 2,\n            -np.pi / 6,\n            (0.0, np.pi),\n            False,\n            GridSampleMode.NEAREST,\n            GridSamplePadMode.BORDER,\n            False,\n            (1, 87, 104, 109),\n        )\n    )\n    TEST_CASES_3D.append(\n        (\n            p,\n            np.pi / 4,\n            (-np.pi / 9, np.pi / 4.5),\n            (np.pi / 9, np.pi / 6),\n            False,\n            \"nearest\",\n            \"border\",\n            True,\n            (1, 89, 105, 104),\n        )\n    )\n    TEST_CASES_3D.append(\n        (\n            p,\n            np.pi / 4,\n            (-np.pi / 9, np.pi / 4.5),\n            (np.pi / 9, np.pi / 6),\n            False,\n            GridSampleMode.NEAREST,\n            GridSamplePadMode.BORDER,\n            True,\n            (1, 89, 105, 104),\n        )\n    )\n    TEST_CASES_3D.append(\n        (\n            p,\n            0.0,\n            (2 * np.pi, 2.06 * np.pi),\n            (-np.pi / 180, np.pi / 180),\n            True,\n            \"nearest\",\n            \"zeros\",\n            True,\n            (1, 48, 64, 80),\n        )\n    )\n    TEST_CASES_3D.append(\n        (\n            p,\n            0.0,\n            (2 * np.pi, 2.06 * np.pi),\n            (-np.pi / 180, np.pi / 180),\n            True,\n            GridSampleMode.NEAREST,\n            GridSamplePadMode.ZEROS,\n            True,\n            (1, 48, 64, 80),\n        )\n    )\n    TEST_CASES_3D.append((p, (-np.pi / 4, 0), 0, 0, False, \"nearest\", \"zeros\", False, (1, 48, 77, 90)))\n    TEST_CASES_3D.append(\n        (p, (-np.pi / 4, 0), 0, 0, False, GridSampleMode.NEAREST, GridSamplePadMode.ZEROS, False, (1, 48, 77, 90))\n    )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_rotated.py_TestRandRotated2D_TestRandRotated2D.test_correct_results.self_assertLessEqual_np_a": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_rotated.py_TestRandRotated2D_TestRandRotated2D.test_correct_results.self_assertLessEqual_np_a", "embedding": null, "metadata": {"file_path": "tests/test_rand_rotated.py", "file_name": "test_rand_rotated.py", "file_type": "text/x-python", "category": "test", "start_line": 108, "end_line": 138, "span_ids": ["TestRandRotated2D", "TestRandRotated2D.test_correct_results"], "tokens": 339}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandRotated2D(NumpyImageTestCase2D):\n    @parameterized.expand(TEST_CASES_2D)\n    def test_correct_results(self, im_type, degrees, keep_size, mode, padding_mode, align_corners):\n        rotate_fn = RandRotated(\n            \"img\",\n            range_x=degrees,\n            prob=1.0,\n            keep_size=keep_size,\n            mode=mode,\n            padding_mode=padding_mode,\n            align_corners=align_corners,\n        )\n        rotate_fn.set_random_state(243)\n        rotated = rotate_fn({\"img\": im_type(self.imt[0]), \"seg\": im_type(self.segn[0])})\n\n        _order = 0 if mode == \"nearest\" else 1\n        if padding_mode == \"border\":\n            _mode = \"nearest\"\n        elif padding_mode == \"reflection\":\n            _mode = \"reflect\"\n        else:\n            _mode = \"constant\"\n        angle = rotate_fn.rand_rotate.x\n        expected = scipy.ndimage.rotate(\n            self.imt[0, 0], -np.rad2deg(angle), (0, 1), not keep_size, order=_order, mode=_mode, prefilter=False\n        )\n        for k, v in rotated.items():\n            rotated[k] = v.cpu() if isinstance(v, torch.Tensor) else v\n        expected = np.stack(expected).astype(np.float32)\n        good = np.sum(np.isclose(expected, rotated[\"img\"][0], atol=1e-3))\n        self.assertLessEqual(np.abs(good - expected.size), 5, \"diff at most 5 pixels\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_shift_intensityd.py_unittest_TestRandShiftIntensityd.test_value.for_p_in_TEST_NDARRAYS_.assert_allclose_result_ke": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_shift_intensityd.py_unittest_TestRandShiftIntensityd.test_value.for_p_in_TEST_NDARRAYS_.assert_allclose_result_ke", "embedding": null, "metadata": {"file_path": "tests/test_rand_shift_intensityd.py", "file_name": "test_rand_shift_intensityd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 31, "span_ids": ["TestRandShiftIntensityd", "TestRandShiftIntensityd.test_value", "docstring"], "tokens": 180}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\n\nfrom monai.transforms import IntensityStatsd, RandShiftIntensityd\nfrom tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, assert_allclose\n\n\nclass TestRandShiftIntensityd(NumpyImageTestCase2D):\n    def test_value(self):\n        key = \"img\"\n        for p in TEST_NDARRAYS:\n            shifter = RandShiftIntensityd(keys=[key], offsets=1.0, prob=1.0)\n            shifter.set_random_state(seed=0)\n            result = shifter({key: p(self.imt)})\n            np.random.seed(0)\n            # simulate the randomize() of transform\n            np.random.random()\n            expected = self.imt + np.random.uniform(low=-1.0, high=1.0)\n            assert_allclose(result[key], p(expected))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_shift_intensityd.py_TestRandShiftIntensityd.test_factor_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_shift_intensityd.py_TestRandShiftIntensityd.test_factor_", "embedding": null, "metadata": {"file_path": "tests/test_rand_shift_intensityd.py", "file_name": "test_rand_shift_intensityd.py", "file_type": "text/x-python", "category": "test", "start_line": 33, "end_line": 49, "span_ids": ["impl", "TestRandShiftIntensityd.test_factor"], "tokens": 189}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandShiftIntensityd(NumpyImageTestCase2D):\n\n    def test_factor(self):\n        key = \"img\"\n        stats = IntensityStatsd(keys=key, ops=\"max\", key_prefix=\"orig\")\n        shifter = RandShiftIntensityd(keys=[key], offsets=1.0, factor_key=[\"orig_max\"], prob=1.0)\n        data = {key: self.imt, key + \"_meta_dict\": {\"affine\": None}}\n        shifter.set_random_state(seed=0)\n        result = shifter(stats(data))\n        np.random.seed(0)\n        # simulate the randomize() of transform\n        np.random.random()\n        expected = self.imt + np.random.uniform(low=-1.0, high=1.0) * np.nanmax(self.imt)\n        np.testing.assert_allclose(result[key], expected)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_spatial_crop_samplesd.py_unittest_TEST_CASE_2._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_spatial_crop_samplesd.py_unittest_TEST_CASE_2._", "embedding": null, "metadata": {"file_path": "tests/test_rand_spatial_crop_samplesd.py", "file_name": "test_rand_spatial_crop_samplesd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 42, "span_ids": ["impl:3", "docstring"], "tokens": 453}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import Compose, RandSpatialCropSamplesd, ToTensord\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTEST_CASE_1 = [\n    {\"keys\": [\"img\", \"seg\"], \"num_samples\": 4, \"roi_size\": [2, 2, 2], \"random_center\": True},\n    {\"img\": np.arange(81).reshape(3, 3, 3, 3), \"seg\": np.arange(81, 0, -1).reshape(3, 3, 3, 3)},\n    [(3, 3, 3, 2), (3, 2, 2, 2), (3, 3, 3, 2), (3, 3, 2, 2)],\n    {\n        \"img\": np.array(\n            [\n                [[[0, 1], [3, 4]], [[9, 10], [12, 13]], [[18, 19], [21, 22]]],\n                [[[27, 28], [30, 31]], [[36, 37], [39, 40]], [[45, 46], [48, 49]]],\n                [[[54, 55], [57, 58]], [[63, 64], [66, 67]], [[72, 73], [75, 76]]],\n            ]\n        ),\n        \"seg\": np.array(\n            [\n                [[[81, 80], [78, 77]], [[72, 71], [69, 68]], [[63, 62], [60, 59]]],\n                [[[54, 53], [51, 50]], [[45, 44], [42, 41]], [[36, 35], [33, 32]]],\n                [[[27, 26], [24, 23]], [[18, 17], [15, 14]], [[9, 8], [6, 5]]],\n            ]\n        ),\n    },\n]\n\nTEST_CASE_2 = []", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_spatial_crop_samplesd.py_for_p_in_TEST_NDARRAYS__for_p_in_TEST_NDARRAYS_.TEST_CASE_2_append_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_spatial_crop_samplesd.py_for_p_in_TEST_NDARRAYS__for_p_in_TEST_NDARRAYS_.TEST_CASE_2_append_", "embedding": null, "metadata": {"file_path": "tests/test_rand_spatial_crop_samplesd.py", "file_name": "test_rand_spatial_crop_samplesd.py", "file_type": "text/x-python", "category": "test", "start_line": 43, "end_line": 79, "span_ids": ["impl:3"], "tokens": 594}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "for p in TEST_NDARRAYS:\n    TEST_CASE_2.append(\n        [\n            {\"keys\": [\"img\", \"seg\"], \"num_samples\": 8, \"roi_size\": [2, 2, 3], \"random_center\": False},\n            {\"img\": p(np.arange(81).reshape(3, 3, 3, 3)), \"seg\": p(np.arange(81, 0, -1).reshape(3, 3, 3, 3))},\n            [\n                (3, 3, 3, 3),\n                (3, 2, 3, 3),\n                (3, 2, 2, 3),\n                (3, 2, 3, 3),\n                (3, 3, 3, 3),\n                (3, 3, 3, 3),\n                (3, 2, 2, 3),\n                (3, 3, 2, 3),\n            ],\n            {\n                \"img\": p(\n                    np.array(\n                        [\n                            [[[0, 1, 2], [3, 4, 5]], [[9, 10, 11], [12, 13, 14]], [[18, 19, 20], [21, 22, 23]]],\n                            [[[27, 28, 29], [30, 31, 32]], [[36, 37, 38], [39, 40, 41]], [[45, 46, 47], [48, 49, 50]]],\n                            [[[54, 55, 56], [57, 58, 59]], [[63, 64, 65], [66, 67, 68]], [[72, 73, 74], [75, 76, 77]]],\n                        ]\n                    )\n                ),\n                \"seg\": p(\n                    np.array(\n                        [\n                            [[[81, 80, 79], [78, 77, 76]], [[72, 71, 70], [69, 68, 67]], [[63, 62, 61], [60, 59, 58]]],\n                            [[[54, 53, 52], [51, 50, 49]], [[45, 44, 43], [42, 41, 40]], [[36, 35, 34], [33, 32, 31]]],\n                            [[[27, 26, 25], [24, 23, 22]], [[18, 17, 16], [15, 14, 13]], [[9, 8, 7], [6, 5, 4]]],\n                        ]\n                    )\n                ),\n            },\n        ]\n    )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_spatial_crop_samplesd.py_TestRandSpatialCropSamplesd_TestRandSpatialCropSamplesd.test_shape.assert_allclose_item_seg": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_spatial_crop_samplesd.py_TestRandSpatialCropSamplesd_TestRandSpatialCropSamplesd.test_shape.assert_allclose_item_seg", "embedding": null, "metadata": {"file_path": "tests/test_rand_spatial_crop_samplesd.py", "file_name": "test_rand_spatial_crop_samplesd.py", "file_type": "text/x-python", "category": "test", "start_line": 82, "end_line": 95, "span_ids": ["TestRandSpatialCropSamplesd", "TestRandSpatialCropSamplesd.test_shape"], "tokens": 185}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandSpatialCropSamplesd(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1, *TEST_CASE_2])\n    def test_shape(self, input_param, input_data, expected_shape, expected_last):\n        xform = RandSpatialCropSamplesd(**input_param)\n        xform.set_random_state(1234)\n        result = xform(input_data)\n        for item, expected in zip(result, expected_shape):\n            self.assertTupleEqual(item[\"img\"].shape, expected)\n            self.assertTupleEqual(item[\"seg\"].shape, expected)\n        for i, item in enumerate(result):\n            self.assertEqual(item[\"img_meta_dict\"][\"patch_index\"], i)\n            self.assertEqual(item[\"seg_meta_dict\"][\"patch_index\"], i)\n        assert_allclose(item[\"img\"], expected_last[\"img\"], type_test=True)\n        assert_allclose(item[\"seg\"], expected_last[\"seg\"], type_test=True)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_weighted_crop.py_unittest_TESTS._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_weighted_crop.py_unittest_TESTS._", "embedding": null, "metadata": {"file_path": "tests/test_rand_weighted_crop.py", "file_name": "test_rand_weighted_crop.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 32, "span_ids": ["get_data", "impl", "docstring"], "tokens": 176}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized.parameterized import parameterized\n\nfrom monai.transforms.croppad.array import RandWeightedCrop\nfrom tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, NumpyImageTestCase3D, assert_allclose\n\n\ndef get_data(ndim):\n    im_gen = NumpyImageTestCase2D() if ndim == 2 else NumpyImageTestCase3D()\n    im_gen.setUp()\n    return im_gen.imt[0], im_gen.seg1[0], im_gen.segn[0]\n\n\nIMT_2D, SEG1_2D, SEGN_2D = get_data(ndim=2)\nIMT_3D, SEG1_3D, SEGN_3D = get_data(ndim=3)\n\n\nTESTS = []", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_weighted_crop.py_for_p_in_TEST_NDARRAYS__for_p_in_TEST_NDARRAYS_.for_q_in_TEST_NDARRAYS_.None_7": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_weighted_crop.py_for_p_in_TEST_NDARRAYS__for_p_in_TEST_NDARRAYS_.for_q_in_TEST_NDARRAYS_.None_7", "embedding": null, "metadata": {"file_path": "tests/test_rand_weighted_crop.py", "file_name": "test_rand_weighted_crop.py", "file_type": "text/x-python", "category": "test", "start_line": 33, "end_line": 148, "span_ids": ["impl"], "tokens": 1101}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "for p in TEST_NDARRAYS:\n    for q in TEST_NDARRAYS:\n        im = SEG1_2D\n        weight = np.zeros_like(im)\n        weight[0, 30, 17] = 1.1\n        weight[0, 40, 31] = 1\n        weight[0, 80, 21] = 1\n        TESTS.append(\n            [\n                \"small roi 2d\",\n                dict(spatial_size=(10, 12), num_samples=3),\n                p(im),\n                q(weight),\n                (1, 10, 12),\n                [[80, 21], [30, 17], [40, 31]],\n            ]\n        )\n        im = IMT_2D\n        TESTS.append(\n            [\n                \"default roi 2d\",\n                dict(spatial_size=(10, -1), num_samples=3),\n                p(im),\n                q(weight),\n                (1, 10, 64),\n                [[14, 32], [105, 32], [20, 32]],\n            ]\n        )\n        im = SEGN_2D\n        weight = np.zeros_like(im)\n        weight[0, 30, 17] = 1.1\n        weight[0, 10, 1] = 1\n        TESTS.append(\n            [\n                \"large roi 2d\",\n                dict(spatial_size=(10000, 400), num_samples=3),\n                p(im),\n                q(weight),\n                (1, 128, 64),\n                [[64, 32], [64, 32], [64, 32]],\n            ]\n        )\n        im = IMT_2D\n        weight = np.zeros_like(im)\n        weight[0, 30, 17] = np.inf\n        weight[0, 10, 1] = -np.inf\n        weight[0, 10, 20] = -np.nan\n        TESTS.append(\n            [\n                \"bad w 2d\",\n                dict(spatial_size=(20, 40), num_samples=3),\n                p(im),\n                q(weight),\n                (1, 20, 40),\n                [[63, 37], [31, 43], [66, 20]],\n            ]\n        )\n        im = SEG1_3D\n        weight = np.zeros_like(im)\n        weight[0, 5, 30, 17] = 1.1\n        weight[0, 8, 40, 31] = 1\n        weight[0, 11, 23, 21] = 1\n        TESTS.append(\n            [\n                \"small roi 3d\",\n                dict(spatial_size=(8, 10, 12), num_samples=3),\n                p(im),\n                q(weight),\n                (1, 8, 10, 12),\n                [[11, 23, 21], [5, 30, 17], [8, 40, 31]],\n            ]\n        )\n        im = IMT_3D\n        weight = np.zeros_like(im)\n        weight[0, 7, 17] = 1.1\n        weight[0, 13, 31] = 1.1\n        weight[0, 24, 21] = 1\n        TESTS.append(\n            [\n                \"default roi 3d\",\n                dict(spatial_size=(10, -1, -1), num_samples=3),\n                p(im),\n                q(weight),\n                (1, 10, 64, 80),\n                [[14, 32, 40], [41, 32, 40], [20, 32, 40]],\n            ]\n        )\n        im = SEGN_3D\n        weight = np.zeros_like(im)\n        weight[0, 30, 17, 20] = 1.1\n        weight[0, 10, 1, 17] = 1\n        TESTS.append(\n            [\n                \"large roi 3d\",\n                dict(spatial_size=(10000, 400, 80), num_samples=3),\n                p(im),\n                q(weight),\n                (1, 48, 64, 80),\n                [[24, 32, 40], [24, 32, 40], [24, 32, 40]],\n            ]\n        )\n        im = IMT_3D\n        weight = np.zeros_like(im)\n        weight[0, 30, 17] = np.inf\n        weight[0, 10, 1] = -np.inf\n        weight[0, 10, 20] = -np.nan\n        TESTS.append(\n            [\n                \"bad w 3d\",\n                dict(spatial_size=(48, 64, 80), num_samples=3),\n                p(im),\n                q(weight),\n                (1, 48, 64, 80),\n                [[24, 32, 40], [24, 32, 40], [24, 32, 40]],\n            ]\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_weighted_crop.py_TestRandWeightedCrop_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_weighted_crop.py_TestRandWeightedCrop_", "embedding": null, "metadata": {"file_path": "tests/test_rand_weighted_crop.py", "file_name": "test_rand_weighted_crop.py", "file_type": "text/x-python", "category": "test", "start_line": 151, "end_line": 172, "span_ids": ["impl:64", "TestRandWeightedCrop", "TestRandWeightedCrop.test_rand_weighted_crop"], "tokens": 212}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandWeightedCrop(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_rand_weighted_crop(self, _, input_params, img, weight, expected_shape, expected_vals):\n        crop = RandWeightedCrop(**input_params)\n        crop.set_random_state(10)\n        result = crop(img, weight)\n        self.assertTrue(len(result) == input_params[\"num_samples\"])\n        assert_allclose(result[0].shape, expected_shape)\n        for c, e in zip(crop.centers, expected_vals):\n            assert_allclose(c, e, type_test=False)\n        # if desired ROI is larger than image, check image is unchanged\n        if all(s >= i for i, s in zip(img.shape[1:], input_params[\"spatial_size\"])):\n            for res in result:\n                self.assertEqual(type(img), type(res))\n                if isinstance(img, torch.Tensor):\n                    self.assertEqual(res.device, img.device)\n                assert_allclose(res, img)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_weighted_cropd.py_unittest_TestRandWeightedCrop.test_rand_weighted_crop_small_roi.for_p_in_TEST_NDARRAYS_.for_q_in_TEST_NDARRAYS_.for_c_e_in_zip_crop_cent.assert_allclose_c_e_typ": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_weighted_cropd.py_unittest_TestRandWeightedCrop.test_rand_weighted_crop_small_roi.for_p_in_TEST_NDARRAYS_.for_q_in_TEST_NDARRAYS_.for_c_e_in_zip_crop_cent.assert_allclose_c_e_typ", "embedding": null, "metadata": {"file_path": "tests/test_rand_weighted_cropd.py", "file_name": "test_rand_weighted_cropd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 37, "span_ids": ["TestRandWeightedCrop.test_rand_weighted_crop_small_roi", "TestRandWeightedCrop", "docstring"], "tokens": 295}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\n\nfrom monai.transforms.croppad.dictionary import RandWeightedCropd\nfrom tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, NumpyImageTestCase3D, assert_allclose\n\n\nclass TestRandWeightedCrop(NumpyImageTestCase2D):\n    def test_rand_weighted_crop_small_roi(self):\n        for p in TEST_NDARRAYS:\n            for q in TEST_NDARRAYS:\n                img = self.seg1[0]\n                n_samples = 3\n                crop = RandWeightedCropd(\"img\", \"w\", (10, 12), n_samples)\n                weight = np.zeros_like(img)\n                weight[0, 30, 17] = 1.1\n                weight[0, 40, 31] = 1\n                weight[0, 80, 21] = 1\n                crop.set_random_state(10)\n                d = {\"img\": p(img), \"w\": q(weight)}\n                result = crop(d)\n                self.assertTrue(len(result) == n_samples)\n                np.testing.assert_allclose(result[0][\"img\"].shape, (1, 10, 12))\n                for c, e in zip(crop.centers, [[80, 21], [30, 17], [40, 31]]):\n                    assert_allclose(c, e, type_test=False)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_weighted_cropd.py_TestRandWeightedCrop.test_rand_weighted_crop_default_roi_TestRandWeightedCrop.test_rand_weighted_crop_default_roi.for_p_in_TEST_NDARRAYS_.for_q_in_TEST_NDARRAYS_.assert_allclose_result_1_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_weighted_cropd.py_TestRandWeightedCrop.test_rand_weighted_crop_default_roi_TestRandWeightedCrop.test_rand_weighted_crop_default_roi.for_p_in_TEST_NDARRAYS_.for_q_in_TEST_NDARRAYS_.assert_allclose_result_1_", "embedding": null, "metadata": {"file_path": "tests/test_rand_weighted_cropd.py", "file_name": "test_rand_weighted_cropd.py", "file_type": "text/x-python", "category": "test", "start_line": 39, "end_line": 56, "span_ids": ["TestRandWeightedCrop.test_rand_weighted_crop_default_roi"], "tokens": 272}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandWeightedCrop(NumpyImageTestCase2D):\n\n    def test_rand_weighted_crop_default_roi(self):\n        for p in TEST_NDARRAYS:\n            for q in TEST_NDARRAYS:\n                img = self.imt[0]\n                n_samples = 3\n                crop = RandWeightedCropd(\"im\", \"weight\", (10, -1), n_samples, \"coords\")\n                weight = np.zeros_like(img)\n                weight[0, 30, 17] = 1.1\n                weight[0, 40, 31] = 1\n                weight[0, 80, 21] = 1\n                crop.set_random_state(10)\n                data = {\"im\": p(img), \"weight\": q(weight), \"others\": np.nan}\n                result = crop(data)\n                self.assertTrue(len(result) == n_samples)\n                np.testing.assert_allclose(result[0][\"im\"].shape, (1, 10, 64))\n                for c, e in zip(crop.centers, [[14, 32], [105, 32], [20, 32]]):\n                    assert_allclose(c, e, type_test=False)\n                assert_allclose(result[1][\"coords\"], [105, 32], type_test=False)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_weighted_cropd.py_TestRandWeightedCrop.test_rand_weighted_crop_large_roi_TestRandWeightedCrop.test_rand_weighted_crop_large_roi.for_p_in_TEST_NDARRAYS_.for_q_in_TEST_NDARRAYS_.assert_allclose_result_1_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_weighted_cropd.py_TestRandWeightedCrop.test_rand_weighted_crop_large_roi_TestRandWeightedCrop.test_rand_weighted_crop_large_roi.for_p_in_TEST_NDARRAYS_.for_q_in_TEST_NDARRAYS_.assert_allclose_result_1_", "embedding": null, "metadata": {"file_path": "tests/test_rand_weighted_cropd.py", "file_name": "test_rand_weighted_cropd.py", "file_type": "text/x-python", "category": "test", "start_line": 58, "end_line": 75, "span_ids": ["TestRandWeightedCrop.test_rand_weighted_crop_large_roi"], "tokens": 288}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandWeightedCrop(NumpyImageTestCase2D):\n\n    def test_rand_weighted_crop_large_roi(self):\n        for p in TEST_NDARRAYS:\n            for q in TEST_NDARRAYS:\n                img = self.segn[0]\n                n_samples = 3\n                crop = RandWeightedCropd((\"img\", \"seg\"), \"weight\", (10000, 400), n_samples, \"location\")\n                weight = np.zeros_like(img)\n                weight[0, 30, 17] = 1.1\n                weight[0, 10, 1] = 1\n                crop.set_random_state(10)\n                data = {\"img\": p(img), \"seg\": p(self.imt[0]), \"weight\": q(weight)}\n                result = crop(data)\n                self.assertTrue(len(result) == n_samples)\n                np.testing.assert_allclose(result[0][\"img\"].shape, (1, 128, 64))\n                np.testing.assert_allclose(result[0][\"seg\"].shape, (1, 128, 64))\n                for c, e in zip(crop.centers, [[64, 32], [64, 32], [64, 32]]):\n                    assert_allclose(c, e, type_test=False)\n                assert_allclose(result[1][\"location\"], [64, 32], type_test=False)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_weighted_cropd.py_TestRandWeightedCrop.test_rand_weighted_crop_bad_w_TestRandWeightedCrop.test_rand_weighted_crop_bad_w.for_p_in_TEST_NDARRAYS_.for_q_in_TEST_NDARRAYS_.for_c_e_in_zip_crop_cent.assert_allclose_c_e_typ": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_weighted_cropd.py_TestRandWeightedCrop.test_rand_weighted_crop_bad_w_TestRandWeightedCrop.test_rand_weighted_crop_bad_w.for_p_in_TEST_NDARRAYS_.for_q_in_TEST_NDARRAYS_.for_c_e_in_zip_crop_cent.assert_allclose_c_e_typ", "embedding": null, "metadata": {"file_path": "tests/test_rand_weighted_cropd.py", "file_name": "test_rand_weighted_cropd.py", "file_type": "text/x-python", "category": "test", "start_line": 77, "end_line": 93, "span_ids": ["TestRandWeightedCrop.test_rand_weighted_crop_bad_w"], "tokens": 274}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandWeightedCrop(NumpyImageTestCase2D):\n\n    def test_rand_weighted_crop_bad_w(self):\n        for p in TEST_NDARRAYS:\n            for q in TEST_NDARRAYS:\n                img = self.imt[0]\n                n_samples = 3\n                crop = RandWeightedCropd((\"img\", \"seg\"), \"w\", (20, 40), n_samples)\n                weight = np.zeros_like(img)\n                weight[0, 30, 17] = np.inf\n                weight[0, 10, 1] = -np.inf\n                weight[0, 10, 20] = -np.nan\n                crop.set_random_state(10)\n                result = crop({\"img\": p(img), \"seg\": p(self.segn[0]), \"w\": q(weight)})\n                self.assertTrue(len(result) == n_samples)\n                np.testing.assert_allclose(result[0][\"img\"].shape, (1, 20, 40))\n                np.testing.assert_allclose(result[0][\"seg\"].shape, (1, 20, 40))\n                for c, e in zip(crop.centers, [[63, 37], [31, 43], [66, 20]]):\n                    assert_allclose(c, e, type_test=False)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_weighted_cropd.py_TestRandWeightedCrop3D_TestRandWeightedCrop3D.test_rand_weighted_crop_small_roi.for_p_in_TEST_NDARRAYS_.for_q_in_TEST_NDARRAYS_.for_c_e_in_zip_crop_cent.assert_allclose_c_e_typ": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_weighted_cropd.py_TestRandWeightedCrop3D_TestRandWeightedCrop3D.test_rand_weighted_crop_small_roi.for_p_in_TEST_NDARRAYS_.for_q_in_TEST_NDARRAYS_.for_c_e_in_zip_crop_cent.assert_allclose_c_e_typ", "embedding": null, "metadata": {"file_path": "tests/test_rand_weighted_cropd.py", "file_name": "test_rand_weighted_cropd.py", "file_type": "text/x-python", "category": "test", "start_line": 96, "end_line": 112, "span_ids": ["TestRandWeightedCrop3D", "TestRandWeightedCrop3D.test_rand_weighted_crop_small_roi"], "tokens": 265}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandWeightedCrop3D(NumpyImageTestCase3D):\n    def test_rand_weighted_crop_small_roi(self):\n        for p in TEST_NDARRAYS:\n            for q in TEST_NDARRAYS:\n                img = self.seg1[0]\n                n_samples = 3\n                crop = RandWeightedCropd(\"img\", \"w\", (8, 10, 12), n_samples)\n                weight = np.zeros_like(img)\n                weight[0, 5, 30, 17] = 1.1\n                weight[0, 8, 40, 31] = 1\n                weight[0, 11, 23, 21] = 1\n                crop.set_random_state(10)\n                result = crop({\"img\": p(img), \"w\": q(weight)})\n                self.assertTrue(len(result) == n_samples)\n                np.testing.assert_allclose(result[0][\"img\"].shape, (1, 8, 10, 12))\n                for c, e in zip(crop.centers, [[11, 23, 21], [5, 30, 17], [8, 40, 31]]):\n                    assert_allclose(c, e, type_test=False)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_weighted_cropd.py_TestRandWeightedCrop3D.test_rand_weighted_crop_default_roi_TestRandWeightedCrop3D.test_rand_weighted_crop_default_roi.for_p_in_TEST_NDARRAYS_.for_q_in_TEST_NDARRAYS_.for_c_e_in_zip_crop_cent.assert_allclose_c_e_typ": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_weighted_cropd.py_TestRandWeightedCrop3D.test_rand_weighted_crop_default_roi_TestRandWeightedCrop3D.test_rand_weighted_crop_default_roi.for_p_in_TEST_NDARRAYS_.for_q_in_TEST_NDARRAYS_.for_c_e_in_zip_crop_cent.assert_allclose_c_e_typ", "embedding": null, "metadata": {"file_path": "tests/test_rand_weighted_cropd.py", "file_name": "test_rand_weighted_cropd.py", "file_type": "text/x-python", "category": "test", "start_line": 114, "end_line": 130, "span_ids": ["TestRandWeightedCrop3D.test_rand_weighted_crop_default_roi"], "tokens": 296}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandWeightedCrop3D(NumpyImageTestCase3D):\n\n    def test_rand_weighted_crop_default_roi(self):\n        for p in TEST_NDARRAYS:\n            for q in TEST_NDARRAYS:\n                img = self.imt[0]\n                n_samples = 3\n                crop = RandWeightedCropd((\"img\", \"seg\"), \"w\", (10, -1, -1), n_samples)\n                weight = np.zeros_like(img)\n                weight[0, 7, 17] = 1.1\n                weight[0, 13, 31] = 1.1\n                weight[0, 24, 21] = 1\n                crop.set_random_state(10)\n                result = crop({\"img\": p(img), \"seg\": p(self.segn[0]), \"w\": q(weight)})\n                self.assertTrue(len(result) == n_samples)\n                np.testing.assert_allclose(result[0][\"img\"].shape, (1, 10, 64, 80))\n                np.testing.assert_allclose(result[0][\"seg\"].shape, (1, 10, 64, 80))\n                for c, e in zip(crop.centers, [[14, 32, 40], [41, 32, 40], [20, 32, 40]]):\n                    assert_allclose(c, e, type_test=False)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_weighted_cropd.py_TestRandWeightedCrop3D.test_rand_weighted_crop_large_roi_TestRandWeightedCrop3D.test_rand_weighted_crop_large_roi.for_p_in_TEST_NDARRAYS_.for_q_in_TEST_NDARRAYS_.for_c_e_in_zip_crop_cent.assert_allclose_c_e_typ": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_weighted_cropd.py_TestRandWeightedCrop3D.test_rand_weighted_crop_large_roi_TestRandWeightedCrop3D.test_rand_weighted_crop_large_roi.for_p_in_TEST_NDARRAYS_.for_q_in_TEST_NDARRAYS_.for_c_e_in_zip_crop_cent.assert_allclose_c_e_typ", "embedding": null, "metadata": {"file_path": "tests/test_rand_weighted_cropd.py", "file_name": "test_rand_weighted_cropd.py", "file_type": "text/x-python", "category": "test", "start_line": 132, "end_line": 146, "span_ids": ["TestRandWeightedCrop3D.test_rand_weighted_crop_large_roi"], "tokens": 247}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandWeightedCrop3D(NumpyImageTestCase3D):\n\n    def test_rand_weighted_crop_large_roi(self):\n        for p in TEST_NDARRAYS:\n            for q in TEST_NDARRAYS:\n                img = self.segn[0]\n                n_samples = 3\n                crop = RandWeightedCropd(\"img\", \"w\", (10000, 400, 80), n_samples)\n                weight = np.zeros_like(img)\n                weight[0, 30, 17, 20] = 1.1\n                weight[0, 10, 1, 17] = 1\n                crop.set_random_state(10)\n                result = crop({\"img\": p(img), \"w\": q(weight)})\n                self.assertTrue(len(result) == n_samples)\n                np.testing.assert_allclose(result[0][\"img\"].shape, (1, 48, 64, 80))\n                for c, e in zip(crop.centers, [[24, 32, 40], [24, 32, 40], [24, 32, 40]]):\n                    assert_allclose(c, e, type_test=False)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_weighted_cropd.py_TestRandWeightedCrop3D.test_rand_weighted_crop_bad_w_TestRandWeightedCrop3D.test_rand_weighted_crop_bad_w.for_p_in_TEST_NDARRAYS_.for_q_in_TEST_NDARRAYS_.for_c_e_in_zip_crop_cent.assert_allclose_c_e_typ": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_weighted_cropd.py_TestRandWeightedCrop3D.test_rand_weighted_crop_bad_w_TestRandWeightedCrop3D.test_rand_weighted_crop_bad_w.for_p_in_TEST_NDARRAYS_.for_q_in_TEST_NDARRAYS_.for_c_e_in_zip_crop_cent.assert_allclose_c_e_typ", "embedding": null, "metadata": {"file_path": "tests/test_rand_weighted_cropd.py", "file_name": "test_rand_weighted_cropd.py", "file_type": "text/x-python", "category": "test", "start_line": 148, "end_line": 164, "span_ids": ["TestRandWeightedCrop3D.test_rand_weighted_crop_bad_w"], "tokens": 294}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandWeightedCrop3D(NumpyImageTestCase3D):\n\n    def test_rand_weighted_crop_bad_w(self):\n        for p in TEST_NDARRAYS:\n            for q in TEST_NDARRAYS:\n                img = self.imt[0]\n                n_samples = 3\n                crop = RandWeightedCropd((\"img\", \"seg\"), \"w\", (48, 64, 80), n_samples)\n                weight = np.zeros_like(img)\n                weight[0, 30, 17] = np.inf\n                weight[0, 10, 1] = -np.inf\n                weight[0, 10, 20] = -np.nan\n                crop.set_random_state(10)\n                result = crop({\"img\": p(img), \"seg\": p(self.segn[0]), \"w\": q(weight)})\n                self.assertTrue(len(result) == n_samples)\n                np.testing.assert_allclose(result[0][\"img\"].shape, (1, 48, 64, 80))\n                np.testing.assert_allclose(result[0][\"seg\"].shape, (1, 48, 64, 80))\n                for c, e in zip(crop.centers, [[24, 32, 40], [24, 32, 40], [24, 32, 40]]):\n                    assert_allclose(c, e, type_test=False)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_zoom.py_unittest_TestRandZoom.test_correct_results.for_p_in_TEST_NDARRAYS_.assert_allclose_zoomed_p": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_zoom.py_unittest_TestRandZoom.test_correct_results.for_p_in_TEST_NDARRAYS_.assert_allclose_zoomed_p", "embedding": null, "metadata": {"file_path": "tests/test_rand_zoom.py", "file_name": "test_rand_zoom.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 38, "span_ids": ["TestRandZoom", "TestRandZoom.test_correct_results", "docstring"], "tokens": 280}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\nfrom scipy.ndimage import zoom as zoom_scipy\n\nfrom monai.transforms import RandZoom\nfrom monai.utils import GridSampleMode, InterpolateMode\nfrom tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, assert_allclose\n\nVALID_CASES = [(0.8, 1.2, \"nearest\", False), (0.8, 1.2, InterpolateMode.NEAREST, False)]\n\n\nclass TestRandZoom(NumpyImageTestCase2D):\n    @parameterized.expand(VALID_CASES)\n    def test_correct_results(self, min_zoom, max_zoom, mode, keep_size):\n        for p in TEST_NDARRAYS:\n            random_zoom = RandZoom(prob=1.0, min_zoom=min_zoom, max_zoom=max_zoom, mode=mode, keep_size=keep_size)\n            random_zoom.set_random_state(1234)\n            zoomed = random_zoom(p(self.imt[0]))\n            expected = [\n                zoom_scipy(channel, zoom=random_zoom._zoom, mode=\"nearest\", order=0, prefilter=False)\n                for channel in self.imt[0]\n            ]\n\n            expected = np.stack(expected).astype(np.float32)\n            assert_allclose(zoomed, p(expected), atol=1.0)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_zoom.py_TestRandZoom.test_keep_size_TestRandZoom.test_keep_size.for_p_in_TEST_NDARRAYS_.None_2": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_zoom.py_TestRandZoom.test_keep_size_TestRandZoom.test_keep_size.for_p_in_TEST_NDARRAYS_.None_2", "embedding": null, "metadata": {"file_path": "tests/test_rand_zoom.py", "file_name": "test_rand_zoom.py", "file_type": "text/x-python", "category": "test", "start_line": 40, "end_line": 49, "span_ids": ["TestRandZoom.test_keep_size"], "tokens": 149}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandZoom(NumpyImageTestCase2D):\n\n    def test_keep_size(self):\n        for p in TEST_NDARRAYS:\n            im = p(self.imt[0])\n            random_zoom = RandZoom(prob=1.0, min_zoom=0.6, max_zoom=0.7, keep_size=True)\n            zoomed = random_zoom(im)\n            self.assertTrue(np.array_equal(zoomed.shape, self.imt.shape[1:]))\n            zoomed = random_zoom(im)\n            self.assertTrue(np.array_equal(zoomed.shape, self.imt.shape[1:]))\n            zoomed = random_zoom(im)\n            self.assertTrue(np.array_equal(zoomed.shape, self.imt.shape[1:]))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_zoom.py_TestRandZoom.test_invalid_inputs_TestRandZoom.test_invalid_inputs.for_p_in_TEST_NDARRAYS_.with_self_assertRaises_ra.random_zoom_p_self_imt_0_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_zoom.py_TestRandZoom.test_invalid_inputs_TestRandZoom.test_invalid_inputs.for_p_in_TEST_NDARRAYS_.with_self_assertRaises_ra.random_zoom_p_self_imt_0_", "embedding": null, "metadata": {"file_path": "tests/test_rand_zoom.py", "file_name": "test_rand_zoom.py", "file_type": "text/x-python", "category": "test", "start_line": 51, "end_line": 62, "span_ids": ["TestRandZoom.test_invalid_inputs"], "tokens": 157}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandZoom(NumpyImageTestCase2D):\n\n    @parameterized.expand(\n        [\n            (\"no_min_zoom\", None, 1.1, \"bilinear\", TypeError),\n            (\"invalid_mode\", 0.9, 1.1, \"s\", ValueError),\n            (\"invalid_mode\", 0.9, 1.1, GridSampleMode.NEAREST, ValueError),\n        ]\n    )\n    def test_invalid_inputs(self, _, min_zoom, max_zoom, mode, raises):\n        for p in TEST_NDARRAYS:\n            with self.assertRaises(raises):\n                random_zoom = RandZoom(prob=1.0, min_zoom=min_zoom, max_zoom=max_zoom, mode=mode)\n                random_zoom(p(self.imt[0]))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_zoomd.py_unittest_TestRandZoomd.test_correct_results.for_p_in_TEST_NDARRAYS_.assert_allclose_zoomed_ke": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_zoomd.py_unittest_TestRandZoomd.test_correct_results.for_p_in_TEST_NDARRAYS_.assert_allclose_zoomed_ke", "embedding": null, "metadata": {"file_path": "tests/test_rand_zoomd.py", "file_name": "test_rand_zoomd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 47, "span_ids": ["TestRandZoomd", "TestRandZoomd.test_correct_results", "docstring"], "tokens": 287}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\nfrom scipy.ndimage import zoom as zoom_scipy\n\nfrom monai.transforms import RandZoomd\nfrom tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, assert_allclose\n\nVALID_CASES = [(0.8, 1.2, \"nearest\", None, False)]\n\n\nclass TestRandZoomd(NumpyImageTestCase2D):\n    @parameterized.expand(VALID_CASES)\n    def test_correct_results(self, min_zoom, max_zoom, mode, align_corners, keep_size):\n        key = \"img\"\n        random_zoom = RandZoomd(\n            key,\n            prob=1.0,\n            min_zoom=min_zoom,\n            max_zoom=max_zoom,\n            mode=mode,\n            align_corners=align_corners,\n            keep_size=keep_size,\n        )\n        for p in TEST_NDARRAYS:\n            random_zoom.set_random_state(1234)\n\n            zoomed = random_zoom({key: p(self.imt[0])})\n            expected = [\n                zoom_scipy(channel, zoom=random_zoom.rand_zoom._zoom, mode=\"nearest\", order=0, prefilter=False)\n                for channel in self.imt[0]\n            ]\n\n            expected = np.stack(expected).astype(np.float32)\n            assert_allclose(zoomed[key], p(expected), atol=1.0)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_zoomd.py_TestRandZoomd.test_keep_size_TestRandZoomd.test_keep_size.for_p_in_TEST_NDARRAYS_.np_testing_assert_array_e": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_zoomd.py_TestRandZoomd.test_keep_size_TestRandZoomd.test_keep_size.for_p_in_TEST_NDARRAYS_.np_testing_assert_array_e", "embedding": null, "metadata": {"file_path": "tests/test_rand_zoomd.py", "file_name": "test_rand_zoomd.py", "file_type": "text/x-python", "category": "test", "start_line": 49, "end_line": 56, "span_ids": ["TestRandZoomd.test_keep_size"], "tokens": 120}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandZoomd(NumpyImageTestCase2D):\n\n    def test_keep_size(self):\n        key = \"img\"\n        random_zoom = RandZoomd(\n            keys=key, prob=1.0, min_zoom=0.6, max_zoom=0.7, keep_size=True, padding_mode=\"constant\", constant_values=2\n        )\n        for p in TEST_NDARRAYS:\n            zoomed = random_zoom({key: p(self.imt[0])})\n            np.testing.assert_array_equal(zoomed[key].shape, self.imt.shape[1:])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_zoomd.py_TestRandZoomd.test_invalid_inputs_TestRandZoomd.test_invalid_inputs.for_p_in_TEST_NDARRAYS_.with_self_assertRaises_ra.random_zoom_key_p_self_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rand_zoomd.py_TestRandZoomd.test_invalid_inputs_TestRandZoomd.test_invalid_inputs.for_p_in_TEST_NDARRAYS_.with_self_assertRaises_ra.random_zoom_key_p_self_", "embedding": null, "metadata": {"file_path": "tests/test_rand_zoomd.py", "file_name": "test_rand_zoomd.py", "file_type": "text/x-python", "category": "test", "start_line": 58, "end_line": 66, "span_ids": ["TestRandZoomd.test_invalid_inputs"], "tokens": 142}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRandZoomd(NumpyImageTestCase2D):\n\n    @parameterized.expand(\n        [(\"no_min_zoom\", None, 1.1, \"bilinear\", TypeError), (\"invalid_order\", 0.9, 1.1, \"s\", ValueError)]\n    )\n    def test_invalid_inputs(self, _, min_zoom, max_zoom, mode, raises):\n        key = \"img\"\n        for p in TEST_NDARRAYS:\n            with self.assertRaises(raises):\n                random_zoom = RandZoomd(key, prob=1.0, min_zoom=min_zoom, max_zoom=max_zoom, mode=mode)\n                random_zoom({key: p(self.imt[0])})", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_resample_datalist.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_resample_datalist.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_resample_datalist.py", "file_name": "test_resample_datalist.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 41, "span_ids": ["TestResampleDatalist.test_value_shape", "TestResampleDatalist", "impl:7", "docstring"], "tokens": 328}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.data import resample_datalist\n\nTEST_CASE_1 = [\n    {\"data\": [1, 2, 3, 4, 5], \"factor\": 2.5, \"random_pick\": True, \"seed\": 123},\n    [1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 2, 4, 5],\n]\n\nTEST_CASE_2 = [\n    {\"data\": [1, 2, 3, 4, 5], \"factor\": 2.5, \"random_pick\": False, \"seed\": 0},\n    [1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3],\n]\n\nTEST_CASE_3 = [{\"data\": [1, 2, 3, 4, 5], \"factor\": 0.6, \"random_pick\": True, \"seed\": 123}, [2, 4, 5]]\n\n\nclass TestResampleDatalist(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3])\n    def test_value_shape(self, input_param, expected):\n        result = resample_datalist(**input_param)\n        np.testing.assert_allclose(result, expected)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_resampler.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_resampler.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_resampler.py", "file_name": "test_resampler.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 164, "span_ids": ["TestResample.test_resample", "TestResample", "impl:12", "docstring"], "tokens": 1612}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import Resample\nfrom monai.transforms.utils import create_grid\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    for q in TEST_NDARRAYS:\n        for device in [None, \"cpu\", \"cuda\"] if torch.cuda.is_available() else [None, \"cpu\"]:\n            TESTS.append(\n                [\n                    dict(padding_mode=\"zeros\", device=device),\n                    {\"grid\": p(create_grid((2, 2))), \"img\": q(np.arange(4).reshape((1, 2, 2)))},\n                    q(np.array([[[0.0, 1.0], [2.0, 3.0]]])),\n                ]\n            )\n            TESTS.append(\n                [\n                    dict(padding_mode=\"zeros\", device=device),\n                    {\"grid\": p(create_grid((4, 4))), \"img\": q(np.arange(4).reshape((1, 2, 2)))},\n                    q(\n                        np.array(\n                            [[[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], [0.0, 2.0, 3.0, 0.0], [0.0, 0.0, 0.0, 0.0]]]\n                        )\n                    ),\n                ]\n            )\n            TESTS.append(\n                [\n                    dict(padding_mode=\"border\", device=device),\n                    {\"grid\": p(create_grid((4, 4))), \"img\": q(np.arange(4).reshape((1, 2, 2)))},\n                    q(\n                        np.array(\n                            [[[0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0], [2.0, 2.0, 3, 3.0], [2.0, 2.0, 3.0, 3.0]]]\n                        )\n                    ),\n                ]\n            )\n            TESTS.append(\n                [\n                    dict(padding_mode=\"reflection\", device=device),\n                    {\"grid\": p(create_grid((4, 4))), \"img\": q(np.arange(4).reshape((1, 2, 2))), \"mode\": \"nearest\"},\n                    q(\n                        np.array(\n                            [[[3.0, 2.0, 3.0, 2.0], [1.0, 0.0, 1.0, 0.0], [3.0, 2.0, 3.0, 2.0], [1.0, 0.0, 1.0, 0.0]]]\n                        )\n                    ),\n                ]\n            )\n            TESTS.append(\n                [\n                    dict(padding_mode=\"zeros\", device=device),\n                    {\n                        \"grid\": p(create_grid((4, 4, 4))),\n                        \"img\": q(np.arange(8).reshape((1, 2, 2, 2))),\n                        \"mode\": \"bilinear\",\n                    },\n                    q(\n                        np.array(\n                            [\n                                [\n                                    [\n                                        [0.0, 0.0, 0.0, 0.0],\n                                        [0.0, 0.0, 0.0, 0.0],\n                                        [0.0, 0.0, 0.0, 0.0],\n                                        [0.0, 0.0, 0.0, 0.0],\n                                    ],\n                                    [\n                                        [0.0, 0.0, 0.0, 0.0],\n                                        [0.0, 0.0, 1.0, 0.0],\n                                        [0.0, 2.0, 3.0, 0.0],\n                                        [0.0, 0.0, 0.0, 0.0],\n                                    ],\n                                    [\n                                        [0.0, 0.0, 0.0, 0.0],\n                                        [0.0, 4.0, 5.0, 0.0],\n                                        [0.0, 6.0, 7.0, 0.0],\n                                        [0.0, 0.0, 0.0, 0.0],\n                                    ],\n                                    [\n                                        [0.0, 0.0, 0.0, 0.0],\n                                        [0.0, 0.0, 0.0, 0.0],\n                                        [0.0, 0.0, 0.0, 0.0],\n                                        [0.0, 0.0, 0.0, 0.0],\n                                    ],\n                                ]\n                            ]\n                        )\n                    ),\n                ]\n            )\n            TESTS.append(\n                [\n                    dict(padding_mode=\"border\", device=device),\n                    {\n                        \"grid\": p(create_grid((4, 4, 4))),\n                        \"img\": q(np.arange(8).reshape((1, 2, 2, 2))),\n                        \"mode\": \"bilinear\",\n                    },\n                    q(\n                        np.array(\n                            [\n                                [\n                                    [\n                                        [0.0, 0.0, 1.0, 1.0],\n                                        [0.0, 0.0, 1.0, 1.0],\n                                        [2.0, 2.0, 3.0, 3.0],\n                                        [2.0, 2.0, 3.0, 3.0],\n                                    ],\n                                    [\n                                        [0.0, 0.0, 1.0, 1.0],\n                                        [0.0, 0.0, 1.0, 1.0],\n                                        [2.0, 2.0, 3.0, 3.0],\n                                        [2.0, 2.0, 3.0, 3.0],\n                                    ],\n                                    [\n                                        [4.0, 4.0, 5.0, 5.0],\n                                        [4.0, 4.0, 5.0, 5.0],\n                                        [6.0, 6.0, 7.0, 7.0],\n                                        [6.0, 6.0, 7.0, 7.0],\n                                    ],\n                                    [\n                                        [4.0, 4.0, 5.0, 5.0],\n                                        [4.0, 4.0, 5.0, 5.0],\n                                        [6.0, 6.0, 7.0, 7.0],\n                                        [6.0, 6.0, 7.0, 7.0],\n                                    ],\n                                ]\n                            ]\n                        )\n                    ),\n                ]\n            )\n\n\nclass TestResample(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_resample(self, input_param, input_data, expected_val):\n        g = Resample(**input_param)\n        result = g(**input_data)\n        if \"device\" in input_data:\n            self.assertEqual(result.device, input_data[\"device\"])\n        assert_allclose(result, expected_val, rtol=1e-4, atol=1e-4)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_resize.py_unittest_TEST_CASE_2._spatial_size_6_mod": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_resize.py_unittest_TEST_CASE_2._spatial_size_6_mod", "embedding": null, "metadata": {"file_path": "tests/test_resize.py", "file_name": "test_resize.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 25, "span_ids": ["docstring"], "tokens": 132}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport skimage.transform\nfrom parameterized import parameterized\n\nfrom monai.transforms import Resize\nfrom tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, assert_allclose\n\nTEST_CASE_0 = [{\"spatial_size\": 15}, (6, 10, 15)]\n\nTEST_CASE_1 = [{\"spatial_size\": 15, \"mode\": \"area\"}, (6, 10, 15)]\n\nTEST_CASE_2 = [{\"spatial_size\": 6, \"mode\": \"trilinear\", \"align_corners\": True}, (2, 4, 6)]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_resize.py_TestResize_TestResize.test_correct_results.for_p_in_TEST_NDARRAYS_.assert_allclose_out_expe": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_resize.py_TestResize_TestResize.test_correct_results.for_p_in_TEST_NDARRAYS_.assert_allclose_out_expe", "embedding": null, "metadata": {"file_path": "tests/test_resize.py", "file_name": "test_resize.py", "file_type": "text/x-python", "category": "test", "start_line": 28, "end_line": 58, "span_ids": ["TestResize", "TestResize.test_correct_results", "TestResize.test_invalid_inputs"], "tokens": 292}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestResize(NumpyImageTestCase2D):\n    def test_invalid_inputs(self):\n        with self.assertRaises(ValueError):\n            resize = Resize(spatial_size=(128, 128, 3), mode=\"order\")\n            resize(self.imt[0])\n\n        with self.assertRaises(ValueError):\n            resize = Resize(spatial_size=(128,), mode=\"order\")\n            resize(self.imt[0])\n\n    @parameterized.expand(\n        [((32, -1), \"area\"), ((32, 32), \"area\"), ((32, 32, 32), \"trilinear\"), ((256, 256), \"bilinear\")]\n    )\n    def test_correct_results(self, spatial_size, mode):\n        resize = Resize(spatial_size, mode=mode)\n        _order = 0\n        if mode.endswith(\"linear\"):\n            _order = 1\n        if spatial_size == (32, -1):\n            spatial_size = (32, 64)\n        expected = [\n            skimage.transform.resize(\n                channel, spatial_size, order=_order, clip=False, preserve_range=False, anti_aliasing=False\n            )\n            for channel in self.imt[0]\n        ]\n\n        expected = np.stack(expected).astype(np.float32)\n        for p in TEST_NDARRAYS:\n            out = resize(p(self.imt[0]))\n            assert_allclose(out, expected, type_test=False, atol=0.9)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_resize.py_TestResize.test_longest_shape_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_resize.py_TestResize.test_longest_shape_", "embedding": null, "metadata": {"file_path": "tests/test_resize.py", "file_name": "test_resize.py", "file_type": "text/x-python", "category": "test", "start_line": 60, "end_line": 75, "span_ids": ["TestResize.test_longest_infinite_decimals", "TestResize.test_longest_shape", "impl:7"], "tokens": 200}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestResize(NumpyImageTestCase2D):\n\n    @parameterized.expand([TEST_CASE_0, TEST_CASE_1, TEST_CASE_2])\n    def test_longest_shape(self, input_param, expected_shape):\n        input_data = np.random.randint(0, 2, size=[3, 4, 7, 10])\n        input_param[\"size_mode\"] = \"longest\"\n        result = Resize(**input_param)(input_data)\n        np.testing.assert_allclose(result.shape[1:], expected_shape)\n\n    def test_longest_infinite_decimals(self):\n        resize = Resize(spatial_size=1008, size_mode=\"longest\", mode=\"bilinear\", align_corners=False)\n        ret = resize(np.random.randint(0, 2, size=[1, 2544, 3032]))\n        self.assertTupleEqual(ret.shape, (1, 846, 1008))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_resize_with_pad_or_crop.py_unittest_TEST_CASES._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_resize_with_pad_or_crop.py_unittest_TEST_CASES._", "embedding": null, "metadata": {"file_path": "tests/test_resize_with_pad_or_crop.py", "file_name": "test_resize_with_pad_or_crop.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 31, "span_ids": ["docstring"], "tokens": 289}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import ResizeWithPadOrCrop\nfrom tests.utils import TEST_NDARRAYS\n\nTEST_CASES = [\n    [{\"spatial_size\": [15, 8, 8], \"mode\": \"constant\"}, (3, 8, 8, 4), (3, 15, 8, 8)],\n    [\n        {\"spatial_size\": [15, 4, 8], \"mode\": \"constant\", \"method\": \"end\", \"constant_values\": 1},\n        (3, 8, 8, 4),\n        (3, 15, 4, 8),\n    ],\n    [{\"spatial_size\": [15, 4, -1], \"mode\": \"constant\"}, (3, 8, 8, 4), (3, 15, 4, 4)],\n    [{\"spatial_size\": [15, 4, -1], \"mode\": \"reflect\"}, (3, 8, 8, 4), (3, 15, 4, 4)],\n    [{\"spatial_size\": [-1, -1, -1], \"mode\": \"reflect\"}, (3, 8, 8, 4), (3, 8, 8, 4)],\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_resize_with_pad_or_crop.py_TestResizeWithPadOrCrop_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_resize_with_pad_or_crop.py_TestResizeWithPadOrCrop_", "embedding": null, "metadata": {"file_path": "tests/test_resize_with_pad_or_crop.py", "file_name": "test_resize_with_pad_or_crop.py", "file_type": "text/x-python", "category": "test", "start_line": 34, "end_line": 51, "span_ids": ["impl:3", "TestResizeWithPadOrCrop", "TestResizeWithPadOrCrop.test_pad_shape"], "tokens": 158}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestResizeWithPadOrCrop(unittest.TestCase):\n    @parameterized.expand(TEST_CASES)\n    def test_pad_shape(self, input_param, input_shape, expected_shape):\n        for p in TEST_NDARRAYS:\n            if isinstance(p(0), torch.Tensor) and (\n                \"constant_values\" in input_param or input_param[\"mode\"] == \"reflect\"\n            ):\n                continue\n            paddcroper = ResizeWithPadOrCrop(**input_param)\n            result = paddcroper(p(np.zeros(input_shape)))\n            np.testing.assert_allclose(result.shape, expected_shape)\n            result = paddcroper(p(np.zeros(input_shape)), mode=\"constant\")\n            np.testing.assert_allclose(result.shape, expected_shape)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_resize_with_pad_or_cropd.py_unittest_TEST_CASES._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_resize_with_pad_or_cropd.py_unittest_TEST_CASES._", "embedding": null, "metadata": {"file_path": "tests/test_resize_with_pad_or_cropd.py", "file_name": "test_resize_with_pad_or_cropd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 31, "span_ids": ["docstring"], "tokens": 350}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import ResizeWithPadOrCropd\nfrom tests.utils import TEST_NDARRAYS\n\nTEST_CASES = [\n    [{\"keys\": \"img\", \"spatial_size\": [15, 8, 8], \"mode\": \"constant\"}, {\"img\": np.zeros((3, 8, 8, 4))}, (3, 15, 8, 8)],\n    [\n        {\"keys\": \"img\", \"spatial_size\": [15, 4, 8], \"mode\": \"constant\", \"method\": \"end\", \"constant_values\": 1},\n        {\"img\": np.zeros((3, 8, 8, 4))},\n        (3, 15, 4, 8),\n    ],\n    [{\"keys\": \"img\", \"spatial_size\": [15, 4, -1], \"mode\": \"constant\"}, {\"img\": np.zeros((3, 8, 8, 4))}, (3, 15, 4, 4)],\n    [{\"keys\": \"img\", \"spatial_size\": [15, 4, -1], \"mode\": \"reflect\"}, {\"img\": np.zeros((3, 8, 8, 4))}, (3, 15, 4, 4)],\n    [{\"keys\": \"img\", \"spatial_size\": [-1, -1, -1], \"mode\": \"reflect\"}, {\"img\": np.zeros((3, 8, 8, 4))}, (3, 8, 8, 4)],\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_resize_with_pad_or_cropd.py_TestResizeWithPadOrCropd_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_resize_with_pad_or_cropd.py_TestResizeWithPadOrCropd_", "embedding": null, "metadata": {"file_path": "tests/test_resize_with_pad_or_cropd.py", "file_name": "test_resize_with_pad_or_cropd.py", "file_type": "text/x-python", "category": "test", "start_line": 34, "end_line": 50, "span_ids": ["TestResizeWithPadOrCropd.test_pad_shape", "impl:3", "TestResizeWithPadOrCropd"], "tokens": 145}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestResizeWithPadOrCropd(unittest.TestCase):\n    @parameterized.expand(TEST_CASES)\n    def test_pad_shape(self, input_param, input_data, expected_val):\n        for p in TEST_NDARRAYS:\n            if isinstance(p(0), torch.Tensor) and (\n                \"constant_values\" in input_param or input_param[\"mode\"] == \"reflect\"\n            ):\n                continue\n            paddcroper = ResizeWithPadOrCropd(**input_param)\n            input_data[\"img\"] = p(input_data[\"img\"])\n            result = paddcroper(input_data)\n            np.testing.assert_allclose(result[\"img\"].shape, expected_val)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_resized.py_unittest_TEST_CASE_3._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_resized.py_unittest_TEST_CASE_3._", "embedding": null, "metadata": {"file_path": "tests/test_resized.py", "file_name": "test_resized.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 30, "span_ids": ["docstring"], "tokens": 205}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport skimage.transform\nfrom parameterized import parameterized\n\nfrom monai.transforms import Resized\nfrom tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, assert_allclose\n\nTEST_CASE_0 = [{\"keys\": \"img\", \"spatial_size\": 15}, (6, 10, 15)]\n\nTEST_CASE_1 = [{\"keys\": \"img\", \"spatial_size\": 15, \"mode\": \"area\"}, (6, 10, 15)]\n\nTEST_CASE_2 = [{\"keys\": \"img\", \"spatial_size\": 6, \"mode\": \"trilinear\", \"align_corners\": True}, (2, 4, 6)]\n\nTEST_CASE_3 = [\n    {\"keys\": [\"img\", \"label\"], \"spatial_size\": 6, \"mode\": [\"trilinear\", \"nearest\"], \"align_corners\": [True, None]},\n    (2, 4, 6),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_resized.py_TestResized_TestResized.test_correct_results.for_p_in_TEST_NDARRAYS_.assert_allclose_out_expe": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_resized.py_TestResized_TestResized.test_correct_results.for_p_in_TEST_NDARRAYS_.assert_allclose_out_expe", "embedding": null, "metadata": {"file_path": "tests/test_resized.py", "file_name": "test_resized.py", "file_type": "text/x-python", "category": "test", "start_line": 33, "end_line": 61, "span_ids": ["TestResized", "TestResized.test_correct_results", "TestResized.test_invalid_inputs"], "tokens": 315}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestResized(NumpyImageTestCase2D):\n    def test_invalid_inputs(self):\n        with self.assertRaises(ValueError):\n            resize = Resized(keys=\"img\", spatial_size=(128, 128, 3), mode=\"order\")\n            resize({\"img\": self.imt[0]})\n\n        with self.assertRaises(ValueError):\n            resize = Resized(keys=\"img\", spatial_size=(128,), mode=\"order\")\n            resize({\"img\": self.imt[0]})\n\n    @parameterized.expand([((32, -1), \"area\"), ((64, 64), \"area\"), ((32, 32, 32), \"area\"), ((256, 256), \"bilinear\")])\n    def test_correct_results(self, spatial_size, mode):\n        resize = Resized(\"img\", spatial_size, mode=mode)\n        _order = 0\n        if mode.endswith(\"linear\"):\n            _order = 1\n        if spatial_size == (32, -1):\n            spatial_size = (32, 64)\n        expected = [\n            skimage.transform.resize(\n                channel, spatial_size, order=_order, clip=False, preserve_range=False, anti_aliasing=False\n            )\n            for channel in self.imt[0]\n        ]\n\n        expected = np.stack(expected).astype(np.float32)\n        for p in TEST_NDARRAYS:\n            out = resize({\"img\": p(self.imt[0])})[\"img\"]\n            assert_allclose(out, expected, type_test=False, atol=0.9)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_resized.py_TestResized.test_longest_shape_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_resized.py_TestResized.test_longest_shape_", "embedding": null, "metadata": {"file_path": "tests/test_resized.py", "file_name": "test_resized.py", "file_type": "text/x-python", "category": "test", "start_line": 63, "end_line": 78, "span_ids": ["TestResized.test_longest_shape", "impl:9"], "tokens": 176}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestResized(NumpyImageTestCase2D):\n\n    @parameterized.expand([TEST_CASE_0, TEST_CASE_1, TEST_CASE_2, TEST_CASE_3])\n    def test_longest_shape(self, input_param, expected_shape):\n        input_data = {\n            \"img\": np.random.randint(0, 2, size=[3, 4, 7, 10]),\n            \"label\": np.random.randint(0, 2, size=[3, 4, 7, 10]),\n        }\n        input_param[\"size_mode\"] = \"longest\"\n        rescaler = Resized(**input_param)\n        result = rescaler(input_data)\n        for k in rescaler.keys:\n            np.testing.assert_allclose(result[k].shape[1:], expected_shape)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rotate.py_unittest_None_2.None_2": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rotate.py_unittest_None_2.None_2", "embedding": null, "metadata": {"file_path": "tests/test_rotate.py", "file_name": "test_rotate.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 43, "span_ids": ["impl:19", "docstring"], "tokens": 512}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom typing import List, Tuple\n\nimport numpy as np\nimport scipy.ndimage\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import Rotate\nfrom tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, NumpyImageTestCase3D\n\nTEST_CASES_2D: List[Tuple] = []\nfor p in TEST_NDARRAYS:\n    TEST_CASES_2D.append((p, np.pi / 6, False, \"bilinear\", \"border\", False))\n    TEST_CASES_2D.append((p, np.pi / 4, True, \"bilinear\", \"border\", False))\n    TEST_CASES_2D.append((p, -np.pi / 4.5, True, \"nearest\", \"reflection\", False))\n    TEST_CASES_2D.append((p, np.pi, False, \"nearest\", \"zeros\", False))\n    TEST_CASES_2D.append((p, -np.pi / 2, False, \"bilinear\", \"zeros\", True))\n\nTEST_CASES_3D: List[Tuple] = []\nfor p in TEST_NDARRAYS:\n    TEST_CASES_3D.append((p, -np.pi / 2, True, \"nearest\", \"border\", False))\n    TEST_CASES_3D.append((p, np.pi / 4, True, \"bilinear\", \"border\", False))\n    TEST_CASES_3D.append((p, -np.pi / 4.5, True, \"nearest\", \"reflection\", False))\n    TEST_CASES_3D.append((p, np.pi, False, \"nearest\", \"zeros\", False))\n    TEST_CASES_3D.append((p, -np.pi / 2, False, \"bilinear\", \"zeros\", False))\n\nTEST_CASES_SHAPE_3D: List[Tuple] = []\nfor p in TEST_NDARRAYS:\n    TEST_CASES_SHAPE_3D.append((p, [-np.pi / 2, 1.0, 2.0], \"nearest\", \"border\", False))\n    TEST_CASES_SHAPE_3D.append((p, [np.pi / 4, 0, 0], \"bilinear\", \"border\", False))\n    TEST_CASES_SHAPE_3D.append((p, [-np.pi / 4.5, -20, 20], \"nearest\", \"reflection\", False))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rotate90.py_unittest_TestRotate90.test_spatial_axes.for_p_in_TEST_NDARRAYS_.assert_allclose_rotated_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rotate90.py_unittest_TestRotate90.test_spatial_axes.for_p_in_TEST_NDARRAYS_.assert_allclose_rotated_", "embedding": null, "metadata": {"file_path": "tests/test_rotate90.py", "file_name": "test_rotate90.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 43, "span_ids": ["TestRotate90.test_k", "TestRotate90.test_rotate90_default", "TestRotate90.test_spatial_axes", "TestRotate90", "docstring"], "tokens": 351}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\n\nfrom monai.transforms import Rotate90\nfrom tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, assert_allclose\n\n\nclass TestRotate90(NumpyImageTestCase2D):\n    def test_rotate90_default(self):\n        rotate = Rotate90()\n        for p in TEST_NDARRAYS:\n            rotated = rotate(p(self.imt[0]))\n            expected = [np.rot90(channel, 1, (0, 1)) for channel in self.imt[0]]\n            expected = np.stack(expected)\n            assert_allclose(rotated, p(expected), rtol=1.0e-5, atol=1.0e-8)\n\n    def test_k(self):\n        rotate = Rotate90(k=2)\n        for p in TEST_NDARRAYS:\n            rotated = rotate(p(self.imt[0]))\n            expected = [np.rot90(channel, 2, (0, 1)) for channel in self.imt[0]]\n            expected = np.stack(expected)\n            assert_allclose(rotated, p(expected), rtol=1.0e-5, atol=1.0e-8)\n\n    def test_spatial_axes(self):\n        rotate = Rotate90(spatial_axes=(0, -1))\n        for p in TEST_NDARRAYS:\n            rotated = rotate(p(self.imt[0]))\n            expected = [np.rot90(channel, 1, (0, -1)) for channel in self.imt[0]]\n            expected = np.stack(expected)\n            assert_allclose(rotated, p(expected), rtol=1.0e-5, atol=1.0e-8)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rotate90.py_TestRotate90.test_prob_k_spatial_axes_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rotate90.py_TestRotate90.test_prob_k_spatial_axes_", "embedding": null, "metadata": {"file_path": "tests/test_rotate90.py", "file_name": "test_rotate90.py", "file_type": "text/x-python", "category": "test", "start_line": 45, "end_line": 56, "span_ids": ["TestRotate90.test_prob_k_spatial_axes", "impl"], "tokens": 134}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestRotate90(NumpyImageTestCase2D):\n\n    def test_prob_k_spatial_axes(self):\n        rotate = Rotate90(k=2, spatial_axes=(0, 1))\n        for p in TEST_NDARRAYS:\n            rotated = rotate(p(self.imt[0]))\n            expected = [np.rot90(channel, 2, (0, 1)) for channel in self.imt[0]]\n            expected = np.stack(expected)\n            assert_allclose(rotated, p(expected), rtol=1.0e-5, atol=1.0e-8)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rotated.py_unittest_None_1.None_4": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_rotated.py_unittest_None_1.None_4", "embedding": null, "metadata": {"file_path": "tests/test_rotated.py", "file_name": "test_rotated.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 37, "span_ids": ["docstring"], "tokens": 386}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom typing import List, Tuple\n\nimport numpy as np\nimport scipy.ndimage\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import Rotated\nfrom tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, NumpyImageTestCase3D\n\nTEST_CASES_2D: List[Tuple] = []\nfor p in TEST_NDARRAYS:\n    TEST_CASES_2D.append((p, -np.pi / 6, False, \"bilinear\", \"border\", False))\n    TEST_CASES_2D.append((p, -np.pi / 4, True, \"bilinear\", \"border\", False))\n    TEST_CASES_2D.append((p, np.pi / 4.5, True, \"nearest\", \"reflection\", False))\n    TEST_CASES_2D.append((p, -np.pi, False, \"nearest\", \"zeros\", False))\n    TEST_CASES_2D.append((p, np.pi / 2, False, \"bilinear\", \"zeros\", True))\n\nTEST_CASES_3D: List[Tuple] = []\nfor p in TEST_NDARRAYS:\n    TEST_CASES_3D.append((p, -np.pi / 6, False, \"bilinear\", \"border\", False))\n    TEST_CASES_3D.append((p, -np.pi / 4, True, \"bilinear\", \"border\", False))\n    TEST_CASES_3D.append((p, np.pi / 4.5, True, \"nearest\", \"reflection\", False))\n    TEST_CASES_3D.append((p, -np.pi, False, \"nearest\", \"zeros\", False))\n    TEST_CASES_3D.append((p, np.pi / 2, False, \"bilinear\", \"zeros\", True))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_save_image.py_os_TEST_CASE_2._torch_randint_0_255_1": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_save_image.py_os_TEST_CASE_2._torch_randint_0_255_1", "embedding": null, "metadata": {"file_path": "tests/test_save_image.py", "file_name": "test_save_image.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 23, "span_ids": ["docstring"], "tokens": 107}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport tempfile\nimport unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import SaveImage\n\nTEST_CASE_1 = [torch.randint(0, 255, (1, 2, 3, 4)), {\"filename_or_obj\": \"testfile0.nii.gz\"}, \".nii.gz\", False]\n\nTEST_CASE_2 = [torch.randint(0, 255, (1, 2, 3, 4)), None, \".nii.gz\", False]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_save_imaged.py_os_TEST_CASE_2._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_save_imaged.py_os_TEST_CASE_2._", "embedding": null, "metadata": {"file_path": "tests/test_save_imaged.py", "file_name": "test_save_imaged.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 35, "span_ids": ["docstring"], "tokens": 155}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport tempfile\nimport unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import SaveImaged\n\nTEST_CASE_1 = [\n    {\"img\": torch.randint(0, 255, (1, 2, 3, 4)), \"img_meta_dict\": {\"filename_or_obj\": \"testfile0.nii.gz\"}},\n    \".nii.gz\",\n    False,\n]\n\nTEST_CASE_2 = [\n    {\n        \"img\": torch.randint(0, 255, (1, 2, 3, 4)),\n        \"img_meta_dict\": {\"filename_or_obj\": \"testfile0.nii.gz\"},\n        \"patch_index\": 6,\n    },\n    \".nii.gz\",\n    False,\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_savitzky_golay_smooth.py_unittest_TEST_CASE_SINE_SMOOTH._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_savitzky_golay_smooth.py_unittest_TEST_CASE_SINE_SMOOTH._", "embedding": null, "metadata": {"file_path": "tests/test_savitzky_golay_smooth.py", "file_name": "test_savitzky_golay_smooth.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 58, "span_ids": ["docstring:13", "docstring"], "tokens": 561}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import SavitzkyGolaySmooth\nfrom tests.utils import TEST_NDARRAYS\n\n# Zero-padding trivial tests\n\nTEST_CASE_SINGLE_VALUE = [\n    {\"window_length\": 3, \"order\": 1},\n    np.expand_dims(np.array([1.0]), 0),  # Input data: Single value\n    np.expand_dims(np.array([1 / 3]), 0),  # Expected output: With a window length of 3 and polyorder 1\n    # output should be equal to mean of 0, 1 and 0 = 1/3 (because input will be zero-padded and a linear fit performed)\n    1e-5,  # absolute tolerance\n]\n\nTEST_CASE_2D_AXIS_2 = [\n    {\"window_length\": 3, \"order\": 1, \"axis\": 2},  # along axis 2 (second spatial dim)\n    np.expand_dims(np.ones((2, 3)), 0),\n    np.expand_dims(np.array([[2 / 3, 1.0, 2 / 3], [2 / 3, 1.0, 2 / 3]]), 0),\n    1e-5,  # absolute tolerance\n]\n\n# Replicated-padding trivial tests\n\nTEST_CASE_SINGLE_VALUE_REP = [\n    {\"window_length\": 3, \"order\": 1, \"mode\": \"replicate\"},\n    np.expand_dims(np.array([1.0]), 0),  # Input data: Single value\n    np.expand_dims(np.array([1.0]), 0),  # Expected output: With a window length of 3 and polyorder 1\n    # output will be equal to mean of [1, 1, 1] = 1 (input will be nearest-neighbour-padded and a linear fit performed)\n    1e-5,  # absolute tolerance\n]\n\n# Sine smoothing\n\nTEST_CASE_SINE_SMOOTH = [\n    {\"window_length\": 3, \"order\": 1},\n    # Sine wave with period equal to savgol window length (windowed to reduce edge effects).\n    np.expand_dims(np.sin(2 * np.pi * 1 / 3 * np.arange(100)) * np.hanning(100), 0),\n    # Should be smoothed out to zeros\n    np.expand_dims(np.zeros(100), 0),\n    # tolerance chosen by examining output of SciPy.signal.savgol_filter() when provided the above input\n    2e-2,  # absolute tolerance\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_scale_intensity.py_TestScaleIntensity.test_channel_wise_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_scale_intensity.py_TestScaleIntensity.test_channel_wise_", "embedding": null, "metadata": {"file_path": "tests/test_scale_intensity.py", "file_name": "test_scale_intensity.py", "file_type": "text/x-python", "category": "test", "start_line": 38, "end_line": 53, "span_ids": ["TestScaleIntensity.test_channel_wise", "impl"], "tokens": 171}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestScaleIntensity(NumpyImageTestCase2D):\n\n    def test_channel_wise(self):\n        for p in TEST_NDARRAYS:\n            scaler = ScaleIntensity(minv=1.0, maxv=2.0, channel_wise=True)\n            data = p(self.imt)\n            result = scaler(data)\n            mina = self.imt.min()\n            maxa = self.imt.max()\n            for i, c in enumerate(data):\n                norm = (c - mina) / (maxa - mina)\n                expected = p((norm * (2.0 - 1.0)) + 1.0)\n                assert_allclose(result[i], expected, type_test=False, rtol=1e-7, atol=0)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_scale_intensity_range_percentiles.py_unittest_TestScaleIntensityRangePercentiles.test_scaling.for_p_in_TEST_NDARRAYS_.assert_allclose_result_p": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_scale_intensity_range_percentiles.py_unittest_TestScaleIntensityRangePercentiles.test_scaling.for_p_in_TEST_NDARRAYS_.assert_allclose_result_p", "embedding": null, "metadata": {"file_path": "tests/test_scale_intensity_range_percentiles.py", "file_name": "test_scale_intensity_range_percentiles.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 34, "span_ids": ["TestScaleIntensityRangePercentiles.test_scaling", "TestScaleIntensityRangePercentiles", "docstring"], "tokens": 219}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\n\nfrom monai.transforms.intensity.array import ScaleIntensityRangePercentiles\nfrom tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, assert_allclose\n\n\nclass TestScaleIntensityRangePercentiles(NumpyImageTestCase2D):\n    def test_scaling(self):\n        img = self.imt[0]\n        lower = 10\n        upper = 99\n        b_min = 0\n        b_max = 255\n\n        a_min = np.percentile(img, lower)\n        a_max = np.percentile(img, upper)\n        expected = (((img - a_min) / (a_max - a_min)) * (b_max - b_min) + b_min).astype(np.uint8)\n        scaler = ScaleIntensityRangePercentiles(lower=lower, upper=upper, b_min=b_min, b_max=b_max, dtype=np.uint8)\n        for p in TEST_NDARRAYS:\n            result = scaler(p(img))\n            assert_allclose(result, p(expected), rtol=1e-4)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_scale_intensityd.py_unittest_TestScaleIntensityd.test_factor_scale.for_p_in_TEST_NDARRAYS_.assert_allclose_result_ke": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_scale_intensityd.py_unittest_TestScaleIntensityd.test_factor_scale.for_p_in_TEST_NDARRAYS_.assert_allclose_result_ke", "embedding": null, "metadata": {"file_path": "tests/test_scale_intensityd.py", "file_name": "test_scale_intensityd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 38, "span_ids": ["TestScaleIntensityd", "TestScaleIntensityd.test_range_scale", "TestScaleIntensityd.test_factor_scale", "docstring"], "tokens": 270}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\n\nfrom monai.transforms import ScaleIntensityd\nfrom tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, assert_allclose\n\n\nclass TestScaleIntensityd(NumpyImageTestCase2D):\n    def test_range_scale(self):\n        key = \"img\"\n        for p in TEST_NDARRAYS:\n            scaler = ScaleIntensityd(keys=[key], minv=1.0, maxv=2.0)\n            result = scaler({key: p(self.imt)})\n            mina = np.min(self.imt)\n            maxa = np.max(self.imt)\n            norm = (self.imt - mina) / (maxa - mina)\n            expected = (norm * (2.0 - 1.0)) + 1.0\n            assert_allclose(result[key], p(expected))\n\n    def test_factor_scale(self):\n        key = \"img\"\n        for p in TEST_NDARRAYS:\n            scaler = ScaleIntensityd(keys=[key], minv=None, maxv=None, factor=0.1)\n            result = scaler({key: p(self.imt)})\n            expected = (self.imt * (1 + 0.1)).astype(np.float32)\n            assert_allclose(result[key], p(expected))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_scale_intensityd.py_TestScaleIntensityd.test_channel_wise_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_scale_intensityd.py_TestScaleIntensityd.test_channel_wise_", "embedding": null, "metadata": {"file_path": "tests/test_scale_intensityd.py", "file_name": "test_scale_intensityd.py", "file_type": "text/x-python", "category": "test", "start_line": 40, "end_line": 56, "span_ids": ["TestScaleIntensityd.test_channel_wise", "impl"], "tokens": 188}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestScaleIntensityd(NumpyImageTestCase2D):\n\n    def test_channel_wise(self):\n        key = \"img\"\n        for p in TEST_NDARRAYS:\n            scaler = ScaleIntensityd(keys=[key], minv=1.0, maxv=2.0, channel_wise=True)\n            data = p(self.imt)\n            result = scaler({key: data})\n            mina = self.imt.min()\n            maxa = self.imt.max()\n            for i, c in enumerate(data):\n                norm = (c - mina) / (maxa - mina)\n                expected = p((norm * (2.0 - 1.0)) + 1.0)\n                assert_allclose(result[key][i], expected, type_test=False, rtol=1e-7, atol=0)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_selfattention.py_unittest_for_dropout_rate_in_np_li.for_hidden_size_in_360_.for_num_heads_in_4_6_8.TEST_CASE_SABLOCK_append_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_selfattention.py_unittest_for_dropout_rate_in_np_li.for_hidden_size_in_360_.for_num_heads_in_4_6_8.TEST_CASE_SABLOCK_append_", "embedding": null, "metadata": {"file_path": "tests/test_selfattention.py", "file_name": "test_selfattention.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 35, "span_ids": ["docstring"], "tokens": 183}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom unittest import skipUnless\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks import eval_mode\nfrom monai.networks.blocks.selfattention import SABlock\nfrom monai.utils import optional_import\n\neinops, has_einops = optional_import(\"einops\")\n\nTEST_CASE_SABLOCK = []\nfor dropout_rate in np.linspace(0, 1, 4):\n    for hidden_size in [360, 480, 600, 768]:\n        for num_heads in [4, 6, 8, 12]:\n\n            test_case = [\n                {\"hidden_size\": hidden_size, \"num_heads\": num_heads, \"dropout_rate\": dropout_rate},\n                (2, 512, hidden_size),\n                (2, 512, hidden_size),\n            ]\n            TEST_CASE_SABLOCK.append(test_case)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_selfattention.py_TestResBlock_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_selfattention.py_TestResBlock_", "embedding": null, "metadata": {"file_path": "tests/test_selfattention.py", "file_name": "test_selfattention.py", "file_type": "text/x-python", "category": "test", "start_line": 38, "end_line": 57, "span_ids": ["impl:10", "TestResBlock.test_ill_arg", "TestResBlock", "TestResBlock.test_shape"], "tokens": 155}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestResBlock(unittest.TestCase):\n    @parameterized.expand(TEST_CASE_SABLOCK)\n    @skipUnless(has_einops, \"Requires einops\")\n    def test_shape(self, input_param, input_shape, expected_shape):\n        net = SABlock(**input_param)\n        with eval_mode(net):\n            result = net(torch.randn(input_shape))\n            self.assertEqual(result.shape, expected_shape)\n\n    def test_ill_arg(self):\n        with self.assertRaises(ValueError):\n            SABlock(hidden_size=128, num_heads=12, dropout_rate=6.0)\n\n        with self.assertRaises(ValueError):\n            SABlock(hidden_size=620, num_heads=8, dropout_rate=0.4)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_senet.py_os_TEST_CASE_PRETRAINED_1._SEResNet50_spatial_di": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_senet.py_os_TEST_CASE_PRETRAINED_1._SEResNet50_spatial_di", "embedding": null, "metadata": {"file_path": "tests/test_senet.py", "file_name": "test_senet.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 45, "span_ids": ["docstring"], "tokens": 319}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport unittest\nfrom typing import TYPE_CHECKING\nfrom unittest import skipUnless\n\nimport torch\nfrom parameterized import parameterized\n\nimport monai.networks.nets.senet as se_mod\nfrom monai.networks import eval_mode\nfrom monai.networks.nets import SENet154, SEResNet50, SEResNet101, SEResNet152, SEResNext50, SEResNext101\nfrom monai.utils import optional_import\nfrom tests.utils import test_is_quick, test_pretrained_networks, test_script_save\n\nif TYPE_CHECKING:\n    import pretrainedmodels\n\n    has_cadene_pretrain = True\nelse:\n    pretrainedmodels, has_cadene_pretrain = optional_import(\"pretrainedmodels\")\n\n\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\n\nNET_ARGS = {\"spatial_dims\": 3, \"in_channels\": 2, \"num_classes\": 2}\nTEST_CASE_1 = [SENet154, NET_ARGS]\nTEST_CASE_2 = [SEResNet50, NET_ARGS]\nTEST_CASE_3 = [SEResNet101, NET_ARGS]\nTEST_CASE_4 = [SEResNet152, NET_ARGS]\nTEST_CASE_5 = [SEResNext50, NET_ARGS]\nTEST_CASE_6 = [SEResNext101, NET_ARGS]\n\nTEST_CASE_PRETRAINED_1 = [SEResNet50, {\"spatial_dims\": 2, \"in_channels\": 3, \"num_classes\": 2, \"pretrained\": True}]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_senet.py_TestPretrainedSENET_TestPretrainedSENET.setUp.if_test_is_quick_.se_mod.SE_NET_MODELS.testing_data_urls": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_senet.py_TestPretrainedSENET_TestPretrainedSENET.setUp.if_test_is_quick_.se_mod.SE_NET_MODELS.testing_data_urls", "embedding": null, "metadata": {"file_path": "tests/test_senet.py", "file_name": "test_senet.py", "file_type": "text/x-python", "category": "test", "start_line": 65, "end_line": 98, "span_ids": ["TestPretrainedSENET.setUp", "TestPretrainedSENET"], "tokens": 521}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestPretrainedSENET(unittest.TestCase):\n    def setUp(self):\n        self.original_urls = se_mod.SE_NET_MODELS.copy()\n        if test_is_quick():\n            testing_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"testing_data\")\n            testing_data_urls = {\n                \"senet154\": {\n                    \"url\": \"https://drive.google.com/uc?id=1e10LFGVIV9L8_Q5Fhwi3X5nU6mDRrCDh\",\n                    \"filename\": \"senet154-c7b49a05.pth\",\n                },\n                \"se_resnet50\": {\n                    \"url\": \"https://drive.google.com/uc?id=1WCeveS0tvjta4Wcp1wAGRi_uyXRfXAGA\",\n                    \"filename\": \"se_resnet50-ce0d4300.pth\",\n                },\n                \"se_resnet101\": {\n                    \"url\": \"https://drive.google.com/uc?id=1Bh0PmLISUltsY8FevtlTbt6vT35clzWg\",\n                    \"filename\": \"se_resnet101-7e38fcc6.pth\",\n                },\n                \"se_resnet152\": {\n                    \"url\": \"https://drive.google.com/uc?id=1fcqpP0ITOcALy_TZAcBdkyf7HcH687J-\",\n                    \"filename\": \"se_resnet152-d17c99b7.pth\",\n                },\n                \"se_resnext50_32x4d\": {\n                    \"url\": \"https://drive.google.com/uc?id=1kRKW8YjGaEwYdQUyhoCIDg1H9ZAoJ-jI\",\n                    \"filename\": \"se_resnext50_32x4d-a260b3a4.pth\",\n                },\n                \"se_resnext101_32x4d\": {\n                    \"url\": \"https://drive.google.com/uc?id=1Tg6Zim1lXgmYgH7FyTXAgihbkq5Jegni\",\n                    \"filename\": \"se_resnext101_32x4d-3b2fe3d8.pth\",\n                },\n            }\n            for item in testing_data_urls:\n                testing_data_urls[item][\"filename\"] = os.path.join(testing_dir, testing_data_urls[item][\"filename\"])\n            se_mod.SE_NET_MODELS = testing_data_urls", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_senet.py_TestPretrainedSENET.tearDown_TestPretrainedSENET.test_senet_shape.with_eval_mode_net_.self_assertEqual_result_s": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_senet.py_TestPretrainedSENET.tearDown_TestPretrainedSENET.test_senet_shape.with_eval_mode_net_.self_assertEqual_result_s", "embedding": null, "metadata": {"file_path": "tests/test_senet.py", "file_name": "test_senet.py", "file_type": "text/x-python", "category": "test", "start_line": 100, "end_line": 111, "span_ids": ["TestPretrainedSENET.tearDown", "TestPretrainedSENET.test_senet_shape"], "tokens": 129}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestPretrainedSENET(unittest.TestCase):\n\n    def tearDown(self):\n        se_mod.SE_NET_MODELS = self.original_urls.copy()\n\n    @parameterized.expand([TEST_CASE_PRETRAINED_1])\n    def test_senet_shape(self, model, input_param):\n        net = test_pretrained_networks(model, input_param, device)\n        input_data = torch.randn(3, 3, 64, 64).to(device)\n        expected_shape = (3, 2)\n        net = net.to(device)\n        with eval_mode(net):\n            result = net(input_data)\n            self.assertEqual(result.shape, expected_shape)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_senet.py_TestPretrainedSENET.test_pretrain_consistency_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_senet.py_TestPretrainedSENET.test_pretrain_consistency_", "embedding": null, "metadata": {"file_path": "tests/test_senet.py", "file_name": "test_senet.py", "file_type": "text/x-python", "category": "test", "start_line": 113, "end_line": 131, "span_ids": ["impl:24", "TestPretrainedSENET.test_pretrain_consistency"], "tokens": 234}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestPretrainedSENET(unittest.TestCase):\n\n    @parameterized.expand([TEST_CASE_PRETRAINED_1])\n    @skipUnless(has_cadene_pretrain, \"Requires `pretrainedmodels` package.\")\n    def test_pretrain_consistency(self, model, input_param):\n        input_data = torch.randn(1, 3, 64, 64).to(device)\n        net = test_pretrained_networks(model, input_param, device)\n        with eval_mode(net):\n            result = net.features(input_data)\n        cadene_net = pretrainedmodels.se_resnet50().to(device)\n        with eval_mode(cadene_net):\n            expected_result = cadene_net.features(input_data)\n        # The difference between Cadene's senet and our version is that\n        # we use nn.Linear as the FC layer, but Cadene's version uses\n        # a conv layer with kernel size equals to 1. It may bring a little difference.\n        self.assertTrue(torch.allclose(result, expected_result, rtol=1e-5, atol=1e-5))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_set_determinism.py_unittest_TestSetDeterminism.test_values.set_determinism_seed_None": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_set_determinism.py_unittest_TestSetDeterminism.test_values.set_determinism_seed_None", "embedding": null, "metadata": {"file_path": "tests/test_set_determinism.py", "file_name": "test_set_determinism.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 52, "span_ids": ["TestSetDeterminism.test_values", "TestSetDeterminism", "docstring"], "tokens": 320}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\n\nfrom monai.utils import get_seed, set_determinism\nfrom tests.utils import SkipIfBeforePyTorchVersion, skip_if_no_cuda\n\n\nclass TestSetDeterminism(unittest.TestCase):\n    def test_values(self):\n        # check system default flags\n        set_determinism(None)\n        self.assertTrue(not torch.backends.cudnn.deterministic)\n        self.assertTrue(get_seed() is None)\n        # set default seed\n        set_determinism()\n        self.assertTrue(get_seed() is not None)\n        self.assertTrue(torch.backends.cudnn.deterministic)\n        self.assertTrue(not torch.backends.cudnn.benchmark)\n        # resume default\n        set_determinism(None)\n        self.assertTrue(not torch.backends.cudnn.deterministic)\n        self.assertTrue(not torch.backends.cudnn.benchmark)\n        self.assertTrue(get_seed() is None)\n        # test seeds\n        seed = 255\n        set_determinism(seed=seed)\n        self.assertEqual(seed, get_seed())\n        a = np.random.randint(seed)\n        b = torch.randint(seed, (1,))\n        # tset when global flag support is disabled\n        torch.backends.disable_global_flags()\n        set_determinism(seed=seed)\n        c = np.random.randint(seed)\n        d = torch.randint(seed, (1,))\n        self.assertEqual(a, c)\n        self.assertEqual(b, d)\n        self.assertTrue(torch.backends.cudnn.deterministic)\n        self.assertTrue(not torch.backends.cudnn.benchmark)\n        set_determinism(seed=None)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_set_determinism.py_TestSetFlag_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_set_determinism.py_TestSetFlag_", "embedding": null, "metadata": {"file_path": "tests/test_set_determinism.py", "file_name": "test_set_determinism.py", "file_type": "text/x-python", "category": "test", "start_line": 53, "end_line": 71, "span_ids": ["TestSetFlag.test_algo", "TestSetFlag.tearDown", "impl", "TestSetFlag.setUp", "TestSetFlag"], "tokens": 167}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestSetFlag(unittest.TestCase):\n    def setUp(self):\n        set_determinism(1, use_deterministic_algorithms=True)\n\n    @SkipIfBeforePyTorchVersion((1, 8))  # beta feature\n    @skip_if_no_cuda\n    def test_algo(self):\n        with self.assertRaises(RuntimeError):\n            x = torch.randn(20, 16, 50, 44, 31, requires_grad=True, device=\"cuda:0\")\n            y = torch.nn.AvgPool3d((3, 2, 2), stride=(2, 1, 2))(x)\n            y.sum().backward()\n\n    def tearDown(self):\n        set_determinism(None, use_deterministic_algorithms=False)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_smartcachedataset.py_TestSmartCacheDataset.test_shuffle_TestSmartCacheDataset.test_shuffle.dataset_shutdown_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_smartcachedataset.py_TestSmartCacheDataset.test_shuffle_TestSmartCacheDataset.test_shuffle.dataset_shutdown_", "embedding": null, "metadata": {"file_path": "tests/test_smartcachedataset.py", "file_name": "test_smartcachedataset.py", "file_type": "text/x-python", "category": "test", "start_line": 104, "end_line": 128, "span_ids": ["TestSmartCacheDataset.test_shuffle"], "tokens": 179}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestSmartCacheDataset(unittest.TestCase):\n\n    def test_shuffle(self):\n        test_data = [{\"image\": f\"test_image{i}.nii.gz\"} for i in range(20)]\n        dataset = SmartCacheDataset(\n            data=test_data,\n            transform=None,\n            replace_rate=0.1,\n            cache_num=16,\n            num_init_workers=4,\n            num_replace_workers=4,\n            shuffle=True,\n            seed=123,\n        )\n\n        dataset.start()\n        for i in range(3):\n            dataset.update_cache()\n\n            if i == 0:\n                self.assertEqual(dataset[15][\"image\"], \"test_image18.nii.gz\")\n            elif i == 1:\n                self.assertEqual(dataset[15][\"image\"], \"test_image13.nii.gz\")\n            else:\n                self.assertEqual(dataset[15][\"image\"], \"test_image5.nii.gz\")\n\n        dataset.shutdown()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_smartcachedataset.py_TestSmartCacheDataset.test_set_data_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_smartcachedataset.py_TestSmartCacheDataset.test_set_data_", "embedding": null, "metadata": {"file_path": "tests/test_smartcachedataset.py", "file_name": "test_smartcachedataset.py", "file_type": "text/x-python", "category": "test", "start_line": 130, "end_line": 183, "span_ids": ["impl:11", "TestSmartCacheDataset.test_set_data", "TestSmartCacheDataset.test_datalist"], "tokens": 471}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestSmartCacheDataset(unittest.TestCase):\n\n    def test_set_data(self):\n        data_list1 = list(range(10))\n\n        transform = Lambda(func=lambda x: np.array([x * 10]))\n\n        dataset = SmartCacheDataset(\n            data=data_list1,\n            transform=transform,\n            cache_rate=0.5,\n            replace_rate=0.4,\n            num_init_workers=4,\n            num_replace_workers=2,\n            shuffle=False,\n            progress=True,\n        )\n\n        num_workers = 2 if sys.platform == \"linux\" else 0\n        dataloader = DataLoader(dataset=dataset, num_workers=num_workers, batch_size=1)\n\n        dataset.start()\n        for i, d in enumerate(dataloader):\n            np.testing.assert_allclose([[data_list1[i] * 10]], d)\n        # replace cache content, move forward 2(5 * 0.4) items\n        dataset.update_cache()\n        for i, d in enumerate(dataloader):\n            np.testing.assert_allclose([[data_list1[i + 2] * 10]], d)\n        # shutdown to update data\n        dataset.shutdown()\n        # update the datalist and fill the cache content\n        data_list2 = list(range(-10, 0))\n        dataset.set_data(data=data_list2)\n        # restart the dataset\n        dataset.start()\n        # rerun with updated cache content\n        for i, d in enumerate(dataloader):\n            np.testing.assert_allclose([[data_list2[i] * 10]], d)\n        # replace cache content, move forward 2(5 * 0.4) items\n        dataset.update_cache()\n        for i, d in enumerate(dataloader):\n            np.testing.assert_allclose([[data_list2[i + 2] * 10]], d)\n        # finally shutdown the dataset\n        dataset.shutdown()\n\n    def test_datalist(self):\n        data_list = [np.array([i]) for i in range(5)]\n        data_list_backup = copy.copy(data_list)\n\n        SmartCacheDataset(data=data_list, transform=None, cache_rate=0.5, replace_rate=0.4, shuffle=True)\n        np.testing.assert_allclose(data_list, data_list_backup)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_spacing.py_unittest_for_p_in_TEST_NDARRAYS_.None_9": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_spacing.py_unittest_for_p_in_TEST_NDARRAYS_.None_9", "embedding": null, "metadata": {"file_path": "tests/test_spacing.py", "file_name": "test_spacing.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 119, "span_ids": ["docstring"], "tokens": 1444}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import Spacing\nfrom monai.utils import ensure_tuple, fall_back_tuple\nfrom tests.utils import TEST_NDARRAYS\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    TESTS.append(\n        [\n            p,\n            {\"pixdim\": (1.0, 1.5), \"padding_mode\": \"zeros\", \"dtype\": float},\n            np.arange(4).reshape((1, 2, 2)) + 1.0,  # data\n            {\"affine\": np.eye(4)},\n            np.array([[[1.0, 1.0], [3.0, 2.0]]]),\n        ]\n    )\n    TESTS.append(\n        [\n            p,\n            {\"pixdim\": 1.0, \"padding_mode\": \"zeros\", \"dtype\": float},\n            np.ones((1, 2, 1, 2)),  # data\n            {\"affine\": np.eye(4)},\n            np.array([[[[1.0, 1.0]], [[1.0, 1.0]]]]),\n        ]\n    )\n    TESTS.append(\n        [\n            p,\n            {\"pixdim\": (1.0, 1.0, 1.0), \"padding_mode\": \"zeros\", \"dtype\": float},\n            np.ones((1, 2, 1, 2)),  # data\n            {\"affine\": np.eye(4)},\n            np.array([[[[1.0, 1.0]], [[1.0, 1.0]]]]),\n        ]\n    )\n    TESTS.append(\n        [\n            p,\n            {\"pixdim\": (1.0, 0.2, 1.5), \"diagonal\": False, \"padding_mode\": \"zeros\", \"align_corners\": True},\n            np.ones((1, 2, 1, 2)),  # data\n            {\"affine\": np.array([[2, 1, 0, 4], [-1, -3, 0, 5], [0, 0, 2.0, 5], [0, 0, 0, 1]])},\n            np.array([[[[0.95527864, 0.95527864]], [[1.0, 1.0]], [[1.0, 1.0]]]]),\n        ]\n    )\n    TESTS.append(\n        [\n            p,\n            {\"pixdim\": (3.0, 1.0), \"padding_mode\": \"zeros\"},\n            np.arange(24).reshape((2, 3, 4)),  # data\n            {\"affine\": np.diag([-3.0, 0.2, 1.5, 1])},\n            np.array([[[0, 0], [4, 0], [8, 0]], [[12, 0], [16, 0], [20, 0]]]),\n        ]\n    )\n    TESTS.append(\n        [\n            p,\n            {\"pixdim\": (3.0, 1.0), \"padding_mode\": \"zeros\"},\n            np.arange(24).reshape((2, 3, 4)),  # data\n            {},\n            np.array([[[0, 1, 2, 3], [0, 0, 0, 0]], [[12, 13, 14, 15], [0, 0, 0, 0]]]),\n        ]\n    )\n    TESTS.append(\n        [\n            p,\n            {\"pixdim\": (1.0, 1.0)},\n            np.arange(24).reshape((2, 3, 4)),  # data\n            {},\n            np.array(\n                [[[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]], [[12, 13, 14, 15], [16, 17, 18, 19], [20, 21, 22, 23]]]\n            ),\n        ]\n    )\n    TESTS.append(\n        [\n            p,\n            {\"pixdim\": (4.0, 5.0, 6.0)},\n            np.arange(24).reshape((1, 2, 3, 4)),  # data\n            {\"affine\": np.array([[-4, 0, 0, 4], [0, 5, 0, -5], [0, 0, 6, -6], [0, 0, 0, 1]])},\n            np.arange(24).reshape((1, 2, 3, 4)),  # data\n        ]\n    )\n    TESTS.append(\n        [\n            p,\n            {\"pixdim\": (4.0, 5.0, 6.0), \"diagonal\": True},\n            np.arange(24).reshape((1, 2, 3, 4)),  # data\n            {\"affine\": np.array([[-4, 0, 0, 4], [0, 5, 0, 0], [0, 0, 6, 0], [0, 0, 0, 1]])},\n            np.array(\n                [[[[12, 13, 14, 15], [16, 17, 18, 19], [20, 21, 22, 23]], [[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]]]]\n            ),\n        ]\n    )\n    TESTS.append(\n        [\n            p,\n            {\"pixdim\": (4.0, 5.0, 6.0), \"padding_mode\": \"border\", \"diagonal\": True},\n            np.arange(24).reshape((1, 2, 3, 4)),  # data\n            {\"affine\": np.array([[-4, 0, 0, -4], [0, 5, 0, 0], [0, 0, 6, 0], [0, 0, 0, 1]])},\n            np.array(\n                [[[[12, 13, 14, 15], [16, 17, 18, 19], [20, 21, 22, 23]], [[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]]]]\n            ),\n        ]\n    )\n    # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_spacing.py_for_p_in_TEST_NDARRAYS_.None_10_for_p_in_TEST_NDARRAYS_.None_14": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_spacing.py_for_p_in_TEST_NDARRAYS_.None_10_for_p_in_TEST_NDARRAYS_.None_14", "embedding": null, "metadata": {"file_path": "tests/test_spacing.py", "file_name": "test_spacing.py", "file_type": "text/x-python", "category": "test", "start_line": 120, "end_line": 194, "span_ids": ["docstring"], "tokens": 1339}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "for p in TEST_NDARRAYS:\n    # ... other code\n    TESTS.append(\n        [\n            p,\n            {\"pixdim\": (4.0, 5.0, 6.0), \"padding_mode\": \"border\", \"diagonal\": True},\n            np.arange(24).reshape((1, 2, 3, 4)),  # data\n            {\"affine\": np.array([[-4, 0, 0, -4], [0, 5, 0, 0], [0, 0, 6, 0], [0, 0, 0, 1]]), \"mode\": \"nearest\"},\n            np.array(\n                [[[[12, 13, 14, 15], [16, 17, 18, 19], [20, 21, 22, 23]], [[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]]]]\n            ),\n        ]\n    )\n    TESTS.append(\n        [\n            p,\n            {\"pixdim\": (1.9, 4.0), \"padding_mode\": \"zeros\", \"diagonal\": True},\n            np.arange(24).reshape((1, 4, 6)),  # data\n            {\"affine\": np.array([[-4, 0, 0, -4], [0, 5, 0, 0], [0, 0, 6, 0], [0, 0, 0, 1]]), \"mode\": \"nearest\"},\n            np.array(\n                [\n                    [\n                        [18.0, 19.0, 20.0, 20.0, 21.0, 22.0, 23.0],\n                        [18.0, 19.0, 20.0, 20.0, 21.0, 22.0, 23.0],\n                        [12.0, 13.0, 14.0, 14.0, 15.0, 16.0, 17.0],\n                        [12.0, 13.0, 14.0, 14.0, 15.0, 16.0, 17.0],\n                        [6.0, 7.0, 8.0, 8.0, 9.0, 10.0, 11.0],\n                        [6.0, 7.0, 8.0, 8.0, 9.0, 10.0, 11.0],\n                        [0.0, 1.0, 2.0, 2.0, 3.0, 4.0, 5.0],\n                    ]\n                ]\n            ),\n        ]\n    )\n    TESTS.append(\n        [\n            p,\n            {\"pixdim\": (5.0, 3.0), \"padding_mode\": \"border\", \"diagonal\": True, \"dtype\": np.float32},\n            np.arange(24).reshape((1, 4, 6)),  # data\n            {\"affine\": np.array([[-4, 0, 0, 0], [0, 5, 0, 0], [0, 0, 6, 0], [0, 0, 0, 1]]), \"mode\": \"bilinear\"},\n            np.array(\n                [\n                    [\n                        [18.0, 18.6, 19.2, 19.8, 20.400002, 21.0, 21.6, 22.2, 22.8],\n                        [10.5, 11.1, 11.700001, 12.299999, 12.900001, 13.5, 14.1, 14.700001, 15.3],\n                        [3.0, 3.6000001, 4.2000003, 4.8, 5.4000006, 6.0, 6.6000004, 7.200001, 7.8],\n                    ]\n                ]\n            ),\n        ]\n    )\n    TESTS.append(\n        [\n            p,\n            {\"pixdim\": (5.0, 3.0), \"padding_mode\": \"zeros\", \"diagonal\": True, \"dtype\": np.float32},\n            np.arange(24).reshape((1, 4, 6)),  # data\n            {\"affine\": np.array([[-4, 0, 0, 0], [0, 5, 0, 0], [0, 0, 6, 0], [0, 0, 0, 1]]), \"mode\": \"bilinear\"},\n            np.array(\n                [\n                    [\n                        [18.0000, 18.6000, 19.2000, 19.8000, 20.4000, 21.0000, 21.6000, 22.2000, 22.8000],\n                        [10.5000, 11.1000, 11.7000, 12.3000, 12.9000, 13.5000, 14.1000, 14.7000, 15.3000],\n                        [3.0000, 3.6000, 4.2000, 4.8000, 5.4000, 6.0000, 6.6000, 7.2000, 7.8000],\n                    ]\n                ]\n            ),\n        ]\n    )\n    TESTS.append(\n        [\n            p,\n            {\"pixdim\": [-1, -1, 0.5], \"padding_mode\": \"zeros\", \"dtype\": float},\n            np.ones((1, 2, 1, 2)),  # data\n            {\"affine\": np.eye(4)},\n            np.array([[[[1.0, 1.0, 1.0]], [[1.0, 1.0, 1.0]]]]),\n        ]\n    )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_spacingd.py_unittest_for_p_in_TEST_NDARRAYS_.None_4": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_spacingd.py_unittest_for_p_in_TEST_NDARRAYS_.None_4", "embedding": null, "metadata": {"file_path": "tests/test_spacingd.py", "file_name": "test_spacingd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 83, "span_ids": ["docstring"], "tokens": 691}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom typing import List, Tuple\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import Spacingd\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTESTS: List[Tuple] = []\nfor p in TEST_NDARRAYS:\n    TESTS.append(\n        (\n            \"spacing 3d\",\n            {\"image\": p(np.ones((2, 10, 15, 20))), \"image_meta_dict\": {\"affine\": p(np.eye(4))}},\n            dict(keys=\"image\", pixdim=(1, 2, 1.4)),\n            (\"image\", \"image_meta_dict\", \"image_transforms\"),\n            (2, 10, 8, 15),\n            p(np.diag([1, 2, 1.4, 1.0])),\n        )\n    )\n    TESTS.append(\n        (\n            \"spacing 2d\",\n            {\"image\": np.ones((2, 10, 20)), \"image_meta_dict\": {\"affine\": np.eye(3)}},\n            dict(keys=\"image\", pixdim=(1, 2)),\n            (\"image\", \"image_meta_dict\", \"image_transforms\"),\n            (2, 10, 10),\n            np.diag((1, 2, 1)),\n        )\n    )\n    TESTS.append(\n        (\n            \"spacing 2d no metadata\",\n            {\"image\": np.ones((2, 10, 20))},\n            dict(keys=\"image\", pixdim=(1, 2)),\n            (\"image\", \"image_meta_dict\", \"image_transforms\"),\n            (2, 10, 10),\n            np.diag((1, 2, 1)),\n        )\n    )\n    TESTS.append(\n        (\n            \"interp all\",\n            {\n                \"image\": np.arange(20).reshape((2, 1, 10)),\n                \"seg\": np.ones((2, 1, 10)),\n                \"image_meta_dict\": {\"affine\": np.eye(4)},\n                \"seg_meta_dict\": {\"affine\": np.eye(4)},\n            },\n            dict(keys=(\"image\", \"seg\"), mode=\"nearest\", pixdim=(1, 0.2)),\n            (\"image\", \"image_meta_dict\", \"image_transforms\", \"seg\", \"seg_meta_dict\", \"seg_transforms\"),\n            (2, 1, 46),\n            np.diag((1, 0.2, 1, 1)),\n        )\n    )\n    TESTS.append(\n        (\n            \"interp sep\",\n            {\n                \"image\": np.ones((2, 1, 10)),\n                \"seg\": np.ones((2, 1, 10)),\n                \"image_meta_dict\": {\"affine\": np.eye(4)},\n                \"seg_meta_dict\": {\"affine\": np.eye(4)},\n            },\n            dict(keys=(\"image\", \"seg\"), mode=(\"bilinear\", \"nearest\"), pixdim=(1, 0.2)),\n            (\"image\", \"image_meta_dict\", \"image_transforms\", \"seg\", \"seg_meta_dict\", \"seg_transforms\"),\n            (2, 1, 46),\n            np.diag((1, 0.2, 1, 1)),\n        )\n    )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_spacingd.py_TestSpacingDCase_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_spacingd.py_TestSpacingDCase_", "embedding": null, "metadata": {"file_path": "tests/test_spacingd.py", "file_name": "test_spacingd.py", "file_type": "text/x-python", "category": "test", "start_line": 86, "end_line": 99, "span_ids": ["TestSpacingDCase.test_spacingd", "impl:9", "TestSpacingDCase"], "tokens": 130}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestSpacingDCase(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_spacingd(self, _, data, kw_args, expected_keys, expected_shape, expected_affine):\n        res = Spacingd(**kw_args)(data)\n        if isinstance(data[\"image\"], torch.Tensor):\n            self.assertEqual(data[\"image\"].device, res[\"image\"].device)\n        self.assertEqual(expected_keys, tuple(sorted(res)))\n        np.testing.assert_allclose(res[\"image\"].shape, expected_shape)\n        assert_allclose(res[\"image_meta_dict\"][\"affine\"], expected_affine)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_spatial_crop.py_unittest_TEST_ERRORS._roi_slices_slice_s": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_spatial_crop.py_unittest_TEST_ERRORS._roi_slices_slice_s", "embedding": null, "metadata": {"file_path": "tests/test_spatial_crop.py", "file_name": "test_spatial_crop.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 32, "span_ids": ["impl:3", "docstring"], "tokens": 521}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import SpatialCrop\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTESTS = [\n    [{\"roi_center\": [1, 1, 1], \"roi_size\": [2, 2, 2]}, (3, 3, 3, 3), (3, 2, 2, 2)],\n    [{\"roi_center\": [1, 1, 1], \"roi_size\": [2, 2, 2]}, (3, 1, 1, 1), (3, 1, 1, 1)],\n    [{\"roi_start\": [0, 0, 0], \"roi_end\": [2, 2, 2]}, (3, 3, 3, 3), (3, 2, 2, 2)],\n    [{\"roi_start\": [0, 0], \"roi_end\": [2, 2]}, (3, 3, 3, 3), (3, 2, 2, 3)],\n    [{\"roi_start\": [0, 0, 0, 0, 0], \"roi_end\": [2, 2, 2, 2, 2]}, (3, 3, 3, 3), (3, 2, 2, 2)],\n    [{\"roi_start\": [0, 0, 0, 0, 0], \"roi_end\": [8, 8, 8, 2, 2]}, (3, 3, 3, 3), (3, 3, 3, 3)],\n    [{\"roi_start\": [1, 0, 0], \"roi_end\": [1, 8, 8]}, (3, 3, 3, 3), (3, 0, 3, 3)],\n    [{\"roi_slices\": [slice(s, e) for s, e in zip([-1, -2, 0], [None, None, 2])]}, (3, 3, 3, 3), (3, 1, 2, 2)],\n]\n\nTEST_ERRORS = [[{\"roi_slices\": [slice(s, e, 2) for s, e in zip([-1, -2, 0], [None, None, 2])]}]]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_spatial_pad.py_unittest_for_mode_in_MODES_.None_1": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_spatial_pad.py_unittest_for_mode_in_MODES_.None_1", "embedding": null, "metadata": {"file_path": "tests/test_spatial_pad.py", "file_name": "test_spatial_pad.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 52, "span_ids": ["docstring"], "tokens": 326}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom typing import List\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import SpatialPad\nfrom monai.utils.enums import NumpyPadMode, PytorchPadMode\nfrom monai.utils.misc import set_determinism\nfrom tests.utils import TEST_NDARRAYS\n\nTESTS = []\n\nMODES = []\n\n# Test modes\nNP_MODES: List = [\n    \"constant\",\n    \"edge\",\n    # `reflect` mode is not supported in some PyTorch versions, skip the test\n    # \"reflect\",\n    \"wrap\",\n]\nMODES += NP_MODES\nMODES += [NumpyPadMode(i) for i in NP_MODES]\n\nPT_MODES: list = [\n    \"constant\",\n    \"replicate\",\n    \"circular\",\n    # `reflect` mode is not supported in some PyTorch versions, skip the test\n    # \"reflect\",\n]\nMODES += PT_MODES\nMODES += [PytorchPadMode(i) for i in PT_MODES]\n\nfor mode in MODES:\n    TESTS.append([{\"spatial_size\": [3, 4], \"method\": \"end\", \"mode\": mode}, (1, 2, 3), (1, 3, 4)])\n\n    TESTS.append([{\"spatial_size\": [15, 4, -1], \"method\": \"symmetric\", \"mode\": mode}, (3, 8, 8, 4), (3, 15, 8, 4)])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_spatial_pad.py_TestSpatialPad_TestSpatialPad.test_pad_shape.for_p_in_TEST_NDARRAYS_.for_results_in_results_1.if_input_param_mode_no.torch_testing_assert_allc": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_spatial_pad.py_TestSpatialPad_TestSpatialPad.test_pad_shape.for_p_in_TEST_NDARRAYS_.for_results_in_results_1.if_input_param_mode_no.torch_testing_assert_allc", "embedding": null, "metadata": {"file_path": "tests/test_spatial_pad.py", "file_name": "test_spatial_pad.py", "file_type": "text/x-python", "category": "test", "start_line": 55, "end_line": 81, "span_ids": ["TestSpatialPad.tearDown", "TestSpatialPad.test_pad_shape", "TestSpatialPad", "TestSpatialPad.get_arr", "TestSpatialPad.setUp"], "tokens": 282}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestSpatialPad(unittest.TestCase):\n    def setUp(self) -> None:\n        set_determinism(seed=0)\n\n    def tearDown(self) -> None:\n        set_determinism(None)\n\n    @staticmethod\n    def get_arr(shape):\n        return np.random.randint(100, size=shape).astype(float)\n\n    @parameterized.expand(TESTS)\n    def test_pad_shape(self, input_param, input_shape, expected_shape):\n        results_1 = []\n        results_2 = []\n        input_data = self.get_arr(input_shape)\n        # check result is the same regardless of input type\n        for p in TEST_NDARRAYS:\n            padder = SpatialPad(**input_param)\n            r1 = padder(p(input_data))\n            r2 = padder(p(input_data), mode=input_param[\"mode\"])\n            results_1.append(r1.cpu() if isinstance(r1, torch.Tensor) else r1)\n            results_2.append(r2.cpu() if isinstance(r2, torch.Tensor) else r2)\n            for results in (results_1, results_2):\n                np.testing.assert_allclose(results[-1].shape, expected_shape)\n                if input_param[\"mode\"] not in (\"empty\", NumpyPadMode.EMPTY):\n                    torch.testing.assert_allclose(results[0], results[-1], atol=0, rtol=1e-5)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_spatial_pad.py_TestSpatialPad.test_pad_kwargs_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_spatial_pad.py_TestSpatialPad.test_pad_kwargs_", "embedding": null, "metadata": {"file_path": "tests/test_spatial_pad.py", "file_name": "test_spatial_pad.py", "file_type": "text/x-python", "category": "test", "start_line": 83, "end_line": 102, "span_ids": ["impl:16", "TestSpatialPad.test_pad_kwargs"], "tokens": 231}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestSpatialPad(unittest.TestCase):\n\n    def test_pad_kwargs(self):\n        for p in TEST_NDARRAYS:\n            input_data = p(np.zeros((3, 8, 4)))\n            if isinstance(input_data, torch.Tensor):\n                result = (\n                    SpatialPad(spatial_size=[15, 8], method=\"end\", mode=\"constant\", value=2)(img=input_data)\n                    .cpu()\n                    .numpy()\n                )\n            else:\n                result = SpatialPad(\n                    spatial_size=[15, 8], method=\"end\", mode=\"constant\", constant_values=((0, 0), (1, 1), (2, 2))\n                )(img=input_data)\n                torch.testing.assert_allclose(result[:, 8:, :4], np.ones((3, 7, 4)), rtol=1e-7, atol=0)\n            torch.testing.assert_allclose(result[:, :, 4:], np.ones((3, 15, 4)) + 1, rtol=1e-7, atol=0)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_split_on_grid_dict.py_TestSplitOnGridDict_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_split_on_grid_dict.py_TestSplitOnGridDict_", "embedding": null, "metadata": {"file_path": "tests/test_split_on_grid_dict.py", "file_name": "test_split_on_grid_dict.py", "file_type": "text/x-python", "category": "test", "start_line": 78, "end_line": 101, "span_ids": ["TestSplitOnGridDict", "impl:57", "TestSplitOnGridDict.test_split_patch_single_call", "TestSplitOnGridDict.test_split_patch_multiple_call"], "tokens": 213}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestSplitOnGridDict(unittest.TestCase):\n    @parameterized.expand(TEST_SINGLE)\n    def test_split_patch_single_call(self, in_type, input_parameters, img_dict, expected):\n        input_dict = {}\n        for k, v in img_dict.items():\n            input_dict[k] = in_type(v)\n        splitter = SplitOnGridDict(**input_parameters)\n        output = splitter(input_dict)[input_parameters[\"keys\"]]\n        assert_allclose(output, expected, type_test=False)\n\n    @parameterized.expand(TEST_MULTIPLE)\n    def test_split_patch_multiple_call(self, in_type, input_parameters, img_list, expected_list):\n        splitter = SplitOnGridDict(**input_parameters)\n        for img_dict, expected in zip(img_list, expected_list):\n            input_dict = {}\n            for k, v in img_dict.items():\n                input_dict[k] = in_type(v)\n            output = splitter(input_dict)[input_parameters[\"keys\"]]\n            assert_allclose(output, expected, type_test=False)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_squeezedim.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_squeezedim.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_squeezedim.py", "file_name": "test_squeezedim.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 47, "span_ids": ["TestSqueezeDim.test_shape", "TestSqueezeDim", "docstring", "impl:9", "TestSqueezeDim.test_invalid_inputs"], "tokens": 378}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import SqueezeDim\nfrom tests.utils import TEST_NDARRAYS\n\nTESTS, TESTS_FAIL = [], []\nfor p in TEST_NDARRAYS:\n    TESTS.append([{\"dim\": None}, p(np.random.rand(1, 2, 1, 3)), (2, 3)])\n    TESTS.append([{\"dim\": 2}, p(np.random.rand(1, 2, 1, 8, 16)), (1, 2, 8, 16)])\n    TESTS.append([{\"dim\": -1}, p(np.random.rand(1, 1, 16, 8, 1)), (1, 1, 16, 8)])\n    TESTS.append([{}, p(np.random.rand(1, 2, 1, 3)), (2, 1, 3)])\n\n    TESTS_FAIL.append([ValueError, {\"dim\": -2}, p(np.random.rand(1, 1, 16, 8, 1))])\n    TESTS_FAIL.append([TypeError, {\"dim\": 0.5}, p(np.random.rand(1, 1, 16, 8, 1))])\n\n\nclass TestSqueezeDim(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_shape(self, input_param, test_data, expected_shape):\n\n        result = SqueezeDim(**input_param)(test_data)\n        self.assertTupleEqual(result.shape, expected_shape)\n\n    @parameterized.expand(TESTS_FAIL)\n    def test_invalid_inputs(self, exception, input_param, test_data):\n\n        with self.assertRaises(exception):\n            SqueezeDim(**input_param)(test_data)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_squeezedimd.py_unittest_for_p_in_TEST_NDARRAYS_.None_6": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_squeezedimd.py_unittest_for_p_in_TEST_NDARRAYS_.None_6", "embedding": null, "metadata": {"file_path": "tests/test_squeezedimd.py", "file_name": "test_squeezedimd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 76, "span_ids": ["docstring"], "tokens": 652}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import SqueezeDimd\nfrom tests.utils import TEST_NDARRAYS\n\nTESTS, TESTS_FAIL = [], []\nfor p in TEST_NDARRAYS:\n    TESTS.append(\n        [\n            {\"keys\": [\"img\", \"seg\"], \"dim\": None},\n            {\"img\": p(np.random.rand(1, 2, 1, 3)), \"seg\": p(np.random.randint(0, 2, size=[1, 2, 1, 3]))},\n            (2, 3),\n        ]\n    )\n\n    TESTS.append(\n        [\n            {\"keys\": [\"img\", \"seg\"], \"dim\": 2},\n            {\"img\": p(np.random.rand(1, 2, 1, 8, 16)), \"seg\": p(np.random.randint(0, 2, size=[1, 2, 1, 8, 16]))},\n            (1, 2, 8, 16),\n        ]\n    )\n\n    TESTS.append(\n        [\n            {\"keys\": [\"img\", \"seg\"], \"dim\": -1},\n            {\"img\": p(np.random.rand(1, 1, 16, 8, 1)), \"seg\": p(np.random.randint(0, 2, size=[1, 1, 16, 8, 1]))},\n            (1, 1, 16, 8),\n        ]\n    )\n\n    TESTS.append(\n        [\n            {\"keys\": [\"img\", \"seg\"]},\n            {\"img\": p(np.random.rand(1, 2, 1, 3)), \"seg\": p(np.random.randint(0, 2, size=[1, 2, 1, 3]))},\n            (2, 1, 3),\n        ]\n    )\n\n    TESTS.append(\n        [\n            {\"keys\": [\"img\", \"seg\"], \"dim\": 0},\n            {\"img\": p(np.random.rand(1, 2, 1, 3)), \"seg\": p(np.random.randint(0, 2, size=[1, 2, 1, 3]))},\n            (2, 1, 3),\n        ]\n    )\n\n    TESTS_FAIL.append(\n        [\n            ValueError,\n            {\"keys\": [\"img\", \"seg\"], \"dim\": -2},\n            {\"img\": p(np.random.rand(1, 1, 16, 8, 1)), \"seg\": p(np.random.randint(0, 2, size=[1, 1, 16, 8, 1]))},\n        ]\n    )\n\n    TESTS_FAIL.append(\n        [\n            TypeError,\n            {\"keys\": [\"img\", \"seg\"], \"dim\": 0.5},\n            {\"img\": p(np.random.rand(1, 1, 16, 8, 1)), \"seg\": p(np.random.randint(0, 2, size=[1, 1, 16, 8, 1]))},\n        ]\n    )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_std_shift_intensity.py_unittest_TestStdShiftIntensity.test_zerostd.for_p_in_TEST_NDARRAYS_.for_nonzero_in_True_Fal.for_channel_wise_in_True.torch_testing_assert_allc": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_std_shift_intensity.py_unittest_TestStdShiftIntensity.test_zerostd.for_p_in_TEST_NDARRAYS_.for_nonzero_in_True_Fal.for_channel_wise_in_True.torch_testing_assert_allc", "embedding": null, "metadata": {"file_path": "tests/test_std_shift_intensity.py", "file_name": "test_std_shift_intensity.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 41, "span_ids": ["TestStdShiftIntensity.test_value", "TestStdShiftIntensity.test_zerostd", "TestStdShiftIntensity", "docstring"], "tokens": 278}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\n\nfrom monai.transforms import ShiftIntensity, StdShiftIntensity\nfrom tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D\n\n\nclass TestStdShiftIntensity(NumpyImageTestCase2D):\n    def test_value(self):\n        for p in TEST_NDARRAYS:\n            imt = p(self.imt)\n            factor = np.random.rand()\n            offset = np.std(self.imt) * factor\n            shifter = ShiftIntensity(offset=offset)\n            expected = shifter(imt)\n            std_shifter = StdShiftIntensity(factor=factor)\n            result = std_shifter(imt)\n            torch.testing.assert_allclose(result, expected, atol=0, rtol=1e-5)\n\n    def test_zerostd(self):\n        for p in TEST_NDARRAYS:\n            image = p(np.ones([2, 3, 3], dtype=np.float32))\n            for nonzero in [True, False]:\n                for channel_wise in [True, False]:\n                    factor = np.random.rand()\n                    std_shifter = StdShiftIntensity(factor=factor, nonzero=nonzero, channel_wise=channel_wise)\n                    result = std_shifter(image)\n                    torch.testing.assert_allclose(result, image, atol=0, rtol=1e-5)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_std_shift_intensity.py_TestStdShiftIntensity.test_nonzero_TestStdShiftIntensity.test_nonzero.for_p_in_TEST_NDARRAYS_.torch_testing_assert_allc": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_std_shift_intensity.py_TestStdShiftIntensity.test_nonzero_TestStdShiftIntensity.test_nonzero.for_p_in_TEST_NDARRAYS_.torch_testing_assert_allc", "embedding": null, "metadata": {"file_path": "tests/test_std_shift_intensity.py", "file_name": "test_std_shift_intensity.py", "file_type": "text/x-python", "category": "test", "start_line": 43, "end_line": 50, "span_ids": ["TestStdShiftIntensity.test_nonzero"], "tokens": 156}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestStdShiftIntensity(NumpyImageTestCase2D):\n\n    def test_nonzero(self):\n        for p in TEST_NDARRAYS:\n            image = p(np.asarray([[4.0, 0.0, 2.0], [0, 2, 4]]))  # std = 1\n            factor = np.random.rand()\n            std_shifter = StdShiftIntensity(factor=factor, nonzero=True)\n            result = std_shifter(image)\n            expected = p(np.asarray([[4 + factor, 0, 2 + factor], [0, 2 + factor, 4 + factor]], dtype=np.float32))\n            torch.testing.assert_allclose(result, expected, atol=0, rtol=1e-5)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_synthetic.py_unittest_TEST_CASES._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_synthetic.py_unittest_TEST_CASES._", "embedding": null, "metadata": {"file_path": "tests/test_synthetic.py", "file_name": "test_synthetic.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 38, "span_ids": ["docstring"], "tokens": 268}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.data import create_test_image_2d, create_test_image_3d\nfrom monai.utils import set_determinism\n\nTEST_CASES = [\n    [2, {\"width\": 64, \"height\": 64, \"rad_max\": 10, \"rad_min\": 4}, 0.1479004, 0.739502, (64, 64), 5],\n    [\n        2,\n        {\"width\": 32, \"height\": 28, \"num_objs\": 3, \"rad_max\": 5, \"rad_min\": 1, \"noise_max\": 0.2},\n        0.1709315,\n        0.4040179,\n        (32, 28),\n        5,\n    ],\n    [\n        3,\n        {\"width\": 64, \"height\": 64, \"depth\": 45, \"num_seg_classes\": 3, \"channel_dim\": -1, \"rad_max\": 10, \"rad_min\": 4},\n        0.025132,\n        0.0753961,\n        (64, 64, 45, 1),\n        3,\n    ],\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_synthetic.py_TestDiceCELoss_TestDiceCELoss.test_create_test_image.None_4": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_synthetic.py_TestDiceCELoss_TestDiceCELoss.test_create_test_image.None_4", "embedding": null, "metadata": {"file_path": "tests/test_synthetic.py", "file_name": "test_synthetic.py", "file_type": "text/x-python", "category": "test", "start_line": 41, "end_line": 52, "span_ids": ["TestDiceCELoss", "TestDiceCELoss.test_create_test_image"], "tokens": 169}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestDiceCELoss(unittest.TestCase):\n    @parameterized.expand(TEST_CASES)\n    def test_create_test_image(self, dim, input_param, expected_img, expected_seg, expected_shape, expected_max_cls):\n        set_determinism(seed=0)\n        if dim == 2:\n            img, seg = create_test_image_2d(**input_param)\n        elif dim == 3:\n            img, seg = create_test_image_3d(**input_param)\n        self.assertEqual(img.shape, expected_shape)\n        self.assertEqual(seg.max(), expected_max_cls)\n        np.testing.assert_allclose(img.mean(), expected_img, atol=1e-7, rtol=1e-7)\n        np.testing.assert_allclose(seg.mean(), expected_seg, atol=1e-7, rtol=1e-7)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_synthetic.py_TestDiceCELoss.test_ill_radius_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_synthetic.py_TestDiceCELoss.test_ill_radius_", "embedding": null, "metadata": {"file_path": "tests/test_synthetic.py", "file_name": "test_synthetic.py", "file_type": "text/x-python", "category": "test", "start_line": 54, "end_line": 65, "span_ids": ["TestDiceCELoss.test_ill_radius", "impl:3"], "tokens": 134}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestDiceCELoss(unittest.TestCase):\n\n    def test_ill_radius(self):\n        with self.assertRaisesRegex(ValueError, \"\"):\n            img, seg = create_test_image_2d(32, 32, rad_max=20)\n        with self.assertRaisesRegex(ValueError, \"\"):\n            img, seg = create_test_image_3d(32, 32, 32, rad_max=10, rad_min=11)\n        with self.assertRaisesRegex(ValueError, \"\"):\n            img, seg = create_test_image_2d(32, 32, rad_max=10, rad_min=0)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_testtimeaugmentation.py_TestTestTimeAugmentation.test_requires_meta_dict_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_testtimeaugmentation.py_TestTestTimeAugmentation.test_requires_meta_dict_", "embedding": null, "metadata": {"file_path": "tests/test_testtimeaugmentation.py", "file_name": "test_testtimeaugmentation.py", "file_type": "text/x-python", "category": "test", "start_line": 151, "end_line": 160, "span_ids": ["TestTestTimeAugmentation.test_requires_meta_dict", "impl:11"], "tokens": 128}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestTestTimeAugmentation(unittest.TestCase):\n\n    @unittest.skipUnless(has_nib, \"Requires nibabel\")\n    def test_requires_meta_dict(self):\n        transforms = Compose([AddChanneld(\"image\"), RandFlipd(\"image\"), Spacingd(\"image\", pixdim=1.1)])\n        tta = TestTimeAugmentation(transforms, batch_size=5, num_workers=0, inferrer_fn=lambda x: x, orig_key=\"image\")\n        tta(self.get_data(1, (20, 20), include_label=False))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_thread_buffer.py_TestDataLoader.test_dataloader_TestDataLoader.test_dataloader.None_1.None_1": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_thread_buffer.py_TestDataLoader.test_dataloader_TestDataLoader.test_dataloader.None_1.None_1", "embedding": null, "metadata": {"file_path": "tests/test_thread_buffer.py", "file_name": "test_thread_buffer.py", "file_type": "text/x-python", "category": "test", "start_line": 44, "end_line": 54, "span_ids": ["TestDataLoader.test_dataloader"], "tokens": 139}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestDataLoader(unittest.TestCase):\n\n    def test_dataloader(self):\n        dataset = Dataset(data=self.datalist, transform=self.transform)\n        dataloader = ThreadDataLoader(dataset=dataset, batch_size=2, num_workers=0)\n\n        for d in dataloader:\n            self.assertEqual(d[\"image\"][0], \"spleen_19.nii.gz\")\n            self.assertEqual(d[\"image\"][1], \"spleen_31.nii.gz\")\n\n        for d in dataloader:\n            self.assertEqual(d[\"label\"][0], \"spleen_label_19.nii.gz\")\n            self.assertEqual(d[\"label\"][1], \"spleen_label_31.nii.gz\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_threshold_intensityd.py_unittest_for_p_in_TEST_NDARRAYS_.None_2": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_threshold_intensityd.py_unittest_for_p_in_TEST_NDARRAYS_.None_2", "embedding": null, "metadata": {"file_path": "tests/test_threshold_intensityd.py", "file_name": "test_threshold_intensityd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 42, "span_ids": ["docstring"], "tokens": 279}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import ThresholdIntensityd\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    TESTS.append(\n        [\n            p,\n            {\"keys\": [\"image\", \"label\", \"extra\"], \"threshold\": 5, \"above\": True, \"cval\": 0},\n            (0, 0, 0, 0, 0, 0, 6, 7, 8, 9),\n        ]\n    )\n    TESTS.append(\n        [\n            p,\n            {\"keys\": [\"image\", \"label\", \"extra\"], \"threshold\": 5, \"above\": False, \"cval\": 0},\n            (0, 1, 2, 3, 4, 0, 0, 0, 0, 0),\n        ]\n    )\n    TESTS.append(\n        [\n            p,\n            {\"keys\": [\"image\", \"label\", \"extra\"], \"threshold\": 5, \"above\": True, \"cval\": 5},\n            (5, 5, 5, 5, 5, 5, 6, 7, 8, 9),\n        ]\n    )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_to_cupy.py_unittest_TestToCupy.test_cupy_input_dtype.cp_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_to_cupy.py_unittest_TestToCupy.test_cupy_input_dtype.cp_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_to_cupy.py", "file_name": "test_to_cupy.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 45, "span_ids": ["TestToCupy.test_cupy_input", "TestToCupy", "TestToCupy.test_cupy_input_dtype", "docstring"], "tokens": 299}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom unittest import skipUnless\n\nimport numpy as np\nimport torch\n\nfrom monai.transforms import ToCupy\nfrom monai.utils import optional_import\nfrom tests.utils import HAS_CUPY, skip_if_no_cuda\n\ncp, _ = optional_import(\"cupy\")\n\n\n@skipUnless(HAS_CUPY, \"CuPy is required.\")\nclass TestToCupy(unittest.TestCase):\n    def test_cupy_input(self):\n        test_data = cp.array([[1, 2], [3, 4]], dtype=cp.float32)\n        test_data = cp.rot90(test_data)\n        self.assertFalse(test_data.flags[\"C_CONTIGUOUS\"])\n        result = ToCupy()(test_data)\n        self.assertTrue(result.dtype == cp.float32)\n        self.assertTrue(isinstance(result, cp.ndarray))\n        self.assertTrue(result.flags[\"C_CONTIGUOUS\"])\n        cp.testing.assert_allclose(result, test_data)\n\n    def test_cupy_input_dtype(self):\n        test_data = cp.array([[1, 2], [3, 4]], dtype=cp.float32)\n        test_data = cp.rot90(test_data)\n        self.assertFalse(test_data.flags[\"C_CONTIGUOUS\"])\n        result = ToCupy(cp.uint8)(test_data)\n        self.assertTrue(result.dtype == cp.uint8)\n        self.assertTrue(isinstance(result, cp.ndarray))\n        self.assertTrue(result.flags[\"C_CONTIGUOUS\"])\n        cp.testing.assert_allclose(result, test_data)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_to_cupy.py_TestToCupy.test_numpy_input_TestToCupy.test_tensor_input.cp_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_to_cupy.py_TestToCupy.test_numpy_input_TestToCupy.test_tensor_input.cp_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_to_cupy.py", "file_name": "test_to_cupy.py", "file_type": "text/x-python", "category": "test", "start_line": 47, "end_line": 75, "span_ids": ["TestToCupy.test_numpy_input_dtype", "TestToCupy.test_numpy_input", "TestToCupy.test_tensor_input"], "tokens": 337}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skipUnless(HAS_CUPY, \"CuPy is required.\")\nclass TestToCupy(unittest.TestCase):\n\n    def test_numpy_input(self):\n        test_data = np.array([[1, 2], [3, 4]], dtype=np.float32)\n        test_data = np.rot90(test_data)\n        self.assertFalse(test_data.flags[\"C_CONTIGUOUS\"])\n        result = ToCupy()(test_data)\n        self.assertTrue(result.dtype == cp.float32)\n        self.assertTrue(isinstance(result, cp.ndarray))\n        self.assertTrue(result.flags[\"C_CONTIGUOUS\"])\n        cp.testing.assert_allclose(result, test_data)\n\n    def test_numpy_input_dtype(self):\n        test_data = np.array([[1, 2], [3, 4]], dtype=np.float32)\n        test_data = np.rot90(test_data)\n        self.assertFalse(test_data.flags[\"C_CONTIGUOUS\"])\n        result = ToCupy(np.uint8)(test_data)\n        self.assertTrue(result.dtype == cp.uint8)\n        self.assertTrue(isinstance(result, cp.ndarray))\n        self.assertTrue(result.flags[\"C_CONTIGUOUS\"])\n        cp.testing.assert_allclose(result, test_data)\n\n    def test_tensor_input(self):\n        test_data = torch.tensor([[1, 2], [3, 4]], dtype=torch.float32)\n        test_data = test_data.rot90()\n        self.assertFalse(test_data.is_contiguous())\n        result = ToCupy()(test_data)\n        self.assertTrue(result.dtype == cp.float32)\n        self.assertTrue(isinstance(result, cp.ndarray))\n        self.assertTrue(result.flags[\"C_CONTIGUOUS\"])\n        cp.testing.assert_allclose(result, test_data)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_to_cupy.py_TestToCupy.test_tensor_cuda_input_TestToCupy.test_tensor_cuda_input.cp_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_to_cupy.py_TestToCupy.test_tensor_cuda_input_TestToCupy.test_tensor_cuda_input.cp_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_to_cupy.py", "file_name": "test_to_cupy.py", "file_type": "text/x-python", "category": "test", "start_line": 77, "end_line": 86, "span_ids": ["TestToCupy.test_tensor_cuda_input"], "tokens": 133}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skipUnless(HAS_CUPY, \"CuPy is required.\")\nclass TestToCupy(unittest.TestCase):\n\n    @skip_if_no_cuda\n    def test_tensor_cuda_input(self):\n        test_data = torch.tensor([[1, 2], [3, 4]], dtype=torch.float32).cuda()\n        test_data = test_data.rot90()\n        self.assertFalse(test_data.is_contiguous())\n        result = ToCupy()(test_data)\n        self.assertTrue(result.dtype == cp.float32)\n        self.assertTrue(isinstance(result, cp.ndarray))\n        self.assertTrue(result.flags[\"C_CONTIGUOUS\"])\n        cp.testing.assert_allclose(result, test_data)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_to_cupy.py_TestToCupy.test_tensor_cuda_input_dtype_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_to_cupy.py_TestToCupy.test_tensor_cuda_input_dtype_", "embedding": null, "metadata": {"file_path": "tests/test_to_cupy.py", "file_name": "test_to_cupy.py", "file_type": "text/x-python", "category": "test", "start_line": 88, "end_line": 111, "span_ids": ["impl:2", "TestToCupy.test_tensor_cuda_input_dtype", "TestToCupy.test_list_tuple"], "tokens": 243}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skipUnless(HAS_CUPY, \"CuPy is required.\")\nclass TestToCupy(unittest.TestCase):\n\n    @skip_if_no_cuda\n    def test_tensor_cuda_input_dtype(self):\n        test_data = torch.tensor([[1, 2], [3, 4]], dtype=torch.uint8).cuda()\n        test_data = test_data.rot90()\n        self.assertFalse(test_data.is_contiguous())\n\n        result = ToCupy(dtype=\"float32\")(test_data)\n        self.assertTrue(result.dtype == cp.float32)\n        self.assertTrue(isinstance(result, cp.ndarray))\n        self.assertTrue(result.flags[\"C_CONTIGUOUS\"])\n        cp.testing.assert_allclose(result, test_data)\n\n    def test_list_tuple(self):\n        test_data = [[1, 2], [3, 4]]\n        result = ToCupy(wrap_sequence=True)(test_data)\n        cp.testing.assert_allclose(result, cp.asarray(test_data))\n        test_data = ((1, 2), (3, 4))\n        result = ToCupy(wrap_sequence=True)(test_data)\n        cp.testing.assert_allclose(result, cp.asarray(test_data))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_to_cupyd.py_TestToCupyd.test_tensor_cuda_input_TestToCupyd.test_tensor_cuda_input.cp_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_to_cupyd.py_TestToCupyd.test_tensor_cuda_input_TestToCupyd.test_tensor_cuda_input.cp_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_to_cupyd.py", "file_name": "test_to_cupyd.py", "file_type": "text/x-python", "category": "test", "start_line": 54, "end_line": 62, "span_ids": ["TestToCupyd.test_tensor_cuda_input"], "tokens": 133}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skipUnless(HAS_CUPY, \"CuPy is required.\")\nclass TestToCupyd(unittest.TestCase):\n\n    @skip_if_no_cuda\n    def test_tensor_cuda_input(self):\n        test_data = torch.tensor([[1, 2], [3, 4]]).cuda()\n        test_data = test_data.rot90()\n        self.assertFalse(test_data.is_contiguous())\n        result = ToCupyd(keys=\"img\")({\"img\": test_data})[\"img\"]\n        self.assertTrue(isinstance(result, cp.ndarray))\n        self.assertTrue(result.flags[\"C_CONTIGUOUS\"])\n        cp.testing.assert_allclose(result, test_data.cpu().numpy())", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_to_device.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_to_device.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_to_device.py", "file_name": "test_to_device.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 41, "span_ids": ["impl:9", "TestToDevice", "TestToDevice.test_value", "docstring"], "tokens": 175}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import ToDevice\nfrom tests.utils import skip_if_no_cuda\n\nTEST_CASE_1 = [\"cuda:0\"]\n\nTEST_CASE_2 = [\"cuda\"]\n\nTEST_CASE_3 = [torch.device(\"cpu:0\")]\n\nTEST_CASE_4 = [\"cpu\"]\n\n\n@skip_if_no_cuda\nclass TestToDevice(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4])\n    def test_value(self, device):\n        converter = ToDevice(device=device, non_blocking=True)\n        data = torch.tensor([1, 2, 3, 4])\n        ret = converter(data)\n        torch.testing.assert_allclose(ret, data.to(device))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_to_deviced.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_to_deviced.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_to_deviced.py", "file_name": "test_to_deviced.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 36, "span_ids": ["TestToDeviced.test_value", "impl", "TestToDeviced", "docstring"], "tokens": 178}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\n\nfrom monai.data import CacheDataset, ThreadDataLoader\nfrom monai.transforms import ToDeviced\nfrom tests.utils import skip_if_no_cuda\n\n\n@skip_if_no_cuda\nclass TestToDeviced(unittest.TestCase):\n    def test_value(self):\n        device = \"cuda:0\"\n        data = [{\"img\": torch.tensor(i)} for i in range(4)]\n        dataset = CacheDataset(\n            data=data, transform=ToDeviced(keys=\"img\", device=device, non_blocking=True), cache_rate=1.0\n        )\n        dataloader = ThreadDataLoader(dataset=dataset, num_workers=0, batch_size=1)\n        for i, d in enumerate(dataloader):\n            torch.testing.assert_allclose(d[\"img\"], torch.tensor([i], device=device))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_to_numpyd.py_unittest_TestToNumpyd.test_cupy_input.assert_allclose_result_t": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_to_numpyd.py_unittest_TestToNumpyd.test_cupy_input.assert_allclose_result_t", "embedding": null, "metadata": {"file_path": "tests/test_to_numpyd.py", "file_name": "test_to_numpyd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 34, "span_ids": ["TestToNumpyd.test_cupy_input", "TestToNumpyd", "docstring"], "tokens": 192}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom unittest import skipUnless\n\nimport numpy as np\nimport torch\n\nfrom monai.transforms import ToNumpyd\nfrom monai.utils import optional_import\nfrom tests.utils import HAS_CUPY, assert_allclose, skip_if_no_cuda\n\ncp, _ = optional_import(\"cupy\")\n\n\nclass TestToNumpyd(unittest.TestCase):\n    @skipUnless(HAS_CUPY, \"CuPy is required.\")\n    def test_cupy_input(self):\n        test_data = cp.array([[1, 2], [3, 4]])\n        test_data = cp.rot90(test_data)\n        self.assertFalse(test_data.flags[\"C_CONTIGUOUS\"])\n        result = ToNumpyd(keys=\"img\")({\"img\": test_data})[\"img\"]\n        self.assertTrue(isinstance(result, np.ndarray))\n        self.assertTrue(result.flags[\"C_CONTIGUOUS\"])\n        assert_allclose(result, test_data.get(), type_test=False)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_to_pil.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_to_pil.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_to_pil.py", "file_name": "test_to_pil.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 51, "span_ids": ["TestToPIL.test_value", "TestToPIL", "impl:15", "docstring"], "tokens": 288}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom typing import TYPE_CHECKING\nfrom unittest import skipUnless\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import ToPIL\nfrom monai.utils import optional_import\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nif TYPE_CHECKING:\n    from PIL.Image import Image as PILImageImage\n    from PIL.Image import fromarray as pil_image_fromarray\n\n    has_pil = True\nelse:\n    pil_image_fromarray, has_pil = optional_import(\"PIL.Image\", name=\"fromarray\")\n    PILImageImage, _ = optional_import(\"PIL.Image\", name=\"Image\")\n\nim = [[1.0, 2.0], [3.0, 4.0]]\nTESTS = []\nfor p in TEST_NDARRAYS:\n    TESTS.append([p(im)])\nif has_pil:\n    TESTS.append([pil_image_fromarray(np.array(im))])\n\n\nclass TestToPIL(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    @skipUnless(has_pil, \"Requires `pillow` package.\")\n    def test_value(self, test_data):\n        result = ToPIL()(test_data)\n        self.assertTrue(isinstance(result, PILImageImage))\n        assert_allclose(np.array(result), test_data, type_test=False)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_to_pild.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_to_pild.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_to_pild.py", "file_name": "test_to_pild.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 49, "span_ids": ["impl:13", "TestToPIL.test_values", "TestToPIL", "docstring"], "tokens": 320}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom typing import TYPE_CHECKING\nfrom unittest import skipUnless\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.transforms import ToPILd\nfrom monai.utils import optional_import\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nif TYPE_CHECKING:\n    from PIL.Image import Image as PILImageImage\n    from PIL.Image import fromarray as pil_image_fromarray\n\n    has_pil = True\nelse:\n    pil_image_fromarray, has_pil = optional_import(\"PIL.Image\", name=\"fromarray\")\n    PILImageImage, _ = optional_import(\"PIL.Image\", name=\"Image\")\n\nim = [[1.0, 2.0], [3.0, 4.0]]\nTESTS = [[{\"keys\": \"image\"}, {\"image\": p(im)}] for p in TEST_NDARRAYS]\nif has_pil:\n    TESTS.append([{\"keys\": \"image\"}, {\"image\": pil_image_fromarray(np.array(im))}])\n\n\nclass TestToPIL(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    @skipUnless(has_pil, \"Requires `pillow` package.\")\n    def test_values(self, input_param, test_data):\n        result = ToPILd(**input_param)(test_data)[input_param[\"keys\"]]\n        self.assertTrue(isinstance(result, PILImageImage))\n        assert_allclose(np.array(result), test_data[input_param[\"keys\"]], type_test=False)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_to_tensor.py_unittest_None_1.TESTS_SINGLE_append_p_5_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_to_tensor.py_unittest_None_1.TESTS_SINGLE_append_p_5_", "embedding": null, "metadata": {"file_path": "tests/test_to_tensor.py", "file_name": "test_to_tensor.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 32, "span_ids": ["docstring"], "tokens": 137}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import ToTensor\nfrom tests.utils import HAS_CUPY, TEST_NDARRAYS, assert_allclose, optional_import\n\ncp, _ = optional_import(\"cupy\")\n\nim = [[1, 2], [3, 4]]\n\nTESTS = []\nTESTS.append((im, (2, 2)))\nfor p in TEST_NDARRAYS:\n    TESTS.append((p(im), (2, 2)))\n\nTESTS_SINGLE = []\nTESTS_SINGLE.append([5])\nfor p in TEST_NDARRAYS:\n    TESTS_SINGLE.append([p(5)])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_to_tensor.py_TestToTensor_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_to_tensor.py_TestToTensor_", "embedding": null, "metadata": {"file_path": "tests/test_to_tensor.py", "file_name": "test_to_tensor.py", "file_type": "text/x-python", "category": "test", "start_line": 35, "end_line": 61, "span_ids": ["TestToTensor", "impl:14", "TestToTensor.test_cupy", "TestToTensor.test_array_input", "TestToTensor.test_single_input"], "tokens": 240}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestToTensor(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_array_input(self, test_data, expected_shape):\n        result = ToTensor(dtype=torch.float32, device=\"cpu\", wrap_sequence=True)(test_data)\n        self.assertTrue(isinstance(result, torch.Tensor))\n        assert_allclose(result, test_data, type_test=False)\n        self.assertTupleEqual(result.shape, expected_shape)\n\n    @parameterized.expand(TESTS_SINGLE)\n    def test_single_input(self, test_data):\n        result = ToTensor()(test_data)\n        self.assertTrue(isinstance(result, torch.Tensor))\n        assert_allclose(result, test_data, type_test=False)\n        self.assertEqual(result.ndim, 0)\n\n    @unittest.skipUnless(HAS_CUPY, \"CuPy is required.\")\n    def test_cupy(self):\n        test_data = [[1, 2], [3, 4]]\n        cupy_array = cp.ascontiguousarray(cp.asarray(test_data))\n        result = ToTensor()(cupy_array)\n        self.assertTrue(isinstance(result, torch.Tensor))\n        assert_allclose(result, test_data, type_test=False)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_torchvision.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_torchvision.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_torchvision.py", "file_name": "test_torchvision.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 66, "span_ids": ["TestTorchVision.test_value", "TestTorchVision", "impl:5", "docstring"], "tokens": 851}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nfrom parameterized import parameterized\n\nfrom monai.transforms import TorchVision\nfrom monai.utils import set_determinism\nfrom tests.utils import TEST_NDARRAYS, SkipIfBeforePyTorchVersion, assert_allclose\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    TESTS.extend(\n        [\n            [\n                {\"name\": \"ColorJitter\"},\n                p([[[0.0, 1.0], [1.0, 2.0]], [[0.0, 1.0], [1.0, 2.0]], [[0.0, 1.0], [1.0, 2.0]]]),\n                p([[[0.0, 1.0], [1.0, 2.0]], [[0.0, 1.0], [1.0, 2.0]], [[0.0, 1.0], [1.0, 2.0]]]),\n            ],\n            [\n                {\"name\": \"ColorJitter\", \"brightness\": 0.5, \"contrast\": 0.5, \"saturation\": [0.1, 0.8], \"hue\": 0.5},\n                p([[[0.0, 1.0], [1.0, 2.0]], [[0.0, 1.0], [1.0, 2.0]], [[0.0, 1.0], [1.0, 2.0]]]),\n                p(\n                    [\n                        [[0.1090, 0.6193], [0.6193, 0.9164]],\n                        [[0.1090, 0.6193], [0.6193, 0.9164]],\n                        [[0.1090, 0.6193], [0.6193, 0.9164]],\n                    ]\n                ),\n            ],\n            [\n                {\"name\": \"Pad\", \"padding\": [1, 1, 1, 1]},\n                p([[[0.0, 1.0], [1.0, 2.0]], [[0.0, 1.0], [1.0, 2.0]], [[0.0, 1.0], [1.0, 2.0]]]),\n                p(\n                    [\n                        [[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 2.0, 0.0], [0.0, 0.0, 0.0, 0.0]],\n                        [[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 2.0, 0.0], [0.0, 0.0, 0.0, 0.0]],\n                        [[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 2.0, 0.0], [0.0, 0.0, 0.0, 0.0]],\n                    ]\n                ),\n            ],\n        ]\n    )\n\n\n@SkipIfBeforePyTorchVersion((1, 7))\nclass TestTorchVision(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_value(self, input_param, input_data, expected_value):\n        set_determinism(seed=0)\n        result = TorchVision(**input_param)(input_data)\n        assert_allclose(result, expected_value, rtol=1e-3)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_torchvision_fully_conv_model.py_TestTorchVisionFullyConvModel_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_torchvision_fully_conv_model.py_TestTorchVisionFullyConvModel_", "embedding": null, "metadata": {"file_path": "tests/test_torchvision_fully_conv_model.py", "file_name": "test_torchvision_fully_conv_model.py", "file_type": "text/x-python", "category": "test", "start_line": 60, "end_line": 82, "span_ids": ["TestTorchVisionFullyConvModel.test_without_pretrained", "impl:18", "TestTorchVisionFullyConvModel", "TestTorchVisionFullyConvModel.test_with_pretrained"], "tokens": 254}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestTorchVisionFullyConvModel(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_0, TEST_CASE_1, TEST_CASE_2, TEST_CASE_3])\n    @skipUnless(has_tv, \"Requires TorchVision.\")\n    def test_without_pretrained(self, input_param, input_shape, expected_shape):\n        net = TorchVisionFullyConvModel(**input_param).to(device)\n        with eval_mode(net):\n            result = net.forward(torch.randn(input_shape).to(device))\n            self.assertEqual(result.shape, expected_shape)\n\n    @parameterized.expand([TEST_CASE_PRETRAINED_0, TEST_CASE_PRETRAINED_1, TEST_CASE_PRETRAINED_2])\n    @skipUnless(has_tv, \"Requires TorchVision.\")\n    def test_with_pretrained(self, input_param, input_shape, expected_shape, expected_value):\n        net = TorchVisionFullyConvModel(**input_param).to(device)\n        with eval_mode(net):\n            result = net.forward(torch.randn(input_shape).to(device))\n            value = next(net.parameters())[0, 0, 0, 0].item()\n            self.assertEqual(value, expected_value)\n            self.assertEqual(result.shape, expected_shape)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_transchex.py_unittest_for_drop_out_in_0_4_.for_in_channels_in_3_.for_img_size_in_224_.for_patch_size_in_16_32.for_num_language_layers_i.for_num_vision_layers_in_.for_num_mixed_layers_in_.for_num_classes_in_8_.TEST_CASE_TRANSCHEX_appen": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_transchex.py_unittest_for_drop_out_in_0_4_.for_in_channels_in_3_.for_img_size_in_224_.for_patch_size_in_16_32.for_num_language_layers_i.for_num_vision_layers_in_.for_num_mixed_layers_in_.for_num_classes_in_8_.TEST_CASE_TRANSCHEX_appen", "embedding": null, "metadata": {"file_path": "tests/test_transchex.py", "file_name": "test_transchex.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 43, "span_ids": ["docstring"], "tokens": 245}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks import eval_mode\nfrom monai.networks.nets.transchex import Transchex\nfrom tests.utils import skip_if_quick\n\nTEST_CASE_TRANSCHEX = []\nfor drop_out in [0.4]:\n    for in_channels in [3]:\n        for img_size in [224]:\n            for patch_size in [16, 32]:\n                for num_language_layers in [2]:\n                    for num_vision_layers in [4]:\n                        for num_mixed_layers in [3]:\n                            for num_classes in [8]:\n                                test_case = [\n                                    {\n                                        \"in_channels\": in_channels,\n                                        \"img_size\": (img_size,) * 2,\n                                        \"patch_size\": (patch_size,) * 2,\n                                        \"num_vision_layers\": num_vision_layers,\n                                        \"num_mixed_layers\": num_mixed_layers,\n                                        \"num_language_layers\": num_language_layers,\n                                        \"num_classes\": num_classes,\n                                        \"drop_out\": drop_out,\n                                    },\n                                    (2, num_classes),  # type: ignore\n                                ]\n                                TEST_CASE_TRANSCHEX.append(test_case)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_transchex.py_TestTranschex_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_transchex.py_TestTranschex_", "embedding": null, "metadata": {"file_path": "tests/test_transchex.py", "file_name": "test_transchex.py", "file_type": "text/x-python", "category": "test", "start_line": 46, "end_line": 83, "span_ids": ["TestTranschex.test_ill_arg", "TestTranschex.test_shape", "impl:14", "TestTranschex"], "tokens": 270}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_quick\nclass TestTranschex(unittest.TestCase):\n    @parameterized.expand(TEST_CASE_TRANSCHEX)\n    def test_shape(self, input_param, expected_shape):\n        net = Transchex(**input_param)\n        with eval_mode(net):\n            result = net(torch.randint(2, (2, 512)), torch.randint(2, (2, 512)), torch.randn((2, 3, 224, 224)))\n            self.assertEqual(result.shape, expected_shape)\n\n    def test_ill_arg(self):\n        with self.assertRaises(ValueError):\n            Transchex(\n                in_channels=3,\n                img_size=(128, 128),\n                patch_size=(16, 16),\n                num_language_layers=2,\n                num_mixed_layers=4,\n                num_vision_layers=2,\n                num_classes=2,\n                drop_out=5.0,\n            )\n\n        with self.assertRaises(ValueError):\n            Transchex(\n                in_channels=1,\n                img_size=(97, 97),\n                patch_size=(16, 16),\n                num_language_layers=6,\n                num_mixed_layers=6,\n                num_vision_layers=8,\n                num_classes=8,\n                drop_out=0.4,\n            )\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_transformerblock.py_unittest_for_dropout_rate_in_np_li.for_hidden_size_in_360_.for_num_heads_in_4_8_1.for_mlp_dim_in_1024_307.TEST_CASE_TRANSFORMERBLOC": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_transformerblock.py_unittest_for_dropout_rate_in_np_li.for_hidden_size_in_360_.for_num_heads_in_4_8_1.for_mlp_dim_in_1024_307.TEST_CASE_TRANSFORMERBLOC", "embedding": null, "metadata": {"file_path": "tests/test_transformerblock.py", "file_name": "test_transformerblock.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 37, "span_ids": ["docstring"], "tokens": 182}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks import eval_mode\nfrom monai.networks.blocks.transformerblock import TransformerBlock\n\nTEST_CASE_TRANSFORMERBLOCK = []\nfor dropout_rate in np.linspace(0, 1, 4):\n    for hidden_size in [360, 480, 600, 768]:\n        for num_heads in [4, 8, 12]:\n            for mlp_dim in [1024, 3072]:\n\n                test_case = [\n                    {\n                        \"hidden_size\": hidden_size,\n                        \"num_heads\": num_heads,\n                        \"mlp_dim\": mlp_dim,\n                        \"dropout_rate\": dropout_rate,\n                    },\n                    (2, 512, hidden_size),\n                    (2, 512, hidden_size),\n                ]\n                TEST_CASE_TRANSFORMERBLOCK.append(test_case)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_transformerblock.py_TestTransformerBlock_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_transformerblock.py_TestTransformerBlock_", "embedding": null, "metadata": {"file_path": "tests/test_transformerblock.py", "file_name": "test_transformerblock.py", "file_type": "text/x-python", "category": "test", "start_line": 40, "end_line": 58, "span_ids": ["TestTransformerBlock.test_shape", "TestTransformerBlock.test_ill_arg", "TestTransformerBlock", "impl:10"], "tokens": 152}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestTransformerBlock(unittest.TestCase):\n    @parameterized.expand(TEST_CASE_TRANSFORMERBLOCK)\n    def test_shape(self, input_param, input_shape, expected_shape):\n        net = TransformerBlock(**input_param)\n        with eval_mode(net):\n            result = net(torch.randn(input_shape))\n            self.assertEqual(result.shape, expected_shape)\n\n    def test_ill_arg(self):\n        with self.assertRaises(ValueError):\n            TransformerBlock(hidden_size=128, num_heads=12, mlp_dim=2048, dropout_rate=4.0)\n\n        with self.assertRaises(ValueError):\n            TransformerBlock(hidden_size=622, num_heads=8, mlp_dim=3072, dropout_rate=0.4)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_transposed.py_unittest_for_p_in_TEST_NDARRAYS_.None_3": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_transposed.py_unittest_for_p_in_TEST_NDARRAYS_.None_3", "embedding": null, "metadata": {"file_path": "tests/test_transposed.py", "file_name": "test_transposed.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 27, "span_ids": ["docstring"], "tokens": 173}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom copy import deepcopy\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import Transposed\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    TESTS.append([p(np.arange(5 * 4).reshape(5, 4)), [1, 0]])\n    TESTS.append([p(np.arange(5 * 4).reshape(5, 4)), None])\n    TESTS.append([p(np.arange(5 * 4 * 3).reshape(5, 4, 3)), [2, 0, 1]])\n    TESTS.append([p(np.arange(5 * 4 * 3).reshape(5, 4, 3)), None])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_unet.py_TEST_CASE_5_ILL_CASES._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_unet.py_TEST_CASE_5_ILL_CASES._", "embedding": null, "metadata": {"file_path": "tests/test_unet.py", "file_name": "test_unet.py", "file_type": "text/x-python", "category": "test", "start_line": 90, "end_line": 161, "span_ids": ["impl:9", "impl:17"], "tokens": 646}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "TEST_CASE_5 = [  # 4-channel 3D, batch 16, LeakyReLU activation\n    {\n        \"spatial_dims\": 3,\n        \"in_channels\": 4,\n        \"out_channels\": 3,\n        \"channels\": (16, 32, 64),\n        \"strides\": (2, 2),\n        \"num_res_units\": 1,\n        \"act\": (Act.LEAKYRELU, {\"negative_slope\": 0.2}),\n    },\n    (16, 4, 32, 64, 48),\n    (16, 3, 32, 64, 48),\n]\n\nTEST_CASE_6 = [  # 4-channel 3D, batch 16, LeakyReLU activation explicit\n    {\n        \"spatial_dims\": 3,\n        \"in_channels\": 4,\n        \"out_channels\": 3,\n        \"channels\": (16, 32, 64),\n        \"strides\": (2, 2),\n        \"num_res_units\": 1,\n        \"act\": (torch.nn.LeakyReLU, {\"negative_slope\": 0.2}),\n    },\n    (16, 4, 32, 64, 48),\n    (16, 3, 32, 64, 48),\n]\n\nCASES = [TEST_CASE_0, TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5, TEST_CASE_6]\n\nILL_CASES = [\n    [\n        {  # len(channels) < 2\n            \"spatial_dims\": 2,\n            \"in_channels\": 1,\n            \"out_channels\": 3,\n            \"channels\": (16,),\n            \"strides\": (2, 2),\n            \"num_res_units\": 0,\n        }\n    ],\n    [\n        {  # len(strides) < len(channels) - 1\n            \"spatial_dims\": 2,\n            \"in_channels\": 1,\n            \"out_channels\": 3,\n            \"channels\": (8, 8, 8),\n            \"strides\": (2,),\n            \"num_res_units\": 0,\n        }\n    ],\n    [\n        {  # len(kernel_size) = 3, spatial_dims = 2\n            \"spatial_dims\": 2,\n            \"in_channels\": 1,\n            \"out_channels\": 3,\n            \"channels\": (8, 8, 8),\n            \"strides\": (2, 2),\n            \"kernel_size\": (3, 3, 3),\n        }\n    ],\n    [\n        {  # len(up_kernel_size) = 2, spatial_dims = 3\n            \"spatial_dims\": 3,\n            \"in_channels\": 1,\n            \"out_channels\": 3,\n            \"channels\": (8, 8, 8),\n            \"strides\": (2, 2),\n            \"up_kernel_size\": (3, 3),\n        }\n    ],\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_unetr.py_unittest_for_dropout_rate_in_0_4_.for_in_channels_in_1_.for_out_channels_in_2_.for_hidden_size_in_768_.for_img_size_in_96_128_.for_feature_size_in_16_.for_num_heads_in_8_.for_mlp_dim_in_3072_.for_norm_name_in_instan.for_pos_embed_in_percep.for_nd_in_2_3_.TEST_CASE_UNETR_append_te": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_unetr.py_unittest_for_dropout_rate_in_0_4_.for_in_channels_in_1_.for_out_channels_in_2_.for_hidden_size_in_768_.for_img_size_in_96_128_.for_feature_size_in_16_.for_num_heads_in_8_.for_mlp_dim_in_3072_.for_norm_name_in_instan.for_pos_embed_in_percep.for_nd_in_2_3_.TEST_CASE_UNETR_append_te", "embedding": null, "metadata": {"file_path": "tests/test_unetr.py", "file_name": "test_unetr.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 52, "span_ids": ["docstring"], "tokens": 318}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks import eval_mode\nfrom monai.networks.nets.unetr import UNETR\n\nTEST_CASE_UNETR = []\nfor dropout_rate in [0.4]:\n    for in_channels in [1]:\n        for out_channels in [2]:\n            for hidden_size in [768]:\n                for img_size in [96, 128]:\n                    for feature_size in [16]:\n                        for num_heads in [8]:\n                            for mlp_dim in [3072]:\n                                for norm_name in [\"instance\"]:\n                                    for pos_embed in [\"perceptron\"]:\n                                        for nd in (2, 3):\n                                            test_case = [\n                                                {\n                                                    \"in_channels\": in_channels,\n                                                    \"out_channels\": out_channels,\n                                                    \"img_size\": (img_size,) * nd,\n                                                    \"hidden_size\": hidden_size,\n                                                    \"feature_size\": feature_size,\n                                                    \"norm_name\": norm_name,\n                                                    \"mlp_dim\": mlp_dim,\n                                                    \"num_heads\": num_heads,\n                                                    \"pos_embed\": pos_embed,\n                                                    \"dropout_rate\": dropout_rate,\n                                                    \"conv_block\": True,\n                                                    \"res_block\": False,\n                                                },\n                                                (2, in_channels, *([img_size] * nd)),\n                                                (2, out_channels, *([img_size] * nd)),\n                                            ]\n                                            if nd == 2:\n                                                test_case[0][\"spatial_dims\"] = 2  # type: ignore\n                                            TEST_CASE_UNETR.append(test_case)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_unetr.py_TestPatchEmbeddingBlock_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_unetr.py_TestPatchEmbeddingBlock_", "embedding": null, "metadata": {"file_path": "tests/test_unetr.py", "file_name": "test_unetr.py", "file_type": "text/x-python", "category": "test", "start_line": 55, "end_line": 123, "span_ids": ["TestPatchEmbeddingBlock", "TestPatchEmbeddingBlock.test_shape", "TestPatchEmbeddingBlock.test_ill_arg", "impl:19"], "tokens": 421}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestPatchEmbeddingBlock(unittest.TestCase):\n    @parameterized.expand(TEST_CASE_UNETR)\n    def test_shape(self, input_param, input_shape, expected_shape):\n        net = UNETR(**input_param)\n        with eval_mode(net):\n            result = net(torch.randn(input_shape))\n            self.assertEqual(result.shape, expected_shape)\n\n    def test_ill_arg(self):\n        with self.assertRaises(ValueError):\n            UNETR(\n                in_channels=1,\n                out_channels=3,\n                img_size=(128, 128, 128),\n                feature_size=16,\n                hidden_size=128,\n                mlp_dim=3072,\n                num_heads=12,\n                pos_embed=\"conv\",\n                norm_name=\"instance\",\n                dropout_rate=5.0,\n            )\n\n        with self.assertRaises(ValueError):\n            UNETR(\n                in_channels=1,\n                out_channels=4,\n                img_size=(32, 32, 32),\n                feature_size=32,\n                hidden_size=512,\n                mlp_dim=3072,\n                num_heads=12,\n                pos_embed=\"conv\",\n                norm_name=\"instance\",\n                dropout_rate=0.5,\n            )\n\n        with self.assertRaises(ValueError):\n            UNETR(\n                in_channels=1,\n                out_channels=3,\n                img_size=(96, 96, 96),\n                feature_size=16,\n                hidden_size=512,\n                mlp_dim=3072,\n                num_heads=14,\n                pos_embed=\"conv\",\n                norm_name=\"batch\",\n                dropout_rate=0.4,\n            )\n\n        with self.assertRaises(ValueError):\n            UNETR(\n                in_channels=1,\n                out_channels=4,\n                img_size=(96, 96, 96),\n                feature_size=8,\n                hidden_size=768,\n                mlp_dim=3072,\n                num_heads=12,\n                pos_embed=\"perc\",\n                norm_name=\"instance\",\n                dropout_rate=0.2,\n            )\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_unetr_block.py_unittest_None_2.for_kernel_size_in_1_3_.for_upsample_kernel_size_.for_stride_in_1_2_.for_res_block_in_False_.for_norm_name_in_instan.for_in_size_in_15_16_.TEST_PRUP_BLOCK_append_te": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_unetr_block.py_unittest_None_2.for_kernel_size_in_1_3_.for_upsample_kernel_size_.for_stride_in_1_2_.for_res_block_in_False_.for_norm_name_in_instan.for_in_size_in_15_16_.TEST_PRUP_BLOCK_append_te", "embedding": null, "metadata": {"file_path": "tests/test_unetr_block.py", "file_name": "test_unetr_block.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 100, "span_ids": ["impl:31", "docstring"], "tokens": 773}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks import eval_mode\nfrom monai.networks.blocks.dynunet_block import get_padding\nfrom monai.networks.blocks.unetr_block import UnetrBasicBlock, UnetrPrUpBlock, UnetrUpBlock\nfrom tests.utils import test_script_save\n\nTEST_CASE_UNETR_BASIC_BLOCK = []\nfor spatial_dims in range(1, 4):\n    for kernel_size in [1, 3]:\n        for stride in [2]:\n            for norm_name in [(\"GROUP\", {\"num_groups\": 16}), (\"batch\", {\"track_running_stats\": False}), \"instance\"]:\n                for in_size in [15, 16]:\n                    padding = get_padding(kernel_size, stride)\n                    if not isinstance(padding, int):\n                        padding = padding[0]\n                    out_size = int((in_size + 2 * padding - kernel_size) / stride) + 1\n                    test_case = [\n                        {\n                            \"spatial_dims\": spatial_dims,\n                            \"in_channels\": 16,\n                            \"out_channels\": 16,\n                            \"kernel_size\": kernel_size,\n                            \"norm_name\": norm_name,\n                            \"stride\": stride,\n                        },\n                        (1, 16, *([in_size] * spatial_dims)),\n                        (1, 16, *([out_size] * spatial_dims)),\n                    ]\n                    TEST_CASE_UNETR_BASIC_BLOCK.append(test_case)\n\nTEST_UP_BLOCK = []\nin_channels, out_channels = 4, 2\nfor spatial_dims in range(1, 4):\n    for kernel_size in [1, 3]:\n        for res_block in [False, True]:\n            for norm_name in [\"instance\"]:\n                for in_size in [15, 16]:\n                    out_size = in_size * stride\n                    test_case = [\n                        {\n                            \"spatial_dims\": spatial_dims,\n                            \"in_channels\": in_channels,\n                            \"out_channels\": out_channels,\n                            \"kernel_size\": kernel_size,\n                            \"norm_name\": norm_name,\n                            \"res_block\": res_block,\n                            \"upsample_kernel_size\": stride,\n                        },\n                        (1, in_channels, *([in_size] * spatial_dims)),\n                        (1, out_channels, *([out_size] * spatial_dims)),\n                        (1, out_channels, *([in_size * stride] * spatial_dims)),\n                    ]\n                    TEST_UP_BLOCK.append(test_case)\n\n\nTEST_PRUP_BLOCK = []\nin_channels, out_channels = 4, 2\nfor spatial_dims in range(1, 4):\n    for kernel_size in [1, 3]:\n        for upsample_kernel_size in [2, 3]:\n            for stride in [1, 2]:\n                for res_block in [False, True]:\n                    for norm_name in [\"instance\"]:\n                        for in_size in [15, 16]:\n                            for num_layer in [0, 2]:\n                                in_size_tmp = in_size\n                                for _ in range(num_layer + 1):\n                                    out_size = in_size_tmp * upsample_kernel_size\n                                    in_size_tmp = out_size\n                            test_case = [\n                                {\n                                    \"spatial_dims\": spatial_dims,\n                                    \"in_channels\": in_channels,\n                                    \"out_channels\": out_channels,\n                                    \"num_layer\": num_layer,\n                                    \"kernel_size\": kernel_size,\n                                    \"norm_name\": norm_name,\n                                    \"stride\": stride,\n                                    \"res_block\": res_block,\n                                    \"upsample_kernel_size\": upsample_kernel_size,\n                                },\n                                (1, in_channels, *([in_size] * spatial_dims)),\n                                (1, out_channels, *([out_size] * spatial_dims)),\n                            ]\n                            TEST_PRUP_BLOCK.append(test_case)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_unetr_block.py_TestResBasicBlock_TestResBasicBlock.test_script.with_eval_mode_net_.test_script_save_net_tes": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_unetr_block.py_TestResBasicBlock_TestResBasicBlock.test_script.with_eval_mode_net_.test_script_save_net_tes", "embedding": null, "metadata": {"file_path": "tests/test_unetr_block.py", "file_name": "test_unetr_block.py", "file_type": "text/x-python", "category": "test", "start_line": 103, "end_line": 122, "span_ids": ["TestResBasicBlock.test_ill_arg", "TestResBasicBlock", "TestResBasicBlock.test_shape", "TestResBasicBlock.test_script"], "tokens": 214}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestResBasicBlock(unittest.TestCase):\n    @parameterized.expand(TEST_CASE_UNETR_BASIC_BLOCK)\n    def test_shape(self, input_param, input_shape, expected_shape):\n        for net in [UnetrBasicBlock(**input_param)]:\n            with eval_mode(net):\n                result = net(torch.randn(input_shape))\n                self.assertEqual(result.shape, expected_shape)\n\n    def test_ill_arg(self):\n        with self.assertRaises(ValueError):\n            UnetrBasicBlock(3, 4, 2, kernel_size=3, stride=1, norm_name=\"norm\")\n        with self.assertRaises(AssertionError):\n            UnetrBasicBlock(3, 4, 2, kernel_size=1, stride=4, norm_name=\"batch\")\n\n    def test_script(self):\n        input_param, input_shape, _ = TEST_CASE_UNETR_BASIC_BLOCK[0]\n        net = UnetrBasicBlock(**input_param)\n        with eval_mode(net):\n            test_data = torch.randn(input_shape)\n            test_script_save(net, test_data)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_unetr_block.py_TestUpBlock_TestUpBlock.test_script.test_script_save_net_tes": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_unetr_block.py_TestUpBlock_TestUpBlock.test_script.test_script_save_net_tes", "embedding": null, "metadata": {"file_path": "tests/test_unetr_block.py", "file_name": "test_unetr_block.py", "file_type": "text/x-python", "category": "test", "start_line": 125, "end_line": 138, "span_ids": ["TestUpBlock.test_shape", "TestUpBlock.test_script", "TestUpBlock"], "tokens": 138}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestUpBlock(unittest.TestCase):\n    @parameterized.expand(TEST_UP_BLOCK)\n    def test_shape(self, input_param, input_shape, expected_shape, skip_shape):\n        net = UnetrUpBlock(**input_param)\n        with eval_mode(net):\n            result = net(torch.randn(input_shape), torch.randn(skip_shape))\n            self.assertEqual(result.shape, expected_shape)\n\n    def test_script(self):\n        input_param, input_shape, _, skip_shape = TEST_UP_BLOCK[0]\n        net = UnetrUpBlock(**input_param)\n        test_data = torch.randn(input_shape)\n        skip_data = torch.randn(skip_shape)\n        test_script_save(net, test_data, skip_data)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_unetr_block.py_TestPrUpBlock_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_unetr_block.py_TestPrUpBlock_", "embedding": null, "metadata": {"file_path": "tests/test_unetr_block.py", "file_name": "test_unetr_block.py", "file_type": "text/x-python", "category": "test", "start_line": 141, "end_line": 158, "span_ids": ["TestPrUpBlock", "impl:52", "TestPrUpBlock.test_script", "TestPrUpBlock.test_shape"], "tokens": 133}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestPrUpBlock(unittest.TestCase):\n    @parameterized.expand(TEST_PRUP_BLOCK)\n    def test_shape(self, input_param, input_shape, expected_shape):\n        net = UnetrPrUpBlock(**input_param)\n        with eval_mode(net):\n            result = net(torch.randn(input_shape))\n            self.assertEqual(result.shape, expected_shape)\n\n    def test_script(self):\n        input_param, input_shape, _ = TEST_PRUP_BLOCK[0]\n        net = UnetrPrUpBlock(**input_param)\n        test_data = torch.randn(input_shape)\n        test_script_save(net, test_data)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_version_leq.py_itertools_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_version_leq.py_itertools_", "embedding": null, "metadata": {"file_path": "tests/test_version_leq.py", "file_name": "test_version_leq.py", "file_type": "text/x-python", "category": "test", "start_line": 13, "end_line": 85, "span_ids": ["impl", "TestVersionCompare.test_compare", "TestVersionCompare", "_pairwise", "impl:5", "docstring"], "tokens": 781}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import itertools\nimport unittest\n\nfrom parameterized import parameterized\n\nfrom monai.utils import version_leq\n\n\n# from pkg_resources\ndef _pairwise(iterable):\n    \"s -> (s0,s1), (s1,s2), (s2, s3), ...\"\n    a, b = itertools.tee(iterable)\n    next(b, None)\n    return zip(a, b)\n\n\n# from pkg_resources\ntorture = \"\"\"\n    0.80.1-3 0.80.1-2 0.80.1-1 0.79.9999+0.80.0pre4-1\n    0.79.9999+0.80.0pre2-3 0.79.9999+0.80.0pre2-2\n    0.77.2-1 0.77.1-1 0.77.0-1\n    \"\"\"\n\nTEST_CASES = (\n    (\"1.6.0\", \"1.6.0\"),\n    (\"1.6.0a0+9907a3e\", \"1.6.0\"),\n    (\"0+unknown\", \"0.6\"),\n    (\"ab\", \"abc\"),\n    (\"0.6rc1\", \"0.6\"),\n    (\"0.6\", \"0.7\"),\n    (\"1.2.a\", \"1.2a\"),\n    (\"1.2-rc1\", \"1.2rc1\"),\n    (\"0.4\", \"0.4.0\"),\n    (\"0.4.0.0\", \"0.4.0\"),\n    (\"0.4.0-0\", \"0.4-0\"),\n    (\"0post1\", \"0.0post1\"),\n    (\"0pre1\", \"0.0c1\"),\n    (\"0.0.0preview1\", \"0c1\"),\n    (\"0.0c1\", \"0-rc1\"),\n    (\"1.2a1\", \"1.2.a.1\"),\n    (\"1.2.a\", \"1.2a\"),\n    (\"2.1\", \"2.1.1\"),\n    (\"2a1\", \"2b0\"),\n    (\"2a1\", \"2.1\"),\n    (\"2.3a1\", \"2.3\"),\n    (\"2.1-1\", \"2.1-2\"),\n    (\"2.1-1\", \"2.1.1\"),\n    (\"2.1\", \"2.1post4\"),\n    (\"2.1a0-20040501\", \"2.1\"),\n    (\"1.1\", \"02.1\"),\n    (\"3.2\", \"3.2.post0\"),\n    (\"3.2post1\", \"3.2post2\"),\n    (\"0.4\", \"4.0\"),\n    (\"0.0.4\", \"0.4.0\"),\n    (\"0post1\", \"0.4post1\"),\n    (\"2.1.0-rc1\", \"2.1.0\"),\n    (\"2.1dev\", \"2.1a0\"),\n    (1.6, \"1.6.0\"),\n    (\"1.6.0\", 1.6),\n    (1.6, 1.7),\n) + tuple(_pairwise(reversed(torture.split())))\n\n\nclass TestVersionCompare(unittest.TestCase):\n    @parameterized.expand(TEST_CASES)\n    def test_compare(self, a, b, expected=True):\n        \"\"\"Test version_leq with `a` and `b`\"\"\"\n        self.assertEqual(version_leq(a, b), expected)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_vit.py_unittest_for_dropout_rate_in_0_6_.for_in_channels_in_4_.for_hidden_size_in_768_.for_img_size_in_96_128_.for_patch_size_in_16_.for_num_heads_in_12_.for_mlp_dim_in_3072_.for_num_layers_in_4_.for_num_classes_in_8_.for_pos_embed_in_conv_.for_classification_in_Fa.for_nd_in_2_3_.TEST_CASE_Vit_append_test": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_vit.py_unittest_for_dropout_rate_in_0_6_.for_in_channels_in_4_.for_hidden_size_in_768_.for_img_size_in_96_128_.for_patch_size_in_16_.for_num_heads_in_12_.for_mlp_dim_in_3072_.for_num_layers_in_4_.for_num_classes_in_8_.for_pos_embed_in_conv_.for_classification_in_Fa.for_nd_in_2_3_.TEST_CASE_Vit_append_test", "embedding": null, "metadata": {"file_path": "tests/test_vit.py", "file_name": "test_vit.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 54, "span_ids": ["docstring"], "tokens": 359}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks import eval_mode\nfrom monai.networks.nets.vit import ViT\n\nTEST_CASE_Vit = []\nfor dropout_rate in [0.6]:\n    for in_channels in [4]:\n        for hidden_size in [768]:\n            for img_size in [96, 128]:\n                for patch_size in [16]:\n                    for num_heads in [12]:\n                        for mlp_dim in [3072]:\n                            for num_layers in [4]:\n                                for num_classes in [8]:\n                                    for pos_embed in [\"conv\"]:\n                                        for classification in [False, True]:\n                                            for nd in (2, 3):\n                                                test_case = [\n                                                    {\n                                                        \"in_channels\": in_channels,\n                                                        \"img_size\": (img_size,) * nd,\n                                                        \"patch_size\": (patch_size,) * nd,\n                                                        \"hidden_size\": hidden_size,\n                                                        \"mlp_dim\": mlp_dim,\n                                                        \"num_layers\": num_layers,\n                                                        \"num_heads\": num_heads,\n                                                        \"pos_embed\": pos_embed,\n                                                        \"classification\": classification,\n                                                        \"num_classes\": num_classes,\n                                                        \"dropout_rate\": dropout_rate,\n                                                    },\n                                                    (2, in_channels, *([img_size] * nd)),\n                                                    (2, (img_size // patch_size) ** nd, hidden_size),\n                                                ]\n                                                if nd == 2:\n                                                    test_case[0][\"spatial_dims\"] = 2  # type: ignore\n                                                if test_case[0][\"classification\"]:  # type: ignore\n                                                    test_case[2] = (2, test_case[0][\"num_classes\"])  # type: ignore\n                                                TEST_CASE_Vit.append(test_case)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_vit.py_TestPatchEmbeddingBlock_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_vit.py_TestPatchEmbeddingBlock_", "embedding": null, "metadata": {"file_path": "tests/test_vit.py", "file_name": "test_vit.py", "file_type": "text/x-python", "category": "test", "start_line": 57, "end_line": 139, "span_ids": ["impl:22", "TestPatchEmbeddingBlock", "TestPatchEmbeddingBlock.test_shape", "TestPatchEmbeddingBlock.test_ill_arg"], "tokens": 523}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestPatchEmbeddingBlock(unittest.TestCase):\n    @parameterized.expand(TEST_CASE_Vit)\n    def test_shape(self, input_param, input_shape, expected_shape):\n        net = ViT(**input_param)\n        with eval_mode(net):\n            result, _ = net(torch.randn(input_shape))\n            self.assertEqual(result.shape, expected_shape)\n\n    def test_ill_arg(self):\n        with self.assertRaises(ValueError):\n            ViT(\n                in_channels=1,\n                img_size=(128, 128, 128),\n                patch_size=(16, 16, 16),\n                hidden_size=128,\n                mlp_dim=3072,\n                num_layers=12,\n                num_heads=12,\n                pos_embed=\"conv\",\n                classification=False,\n                dropout_rate=5.0,\n            )\n\n        with self.assertRaises(ValueError):\n            ViT(\n                in_channels=1,\n                img_size=(32, 32, 32),\n                patch_size=(64, 64, 64),\n                hidden_size=512,\n                mlp_dim=3072,\n                num_layers=12,\n                num_heads=8,\n                pos_embed=\"perceptron\",\n                classification=False,\n                dropout_rate=0.3,\n            )\n\n        with self.assertRaises(ValueError):\n            ViT(\n                in_channels=1,\n                img_size=(96, 96, 96),\n                patch_size=(8, 8, 8),\n                hidden_size=512,\n                mlp_dim=3072,\n                num_layers=12,\n                num_heads=14,\n                pos_embed=\"conv\",\n                classification=False,\n                dropout_rate=0.3,\n            )\n\n        with self.assertRaises(ValueError):\n            ViT(\n                in_channels=1,\n                img_size=(97, 97, 97),\n                patch_size=(4, 4, 4),\n                hidden_size=768,\n                mlp_dim=3072,\n                num_layers=12,\n                num_heads=8,\n                pos_embed=\"perceptron\",\n                classification=True,\n                dropout_rate=0.3,\n            )\n\n        with self.assertRaises(ValueError):\n            ViT(\n                in_channels=4,\n                img_size=(96, 96, 96),\n                patch_size=(16, 16, 16),\n                hidden_size=768,\n                mlp_dim=3072,\n                num_layers=12,\n                num_heads=12,\n                pos_embed=\"perc\",\n                classification=False,\n                dropout_rate=0.3,\n            )\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_vote_ensemble.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_vote_ensemble.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_vote_ensemble.py", "file_name": "test_vote_ensemble.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 83, "span_ids": ["TestVoteEnsemble", "impl:10", "TestVoteEnsemble.test_value", "docstring"], "tokens": 576}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import VoteEnsemble\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    # shape: [2, 1, 1]\n    TESTS.append(\n        [\n            {\"num_classes\": None},\n            [p(torch.tensor([[[1]], [[0]]])), p(torch.tensor([[[1]], [[0]]])), p(torch.tensor([[[0]], [[1]]]))],\n            p(torch.tensor([[[1.0]], [[0.0]]])),\n        ]\n    )\n\n    # shape: [1, 2, 1, 1]\n    TESTS.append(\n        [\n            {\"num_classes\": None},\n            p(\n                torch.stack(\n                    [torch.tensor([[[[1]], [[0]]]]), torch.tensor([[[[1]], [[0]]]]), torch.tensor([[[[0]], [[1]]]])]\n                )\n            ),\n            p(torch.tensor([[[[1.0]], [[0.0]]]])),\n        ]\n    )\n\n    # shape: [1, 2, 1]\n    TESTS.append(\n        [\n            {\"num_classes\": 3},\n            [p(torch.tensor([[[0], [2]]])), p(torch.tensor([[[0], [2]]])), p(torch.tensor([[[1], [1]]]))],\n            p(torch.tensor([[[0], [2]]])),\n        ]\n    )\n\n    # shape: [1, 2, 1]\n    TESTS.append(\n        [\n            {\"num_classes\": 5},\n            [p(torch.tensor([[[0], [2]]])), p(torch.tensor([[[0], [2]]])), p(torch.tensor([[[1], [1]]]))],\n            p(torch.tensor([[[0], [2]]])),\n        ]\n    )\n\n    # shape: [1]\n    TESTS.append(\n        [{\"num_classes\": 3}, [p(torch.tensor([2])), p(torch.tensor([2])), p(torch.tensor([1]))], p(torch.tensor([2]))]\n    )\n\n    # shape: 1\n    TESTS.append([{\"num_classes\": 3}, [p(torch.tensor(2)), p(torch.tensor(2)), p(torch.tensor(1))], p(torch.tensor(2))])\n\n\nclass TestVoteEnsemble(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_value(self, input_param, img, expected_value):\n        result = VoteEnsemble(**input_param)(img)\n        if isinstance(img, torch.Tensor):\n            self.assertIsInstance(result, torch.Tensor)\n            self.assertEqual(result.device, img.device)\n        assert_allclose(result, expected_value)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_vote_ensembled.py_unittest_for_p_in_TEST_NDARRAYS_.None_4": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_vote_ensembled.py_unittest_for_p_in_TEST_NDARRAYS_.None_4", "embedding": null, "metadata": {"file_path": "tests/test_vote_ensembled.py", "file_name": "test_vote_ensembled.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 83, "span_ids": ["docstring"], "tokens": 625}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import VoteEnsembled\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    # shape: [1, 2, 1, 1]\n    TESTS.append(\n        [\n            {\"keys\": [\"pred0\", \"pred1\", \"pred2\"], \"output_key\": \"output\", \"num_classes\": None},\n            {\n                \"pred0\": p(torch.tensor([[[[1]], [[0]]]])),\n                \"pred1\": p(torch.tensor([[[[1]], [[0]]]])),\n                \"pred2\": p(torch.tensor([[[[0]], [[1]]]])),\n            },\n            p(torch.tensor([[[[1.0]], [[0.0]]]])),\n        ]\n    )\n\n    # shape: [1, 2, 1, 1]\n    TESTS.append(\n        [\n            {\"keys\": \"output\", \"output_key\": \"output\", \"num_classes\": None},\n            {\n                \"output\": p(\n                    torch.stack(\n                        [torch.tensor([[[[1]], [[0]]]]), torch.tensor([[[[1]], [[0]]]]), torch.tensor([[[[0]], [[1]]]])]\n                    )\n                )\n            },\n            p(torch.tensor([[[[1.0]], [[0.0]]]])),\n        ]\n    )\n\n    # shape: [1, 2, 1]\n    TESTS.append(\n        [\n            {\"keys\": [\"pred0\", \"pred1\", \"pred2\"], \"output_key\": \"output\", \"num_classes\": 3},\n            {\n                \"pred0\": p(torch.tensor([[[0], [2]]])),\n                \"pred1\": p(torch.tensor([[[0], [2]]])),\n                \"pred2\": p(torch.tensor([[[1], [1]]])),\n            },\n            p(torch.tensor([[[0], [2]]])),\n        ]\n    )\n\n    # shape: [1, 2, 1]\n    TESTS.append(\n        [\n            {\"keys\": [\"pred0\", \"pred1\", \"pred2\"], \"output_key\": \"output\", \"num_classes\": 5},\n            {\n                \"pred0\": p(torch.tensor([[[0], [2]]])),\n                \"pred1\": p(torch.tensor([[[0], [2]]])),\n                \"pred2\": p(torch.tensor([[[1], [1]]])),\n            },\n            p(torch.tensor([[[0], [2]]])),\n        ]\n    )\n\n    # shape: [1]\n    TESTS.append(\n        [\n            {\"keys\": [\"pred0\", \"pred1\", \"pred2\"], \"output_key\": \"output\", \"num_classes\": 3},\n            {\"pred0\": p(torch.tensor([2])), \"pred1\": p(torch.tensor([2])), \"pred2\": p(torch.tensor([1]))},\n            p(torch.tensor([2])),\n        ]\n    )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_zoom.py_TestZoom_TestZoom.test_correct_results.for_p_in_TEST_NDARRAYS_.assert_allclose_zoomed_p": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_zoom.py_TestZoom_TestZoom.test_correct_results.for_p_in_TEST_NDARRAYS_.assert_allclose_zoomed_p", "embedding": null, "metadata": {"file_path": "tests/test_zoom.py", "file_name": "test_zoom.py", "file_type": "text/x-python", "category": "test", "start_line": 27, "end_line": 40, "span_ids": ["TestZoom", "TestZoom.test_correct_results"], "tokens": 161}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestZoom(NumpyImageTestCase2D):\n    @parameterized.expand(VALID_CASES)\n    def test_correct_results(self, zoom, mode):\n        for p in TEST_NDARRAYS:\n            zoom_fn = Zoom(zoom=zoom, mode=mode, keep_size=False)\n            zoomed = zoom_fn(p(self.imt[0]))\n            _order = 0\n            if mode.endswith(\"linear\"):\n                _order = 1\n            expected = []\n            for channel in self.imt[0]:\n                expected.append(zoom_scipy(channel, zoom=zoom, mode=\"nearest\", order=_order, prefilter=False))\n            expected = np.stack(expected).astype(np.float32)\n            assert_allclose(zoomed, p(expected), atol=1.0)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_zoom.py_TestZoom.test_keep_size_TestZoom.test_invalid_inputs.for_p_in_TEST_NDARRAYS_.with_self_assertRaises_ra.zoom_fn_p_self_imt_0_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_zoom.py_TestZoom.test_keep_size_TestZoom.test_invalid_inputs.for_p_in_TEST_NDARRAYS_.with_self_assertRaises_ra.zoom_fn_p_self_imt_0_", "embedding": null, "metadata": {"file_path": "tests/test_zoom.py", "file_name": "test_zoom.py", "file_type": "text/x-python", "category": "test", "start_line": 42, "end_line": 57, "span_ids": ["TestZoom.test_keep_size", "TestZoom.test_invalid_inputs"], "tokens": 202}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestZoom(NumpyImageTestCase2D):\n\n    def test_keep_size(self):\n        for p in TEST_NDARRAYS:\n            zoom_fn = Zoom(zoom=[0.6, 0.6], keep_size=True, align_corners=True)\n            zoomed = zoom_fn(p(self.imt[0]), mode=\"bilinear\")\n            assert_allclose(zoomed.shape, self.imt.shape[1:])\n\n            zoom_fn = Zoom(zoom=[1.3, 1.3], keep_size=True)\n            zoomed = zoom_fn(p(self.imt[0]))\n            assert_allclose(zoomed.shape, self.imt.shape[1:])\n\n    @parameterized.expand(INVALID_CASES)\n    def test_invalid_inputs(self, zoom, mode, raises):\n        for p in TEST_NDARRAYS:\n            with self.assertRaises(raises):\n                zoom_fn = Zoom(zoom=zoom, mode=mode)\n                zoom_fn(p(self.imt[0]))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_zoomd.py_TestZoomd_TestZoomd.test_correct_results.for_p_in_TEST_NDARRAYS_.assert_allclose_zoomed_ke": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_zoomd.py_TestZoomd_TestZoomd.test_correct_results.for_p_in_TEST_NDARRAYS_.assert_allclose_zoomed_ke", "embedding": null, "metadata": {"file_path": "tests/test_zoomd.py", "file_name": "test_zoomd.py", "file_type": "text/x-python", "category": "test", "start_line": 26, "end_line": 41, "span_ids": ["TestZoomd.test_correct_results", "TestZoomd"], "tokens": 178}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestZoomd(NumpyImageTestCase2D):\n    @parameterized.expand(VALID_CASES)\n    def test_correct_results(self, zoom, mode, keep_size):\n        key = \"img\"\n        zoom_fn = Zoomd(key, zoom=zoom, mode=mode, keep_size=keep_size)\n        for p in TEST_NDARRAYS:\n            zoomed = zoom_fn({key: p(self.imt[0])})\n            _order = 0\n            if mode.endswith(\"linear\"):\n                _order = 1\n            expected = [\n                zoom_scipy(channel, zoom=zoom, mode=\"nearest\", order=_order, prefilter=False) for channel in self.imt[0]\n            ]\n\n            expected = np.stack(expected).astype(np.float32)\n            assert_allclose(zoomed[key], p(expected), atol=1.0)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/testing_data/integration_answers.py_np_EXPECTED_ANSWERS": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/testing_data/integration_answers.py_np_EXPECTED_ANSWERS", "embedding": null, "metadata": {"file_path": "tests/testing_data/integration_answers.py", "file_name": "integration_answers.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 436, "span_ids": ["docstring"], "tokens": 14}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import numpy as np\n\nEXPECTED_ANSWERS =\n # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/testing_data/integration_answers.py_test_integration_value_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/testing_data/integration_answers.py_test_integration_value_", "embedding": null, "metadata": {"file_path": "tests/testing_data/integration_answers.py", "file_name": "integration_answers.py", "file_type": "text/x-python", "category": "implementation", "start_line": 586, "end_line": 597, "span_ids": ["test_integration_value"], "tokens": 128}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def test_integration_value(test_name, key, data, rtol=1e-2):\n    for (idx, expected) in enumerate(EXPECTED_ANSWERS):\n        if test_name not in expected:\n            continue\n        if key not in expected[test_name]:\n            continue\n        value = expected[test_name][key]\n        if np.allclose(data, value, rtol=rtol):\n            print(f\"matched {idx} result of {test_name}, {key}, {rtol}.\")\n            return True\n    raise ValueError(f\"no matched results for {test_name}, {key}. {data}.\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/utils.py_copy_clone.return.copy_deepcopy_data_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/utils.py_copy_clone.return.copy_deepcopy_data_", "embedding": null, "metadata": {"file_path": "tests/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 58, "span_ids": ["clone", "docstring"], "tokens": 270}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import copy\nimport datetime\nimport functools\nimport importlib\nimport os\nimport queue\nimport sys\nimport tempfile\nimport time\nimport traceback\nimport unittest\nimport warnings\nfrom functools import partial\nfrom subprocess import PIPE, Popen\nfrom typing import Callable, Optional, Tuple\nfrom urllib.error import HTTPError, URLError\n\nimport numpy as np\nimport torch\nimport torch.distributed as dist\n\nfrom monai.config import NdarrayTensor\nfrom monai.config.deviceconfig import USE_COMPILED\nfrom monai.config.type_definitions import NdarrayOrTensor\nfrom monai.data import create_test_image_2d, create_test_image_3d\nfrom monai.networks import convert_to_torchscript\nfrom monai.utils import optional_import\nfrom monai.utils.module import pytorch_after, version_leq\nfrom monai.utils.type_conversion import convert_data_type\n\nnib, _ = optional_import(\"nibabel\")\n\nquick_test_var = \"QUICKTEST\"\n_tf32_enabled = None\n\n\ndef clone(data: NdarrayTensor) -> NdarrayTensor:\n    \"\"\"\n    Clone data independent of type.\n\n    Args:\n        data (NdarrayTensor): This can be a Pytorch Tensor or numpy array.\n\n    Returns:\n        Any: Cloned data object\n    \"\"\"\n    return copy.deepcopy(data)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/utils.py_assert_allclose_assert_allclose.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/utils.py_assert_allclose_assert_allclose.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 62, "end_line": 93, "span_ids": ["assert_allclose"], "tokens": 320}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def assert_allclose(\n    actual: NdarrayOrTensor,\n    desired: NdarrayOrTensor,\n    type_test: bool = True,\n    device_test: bool = False,\n    *args,\n    **kwargs,\n):\n    \"\"\"\n    Assert that types and all values of two data objects are close.\n\n    Args:\n        actual: Pytorch Tensor or numpy array for comparison.\n        desired: Pytorch Tensor or numpy array to compare against.\n        type_test: whether to test that `actual` and `desired` are both numpy arrays or torch tensors.\n        device_test: whether to test the device property.\n        args: extra arguments to pass on to `np.testing.assert_allclose`.\n        kwargs: extra arguments to pass on to `np.testing.assert_allclose`.\n\n\n    \"\"\"\n    if type_test:\n        # check both actual and desired are of the same type\n        np.testing.assert_equal(isinstance(actual, np.ndarray), isinstance(desired, np.ndarray), \"numpy type\")\n        np.testing.assert_equal(isinstance(actual, torch.Tensor), isinstance(desired, torch.Tensor), \"torch type\")\n\n    if isinstance(desired, torch.Tensor) or isinstance(actual, torch.Tensor):\n        if device_test:\n            np.testing.assert_equal(str(actual.device), str(desired.device), \"torch device check\")  # type: ignore\n        actual = actual.cpu().numpy() if isinstance(actual, torch.Tensor) else actual\n        desired = desired.cpu().numpy() if isinstance(desired, torch.Tensor) else desired\n    np.testing.assert_allclose(actual, desired, *args, **kwargs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/utils.py_skip_if_quick_skip_if_windows.return.unittest_skipIf_sys_platf": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/utils.py_skip_if_quick_skip_if_windows.return.unittest_skipIf_sys_platf", "embedding": null, "metadata": {"file_path": "tests/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 135, "end_line": 187, "span_ids": ["skip_if_no_cpp_extension", "skip_if_quick", "SkipIfNoModule", "SkipIfNoModule.__init__", "skip_if_no_cuda", "SkipIfNoModule.__call__", "SkipIfModule", "SkipIfModule.__init__", "skip_if_windows", "SkipIfModule.__call__"], "tokens": 374}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def skip_if_quick(obj):\n    \"\"\"\n    Skip the unit tests if environment variable `quick_test_var=true`.\n    For example, the user can skip the relevant tests by setting ``export QUICKTEST=true``.\n    \"\"\"\n    is_quick = test_is_quick()\n\n    return unittest.skipIf(is_quick, \"Skipping slow tests\")(obj)\n\n\nclass SkipIfNoModule:\n    \"\"\"Decorator to be used if test should be skipped\n    when optional module is not present.\"\"\"\n\n    def __init__(self, module_name):\n        self.module_name = module_name\n        self.module_missing = not optional_import(self.module_name)[1]\n\n    def __call__(self, obj):\n        return unittest.skipIf(self.module_missing, f\"optional module not present: {self.module_name}\")(obj)\n\n\nclass SkipIfModule:\n    \"\"\"Decorator to be used if test should be skipped\n    when optional module is present.\"\"\"\n\n    def __init__(self, module_name):\n        self.module_name = module_name\n        self.module_avail = optional_import(self.module_name)[1]\n\n    def __call__(self, obj):\n        return unittest.skipIf(self.module_avail, f\"Skipping because optional module present: {self.module_name}\")(obj)\n\n\ndef skip_if_no_cpp_extension(obj):\n    \"\"\"\n    Skip the unit tests if the cpp extension is not available\n    \"\"\"\n    return unittest.skipUnless(USE_COMPILED, \"Skipping cpp extension tests\")(obj)\n\n\ndef skip_if_no_cuda(obj):\n    \"\"\"\n    Skip the unit tests if torch.cuda.is_available is False\n    \"\"\"\n    return unittest.skipUnless(torch.cuda.is_available(), \"Skipping CUDA-based tests\")(obj)\n\n\ndef skip_if_windows(obj):\n    \"\"\"\n    Skip the unit tests if platform is win32\n    \"\"\"\n    return unittest.skipIf(sys.platform == \"win32\", \"Skipping tests on Windows\")(obj)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/utils.py_test_script_save_test_script_save.with_tempfile_TemporaryDi.convert_to_torchscript_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/utils.py_test_script_save_test_script_save.with_tempfile_TemporaryDi.convert_to_torchscript_", "embedding": null, "metadata": {"file_path": "tests/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 581, "end_line": 600, "span_ids": ["test_script_save"], "tokens": 194}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def test_script_save(net, *inputs, device=None, rtol=1e-4, atol=0.0):\n    \"\"\"\n    Test the ability to save `net` as a Torchscript object, reload it, and apply inference. The value `inputs` is\n    forward-passed through the original and loaded copy of the network and their results returned.\n    The forward pass for both is done without gradient accumulation.\n\n    The test will be performed with CUDA if available, else CPU.\n    \"\"\"\n    # TODO: would be nice to use GPU if available, but it currently causes CI failures.\n    device = \"cpu\"\n    with tempfile.TemporaryDirectory() as tempdir:\n        convert_to_torchscript(\n            model=net,\n            filename_or_obj=os.path.join(tempdir, \"model.ts\"),\n            verify=True,\n            inputs=inputs,\n            device=device,\n            rtol=rtol,\n            atol=atol,\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/decathlon_datalist.py_json__compute_path_1._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/decathlon_datalist.py_json__compute_path_1._", "embedding": null, "metadata": {"file_path": "monai/data/decathlon_datalist.py", "file_name": "decathlon_datalist.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 30, "span_ids": ["_compute_path", "_compute_path_1", "docstring"], "tokens": 129}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import json\nimport os\nimport warnings\nfrom pathlib import Path\nfrom typing import Dict, List, Optional, Sequence, Union, overload\n\nfrom monai.config import KeysCollection, PathLike\nfrom monai.data.utils import partition_dataset, select_cross_validation_folds\nfrom monai.utils import ensure_tuple\n\n\n@overload\ndef _compute_path(base_dir: PathLike, element: PathLike, check_path: bool = False) -> str:\n    ...\n\n\n@overload\ndef _compute_path(base_dir: PathLike, element: List[PathLike], check_path: bool = False) -> List[str]:\n    ...", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/decathlon_datalist.py__compute_path_2__compute_path_2.return.element": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/decathlon_datalist.py__compute_path_2__compute_path_2.return.element", "embedding": null, "metadata": {"file_path": "monai/data/decathlon_datalist.py", "file_name": "decathlon_datalist.py", "file_type": "text/x-python", "category": "implementation", "start_line": 33, "end_line": 60, "span_ids": ["_compute_path_2"], "tokens": 236}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _compute_path(base_dir, element, check_path=False):\n    \"\"\"\n    Args:\n        base_dir: the base directory of the dataset.\n        element: file path(s) to append to directory.\n        check_path: if `True`, only compute when the result is an existing path.\n\n    Raises:\n        TypeError: When ``element`` contains a non ``str``.\n        TypeError: When ``element`` type is not in ``Union[list, str]``.\n\n    \"\"\"\n\n    def _join_path(base_dir: PathLike, item: PathLike):\n        result = os.path.normpath(os.path.join(base_dir, item))\n        if check_path and not os.path.exists(result):\n            # if not an existing path, don't join with base dir\n            return f\"{item}\"\n        return f\"{result}\"\n\n    if isinstance(element, (str, os.PathLike)):\n        return _join_path(base_dir, element)\n    if isinstance(element, list):\n        for e in element:\n            if not isinstance(e, (str, os.PathLike)):\n                return element\n        return [_join_path(base_dir, e) for e in element]\n    return element", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/decathlon_datalist.py_load_decathlon_properties_load_decathlon_properties.return.properties": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/decathlon_datalist.py_load_decathlon_properties_load_decathlon_properties.return.properties", "embedding": null, "metadata": {"file_path": "monai/data/decathlon_datalist.py", "file_name": "decathlon_datalist.py", "file_type": "text/x-python", "category": "implementation", "start_line": 134, "end_line": 156, "span_ids": ["load_decathlon_properties"], "tokens": 240}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def load_decathlon_properties(data_property_file_path: PathLike, property_keys: Union[Sequence[str], str]) -> Dict:\n    \"\"\"Load the properties from the JSON file contains data property with specified `property_keys`.\n\n    Args:\n        data_property_file_path: the path to the JSON file of data properties.\n        property_keys: expected keys to load from the JSON file, for example, we have these keys\n            in the decathlon challenge:\n            `name`, `description`, `reference`, `licence`, `tensorImageSize`,\n            `modality`, `labels`, `numTraining`, `numTest`, etc.\n\n    \"\"\"\n    data_property_file_path = Path(data_property_file_path)\n    if not data_property_file_path.is_file():\n        raise ValueError(f\"Data property file {data_property_file_path} does not exist.\")\n    with open(data_property_file_path) as json_file:\n        json_data = json.load(json_file)\n\n    properties = {}\n    for key in ensure_tuple(property_keys):\n        if key not in json_data:\n            raise KeyError(f\"key {key} is not in the data property file.\")\n        properties[key] = json_data[key]\n    return properties", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/decathlon_datalist.py_check_missing_files_check_missing_files.return.missing_files": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/decathlon_datalist.py_check_missing_files_check_missing_files.return.missing_files", "embedding": null, "metadata": {"file_path": "monai/data/decathlon_datalist.py", "file_name": "decathlon_datalist.py", "file_type": "text/x-python", "category": "implementation", "start_line": 159, "end_line": 194, "span_ids": ["check_missing_files"], "tokens": 328}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def check_missing_files(\n    datalist: List[Dict], keys: KeysCollection, root_dir: Optional[PathLike] = None, allow_missing_keys: bool = False\n):\n    \"\"\"Checks whether some files in the Decathlon datalist are missing.\n    It would be helpful to check missing files before a heavy training run.\n\n    Args:\n        datalist: a list of data items, every item is a dictionary.\n            usually generated by `load_decathlon_datalist` API.\n        keys: expected keys to check in the datalist.\n        root_dir: if not None, provides the root dir for the relative file paths in `datalist`.\n        allow_missing_keys: whether allow missing keys in the datalist items.\n            if False, raise exception if missing. default to False.\n\n    Returns:\n        A list of missing filenames.\n\n    \"\"\"\n    missing_files = []\n    for item in datalist:\n        for k in ensure_tuple(keys):\n            if k not in item:\n                if not allow_missing_keys:\n                    raise ValueError(f\"key `{k}` is missing in the datalist item: {item}\")\n                continue\n\n            for f in ensure_tuple(item[k]):\n                if not isinstance(f, (str, os.PathLike)):\n                    raise ValueError(f\"filepath of key `{k}` must be a string or a list of strings, but got: {f}.\")\n                f = Path(f)\n                if isinstance(root_dir, (str, os.PathLike)):\n                    f = Path(root_dir).joinpath(f)\n                if not f.exists():\n                    missing_files.append(f)\n\n    return missing_files", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/decathlon_datalist.py_create_cross_validation_datalist_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/decathlon_datalist.py_create_cross_validation_datalist_", "embedding": null, "metadata": {"file_path": "monai/data/decathlon_datalist.py", "file_name": "decathlon_datalist.py", "file_type": "text/x-python", "category": "implementation", "start_line": 197, "end_line": 251, "span_ids": ["create_cross_validation_datalist"], "tokens": 611}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def create_cross_validation_datalist(\n    datalist: List[Dict],\n    nfolds: int,\n    train_folds: Union[Sequence[int], int],\n    val_folds: Union[Sequence[int], int],\n    train_key: str = \"training\",\n    val_key: str = \"validation\",\n    filename: Optional[Union[Path, str]] = None,\n    shuffle: bool = True,\n    seed: int = 0,\n    check_missing: bool = False,\n    keys: Optional[KeysCollection] = None,\n    root_dir: Optional[str] = None,\n    allow_missing_keys: bool = False,\n    raise_error: bool = True,\n):\n    \"\"\"\n    Utility to create new Decathlon style datalist based on cross validation partition.\n\n    Args:\n        datalist: loaded list of dictionaries for all the items to partition.\n        nfolds: number of the kfold split.\n        train_folds: indices of folds for training part.\n        val_folds: indices of folds for validation part.\n        train_key: the key of train part in the new datalist, defaults to \"training\".\n        val_key: the key of validation part in the new datalist, defaults to \"validation\".\n        filename: if not None and ends with \".json\", save the new datalist into JSON file.\n        shuffle: whether to shuffle the datalist before partition, defaults to `True`.\n        seed: if `shuffle` is True, set the random seed, defaults to `0`.\n        check_missing: whether to check all the files specified by `keys` are existing.\n        keys: if not None and check_missing_files is True, the expected keys to check in the datalist.\n        root_dir: if not None, provides the root dir for the relative file paths in `datalist`.\n        allow_missing_keys: if check_missing_files is `True`, whether allow missing keys in the datalist items.\n            if False, raise exception if missing. default to False.\n        raise_error: when found missing files, if `True`, raise exception and stop, if `False`, print warning.\n\n    \"\"\"\n    if check_missing and keys is not None:\n        files = check_missing_files(datalist, keys, root_dir, allow_missing_keys)\n        if files:\n            msg = f\"some files of the datalist are missing: {files}\"\n            if raise_error:\n                raise ValueError(msg)\n            warnings.warn(msg)\n\n    data = partition_dataset(data=datalist, num_partitions=nfolds, shuffle=shuffle, seed=seed)\n    train_list = select_cross_validation_folds(partitions=data, folds=train_folds)\n    val_list = select_cross_validation_folds(partitions=data, folds=val_folds)\n    ret = {train_key: train_list, val_key: val_list}\n    if isinstance(filename, (str, Path)):\n        with open(filename, \"w\") as f:\n            json.dump(ret, f, indent=4)\n\n    return ret", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/iterable_dataset.py_from_typing_import_Any_C_IterableDataset.__iter__.for_i_item_in_enumerate_.if_i_num_workers_id_.yield_item": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/iterable_dataset.py_from_typing_import_Any_C_IterableDataset.__iter__.for_i_item_in_enumerate_.if_i_num_workers_id_.yield_item", "embedding": null, "metadata": {"file_path": "monai/data/iterable_dataset.py", "file_name": "iterable_dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 61, "span_ids": ["IterableDataset.__init__", "IterableDataset.__iter__", "IterableDataset", "docstring"], "tokens": 429}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Union\n\nimport numpy as np\nfrom torch.utils.data import IterableDataset as _TorchIterableDataset\nfrom torch.utils.data import get_worker_info\n\nfrom monai.data.utils import convert_tables_to_dicts\nfrom monai.transforms import apply_transform\nfrom monai.transforms.transform import Randomizable\nfrom monai.utils import deprecated_arg, optional_import\n\npd, _ = optional_import(\"pandas\")\n\n\nclass IterableDataset(_TorchIterableDataset):\n    \"\"\"\n    A generic dataset for iterable data source and an optional callable data transform\n    when fetching a data sample. Inherit from PyTorch IterableDataset:\n    https://pytorch.org/docs/stable/data.html?highlight=iterabledataset#torch.utils.data.IterableDataset.\n    For example, typical input data can be web data stream which can support multi-process access.\n\n    To accelerate the loading process, it can support multi-processing based on PyTorch DataLoader workers,\n    every process executes transforms on part of every loaded data.\n    Note that the order of output data may not match data source in multi-processing mode.\n    And each worker process will have a different copy of the dataset object, need to guarantee\n    process-safe from data source or DataLoader.\n\n    \"\"\"\n\n    def __init__(self, data: Iterable, transform: Optional[Callable] = None) -> None:\n        \"\"\"\n        Args:\n            data: input data source to load and transform to generate dataset for model.\n            transform: a callable data transform on input data.\n        \"\"\"\n        self.data = data\n        self.transform = transform\n        self.source = None\n\n    def __iter__(self):\n        info = get_worker_info()\n        num_workers = info.num_workers if info is not None else 1\n        id = info.id if info is not None else 0\n\n        self.source = iter(self.data)\n        for i, item in enumerate(self.source):\n            if i % num_workers == id:\n                if self.transform is not None:\n                    item = apply_transform(self.transform, item)\n                yield item", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/iterable_dataset.py_ShuffleBuffer_ShuffleBuffer.__init__.self._idx.0": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/iterable_dataset.py_ShuffleBuffer_ShuffleBuffer.__init__.self._idx.0", "embedding": null, "metadata": {"file_path": "monai/data/iterable_dataset.py", "file_name": "iterable_dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 64, "end_line": 82, "span_ids": ["ShuffleBuffer.__init__", "ShuffleBuffer"], "tokens": 202}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ShuffleBuffer(Randomizable, IterableDataset):\n    \"\"\"\n    Extend the IterableDataset with a buffer and randomly pop items.\n\n    Args:\n        data: input data source to load and transform to generate dataset for model.\n        transform: a callable data transform on input data.\n        buffer_size: size of the buffer to store items and randomly pop, default to 512.\n        seed: random seed to initialize the random state of all workers, set `seed += 1` in\n            every iter() call, refer to the PyTorch idea:\n            https://github.com/pytorch/pytorch/blob/v1.10.0/torch/utils/data/distributed.py#L98.\n\n    \"\"\"\n\n    def __init__(self, data, transform=None, buffer_size: int = 512, seed: int = 0) -> None:\n        super().__init__(data=data, transform=transform)\n        self.size = buffer_size\n        self.seed = seed\n        self._idx = 0", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/iterable_dataset.py_ShuffleBuffer.__iter___ShuffleBuffer.set_random_state.raise_NotImplementedError": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/iterable_dataset.py_ShuffleBuffer.__iter___ShuffleBuffer.set_random_state.raise_NotImplementedError", "embedding": null, "metadata": {"file_path": "monai/data/iterable_dataset.py", "file_name": "iterable_dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 84, "end_line": 119, "span_ids": ["ShuffleBuffer.set_random_state", "ShuffleBuffer.__iter__", "ShuffleBuffer.randomize"], "tokens": 276}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ShuffleBuffer(Randomizable, IterableDataset):\n\n    def __iter__(self):\n        \"\"\"\n        Fetch data from the source, if buffer is not full, fill into buffer, otherwise,\n        randomly pop items from the buffer.\n        After loading all the data from source, randomly pop items from the buffer.\n\n        \"\"\"\n        self.seed += 1\n        super().set_random_state(seed=self.seed)  # make all workers in sync\n        buffer = []\n        source = self.data\n\n        def _pop_item():\n            self.randomize(len(buffer))\n            # switch random index data and the last index data\n            ret, buffer[self._idx] = buffer[self._idx], buffer[-1]\n            buffer.pop()\n            return ret\n\n        def _get_item():\n            for item in source:\n                if len(buffer) >= self.size:\n                    yield _pop_item()\n                buffer.append(item)\n\n            while buffer:\n                yield _pop_item()\n\n        self.data = _get_item()\n        return super().__iter__()\n\n    def randomize(self, size: int) -> None:\n        self._idx = self.R.randint(size)\n\n    def set_random_state(self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None):\n        raise NotImplementedError(f\"`set_random_state` is not available in {self.__class__.__name__}.\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/test_time_augmentation.py_warnings__identity.return.x": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/test_time_augmentation.py_warnings__identity.return.x", "embedding": null, "metadata": {"file_path": "monai/data/test_time_augmentation.py", "file_name": "test_time_augmentation.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 42, "span_ids": ["_identity", "docstring"], "tokens": 221}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import warnings\nfrom copy import deepcopy\nfrom typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\n\nfrom monai.data.dataloader import DataLoader\nfrom monai.data.dataset import Dataset\nfrom monai.data.utils import list_data_collate, pad_list_data_collate\nfrom monai.transforms.compose import Compose\nfrom monai.transforms.inverse import InvertibleTransform\nfrom monai.transforms.inverse_batch_transform import BatchInverseTransform\nfrom monai.transforms.transform import Randomizable\nfrom monai.transforms.utils import allow_missing_keys_mode, convert_inverse_interp_mode\nfrom monai.utils.enums import CommonKeys, TraceKeys\nfrom monai.utils.module import optional_import\nfrom monai.utils.type_conversion import convert_data_type\n\nif TYPE_CHECKING:\n    from tqdm import tqdm\n\n    has_tqdm = True\nelse:\n    tqdm, has_tqdm = optional_import(\"tqdm\", name=\"tqdm\")\n\n__all__ = [\"TestTimeAugmentation\"]\n\n\ndef _identity(x):\n    return x", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/test_time_augmentation.py_TestTimeAugmentation._check_transforms_TestTimeAugmentation._check_transforms.for_r_i_in_zip_randoms_.if_r_and_not_i_.warnings_warn_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/test_time_augmentation.py_TestTimeAugmentation._check_transforms_TestTimeAugmentation._check_transforms.for_r_i_in_zip_randoms_.if_r_and_not_i_.warnings_warn_", "embedding": null, "metadata": {"file_path": "monai/data/test_time_augmentation.py", "file_name": "test_time_augmentation.py", "file_type": "text/x-python", "category": "test", "start_line": 128, "end_line": 143, "span_ids": ["TestTimeAugmentation._check_transforms"], "tokens": 210}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestTimeAugmentation:\n\n    def _check_transforms(self):\n        \"\"\"Should be at least 1 random transform, and all random transforms should be invertible.\"\"\"\n        ts = [self.transform] if not isinstance(self.transform, Compose) else self.transform.transforms\n        randoms = np.array([isinstance(t, Randomizable) for t in ts])\n        invertibles = np.array([isinstance(t, InvertibleTransform) for t in ts])\n        # check at least 1 random\n        if sum(randoms) == 0:\n            raise RuntimeError(\n                \"Requires a `Randomizable` transform or a `Compose` containing at least one `Randomizable` transform.\"\n            )\n        # check that whenever randoms is True, invertibles is also true\n        for r, i in zip(randoms, invertibles):\n            if r and not i:\n                warnings.warn(\n                    f\"Not all applied random transform(s) are invertible. Problematic transform: {type(r).__name__}\"\n                )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/workflow.py_Workflow._register_metrics._compare_metrics_Workflow._register_metrics._compare_metrics.if_key_metric_name_is_not.if_self_metric_cmp_fn_cur.None_1": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/workflow.py_Workflow._register_metrics._compare_metrics_Workflow._register_metrics._compare_metrics.if_key_metric_name_is_not.if_self_metric_cmp_fn_cur.None_1", "embedding": null, "metadata": {"file_path": "monai/engines/workflow.py", "file_name": "workflow.py", "file_type": "text/x-python", "category": "implementation", "start_line": 227, "end_line": 242, "span_ids": ["Workflow._register_metrics"], "tokens": 228}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Workflow(IgniteEngine):\n\n    def _register_metrics(self, k_metric: Dict, add_metrics: Optional[Dict] = None):\n        # ... other code\n\n        @self.on(Events.EPOCH_COMPLETED)\n        def _compare_metrics(engine: Engine) -> None:\n            key_metric_name = engine.state.key_metric_name  # type: ignore\n            if key_metric_name is not None:\n                current_val_metric = engine.state.metrics[key_metric_name]\n                if not is_scalar(current_val_metric):\n                    warnings.warn(\n                        \"key metric is not a scalar value, skip the metric comparison with the current best metric.\"\n                        \"please set other metrics as the key metric, or change the `reduction` mode to 'mean'.\"\n                    )\n                    return\n\n                if self.metric_cmp_fn(current_val_metric, engine.state.best_metric):  # type: ignore\n                    self.logger.info(f\"Got new best metric of {key_metric_name}: {current_val_metric}\")\n                    engine.state.best_metric = current_val_metric  # type: ignore\n                    engine.state.best_metric_epoch = engine.state.epoch  # type: ignore", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/workflow.py_Workflow._register_handlers_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/workflow.py_Workflow._register_handlers_", "embedding": null, "metadata": {"file_path": "monai/engines/workflow.py", "file_name": "workflow.py", "file_type": "text/x-python", "category": "implementation", "start_line": 244, "end_line": 274, "span_ids": ["Workflow._register_handlers", "Workflow.run", "Workflow._iteration"], "tokens": 240}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Workflow(IgniteEngine):\n\n    def _register_handlers(self, handlers: Sequence):\n        \"\"\"\n        Register the handlers to the engine, supports ignite Handlers with `attach` API.\n\n        \"\"\"\n        handlers_ = ensure_tuple(handlers)\n        for handler in handlers_:\n            handler.attach(self)\n\n    def run(self) -> None:\n        \"\"\"\n        Execute training, validation or evaluation based on Ignite Engine.\n\n        \"\"\"\n        super().run(data=self.data_loader, max_epochs=self.state.max_epochs)\n\n    def _iteration(self, engine: Engine, batchdata: Dict[str, torch.Tensor]):\n        \"\"\"\n        Abstract callback function for the processing logic of 1 iteration in Ignite Engine.\n        Need subclass to implement different logics, like SupervisedTrainer/Evaluator, GANTrainer, etc.\n\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n            batchdata: input data for this iteration, usually can be dictionary or tuple of Tensor data.\n\n        Raises:\n            NotImplementedError: When the subclass does not override this method.\n\n        \"\"\"\n        raise NotImplementedError(f\"Subclass {self.__class__.__name__} must implement this method.\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/ignite_metric.py_IgniteMetric.compute_IgniteMetric.compute.return.result": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/ignite_metric.py_IgniteMetric.compute_IgniteMetric.compute.return.result", "embedding": null, "metadata": {"file_path": "monai/handlers/ignite_metric.py", "file_name": "ignite_metric.py", "file_type": "text/x-python", "category": "implementation", "start_line": 84, "end_line": 108, "span_ids": ["IgniteMetric.compute"], "tokens": 205}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class IgniteMetric(Metric):\n\n    def compute(self) -> Any:\n        \"\"\"\n        Raises:\n            NotComputableError: When ``compute`` is called before an ``update`` occurs.\n\n        \"\"\"\n        result = self.metric_fn.aggregate()\n        if isinstance(result, (tuple, list)):\n            if len(result) > 1:\n                warnings.warn(\"metric handler can only record the first value of result list.\")\n            result = result[0]\n\n        self._is_reduced = True\n\n        # save score of every image into engine.state for other components\n        if self.save_details:\n            if self._engine is None or self._name is None:\n                raise RuntimeError(\"please call the attach() function to connect expected engine first.\")\n            self._engine.state.metric_details[self._name] = self.metric_fn.get_buffer()  # type: ignore\n\n        if isinstance(result, torch.Tensor):\n            result = result.squeeze()\n            if result.ndim == 0:\n                result = result.item()\n        return result", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/__init__.py_ContrastiveLoss_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/__init__.py_ContrastiveLoss_", "embedding": null, "metadata": {"file_path": "monai/losses/__init__.py", "file_name": "__init__.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 32, "span_ids": ["docstring"], "tokens": 144}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from .contrastive import ContrastiveLoss\nfrom .deform import BendingEnergyLoss\nfrom .dice import (\n    Dice,\n    DiceCELoss,\n    DiceFocalLoss,\n    DiceLoss,\n    GeneralizedDiceLoss,\n    GeneralizedWassersteinDiceLoss,\n    MaskedDiceLoss,\n    dice_ce,\n    dice_focal,\n    generalized_dice,\n    generalized_wasserstein_dice,\n)\nfrom .focal_loss import FocalLoss\nfrom .image_dissimilarity import GlobalMutualInformationLoss, LocalNormalizedCrossCorrelationLoss\nfrom .multi_scale import MultiScaleLoss\nfrom .spatial_mask import MaskedLoss\nfrom .tversky import TverskyLoss", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/contrastive.py_ContrastiveLoss.forward_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/contrastive.py_ContrastiveLoss.forward_", "embedding": null, "metadata": {"file_path": "monai/losses/contrastive.py", "file_name": "contrastive.py", "file_type": "text/x-python", "category": "implementation", "start_line": 53, "end_line": 89, "span_ids": ["ContrastiveLoss.forward"], "tokens": 380}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ContrastiveLoss(_Loss):\n\n    def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:\n        \"\"\"\n        Args:\n            input: the shape should be B[F].\n            target: the shape should be B[F].\n        \"\"\"\n        if len(target.shape) > 2 or len(input.shape) > 2:\n            raise ValueError(\n                f\"Either target or input has dimensions greater than 2 where target \"\n                f\"shape is ({target.shape}) and input shape is ({input.shape})\"\n            )\n\n        if target.shape != input.shape:\n            raise ValueError(f\"ground truth has differing shape ({target.shape}) from input ({input.shape})\")\n\n        temperature_tensor = torch.tensor(self.temperature).to(input.device)\n\n        norm_i = F.normalize(input, dim=1)\n        norm_j = F.normalize(target, dim=1)\n\n        negatives_mask = ~torch.eye(self.batch_size * 2, self.batch_size * 2, dtype=torch.bool)\n        negatives_mask = torch.tensor(negatives_mask, dtype=torch.float)\n        negatives_mask = torch.clone(torch.as_tensor(negatives_mask)).to(input.device)\n\n        repr = torch.cat([norm_i, norm_j], dim=0)\n        sim_matrix = F.cosine_similarity(repr.unsqueeze(1), repr.unsqueeze(0), dim=2)\n        sim_ij = torch.diag(sim_matrix, self.batch_size)\n        sim_ji = torch.diag(sim_matrix, -self.batch_size)\n\n        positives = torch.cat([sim_ij, sim_ji], dim=0)\n        nominator = torch.exp(positives / temperature_tensor)\n        denominator = negatives_mask * torch.exp(sim_matrix / temperature_tensor)\n\n        loss_partial = -torch.log(nominator / torch.sum(denominator, dim=1))\n\n        return torch.sum(loss_partial) / (2 * self.batch_size)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/dice.py_DiceLoss.forward_DiceLoss.forward.f.1_0_2_0_intersection": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/dice.py_DiceLoss.forward_DiceLoss.forward.f.1_0_2_0_intersection", "embedding": null, "metadata": {"file_path": "monai/losses/dice.py", "file_name": "dice.py", "file_type": "text/x-python", "category": "implementation", "start_line": 106, "end_line": 179, "span_ids": ["DiceLoss.forward"], "tokens": 687}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DiceLoss(_Loss):\n\n    def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:\n        \"\"\"\n        Args:\n            input: the shape should be BNH[WD], where N is the number of classes.\n            target: the shape should be BNH[WD] or B1H[WD], where N is the number of classes.\n\n        Raises:\n            AssertionError: When input and target (after one hot transform if set)\n                have different shapes.\n            ValueError: When ``self.reduction`` is not one of [\"mean\", \"sum\", \"none\"].\n\n        Example:\n            >>> from monai.losses.dice import *  # NOQA\n            >>> import torch\n            >>> from monai.losses.dice import DiceLoss\n            >>> B, C, H, W = 7, 5, 3, 2\n            >>> input = torch.rand(B, C, H, W)\n            >>> target_idx = torch.randint(low=0, high=C - 1, size=(B, H, W)).long()\n            >>> target = one_hot(target_idx[:, None, ...], num_classes=C)\n            >>> self = DiceLoss(reduction='none')\n            >>> loss = self(input, target)\n            >>> assert np.broadcast_shapes(loss.shape, input.shape) == input.shape\n        \"\"\"\n        if self.sigmoid:\n            input = torch.sigmoid(input)\n\n        n_pred_ch = input.shape[1]\n        if self.softmax:\n            if n_pred_ch == 1:\n                warnings.warn(\"single channel prediction, `softmax=True` ignored.\")\n            else:\n                input = torch.softmax(input, 1)\n\n        if self.other_act is not None:\n            input = self.other_act(input)\n\n        if self.to_onehot_y:\n            if n_pred_ch == 1:\n                warnings.warn(\"single channel prediction, `to_onehot_y=True` ignored.\")\n            else:\n                target = one_hot(target, num_classes=n_pred_ch)\n\n        if not self.include_background:\n            if n_pred_ch == 1:\n                warnings.warn(\"single channel prediction, `include_background=False` ignored.\")\n            else:\n                # if skipping background, removing first channel\n                target = target[:, 1:]\n                input = input[:, 1:]\n\n        if target.shape != input.shape:\n            raise AssertionError(f\"ground truth has different shape ({target.shape}) from input ({input.shape})\")\n\n        # reducing only spatial dimensions (not batch nor channels)\n        reduce_axis: List[int] = torch.arange(2, len(input.shape)).tolist()\n        if self.batch:\n            # reducing spatial dimensions and batch\n            reduce_axis = [0] + reduce_axis\n\n        intersection = torch.sum(target * input, dim=reduce_axis)\n\n        if self.squared_pred:\n            target = torch.pow(target, 2)\n            input = torch.pow(input, 2)\n\n        ground_o = torch.sum(target, dim=reduce_axis)\n        pred_o = torch.sum(input, dim=reduce_axis)\n\n        denominator = ground_o + pred_o\n\n        if self.jaccard:\n            denominator = 2.0 * (denominator - intersection)\n\n        f: torch.Tensor = 1.0 - (2.0 * intersection + self.smooth_nr) / (denominator + self.smooth_dr)\n        # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/dice.py_DiceLoss.forward.if_self_reduction_Loss_DiceLoss.forward.return.f": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/dice.py_DiceLoss.forward.if_self_reduction_Loss_DiceLoss.forward.return.f", "embedding": null, "metadata": {"file_path": "monai/losses/dice.py", "file_name": "dice.py", "file_type": "text/x-python", "category": "implementation", "start_line": 181, "end_line": 193, "span_ids": ["DiceLoss.forward"], "tokens": 196}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DiceLoss(_Loss):\n\n    def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:\n        # ... other code\n\n        if self.reduction == LossReduction.MEAN.value:\n            f = torch.mean(f)  # the batch and channel average\n        elif self.reduction == LossReduction.SUM.value:\n            f = torch.sum(f)  # sum over the batch and channel dims\n        elif self.reduction == LossReduction.NONE.value:\n            # If we are not computing voxelwise loss components at least\n            # make sure a none reduction maintains a broadcastable shape\n            broadcast_shape = list(f.shape[0:2]) + [1] * (len(input.shape) - 2)\n            f = f.view(broadcast_shape)\n        else:\n            raise ValueError(f'Unsupported reduction: {self.reduction}, available options are [\"mean\", \"sum\", \"none\"].')\n\n        return f", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/focal_loss.py_warnings_FocalLoss._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/focal_loss.py_warnings_FocalLoss._", "embedding": null, "metadata": {"file_path": "monai/losses/focal_loss.py", "file_name": "focal_loss.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 63, "span_ids": ["FocalLoss", "docstring"], "tokens": 610}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import warnings\nfrom typing import Optional, Sequence, Union\n\nimport torch\nimport torch.nn.functional as F\nfrom torch.nn.modules.loss import _Loss\n\nfrom monai.networks import one_hot\nfrom monai.utils import LossReduction\n\n\nclass FocalLoss(_Loss):\n    \"\"\"\n    FocalLoss is an extension of BCEWithLogitsLoss that down-weights loss from\n    high confidence correct predictions.\n\n    Reimplementation of the Focal Loss (with a build-in sigmoid activation) described in:\n\n        - \"Focal Loss for Dense Object Detection\", T. Lin et al., ICCV 2017\n        - \"AnatomyNet: Deep learning for fast and fully automated whole\u2010volume segmentation of head and neck anatomy\",\n          Zhu et al., Medical Physics 2018\n\n    Example:\n        >>> import torch\n        >>> from monai.losses import FocalLoss\n        >>> from torch.nn import BCEWithLogitsLoss\n        >>> shape = B, N, *DIMS = 2, 3, 5, 7, 11\n        >>> input = torch.rand(*shape)\n        >>> target = torch.rand(*shape)\n        >>> # Demonstrate equivalence to BCE when gamma=0\n        >>> fl_g0_criterion = FocalLoss(reduction='none', gamma=0)\n        >>> fl_g0_loss = fl_g0_criterion(input, target)\n        >>> bce_criterion = BCEWithLogitsLoss(reduction='none')\n        >>> bce_loss = bce_criterion(input, target)\n        >>> assert torch.allclose(fl_g0_loss, bce_loss)\n        >>> # Demonstrate \"focus\" by setting gamma > 0.\n        >>> fl_g2_criterion = FocalLoss(reduction='none', gamma=2)\n        >>> fl_g2_loss = fl_g2_criterion(input, target)\n        >>> # Mark easy and hard cases\n        >>> is_easy = (target > 0.7) & (input > 0.7)\n        >>> is_hard = (target > 0.7) & (input < 0.3)\n        >>> easy_loss_g0 = fl_g0_loss[is_easy].mean()\n        >>> hard_loss_g0 = fl_g0_loss[is_hard].mean()\n        >>> easy_loss_g2 = fl_g2_loss[is_easy].mean()\n        >>> hard_loss_g2 = fl_g2_loss[is_hard].mean()\n        >>> # Gamma > 0 causes the loss function to \"focus\" on the hard\n        >>> # cases.  IE, easy cases are downweighted, so hard cases\n        >>> # receive a higher proportion of the loss.\n        >>> hard_to_easy_ratio_g2 = hard_loss_g2 / easy_loss_g2\n        >>> hard_to_easy_ratio_g0 = hard_loss_g0 / easy_loss_g0\n        >>> assert hard_to_easy_ratio_g2 > hard_to_easy_ratio_g0\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/focal_loss.py_FocalLoss.__init___FocalLoss.__init__.self.weight.weight": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/focal_loss.py_FocalLoss.__init___FocalLoss.__init__.self.weight.weight", "embedding": null, "metadata": {"file_path": "monai/losses/focal_loss.py", "file_name": "focal_loss.py", "file_type": "text/x-python", "category": "implementation", "start_line": 65, "end_line": 103, "span_ids": ["FocalLoss.__init__"], "tokens": 502}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class FocalLoss(_Loss):\n\n    def __init__(\n        self,\n        include_background: bool = True,\n        to_onehot_y: bool = False,\n        gamma: float = 2.0,\n        weight: Optional[Union[Sequence[float], float, int, torch.Tensor]] = None,\n        reduction: Union[LossReduction, str] = LossReduction.MEAN,\n    ) -> None:\n        \"\"\"\n        Args:\n            include_background: if False, channel index 0 (background category) is excluded from the calculation.\n            to_onehot_y: whether to convert `y` into the one-hot format. Defaults to False.\n            gamma: value of the exponent gamma in the definition of the Focal loss.\n            weight: weights to apply to the voxels of each class. If None no weights are applied.\n                This corresponds to the weights `\\alpha` in [1].\n                The input can be a single value (same weight for all classes), a sequence of values (the length\n                of the sequence should be the same as the number of classes, if not ``include_background``, the\n                number should not include class 0).\n                The value/values should be no less than 0. Defaults to None.\n            reduction: {``\"none\"``, ``\"mean\"``, ``\"sum\"``}\n                Specifies the reduction to apply to the output. Defaults to ``\"mean\"``.\n\n                - ``\"none\"``: no reduction will be applied.\n                - ``\"mean\"``: the sum of the output will be divided by the number of elements in the output.\n                - ``\"sum\"``: the output will be summed.\n\n        Example:\n            >>> import torch\n            >>> from monai.losses import FocalLoss\n            >>> pred = torch.tensor([[1, 0], [0, 1], [1, 0]], dtype=torch.float32)\n            >>> grnd = torch.tensor([[0], [1], [0]], dtype=torch.int64)\n            >>> fl = FocalLoss(to_onehot_y=True)\n            >>> fl(pred, grnd)\n        \"\"\"\n        super().__init__(reduction=LossReduction(reduction).value)\n        self.include_background = include_background\n        self.to_onehot_y = to_onehot_y\n        self.gamma = gamma\n        self.weight: Optional[Union[Sequence[float], float, int, torch.Tensor]] = weight", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/focal_loss.py_FocalLoss.forward_FocalLoss.forward.p.F_logsigmoid_i_t_2_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/focal_loss.py_FocalLoss.forward_FocalLoss.forward.p.F_logsigmoid_i_t_2_", "embedding": null, "metadata": {"file_path": "monai/losses/focal_loss.py", "file_name": "focal_loss.py", "file_type": "text/x-python", "category": "implementation", "start_line": 105, "end_line": 178, "span_ids": ["FocalLoss.forward"], "tokens": 806}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class FocalLoss(_Loss):\n\n    def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:\n        \"\"\"\n        Args:\n            input: the shape should be BNH[WD], where N is the number of classes.\n                The input should be the original logits since it will be transformed by\n                a sigmoid in the forward function.\n            target: the shape should be BNH[WD] or B1H[WD], where N is the number of classes.\n\n        Raises:\n            ValueError: When input and target (after one hot transform if set)\n                have different shapes.\n            ValueError: When ``self.reduction`` is not one of [\"mean\", \"sum\", \"none\"].\n            ValueError: When ``self.weight`` is a sequence and the length is not equal to the\n                number of classes.\n            ValueError: When ``self.weight`` is/contains a value that is less than 0.\n\n        \"\"\"\n        n_pred_ch = input.shape[1]\n\n        if self.to_onehot_y:\n            if n_pred_ch == 1:\n                warnings.warn(\"single channel prediction, `to_onehot_y=True` ignored.\")\n            else:\n                target = one_hot(target, num_classes=n_pred_ch)\n\n        if not self.include_background:\n            if n_pred_ch == 1:\n                warnings.warn(\"single channel prediction, `include_background=False` ignored.\")\n            else:\n                # if skipping background, removing first channel\n                target = target[:, 1:]\n                input = input[:, 1:]\n\n        if target.shape != input.shape:\n            raise ValueError(f\"ground truth has different shape ({target.shape}) from input ({input.shape})\")\n\n        i = input\n        t = target\n\n        # Change the shape of input and target to B x N x num_voxels.\n        b, n = t.shape[:2]\n        i = i.reshape(b, n, -1)\n        t = t.reshape(b, n, -1)\n\n        # computing binary cross entropy with logits\n        # see also https://github.com/pytorch/pytorch/blob/v1.9.0/aten/src/ATen/native/Loss.cpp#L231\n        max_val = (-i).clamp(min=0)\n        ce = i - i * t + max_val + ((-max_val).exp() + (-i - max_val).exp()).log()\n\n        if self.weight is not None:\n            class_weight: Optional[torch.Tensor] = None\n            if isinstance(self.weight, (float, int)):\n                class_weight = torch.as_tensor([self.weight] * i.size(1))\n            else:\n                class_weight = torch.as_tensor(self.weight)\n                if class_weight.size(0) != i.size(1):\n                    raise ValueError(\n                        \"the length of the weight sequence should be the same as the number of classes. \"\n                        + \"If `include_background=False`, the number should not include class 0.\"\n                    )\n            if class_weight.min() < 0:\n                raise ValueError(\"the value/values of weights should be no less than 0.\")\n            class_weight = class_weight.to(i)\n            # Convert the weight to a map in which each voxel\n            # has the weight associated with the ground-truth label\n            # associated with this voxel in target.\n            at = class_weight[None, :, None]  # N => 1,N,1\n            at = at.expand((t.size(0), -1, t.size(2)))  # 1,N,1 => B,N,H*W\n            # Multiply the log proba by their weights.\n            ce = ce * at\n\n        # Compute the loss mini-batch.\n        # (1-p_t)^gamma * log(p_t) with reduced chance of overflow\n        p = F.logsigmoid(-i * (t * 2.0 - 1.0))\n        # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/focal_loss.py_FocalLoss.forward.flat_loss_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/focal_loss.py_FocalLoss.forward.flat_loss_", "embedding": null, "metadata": {"file_path": "monai/losses/focal_loss.py", "file_name": "focal_loss.py", "file_type": "text/x-python", "category": "implementation", "start_line": 179, "end_line": 201, "span_ids": ["FocalLoss.forward"], "tokens": 269}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class FocalLoss(_Loss):\n\n    def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:\n        # ... other code\n        flat_loss: torch.Tensor = (p * self.gamma).exp() * ce\n\n        # Previously there was a mean over the last dimension, which did not\n        # return a compatible BCE loss. To maintain backwards compatible\n        # behavior we have a flag that performs this extra step, disable or\n        # parameterize if necessary. (Or justify why the mean should be there)\n        average_spatial_dims = True\n\n        if self.reduction == LossReduction.SUM.value:\n            if average_spatial_dims:\n                flat_loss = flat_loss.mean(dim=-1)\n            loss = flat_loss.sum()\n        elif self.reduction == LossReduction.MEAN.value:\n            if average_spatial_dims:\n                flat_loss = flat_loss.mean(dim=-1)\n            loss = flat_loss.mean()\n        elif self.reduction == LossReduction.NONE.value:\n            spacetime_dims = input.shape[2:]\n            loss = flat_loss.reshape([b, n] + list(spacetime_dims))\n        else:\n            raise ValueError(f'Unsupported reduction: {self.reduction}, available options are [\"mean\", \"sum\", \"none\"].')\n        return loss", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/cumulative_average.py_torch_CumulativeAverage.reset.self.not_nans.None": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/cumulative_average.py_torch_CumulativeAverage.reset.self.not_nans.None", "embedding": null, "metadata": {"file_path": "monai/metrics/cumulative_average.py", "file_name": "cumulative_average.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 52, "span_ids": ["CumulativeAverage.__init__", "CumulativeAverage.reset", "CumulativeAverage", "docstring"], "tokens": 266}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import torch\n\nfrom monai.transforms import isnan\nfrom monai.utils import convert_data_type\n\nfrom .metric import Cumulative\n\n\nclass CumulativeAverage(Cumulative):\n    \"\"\"\n    Cumulatively record data value and aggregate for the average value.\n    It supports single class or multi-class data, for example,\n    value can be 0.44 (a loss value) or [0.3, 0.4] (metrics of two classes).\n    It also supports distributed data parallel, sync data when aggregating.\n    For example, recording loss values and compute the overall average value in every 5 iterations:\n\n    .. code-block:: python\n\n        average = CumulativeAverage()\n        for i, d in enumerate(dataloader):\n            loss = ...\n            average.append(loss)\n            if i % 5 == 0:\n                print(f\"cumulative average of loss: {average.aggregate()}\")\n        average.reset()\n\n    \"\"\"\n\n    def __init__(self) -> None:\n        super().__init__()\n        self.sum = None\n        self.not_nans = None\n\n    def reset(self):\n        \"\"\"\n        Reset all the running status, including buffers, sum, not nans count, etc.\n\n        \"\"\"\n        super().reset()\n        self.sum = None\n        self.not_nans = None", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/cumulative_average.py_CumulativeAverage.aggregate_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/cumulative_average.py_CumulativeAverage.aggregate_", "embedding": null, "metadata": {"file_path": "monai/metrics/cumulative_average.py", "file_name": "cumulative_average.py", "file_type": "text/x-python", "category": "implementation", "start_line": 54, "end_line": 73, "span_ids": ["CumulativeAverage.aggregate"], "tokens": 172}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CumulativeAverage(Cumulative):\n\n    def aggregate(self):  # type: ignore\n        \"\"\"\n        Sync data from all the ranks and compute the average value with previous sum value.\n\n        \"\"\"\n        data = self.get_buffer()\n\n        # compute SUM across the batch dimension\n        nans = isnan(data)\n        not_nans = convert_data_type((~nans), dtype=torch.float32)[0].sum(0)\n        data[nans] = 0\n        f = data.sum(0)\n\n        # clear the buffer for next update\n        super().reset()\n        self.sum = f if self.sum is None else (self.sum + f)\n        self.not_nans = not_nans if self.not_nans is None else (self.not_nans + not_nans)\n\n        return self.sum / self.not_nans", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/metric.py_IterationMetric_IterationMetric.__call__.raise_ValueError_y_pred_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/metric.py_IterationMetric_IterationMetric.__call__.raise_ValueError_y_pred_", "embedding": null, "metadata": {"file_path": "monai/metrics/metric.py", "file_name": "metric.py", "file_type": "text/x-python", "category": "implementation", "start_line": 38, "end_line": 73, "span_ids": ["IterationMetric", "IterationMetric.__call__"], "tokens": 415}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class IterationMetric(Metric):\n    \"\"\"\n    Base class for metrics computation at the iteration level, that is, on a min-batch of samples\n    usually using the model outcome of one iteration.\n\n    `__call__` is designed to handle `y_pred` and `y` (optional) in torch tensors or a list/tuple of tensors.\n\n    Subclasses typically implement the `_compute_tensor` function for the actual tensor computation logic.\n    \"\"\"\n\n    def __call__(self, y_pred: TensorOrList, y: Optional[TensorOrList] = None):  # type: ignore\n        \"\"\"\n        Execute basic computation for model prediction `y_pred` and ground truth `y` (optional).\n        It supports inputs of a list of \"channel-first\" Tensor and a \"batch-first\" Tensor.\n\n        Args:\n            y_pred: the raw model prediction data at one iteration, must be a list of `channel-first` Tensor\n                or a `batch-first` Tensor.\n            y: the ground truth to compute, must be a list of `channel-first` Tensor\n                or a `batch-first` Tensor.\n\n        Returns:\n            The computed metric values at the iteration level.\n            The output shape could be a `batch-first` tensor or a list of `batch-first` tensors.\n            When it's a list of tensors, each item in the list can represent a specific type of metric.\n\n        \"\"\"\n        ret: TensorOrList\n        # handling a list of channel-first data\n        if isinstance(y_pred, (list, tuple)) or isinstance(y, (list, tuple)):\n            return self._compute_list(y_pred, y)\n        # handling a single batch-first data\n        if isinstance(y_pred, torch.Tensor):\n            y_ = y.detach() if isinstance(y, torch.Tensor) else None\n            return self._compute_tensor(y_pred.detach(), y_)\n        raise ValueError(\"y_pred or y must be a list/tuple of `channel-first` Tensors or a `batch-first` Tensor.\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/metric.py_IterationMetric._compute_list_IterationMetric._compute_list.return.ret": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/metric.py_IterationMetric._compute_list_IterationMetric._compute_list.return.ret", "embedding": null, "metadata": {"file_path": "monai/metrics/metric.py", "file_name": "metric.py", "file_type": "text/x-python", "category": "implementation", "start_line": 75, "end_line": 100, "span_ids": ["IterationMetric._compute_list"], "tokens": 394}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class IterationMetric(Metric):\n\n    def _compute_list(self, y_pred: TensorOrList, y: Optional[TensorOrList] = None):\n        \"\"\"\n        Execute the metric computation for `y_pred` and `y` in a list of \"channel-first\" tensors.\n\n        The return value is a \"batch-first\" tensor, or a list of \"batch-first\" tensors.\n        When it's a list of tensors, each item in the list can represent a specific type of metric values.\n\n        For example, `self._compute_tensor` may be implemented as returning a list of `batch_size` items,\n        where each item is a tuple of three values `tp`, `fp`, `fn` for true positives, false positives,\n        and false negatives respectively. This function will return a list of three items,\n        (`tp_batched`, `fp_batched`, `fn_batched`), where each item is a `batch_size`-length tensor.\n\n        Note: subclass may enhance the operation to have multi-thread support.\n        \"\"\"\n        if y is not None:\n            ret = [self._compute_tensor(p.detach().unsqueeze(0), y_.detach().unsqueeze(0)) for p, y_ in zip(y_pred, y)]\n        else:\n            ret = [self._compute_tensor(p_.detach().unsqueeze(0), None) for p_ in y_pred]\n\n        # concat the list of results (e.g. a batch of evaluation scores)\n        if isinstance(ret[0], torch.Tensor):\n            return torch.cat(ret, dim=0)\n        # the result is a list of sequence of tensors (e.g. a batch of multi-class results)\n        if isinstance(ret[0], (list, tuple)) and all(isinstance(i, torch.Tensor) for i in ret[0]):\n            return [torch.cat(batch_i, dim=0) for batch_i in zip(*ret)]\n        return ret", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/metric.py_IterationMetric._compute_tensor_IterationMetric._compute_tensor.raise_NotImplementedError": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/metric.py_IterationMetric._compute_tensor_IterationMetric._compute_tensor.raise_NotImplementedError", "embedding": null, "metadata": {"file_path": "monai/metrics/metric.py", "file_name": "metric.py", "file_type": "text/x-python", "category": "implementation", "start_line": 102, "end_line": 109, "span_ids": ["IterationMetric._compute_tensor"], "tokens": 119}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class IterationMetric(Metric):\n\n    @abstractmethod\n    def _compute_tensor(self, y_pred: torch.Tensor, y: Optional[torch.Tensor] = None):\n        \"\"\"\n        Computation logic for `y_pred` and `y` of an iteration, the data should be \"batch-first\" Tensors.\n        A subclass should implement its own computation logic.\n        The return value is usually a \"batch_first\" tensor, or a list of \"batch_first\" tensors.\n        \"\"\"\n        raise NotImplementedError(f\"Subclass {self.__class__.__name__} must implement this method.\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/metric.py_Cumulative.extend_Cumulative.extend.self._synced.False": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/metric.py_Cumulative.extend_Cumulative.extend.self._synced.False", "embedding": null, "metadata": {"file_path": "monai/metrics/metric.py", "file_name": "metric.py", "file_type": "text/x-python", "category": "implementation", "start_line": 190, "end_line": 213, "span_ids": ["Cumulative.extend"], "tokens": 279}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Cumulative:\n\n    def extend(self, *data) -> None:\n        \"\"\"\n        Extend the local buffers with new (\"batch-first\") data.\n        A buffer will be allocated for each `data` item.\n        Compared with `self.append`, this method adds a \"batch\" of data to the local buffers.\n\n        Args:\n            data: each item can be a \"batch-first\" tensor or a list of \"channel-first\" tensors.\n                they will be concatenated at the 0-th dimension when `get_buffer()` is called.\n        \"\"\"\n        if self._buffers is None:\n            self._buffers = [[] for _ in data]\n        for b, d in zip(self._buffers, data):\n            # converting to pytorch tensors so that we can use the distributed API\n            d_t: torch.Tensor\n            d_t, *_ = convert_data_type(d, output_type=torch.Tensor, wrap_sequence=True)  # type: ignore\n            try:\n                b.extend([x[0] for x in torch.split(d_t, 1, dim=0)])\n            except (AttributeError, IndexError, RuntimeError) as e:\n                raise TypeError(\n                    f\"{e}. `data` should be a batch-first tensor or\"\n                    f\" a list of channel-first tensors, got {type(d_t)}\"\n                ) from e\n        self._synced = False", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/metric.py_Cumulative.append_Cumulative.append.self._synced.False": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/metric.py_Cumulative.append_Cumulative.append.self._synced.False", "embedding": null, "metadata": {"file_path": "monai/metrics/metric.py", "file_name": "metric.py", "file_type": "text/x-python", "category": "implementation", "start_line": 215, "end_line": 234, "span_ids": ["Cumulative.append"], "tokens": 201}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Cumulative:\n\n    def append(self, *data) -> None:\n        \"\"\"\n        Add samples to the local cumulative buffers.\n        A buffer will be allocated for each `data` item.\n        Compared with `self.extend`, this method adds a single sample (instead\n        of a \"batch\") to the local buffers.\n\n        Args:\n            data: each item will be converted into a torch tensor.\n                they will be stacked at the 0-th dim with a new dimension when `get_buffer()` is called.\n\n        \"\"\"\n        if self._buffers is None:\n            self._buffers = [[] for _ in data]\n        for b, d in zip(self._buffers, data):\n            # converting to pytorch tensors so that we can use the distributed API\n            d_t: torch.Tensor\n            d_t, *_ = convert_data_type(d, output_type=torch.Tensor, wrap_sequence=True)  # type: ignore\n            b.append(d_t)\n        self._synced = False", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/metric.py_Cumulative.aggregate_Cumulative._sync.self._synced.True": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/metric.py_Cumulative.aggregate_Cumulative._sync.self._synced.True", "embedding": null, "metadata": {"file_path": "monai/metrics/metric.py", "file_name": "metric.py", "file_type": "text/x-python", "category": "implementation", "start_line": 236, "end_line": 259, "span_ids": ["Cumulative._sync", "Cumulative.aggregate"], "tokens": 206}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Cumulative:\n\n    @abstractmethod\n    def aggregate(self, *args: Any, **kwargs: Any):\n        \"\"\"\n        Aggregate final results based on the gathered buffers.\n        This method is expected to use `get_buffer` to gather the local buffer contents.\n\n        \"\"\"\n        raise NotImplementedError(f\"Subclass {self.__class__.__name__} must implement this method.\")\n\n    def _sync(self):\n        \"\"\"\n        All gather the buffers across distributed ranks for aggregating.\n        Each buffer will be concatenated as a PyTorch Tensor.\n\n        \"\"\"\n        if self._synced or self._buffers is None:\n            return\n        try:\n            self._synced_tensors = [\n                evenly_divisible_all_gather(torch.stack(b, dim=0), concat=True) for b in self._buffers\n            ]\n        except (RuntimeError, TypeError, ValueError) as e:\n            raise TypeError(f\"{e}. unable to sync buffer contents: {self._buffers}.\") from e\n        self._synced = True", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/metric.py_Cumulative.__len___Cumulative.get_buffer.return.self__synced_tensors_0_i": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/metric.py_Cumulative.__len___Cumulative.get_buffer.return.self__synced_tensors_0_i", "embedding": null, "metadata": {"file_path": "monai/metrics/metric.py", "file_name": "metric.py", "file_type": "text/x-python", "category": "implementation", "start_line": 261, "end_line": 279, "span_ids": ["Cumulative.get_buffer", "Cumulative.__len__"], "tokens": 148}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Cumulative:\n\n    def __len__(self):\n        \"\"\"\n        Return the length of the largest buffer.\n        Note that the method will trigger synchronization of the local buffers.\n        \"\"\"\n        self._sync()\n        if not self._synced_tensors:\n            return 0\n        return max(len(x) for x in self._synced_tensors)\n\n    def get_buffer(self):\n        \"\"\"\n        Get the synchronized list of buffers.\n        A typical usage is to generate the metrics report based on the raw metric details.\n        Each buffer is a PyTorch Tensor.\n\n        \"\"\"\n        self._sync()\n        return self._synced_tensors[0] if len(self._synced_tensors) == 1 else self._synced_tensors", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/simplelayers.py_apply_filter_apply_filter.return.output_view_batch_chns_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/layers/simplelayers.py_apply_filter_apply_filter.return.output_view_batch_chns_", "embedding": null, "metadata": {"file_path": "monai/networks/layers/simplelayers.py", "file_name": "simplelayers.py", "file_type": "text/x-python", "category": "implementation", "start_line": 253, "end_line": 305, "span_ids": ["apply_filter"], "tokens": 653}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def apply_filter(x: torch.Tensor, kernel: torch.Tensor, **kwargs) -> torch.Tensor:\n    \"\"\"\n    Filtering `x` with `kernel` independently for each batch and channel respectively.\n\n    Args:\n        x: the input image, must have shape (batch, channels, H[, W, D]).\n        kernel: `kernel` must at least have the spatial shape (H_k[, W_k, D_k]).\n            `kernel` shape must be broadcastable to the `batch` and `channels` dimensions of `x`.\n        kwargs: keyword arguments passed to `conv*d()` functions.\n\n    Returns:\n        The filtered `x`.\n\n    Examples:\n\n    .. code-block:: python\n\n        >>> import torch\n        >>> from monai.networks.layers import apply_filter\n        >>> img = torch.rand(2, 5, 10, 10)  # batch_size 2, channels 5, 10x10 2D images\n        >>> out = apply_filter(img, torch.rand(3, 3))   # spatial kernel\n        >>> out = apply_filter(img, torch.rand(5, 3, 3))  # channel-wise kernels\n        >>> out = apply_filter(img, torch.rand(2, 5, 3, 3))  # batch-, channel-wise kernels\n\n    \"\"\"\n    if not isinstance(x, torch.Tensor):\n        raise TypeError(f\"x must be a torch.Tensor but is {type(x).__name__}.\")\n    batch, chns, *spatials = x.shape\n    n_spatial = len(spatials)\n    if n_spatial > 3:\n        raise NotImplementedError(f\"Only spatial dimensions up to 3 are supported but got {n_spatial}.\")\n    k_size = len(kernel.shape)\n    if k_size < n_spatial or k_size > n_spatial + 2:\n        raise ValueError(\n            f\"kernel must have {n_spatial} ~ {n_spatial + 2} dimensions to match the input shape {x.shape}.\"\n        )\n    kernel = kernel.to(x)\n    # broadcast kernel size to (batch chns, spatial_kernel_size)\n    kernel = kernel.expand(batch, chns, *kernel.shape[(k_size - n_spatial) :])\n    kernel = kernel.reshape(-1, 1, *kernel.shape[2:])  # group=1\n    x = x.view(1, kernel.shape[0], *spatials)\n    conv = [F.conv1d, F.conv2d, F.conv3d][n_spatial - 1]\n    if \"padding\" not in kwargs:\n        if pytorch_after(1, 10):\n            kwargs[\"padding\"] = \"same\"\n        else:\n            # even-sized kernels are not supported\n            kwargs[\"padding\"] = [(k - 1) // 2 for k in kernel.shape[2:]]\n\n    if \"stride\" not in kwargs:\n        kwargs[\"stride\"] = 1\n    output = conv(x, kernel, groups=kernel.shape[0], bias=None, **kwargs)\n    return output.view(batch, chns, *output.shape[2:])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dynunet.py_DynUNet.check_kernel_stride_DynUNet.check_kernel_stride.for_idx_k_i_in_enumerate.if_not_isinstance_stride_.if_len_stride_self_sp.raise_ValueError_error_ms": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dynunet.py_DynUNet.check_kernel_stride_DynUNet.check_kernel_stride.for_idx_k_i_in_enumerate.if_not_isinstance_stride_.if_len_stride_self_sp.raise_ValueError_error_ms", "embedding": null, "metadata": {"file_path": "monai/networks/nets/dynunet.py", "file_name": "dynunet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 208, "end_line": 223, "span_ids": ["DynUNet.check_kernel_stride"], "tokens": 193}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DynUNet(nn.Module):\n\n    def check_kernel_stride(self):\n        kernels, strides = self.kernel_size, self.strides\n        error_msg = \"length of kernel_size and strides should be the same, and no less than 3.\"\n        if len(kernels) != len(strides) or len(kernels) < 3:\n            raise ValueError(error_msg)\n\n        for idx, k_i in enumerate(kernels):\n            kernel, stride = k_i, strides[idx]\n            if not isinstance(kernel, int):\n                error_msg = f\"length of kernel_size in block {idx} should be the same as spatial_dims.\"\n                if len(kernel) != self.spatial_dims:\n                    raise ValueError(error_msg)\n            if not isinstance(stride, int):\n                error_msg = f\"length of stride in block {idx} should be the same as spatial_dims.\"\n                if len(stride) != self.spatial_dims:\n                    raise ValueError(error_msg)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dynunet.py_DynUNet.check_deep_supr_num_DynUNet.get_downsamples.return.self_get_module_list_inp_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dynunet.py_DynUNet.check_deep_supr_num_DynUNet.get_downsamples.return.self_get_module_list_inp_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/dynunet.py", "file_name": "dynunet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 246, "end_line": 301, "span_ids": ["DynUNet.get_bottleneck", "DynUNet.check_deep_supr_num", "DynUNet.check_filters", "DynUNet.get_output_block", "DynUNet.get_downsamples", "DynUNet.get_input_block", "DynUNet.forward"], "tokens": 456}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DynUNet(nn.Module):\n\n    def check_deep_supr_num(self):\n        deep_supr_num, strides = self.deep_supr_num, self.strides\n        num_up_layers = len(strides) - 1\n        if deep_supr_num >= num_up_layers:\n            raise ValueError(\"deep_supr_num should be less than the number of up sample layers.\")\n        if deep_supr_num < 1:\n            raise ValueError(\"deep_supr_num should be larger than 0.\")\n\n    def check_filters(self):\n        filters = self.filters\n        if len(filters) < len(self.strides):\n            raise ValueError(\"length of filters should be no less than the length of strides.\")\n        else:\n            self.filters = filters[: len(self.strides)]\n\n    def forward(self, x):\n        out = self.skip_layers(x)\n        out = self.output_block(out)\n        if self.training and self.deep_supervision:\n            out_all = [out]\n            for feature_map in self.heads:\n                out_all.append(interpolate(feature_map, out.shape[2:]))\n            return torch.stack(out_all, dim=1)\n        return out\n\n    def get_input_block(self):\n        return self.conv_block(\n            self.spatial_dims,\n            self.in_channels,\n            self.filters[0],\n            self.kernel_size[0],\n            self.strides[0],\n            self.norm_name,\n            self.act_name,\n            dropout=self.dropout,\n        )\n\n    def get_bottleneck(self):\n        return self.conv_block(\n            self.spatial_dims,\n            self.filters[-2],\n            self.filters[-1],\n            self.kernel_size[-1],\n            self.strides[-1],\n            self.norm_name,\n            self.act_name,\n            dropout=self.dropout,\n        )\n\n    def get_output_block(self, idx: int):\n        return UnetOutBlock(self.spatial_dims, self.filters[idx], self.out_channels, dropout=self.dropout)\n\n    def get_downsamples(self):\n        inp, out = self.filters[:-2], self.filters[1:-1]\n        strides, kernel_size = self.strides[1:-1], self.kernel_size[1:-1]\n        return self.get_module_list(inp, out, kernel_size, strides, self.conv_block)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dynunet.py_DynUNet.get_upsamples_DynUNet.get_upsamples.return.self_get_module_list_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dynunet.py_DynUNet.get_upsamples_DynUNet.get_upsamples.return.self_get_module_list_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/dynunet.py", "file_name": "dynunet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 283, "end_line": 289, "span_ids": ["DynUNet.get_upsamples"], "tokens": 112}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DynUNet(nn.Module):\n\n    def get_upsamples(self):\n        inp, out = self.filters[1:][::-1], self.filters[:-1][::-1]\n        strides, kernel_size = self.strides[1:][::-1], self.kernel_size[1:][::-1]\n        upsample_kernel_size = self.upsample_kernel_size[::-1]\n        return self.get_module_list(\n            inp, out, kernel_size, strides, UnetUpBlock, upsample_kernel_size, trans_bias=self.trans_bias\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/vitautoenc.py_ViTAutoEnc.forward_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/vitautoenc.py_ViTAutoEnc.forward_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/vitautoenc.py", "file_name": "vitautoenc.py", "file_type": "text/x-python", "category": "implementation", "start_line": 102, "end_line": 120, "span_ids": ["ViTAutoEnc.forward"], "tokens": 183}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ViTAutoEnc(nn.Module):\n\n    def forward(self, x):\n        \"\"\"\n        Args:\n            x: input tensor must have isotropic spatial dimensions,\n                such as ``[batch_size, channels, sp_size, sp_size[, sp_size]]``.\n        \"\"\"\n        x = self.patch_embedding(x)\n        hidden_states_out = []\n        for blk in self.blocks:\n            x = blk(x)\n            hidden_states_out.append(x)\n        x = self.norm(x)\n        x = x.transpose(1, 2)\n        d = [round(math.pow(x.shape[2], 1 / self.spatial_dims))] * self.spatial_dims\n        x = torch.reshape(x, [x.shape[0], x.shape[1], *d])\n        x = self.conv3d_transpose(x)\n        x = self.conv3d_transpose_1(x)\n        return x, hidden_states_out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_ScaleIntensity.__call___ScaleIntensity.__call__.return.ret": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_ScaleIntensity.__call___ScaleIntensity.__call__.return.ret", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 438, "end_line": 457, "span_ids": ["ScaleIntensity.__call__"], "tokens": 217}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ScaleIntensity(Transform):\n\n    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:\n        \"\"\"\n        Apply the transform to `img`.\n\n        Raises:\n            ValueError: When ``self.minv=None`` or ``self.maxv=None`` and ``self.factor=None``. Incompatible values.\n\n        \"\"\"\n        ret: NdarrayOrTensor\n        if self.minv is not None or self.maxv is not None:\n            if self.channel_wise:\n                out = [rescale_array(d, self.minv, self.maxv, dtype=self.dtype) for d in img]\n                ret = torch.stack(out) if isinstance(img, torch.Tensor) else np.stack(out)  # type: ignore\n            else:\n                ret = rescale_array(img, self.minv, self.maxv, dtype=self.dtype)\n        else:\n            ret = (img * (1 + self.factor)) if self.factor is not None else img\n\n        ret, *_ = convert_data_type(ret, dtype=self.dtype or img.dtype)\n        return ret", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_ScaleIntensityRange_ScaleIntensityRange.__init__.self.dtype.dtype": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_ScaleIntensityRange_ScaleIntensityRange.__init__.self.dtype.dtype", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 720, "end_line": 753, "span_ids": ["ScaleIntensityRange", "ScaleIntensityRange.__init__"], "tokens": 300}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ScaleIntensityRange(Transform):\n    \"\"\"\n    Apply specific intensity scaling to the whole numpy array.\n    Scaling from [a_min, a_max] to [b_min, b_max] with clip option.\n\n    When `b_min` or `b_max` are `None`, `scacled_array * (b_max - b_min) + b_min` will be skipped.\n    If `clip=True`, when `b_min`/`b_max` is None, the clipping is not performed on the corresponding edge.\n\n    Args:\n        a_min: intensity original range min.\n        a_max: intensity original range max.\n        b_min: intensity target range min.\n        b_max: intensity target range max.\n        clip: whether to perform clip after scaling.\n        dtype: output data type, if None, same as input image. defaults to float32.\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __init__(\n        self,\n        a_min: float,\n        a_max: float,\n        b_min: Optional[float] = None,\n        b_max: Optional[float] = None,\n        clip: bool = False,\n        dtype: DtypeLike = np.float32,\n    ) -> None:\n        self.a_min = a_min\n        self.a_max = a_max\n        self.b_min = b_min\n        self.b_max = b_max\n        self.clip = clip\n        self.dtype = dtype", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_ScaleIntensityRange.__call___ScaleIntensityRange.__call__.return.ret": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_ScaleIntensityRange.__call___ScaleIntensityRange.__call__.return.ret", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 755, "end_line": 773, "span_ids": ["ScaleIntensityRange.__call__"], "tokens": 200}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ScaleIntensityRange(Transform):\n\n    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:\n        \"\"\"\n        Apply the transform to `img`.\n        \"\"\"\n        dtype = self.dtype or img.dtype\n        if self.a_max - self.a_min == 0.0:\n            warn(\"Divide by zero (a_min == a_max)\", Warning)\n            if self.b_min is None:\n                return img - self.a_min\n            return img - self.a_min + self.b_min\n\n        img = (img - self.a_min) / (self.a_max - self.a_min)\n        if (self.b_min is not None) and (self.b_max is not None):\n            img = img * (self.b_max - self.b_min) + self.b_min\n        if self.clip:\n            img = clip(img, self.b_min, self.b_max)\n        ret, *_ = convert_data_type(img, dtype=dtype)\n\n        return ret", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_ScaleIntensityRangePercentiles.__init___ScaleIntensityRangePercentiles.__init__.self.dtype.dtype": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_ScaleIntensityRangePercentiles.__init___ScaleIntensityRangePercentiles.__init__.self.dtype.dtype", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 929, "end_line": 951, "span_ids": ["ScaleIntensityRangePercentiles.__init__"], "tokens": 199}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ScaleIntensityRangePercentiles(Transform):\n\n    def __init__(\n        self,\n        lower: float,\n        upper: float,\n        b_min: Optional[float],\n        b_max: Optional[float],\n        clip: bool = False,\n        relative: bool = False,\n        channel_wise: bool = False,\n        dtype: DtypeLike = np.float32,\n    ) -> None:\n        if lower < 0.0 or lower > 100.0:\n            raise ValueError(\"Percentiles must be in the range [0, 100]\")\n        if upper < 0.0 or upper > 100.0:\n            raise ValueError(\"Percentiles must be in the range [0, 100]\")\n        self.lower = lower\n        self.upper = upper\n        self.b_min = b_min\n        self.b_max = b_max\n        self.clip = clip\n        self.relative = relative\n        self.channel_wise = channel_wise\n        self.dtype = dtype", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_HistogramNormalize_HistogramNormalize.__init__.self.dtype.dtype": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_HistogramNormalize_HistogramNormalize.__init__.self.dtype.dtype", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1993, "end_line": 2024, "span_ids": ["HistogramNormalize.__init__", "HistogramNormalize"], "tokens": 303}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class HistogramNormalize(Transform):\n    \"\"\"\n    Apply the histogram normalization to input image.\n    Refer to: https://github.com/facebookresearch/CovidPrognosis/blob/master/covidprognosis/data/transforms.py#L83.\n\n    Args:\n        num_bins: number of the bins to use in histogram, default to `256`. for more details:\n            https://numpy.org/doc/stable/reference/generated/numpy.histogram.html.\n        min: the min value to normalize input image, default to `0`.\n        max: the max value to normalize input image, default to `255`.\n        mask: if provided, must be ndarray of bools or 0s and 1s, and same shape as `image`.\n            only points at which `mask==True` are used for the equalization.\n            can also provide the mask along with img at runtime.\n        dtype: data type of the output, if None, same as input image. default to `float32`.\n\n    \"\"\"\n\n    backend = [TransformBackends.NUMPY]\n\n    def __init__(\n        self,\n        num_bins: int = 256,\n        min: int = 0,\n        max: int = 255,\n        mask: Optional[NdarrayOrTensor] = None,\n        dtype: DtypeLike = np.float32,\n    ) -> None:\n        self.num_bins = num_bins\n        self.min = min\n        self.max = max\n        self.mask = mask\n        self.dtype = dtype", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_HistogramNormalize.__call___": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_HistogramNormalize.__call___", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 2026, "end_line": 2038, "span_ids": ["HistogramNormalize.__call__"], "tokens": 170}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class HistogramNormalize(Transform):\n\n    def __call__(self, img: NdarrayOrTensor, mask: Optional[NdarrayOrTensor] = None) -> NdarrayOrTensor:\n        img_np: np.ndarray\n        img_np, *_ = convert_data_type(img, np.ndarray)  # type: ignore\n        mask = mask if mask is not None else self.mask\n        mask_np: Optional[np.ndarray] = None\n        if mask is not None:\n            mask_np, *_ = convert_data_type(mask, np.ndarray)  # type: ignore\n\n        ret = equalize_hist(img=img_np, mask=mask_np, num_bins=self.num_bins, min=self.min, max=self.max)\n        out, *_ = convert_to_dst_type(src=ret, dst=img, dtype=self.dtype or img.dtype)\n\n        return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/inverse.py_os_TraceableTransform.trace_key.return.str_key_TraceKeys_KEY_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/inverse.py_os_TraceableTransform.trace_key.return.str_key_TraceKeys_KEY_", "embedding": null, "metadata": {"file_path": "monai/transforms/inverse.py", "file_name": "inverse.py", "file_type": "text/x-python", "category": "implementation", "start_line": 11, "end_line": 52, "span_ids": ["TraceableTransform", "TraceableTransform.set_tracing", "TraceableTransform.trace_key", "docstring"], "tokens": 355}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nfrom typing import Hashable, Mapping, Optional, Tuple\n\nimport torch\n\nfrom monai.transforms.transform import Transform\nfrom monai.utils.enums import TraceKeys\n\n__all__ = [\"TraceableTransform\", \"InvertibleTransform\"]\n\n\nclass TraceableTransform(Transform):\n    \"\"\"\n    Maintains a stack of applied transforms. The stack is inserted as pairs of\n    `trace_key: list of transforms` to each data dictionary.\n\n    The ``__call__`` method of this transform class must be implemented so\n    that the transformation information for each key is stored when\n    ``__call__`` is called. If the transforms were applied to keys \"image\" and\n    \"label\", there will be two extra keys in the dictionary: \"image_transforms\"\n    and \"label_transforms\" (based on `TraceKeys.KEY_SUFFIX`). Each list\n    contains a list of the transforms applied to that key.\n\n    The information in ``data[key_transform]`` will be compatible with the\n    default collate since it only stores strings, numbers and arrays.\n\n    `tracing` could be enabled by `self.set_tracing` or setting\n    `MONAI_TRACE_TRANSFORM` when initializing the class.\n    \"\"\"\n\n    tracing = False if os.environ.get(\"MONAI_TRACE_TRANSFORM\", \"1\") == \"0\" else True\n\n    def set_tracing(self, tracing: bool) -> None:\n        \"\"\"Set whether to trace transforms.\"\"\"\n        self.tracing = tracing\n\n    @staticmethod\n    def trace_key(key: Hashable = None):\n        \"\"\"The key to store the stack of applied transforms.\"\"\"\n        if key is None:\n            return TraceKeys.KEY_SUFFIX\n        return str(key) + TraceKeys.KEY_SUFFIX", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/inverse.py_TraceableTransform.push_transform_TraceableTransform.pop_transform.return.data_get_self_trace_key_k": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/inverse.py_TraceableTransform.push_transform_TraceableTransform.pop_transform.return.data_get_self_trace_key_k", "embedding": null, "metadata": {"file_path": "monai/transforms/inverse.py", "file_name": "inverse.py", "file_type": "text/x-python", "category": "implementation", "start_line": 54, "end_line": 81, "span_ids": ["TraceableTransform.pop_transform", "TraceableTransform.push_transform"], "tokens": 326}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TraceableTransform(Transform):\n\n    def push_transform(\n        self, data: Mapping, key: Hashable = None, extra_info: Optional[dict] = None, orig_size: Optional[Tuple] = None\n    ) -> None:\n        \"\"\"PUsh to a stack of applied transforms for that key.\"\"\"\n        if not self.tracing:\n            return\n        info = {TraceKeys.CLASS_NAME: self.__class__.__name__, TraceKeys.ID: id(self)}\n        if orig_size is not None:\n            info[TraceKeys.ORIG_SIZE] = orig_size\n        elif key in data and hasattr(data[key], \"shape\"):\n            info[TraceKeys.ORIG_SIZE] = data[key].shape[1:]\n        if extra_info is not None:\n            info[TraceKeys.EXTRA_INFO] = extra_info\n        # If class is randomizable transform, store whether the transform was actually performed (based on `prob`)\n        if hasattr(self, \"_do_transform\"):  # RandomizableTransform\n            info[TraceKeys.DO_TRANSFORM] = self._do_transform  # type: ignore\n        # If this is the first, create list\n        if self.trace_key(key) not in data:\n            if not isinstance(data, dict):\n                data = dict(data)\n            data[self.trace_key(key)] = []\n        data[self.trace_key(key)].append(info)\n\n    def pop_transform(self, data: Mapping, key: Hashable = None):\n        \"\"\"Remove the most recent applied transform.\"\"\"\n        if not self.tracing:\n            return\n        return data.get(self.trace_key(key), []).pop()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/inverse.py_InvertibleTransform_InvertibleTransform._Classes_for_invertible": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/inverse.py_InvertibleTransform_InvertibleTransform._Classes_for_invertible", "embedding": null, "metadata": {"file_path": "monai/transforms/inverse.py", "file_name": "inverse.py", "file_type": "text/x-python", "category": "implementation", "start_line": 84, "end_line": 119, "span_ids": ["InvertibleTransform"], "tokens": 399}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class InvertibleTransform(TraceableTransform):\n    \"\"\"Classes for invertible transforms.\n\n    This class exists so that an ``invert`` method can be implemented. This allows, for\n    example, images to be cropped, rotated, padded, etc., during training and inference,\n    and after be returned to their original size before saving to file for comparison in\n    an external viewer.\n\n    When the ``inverse`` method is called:\n\n        - the inverse is called on each key individually, which allows for\n          different parameters being passed to each label (e.g., different\n          interpolation for image and label).\n\n        - the inverse transforms are applied in a last- in-first-out order. As\n          the inverse is applied, its entry is removed from the list detailing\n          the applied transformations. That is to say that during the forward\n          pass, the list of applied transforms grows, and then during the\n          inverse it shrinks back down to an empty list.\n\n    We currently check that the ``id()`` of the transform is the same in the forward and\n    inverse directions. This is a useful check to ensure that the inverses are being\n    processed in the correct order.\n\n    Note to developers: When converting a transform to an invertible transform, you need to:\n\n        #. Inherit from this class.\n        #. In ``__call__``, add a call to ``push_transform``.\n        #. Any extra information that might be needed for the inverse can be included with the\n           dictionary ``extra_info``. This dictionary should have the same keys regardless of\n           whether ``do_transform`` was `True` or `False` and can only contain objects that are\n           accepted in pytorch data loader's collate function (e.g., `None` is not allowed).\n        #. Implement an ``inverse`` method. Make sure that after performing the inverse,\n           ``pop_transform`` is called.\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/inverse.py_InvertibleTransform.check_transforms_match_InvertibleTransform.check_transforms_match.raise_RuntimeError_f_Erro": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/inverse.py_InvertibleTransform.check_transforms_match_InvertibleTransform.check_transforms_match.raise_RuntimeError_f_Erro", "embedding": null, "metadata": {"file_path": "monai/transforms/inverse.py", "file_name": "inverse.py", "file_type": "text/x-python", "category": "implementation", "start_line": 121, "end_line": 130, "span_ids": ["InvertibleTransform.check_transforms_match"], "tokens": 151}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class InvertibleTransform(TraceableTransform):\n\n    def check_transforms_match(self, transform: Mapping) -> None:\n        \"\"\"Check transforms are of same instance.\"\"\"\n        xform_name = transform.get(TraceKeys.CLASS_NAME, \"\")\n        xform_id = transform.get(TraceKeys.ID, \"\")\n        if xform_id == id(self):\n            return\n        # basic check if multiprocessing uses 'spawn' (objects get recreated so don't have same ID)\n        if torch.multiprocessing.get_start_method() in (\"spawn\", None) and xform_name == self.__class__.__name__:\n            return\n        raise RuntimeError(f\"Error inverting the most recently applied invertible transform {xform_name} {xform_id}.\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/array.py_Activations.__call___Activations.__call__.return.out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/array.py_Activations.__call___Activations.__call__.return.out", "embedding": null, "metadata": {"file_path": "monai/transforms/post/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 70, "end_line": 109, "span_ids": ["Activations.__call__"], "tokens": 375}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Activations(Transform):\n\n    def __call__(\n        self,\n        img: NdarrayOrTensor,\n        sigmoid: Optional[bool] = None,\n        softmax: Optional[bool] = None,\n        other: Optional[Callable] = None,\n    ) -> NdarrayOrTensor:\n        \"\"\"\n        Args:\n            sigmoid: whether to execute sigmoid function on model output before transform.\n                Defaults to ``self.sigmoid``.\n            softmax: whether to execute softmax function on model output before transform.\n                Defaults to ``self.softmax``.\n            other: callable function to execute other activation layers, for example:\n                `other = torch.tanh`. Defaults to ``self.other``.\n\n        Raises:\n            ValueError: When ``sigmoid=True`` and ``softmax=True``. Incompatible values.\n            TypeError: When ``other`` is not an ``Optional[Callable]``.\n            ValueError: When ``self.other=None`` and ``other=None``. Incompatible values.\n\n        \"\"\"\n        if sigmoid and softmax:\n            raise ValueError(\"Incompatible values: sigmoid=True and softmax=True.\")\n        if other is not None and not callable(other):\n            raise TypeError(f\"other must be None or callable but is {type(other).__name__}.\")\n\n        # convert to float as activation must operate on float tensor\n        img_t: torch.Tensor\n        img_t, *_ = convert_data_type(img, torch.Tensor, dtype=torch.float)  # type: ignore\n        if sigmoid or self.sigmoid:\n            img_t = torch.sigmoid(img_t)\n        if softmax or self.softmax:\n            img_t = torch.softmax(img_t, dim=0)\n\n        act_func = self.other if other is None else other\n        if act_func is not None:\n            img_t = act_func(img_t)\n        out, *_ = convert_to_dst_type(img_t, img)\n        return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/array.py_AsDiscrete.__call___AsDiscrete.__call__.return.img": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/array.py_AsDiscrete.__call___AsDiscrete.__call__.return.img", "embedding": null, "metadata": {"file_path": "monai/transforms/post/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 188, "end_line": 256, "span_ids": ["AsDiscrete.__call__"], "tokens": 785}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AsDiscrete(Transform):\n\n    @deprecated_arg(name=\"n_classes\", new_name=\"num_classes\", since=\"0.6\", msg_suffix=\"please use `to_onehot` instead.\")\n    @deprecated_arg(\"num_classes\", since=\"0.7\", msg_suffix=\"please use `to_onehot` instead.\")\n    @deprecated_arg(\"logit_thresh\", since=\"0.7\", msg_suffix=\"please use `threshold` instead.\")\n    @deprecated_arg(\n        name=\"threshold_values\", new_name=\"threshold\", since=\"0.7\", msg_suffix=\"please use `threshold` instead.\"\n    )\n    def __call__(\n        self,\n        img: NdarrayOrTensor,\n        argmax: Optional[bool] = None,\n        to_onehot: Optional[int] = None,\n        threshold: Optional[float] = None,\n        rounding: Optional[str] = None,\n        n_classes: Optional[int] = None,  # deprecated\n        num_classes: Optional[int] = None,  # deprecated\n        logit_thresh: Optional[float] = None,  # deprecated\n        threshold_values: Optional[bool] = None,  # deprecated\n    ) -> NdarrayOrTensor:\n        \"\"\"\n        Args:\n            img: the input tensor data to convert, if no channel dimension when converting to `One-Hot`,\n                will automatically add it.\n            argmax: whether to execute argmax function on input data before transform.\n                Defaults to ``self.argmax``.\n            to_onehot: if not None, convert input data into the one-hot format with specified number of classes.\n                Defaults to ``self.to_onehot``.\n            threshold: if not None, threshold the float values to int number 0 or 1 with specified threshold value.\n                Defaults to ``self.threshold``.\n            rounding: if not None, round the data according to the specified option,\n                available options: [\"torchrounding\"].\n\n        .. deprecated:: 0.6.0\n            ``n_classes`` is deprecated, use ``to_onehot`` instead.\n\n        .. deprecated:: 0.7.0\n            ``num_classes`` is deprecated, use ``to_onehot`` instead.\n            ``logit_thresh`` is deprecated, use ``threshold`` instead.\n            ``threshold_values`` is deprecated, use ``threshold`` instead.\n\n        \"\"\"\n        if isinstance(to_onehot, bool):\n            warnings.warn(\"`to_onehot=True/False` is deprecated, please use `to_onehot=num_classes` instead.\")\n            to_onehot = num_classes if to_onehot else None\n        if isinstance(threshold, bool):\n            warnings.warn(\"`threshold_values=True/False` is deprecated, please use `threshold=value` instead.\")\n            threshold = logit_thresh if threshold else None\n\n        img_t: torch.Tensor\n        img_t, *_ = convert_data_type(img, torch.Tensor)  # type: ignore\n        if argmax or self.argmax:\n            img_t = torch.argmax(img_t, dim=0, keepdim=True)\n\n        to_onehot = self.to_onehot if to_onehot is None else to_onehot\n        if to_onehot is not None:\n            if not isinstance(to_onehot, int):\n                raise AssertionError(\"the number of classes for One-Hot must be an integer.\")\n            img_t = one_hot(img_t, num_classes=to_onehot, dim=0)\n\n        threshold = self.threshold if threshold is None else threshold\n        if threshold is not None:\n            img_t = img_t >= threshold\n\n        rounding = self.rounding if rounding is None else rounding\n        if rounding is not None:\n            look_up_option(rounding, [\"torchrounding\"])\n            img_t = torch.round(img_t)\n\n        img, *_ = convert_to_dst_type(img_t, img, dtype=torch.float)\n        return img", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/array.py_KeepLargestConnectedComponent.backend_KeepLargestConnectedComponent.__init__.self.connectivity.connectivity": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/array.py_KeepLargestConnectedComponent.backend_KeepLargestConnectedComponent.__init__.self.connectivity.connectivity", "embedding": null, "metadata": {"file_path": "monai/transforms/post/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 269, "end_line": 291, "span_ids": ["KeepLargestConnectedComponent.__init__", "KeepLargestConnectedComponent"], "tokens": 262}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class KeepLargestConnectedComponent(Transform):\n\n    backend = [TransformBackends.NUMPY]\n\n    def __init__(\n        self, applied_labels: Union[Sequence[int], int], independent: bool = True, connectivity: Optional[int] = None\n    ) -> None:\n        \"\"\"\n        Args:\n            applied_labels: Labels for applying the connected component analysis on.\n                If only one channel. The pixel whose value is in this list will be analyzed.\n                If the data is in one-hot format, this is used to determine which channels to apply.\n            independent: whether to treat ``applied_labels`` as a union of foreground labels.\n                If ``True``, the connected component analysis will be performed on each foreground label independently\n                and return the intersection of the largest components.\n                If ``False``, the analysis will be performed on the union of foreground labels.\n                default is `True`.\n            connectivity: Maximum number of orthogonal hops to consider a pixel/voxel as a neighbor.\n                Accepted values are ranging from  1 to input.ndim. If ``None``, a full\n                connectivity of ``input.ndim`` is used.\n        \"\"\"\n        super().__init__()\n        self.applied_labels = ensure_tuple(applied_labels)\n        self.independent = independent\n        self.connectivity = connectivity", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/array.py_KeepLargestConnectedComponent.__call___KeepLargestConnectedComponent.__call__.return.img": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/array.py_KeepLargestConnectedComponent.__call___KeepLargestConnectedComponent.__call__.return.img", "embedding": null, "metadata": {"file_path": "monai/transforms/post/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 293, "end_line": 323, "span_ids": ["KeepLargestConnectedComponent.__call__"], "tokens": 340}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class KeepLargestConnectedComponent(Transform):\n\n    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:\n        \"\"\"\n        Args:\n            img: shape must be (C, spatial_dim1[, spatial_dim2, ...]).\n\n        Returns:\n            An array with shape (C, spatial_dim1[, spatial_dim2, ...]).\n        \"\"\"\n\n        is_onehot = img.shape[0] > 1\n        if self.independent:\n            for i in self.applied_labels:\n                foreground = img[i] > 0 if is_onehot else img[0] == i\n                mask = get_largest_connected_component_mask(foreground, self.connectivity)\n                if is_onehot:\n                    img[i][foreground != mask] = 0\n                else:\n                    img[0][foreground != mask] = 0\n            return img\n        if not is_onehot:  # not one-hot, union of labels\n            labels, *_ = convert_to_dst_type(self.applied_labels, dst=img, wrap_sequence=True)\n            foreground = (img[..., None] == labels).any(-1)[0]\n            mask = get_largest_connected_component_mask(foreground, self.connectivity)\n            img[0][foreground != mask] = 0\n            return img\n        # one-hot, union of labels\n        foreground = (img[self.applied_labels, ...] == 1).any(0)\n        mask = get_largest_connected_component_mask(foreground, self.connectivity)\n        for i in self.applied_labels:\n            img[i][foreground != mask] = 0\n        return img", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/array.py_LabelFilter.__call___LabelFilter.__call__.return.np_asarray_np_where_np_is": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/array.py_LabelFilter.__call___LabelFilter.__call__.return.np_asarray_np_where_np_is", "embedding": null, "metadata": {"file_path": "monai/transforms/post/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 391, "end_line": 415, "span_ids": ["LabelFilter.__call__"], "tokens": 252}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LabelFilter:\n\n    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:\n        \"\"\"\n        Filter the image on the `applied_labels`.\n\n        Args:\n            img: Pytorch tensor or numpy array of any shape.\n\n        Raises:\n            NotImplementedError: The provided image was not a Pytorch Tensor or numpy array.\n\n        Returns:\n            Pytorch tensor or numpy array of the same shape as the input.\n        \"\"\"\n        if not isinstance(img, (np.ndarray, torch.Tensor)):\n            raise NotImplementedError(f\"{self.__class__} can not handle data of type {type(img)}.\")\n\n        if isinstance(img, torch.Tensor):\n            if hasattr(torch, \"isin\"):  # `isin` is new in torch 1.10.0\n                appl_lbls = torch.as_tensor(self.applied_labels, device=img.device)\n                return torch.where(torch.isin(img, appl_lbls), img, torch.tensor(0.0).to(img))\n            else:\n                out = self(img.detach().cpu().numpy())\n                out, *_ = convert_to_dst_type(out, img)\n                return out\n        return np.asarray(np.where(np.isin(img, self.applied_labels), img, 0))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/array.py_LabelToContour.__call___LabelToContour.__call__.return.output": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/array.py_LabelToContour.__call___LabelToContour.__call__.return.output", "embedding": null, "metadata": {"file_path": "monai/transforms/post/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 484, "end_line": 513, "span_ids": ["LabelToContour.__call__"], "tokens": 391}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LabelToContour(Transform):\n\n    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:\n        \"\"\"\n        Args:\n            img: torch tensor data to extract the contour, with shape: [channels, height, width[, depth]]\n\n        Raises:\n            ValueError: When ``image`` ndim is not one of [3, 4].\n\n        Returns:\n            A torch tensor with the same shape as img, note:\n                1. it's the binary classification result of whether a pixel is edge or not.\n                2. in order to keep the original shape of mask image, we use padding as default.\n                3. the edge detection is just approximate because it defects inherent to Laplace kernel,\n                   ideally the edge should be thin enough, but now it has a thickness.\n\n        \"\"\"\n        img_: torch.Tensor = convert_data_type(img, torch.Tensor)[0]  # type: ignore\n        spatial_dims = len(img_.shape) - 1\n        img_ = img_.unsqueeze(0)  # adds a batch dim\n        if spatial_dims == 2:\n            kernel = torch.tensor([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]], dtype=torch.float32)\n        elif spatial_dims == 3:\n            kernel = -1.0 * torch.ones(3, 3, 3, dtype=torch.float32)\n            kernel[1, 1, 1] = 26.0\n        else:\n            raise ValueError(f\"{self.__class__} can only handle 2D or 3D images.\")\n        contour_img = apply_filter(img_, kernel)\n        contour_img.clamp_(min=0.0, max=1.0)\n        output, *_ = convert_to_dst_type(contour_img.squeeze(0), img)\n        return output", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_get_largest_connected_component_mask_get_largest_connected_component_mask.return.largest_cc": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_get_largest_connected_component_mask_get_largest_connected_component_mask.return.largest_cc", "embedding": null, "metadata": {"file_path": "monai/transforms/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 928, "end_line": 944, "span_ids": ["get_largest_connected_component_mask"], "tokens": 245}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_largest_connected_component_mask(img: NdarrayOrTensor, connectivity: Optional[int] = None) -> NdarrayOrTensor:\n    \"\"\"\n    Gets the largest connected component mask of an image.\n\n    Args:\n        img: Image to get largest connected component from. Shape is (spatial_dim1 [, spatial_dim2, ...])\n        connectivity: Maximum number of orthogonal hops to consider a pixel/voxel as a neighbor.\n            Accepted values are ranging from  1 to input.ndim. If ``None``, a full\n            connectivity of ``input.ndim`` is used.\n    \"\"\"\n    img_arr: np.ndarray = convert_data_type(img, np.ndarray)[0]  # type: ignore\n    largest_cc: np.ndarray = np.zeros(shape=img_arr.shape, dtype=img_arr.dtype)\n    img_arr = measure.label(img_arr, connectivity=connectivity)\n    if img_arr.max() != 0:\n        largest_cc[...] = img_arr == (np.argmax(np.bincount(img_arr.flat)[1:]) + 1)\n    largest_cc = convert_to_dst_type(largest_cc, dst=img, dtype=largest_cc.dtype)[0]  # type: ignore\n    return largest_cc", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_equalize_hist_equalize_hist.return.img_reshape_orig_shape_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_equalize_hist_equalize_hist.return.img_reshape_orig_shape_", "embedding": null, "metadata": {"file_path": "monai/transforms/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1248, "end_line": 1281, "span_ids": ["equalize_hist"], "tokens": 356}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def equalize_hist(\n    img: np.ndarray, mask: Optional[np.ndarray] = None, num_bins: int = 256, min: int = 0, max: int = 255\n) -> np.ndarray:\n    \"\"\"\n    Utility to equalize input image based on the histogram.\n    If `skimage` installed, will leverage `skimage.exposure.histogram`, otherwise, use\n    `np.histogram` instead.\n\n    Args:\n        img: input image to equalize.\n        mask: if provided, must be ndarray of bools or 0s and 1s, and same shape as `image`.\n            only points at which `mask==True` are used for the equalization.\n        num_bins: number of the bins to use in histogram, default to `256`. for more details:\n            https://numpy.org/doc/stable/reference/generated/numpy.histogram.html.\n        min: the min value to normalize input image, default to `0`.\n        max: the max value to normalize input image, default to `255`.\n\n    \"\"\"\n\n    orig_shape = img.shape\n    hist_img = img[np.array(mask, dtype=bool)] if mask is not None else img\n    if has_skimage:\n        hist, bins = exposure.histogram(hist_img.flatten(), num_bins)\n    else:\n        hist, bins = np.histogram(hist_img.flatten(), num_bins)\n        bins = (bins[:-1] + bins[1:]) / 2\n\n    cum = hist.cumsum()\n    # normalize the cumulative result\n    cum = rescale_array(arr=cum, minv=min, maxv=max)\n\n    # apply linear interpolation\n    img = np.interp(img.flatten(), bins, cum)\n    return img.reshape(orig_shape)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils_create_transform_ims.py_pre_process_data_get_2d_slice.return.out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils_create_transform_ims.py_pre_process_data_get_2d_slice.return.out", "embedding": null, "metadata": {"file_path": "monai/transforms/utils_create_transform_ims.py", "file_name": "utils_create_transform_ims.py", "file_type": "text/x-python", "category": "implementation", "start_line": 238, "end_line": 264, "span_ids": ["pre_process_data", "get_2d_slice"], "tokens": 249}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def pre_process_data(data, ndim, is_map, is_post):\n    \"\"\"If transform requires 2D data, then convert to 2D\"\"\"\n    if ndim == 2:\n        for k in keys:\n            data[k] = data[k][..., data[k].shape[-1] // 2]\n\n    if is_map:\n        return data\n    return data[CommonKeys.LABEL] if is_post else data[CommonKeys.IMAGE]\n\n\ndef get_2d_slice(image, view, is_label):\n    \"\"\"If image is 3d, get the central slice. If is already 2d, return as-is.\n    If image is label, set 0 to np.nan.\n    \"\"\"\n    if image.ndim == 2:\n        out = image\n    else:\n        shape = image.shape\n        slices = [slice(0, s) for s in shape]\n        _slice = shape[view] // 2\n        slices[view] = slice(_slice, _slice + 1)\n        slices = tuple(slices)\n        out = np.squeeze(image[slices], view)\n    if is_label:\n        out[out == 0] = np.nan\n    return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils_pytorch_numpy_unification.py_from_typing_import_Option___all__._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils_pytorch_numpy_unification.py_from_typing_import_Option___all__._", "embedding": null, "metadata": {"file_path": "monai/transforms/utils_pytorch_numpy_unification.py", "file_name": "utils_pytorch_numpy_unification.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 39, "span_ids": ["docstring"], "tokens": 139}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Optional, Sequence, Union\n\nimport numpy as np\nimport torch\n\nfrom monai.config.type_definitions import NdarrayOrTensor\nfrom monai.utils.misc import is_module_ver_at_least\n\n__all__ = [\n    \"moveaxis\",\n    \"in1d\",\n    \"clip\",\n    \"percentile\",\n    \"where\",\n    \"nonzero\",\n    \"floor_divide\",\n    \"unravel_index\",\n    \"unravel_indices\",\n    \"ravel\",\n    \"any_np_pt\",\n    \"maximum\",\n    \"concatenate\",\n    \"cumsum\",\n    \"isfinite\",\n    \"searchsorted\",\n    \"repeat\",\n    \"isnan\",\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/enums.py_InterpolateMode_ForwardMode.EVAL._eval_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/enums.py_InterpolateMode_ForwardMode.EVAL._eval_", "embedding": null, "metadata": {"file_path": "monai/utils/enums.py", "file_name": "enums.py", "file_type": "text/x-python", "category": "implementation", "start_line": 75, "end_line": 211, "span_ids": ["ChannelMatching", "Average", "LossReduction", "BlendMode", "MetricReduction", "Weight", "InterpolateMode", "Method", "GridSamplePadMode", "SkipMode", "PytorchPadMode", "UpsampleMode", "ForwardMode"], "tokens": 745}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class InterpolateMode(Enum):\n    \"\"\"\n    See also: https://pytorch.org/docs/stable/nn.functional.html#interpolate\n    \"\"\"\n\n    NEAREST = \"nearest\"\n    LINEAR = \"linear\"\n    BILINEAR = \"bilinear\"\n    BICUBIC = \"bicubic\"\n    TRILINEAR = \"trilinear\"\n    AREA = \"area\"\n\n\nclass UpsampleMode(Enum):\n    \"\"\"\n    See also: :py:class:`monai.networks.blocks.UpSample`\n    \"\"\"\n\n    DECONV = \"deconv\"\n    NONTRAINABLE = \"nontrainable\"  # e.g. using torch.nn.Upsample\n    PIXELSHUFFLE = \"pixelshuffle\"\n\n\nclass BlendMode(Enum):\n    \"\"\"\n    See also: :py:class:`monai.data.utils.compute_importance_map`\n    \"\"\"\n\n    CONSTANT = \"constant\"\n    GAUSSIAN = \"gaussian\"\n\n\nclass PytorchPadMode(Enum):\n    \"\"\"\n    See also: https://pytorch.org/docs/stable/nn.functional.html#pad\n    \"\"\"\n\n    CONSTANT = \"constant\"\n    REFLECT = \"reflect\"\n    REPLICATE = \"replicate\"\n    CIRCULAR = \"circular\"\n\n\nclass GridSamplePadMode(Enum):\n    \"\"\"\n    See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample\n    \"\"\"\n\n    ZEROS = \"zeros\"\n    BORDER = \"border\"\n    REFLECTION = \"reflection\"\n\n\nclass Average(Enum):\n    \"\"\"\n    See also: :py:class:`monai.metrics.rocauc.compute_roc_auc`\n    \"\"\"\n\n    MACRO = \"macro\"\n    WEIGHTED = \"weighted\"\n    MICRO = \"micro\"\n    NONE = \"none\"\n\n\nclass MetricReduction(Enum):\n    \"\"\"\n    See also: :py:class:`monai.metrics.meandice.DiceMetric`\n    \"\"\"\n\n    NONE = \"none\"\n    MEAN = \"mean\"\n    SUM = \"sum\"\n    MEAN_BATCH = \"mean_batch\"\n    SUM_BATCH = \"sum_batch\"\n    MEAN_CHANNEL = \"mean_channel\"\n    SUM_CHANNEL = \"sum_channel\"\n\n\nclass LossReduction(Enum):\n    \"\"\"\n    See also:\n        - :py:class:`monai.losses.dice.DiceLoss`\n        - :py:class:`monai.losses.dice.GeneralizedDiceLoss`\n        - :py:class:`monai.losses.focal_loss.FocalLoss`\n        - :py:class:`monai.losses.tversky.TverskyLoss`\n    \"\"\"\n\n    NONE = \"none\"\n    MEAN = \"mean\"\n    SUM = \"sum\"\n\n\nclass Weight(Enum):\n    \"\"\"\n    See also: :py:class:`monai.losses.dice.GeneralizedDiceLoss`\n    \"\"\"\n\n    SQUARE = \"square\"\n    SIMPLE = \"simple\"\n    UNIFORM = \"uniform\"\n\n\nclass ChannelMatching(Enum):\n    \"\"\"\n    See also: :py:class:`monai.networks.nets.HighResBlock`\n    \"\"\"\n\n    PAD = \"pad\"\n    PROJECT = \"project\"\n\n\nclass SkipMode(Enum):\n    \"\"\"\n    See also: :py:class:`monai.networks.layers.SkipConnection`\n    \"\"\"\n\n    CAT = \"cat\"\n    ADD = \"add\"\n    MUL = \"mul\"\n\n\nclass Method(Enum):\n    \"\"\"\n    See also: :py:class:`monai.transforms.croppad.array.SpatialPad`\n    \"\"\"\n\n    SYMMETRIC = \"symmetric\"\n    END = \"end\"\n\n\nclass ForwardMode(Enum):\n    \"\"\"\n    See also: :py:class:`monai.transforms.engines.evaluator.Evaluator`\n    \"\"\"\n\n    TRAIN = \"train\"\n    EVAL = \"eval\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/enums.py_TraceKeys_InverseKeys.NONE._none_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/enums.py_TraceKeys_InverseKeys.NONE._none_", "embedding": null, "metadata": {"file_path": "monai/utils/enums.py", "file_name": "enums.py", "file_type": "text/x-python", "category": "implementation", "start_line": 214, "end_line": 242, "span_ids": ["TraceKeys", "InverseKeys"], "tokens": 187}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TraceKeys:\n    \"\"\"Extra meta data keys used for traceable transforms.\"\"\"\n\n    CLASS_NAME = \"class\"\n    ID = \"id\"\n    ORIG_SIZE = \"orig_size\"\n    EXTRA_INFO = \"extra_info\"\n    DO_TRANSFORM = \"do_transforms\"\n    KEY_SUFFIX = \"_transforms\"\n    NONE = \"none\"\n\n\n@deprecated(since=\"0.8.0\", msg_suffix=\"use monai.utils.enums.TraceKeys instead.\")\nclass InverseKeys:\n    \"\"\"\n    Extra meta data keys used for inverse transforms.\n\n    .. deprecated:: 0.8.0\n        Use :class:`monai.utils.enums.TraceKeys` instead.\n\n    \"\"\"\n\n    CLASS_NAME = \"class\"\n    ID = \"id\"\n    ORIG_SIZE = \"orig_size\"\n    EXTRA_INFO = \"extra_info\"\n    DO_TRANSFORM = \"do_transforms\"\n    KEY_SUFFIX = \"_transforms\"\n    NONE = \"none\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/module.py_require_pkg_require_pkg._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/module.py_require_pkg_require_pkg._", "embedding": null, "metadata": {"file_path": "monai/utils/module.py", "file_name": "module.py", "file_type": "text/x-python", "category": "implementation", "start_line": 353, "end_line": 366, "span_ids": ["require_pkg"], "tokens": 152}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def require_pkg(\n    pkg_name: str, version: str = \"\", version_checker: Callable[..., bool] = min_version, raise_error: bool = True\n):\n    \"\"\"\n    Decorator function to check the required package installation.\n\n    Args:\n        pkg_name: required package name, like: \"itk\", \"nibabel\", etc.\n        version: required version string used by the version_checker.\n        version_checker: a callable to check the module version, defaults to `monai.utils.min_version`.\n        raise_error: if True, raise `OptionalImportError` error if the required package is not installed\n            or the version doesn't match requirement, if False, print the error in a warning.\n\n    \"\"\"\n    # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/module.py_require_pkg._decorator_require_pkg.return._decorator": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/module.py_require_pkg._decorator_require_pkg.return._decorator", "embedding": null, "metadata": {"file_path": "monai/utils/module.py", "file_name": "module.py", "file_type": "text/x-python", "category": "implementation", "start_line": 368, "end_line": 389, "span_ids": ["require_pkg"], "tokens": 190}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def require_pkg(\n    pkg_name: str, version: str = \"\", version_checker: Callable[..., bool] = min_version, raise_error: bool = True\n):\n\n    def _decorator(obj):\n        is_func = isinstance(obj, FunctionType)\n        call_obj = obj if is_func else obj.__init__\n        _, has = optional_import(module=pkg_name, version=version, version_checker=version_checker)\n\n        @wraps(call_obj)\n        def _wrapper(*args, **kwargs):\n            if not has:\n                err_msg = f\"required package `{pkg_name}` is not installed or the version doesn't match requirement.\"\n                if raise_error:\n                    raise OptionalImportError(err_msg)\n                else:\n                    warnings.warn(err_msg)\n\n            return call_obj(*args, **kwargs)\n\n        if is_func:\n            return _wrapper\n        obj.__init__ = _wrapper\n        return obj\n\n    return _decorator", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/img2tensorboard.py__image3_animated_gif__image3_animated_gif.return.summary_value_image_summ": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/img2tensorboard.py__image3_animated_gif__image3_animated_gif.return.summary_value_image_summ", "embedding": null, "metadata": {"file_path": "monai/visualize/img2tensorboard.py", "file_name": "img2tensorboard.py", "file_type": "text/x-python", "category": "implementation", "start_line": 36, "end_line": 68, "span_ids": ["_image3_animated_gif"], "tokens": 462}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _image3_animated_gif(\n    tag: str, image: Union[np.ndarray, torch.Tensor], writer, frame_dim: int = 0, scale_factor: float = 1.0\n):\n    \"\"\"Function to actually create the animated gif.\n\n    Args:\n        tag: Data identifier\n        image: 3D image tensors expected to be in `HWD` format\n        writer: the tensorboard writer to plot image\n        frame_dim: the dimension used as frames for GIF image, expect data shape as `HWD`, default to `0`.\n        scale_factor: amount to multiply values by. if the image data is between 0 and 1, using 255 for this value will\n            scale it to displayable range\n    \"\"\"\n    if len(image.shape) != 3:\n        raise AssertionError(\"3D image tensors expected to be in `HWD` format, len(image.shape) != 3\")\n\n    image_np: np.ndarray\n    image_np, *_ = convert_data_type(image, output_type=np.ndarray)  # type: ignore\n    ims = [(i * scale_factor).astype(np.uint8, copy=False) for i in np.moveaxis(image_np, frame_dim, 0)]\n    ims = [GifImage.fromarray(im) for im in ims]\n    img_str = b\"\"\n    for b_data in PIL.GifImagePlugin.getheader(ims[0])[0]:\n        img_str += b_data\n    img_str += b\"\\x21\\xFF\\x0B\\x4E\\x45\\x54\\x53\\x43\\x41\\x50\" b\"\\x45\\x32\\x2E\\x30\\x03\\x01\\x00\\x00\\x00\"\n    for i in ims:\n        for b_data in PIL.GifImagePlugin.getdata(i):\n            img_str += b_data\n    img_str += b\"\\x3B\"\n\n    summary = SummaryX if has_tensorboardx and isinstance(writer, SummaryWriterX) else Summary\n    summary_image_str = summary.Image(height=10, width=10, colorspace=1, encoded_image_string=img_str)\n    image_summary = summary.Value(tag=tag, image=summary_image_str)\n    return summary(value=[image_summary])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/img2tensorboard.py_add_animated_gif_add_animated_gif.for_s_in_summary_.writer__get_file_writer_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/img2tensorboard.py_add_animated_gif_add_animated_gif.for_s_in_summary_.writer__get_file_writer_", "embedding": null, "metadata": {"file_path": "monai/visualize/img2tensorboard.py", "file_name": "img2tensorboard.py", "file_type": "text/x-python", "category": "implementation", "start_line": 107, "end_line": 134, "span_ids": ["add_animated_gif"], "tokens": 305}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def add_animated_gif(\n    writer: SummaryWriter,\n    tag: str,\n    image_tensor: Union[np.ndarray, torch.Tensor],\n    max_out: int = 3,\n    frame_dim: int = -3,\n    scale_factor: float = 1.0,\n    global_step: Optional[int] = None,\n) -> None:\n    \"\"\"Creates an animated gif out of an image tensor in 'CHWD' format and writes it with SummaryWriter.\n\n    Args:\n        writer: Tensorboard SummaryWriter to write to\n        tag: Data identifier\n        image_tensor: tensor for the image to add, expected to be in `CHWD` format\n        max_out: maximum number of image channels to animate through\n        frame_dim: the dimension used as frames for GIF image, expect input data shape as `CHWD`,\n            default to `-3` (the first spatial dim)\n        scale_factor: amount to multiply values by. If the image data is between 0 and 1, using 255 for this value will\n            scale it to displayable range\n        global_step: Global step value to record\n    \"\"\"\n    summary = make_animated_gif_summary(\n        tag=tag, image=image_tensor, writer=writer, max_out=max_out, frame_dim=frame_dim, scale_factor=scale_factor\n    )\n    for s in summary:\n        # add GIF for every channel separately\n        writer._get_file_writer().add_summary(s, global_step)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/utils.py_from_typing_import_Option___all__._matshow3d_blend_imag": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/utils.py_from_typing_import_Option___all__._matshow3d_blend_imag", "embedding": null, "metadata": {"file_path": "monai/visualize/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 26, "span_ids": ["docstring"], "tokens": 125}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Optional\n\nimport numpy as np\n\nfrom monai.config.type_definitions import NdarrayOrTensor\nfrom monai.transforms.croppad.array import SpatialPad\nfrom monai.transforms.utils import rescale_array\nfrom monai.transforms.utils_pytorch_numpy_unification import repeat, where\nfrom monai.utils.module import optional_import\nfrom monai.utils.type_conversion import convert_data_type, convert_to_dst_type\n\nplt, _ = optional_import(\"matplotlib\", name=\"pyplot\")\ncm, _ = optional_import(\"matplotlib\", name=\"cm\")\n\n__all__ = [\"matshow3d\", \"blend_images\"]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/utils.py_blend_images_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/utils.py_blend_images_", "embedding": null, "metadata": {"file_path": "monai/visualize/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 136, "end_line": 180, "span_ids": ["blend_images"], "tokens": 540}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def blend_images(\n    image: NdarrayOrTensor, label: NdarrayOrTensor, alpha: float = 0.5, cmap: str = \"hsv\", rescale_arrays: bool = True\n):\n    \"\"\"\n    Blend an image and a label. Both should have the shape CHW[D].\n    The image may have C==1 or 3 channels (greyscale or RGB).\n    The label is expected to have C==1.\n\n    Args:\n        image: the input image to blend with label data.\n        label: the input label to blend with image data.\n        alpha: when blending image and label, `alpha` is the weight for the image region mapping to `label != 0`,\n            and `1 - alpha` is the weight for the label region that `label != 0`, default to `0.5`.\n        cmap: specify colormap in the matplotlib, default to `hsv`, for more details, please refer to:\n            https://matplotlib.org/2.0.2/users/colormaps.html.\n        rescale_arrays: whether to rescale the array to [0, 1] first, default to `True`.\n\n    \"\"\"\n\n    if label.shape[0] != 1:\n        raise ValueError(\"Label should have 1 channel\")\n    if image.shape[0] not in (1, 3):\n        raise ValueError(\"Image should have 1 or 3 channels\")\n    # rescale arrays to [0, 1] if desired\n    if rescale_arrays:\n        image = rescale_array(image)\n        label = rescale_array(label)\n    # convert image to rgb (if necessary) and then rgba\n    if image.shape[0] == 1:\n        image = repeat(image, 3, axis=0)\n\n    def get_label_rgb(cmap: str, label: NdarrayOrTensor):\n        _cmap = cm.get_cmap(cmap)\n        label_np: np.ndarray\n        label_np, *_ = convert_data_type(label, np.ndarray)  # type: ignore\n        label_rgb_np = _cmap(label_np[0])\n        label_rgb_np = np.moveaxis(label_rgb_np, -1, 0)[:3]\n        label_rgb, *_ = convert_to_dst_type(label_rgb_np, label)\n        return label_rgb\n\n    label_rgb = get_label_rgb(cmap, label)\n    w_image = where(label == 0, 1.0, alpha)\n    w_label = where(label == 0, 0.0, 1 - alpha)\n    return w_image * image + w_label * label_rgb", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_activations.py_unittest_TEST_CASE_5._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_activations.py_unittest_TEST_CASE_5._", "embedding": null, "metadata": {"file_path": "tests/test_activations.py", "file_name": "test_activations.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 66, "span_ids": ["impl:9", "docstring"], "tokens": 625}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks.layers.factories import Act\nfrom monai.transforms import Activations\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTEST_CASES = []\nfor p in TEST_NDARRAYS:\n    TEST_CASES.append(\n        [\n            {\"sigmoid\": True, \"softmax\": False, \"other\": None},\n            p([[[0.0, 1.0], [2.0, 3.0]]]),\n            p([[[0.5000, 0.7311], [0.8808, 0.9526]]]),\n            (1, 2, 2),\n        ]\n    )\n\n    TEST_CASES.append(\n        [\n            {\"sigmoid\": False, \"softmax\": True, \"other\": None},\n            p([[[0.0, 1.0]], [[2.0, 3.0]]]),\n            p([[[0.1192, 0.1192]], [[0.8808, 0.8808]]]),\n            (2, 1, 2),\n        ]\n    )\n\n    TEST_CASES.append(\n        [\n            {\"sigmoid\": False, \"softmax\": False, \"other\": torch.tanh},\n            p([[[0.0, 1.0], [2.0, 3.0]]]),\n            p([[[0.0000, 0.7616], [0.9640, 0.9951]]]),\n            (1, 2, 2),\n        ]\n    )\n\nTEST_CASE_4 = [\n    \"swish\",\n    torch.tensor([[[-10, -8, -6, -4, -2], [0, 2, 4, 6, 8]]], dtype=torch.float32),\n    torch.tensor(\n        [[[-4.54e-04, -2.68e-03, -1.48e-02, -7.19e-02, -2.38e-01], [0.00e00, 1.76e00, 3.93e00, 5.99e00, 8.00e00]]]\n    ),\n    (1, 2, 5),\n]\n\nTEST_CASE_5 = [\n    \"memswish\",\n    torch.tensor([[[-10, -8, -6, -4, -2], [0, 2, 4, 6, 8]]], dtype=torch.float32),\n    torch.tensor(\n        [[[-4.54e-04, -2.68e-03, -1.48e-02, -7.19e-02, -2.38e-01], [0.00e00, 1.76e00, 3.93e00, 5.99e00, 8.00e00]]]\n    ),\n    (1, 2, 5),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_activations.py_TEST_CASE_6_TEST_CASE_6._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_activations.py_TEST_CASE_6_TEST_CASE_6._", "embedding": null, "metadata": {"file_path": "tests/test_activations.py", "file_name": "test_activations.py", "file_type": "text/x-python", "category": "test", "start_line": 68, "end_line": 75, "span_ids": ["impl:9"], "tokens": 146}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "TEST_CASE_6 = [\n    \"mish\",\n    torch.tensor([[[-10, -8, -6, -4, -2], [0, 2, 4, 6, 8]]], dtype=torch.float32),\n    torch.tensor(\n        [[[-4.54e-04, -2.68e-03, -1.49e-02, -7.26e-02, -2.53e-01], [0.00e00, 1.94e00, 4.00e00, 6.00e00, 8.00e00]]]\n    ),\n    (1, 2, 5),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_activationsd.py_unittest_for_p_in_TEST_NDARRAYS_.None_2": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_activationsd.py_unittest_for_p_in_TEST_NDARRAYS_.None_2", "embedding": null, "metadata": {"file_path": "tests/test_activationsd.py", "file_name": "test_activationsd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 47, "span_ids": ["docstring"], "tokens": 482}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import Activationsd\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTEST_CASES = []\nfor p in TEST_NDARRAYS:\n    TEST_CASES.append(\n        [\n            {\"keys\": [\"pred\", \"label\"], \"sigmoid\": False, \"softmax\": [True, False], \"other\": None},\n            {\"pred\": p([[[0.0, 1.0]], [[2.0, 3.0]]]), \"label\": p([[[0.0, 1.0]], [[2.0, 3.0]]])},\n            {\"pred\": p([[[0.1192, 0.1192]], [[0.8808, 0.8808]]]), \"label\": p([[[0.0, 1.0]], [[2.0, 3.0]]])},\n            (2, 1, 2),\n        ]\n    )\n\n    TEST_CASES.append(\n        [\n            {\"keys\": [\"pred\", \"label\"], \"sigmoid\": False, \"softmax\": False, \"other\": [torch.tanh, None]},\n            {\"pred\": p([[[0.0, 1.0], [2.0, 3.0]]]), \"label\": p([[[0.0, 1.0], [2.0, 3.0]]])},\n            {\"pred\": p([[[0.0000, 0.7616], [0.9640, 0.9951]]]), \"label\": p([[[0.0, 1.0], [2.0, 3.0]]])},\n            (1, 2, 2),\n        ]\n    )\n\n    TEST_CASES.append(\n        [\n            {\"keys\": \"pred\", \"sigmoid\": False, \"softmax\": False, \"other\": torch.tanh},\n            {\"pred\": p([[[0.0, 1.0], [2.0, 3.0]]])},\n            {\"pred\": p([[[0.0000, 0.7616], [0.9640, 0.9951]]])},\n            (1, 2, 2),\n        ]\n    )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_apply_filter.py_unittest_ApplyFilterTestCase.test_1d.if_torch_cuda_is_availabl.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_apply_filter.py_unittest_ApplyFilterTestCase.test_1d.if_torch_cuda_is_availabl.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_apply_filter.py", "file_name": "test_apply_filter.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 28, "span_ids": ["ApplyFilterTestCase", "ApplyFilterTestCase.test_1d", "docstring"], "tokens": 201}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\n\nfrom monai.networks.layers import apply_filter\n\n\nclass ApplyFilterTestCase(unittest.TestCase):\n    def test_1d(self):\n        a = torch.tensor([[list(range(10))]], dtype=torch.float)\n        out = apply_filter(a, torch.tensor([-1, 0, 1]), stride=1)\n        expected = np.array([[[1.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, -8.0]]])\n        np.testing.assert_allclose(out.cpu().numpy(), expected, rtol=1e-4)\n        if torch.cuda.is_available():\n            out = apply_filter(a.cuda(), torch.tensor([-1, 0, 1]).cuda())\n            np.testing.assert_allclose(out.cpu().numpy(), expected, rtol=1e-4)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_apply_filter.py_ApplyFilterTestCase.test_2d_ApplyFilterTestCase.test_2d.if_torch_cuda_is_availabl.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_apply_filter.py_ApplyFilterTestCase.test_2d_ApplyFilterTestCase.test_2d.if_torch_cuda_is_availabl.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_apply_filter.py", "file_name": "test_apply_filter.py", "file_type": "text/x-python", "category": "test", "start_line": 30, "end_line": 44, "span_ids": ["ApplyFilterTestCase.test_2d"], "tokens": 304}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ApplyFilterTestCase(unittest.TestCase):\n\n    def test_2d(self):\n        a = torch.tensor([[[list(range(7)), list(range(7, 0, -1)), list(range(7))]]], dtype=torch.float)\n        expected = np.array(\n            [\n                [14.0, 21.0, 21.0, 21.0, 21.0, 21.0, 14.0],\n                [15.0, 24.0, 27.0, 30.0, 33.0, 36.0, 25.0],\n                [14.0, 21.0, 21.0, 21.0, 21.0, 21.0, 14.0],\n            ]\n        )\n        expected = expected[None][None]\n        out = apply_filter(a, torch.tensor([[1, 1, 1], [1, 1, 1], [1, 1, 1]]))\n        np.testing.assert_allclose(out.cpu().numpy(), expected, rtol=1e-4)\n        if torch.cuda.is_available():\n            out = apply_filter(a.cuda(), torch.tensor([[1, 1, 1], [1, 1, 1], [1, 1, 1]]).cuda())\n            np.testing.assert_allclose(out.cpu().numpy(), expected, rtol=1e-4)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_apply_filter.py_ApplyFilterTestCase.test_3d_ApplyFilterTestCase.test_3d.for_kernel_in_k_k_None_.if_torch_cuda_is_availabl.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_apply_filter.py_ApplyFilterTestCase.test_3d_ApplyFilterTestCase.test_3d.for_kernel_in_k_k_None_.if_torch_cuda_is_availabl.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_apply_filter.py", "file_name": "test_apply_filter.py", "file_type": "text/x-python", "category": "test", "start_line": 46, "end_line": 75, "span_ids": ["ApplyFilterTestCase.test_3d"], "tokens": 466}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ApplyFilterTestCase(unittest.TestCase):\n\n    def test_3d(self):\n        a = torch.tensor(\n            [[list(range(7)), list(range(7)), list(range(7))], [list(range(7)), list(range(7)), list(range(7))]],\n            dtype=torch.float,\n        )\n        a = a[None][None]\n        a = a.expand(2, 3, -1, -1, -1)\n        expected = np.array(\n            [\n                [\n                    [2.0, 6.0, 12.0, 18.0, 24.0, 30.0, 22.0],\n                    [3.0, 9.0, 18.0, 27.0, 36.0, 45.0, 33.0],\n                    [2.0, 6.0, 12.0, 18.0, 24.0, 30.0, 22.0],\n                ],\n                [\n                    [2.0, 6.0, 12.0, 18.0, 24.0, 30.0, 22.0],\n                    [3.0, 9.0, 18.0, 27.0, 36.0, 45.0, 33.0],\n                    [2.0, 6.0, 12.0, 18.0, 24.0, 30.0, 22.0],\n                ],\n            ]\n        )\n        expected = expected\n        # testing shapes\n        k = torch.tensor([[[1, 1, 1], [1, 1, 1], [1, 1, 1]]])\n        for kernel in (k, k[None], k[None][None]):\n            out = apply_filter(a, kernel)\n            np.testing.assert_allclose(out.cpu().numpy()[1][2], expected, rtol=1e-4)\n            if torch.cuda.is_available():\n                out = apply_filter(a.cuda(), kernel.cuda())\n                np.testing.assert_allclose(out.cpu().numpy()[0][1], expected, rtol=1e-4)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_apply_filter.py_ApplyFilterTestCase.test_wrong_args_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_apply_filter.py_ApplyFilterTestCase.test_wrong_args_", "embedding": null, "metadata": {"file_path": "tests/test_apply_filter.py", "file_name": "test_apply_filter.py", "file_type": "text/x-python", "category": "test", "start_line": 77, "end_line": 88, "span_ids": ["ApplyFilterTestCase.test_wrong_args", "impl"], "tokens": 141}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ApplyFilterTestCase(unittest.TestCase):\n\n    def test_wrong_args(self):\n        with self.assertRaisesRegex(ValueError, \"\"):\n            apply_filter(torch.ones((1, 2, 3, 2)), torch.ones((2,)))\n        with self.assertRaisesRegex(NotImplementedError, \"\"):\n            apply_filter(torch.ones((1, 1, 1, 2, 3, 2)), torch.ones((2,)))\n        with self.assertRaisesRegex(TypeError, \"\"):\n            apply_filter(((1, 1, 1, 2, 3, 2)), torch.ones((2,)))  # type: ignore\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_blend_images.py_unittest_for_p_in_TEST_NDARRAYS_.None_1": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_blend_images.py_unittest_for_p_in_TEST_NDARRAYS_.None_1", "embedding": null, "metadata": {"file_path": "tests/test_blend_images.py", "file_name": "test_blend_images.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 32, "span_ids": ["docstring"], "tokens": 167}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom unittest.case import skipUnless\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.data.synthetic import create_test_image_2d, create_test_image_3d\nfrom monai.transforms.utils_pytorch_numpy_unification import moveaxis\nfrom monai.utils.module import optional_import\nfrom monai.visualize.utils import blend_images\nfrom tests.utils import TEST_NDARRAYS\n\nplt, has_matplotlib = optional_import(\"matplotlib.pyplot\")\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    image, label = create_test_image_2d(100, 101)\n    TESTS.append((p(image), p(label)))\n\n    image, label = create_test_image_3d(100, 101, 102)\n    TESTS.append((p(image), p(label)))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_blend_images.py_TestBlendImages_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_blend_images.py_TestBlendImages_", "embedding": null, "metadata": {"file_path": "tests/test_blend_images.py", "file_name": "test_blend_images.py", "file_type": "text/x-python", "category": "test", "start_line": 35, "end_line": 54, "span_ids": ["TestBlendImages.test_blend", "impl:9", "TestBlendImages"], "tokens": 163}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skipUnless(has_matplotlib, \"Matplotlib required\")\nclass TestBlendImages(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_blend(self, image, label):\n        blended = blend_images(image[None], label[None])\n        self.assertEqual(type(image), type(blended))\n        if isinstance(blended, torch.Tensor):\n            self.assertEqual(blended.device, image.device)\n            blended = blended.cpu().numpy()\n        self.assertEqual((3,) + image.shape, blended.shape)\n\n        blended = moveaxis(blended, 0, -1)  # move RGB component to end\n        if blended.ndim > 3:\n            blended = blended[blended.shape[0] // 2]\n        plt.imshow(blended)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_check_missing_files.py_os_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_check_missing_files.py_os_", "embedding": null, "metadata": {"file_path": "tests/test_check_missing_files.py", "file_name": "test_check_missing_files.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 57, "span_ids": ["TestCheckMissingFiles", "TestCheckMissingFiles.test_content", "impl", "docstring"], "tokens": 433}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport tempfile\nimport unittest\nfrom pathlib import Path\n\nimport nibabel as nib\nimport numpy as np\n\nfrom monai.data import check_missing_files\n\n\nclass TestCheckMissingFiles(unittest.TestCase):\n    def test_content(self):\n        test_image = nib.Nifti1Image(np.random.randint(0, 2, size=[128, 128, 128]), np.eye(4))\n        with tempfile.TemporaryDirectory() as tempdir:\n            nib.save(test_image, os.path.join(tempdir, \"test_image1.nii.gz\"))\n            nib.save(test_image, os.path.join(tempdir, \"test_label1.nii.gz\"))\n            nib.save(test_image, os.path.join(tempdir, \"test_extra1.nii.gz\"))\n            nib.save(test_image, os.path.join(tempdir, \"test_image2.nii.gz\"))\n\n            datalist = [\n                {\n                    \"image\": os.path.join(tempdir, \"test_image1.nii.gz\"),\n                    \"label\": [os.path.join(tempdir, \"test_label1.nii.gz\"), os.path.join(tempdir, \"test_extra1.nii.gz\")],\n                },\n                {\n                    \"image\": Path(os.path.join(tempdir, \"test_image2.nii.gz\")),\n                    \"label\": Path(os.path.join(tempdir, \"test_label_missing.nii.gz\")),\n                },\n            ]\n\n            missings = check_missing_files(datalist=datalist, keys=[\"image\", \"label\"])\n            self.assertEqual(len(missings), 1)\n            self.assertEqual(str(missings[0]), os.path.join(tempdir, \"test_label_missing.nii.gz\"))\n\n            # test with missing key and relative path\n            datalist = [{\"image\": \"test_image1.nii.gz\", \"label\": \"test_label_missing.nii.gz\"}]\n            missings = check_missing_files(\n                datalist=datalist, keys=[\"image\", \"label\", \"test\"], root_dir=tempdir, allow_missing_keys=True\n            )\n            self.assertEqual(f\"{missings[0]}\", os.path.join(tempdir, \"test_label_missing.nii.gz\"))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_contrastive_loss.py_unittest_TEST_CASES._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_contrastive_loss.py_unittest_TEST_CASES._", "embedding": null, "metadata": {"file_path": "tests/test_contrastive_loss.py", "file_name": "test_contrastive_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 52, "span_ids": ["docstring"], "tokens": 601}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.losses import ContrastiveLoss\n\nTEST_CASES = [\n    [  # shape: (1, 4), (1, 4)\n        {\"temperature\": 0.5, \"batch_size\": 1},\n        {\"input\": torch.tensor([[1.0, 1.0, 0.0, 0.0]]), \"target\": torch.tensor([[1.0, 1.0, 0.0, 0.0]])},\n        0.0,\n    ],\n    [  # shape: (2, 4), (2, 4)\n        {\"temperature\": 0.5, \"batch_size\": 2},\n        {\n            \"input\": torch.tensor([[1.0, 1.0, 0.0, 0.0], [1.0, 1.0, 0.0, 0.0]]),\n            \"target\": torch.tensor([[1.0, 1.0, 0.0, 0.0], [1.0, 1.0, 0.0, 0.0]]),\n        },\n        1.0986,\n    ],\n    [  # shape: (1, 4), (1, 4)\n        {\"temperature\": 0.5, \"batch_size\": 2},\n        {\n            \"input\": torch.tensor([[1.0, 2.0, 3.0, 4.0], [1.0, 1.0, 0.0, 0.0]]),\n            \"target\": torch.tensor([[0.0, 0.0, 0.0, 0.0], [1.0, 1.0, 0.0, 0.0]]),\n        },\n        0.8719,\n    ],\n    [  # shape: (1, 4), (1, 4)\n        {\"temperature\": 0.5, \"batch_size\": 1},\n        {\"input\": torch.tensor([[0.0, 0.0, 1.0, 1.0]]), \"target\": torch.tensor([[1.0, 1.0, 0.0, 0.0]])},\n        0.0,\n    ],\n    [  # shape: (1, 4), (1, 4)\n        {\"temperature\": 0.05, \"batch_size\": 1},\n        {\"input\": torch.tensor([[0.0, 0.0, 1.0, 1.0]]), \"target\": torch.tensor([[1.0, 1.0, 0.0, 0.0]])},\n        0.0,\n    ],\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_contrastive_loss.py_TestContrastiveLoss_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_contrastive_loss.py_TestContrastiveLoss_", "embedding": null, "metadata": {"file_path": "tests/test_contrastive_loss.py", "file_name": "test_contrastive_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 55, "end_line": 80, "span_ids": ["impl:3", "TestContrastiveLoss.test_ill_shape", "TestContrastiveLoss.test_result", "TestContrastiveLoss", "TestContrastiveLoss.test_with_cuda"], "tokens": 267}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestContrastiveLoss(unittest.TestCase):\n    @parameterized.expand(TEST_CASES)\n    def test_result(self, input_param, input_data, expected_val):\n        contrastiveloss = ContrastiveLoss(**input_param)\n        result = contrastiveloss(**input_data)\n        np.testing.assert_allclose(result.detach().cpu().numpy(), expected_val, atol=1e-4, rtol=1e-4)\n\n    def test_ill_shape(self):\n        loss = ContrastiveLoss(temperature=0.5, batch_size=1)\n        with self.assertRaisesRegex(ValueError, \"\"):\n            loss(torch.ones((1, 2, 3)), torch.ones((1, 1, 2, 3)))\n\n    def test_with_cuda(self):\n        loss = ContrastiveLoss(temperature=0.5, batch_size=1)\n        i = torch.ones((1, 10))\n        j = torch.ones((1, 10))\n        if torch.cuda.is_available():\n            i = i.cuda()\n            j = j.cuda()\n        output = loss(i, j)\n        np.testing.assert_allclose(output.detach().cpu().numpy(), 0.0, atol=1e-4, rtol=1e-4)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_create_cross_validation_datalist.py_os_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_create_cross_validation_datalist.py_os_", "embedding": null, "metadata": {"file_path": "tests/test_create_cross_validation_datalist.py", "file_name": "test_create_cross_validation_datalist.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 57, "span_ids": ["TestCreateCrossValidationDatalist", "impl", "TestCreateCrossValidationDatalist.test_content", "docstring"], "tokens": 327}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport tempfile\nimport unittest\nfrom pathlib import Path\n\nfrom monai.data import create_cross_validation_datalist, load_decathlon_datalist\n\n\nclass TestCreateCrossValidationDatalist(unittest.TestCase):\n    def test_content(self):\n        with tempfile.TemporaryDirectory() as tempdir:\n            datalist = []\n            for i in range(5):\n                image = os.path.join(tempdir, f\"test_image{i}.nii.gz\")\n                label = os.path.join(tempdir, f\"test_label{i}.nii.gz\")\n                Path(image).touch()\n                Path(label).touch()\n                datalist.append({\"image\": image, \"label\": label})\n\n            filename = os.path.join(tempdir, \"test_datalist.json\")\n            result = create_cross_validation_datalist(\n                datalist=datalist,\n                nfolds=5,\n                train_folds=[0, 1, 2, 3],\n                val_folds=4,\n                train_key=\"test_train\",\n                val_key=\"test_val\",\n                filename=Path(filename),\n                shuffle=True,\n                seed=123,\n                check_missing=True,\n                keys=[\"image\", \"label\"],\n                root_dir=None,\n                allow_missing_keys=False,\n                raise_error=True,\n            )\n\n            loaded = load_decathlon_datalist(filename, data_list_key=\"test_train\")\n            for r, l in zip(result[\"test_train\"], loaded):\n                self.assertEqual(r[\"image\"], l[\"image\"])\n                self.assertEqual(r[\"label\"], l[\"label\"])\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cumulative.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cumulative.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_cumulative.py", "file_name": "test_cumulative.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 60, "span_ids": ["impl", "TestCumulative.test_single", "TestCumulative.test_ill", "TestCumulative.test_multi", "docstring", "TestCumulative"], "tokens": 269}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\n\nfrom monai.metrics import Cumulative\nfrom tests.utils import assert_allclose\n\n\nclass TestCumulative(unittest.TestCase):\n    def test_single(self):\n        c = Cumulative()\n        c.extend([2, 3])\n        c.append(1)\n        assert_allclose(c.get_buffer(), torch.tensor([2, 3, 1]))\n\n    def test_multi(self):\n        c = Cumulative()\n        c.extend([2, 3], [4, 6])\n        c.append(1)\n        assert_allclose(c.get_buffer()[0], torch.tensor([2, 3, 1]))\n        assert_allclose(c.get_buffer()[1], torch.tensor([4, 6]))\n\n        c.reset()\n        c.append()\n        c.extend()\n        self.assertEqual(c.get_buffer(), [])\n\n        c.reset()\n\n    def test_ill(self):\n        c = Cumulative()\n        with self.assertRaises(TypeError):\n            c.extend(None)\n        with self.assertRaises(TypeError):\n            c.extend([])\n        with self.assertRaises(TypeError):\n            c.extend(1)\n        with self.assertRaises(TypeError):\n            c.append([])\n            c.append([1, 2])\n            c.get_buffer()\n        with self.assertRaises(TypeError):\n            c.append(None)\n            c.get_buffer()\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cumulative_average.py_unittest_TEST_CASE_4._torch_as_tensor_0_1_t": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cumulative_average.py_unittest_TEST_CASE_4._torch_as_tensor_0_1_t", "embedding": null, "metadata": {"file_path": "tests/test_cumulative_average.py", "file_name": "test_cumulative_average.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 36, "span_ids": ["docstring"], "tokens": 237}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.metrics import CumulativeAverage\n\n# single class value\nTEST_CASE_1 = [[torch.as_tensor([[0.1]]), torch.as_tensor([[0.2]]), torch.as_tensor([[0.3]])], torch.as_tensor([0.2])]\n\n# multi-class value\nTEST_CASE_2 = [\n    [torch.as_tensor([[0.1, 0.2]]), torch.as_tensor([[0.2, 0.3]]), torch.as_tensor([[0.3, 0.4]])],\n    torch.as_tensor([0.2, 0.3]),\n]\n\n# Nan value\nTEST_CASE_3 = [\n    [torch.as_tensor([[0.1]]), torch.as_tensor([[0.2]]), torch.as_tensor([[float(\"nan\")]])],\n    torch.as_tensor([0.15]),\n]\n\n# different input shape\nTEST_CASE_4 = [[torch.as_tensor(0.1), torch.as_tensor(0.2), torch.as_tensor(0.3)], torch.as_tensor(0.2)]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cumulative_average.py_TestCumulativeAverage_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cumulative_average.py_TestCumulativeAverage_", "embedding": null, "metadata": {"file_path": "tests/test_cumulative_average.py", "file_name": "test_cumulative_average.py", "file_type": "text/x-python", "category": "test", "start_line": 39, "end_line": 64, "span_ids": ["impl:9", "TestCumulativeAverage", "TestCumulativeAverage.test_value", "TestCumulativeAverage.test_numpy_array"], "tokens": 212}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestCumulativeAverage(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4])\n    def test_value(self, input_data, expected_value):\n        average = CumulativeAverage()\n        func = average.append if input_data[0].ndim < 2 else average.extend\n        func(input_data[0])\n        func(input_data[1])\n        result = average.aggregate()\n        # continue to update new data\n        func(input_data[2])\n        result = average.aggregate()\n        torch.testing.assert_allclose(result, expected_value)\n\n    def test_numpy_array(self):\n        class TestCumulativeAverage(CumulativeAverage):\n            def get_buffer(self):\n                return np.array([[1, 2], [3, np.nan]])\n\n        average = TestCumulativeAverage()\n        result = average.aggregate()\n        np.testing.assert_allclose(result, np.array([2.0, 2.0]))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cumulative_average_dist.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_cumulative_average_dist.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_cumulative_average_dist.py", "file_name": "test_cumulative_average_dist.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 48, "span_ids": ["DistributedCumulativeAverage", "DistributedCumulativeAverage.test_value", "impl", "docstring"], "tokens": 345}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nimport torch.distributed as dist\n\nfrom monai.metrics import CumulativeAverage\nfrom tests.utils import DistCall, DistTestCase\n\n\nclass DistributedCumulativeAverage(DistTestCase):\n    @DistCall(nnodes=1, nproc_per_node=2)\n    def test_value(self):\n        rank = dist.get_rank()\n        input_data = [\n            [torch.as_tensor([[0.1]]), torch.as_tensor([[0.2]]), torch.as_tensor([[0.3]])],\n            [torch.as_tensor([[0.1]]), torch.as_tensor([[0.2]]), torch.as_tensor([[float(\"nan\")]])],\n            [torch.as_tensor([[0.1, 0.2]]), torch.as_tensor([[0.2, 0.3]]), torch.as_tensor([[0.3, 0.4]])],\n            [torch.as_tensor(0.1), torch.as_tensor(0.2), torch.as_tensor(0.3)],\n        ]\n        expected = [torch.as_tensor([0.2]), torch.as_tensor([0.15]), torch.as_tensor([0.2, 0.3]), torch.as_tensor(0.2)]\n        average = CumulativeAverage()\n\n        for i, e in zip(input_data, expected):\n            func = average.append if i[0].ndim < 2 else average.extend\n            if rank == 0:\n                func(i[0])\n                func(i[1])\n            else:\n                func(i[2])\n            result = average.aggregate()\n            torch.testing.assert_allclose(result, e)\n            average.reset()\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dynunet_block.py_unittest_None_1.for_kernel_size_in_1_3_.for_stride_in_1_2_.for_norm_name_in_batch_.for_in_size_in_15_16_.for_trans_bias_in_True_.TEST_UP_BLOCK_append_test": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dynunet_block.py_unittest_None_1.for_kernel_size_in_1_3_.for_stride_in_1_2_.for_norm_name_in_batch_.for_in_size_in_15_16_.for_trans_bias_in_True_.TEST_UP_BLOCK_append_test", "embedding": null, "metadata": {"file_path": "tests/test_dynunet_block.py", "file_name": "test_dynunet_block.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 70, "span_ids": ["docstring"], "tokens": 535}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks import eval_mode\nfrom monai.networks.blocks.dynunet_block import UnetBasicBlock, UnetResBlock, UnetUpBlock, get_padding\nfrom tests.utils import test_script_save\n\nTEST_CASE_RES_BASIC_BLOCK = []\nfor spatial_dims in range(2, 4):\n    for kernel_size in [1, 3]:\n        for stride in [1, 2]:\n            for norm_name in [(\"GROUP\", {\"num_groups\": 16}), (\"batch\", {\"track_running_stats\": False}), \"instance\"]:\n                for in_size in [15, 16]:\n                    padding = get_padding(kernel_size, stride)\n                    if not isinstance(padding, int):\n                        padding = padding[0]\n                    out_size = int((in_size + 2 * padding - kernel_size) / stride) + 1\n                    test_case = [\n                        {\n                            \"spatial_dims\": spatial_dims,\n                            \"in_channels\": 16,\n                            \"out_channels\": 16,\n                            \"kernel_size\": kernel_size,\n                            \"norm_name\": norm_name,\n                            \"act_name\": (\"leakyrelu\", {\"inplace\": True, \"negative_slope\": 0.1}),\n                            \"stride\": stride,\n                        },\n                        (1, 16, *([in_size] * spatial_dims)),\n                        (1, 16, *([out_size] * spatial_dims)),\n                    ]\n                    TEST_CASE_RES_BASIC_BLOCK.append(test_case)\n\nTEST_UP_BLOCK = []\nin_channels, out_channels = 4, 2\nfor spatial_dims in range(2, 4):\n    for kernel_size in [1, 3]:\n        for stride in [1, 2]:\n            for norm_name in [\"batch\", \"instance\"]:\n                for in_size in [15, 16]:\n                    for trans_bias in [True, False]:\n                        out_size = in_size * stride\n                        test_case = [\n                            {\n                                \"spatial_dims\": spatial_dims,\n                                \"in_channels\": in_channels,\n                                \"out_channels\": out_channels,\n                                \"kernel_size\": kernel_size,\n                                \"norm_name\": norm_name,\n                                \"stride\": stride,\n                                \"upsample_kernel_size\": stride,\n                                \"trans_bias\": trans_bias,\n                            },\n                            (1, in_channels, *([in_size] * spatial_dims)),\n                            (1, out_channels, *([out_size] * spatial_dims)),\n                            (1, out_channels, *([in_size * stride] * spatial_dims)),\n                        ]\n                        TEST_UP_BLOCK.append(test_case)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_focal_loss.py_TestFocalLoss.test_consistency_with_cross_entropy_2d_no_reduction_TestFocalLoss.test_consistency_with_cross_entropy_2d_no_reduction._self_assertAlmostEqual_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_focal_loss.py_TestFocalLoss.test_consistency_with_cross_entropy_2d_no_reduction_TestFocalLoss.test_consistency_with_cross_entropy_2d_no_reduction._self_assertAlmostEqual_", "embedding": null, "metadata": {"file_path": "tests/test_focal_loss.py", "file_name": "test_focal_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 47, "end_line": 74, "span_ids": ["TestFocalLoss.test_consistency_with_cross_entropy_2d_no_reduction"], "tokens": 332}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestFocalLoss(unittest.TestCase):\n\n    def test_consistency_with_cross_entropy_2d_no_reduction(self):\n        \"\"\"For gamma=0 the focal loss reduces to the cross entropy loss\"\"\"\n        import numpy as np\n\n        focal_loss = FocalLoss(to_onehot_y=False, gamma=0.0, reduction=\"none\", weight=1.0)\n        ce = nn.BCEWithLogitsLoss(reduction=\"none\")\n        max_error = 0\n        class_num = 10\n        batch_size = 128\n        for _ in range(100):\n            # Create a random tensor of shape (batch_size, class_num, 8, 4)\n            x = torch.rand(batch_size, class_num, 8, 4, requires_grad=True)\n            # Create a random batch of classes\n            l = torch.randint(low=0, high=2, size=(batch_size, class_num, 8, 4)).float()\n            if torch.cuda.is_available():\n                x = x.cuda()\n                l = l.cuda()\n            output0 = focal_loss(x, l)\n            output1 = ce(x, l)\n            a = output0.cpu().detach().numpy()\n            b = output1.cpu().detach().numpy()\n            error = np.abs(a - b)\n            max_error = np.maximum(error, max_error)\n            # if np.all(np.abs(a - b) > max_error):\n            #     max_error = np.abs(a - b)\n\n        assert np.allclose(max_error, 0)\n        # self.assertAlmostEqual(max_error, 0.0, places=3)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_global_mutual_information_loss.py_os_EXPECTED_VALUE._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_global_mutual_information_loss.py_os_EXPECTED_VALUE._", "embedding": null, "metadata": {"file_path": "tests/test_global_mutual_information_loss.py", "file_name": "test_global_mutual_information_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 11, "end_line": 52, "span_ids": ["docstring"], "tokens": 376}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport unittest\n\nimport numpy as np\nimport torch\n\nfrom monai import transforms\nfrom monai.apps import download_url\nfrom monai.losses.image_dissimilarity import GlobalMutualInformationLoss\nfrom tests.utils import SkipIfBeforePyTorchVersion\n\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\nFILE_URL = \"https://drive.google.com/uc?id=17tsDLvG_GZm7a4fCVMCv-KyDx0hqq1ji\"\nFILE_PATH = os.path.join(os.path.dirname(__file__), \"testing_data\", \"temp_\" + \"mri.nii\")\n\nEXPECTED_VALUE = {\n    \"xyz_translation\": [\n        -1.5860259532928467,\n        -0.5957175493240356,\n        -0.3855515122413635,\n        -0.28728482127189636,\n        -0.23416118323802948,\n        -0.19534644484519958,\n        -0.17001715302467346,\n        -0.15043553709983826,\n        -0.1366637945175171,\n        -0.12534910440444946,\n    ],\n    \"xyz_rotation\": [\n        -1.5860259532928467,\n        -0.29977330565452576,\n        -0.18411292135715485,\n        -0.1582011878490448,\n        -0.16107326745986938,\n        -0.165723517537117,\n        -0.1970357596874237,\n        -0.1755618453025818,\n        -0.17100191116333008,\n        -0.17264796793460846,\n    ],\n}", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_global_mutual_information_loss.py_TestGlobalMutualInformationLoss_TestGlobalMutualInformationLoss.test_bspline.transformation.return.transformation_img_FI": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_global_mutual_information_loss.py_TestGlobalMutualInformationLoss_TestGlobalMutualInformationLoss.test_bspline.transformation.return.transformation_img_FI", "embedding": null, "metadata": {"file_path": "tests/test_global_mutual_information_loss.py", "file_name": "test_global_mutual_information_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 55, "end_line": 87, "span_ids": ["TestGlobalMutualInformationLoss", "TestGlobalMutualInformationLoss.setUp", "TestGlobalMutualInformationLoss.test_bspline"], "tokens": 347}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestGlobalMutualInformationLoss(unittest.TestCase):\n    def setUp(self):\n        download_url(FILE_URL, FILE_PATH)\n\n    @SkipIfBeforePyTorchVersion((1, 9))\n    def test_bspline(self):\n        loss_fn = GlobalMutualInformationLoss(kernel_type=\"b-spline\", num_bins=32, sigma_ratio=0.015)\n\n        transform_params_dict = {\n            \"xyz_translation\": [(i, i, i) for i in range(10)],\n            \"xyz_rotation\": [(np.pi / 100 * i, np.pi / 100 * i, np.pi / 100 * i) for i in range(10)],\n        }\n\n        def transformation(translate_params=(0.0, 0.0, 0.0), rotate_params=(0.0, 0.0, 0.0)):\n            \"\"\"\n            Read and transform Prostate_T2W_AX_1.nii\n            Args:\n                translate_params: a tuple of 3 floats, translation is in pixel/voxel relative to the center of the input\n                        image. Defaults to no translation.\n                rotate_params: a rotation angle in radians, a tuple of 3 floats for 3D.\n                        Defaults to no rotation.\n            Returns:\n                numpy array of shape HWD\n            \"\"\"\n            transform_list = [\n                transforms.LoadImaged(keys=\"img\"),\n                transforms.Affined(\n                    keys=\"img\", translate_params=translate_params, rotate_params=rotate_params, device=None\n                ),\n                transforms.NormalizeIntensityd(keys=[\"img\"]),\n            ]\n            transformation = transforms.Compose(transform_list)\n            return transformation({\"img\": FILE_PATH})[\"img\"]\n        # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_global_mutual_information_loss.py_TestGlobalMutualInformationLoss.test_bspline.a1_TestGlobalMutualInformationLoss.test_bspline.for_mode_in_transform_par.for_transform_params_exp.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_global_mutual_information_loss.py_TestGlobalMutualInformationLoss.test_bspline.a1_TestGlobalMutualInformationLoss.test_bspline.for_mode_in_transform_par.for_transform_params_exp.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_global_mutual_information_loss.py", "file_name": "test_global_mutual_information_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 89, "end_line": 102, "span_ids": ["TestGlobalMutualInformationLoss.test_bspline"], "tokens": 239}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestGlobalMutualInformationLoss(unittest.TestCase):\n\n    @SkipIfBeforePyTorchVersion((1, 9))\n    def test_bspline(self):\n        # ... other code\n\n        a1 = transformation()\n        a1 = torch.tensor(a1).unsqueeze(0).unsqueeze(0).to(device)\n\n        for mode in transform_params_dict.keys():\n            transform_params_list = transform_params_dict[mode]\n            expected_value_list = EXPECTED_VALUE[mode]\n            for transform_params, expected_value in zip(transform_params_list, expected_value_list):\n                a2 = transformation(\n                    translate_params=transform_params if \"translation\" in mode else (0.0, 0.0, 0.0),\n                    rotate_params=transform_params if \"rotation\" in mode else (0.0, 0.0, 0.0),\n                )\n                a2 = torch.tensor(a2).unsqueeze(0).unsqueeze(0).to(device)\n                result = loss_fn(a2, a1).detach().cpu().numpy()\n                np.testing.assert_allclose(result, expected_value, rtol=1e-3, atol=5e-3)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_global_mutual_information_loss.py_TestGlobalMutualInformationLoss.test_ill_shape_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_global_mutual_information_loss.py_TestGlobalMutualInformationLoss.test_ill_shape_", "embedding": null, "metadata": {"file_path": "tests/test_global_mutual_information_loss.py", "file_name": "test_global_mutual_information_loss.py", "file_type": "text/x-python", "category": "test", "start_line": 104, "end_line": 126, "span_ids": ["impl:9", "TestGlobalMutualInformationLoss.test_ill_shape", "TestGlobalMutualInformationLoss.test_ill_opts"], "tokens": 277}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestGlobalMutualInformationLoss(unittest.TestCase):\n\n    def test_ill_shape(self):\n        loss = GlobalMutualInformationLoss()\n        with self.assertRaisesRegex(ValueError, \"\"):\n            loss.forward(torch.ones((1, 2), dtype=torch.float), torch.ones((1, 3), dtype=torch.float, device=device))\n        with self.assertRaisesRegex(ValueError, \"\"):\n            loss.forward(torch.ones((1, 3, 3), dtype=torch.float), torch.ones((1, 3), dtype=torch.float, device=device))\n\n    def test_ill_opts(self):\n        pred = torch.ones((1, 3, 3, 3, 3), dtype=torch.float, device=device)\n        target = torch.ones((1, 3, 3, 3, 3), dtype=torch.float, device=device)\n        with self.assertRaisesRegex(ValueError, \"\"):\n            GlobalMutualInformationLoss(num_bins=0)(pred, target)\n        with self.assertRaisesRegex(ValueError, \"\"):\n            GlobalMutualInformationLoss(num_bins=-1)(pred, target)\n        with self.assertRaisesRegex(ValueError, \"\"):\n            GlobalMutualInformationLoss(reduction=\"unknown\")(pred, target)\n        with self.assertRaisesRegex(ValueError, \"\"):\n            GlobalMutualInformationLoss(reduction=None)(pred, target)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_confusion_matrix.py_TestHandlerConfusionMatrix_TestHandlerConfusionMatrix.test_compute.torch_testing_assert_allc": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_confusion_matrix.py_TestHandlerConfusionMatrix_TestHandlerConfusionMatrix.test_compute.torch_testing_assert_allc", "embedding": null, "metadata": {"file_path": "tests/test_handler_confusion_matrix.py", "file_name": "test_handler_confusion_matrix.py", "file_type": "text/x-python", "category": "test", "start_line": 47, "end_line": 63, "span_ids": ["TestHandlerConfusionMatrix.test_compute", "TestHandlerConfusionMatrix"], "tokens": 194}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestHandlerConfusionMatrix(unittest.TestCase):\n    # TODO test multi node averaged confusion matrix\n\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3])\n    def test_compute(self, input_params, expected_avg):\n        metric = ConfusionMatrix(**input_params)\n        # test input a list of channel-first tensor\n        y_pred = [torch.Tensor([[0], [1]]), torch.Tensor([[1], [0]])]\n        y = [torch.Tensor([[0], [1]]), torch.Tensor([[0], [1]])]\n        metric.update([y_pred, y])\n\n        y_pred = torch.Tensor([[[0], [1]], [[1], [0]]])\n        y = torch.Tensor([[[0], [1]], [[1], [0]]])\n        metric.update([y_pred, y])\n\n        avg_metric = metric.compute()\n        torch.testing.assert_allclose(avg_metric, expected_avg)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_hausdorff_distance.py_TestHandlerHausdorffDistance_TestHandlerHausdorffDistance.test_compute.None_8": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_hausdorff_distance.py_TestHandlerHausdorffDistance_TestHandlerHausdorffDistance.test_compute.None_8", "embedding": null, "metadata": {"file_path": "tests/test_handler_hausdorff_distance.py", "file_name": "test_handler_hausdorff_distance.py", "file_type": "text/x-python", "category": "test", "start_line": 60, "end_line": 83, "span_ids": ["TestHandlerHausdorffDistance", "TestHandlerHausdorffDistance.test_compute"], "tokens": 206}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestHandlerHausdorffDistance(unittest.TestCase):\n    # TODO test multi node Hausdorff Distance\n\n    def test_compute(self):\n        hd_metric = HausdorffDistance(include_background=True)\n\n        def _val_func(engine, batch):\n            pass\n\n        engine = Engine(_val_func)\n        hd_metric.attach(engine, \"hausdorff_distance\")\n\n        y_pred, y = TEST_SAMPLE_1\n        hd_metric.update([y_pred, y])\n        self.assertEqual(hd_metric.compute(), 10)\n        y_pred, y = TEST_SAMPLE_2\n        hd_metric.update([y_pred, y])\n        self.assertEqual(hd_metric.compute(), 5)\n        y_pred, y = TEST_SAMPLE_3\n        hd_metric.update([y_pred, y])\n        self.assertEqual(hd_metric.compute(), float(\"inf\"))\n        y_pred, y = TEST_SAMPLE_4\n        hd_metric.update([y_pred, y])\n        self.assertEqual(hd_metric.compute(), float(\"inf\"))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_hausdorff_distance.py_TestHandlerHausdorffDistance.test_shape_mismatch_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_hausdorff_distance.py_TestHandlerHausdorffDistance.test_shape_mismatch_", "embedding": null, "metadata": {"file_path": "tests/test_handler_hausdorff_distance.py", "file_name": "test_handler_hausdorff_distance.py", "file_type": "text/x-python", "category": "test", "start_line": 85, "end_line": 110, "span_ids": ["TestHandlerHausdorffDistance.test_shape_mismatch", "impl:15", "TestHandlerHausdorffDistance.test_reduction"], "tokens": 221}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestHandlerHausdorffDistance(unittest.TestCase):\n\n    def test_shape_mismatch(self):\n        hd_metric = HausdorffDistance(include_background=True)\n        with self.assertRaises((AssertionError, ValueError)):\n            y_pred = TEST_SAMPLE_1[0]\n            y = torch.ones((1, 1, 10, 10, 10))\n            hd_metric.update([y_pred, y])\n\n    def test_reduction(self):\n        hd_metric = HausdorffDistance(include_background=True, reduction=\"mean_channel\")\n\n        def _val_func(engine, batch):\n            pass\n\n        engine = Engine(_val_func)\n        hd_metric.attach(engine, \"hausdorff_distance\")\n\n        y_pred, y = TEST_SAMPLE_1\n        hd_metric.update([y_pred, y])\n        y_pred, y = TEST_SAMPLE_2\n        hd_metric.update([y_pred, y])\n        torch.testing.assert_allclose(hd_metric.compute().float(), torch.tensor([10.0, 0.0]))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_mean_dice.py_unittest_TEST_CASE_3._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_mean_dice.py_unittest_TEST_CASE_3._", "embedding": null, "metadata": {"file_path": "tests/test_handler_mean_dice.py", "file_name": "test_handler_mean_dice.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 26, "span_ids": ["docstring"], "tokens": 162}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom ignite.engine import Engine, Events\nfrom parameterized import parameterized\n\nfrom monai.handlers import MeanDice, from_engine\n\nTEST_CASE_1 = [{\"include_background\": True, \"output_transform\": from_engine([\"pred\", \"label\"])}, 0.75, (4, 2)]\nTEST_CASE_2 = [{\"include_background\": False, \"output_transform\": from_engine([\"pred\", \"label\"])}, 0.66666, (4, 1)]\nTEST_CASE_3 = [\n    {\"reduction\": \"mean_channel\", \"output_transform\": from_engine([\"pred\", \"label\"])},\n    torch.Tensor([1.0, 0.0, 1.0, 1.0]),\n    (4, 2),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_surface_distance.py_TestHandlerSurfaceDistance_TestHandlerSurfaceDistance.test_compute.None_8": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_surface_distance.py_TestHandlerSurfaceDistance_TestHandlerSurfaceDistance.test_compute.None_8", "embedding": null, "metadata": {"file_path": "tests/test_handler_surface_distance.py", "file_name": "test_handler_surface_distance.py", "file_type": "text/x-python", "category": "test", "start_line": 60, "end_line": 83, "span_ids": ["TestHandlerSurfaceDistance.test_compute", "TestHandlerSurfaceDistance"], "tokens": 207}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestHandlerSurfaceDistance(unittest.TestCase):\n    # TODO test multi node Surface Distance\n\n    def test_compute(self):\n        sur_metric = SurfaceDistance(include_background=True)\n\n        def _val_func(engine, batch):\n            pass\n\n        engine = Engine(_val_func)\n        sur_metric.attach(engine, \"surface_distance\")\n\n        y_pred, y = TEST_SAMPLE_1\n        sur_metric.update([y_pred, y])\n        self.assertAlmostEqual(sur_metric.compute(), 4.17133, places=4)\n        y_pred, y = TEST_SAMPLE_2\n        sur_metric.update([y_pred, y])\n        self.assertAlmostEqual(sur_metric.compute(), 2.08566, places=4)\n        y_pred, y = TEST_SAMPLE_3\n        sur_metric.update([y_pred, y])\n        self.assertAlmostEqual(sur_metric.compute(), float(\"inf\"))\n        y_pred, y = TEST_SAMPLE_4\n        sur_metric.update([y_pred, y])\n        self.assertAlmostEqual(sur_metric.compute(), float(\"inf\"))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_surface_distance.py_TestHandlerSurfaceDistance.test_shape_mismatch_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_handler_surface_distance.py_TestHandlerSurfaceDistance.test_shape_mismatch_", "embedding": null, "metadata": {"file_path": "tests/test_handler_surface_distance.py", "file_name": "test_handler_surface_distance.py", "file_type": "text/x-python", "category": "test", "start_line": 85, "end_line": 110, "span_ids": ["impl:15", "TestHandlerSurfaceDistance.test_reduction", "TestHandlerSurfaceDistance.test_shape_mismatch"], "tokens": 210}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestHandlerSurfaceDistance(unittest.TestCase):\n\n    def test_shape_mismatch(self):\n        sur_metric = SurfaceDistance(include_background=True)\n        with self.assertRaises((AssertionError, ValueError)):\n            y_pred = TEST_SAMPLE_1[0]\n            y = torch.ones((1, 1, 10, 10, 10))\n            sur_metric.update([y_pred, y])\n\n    def test_reduction(self):\n        sur_metric = SurfaceDistance(include_background=True, reduction=\"mean_channel\")\n\n        def _val_func(engine, batch):\n            pass\n\n        engine = Engine(_val_func)\n        sur_metric.attach(engine, \"surface_distance\")\n\n        y_pred, y = TEST_SAMPLE_1\n        sur_metric.update([y_pred, y])\n        y_pred, y = TEST_SAMPLE_2\n        sur_metric.update([y_pred, y])\n        torch.testing.assert_allclose(sur_metric.compute().float(), torch.tensor([4.1713, 0.0000]))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_keep_largest_connected_component.py_unittest_grid_3._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_keep_largest_connected_component.py_unittest_grid_3._", "embedding": null, "metadata": {"file_path": "tests/test_keep_largest_connected_component.py", "file_name": "test_keep_largest_connected_component.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 44, "span_ids": ["docstring"], "tokens": 606}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import KeepLargestConnectedComponent\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\ngrid_1 = [[[0, 0, 1, 0, 0], [0, 2, 1, 1, 1], [1, 2, 1, 0, 0], [1, 2, 0, 1, 0], [2, 2, 0, 0, 2]]]\ngrid_2 = [[[0, 0, 0, 0, 1], [0, 0, 1, 1, 1], [1, 0, 1, 1, 2], [1, 0, 1, 2, 2], [0, 0, 0, 0, 1]]]\ngrid_3 = [\n    [\n        [1.0, 1.0, 0.0, 1.0, 1.0],\n        [1.0, 0.0, 0.0, 0.0, 0.0],\n        [0.0, 0.0, 0.0, 1.0, 1.0],\n        [0.0, 0.0, 1.0, 0.0, 1.0],\n        [0.0, 0.0, 1.0, 1.0, 0.0],\n    ],\n    [\n        [0.0, 0.0, 1.0, 0.0, 0.0],\n        [0.0, 0.0, 1.0, 1.0, 1.0],\n        [1.0, 0.0, 1.0, 0.0, 0.0],\n        [1.0, 0.0, 0.0, 1.0, 0.0],\n        [0.0, 0.0, 0.0, 0.0, 0.0],\n    ],\n    [\n        [0.0, 0.0, 0.0, 0.0, 0.0],\n        [0.0, 1.0, 0.0, 0.0, 0.0],\n        [0.0, 1.0, 0.0, 0.0, 0.0],\n        [0.0, 1.0, 0.0, 0.0, 0.0],\n        [1.0, 1.0, 0.0, 0.0, 1.0],\n    ],\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_keep_largest_connected_component.py_grid_4_TESTS._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_keep_largest_connected_component.py_grid_4_TESTS._", "embedding": null, "metadata": {"file_path": "tests/test_keep_largest_connected_component.py", "file_name": "test_keep_largest_connected_component.py", "file_type": "text/x-python", "category": "test", "start_line": 45, "end_line": 70, "span_ids": ["impl:7"], "tokens": 412}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "grid_4 = [\n    [\n        [1.0, 1.0, 1.0, 1.0, 0.0],\n        [1.0, 1.0, 0.0, 0.0, 0.0],\n        [0.0, 1.0, 0.0, 0.0, 0.0],\n        [0.0, 1.0, 0.0, 0.0, 0.0],\n        [1.0, 1.0, 1.0, 1.0, 0.0],\n    ],\n    [\n        [0.0, 0.0, 0.0, 0.0, 1.0],\n        [0.0, 0.0, 1.0, 1.0, 1.0],\n        [1.0, 0.0, 1.0, 1.0, 0.0],\n        [1.0, 0.0, 1.0, 0.0, 0.0],\n        [0.0, 0.0, 0.0, 0.0, 1.0],\n    ],\n    [\n        [0.0, 0.0, 0.0, 0.0, 0.0],\n        [0.0, 0.0, 0.0, 0.0, 0.0],\n        [0.0, 0.0, 0.0, 0.0, 1.0],\n        [0.0, 0.0, 0.0, 1.0, 1.0],\n        [0.0, 0.0, 0.0, 0.0, 0.0],\n    ],\n]\n\n\nTESTS = []", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_keep_largest_connected_component.py_for_p_in_TEST_NDARRAYS__for_p_in_TEST_NDARRAYS_.None_9": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_keep_largest_connected_component.py_for_p_in_TEST_NDARRAYS__for_p_in_TEST_NDARRAYS_.None_9", "embedding": null, "metadata": {"file_path": "tests/test_keep_largest_connected_component.py", "file_name": "test_keep_largest_connected_component.py", "file_type": "text/x-python", "category": "test", "start_line": 71, "end_line": 160, "span_ids": ["impl:7"], "tokens": 1263}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "for p in TEST_NDARRAYS:\n    TESTS.append(\n        [\n            \"value_1\",\n            {\"independent\": False, \"applied_labels\": 1},\n            p(grid_1),\n            torch.tensor([[[0, 0, 1, 0, 0], [0, 2, 1, 1, 1], [0, 2, 1, 0, 0], [0, 2, 0, 1, 0], [2, 2, 0, 0, 2]]]),\n        ]\n    )\n\n    TESTS.append(\n        [\n            \"value_2\",\n            {\"independent\": False, \"applied_labels\": [2]},\n            p(grid_1),\n            torch.tensor([[[0, 0, 1, 0, 0], [0, 2, 1, 1, 1], [1, 2, 1, 0, 0], [1, 2, 0, 1, 0], [2, 2, 0, 0, 0]]]),\n        ]\n    )\n\n    TESTS.append(\n        [\n            \"independent_value_1_2\",\n            {\"independent\": True, \"applied_labels\": [1, 2]},\n            p(grid_1),\n            torch.tensor([[[0, 0, 1, 0, 0], [0, 2, 1, 1, 1], [0, 2, 1, 0, 0], [0, 2, 0, 1, 0], [2, 2, 0, 0, 0]]]),\n        ]\n    )\n\n    TESTS.append(\n        [\n            \"dependent_value_1_2\",\n            {\"independent\": False, \"applied_labels\": [1, 2]},\n            p(grid_1),\n            torch.tensor([[[0, 0, 1, 0, 0], [0, 2, 1, 1, 1], [1, 2, 1, 0, 0], [1, 2, 0, 1, 0], [2, 2, 0, 0, 2]]]),\n        ]\n    )\n\n    TESTS.append(\n        [\n            \"value_1\",\n            {\"independent\": True, \"applied_labels\": [1]},\n            p(grid_2),\n            torch.tensor([[[0, 0, 0, 0, 1], [0, 0, 1, 1, 1], [0, 0, 1, 1, 2], [0, 0, 1, 2, 2], [0, 0, 0, 0, 0]]]),\n        ]\n    )\n\n    TESTS.append(\n        [\n            \"independent_value_1_2\",\n            {\"independent\": True, \"applied_labels\": [1, 2]},\n            p(grid_2),\n            torch.tensor([[[0, 0, 0, 0, 1], [0, 0, 1, 1, 1], [0, 0, 1, 1, 2], [0, 0, 1, 2, 2], [0, 0, 0, 0, 0]]]),\n        ]\n    )\n\n    TESTS.append(\n        [\n            \"dependent_value_1_2\",\n            {\"independent\": False, \"applied_labels\": [1, 2]},\n            p(grid_2),\n            torch.tensor([[[0, 0, 0, 0, 1], [0, 0, 1, 1, 1], [0, 0, 1, 1, 2], [0, 0, 1, 2, 2], [0, 0, 0, 0, 1]]]),\n        ]\n    )\n\n    TESTS.append(\n        [\n            \"value_1_connect_1\",\n            {\"independent\": False, \"applied_labels\": [1], \"connectivity\": 1},\n            p(grid_1),\n            torch.tensor([[[0, 0, 1, 0, 0], [0, 2, 1, 1, 1], [0, 2, 1, 0, 0], [0, 2, 0, 0, 0], [2, 2, 0, 0, 2]]]),\n        ]\n    )\n\n    TESTS.append(\n        [\n            \"independent_value_1_2_connect_1\",\n            {\"independent\": True, \"applied_labels\": [1, 2], \"connectivity\": 1},\n            p(grid_1),\n            torch.tensor([[[0, 0, 1, 0, 0], [0, 2, 1, 1, 1], [0, 2, 1, 0, 0], [0, 2, 0, 0, 0], [2, 2, 0, 0, 0]]]),\n        ]\n    )\n\n    TESTS.append(\n        [\n            \"dependent_value_1_2_connect_1\",\n            {\"independent\": False, \"applied_labels\": [1, 2], \"connectivity\": 1},\n            p(grid_1),\n            torch.tensor([[[0, 0, 1, 0, 0], [0, 2, 1, 1, 1], [1, 2, 1, 0, 0], [1, 2, 0, 0, 0], [2, 2, 0, 0, 0]]]),\n        ]\n    )\n    # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_keep_largest_connected_component.py_for_p_in_TEST_NDARRAYS_.None_10_for_p_in_TEST_NDARRAYS_.None_12": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_keep_largest_connected_component.py_for_p_in_TEST_NDARRAYS_.None_10_for_p_in_TEST_NDARRAYS_.None_12", "embedding": null, "metadata": {"file_path": "tests/test_keep_largest_connected_component.py", "file_name": "test_keep_largest_connected_component.py", "file_type": "text/x-python", "category": "test", "start_line": 162, "end_line": 259, "span_ids": ["impl:7"], "tokens": 1429}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "for p in TEST_NDARRAYS:\n    # ... other code\n\n    TESTS.append(\n        [\n            \"onehot_independent_batch_2_apply_label_1_connect_1\",\n            {\"independent\": True, \"applied_labels\": [1], \"connectivity\": 1},\n            p(grid_3),\n            torch.tensor(\n                [\n                    [\n                        [1.0, 1.0, 0.0, 1.0, 1.0],\n                        [1.0, 0.0, 0.0, 0.0, 0.0],\n                        [0.0, 0.0, 0.0, 1.0, 1.0],\n                        [0.0, 0.0, 1.0, 0.0, 1.0],\n                        [0.0, 0.0, 1.0, 1.0, 0.0],\n                    ],\n                    [\n                        [0.0, 0.0, 1.0, 0.0, 0.0],\n                        [0.0, 0.0, 1.0, 1.0, 1.0],\n                        [0.0, 0.0, 1.0, 0.0, 0.0],\n                        [0.0, 0.0, 0.0, 0.0, 0.0],\n                        [0.0, 0.0, 0.0, 0.0, 0.0],\n                    ],\n                    [\n                        [0.0, 0.0, 0.0, 0.0, 0.0],\n                        [0.0, 1.0, 0.0, 0.0, 0.0],\n                        [0.0, 1.0, 0.0, 0.0, 0.0],\n                        [0.0, 1.0, 0.0, 0.0, 0.0],\n                        [1.0, 1.0, 0.0, 0.0, 1.0],\n                    ],\n                ]\n            ),\n        ]\n    )\n\n    TESTS.append(\n        [\n            \"onehot_independent_batch_2_apply_label_1_connect_2\",\n            {\"independent\": True, \"applied_labels\": [1], \"connectivity\": 2},\n            p(grid_3),\n            torch.tensor(\n                [\n                    [\n                        [1.0, 1.0, 0.0, 1.0, 1.0],\n                        [1.0, 0.0, 0.0, 0.0, 0.0],\n                        [0.0, 0.0, 0.0, 1.0, 1.0],\n                        [0.0, 0.0, 1.0, 0.0, 1.0],\n                        [0.0, 0.0, 1.0, 1.0, 0.0],\n                    ],\n                    [\n                        [0.0, 0.0, 1.0, 0.0, 0.0],\n                        [0.0, 0.0, 1.0, 1.0, 1.0],\n                        [0.0, 0.0, 1.0, 0.0, 0.0],\n                        [0.0, 0.0, 0.0, 1.0, 0.0],\n                        [0.0, 0.0, 0.0, 0.0, 0.0],\n                    ],\n                    [\n                        [0.0, 0.0, 0.0, 0.0, 0.0],\n                        [0.0, 1.0, 0.0, 0.0, 0.0],\n                        [0.0, 1.0, 0.0, 0.0, 0.0],\n                        [0.0, 1.0, 0.0, 0.0, 0.0],\n                        [1.0, 1.0, 0.0, 0.0, 1.0],\n                    ],\n                ]\n            ),\n        ]\n    )\n\n    TESTS.append(\n        [\n            \"onehot_independent_batch_2_apply_label_1_2_connect_2\",\n            {\"independent\": True, \"applied_labels\": [1, 2], \"connectivity\": 2},\n            p(grid_3),\n            torch.tensor(\n                [\n                    [\n                        [1.0, 1.0, 0.0, 1.0, 1.0],\n                        [1.0, 0.0, 0.0, 0.0, 0.0],\n                        [0.0, 0.0, 0.0, 1.0, 1.0],\n                        [0.0, 0.0, 1.0, 0.0, 1.0],\n                        [0.0, 0.0, 1.0, 1.0, 0.0],\n                    ],\n                    [\n                        [0.0, 0.0, 1.0, 0.0, 0.0],\n                        [0.0, 0.0, 1.0, 1.0, 1.0],\n                        [0.0, 0.0, 1.0, 0.0, 0.0],\n                        [0.0, 0.0, 0.0, 1.0, 0.0],\n                        [0.0, 0.0, 0.0, 0.0, 0.0],\n                    ],\n                    [\n                        [0.0, 0.0, 0.0, 0.0, 0.0],\n                        [0.0, 1.0, 0.0, 0.0, 0.0],\n                        [0.0, 1.0, 0.0, 0.0, 0.0],\n                        [0.0, 1.0, 0.0, 0.0, 0.0],\n                        [1.0, 1.0, 0.0, 0.0, 0.0],\n                    ],\n                ]\n            ),\n        ]\n    )\n    # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_keep_largest_connected_component.py_for_p_in_TEST_NDARRAYS_.None_13_for_p_in_TEST_NDARRAYS_.None_14": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_keep_largest_connected_component.py_for_p_in_TEST_NDARRAYS_.None_13_for_p_in_TEST_NDARRAYS_.None_14", "embedding": null, "metadata": {"file_path": "tests/test_keep_largest_connected_component.py", "file_name": "test_keep_largest_connected_component.py", "file_type": "text/x-python", "category": "test", "start_line": 261, "end_line": 325, "span_ids": ["impl:7"], "tokens": 961}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "for p in TEST_NDARRAYS:\n    # ... other code\n\n    TESTS.append(\n        [\n            \"onehot_dependent_batch_2_apply_label_1_2_connect_2\",\n            {\"independent\": False, \"applied_labels\": [1, 2], \"connectivity\": 2},\n            p(grid_4),\n            torch.tensor(\n                [\n                    [\n                        [1.0, 1.0, 1.0, 1.0, 0.0],\n                        [1.0, 1.0, 0.0, 0.0, 0.0],\n                        [0.0, 1.0, 0.0, 0.0, 0.0],\n                        [0.0, 1.0, 0.0, 0.0, 0.0],\n                        [1.0, 1.0, 1.0, 1.0, 0.0],\n                    ],\n                    [\n                        [0.0, 0.0, 0.0, 0.0, 1.0],\n                        [0.0, 0.0, 1.0, 1.0, 1.0],\n                        [0.0, 0.0, 1.0, 1.0, 0.0],\n                        [0.0, 0.0, 1.0, 0.0, 0.0],\n                        [0.0, 0.0, 0.0, 0.0, 1.0],\n                    ],\n                    [\n                        [0.0, 0.0, 0.0, 0.0, 0.0],\n                        [0.0, 0.0, 0.0, 0.0, 0.0],\n                        [0.0, 0.0, 0.0, 0.0, 1.0],\n                        [0.0, 0.0, 0.0, 1.0, 1.0],\n                        [0.0, 0.0, 0.0, 0.0, 0.0],\n                    ],\n                ]\n            ),\n        ]\n    )\n\n    TESTS.append(\n        [\n            \"onehot_dependent_batch_2_apply_label_1_2_connect_1\",\n            {\"independent\": False, \"applied_labels\": [1, 2], \"connectivity\": 1},\n            p(grid_4),\n            torch.tensor(\n                [\n                    [\n                        [1.0, 1.0, 1.0, 1.0, 0.0],\n                        [1.0, 1.0, 0.0, 0.0, 0.0],\n                        [0.0, 1.0, 0.0, 0.0, 0.0],\n                        [0.0, 1.0, 0.0, 0.0, 0.0],\n                        [1.0, 1.0, 1.0, 1.0, 0.0],\n                    ],\n                    [\n                        [0.0, 0.0, 0.0, 0.0, 1.0],\n                        [0.0, 0.0, 1.0, 1.0, 1.0],\n                        [0.0, 0.0, 1.0, 1.0, 0.0],\n                        [0.0, 0.0, 1.0, 0.0, 0.0],\n                        [0.0, 0.0, 0.0, 0.0, 1.0],\n                    ],\n                    [\n                        [0.0, 0.0, 0.0, 0.0, 0.0],\n                        [0.0, 0.0, 0.0, 0.0, 0.0],\n                        [0.0, 0.0, 0.0, 0.0, 1.0],\n                        [0.0, 0.0, 0.0, 1.0, 1.0],\n                        [0.0, 0.0, 0.0, 0.0, 0.0],\n                    ],\n                ]\n            ),\n        ]\n    )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_keep_largest_connected_component.py_INVALID_CASES_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_keep_largest_connected_component.py_INVALID_CASES_", "embedding": null, "metadata": {"file_path": "tests/test_keep_largest_connected_component.py", "file_name": "test_keep_largest_connected_component.py", "file_type": "text/x-python", "category": "test", "start_line": 327, "end_line": 349, "span_ids": ["TestKeepLargestConnectedComponent", "impl:27", "TestKeepLargestConnectedComponent.test_raise_exception", "TestKeepLargestConnectedComponent.test_correct_results", "impl:32"], "tokens": 194}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "INVALID_CASES = []\nfor p in TEST_NDARRAYS:\n    INVALID_CASES.append([\"no_applied_labels_for_single_channel\", {\"independent\": False}, p(grid_1), TypeError])\n    INVALID_CASES.append([\"no_applied_labels_for_multi_channel\", {\"independent\": False}, p(grid_3), TypeError])\n\n\nclass TestKeepLargestConnectedComponent(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_correct_results(self, _, args, input_image, expected):\n        converter = KeepLargestConnectedComponent(**args)\n        result = converter(input_image)\n        assert_allclose(result, expected, type_test=False)\n\n    @parameterized.expand(INVALID_CASES)\n    def test_raise_exception(self, _, args, input_image, expected_error):\n        with self.assertRaises(expected_error):\n            converter = KeepLargestConnectedComponent(**args)\n            _ = converter(input_image)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_keep_largest_connected_componentd.py_grid_4_VALID_CASES._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_keep_largest_connected_componentd.py_grid_4_VALID_CASES._", "embedding": null, "metadata": {"file_path": "tests/test_keep_largest_connected_componentd.py", "file_name": "test_keep_largest_connected_componentd.py", "file_type": "text/x-python", "category": "test", "start_line": 45, "end_line": 69, "span_ids": ["impl:7"], "tokens": 413}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "grid_4 = [\n    [\n        [1.0, 1.0, 1.0, 1.0, 0.0],\n        [1.0, 1.0, 0.0, 0.0, 0.0],\n        [0.0, 1.0, 0.0, 0.0, 0.0],\n        [0.0, 1.0, 0.0, 0.0, 0.0],\n        [1.0, 1.0, 1.0, 1.0, 0.0],\n    ],\n    [\n        [0.0, 0.0, 0.0, 0.0, 1.0],\n        [0.0, 0.0, 1.0, 1.0, 1.0],\n        [1.0, 0.0, 1.0, 1.0, 0.0],\n        [1.0, 0.0, 1.0, 0.0, 0.0],\n        [0.0, 0.0, 0.0, 0.0, 1.0],\n    ],\n    [\n        [0.0, 0.0, 0.0, 0.0, 0.0],\n        [0.0, 0.0, 0.0, 0.0, 0.0],\n        [0.0, 0.0, 0.0, 0.0, 1.0],\n        [0.0, 0.0, 0.0, 1.0, 1.0],\n        [0.0, 0.0, 0.0, 0.0, 0.0],\n    ],\n]\n\nVALID_CASES = []", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_keep_largest_connected_componentd.py_for_p_in_TEST_NDARRAYS__for_p_in_TEST_NDARRAYS_.None_9": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_keep_largest_connected_componentd.py_for_p_in_TEST_NDARRAYS__for_p_in_TEST_NDARRAYS_.None_9", "embedding": null, "metadata": {"file_path": "tests/test_keep_largest_connected_componentd.py", "file_name": "test_keep_largest_connected_componentd.py", "file_type": "text/x-python", "category": "test", "start_line": 70, "end_line": 159, "span_ids": ["impl:7"], "tokens": 1363}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "for p in TEST_NDARRAYS:\n    VALID_CASES.append(\n        [\n            \"value_1\",\n            {\"keys\": [\"img\"], \"independent\": False, \"applied_labels\": 1},\n            {\"img\": p(grid_1)},\n            torch.tensor([[[0, 0, 1, 0, 0], [0, 2, 1, 1, 1], [0, 2, 1, 0, 0], [0, 2, 0, 1, 0], [2, 2, 0, 0, 2]]]),\n        ]\n    )\n\n    VALID_CASES.append(\n        [\n            \"value_2\",\n            {\"keys\": [\"img\"], \"independent\": False, \"applied_labels\": [2]},\n            {\"img\": p(grid_1)},\n            torch.tensor([[[0, 0, 1, 0, 0], [0, 2, 1, 1, 1], [1, 2, 1, 0, 0], [1, 2, 0, 1, 0], [2, 2, 0, 0, 0]]]),\n        ]\n    )\n\n    VALID_CASES.append(\n        [\n            \"independent_value_1_2\",\n            {\"keys\": [\"img\"], \"independent\": True, \"applied_labels\": [1, 2]},\n            {\"img\": p(grid_1)},\n            torch.tensor([[[0, 0, 1, 0, 0], [0, 2, 1, 1, 1], [0, 2, 1, 0, 0], [0, 2, 0, 1, 0], [2, 2, 0, 0, 0]]]),\n        ]\n    )\n\n    VALID_CASES.append(\n        [\n            \"dependent_value_1_2\",\n            {\"keys\": [\"img\"], \"independent\": False, \"applied_labels\": [1, 2]},\n            {\"img\": p(grid_1)},\n            torch.tensor([[[0, 0, 1, 0, 0], [0, 2, 1, 1, 1], [1, 2, 1, 0, 0], [1, 2, 0, 1, 0], [2, 2, 0, 0, 2]]]),\n        ]\n    )\n\n    VALID_CASES.append(\n        [\n            \"value_1\",\n            {\"keys\": [\"img\"], \"independent\": True, \"applied_labels\": [1]},\n            {\"img\": p(grid_2)},\n            torch.tensor([[[0, 0, 0, 0, 1], [0, 0, 1, 1, 1], [0, 0, 1, 1, 2], [0, 0, 1, 2, 2], [0, 0, 0, 0, 0]]]),\n        ]\n    )\n\n    VALID_CASES.append(\n        [\n            \"independent_value_1_2\",\n            {\"keys\": [\"img\"], \"independent\": True, \"applied_labels\": [1, 2]},\n            {\"img\": p(grid_2)},\n            torch.tensor([[[0, 0, 0, 0, 1], [0, 0, 1, 1, 1], [0, 0, 1, 1, 2], [0, 0, 1, 2, 2], [0, 0, 0, 0, 0]]]),\n        ]\n    )\n\n    VALID_CASES.append(\n        [\n            \"dependent_value_1_2\",\n            {\"keys\": [\"img\"], \"independent\": False, \"applied_labels\": [1, 2]},\n            {\"img\": p(grid_2)},\n            torch.tensor([[[0, 0, 0, 0, 1], [0, 0, 1, 1, 1], [0, 0, 1, 1, 2], [0, 0, 1, 2, 2], [0, 0, 0, 0, 1]]]),\n        ]\n    )\n\n    VALID_CASES.append(\n        [\n            \"value_1_connect_1\",\n            {\"keys\": [\"img\"], \"independent\": False, \"applied_labels\": [1], \"connectivity\": 1},\n            {\"img\": p(grid_1)},\n            torch.tensor([[[0, 0, 1, 0, 0], [0, 2, 1, 1, 1], [0, 2, 1, 0, 0], [0, 2, 0, 0, 0], [2, 2, 0, 0, 2]]]),\n        ]\n    )\n\n    VALID_CASES.append(\n        [\n            \"independent_value_1_2_connect_1\",\n            {\"keys\": [\"img\"], \"independent\": True, \"applied_labels\": [1, 2], \"connectivity\": 1},\n            {\"img\": p(grid_1)},\n            torch.tensor([[[0, 0, 1, 0, 0], [0, 2, 1, 1, 1], [0, 2, 1, 0, 0], [0, 2, 0, 0, 0], [2, 2, 0, 0, 0]]]),\n        ]\n    )\n\n    VALID_CASES.append(\n        [\n            \"dependent_value_1_2_connect_1\",\n            {\"keys\": [\"img\"], \"independent\": False, \"applied_labels\": [1, 2], \"connectivity\": 1},\n            {\"img\": p(grid_1)},\n            torch.tensor([[[0, 0, 1, 0, 0], [0, 2, 1, 1, 1], [1, 2, 1, 0, 0], [1, 2, 0, 0, 0], [2, 2, 0, 0, 0]]]),\n        ]\n    )\n    # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_keep_largest_connected_componentd.py_for_p_in_TEST_NDARRAYS_.None_10_for_p_in_TEST_NDARRAYS_.None_12": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_keep_largest_connected_componentd.py_for_p_in_TEST_NDARRAYS_.None_10_for_p_in_TEST_NDARRAYS_.None_12", "embedding": null, "metadata": {"file_path": "tests/test_keep_largest_connected_componentd.py", "file_name": "test_keep_largest_connected_componentd.py", "file_type": "text/x-python", "category": "test", "start_line": 161, "end_line": 258, "span_ids": ["impl:7"], "tokens": 1459}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "for p in TEST_NDARRAYS:\n    # ... other code\n\n    VALID_CASES.append(\n        [\n            \"onehot_independent_batch_2_apply_label_1_connect_1\",\n            {\"keys\": [\"img\"], \"independent\": True, \"applied_labels\": [1], \"connectivity\": 1},\n            {\"img\": p(grid_3)},\n            torch.tensor(\n                [\n                    [\n                        [1.0, 1.0, 0.0, 1.0, 1.0],\n                        [1.0, 0.0, 0.0, 0.0, 0.0],\n                        [0.0, 0.0, 0.0, 1.0, 1.0],\n                        [0.0, 0.0, 1.0, 0.0, 1.0],\n                        [0.0, 0.0, 1.0, 1.0, 0.0],\n                    ],\n                    [\n                        [0.0, 0.0, 1.0, 0.0, 0.0],\n                        [0.0, 0.0, 1.0, 1.0, 1.0],\n                        [0.0, 0.0, 1.0, 0.0, 0.0],\n                        [0.0, 0.0, 0.0, 0.0, 0.0],\n                        [0.0, 0.0, 0.0, 0.0, 0.0],\n                    ],\n                    [\n                        [0.0, 0.0, 0.0, 0.0, 0.0],\n                        [0.0, 1.0, 0.0, 0.0, 0.0],\n                        [0.0, 1.0, 0.0, 0.0, 0.0],\n                        [0.0, 1.0, 0.0, 0.0, 0.0],\n                        [1.0, 1.0, 0.0, 0.0, 1.0],\n                    ],\n                ]\n            ),\n        ]\n    )\n\n    VALID_CASES.append(\n        [\n            \"onehot_independent_batch_2_apply_label_1_connect_2\",\n            {\"keys\": [\"img\"], \"independent\": True, \"applied_labels\": [1], \"connectivity\": 2},\n            {\"img\": p(grid_3)},\n            torch.tensor(\n                [\n                    [\n                        [1.0, 1.0, 0.0, 1.0, 1.0],\n                        [1.0, 0.0, 0.0, 0.0, 0.0],\n                        [0.0, 0.0, 0.0, 1.0, 1.0],\n                        [0.0, 0.0, 1.0, 0.0, 1.0],\n                        [0.0, 0.0, 1.0, 1.0, 0.0],\n                    ],\n                    [\n                        [0.0, 0.0, 1.0, 0.0, 0.0],\n                        [0.0, 0.0, 1.0, 1.0, 1.0],\n                        [0.0, 0.0, 1.0, 0.0, 0.0],\n                        [0.0, 0.0, 0.0, 1.0, 0.0],\n                        [0.0, 0.0, 0.0, 0.0, 0.0],\n                    ],\n                    [\n                        [0.0, 0.0, 0.0, 0.0, 0.0],\n                        [0.0, 1.0, 0.0, 0.0, 0.0],\n                        [0.0, 1.0, 0.0, 0.0, 0.0],\n                        [0.0, 1.0, 0.0, 0.0, 0.0],\n                        [1.0, 1.0, 0.0, 0.0, 1.0],\n                    ],\n                ]\n            ),\n        ]\n    )\n\n    VALID_CASES.append(\n        [\n            \"onehot_independent_batch_2_apply_label_1_2_connect_2\",\n            {\"keys\": [\"img\"], \"independent\": True, \"applied_labels\": [1, 2], \"connectivity\": 2},\n            {\"img\": p(grid_3)},\n            torch.tensor(\n                [\n                    [\n                        [1.0, 1.0, 0.0, 1.0, 1.0],\n                        [1.0, 0.0, 0.0, 0.0, 0.0],\n                        [0.0, 0.0, 0.0, 1.0, 1.0],\n                        [0.0, 0.0, 1.0, 0.0, 1.0],\n                        [0.0, 0.0, 1.0, 1.0, 0.0],\n                    ],\n                    [\n                        [0.0, 0.0, 1.0, 0.0, 0.0],\n                        [0.0, 0.0, 1.0, 1.0, 1.0],\n                        [0.0, 0.0, 1.0, 0.0, 0.0],\n                        [0.0, 0.0, 0.0, 1.0, 0.0],\n                        [0.0, 0.0, 0.0, 0.0, 0.0],\n                    ],\n                    [\n                        [0.0, 0.0, 0.0, 0.0, 0.0],\n                        [0.0, 1.0, 0.0, 0.0, 0.0],\n                        [0.0, 1.0, 0.0, 0.0, 0.0],\n                        [0.0, 1.0, 0.0, 0.0, 0.0],\n                        [1.0, 1.0, 0.0, 0.0, 0.0],\n                    ],\n                ]\n            ),\n        ]\n    )\n    # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_keep_largest_connected_componentd.py_for_p_in_TEST_NDARRAYS_.None_13_for_p_in_TEST_NDARRAYS_.None_14": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_keep_largest_connected_componentd.py_for_p_in_TEST_NDARRAYS_.None_13_for_p_in_TEST_NDARRAYS_.None_14", "embedding": null, "metadata": {"file_path": "tests/test_keep_largest_connected_componentd.py", "file_name": "test_keep_largest_connected_componentd.py", "file_type": "text/x-python", "category": "test", "start_line": 260, "end_line": 324, "span_ids": ["impl:7"], "tokens": 981}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "for p in TEST_NDARRAYS:\n    # ... other code\n\n    VALID_CASES.append(\n        [\n            \"onehot_dependent_batch_2_apply_label_1_2_connect_2\",\n            {\"keys\": [\"img\"], \"independent\": False, \"applied_labels\": [1, 2], \"connectivity\": 2},\n            {\"img\": p(grid_4)},\n            torch.tensor(\n                [\n                    [\n                        [1.0, 1.0, 1.0, 1.0, 0.0],\n                        [1.0, 1.0, 0.0, 0.0, 0.0],\n                        [0.0, 1.0, 0.0, 0.0, 0.0],\n                        [0.0, 1.0, 0.0, 0.0, 0.0],\n                        [1.0, 1.0, 1.0, 1.0, 0.0],\n                    ],\n                    [\n                        [0.0, 0.0, 0.0, 0.0, 1.0],\n                        [0.0, 0.0, 1.0, 1.0, 1.0],\n                        [0.0, 0.0, 1.0, 1.0, 0.0],\n                        [0.0, 0.0, 1.0, 0.0, 0.0],\n                        [0.0, 0.0, 0.0, 0.0, 1.0],\n                    ],\n                    [\n                        [0.0, 0.0, 0.0, 0.0, 0.0],\n                        [0.0, 0.0, 0.0, 0.0, 0.0],\n                        [0.0, 0.0, 0.0, 0.0, 1.0],\n                        [0.0, 0.0, 0.0, 1.0, 1.0],\n                        [0.0, 0.0, 0.0, 0.0, 0.0],\n                    ],\n                ]\n            ),\n        ]\n    )\n\n    VALID_CASES.append(\n        [\n            \"onehot_dependent_batch_2_apply_label_1_2_connect_1\",\n            {\"keys\": [\"img\"], \"independent\": False, \"applied_labels\": [1, 2], \"connectivity\": 1},\n            {\"img\": p(grid_4)},\n            torch.tensor(\n                [\n                    [\n                        [1.0, 1.0, 1.0, 1.0, 0.0],\n                        [1.0, 1.0, 0.0, 0.0, 0.0],\n                        [0.0, 1.0, 0.0, 0.0, 0.0],\n                        [0.0, 1.0, 0.0, 0.0, 0.0],\n                        [1.0, 1.0, 1.0, 1.0, 0.0],\n                    ],\n                    [\n                        [0.0, 0.0, 0.0, 0.0, 1.0],\n                        [0.0, 0.0, 1.0, 1.0, 1.0],\n                        [0.0, 0.0, 1.0, 1.0, 0.0],\n                        [0.0, 0.0, 1.0, 0.0, 0.0],\n                        [0.0, 0.0, 0.0, 0.0, 1.0],\n                    ],\n                    [\n                        [0.0, 0.0, 0.0, 0.0, 0.0],\n                        [0.0, 0.0, 0.0, 0.0, 0.0],\n                        [0.0, 0.0, 0.0, 0.0, 1.0],\n                        [0.0, 0.0, 0.0, 1.0, 1.0],\n                        [0.0, 0.0, 0.0, 0.0, 0.0],\n                    ],\n                ]\n            ),\n        ]\n    )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_keep_largest_connected_componentd.py_INVALID_CASES_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_keep_largest_connected_componentd.py_INVALID_CASES_", "embedding": null, "metadata": {"file_path": "tests/test_keep_largest_connected_componentd.py", "file_name": "test_keep_largest_connected_componentd.py", "file_type": "text/x-python", "category": "test", "start_line": 326, "end_line": 352, "span_ids": ["impl:27", "TestKeepLargestConnectedComponentd.test_correct_results", "TestKeepLargestConnectedComponentd.test_raise_exception", "impl:32", "TestKeepLargestConnectedComponentd"], "tokens": 227}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "INVALID_CASES = []\nfor p in TEST_NDARRAYS:\n    INVALID_CASES.append(\n        [\"no_applied_labels_for_single_channel\", {\"keys\": [\"img\"], \"independent\": False}, {\"img\": p(grid_1)}, TypeError]\n    )\n    INVALID_CASES.append(\n        [\"no_applied_labels_for_multi_channel\", {\"keys\": [\"img\"], \"independent\": False}, {\"img\": p(grid_3)}, TypeError]\n    )\n\n\nclass TestKeepLargestConnectedComponentd(unittest.TestCase):\n    @parameterized.expand(VALID_CASES)\n    def test_correct_results(self, _, args, input_dict, expected):\n        converter = KeepLargestConnectedComponentd(**args)\n        result = converter(input_dict)\n        assert_allclose(result[\"img\"], expected, type_test=False)\n\n    @parameterized.expand(INVALID_CASES)\n    def test_raise_exception(self, _, args, input_dict, expected_error):\n        with self.assertRaises(expected_error):\n            converter = KeepLargestConnectedComponentd(**args)\n            _ = converter(input_dict)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_label_filterd.py_unittest_INVALID_CASES._ITEST_CASE_1_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_label_filterd.py_unittest_INVALID_CASES._ITEST_CASE_1_", "embedding": null, "metadata": {"file_path": "tests/test_label_filterd.py", "file_name": "test_label_filterd.py", "file_type": "text/x-python", "category": "test", "start_line": 13, "end_line": 57, "span_ids": ["docstring"], "tokens": 390}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms.post.dictionary import LabelFilterd\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\ngrid_1 = torch.tensor([[[[1, 2, 3], [4, 5, 6], [7, 8, 9]]]])\n\nVALID_TESTS = []\nfor p in TEST_NDARRAYS:\n    VALID_TESTS.append(\n        [\n            \"filter_single_label\",\n            {\"applied_labels\": 3},\n            p(grid_1),\n            p(torch.tensor([[[[0, 0, 3], [0, 0, 0], [0, 0, 0]]]])),\n        ]\n    )\n\n    VALID_TESTS.append(\n        [\n            \"filter_single_label_list\",\n            {\"applied_labels\": [3]},\n            p(grid_1),\n            p(torch.tensor([[[[0, 0, 3], [0, 0, 0], [0, 0, 0]]]])),\n        ]\n    )\n\n    VALID_TESTS.append(\n        [\n            \"filter_multi_label\",\n            {\"applied_labels\": [3, 5, 8]},\n            p(grid_1),\n            p(torch.tensor([[[[0, 0, 3], [0, 5, 0], [0, 8, 0]]]])),\n        ]\n    )\n\n    VALID_TESTS.append([\"filter_all\", {\"applied_labels\": [1, 2, 3, 4, 5, 6, 7, 8, 9]}, p(grid_1), p(grid_1)])\n\n\nITEST_CASE_1 = [\"invalid_image_data_type\", {\"applied_labels\": 1}, [[[[1, 1, 1]]]], NotImplementedError]\n\nINVALID_CASES = [ITEST_CASE_1]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_label_filterd.py_TestLabelFilter_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_label_filterd.py_TestLabelFilter_", "embedding": null, "metadata": {"file_path": "tests/test_label_filterd.py", "file_name": "test_label_filterd.py", "file_type": "text/x-python", "category": "test", "start_line": 60, "end_line": 79, "span_ids": ["TestLabelFilter.test_raise_exception", "TestLabelFilter.test_correct_results", "TestLabelFilter", "impl:14"], "tokens": 162}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestLabelFilter(unittest.TestCase):\n    @parameterized.expand(VALID_TESTS)\n    def test_correct_results(self, _, args, input_image, expected):\n        converter = LabelFilterd(keys=\"image\", **args)\n        result = converter({\"image\": input_image})[\"image\"]\n        assert_allclose(result, expected)\n\n    @parameterized.expand(INVALID_CASES)\n    def test_raise_exception(self, _, args, input_image, expected_error):\n        with self.assertRaises(expected_error):\n            converter = LabelFilterd(keys=\"image\", **args)\n            if isinstance(input_image, torch.Tensor) and torch.cuda.is_available():\n                _ = converter({\"image\": input_image.cuda()})\n            else:\n                _ = converter({\"image\": input_image})\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_label_to_contour.py_gen_fixed_cube_gen_fixed_cube.return.array_type_cube_array_t": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_label_to_contour.py_gen_fixed_cube_gen_fixed_cube.return.array_type_cube_array_t", "embedding": null, "metadata": {"file_path": "tests/test_label_to_contour.py", "file_name": "test_label_to_contour.py", "file_type": "text/x-python", "category": "test", "start_line": 104, "end_line": 114, "span_ids": ["gen_fixed_cube"], "tokens": 137}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def gen_fixed_cube(array_type):\n    scale, core_start, core_end = 8, 1, 7\n    cube = np.zeros((scale, scale, scale))\n    cube[core_start:core_end, core_start:core_end, core_start:core_end] = torch.ones(\n        core_end - core_start, core_end - core_start, core_end - core_start\n    )\n    cube = cube[None]\n\n    batch_size, channels = 10, 6\n    cube = np.tile(cube, (batch_size, channels, 1, 1, 1))\n    return array_type(cube), array_type(expected_output_for_cube)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_label_to_contourd.py_gen_fixed_cube_gen_fixed_cube.return.array_type_cube_array_t": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_label_to_contourd.py_gen_fixed_cube_gen_fixed_cube.return.array_type_cube_array_t", "embedding": null, "metadata": {"file_path": "tests/test_label_to_contourd.py", "file_name": "test_label_to_contourd.py", "file_type": "text/x-python", "category": "test", "start_line": 104, "end_line": 114, "span_ids": ["gen_fixed_cube"], "tokens": 137}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def gen_fixed_cube(array_type):\n    scale, core_start, core_end = 8, 1, 7\n    cube = np.zeros((scale, scale, scale))\n    cube[core_start:core_end, core_start:core_end, core_start:core_end] = torch.ones(\n        core_end - core_start, core_end - core_start, core_end - core_start\n    )\n    cube = cube[None]\n\n    batch_size, channels = 10, 6\n    cube = np.tile(cube, (batch_size, channels, 1, 1, 1))\n    return array_type(cube), array_type(expected_output_for_cube)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nifti_saver.py_TestNiftiSaver.test_saved_3d_no_resize_content_TestNiftiSaver.test_saved_3d_no_resize_content.with_tempfile_TemporaryDi.for_i_in_range_8_.self_assertEqual_img_shap": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nifti_saver.py_TestNiftiSaver.test_saved_3d_no_resize_content_TestNiftiSaver.test_saved_3d_no_resize_content.with_tempfile_TemporaryDi.for_i_in_range_8_.self_assertEqual_img_shap", "embedding": null, "metadata": {"file_path": "tests/test_nifti_saver.py", "file_name": "test_nifti_saver.py", "file_type": "text/x-python", "category": "test", "start_line": 67, "end_line": 84, "span_ids": ["TestNiftiSaver.test_saved_3d_no_resize_content"], "tokens": 272}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestNiftiSaver(unittest.TestCase):\n\n    def test_saved_3d_no_resize_content(self):\n        with tempfile.TemporaryDirectory() as tempdir:\n\n            saver = NiftiSaver(\n                output_dir=tempdir, output_postfix=\"seg\", output_ext=\".nii.gz\", dtype=np.float32, resample=False\n            )\n\n            meta_data = {\n                \"filename_or_obj\": [\"testfile\" + str(i) + \".nii.gz\" for i in range(8)],\n                \"spatial_shape\": [(10, 10, 2)] * 8,\n                \"affine\": [np.diag(np.ones(4)) * 5] * 8,\n                \"original_affine\": [np.diag(np.ones(4)) * 1.0] * 8,\n            }\n            saver.save_batch(torch.randint(0, 255, (8, 8, 1, 2, 2)), meta_data)\n            for i in range(8):\n                filepath = os.path.join(tempdir, \"testfile\" + str(i), \"testfile\" + str(i) + \"_seg.nii.gz\")\n                img, _ = LoadImage(\"nibabelreader\")(filepath)\n                self.assertEqual(img.shape, (1, 2, 2, 8))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_numpy_reader.py_os_TestNumpyReader.test_npy.None_2": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_numpy_reader.py_os_TestNumpyReader.test_npy.None_2", "embedding": null, "metadata": {"file_path": "tests/test_numpy_reader.py", "file_name": "test_numpy_reader.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 35, "span_ids": ["TestNumpyReader", "TestNumpyReader.test_npy", "docstring"], "tokens": 177}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport sys\nimport tempfile\nimport unittest\n\nimport numpy as np\nimport torch\n\nfrom monai.data import DataLoader, Dataset, NumpyReader\nfrom monai.transforms import LoadImaged\n\n\nclass TestNumpyReader(unittest.TestCase):\n    def test_npy(self):\n        test_data = np.random.randint(0, 256, size=[3, 4, 4])\n        with tempfile.TemporaryDirectory() as tempdir:\n            filepath = os.path.join(tempdir, \"test_data.npy\")\n            np.save(filepath, test_data)\n\n            reader = NumpyReader()\n            result = reader.get_data(reader.read(filepath))\n        np.testing.assert_allclose(result[1][\"spatial_shape\"], test_data.shape)\n        np.testing.assert_allclose(result[0].shape, test_data.shape)\n        np.testing.assert_allclose(result[0], test_data)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_numpy_reader.py_TestNumpyReader.test_npz1_TestNumpyReader.test_npz1.None_2": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_numpy_reader.py_TestNumpyReader.test_npz1_TestNumpyReader.test_npz1.None_2", "embedding": null, "metadata": {"file_path": "tests/test_numpy_reader.py", "file_name": "test_numpy_reader.py", "file_type": "text/x-python", "category": "test", "start_line": 37, "end_line": 47, "span_ids": ["TestNumpyReader.test_npz1"], "tokens": 141}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestNumpyReader(unittest.TestCase):\n\n    def test_npz1(self):\n        test_data1 = np.random.randint(0, 256, size=[3, 4, 4])\n        with tempfile.TemporaryDirectory() as tempdir:\n            filepath = os.path.join(tempdir, \"test_data.npy\")\n            np.save(filepath, test_data1)\n\n            reader = NumpyReader()\n            result = reader.get_data(reader.read(filepath))\n        np.testing.assert_allclose(result[1][\"spatial_shape\"], test_data1.shape)\n        np.testing.assert_allclose(result[0].shape, test_data1.shape)\n        np.testing.assert_allclose(result[0], test_data1)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_numpy_reader.py_TestNumpyReader.test_npz2_TestNumpyReader.test_npz2.None_2": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_numpy_reader.py_TestNumpyReader.test_npz2_TestNumpyReader.test_npz2.None_2", "embedding": null, "metadata": {"file_path": "tests/test_numpy_reader.py", "file_name": "test_numpy_reader.py", "file_type": "text/x-python", "category": "test", "start_line": 49, "end_line": 60, "span_ids": ["TestNumpyReader.test_npz2"], "tokens": 185}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestNumpyReader(unittest.TestCase):\n\n    def test_npz2(self):\n        test_data1 = np.random.randint(0, 256, size=[3, 4, 4])\n        test_data2 = np.random.randint(0, 256, size=[3, 4, 4])\n        with tempfile.TemporaryDirectory() as tempdir:\n            filepath = os.path.join(tempdir, \"test_data.npz\")\n            np.savez(filepath, test_data1, test_data2)\n\n            reader = NumpyReader()\n            result = reader.get_data(reader.read(filepath))\n        np.testing.assert_allclose(result[1][\"spatial_shape\"], test_data1.shape)\n        np.testing.assert_allclose(result[0].shape, (2, 3, 4, 4))\n        np.testing.assert_allclose(result[0], np.stack([test_data1, test_data2]))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_numpy_reader.py_TestNumpyReader.test_npz3_TestNumpyReader.test_npz3.None_2": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_numpy_reader.py_TestNumpyReader.test_npz3_TestNumpyReader.test_npz3.None_2", "embedding": null, "metadata": {"file_path": "tests/test_numpy_reader.py", "file_name": "test_numpy_reader.py", "file_type": "text/x-python", "category": "test", "start_line": 62, "end_line": 73, "span_ids": ["TestNumpyReader.test_npz3"], "tokens": 199}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestNumpyReader(unittest.TestCase):\n\n    def test_npz3(self):\n        test_data1 = np.random.randint(0, 256, size=[3, 4, 4])\n        test_data2 = np.random.randint(0, 256, size=[3, 4, 4])\n        with tempfile.TemporaryDirectory() as tempdir:\n            filepath = os.path.join(tempdir, \"test_data.npz\")\n            np.savez(filepath, test1=test_data1, test2=test_data2)\n\n            reader = NumpyReader(npz_keys=[\"test1\", \"test2\"])\n            result = reader.get_data(reader.read(filepath))\n        np.testing.assert_allclose(result[1][\"spatial_shape\"], test_data1.shape)\n        np.testing.assert_allclose(result[0].shape, (2, 3, 4, 4))\n        np.testing.assert_allclose(result[0], np.stack([test_data1, test_data2]))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_numpy_reader.py_TestNumpyReader.test_npy_pickle_TestNumpyReader.test_npy_pickle.None_1": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_numpy_reader.py_TestNumpyReader.test_npy_pickle_TestNumpyReader.test_npy_pickle.None_1", "embedding": null, "metadata": {"file_path": "tests/test_numpy_reader.py", "file_name": "test_numpy_reader.py", "file_type": "text/x-python", "category": "test", "start_line": 75, "end_line": 84, "span_ids": ["TestNumpyReader.test_npy_pickle"], "tokens": 135}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestNumpyReader(unittest.TestCase):\n\n    def test_npy_pickle(self):\n        test_data = {\"test\": np.random.randint(0, 256, size=[3, 4, 4])}\n        with tempfile.TemporaryDirectory() as tempdir:\n            filepath = os.path.join(tempdir, \"test_data.npy\")\n            np.save(filepath, test_data, allow_pickle=True)\n\n            reader = NumpyReader()\n            result = reader.get_data(reader.read(filepath))[0].item()\n        np.testing.assert_allclose(result[\"test\"].shape, test_data[\"test\"].shape)\n        np.testing.assert_allclose(result[\"test\"], test_data[\"test\"])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_numpy_reader.py_TestNumpyReader.test_kwargs_TestNumpyReader.test_kwargs.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_numpy_reader.py_TestNumpyReader.test_kwargs_TestNumpyReader.test_kwargs.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_numpy_reader.py", "file_name": "test_numpy_reader.py", "file_type": "text/x-python", "category": "test", "start_line": 86, "end_line": 94, "span_ids": ["TestNumpyReader.test_kwargs"], "tokens": 127}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestNumpyReader(unittest.TestCase):\n\n    def test_kwargs(self):\n        test_data = {\"test\": np.random.randint(0, 256, size=[3, 4, 4])}\n        with tempfile.TemporaryDirectory() as tempdir:\n            filepath = os.path.join(tempdir, \"test_data.npy\")\n            np.save(filepath, test_data, allow_pickle=True)\n\n            reader = NumpyReader(mmap_mode=\"r\")\n            result = reader.get_data(reader.read(filepath, mmap_mode=None))[0].item()\n        np.testing.assert_allclose(result[\"test\"].shape, test_data[\"test\"].shape)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_numpy_reader.py_TestNumpyReader.test_dataloader_TestNumpyReader.test_dataloader.with_tempfile_TemporaryDi.for_i_in_range_4_.for_d_in_loader_.for_c_in_d_image_.torch_testing_assert_allc": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_numpy_reader.py_TestNumpyReader.test_dataloader_TestNumpyReader.test_dataloader.with_tempfile_TemporaryDi.for_i_in_range_4_.for_d_in_loader_.for_c_in_d_image_.torch_testing_assert_allc", "embedding": null, "metadata": {"file_path": "tests/test_numpy_reader.py", "file_name": "test_numpy_reader.py", "file_type": "text/x-python", "category": "test", "start_line": 96, "end_line": 115, "span_ids": ["TestNumpyReader.test_dataloader"], "tokens": 214}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestNumpyReader(unittest.TestCase):\n\n    def test_dataloader(self):\n        test_data = np.random.randint(0, 256, size=[3, 4, 5])\n        datalist = []\n        with tempfile.TemporaryDirectory() as tempdir:\n            for i in range(4):\n                filepath = os.path.join(tempdir, f\"test_data{i}.npz\")\n                np.savez(filepath, test_data)\n                datalist.append({\"image\": filepath})\n\n                num_workers = 2 if sys.platform == \"linux\" else 0\n                loader = DataLoader(\n                    Dataset(data=datalist, transform=LoadImaged(keys=\"image\", reader=NumpyReader())),\n                    batch_size=2,\n                    num_workers=num_workers,\n                )\n                for d in loader:\n                    for s in d[\"image_meta_dict\"][\"spatial_shape\"]:\n                        torch.testing.assert_allclose(s, torch.as_tensor([3, 4, 5]))\n                    for c in d[\"image\"]:\n                        torch.testing.assert_allclose(c, test_data)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_numpy_reader.py_TestNumpyReader.test_channel_dim_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_numpy_reader.py_TestNumpyReader.test_channel_dim_", "embedding": null, "metadata": {"file_path": "tests/test_numpy_reader.py", "file_name": "test_numpy_reader.py", "file_type": "text/x-python", "category": "test", "start_line": 117, "end_line": 131, "span_ids": ["TestNumpyReader.test_channel_dim", "impl"], "tokens": 141}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestNumpyReader(unittest.TestCase):\n\n    def test_channel_dim(self):\n        test_data = np.random.randint(0, 256, size=[3, 4, 5, 2])\n        with tempfile.TemporaryDirectory() as tempdir:\n            filepath = os.path.join(tempdir, \"test_data.npy\")\n            np.save(filepath, test_data)\n\n            reader = NumpyReader(channel_dim=-1)\n            result = reader.get_data(reader.read(filepath))\n        np.testing.assert_allclose(result[1][\"spatial_shape\"], test_data.shape[:-1])\n        self.assertEqual(result[1][\"original_channel_dim\"], -1)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_plot_2d_or_3d_image.py_TestPlot2dOr3dImage_TestPlot2dOr3dImage.test_tb_image.with_tempfile_TemporaryDi.self_assertTrue_len_glob_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_plot_2d_or_3d_image.py_TestPlot2dOr3dImage_TestPlot2dOr3dImage.test_tb_image.with_tempfile_TemporaryDi.self_assertTrue_len_glob_", "embedding": null, "metadata": {"file_path": "tests/test_plot_2d_or_3d_image.py", "file_name": "test_plot_2d_or_3d_image.py", "file_type": "text/x-python", "category": "test", "start_line": 37, "end_line": 45, "span_ids": ["TestPlot2dOr3dImage", "TestPlot2dOr3dImage.test_tb_image"], "tokens": 124}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestPlot2dOr3dImage(unittest.TestCase):\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5])\n    def test_tb_image(self, shape):\n        with tempfile.TemporaryDirectory() as tempdir:\n            writer = SummaryWriter(log_dir=tempdir)\n            plot_2d_or_3d_image(torch.zeros(shape), 0, writer, max_channels=3, frame_dim=-1)\n            writer.flush()\n            writer.close()\n            self.assertTrue(len(glob.glob(tempdir)) > 0)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_plot_2d_or_3d_image.py_TestPlot2dOr3dImage.test_tbx_image_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_plot_2d_or_3d_image.py_TestPlot2dOr3dImage.test_tbx_image_", "embedding": null, "metadata": {"file_path": "tests/test_plot_2d_or_3d_image.py", "file_name": "test_plot_2d_or_3d_image.py", "file_type": "text/x-python", "category": "test", "start_line": 47, "end_line": 70, "span_ids": ["TestPlot2dOr3dImage.test_tbx_video", "TestPlot2dOr3dImage.test_tbx_image", "impl:12"], "tokens": 244}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestPlot2dOr3dImage(unittest.TestCase):\n\n    @SkipIfNoModule(\"tensorboardX\")\n    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5])\n    def test_tbx_image(self, shape):\n        with tempfile.TemporaryDirectory() as tempdir:\n            writer = SummaryWriterX(log_dir=tempdir)\n            plot_2d_or_3d_image(torch.zeros(shape), 0, writer, max_channels=2)\n            writer.flush()\n            writer.close()\n            self.assertTrue(len(glob.glob(tempdir)) > 0)\n\n    @SkipIfNoModule(\"tensorboardX\")\n    @parameterized.expand([TEST_CASE_5])\n    def test_tbx_video(self, shape):\n        with tempfile.TemporaryDirectory() as tempdir:\n            writer = SummaryWriterX(log_dir=tempdir)\n            plot_2d_or_3d_image(torch.rand(shape), 0, writer, max_channels=3)\n            writer.flush()\n            writer.close()\n            self.assertTrue(len(glob.glob(tempdir)) > 0)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_probnms.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_probnms.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_probnms.py", "file_name": "test_probnms.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 71, "span_ids": ["TestProbNMS.test_output", "impl:46", "TestProbNMS", "docstring"], "tokens": 908}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms.post.array import ProbNMS\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    probs_map_1 = p(np.random.rand(100, 100).clip(0, 0.5))\n    TESTS.append([{\"spatial_dims\": 2, \"prob_threshold\": 0.5, \"box_size\": 10}, probs_map_1, []])\n\n    probs_map_2 = p(np.random.rand(100, 100).clip(0, 0.5))\n    probs_map_2[33, 33] = 0.7\n    probs_map_2[66, 66] = 0.9\n    expected_2 = [[0.9, 66, 66], [0.7, 33, 33]]\n    TESTS.append([{\"spatial_dims\": 2, \"prob_threshold\": 0.5, \"box_size\": [10, 10]}, probs_map_2, expected_2])\n\n    probs_map_3 = p(np.random.rand(100, 100).clip(0, 0.5))\n    probs_map_3[56, 58] = 0.7\n    probs_map_3[60, 66] = 0.8\n    probs_map_3[66, 66] = 0.9\n    expected_3 = [[0.9, 66, 66], [0.8, 60, 66]]\n    TESTS.append([{\"spatial_dims\": 2, \"prob_threshold\": 0.5, \"box_size\": (10, 20)}, probs_map_3, expected_3])\n\n    probs_map_4 = p(np.random.rand(100, 100).clip(0, 0.5))\n    probs_map_4[33, 33] = 0.7\n    probs_map_4[66, 66] = 0.9\n    expected_4 = [[0.9, 66, 66]]\n    TESTS.append([{\"spatial_dims\": 2, \"prob_threshold\": 0.8, \"box_size\": 10}, probs_map_4, expected_4])\n\n    probs_map_5 = p(np.random.rand(100, 100).clip(0, 0.5))\n    TESTS.append([{\"spatial_dims\": 2, \"prob_threshold\": 0.5, \"sigma\": 0.1}, probs_map_5, []])\n\n    probs_map_6 = p(np.random.rand(100, 100).clip(0, 0.5))\n    probs_map_6[33, 33] = 0.7\n    probs_map_6[66, 66] = 0.9\n    expected_6 = [[0.9, 66, 66], [0.7, 33, 33]]\n    TESTS.append([{\"spatial_dims\": 2, \"prob_threshold\": 0.5, \"sigma\": 0.1}, probs_map_6, expected_6])\n\n    probs_map_3d = p(torch.rand([50, 50, 50]).uniform_(0, 0.5))\n    probs_map_3d[25, 25, 25] = 0.7\n    probs_map_3d[45, 45, 45] = 0.9\n    expected_3d = [[0.9, 45, 45, 45], [0.7, 25, 25, 25]]\n    TESTS.append([{\"spatial_dims\": 3, \"prob_threshold\": 0.5, \"box_size\": (10, 10, 10)}, probs_map_3d, expected_3d])\n\n\nclass TestProbNMS(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_output(self, class_args, probs_map, expected):\n        nms = ProbNMS(**class_args)\n        output = nms(probs_map)\n        assert_allclose(output, expected)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_probnmsd.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_probnmsd.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_probnmsd.py", "file_name": "test_probnmsd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 78, "span_ids": ["TestProbNMS.test_output", "impl:46", "TestProbNMS", "docstring"], "tokens": 964}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom typing import Any, List\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms.post.dictionary import ProbNMSD\nfrom tests.utils import TEST_NDARRAYS\n\nTESTS: List[Any] = []\nfor p in TEST_NDARRAYS:\n    probs_map_1 = p(np.random.rand(100, 100).clip(0, 0.5))\n    TESTS.append([{\"spatial_dims\": 2, \"prob_threshold\": 0.5, \"box_size\": 10}, {\"prob_map\": probs_map_1}, []])\n\n    probs_map_2 = p(np.random.rand(100, 100).clip(0, 0.5))\n    probs_map_2[33, 33] = 0.7\n    probs_map_2[66, 66] = 0.9\n    expected_2 = [[0.9, 66, 66], [0.7, 33, 33]]\n    TESTS.append(\n        [{\"spatial_dims\": 2, \"prob_threshold\": 0.5, \"box_size\": [10, 10]}, {\"prob_map\": probs_map_2}, expected_2]\n    )\n\n    probs_map_3 = p(np.random.rand(100, 100).clip(0, 0.5))\n    probs_map_3[56, 58] = 0.7\n    probs_map_3[60, 66] = 0.8\n    probs_map_3[66, 66] = 0.9\n    expected_3 = [[0.9, 66, 66], [0.8, 60, 66]]\n    TESTS.append(\n        [{\"spatial_dims\": 2, \"prob_threshold\": 0.5, \"box_size\": (10, 20)}, {\"prob_map\": probs_map_3}, expected_3]\n    )\n\n    probs_map_4 = p(np.random.rand(100, 100).clip(0, 0.5))\n    probs_map_4[33, 33] = 0.7\n    probs_map_4[66, 66] = 0.9\n    expected_4 = [[0.9, 66, 66]]\n    TESTS.append([{\"spatial_dims\": 2, \"prob_threshold\": 0.8, \"box_size\": 10}, {\"prob_map\": probs_map_4}, expected_4])\n\n    probs_map_5 = p(np.random.rand(100, 100).clip(0, 0.5))\n    TESTS.append([{\"spatial_dims\": 2, \"prob_threshold\": 0.5, \"sigma\": 0.1}, {\"prob_map\": probs_map_5}, []])\n\n    probs_map_6 = p(np.random.rand(100, 100).clip(0, 0.5))\n    probs_map_6[33, 33] = 0.7\n    probs_map_6[66, 66] = 0.9\n    expected_6 = [[0.9, 66, 66], [0.7, 33, 33]]\n    TESTS.append([{\"spatial_dims\": 2, \"prob_threshold\": 0.5, \"sigma\": 0.1}, {\"prob_map\": probs_map_6}, expected_6])\n\n    probs_map_3d = p(torch.rand([50, 50, 50]).uniform_(0, 0.5))\n    probs_map_3d[25, 25, 25] = 0.7\n    probs_map_3d[45, 45, 45] = 0.9\n    expected_3d = [[0.9, 45, 45, 45], [0.7, 25, 25, 25]]\n    TESTS.append(\n        [{\"spatial_dims\": 3, \"prob_threshold\": 0.5, \"box_size\": (10, 10, 10)}, {\"prob_map\": probs_map_3d}, expected_3d]\n    )\n\n\nclass TestProbNMS(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_output(self, class_args, probs_map, expected):\n        nms = ProbNMSD(keys=\"prob_map\", **class_args)\n        output = nms(probs_map)\n        np.testing.assert_allclose(output[\"prob_map\"], expected)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_require_pkg.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_require_pkg.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_require_pkg.py", "file_name": "test_require_pkg.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 78, "span_ids": ["TestRequirePkg.test_warning", "impl", "TestRequirePkg.test_func_versions_exception", "TestRequirePkg.test_class_exception", "TestRequirePkg.test_func_exception", "TestRequirePkg.test_class", "docstring", "TestRequirePkg.test_class_version_exception", "TestRequirePkg", "TestRequirePkg.test_function"], "tokens": 330}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nfrom monai.utils import OptionalImportError, min_version, require_pkg\n\n\nclass TestRequirePkg(unittest.TestCase):\n    def test_class(self):\n        @require_pkg(pkg_name=\"torch\", version=\"1.4\", version_checker=min_version)\n        class TestClass:\n            pass\n\n        TestClass()\n\n    def test_function(self):\n        @require_pkg(pkg_name=\"torch\", version=\"1.4\", version_checker=min_version)\n        def test_func(x):\n            return x\n\n        test_func(x=None)\n\n    def test_warning(self):\n        @require_pkg(pkg_name=\"test123\", raise_error=False)\n        def test_func(x):\n            return x\n\n        test_func(x=None)\n\n    def test_class_exception(self):\n        with self.assertRaises(OptionalImportError):\n\n            @require_pkg(pkg_name=\"test123\")\n            class TestClass:\n                pass\n\n            TestClass()\n\n    def test_class_version_exception(self):\n        with self.assertRaises(OptionalImportError):\n\n            @require_pkg(pkg_name=\"torch\", version=\"10000\", version_checker=min_version)\n            class TestClass:\n                pass\n\n            TestClass()\n\n    def test_func_exception(self):\n        with self.assertRaises(OptionalImportError):\n\n            @require_pkg(pkg_name=\"test123\")\n            def test_func(x):\n                return x\n\n            test_func(x=None)\n\n    def test_func_versions_exception(self):\n        with self.assertRaises(OptionalImportError):\n\n            @require_pkg(pkg_name=\"torch\", version=\"10000\", version_checker=min_version)\n            def test_func(x):\n                return x\n\n            test_func(x=None)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_scale_intensity.py_unittest_TestScaleIntensity.test_range_scale.for_p_in_TEST_NDARRAYS_.assert_allclose_result_e": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_scale_intensity.py_unittest_TestScaleIntensity.test_range_scale.for_p_in_TEST_NDARRAYS_.assert_allclose_result_e", "embedding": null, "metadata": {"file_path": "tests/test_scale_intensity.py", "file_name": "test_scale_intensity.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 30, "span_ids": ["TestScaleIntensity.test_range_scale", "TestScaleIntensity", "docstring"], "tokens": 187}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\n\nfrom monai.transforms import ScaleIntensity\nfrom monai.transforms.utils import rescale_array\nfrom tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, assert_allclose\n\n\nclass TestScaleIntensity(NumpyImageTestCase2D):\n    def test_range_scale(self):\n        for p in TEST_NDARRAYS:\n            scaler = ScaleIntensity(minv=1.0, maxv=2.0)\n            result = scaler(p(self.imt))\n            mina = self.imt.min()\n            maxa = self.imt.max()\n            norm = (self.imt - mina) / (maxa - mina)\n            expected = p((norm * (2.0 - 1.0)) + 1.0)\n            assert_allclose(result, expected, type_test=False, rtol=1e-7, atol=0)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_scale_intensity.py_TestScaleIntensity.test_factor_scale_TestScaleIntensity.test_max_none.for_p_in_TEST_NDARRAYS_.assert_allclose_result_e": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_scale_intensity.py_TestScaleIntensity.test_factor_scale_TestScaleIntensity.test_max_none.for_p_in_TEST_NDARRAYS_.assert_allclose_result_e", "embedding": null, "metadata": {"file_path": "tests/test_scale_intensity.py", "file_name": "test_scale_intensity.py", "file_type": "text/x-python", "category": "test", "start_line": 32, "end_line": 44, "span_ids": ["TestScaleIntensity.test_factor_scale", "TestScaleIntensity.test_max_none"], "tokens": 192}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestScaleIntensity(NumpyImageTestCase2D):\n\n    def test_factor_scale(self):\n        for p in TEST_NDARRAYS:\n            scaler = ScaleIntensity(minv=None, maxv=None, factor=0.1)\n            result = scaler(p(self.imt))\n            expected = p((self.imt * (1 + 0.1)).astype(np.float32))\n            assert_allclose(result, p(expected), rtol=1e-7, atol=0)\n\n    def test_max_none(self):\n        for p in TEST_NDARRAYS:\n            scaler = ScaleIntensity(minv=0.0, maxv=None, factor=0.1)\n            result = scaler(p(self.imt))\n            expected = rescale_array(p(self.imt), minv=0.0, maxv=None)\n            assert_allclose(result, expected, rtol=1e-3, atol=1e-3)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_scale_intensity.py_TestScaleIntensity.test_int_TestScaleIntensity.test_int.for_p_in_TEST_NDARRAYS_.assert_allclose_result_e": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_scale_intensity.py_TestScaleIntensity.test_int_TestScaleIntensity.test_int.for_p_in_TEST_NDARRAYS_.assert_allclose_result_e", "embedding": null, "metadata": {"file_path": "tests/test_scale_intensity.py", "file_name": "test_scale_intensity.py", "file_type": "text/x-python", "category": "test", "start_line": 46, "end_line": 56, "span_ids": ["TestScaleIntensity.test_int"], "tokens": 170}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestScaleIntensity(NumpyImageTestCase2D):\n\n    def test_int(self):\n        \"\"\"integers should be handled by converting them to floats first.\"\"\"\n        for p in TEST_NDARRAYS:\n            scaler = ScaleIntensity(minv=1.0, maxv=2.0)\n            result = scaler(p(self.imt.astype(int)))\n            _imt = self.imt.astype(int).astype(np.float32)\n            mina = _imt.min()\n            maxa = _imt.max()\n            norm = (_imt - mina) / (maxa - mina)\n            expected = p((norm * (2.0 - 1.0)) + 1.0)\n            assert_allclose(result, expected, type_test=False, rtol=1e-7, atol=0)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_scale_intensity_range.py_unittest_IntensityScaleIntensityRange.test_image_scale_intensity_range.for_p_in_TEST_NDARRAYS_.assert_allclose_scaled_p": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_scale_intensity_range.py_unittest_IntensityScaleIntensityRange.test_image_scale_intensity_range.for_p_in_TEST_NDARRAYS_.assert_allclose_scaled_p", "embedding": null, "metadata": {"file_path": "tests/test_scale_intensity_range.py", "file_name": "test_scale_intensity_range.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 27, "span_ids": ["IntensityScaleIntensityRange", "IntensityScaleIntensityRange.test_image_scale_intensity_range", "docstring"], "tokens": 158}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\n\nfrom monai.transforms import ScaleIntensityRange\nfrom tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, assert_allclose\n\n\nclass IntensityScaleIntensityRange(NumpyImageTestCase2D):\n    def test_image_scale_intensity_range(self):\n        scaler = ScaleIntensityRange(a_min=20, a_max=108, b_min=50, b_max=80, dtype=np.uint8)\n        for p in TEST_NDARRAYS:\n            scaled = scaler(p(self.imt))\n            self.assertTrue(scaled.dtype, np.uint8)\n            expected = (((self.imt - 20) / 88) * 30 + 50).astype(np.uint8)\n            assert_allclose(scaled, p(expected))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_scale_intensity_range.py_IntensityScaleIntensityRange.test_image_scale_intensity_range_none_clip_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_scale_intensity_range.py_IntensityScaleIntensityRange.test_image_scale_intensity_range_none_clip_", "embedding": null, "metadata": {"file_path": "tests/test_scale_intensity_range.py", "file_name": "test_scale_intensity_range.py", "file_type": "text/x-python", "category": "test", "start_line": 29, "end_line": 40, "span_ids": ["IntensityScaleIntensityRange.test_image_scale_intensity_range_none_clip", "impl"], "tokens": 137}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class IntensityScaleIntensityRange(NumpyImageTestCase2D):\n\n    def test_image_scale_intensity_range_none_clip(self):\n        scaler = ScaleIntensityRange(a_min=20, a_max=108, b_min=None, b_max=80, clip=True, dtype=np.uint8)\n        for p in TEST_NDARRAYS:\n            scaled = scaler(p(self.imt))\n            self.assertTrue(scaled.dtype, np.uint8)\n            expected = (np.clip((self.imt - 20) / 88, None, 80)).astype(np.uint8)\n            assert_allclose(scaled, p(expected))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_scale_intensity_range_percentiles.py_TestScaleIntensityRangePercentiles.test_relative_scaling_TestScaleIntensityRangePercentiles.test_relative_scaling.None_1.assert_allclose_result_p": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_scale_intensity_range_percentiles.py_TestScaleIntensityRangePercentiles.test_relative_scaling_TestScaleIntensityRangePercentiles.test_relative_scaling.None_1.assert_allclose_result_p", "embedding": null, "metadata": {"file_path": "tests/test_scale_intensity_range_percentiles.py", "file_name": "test_scale_intensity_range_percentiles.py", "file_type": "text/x-python", "category": "test", "start_line": 36, "end_line": 60, "span_ids": ["TestScaleIntensityRangePercentiles.test_relative_scaling"], "tokens": 318}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestScaleIntensityRangePercentiles(NumpyImageTestCase2D):\n\n    def test_relative_scaling(self):\n        img = self.imt[0]\n        lower = 10\n        upper = 99\n        b_min = 100\n        b_max = 300\n        scaler = ScaleIntensityRangePercentiles(lower=lower, upper=upper, b_min=b_min, b_max=b_max, relative=True)\n\n        expected_a_min = np.percentile(img, lower)\n        expected_a_max = np.percentile(img, upper)\n        expected_b_min = ((b_max - b_min) * (lower / 100.0)) + b_min\n        expected_b_max = ((b_max - b_min) * (upper / 100.0)) + b_min\n        expected_img = (img - expected_a_min) / (expected_a_max - expected_a_min)\n        expected_img = (expected_img * (expected_b_max - expected_b_min)) + expected_b_min\n\n        for p in TEST_NDARRAYS:\n            result = scaler(p(img))\n            assert_allclose(result, p(expected_img), rtol=1e-3)\n\n        scaler = ScaleIntensityRangePercentiles(\n            lower=lower, upper=upper, b_min=b_min, b_max=b_max, relative=True, clip=True\n        )\n        for p in TEST_NDARRAYS:\n            result = scaler(p(img))\n            assert_allclose(result, p(np.clip(expected_img, expected_b_min, expected_b_max)), rtol=1e-4)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_scale_intensity_range_percentilesd.py_unittest_TestScaleIntensityRangePercentilesd.test_scaling.for_p_in_TEST_NDARRAYS_.assert_allclose_p_expecte": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_scale_intensity_range_percentilesd.py_unittest_TestScaleIntensityRangePercentilesd.test_scaling.for_p_in_TEST_NDARRAYS_.assert_allclose_p_expecte", "embedding": null, "metadata": {"file_path": "tests/test_scale_intensity_range_percentilesd.py", "file_name": "test_scale_intensity_range_percentilesd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 37, "span_ids": ["TestScaleIntensityRangePercentilesd", "TestScaleIntensityRangePercentilesd.test_scaling", "docstring"], "tokens": 233}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\n\nfrom monai.transforms.intensity.dictionary import ScaleIntensityRangePercentilesd\nfrom tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, assert_allclose\n\n\nclass TestScaleIntensityRangePercentilesd(NumpyImageTestCase2D):\n    def test_scaling(self):\n        img = self.imt\n        lower = 10\n        upper = 99\n        b_min = 0\n        b_max = 255\n\n        a_min = np.percentile(img, lower)\n        a_max = np.percentile(img, upper)\n        expected = (((img - a_min) / (a_max - a_min)) * (b_max - b_min) + b_min).astype(np.uint8)\n\n        for p in TEST_NDARRAYS:\n            data = {\"img\": p(img)}\n            scaler = ScaleIntensityRangePercentilesd(\n                keys=data.keys(), lower=lower, upper=upper, b_min=b_min, b_max=b_max, dtype=np.uint8\n            )\n            assert_allclose(p(expected), scaler(data)[\"img\"], rtol=1e-4)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_scale_intensity_range_percentilesd.py_TestScaleIntensityRangePercentilesd.test_relative_scaling_TestScaleIntensityRangePercentilesd.test_relative_scaling.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_scale_intensity_range_percentilesd.py_TestScaleIntensityRangePercentilesd.test_relative_scaling_TestScaleIntensityRangePercentilesd.test_relative_scaling.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_scale_intensity_range_percentilesd.py", "file_name": "test_scale_intensity_range_percentilesd.py", "file_type": "text/x-python", "category": "test", "start_line": 40, "end_line": 59, "span_ids": ["TestScaleIntensityRangePercentilesd.test_relative_scaling"], "tokens": 236}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestScaleIntensityRangePercentilesd(NumpyImageTestCase2D):\n\n    def test_relative_scaling(self):\n        img = self.imt\n        data = {}\n        data[\"img\"] = img\n        lower = 10\n        upper = 99\n        b_min = 100\n        b_max = 300\n        scaler = ScaleIntensityRangePercentilesd(\n            keys=data.keys(), lower=lower, upper=upper, b_min=b_min, b_max=b_max, relative=True\n        )\n\n        expected_a_min = np.percentile(img, lower)\n        expected_a_max = np.percentile(img, upper)\n        expected_b_min = ((b_max - b_min) * (lower / 100.0)) + b_min\n        expected_b_max = ((b_max - b_min) * (upper / 100.0)) + b_min\n        expected_img = (img - expected_a_min) / (expected_a_max - expected_a_min)\n        expected_img = (expected_img * (expected_b_max - expected_b_min)) + expected_b_min\n\n        np.testing.assert_allclose(expected_img, scaler(data)[\"img\"])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_separable_filter.py_unittest_SeparableFilterTestCase.test_1d.if_torch_cuda_is_availabl.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_separable_filter.py_unittest_SeparableFilterTestCase.test_1d.if_torch_cuda_is_availabl.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_separable_filter.py", "file_name": "test_separable_filter.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 28, "span_ids": ["SeparableFilterTestCase", "SeparableFilterTestCase.test_1d", "docstring"], "tokens": 204}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\n\nfrom monai.networks.layers import separable_filtering\n\n\nclass SeparableFilterTestCase(unittest.TestCase):\n    def test_1d(self):\n        a = torch.tensor([[list(range(10))]], dtype=torch.float)\n        out = separable_filtering(a, torch.tensor([-1, 0, 1]))\n        expected = np.array([[[1.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, -8.0]]])\n        np.testing.assert_allclose(out.cpu().numpy(), expected, rtol=1e-4)\n        if torch.cuda.is_available():\n            out = separable_filtering(a.cuda(), torch.tensor([-1, 0, 1]).cuda())\n            np.testing.assert_allclose(out.cpu().numpy(), expected, rtol=1e-4)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_separable_filter.py_SeparableFilterTestCase.test_2d_SeparableFilterTestCase.test_2d.if_torch_cuda_is_availabl.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_separable_filter.py_SeparableFilterTestCase.test_2d_SeparableFilterTestCase.test_2d.if_torch_cuda_is_availabl.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_separable_filter.py", "file_name": "test_separable_filter.py", "file_type": "text/x-python", "category": "test", "start_line": 30, "end_line": 44, "span_ids": ["SeparableFilterTestCase.test_2d"], "tokens": 277}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SeparableFilterTestCase(unittest.TestCase):\n\n    def test_2d(self):\n        a = torch.tensor([[[list(range(7)), list(range(7, 0, -1)), list(range(7))]]], dtype=torch.float)\n        expected = np.array(\n            [\n                [28.0, 28.0, 28.0, 28.0, 28.0, 28.0],\n                [30.0, 34.0, 38.0, 42.0, 46.0, 50.0],\n                [28.0, 28.0, 28.0, 28.0, 28.0, 28.0],\n            ]\n        )\n        expected = expected[None][None]\n        out = separable_filtering(a, [torch.tensor([1, 1, 1]), torch.tensor([2, 2])])\n        np.testing.assert_allclose(out.cpu().numpy(), expected, rtol=1e-4)\n        if torch.cuda.is_available():\n            out = separable_filtering(a.cuda(), [torch.tensor([1, 1, 1]).cuda(), torch.tensor([2, 2]).cuda()])\n            np.testing.assert_allclose(out.cpu().numpy(), expected, rtol=1e-4)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_separable_filter.py_SeparableFilterTestCase.test_3d_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_separable_filter.py_SeparableFilterTestCase.test_3d_", "embedding": null, "metadata": {"file_path": "tests/test_separable_filter.py", "file_name": "test_separable_filter.py", "file_type": "text/x-python", "category": "test", "start_line": 46, "end_line": 86, "span_ids": ["SeparableFilterTestCase.test_3d", "SeparableFilterTestCase.test_wrong_args", "impl"], "tokens": 532}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SeparableFilterTestCase(unittest.TestCase):\n\n    def test_3d(self):\n        a = torch.tensor(\n            [[list(range(7)), list(range(7)), list(range(7))], [list(range(7)), list(range(7)), list(range(7))]],\n            dtype=torch.float,\n        )\n        a = a[None][None]\n        a = a.expand(2, 3, -1, -1, -1)\n        expected = np.array(\n            [\n                [\n                    [4.0, 12.0, 24.0, 36.0, 48.0, 60.0, 44.0],\n                    [6.0, 18.0, 36.0, 54.0, 72.0, 90.0, 66.0],\n                    [4.0, 12.0, 24.0, 36.0, 48.0, 60.0, 44.0],\n                ],\n                [\n                    [4.0, 12.0, 24.0, 36.0, 48.0, 60.0, 44.0],\n                    [6.0, 18.0, 36.0, 54.0, 72.0, 90.0, 66.0],\n                    [4.0, 12.0, 24.0, 36.0, 48.0, 60.0, 44.0],\n                ],\n            ]\n        )\n        expected = expected\n        # testing shapes\n        k = torch.tensor([1, 1, 1])\n        for kernel in (k, [k] * 3):\n            out = separable_filtering(a, kernel)\n            np.testing.assert_allclose(out.cpu().numpy()[1][2], expected, rtol=1e-4)\n            if torch.cuda.is_available():\n                out = separable_filtering(\n                    a.cuda(), kernel.cuda() if isinstance(kernel, torch.Tensor) else [k.cuda() for k in kernel]\n                )\n                np.testing.assert_allclose(out.cpu().numpy()[0][1], expected, rtol=1e-4)\n\n    def test_wrong_args(self):\n        with self.assertRaisesRegex(TypeError, \"\"):\n            separable_filtering(((1, 1, 1, 2, 3, 2)), torch.ones((2,)))  # type: ignore\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_testtimeaugmentation.py_TestTestTimeAugmentation.test_fail_non_random_TestTestTimeAugmentation.test_warn_random_but_has_no_invertible.with_self_assertWarns_Use.tta_self_get_data_1_20_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_testtimeaugmentation.py_TestTestTimeAugmentation.test_fail_non_random_TestTestTimeAugmentation.test_warn_random_but_has_no_invertible.with_self_assertWarns_Use.tta_self_get_data_1_20_", "embedding": null, "metadata": {"file_path": "tests/test_testtimeaugmentation.py", "file_name": "test_testtimeaugmentation.py", "file_type": "text/x-python", "category": "test", "start_line": 139, "end_line": 150, "span_ids": ["TestTestTimeAugmentation.test_warn_random_but_has_no_invertible", "TestTestTimeAugmentation.test_fail_non_random"], "tokens": 172}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestTestTimeAugmentation(unittest.TestCase):\n\n    def test_fail_non_random(self):\n        transforms = Compose([AddChanneld(\"im\"), SpatialPadd(\"im\", 1)])\n        with self.assertRaises(RuntimeError):\n            TestTimeAugmentation(transforms, None, None, None)\n\n    def test_warn_random_but_has_no_invertible(self):\n        transforms = Compose(\n            [AddChanneld(\"image\"), RandFlipd(\"image\", prob=1.0), RandScaleIntensityd(\"image\", 0.1, prob=1.0)]\n        )\n        with self.assertWarns(UserWarning):\n            tta = TestTimeAugmentation(transforms, 5, 0, orig_key=\"image\")\n            tta(self.get_data(1, (20, 20), data_type=np.float32))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_testtimeaugmentation.py_TestTestTimeAugmentation.test_warn_random_but_all_not_invertible_TestTestTimeAugmentation.test_image_no_label.tta_self_get_data_1_20_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_testtimeaugmentation.py_TestTestTimeAugmentation.test_warn_random_but_all_not_invertible_TestTestTimeAugmentation.test_image_no_label.tta_self_get_data_1_20_", "embedding": null, "metadata": {"file_path": "tests/test_testtimeaugmentation.py", "file_name": "test_testtimeaugmentation.py", "file_type": "text/x-python", "category": "test", "start_line": 152, "end_line": 168, "span_ids": ["TestTestTimeAugmentation.test_single_transform", "TestTestTimeAugmentation.test_warn_random_but_all_not_invertible", "TestTestTimeAugmentation.test_image_no_label"], "tokens": 273}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestTestTimeAugmentation(unittest.TestCase):\n\n    def test_warn_random_but_all_not_invertible(self):\n        \"\"\"test with no invertible stack\"\"\"\n        transforms = Compose([AddChanneld(\"image\"), RandScaleIntensityd(\"image\", 0.1, prob=1.0)])\n        with self.assertWarns(UserWarning):\n            tta = TestTimeAugmentation(transforms, 1, 0, orig_key=\"image\")\n            tta(self.get_data(1, (20, 20), data_type=np.float32))\n\n    def test_single_transform(self):\n        for p in TEST_NDARRAYS:\n            transforms = RandFlipd([\"image\", \"label\"], prob=1.0)\n            tta = TestTimeAugmentation(transforms, batch_size=5, num_workers=0, inferrer_fn=lambda x: x)\n            tta(self.get_data(1, (20, 20), data_type=p))\n\n    def test_image_no_label(self):\n        transforms = RandFlipd([\"image\"], prob=1.0)\n        tta = TestTimeAugmentation(transforms, batch_size=5, num_workers=0, inferrer_fn=lambda x: x, orig_key=\"image\")\n        tta(self.get_data(1, (20, 20), include_label=False))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_traceable_transform.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_traceable_transform.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_traceable_transform.py", "file_name": "test_traceable_transform.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 54, "span_ids": ["_TraceTest", "TestTraceable", "impl", "TestTraceable.test_default", "_TraceTest.__call__", "docstring", "_TraceTest.pop"], "tokens": 262}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nfrom monai.transforms.inverse import TraceableTransform\n\n\nclass _TraceTest(TraceableTransform):\n    def __call__(self, data):\n        self.push_transform(data)\n        return data\n\n    def pop(self, data):\n        self.pop_transform(data)\n        return data\n\n\nclass TestTraceable(unittest.TestCase):\n    def test_default(self):\n        expected_key = \"_transforms\"\n        a = _TraceTest()\n        self.assertEqual(a.trace_key(), expected_key)\n\n        data = {\"image\": \"test\"}\n        data = a(data)  # adds to the stack\n        self.assertTrue(isinstance(data[expected_key], list))\n        self.assertEqual(data[expected_key][0][\"class\"], \"_TraceTest\")\n\n        data = a(data)  # adds to the stack\n        self.assertEqual(len(data[expected_key]), 2)\n        self.assertEqual(data[expected_key][-1][\"class\"], \"_TraceTest\")\n\n        with self.assertRaises(IndexError):\n            a.pop({\"test\": \"test\"})  # no stack in the data\n        data = a.pop(data)\n        data = a.pop(data)\n        self.assertEqual(data[expected_key], [])\n\n        with self.assertRaises(IndexError):  # no more items\n            a.pop(data)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_vitautoenc.py_TestPatchEmbeddingBlock_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_vitautoenc.py_TestPatchEmbeddingBlock_", "embedding": null, "metadata": {"file_path": "tests/test_vitautoenc.py", "file_name": "test_vitautoenc.py", "file_type": "text/x-python", "category": "test", "start_line": 44, "end_line": 121, "span_ids": ["impl:11", "TestPatchEmbeddingBlock", "TestPatchEmbeddingBlock.test_shape", "TestPatchEmbeddingBlock.test_ill_arg"], "tokens": 517}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestPatchEmbeddingBlock(unittest.TestCase):\n    @parameterized.expand(TEST_CASE_Vitautoenc)\n    def test_shape(self, input_param, input_shape, expected_shape):\n        net = ViTAutoEnc(**input_param)\n        with eval_mode(net):\n            result, _ = net(torch.randn(input_shape))\n            self.assertEqual(result.shape, expected_shape)\n\n    def test_ill_arg(self):\n        with self.assertRaises(ValueError):\n            ViTAutoEnc(\n                in_channels=1,\n                img_size=(128, 128, 128),\n                patch_size=(16, 16, 16),\n                hidden_size=128,\n                mlp_dim=3072,\n                num_layers=12,\n                num_heads=12,\n                pos_embed=\"conv\",\n                dropout_rate=5.0,\n            )\n\n        with self.assertRaises(ValueError):\n            ViTAutoEnc(\n                in_channels=1,\n                img_size=(32, 32, 32),\n                patch_size=(64, 64, 64),\n                hidden_size=512,\n                mlp_dim=3072,\n                num_layers=12,\n                num_heads=8,\n                pos_embed=\"perceptron\",\n                dropout_rate=0.3,\n            )\n\n        with self.assertRaises(ValueError):\n            ViTAutoEnc(\n                in_channels=1,\n                img_size=(96, 96, 96),\n                patch_size=(8, 8, 8),\n                hidden_size=512,\n                mlp_dim=3072,\n                num_layers=12,\n                num_heads=14,\n                pos_embed=\"conv\",\n                dropout_rate=0.3,\n            )\n\n        with self.assertRaises(ValueError):\n            ViTAutoEnc(\n                in_channels=1,\n                img_size=(97, 97, 97),\n                patch_size=(4, 4, 4),\n                hidden_size=768,\n                mlp_dim=3072,\n                num_layers=12,\n                num_heads=8,\n                pos_embed=\"perceptron\",\n                dropout_rate=0.3,\n            )\n\n        with self.assertRaises(ValueError):\n            ViTAutoEnc(\n                in_channels=4,\n                img_size=(96, 96, 96),\n                patch_size=(16, 16, 16),\n                hidden_size=768,\n                mlp_dim=3072,\n                num_layers=12,\n                num_heads=12,\n                pos_embed=\"perc\",\n                dropout_rate=0.3,\n            )\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_wsireader.py_WSIReaderTests.Tests.test_with_dataloader_WSIReaderTests.Tests.test_with_dataloader.self_assertTupleEqual_dat": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_wsireader.py_WSIReaderTests.Tests.test_with_dataloader_WSIReaderTests.Tests.test_with_dataloader.self_assertTupleEqual_dat", "embedding": null, "metadata": {"file_path": "tests/test_wsireader.py", "file_name": "test_wsireader.py", "file_type": "text/x-python", "category": "test", "start_line": 168, "end_line": 181, "span_ids": ["WSIReaderTests.Tests.test_with_dataloader"], "tokens": 161}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class WSIReaderTests:\n    class Tests(unittest.TestCase):\n\n        @parameterized.expand([TEST_CASE_TRANSFORM_0])\n        def test_with_dataloader(self, file_path, level, expected_spatial_shape, expected_shape):\n            train_transform = Compose(\n                [\n                    LoadImaged(keys=[\"image\"], reader=WSIReader, backend=self.backend, level=level),\n                    ToTensord(keys=[\"image\"]),\n                ]\n            )\n            dataset = Dataset([{\"image\": file_path}], transform=train_transform)\n            data_loader = DataLoader(dataset)\n            data: dict = first(data_loader)\n            for s in data[\"image_meta_dict\"][\"spatial_shape\"]:\n                torch.testing.assert_allclose(s, expected_spatial_shape)\n            self.assertTupleEqual(data[\"image\"].shape, expected_shape)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_wsireader.py_TestCuCIM_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_wsireader.py_TestCuCIM_", "embedding": null, "metadata": {"file_path": "tests/test_wsireader.py", "file_name": "test_wsireader.py", "file_type": "text/x-python", "category": "test", "start_line": 184, "end_line": 207, "span_ids": ["impl:36", "TestTiffFile.setUpClass", "TestCuCIM", "TestCuCIM.setUpClass", "TestTiffFile", "TestOpenSlide.setUpClass", "TestOpenSlide"], "tokens": 141}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skipUnless(has_cucim, \"Requires cucim\")\nclass TestCuCIM(WSIReaderTests.Tests):\n    @classmethod\n    def setUpClass(cls):\n        cls.backend = \"cucim\"\n\n\n@skipUnless(has_osl, \"Requires OpenSlide\")\nclass TestOpenSlide(WSIReaderTests.Tests):\n    @classmethod\n    def setUpClass(cls):\n        cls.backend = \"openslide\"\n\n\n@skipUnless(has_tiff, \"Requires TiffFile\")\nclass TestTiffFile(WSIReaderTests.Tests):\n    @classmethod\n    def setUpClass(cls):\n        cls.backend = \"tifffile\"\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/datasets.py_sys___all__._MedNISTDataset_Decat": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/datasets.py_sys___all__._MedNISTDataset_Decat", "embedding": null, "metadata": {"file_path": "monai/apps/datasets.py", "file_name": "datasets.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 30, "span_ids": ["docstring"], "tokens": 123}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import sys\nfrom pathlib import Path\nfrom typing import Callable, Dict, List, Optional, Sequence, Union\n\nimport numpy as np\n\nfrom monai.apps.utils import download_and_extract\nfrom monai.config.type_definitions import PathLike\nfrom monai.data import (\n    CacheDataset,\n    load_decathlon_datalist,\n    load_decathlon_properties,\n    partition_dataset,\n    select_cross_validation_folds,\n)\nfrom monai.transforms import LoadImaged, Randomizable\nfrom monai.utils import ensure_tuple\n\n__all__ = [\"MedNISTDataset\", \"DecathlonDataset\", \"CrossValidation\"]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/metrics/lesion_froc.py_LesionFROC.compute_fp_tp_LesionFROC.compute_fp_tp.return.np_array_total_fp_probs_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/metrics/lesion_froc.py_LesionFROC.compute_fp_tp_LesionFROC.compute_fp_tp.return.np_array_total_fp_probs_", "embedding": null, "metadata": {"file_path": "monai/apps/pathology/metrics/lesion_froc.py", "file_name": "lesion_froc.py", "file_type": "text/x-python", "category": "implementation", "start_line": 124, "end_line": 150, "span_ids": ["LesionFROC.compute_fp_tp"], "tokens": 231}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class LesionFROC:\n\n    def compute_fp_tp(self):\n        \"\"\"\n        Compute false positive and true positive probabilities for tumor detection,\n        by comparing the model outputs with the prepared ground truths for all samples\n\n        \"\"\"\n        total_fp_probs, total_tp_probs = [], []\n        total_num_targets = 0\n        num_images = len(self.data)\n\n        for sample in tqdm(self.data):\n            probs, y_coord, x_coord = self.prepare_inference_result(sample)\n            ground_truth, itc_labels = self.prepare_ground_truth(sample)\n            # compute FP and TP probabilities for a pair of an image and an ground truth mask\n            fp_probs, tp_probs, num_targets = compute_fp_tp_probs(\n                probs=probs,\n                y_coord=y_coord,\n                x_coord=x_coord,\n                evaluation_mask=ground_truth,\n                labels_to_exclude=itc_labels,\n                resolution_level=sample[\"level\"],\n            )\n            total_fp_probs.extend(fp_probs)\n            total_tp_probs.extend(tp_probs)\n            total_num_targets += num_targets\n\n        return np.array(total_fp_probs), np.array(total_tp_probs), total_num_targets, num_images", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/transforms/spatial/array.py_SplitOnGrid.__call___SplitOnGrid.get_params.return.patch_size_steps": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/transforms/spatial/array.py_SplitOnGrid.__call___SplitOnGrid.get_params.return.patch_size_steps", "embedding": null, "metadata": {"file_path": "monai/apps/pathology/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 58, "end_line": 107, "span_ids": ["SplitOnGrid.get_params", "SplitOnGrid.__call__"], "tokens": 468}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SplitOnGrid(Transform):\n\n    def __call__(self, image: NdarrayOrTensor) -> NdarrayOrTensor:\n        if self.grid_size == (1, 1) and self.patch_size is None:\n            if isinstance(image, torch.Tensor):\n                return torch.stack([image])\n            elif isinstance(image, np.ndarray):\n                return np.stack([image])  # type: ignore\n            else:\n                raise ValueError(f\"Input type [{type(image)}] is not supported.\")\n\n        patch_size, steps = self.get_params(image.shape[1:])\n        patches: NdarrayOrTensor\n        if isinstance(image, torch.Tensor):\n            patches = (\n                image.unfold(1, patch_size[0], steps[0])\n                .unfold(2, patch_size[1], steps[1])\n                .flatten(1, 2)\n                .transpose(0, 1)\n                .contiguous()\n            )\n        elif isinstance(image, np.ndarray):\n            x_step, y_step = steps\n            c_stride, x_stride, y_stride = image.strides\n            n_channels = image.shape[0]\n            patches = as_strided(\n                image,\n                shape=(*self.grid_size, n_channels, patch_size[0], patch_size[1]),\n                strides=(x_stride * x_step, y_stride * y_step, c_stride, x_stride, y_stride),\n                writeable=False,\n            )\n            # flatten the first two dimensions\n            patches = patches.reshape(np.prod(patches.shape[:2]), *patches.shape[2:])\n            # make it a contiguous array\n            patches = np.ascontiguousarray(patches)\n        else:\n            raise ValueError(f\"Input type [{type(image)}] is not supported.\")\n\n        return patches\n\n    def get_params(self, image_size):\n        if self.patch_size is None:\n            patch_size = tuple(image_size[i] // self.grid_size[i] for i in range(2))\n        else:\n            patch_size = self.patch_size\n\n        steps = tuple(\n            (image_size[i] - patch_size[i]) // (self.grid_size[i] - 1) if self.grid_size[i] > 1 else image_size[i]\n            for i in range(2)\n        )\n\n        return patch_size, steps", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/transforms/spatial/array.py_TileOnGrid_TileOnGrid.__init__.if_self_filter_mode_not_i.raise_ValueError_Unsuppo": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/transforms/spatial/array.py_TileOnGrid_TileOnGrid.__init__.if_self_filter_mode_not_i.raise_ValueError_Unsuppo", "embedding": null, "metadata": {"file_path": "monai/apps/pathology/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 110, "end_line": 163, "span_ids": ["TileOnGrid", "TileOnGrid.__init__"], "tokens": 475}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TileOnGrid(Randomizable, Transform):\n    \"\"\"\n    Tile the 2D image into patches on a grid and maintain a subset of it.\n    This transform works only with np.ndarray inputs for 2D images.\n\n    Args:\n        tile_count: number of tiles to extract, if None extracts all non-background tiles\n            Defaults to ``None``.\n        tile_size: size of the square tile\n            Defaults to ``256``.\n        step: step size\n            Defaults to ``None`` (same as tile_size)\n        random_offset: Randomize position of the grid, instead of starting from the top-left corner\n            Defaults to ``False``.\n        pad_full: pad image to the size evenly divisible by tile_size\n            Defaults to ``False``.\n        background_val: the background constant (e.g. 255 for white background)\n            Defaults to ``255``.\n        filter_mode: mode must be in [\"min\", \"max\", \"random\"]. If total number of tiles is more than tile_size,\n            then sort by intensity sum, and take the smallest (for min), largest (for max) or random (for random) subset\n            Defaults to ``min`` (which assumes background is high value)\n\n    \"\"\"\n\n    backend = [TransformBackends.NUMPY]\n\n    def __init__(\n        self,\n        tile_count: Optional[int] = None,\n        tile_size: int = 256,\n        step: Optional[int] = None,\n        random_offset: bool = False,\n        pad_full: bool = False,\n        background_val: int = 255,\n        filter_mode: str = \"min\",\n    ):\n        self.tile_count = tile_count\n        self.tile_size = tile_size\n        self.random_offset = random_offset\n        self.pad_full = pad_full\n        self.background_val = background_val\n        self.filter_mode = filter_mode\n\n        if step is None:\n            # non-overlapping grid\n            self.step = self.tile_size\n        else:\n            self.step = step\n\n        self.offset = (0, 0)\n        self.random_idxs = np.array((0,))\n\n        if self.filter_mode not in [\"min\", \"max\", \"random\"]:\n            raise ValueError(\"Unsupported filter_mode, must be [min, max or random]: \" + str(self.filter_mode))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/transforms/spatial/array.py_TileOnGrid.randomize_TileOnGrid.randomize.if_self_tile_count_is_not.else_.self.random_idxs.np_array_0_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/transforms/spatial/array.py_TileOnGrid.randomize_TileOnGrid.randomize.if_self_tile_count_is_not.else_.self.random_idxs.np_array_0_", "embedding": null, "metadata": {"file_path": "monai/apps/pathology/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 165, "end_line": 190, "span_ids": ["TileOnGrid.randomize"], "tokens": 284}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TileOnGrid(Randomizable, Transform):\n\n    def randomize(self, img_size: Sequence[int]) -> None:\n\n        c, h, w = img_size\n\n        self.offset = (0, 0)\n        if self.random_offset:\n            pad_h = h % self.tile_size\n            pad_w = w % self.tile_size\n            self.offset = (self.R.randint(pad_h) if pad_h > 0 else 0, self.R.randint(pad_w) if pad_w > 0 else 0)\n            h = h - self.offset[0]\n            w = w - self.offset[1]\n\n        if self.pad_full:\n            pad_h = (self.tile_size - h % self.tile_size) % self.tile_size\n            pad_w = (self.tile_size - w % self.tile_size) % self.tile_size\n            h = h + pad_h\n            w = w + pad_w\n\n        h_n = (h - self.tile_size + self.step) // self.step\n        w_n = (w - self.tile_size + self.step) // self.step\n        tile_total = h_n * w_n\n\n        if self.tile_count is not None and tile_total > self.tile_count:\n            self.random_idxs = self.R.choice(range(tile_total), self.tile_count, replace=False)\n        else:\n            self.random_idxs = np.array((0,))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/transforms/spatial/array.py_TileOnGrid.__call___": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/transforms/spatial/array.py_TileOnGrid.__call___", "embedding": null, "metadata": {"file_path": "monai/apps/pathology/transforms/spatial/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 192, "end_line": 264, "span_ids": ["TileOnGrid.__call__"], "tokens": 822}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TileOnGrid(Randomizable, Transform):\n\n    def __call__(self, image: NdarrayOrTensor) -> NdarrayOrTensor:\n        img_np: np.ndarray\n        img_np, *_ = convert_data_type(image, np.ndarray)  # type: ignore\n\n        # add random offset\n        self.randomize(img_size=img_np.shape)\n\n        if self.random_offset and (self.offset[0] > 0 or self.offset[1] > 0):\n            img_np = img_np[:, self.offset[0] :, self.offset[1] :]\n\n        # pad to full size, divisible by tile_size\n        if self.pad_full:\n            c, h, w = img_np.shape\n            pad_h = (self.tile_size - h % self.tile_size) % self.tile_size\n            pad_w = (self.tile_size - w % self.tile_size) % self.tile_size\n            img_np = np.pad(\n                img_np,\n                [[0, 0], [pad_h // 2, pad_h - pad_h // 2], [pad_w // 2, pad_w - pad_w // 2]],\n                constant_values=self.background_val,\n            )\n\n        # extact tiles\n        x_step, y_step = self.step, self.step\n        h_tile, w_tile = self.tile_size, self.tile_size\n        c_image, h_image, w_image = img_np.shape\n        c_stride, x_stride, y_stride = img_np.strides\n        llw = as_strided(\n            img_np,\n            shape=((h_image - h_tile) // x_step + 1, (w_image - w_tile) // y_step + 1, c_image, h_tile, w_tile),\n            strides=(x_stride * x_step, y_stride * y_step, c_stride, x_stride, y_stride),\n            writeable=False,\n        )\n        img_np = llw.reshape(-1, c_image, h_tile, w_tile)\n\n        # if keeping all patches\n        if self.tile_count is None:\n            # retain only patches with significant foreground content to speed up inference\n            # FYI, this returns a variable number of tiles, so the batch_size must be 1 (per gpu), e.g during inference\n            thresh = 0.999 * 3 * self.background_val * self.tile_size * self.tile_size\n            if self.filter_mode == \"min\":\n                # default, keep non-background tiles (small values)\n                idxs = np.argwhere(img_np.sum(axis=(1, 2, 3)) < thresh)\n                img_np = img_np[idxs.reshape(-1)]\n            elif self.filter_mode == \"max\":\n                idxs = np.argwhere(img_np.sum(axis=(1, 2, 3)) >= thresh)\n                img_np = img_np[idxs.reshape(-1)]\n\n        else:\n            if len(img_np) > self.tile_count:\n\n                if self.filter_mode == \"min\":\n                    # default, keep non-background tiles (smallest values)\n                    idxs = np.argsort(img_np.sum(axis=(1, 2, 3)))[: self.tile_count]\n                    img_np = img_np[idxs]\n                elif self.filter_mode == \"max\":\n                    idxs = np.argsort(img_np.sum(axis=(1, 2, 3)))[-self.tile_count :]\n                    img_np = img_np[idxs]\n                else:\n                    # random subset (more appropriate for WSIs without distinct background)\n                    if self.random_idxs is not None:\n                        img_np = img_np[self.random_idxs]\n\n            elif len(img_np) < self.tile_count:\n                img_np = np.pad(\n                    img_np,\n                    [[0, self.tile_count - len(img_np)], [0, 0], [0, 0], [0, 0]],\n                    constant_values=self.background_val,\n                )\n\n        image, *_ = convert_to_dst_type(src=img_np, dst=image, dtype=image.dtype)\n\n        return image", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/transforms/spatial/dictionary.py_copy___all__._SplitOnGridd_SplitOn": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/transforms/spatial/dictionary.py_copy___all__._SplitOnGridd_SplitOn", "embedding": null, "metadata": {"file_path": "monai/apps/pathology/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 21, "span_ids": ["docstring"], "tokens": 107}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import copy\nfrom typing import Any, Dict, Hashable, List, Mapping, Optional, Tuple, Union\n\nfrom monai.config import KeysCollection\nfrom monai.config.type_definitions import NdarrayOrTensor\nfrom monai.transforms.transform import MapTransform, Randomizable\n\nfrom .array import SplitOnGrid, TileOnGrid\n\n__all__ = [\"SplitOnGridd\", \"SplitOnGridD\", \"SplitOnGridDict\", \"TileOnGridd\", \"TileOnGridD\", \"TileOnGridDict\"]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/transforms/spatial/dictionary.py_SplitOnGridd_SplitOnGridd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/transforms/spatial/dictionary.py_SplitOnGridd_SplitOnGridd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/apps/pathology/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 24, "end_line": 55, "span_ids": ["SplitOnGridd.__call__", "SplitOnGridd.__init__", "SplitOnGridd"], "tokens": 314}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SplitOnGridd(MapTransform):\n    \"\"\"\n    Split the image into patches based on the provided grid shape.\n    This transform works only with torch.Tensor inputs.\n\n    Args:\n        grid_size: a tuple or an integer define the shape of the grid upon which to extract patches.\n            If it's an integer, the value will be repeated for each dimension. Default is 2x2\n        patch_size: a tuple or an integer that defines the output patch sizes.\n            If it's an integer, the value will be repeated for each dimension.\n            The default is (0, 0), where the patch size will be inferred from the grid shape.\n\n    Note: the shape of the input image is inferred based on the first image used.\n    \"\"\"\n\n    backend = SplitOnGrid.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        grid_size: Union[int, Tuple[int, int]] = (2, 2),\n        patch_size: Optional[Union[int, Tuple[int, int]]] = None,\n        allow_missing_keys: bool = False,\n    ):\n        super().__init__(keys, allow_missing_keys)\n        self.splitter = SplitOnGrid(grid_size=grid_size, patch_size=patch_size)\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key in self.key_iterator(d):\n            d[key] = self.splitter(d[key])\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/transforms/spatial/dictionary.py_TileOnGridd_TileOnGridd.randomize._type_ignore": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/transforms/spatial/dictionary.py_TileOnGridd_TileOnGridd.randomize._type_ignore", "embedding": null, "metadata": {"file_path": "monai/apps/pathology/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 58, "end_line": 113, "span_ids": ["TileOnGridd.__init__", "TileOnGridd", "TileOnGridd.randomize"], "tokens": 477}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TileOnGridd(Randomizable, MapTransform):\n    \"\"\"\n    Tile the 2D image into patches on a grid and maintain a subset of it.\n    This transform works only with np.ndarray inputs for 2D images.\n\n    Args:\n        tile_count: number of tiles to extract, if None extracts all non-background tiles\n            Defaults to ``None``.\n        tile_size: size of the square tile\n            Defaults to ``256``.\n        step: step size\n            Defaults to ``None`` (same as tile_size)\n        random_offset: Randomize position of the grid, instead of starting from the top-left corner\n            Defaults to ``False``.\n        pad_full: pad image to the size evenly divisible by tile_size\n            Defaults to ``False``.\n        background_val: the background constant (e.g. 255 for white background)\n            Defaults to ``255``.\n        filter_mode: mode must be in [\"min\", \"max\", \"random\"]. If total number of tiles is more than tile_size,\n            then sort by intensity sum, and take the smallest (for min), largest (for max) or random (for random) subset\n            Defaults to ``min`` (which assumes background is high value)\n\n    \"\"\"\n\n    backend = SplitOnGrid.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        tile_count: Optional[int] = None,\n        tile_size: int = 256,\n        step: Optional[int] = None,\n        random_offset: bool = False,\n        pad_full: bool = False,\n        background_val: int = 255,\n        filter_mode: str = \"min\",\n        allow_missing_keys: bool = False,\n        return_list_of_dicts: bool = False,\n    ):\n        super().__init__(keys, allow_missing_keys)\n\n        self.return_list_of_dicts = return_list_of_dicts\n        self.seed = None\n\n        self.splitter = TileOnGrid(\n            tile_count=tile_count,\n            tile_size=tile_size,\n            step=step,\n            random_offset=random_offset,\n            pad_full=pad_full,\n            background_val=background_val,\n            filter_mode=filter_mode,\n        )\n\n    def randomize(self, data: Any = None) -> None:\n        self.seed = self.R.randint(10000)  # type: ignore", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/transforms/spatial/dictionary.py_TileOnGridd.__call___": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/pathology/transforms/spatial/dictionary.py_TileOnGridd.__call___", "embedding": null, "metadata": {"file_path": "monai/apps/pathology/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 115, "end_line": 137, "span_ids": ["impl:3", "TileOnGridd.__call__"], "tokens": 209}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TileOnGridd(Randomizable, MapTransform):\n\n    def __call__(\n        self, data: Mapping[Hashable, NdarrayOrTensor]\n    ) -> Union[Dict[Hashable, NdarrayOrTensor], List[Dict[Hashable, NdarrayOrTensor]]]:\n\n        self.randomize()\n\n        d = dict(data)\n        for key in self.key_iterator(d):\n            self.splitter.set_random_state(seed=self.seed)  # same random seed for all keys\n            d[key] = self.splitter(d[key])\n\n        if self.return_list_of_dicts:\n            d_list = []\n            for i in range(len(d[self.keys[0]])):\n                d_list.append({k: d[k][i] if k in self.keys else copy.deepcopy(d[k]) for k in d.keys()})\n            d = d_list  # type: ignore\n\n        return d\n\n\nSplitOnGridDict = SplitOnGridD = SplitOnGridd\nTileOnGridDict = TileOnGridD = TileOnGridd", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/utils.py_hashlib_SUPPORTED_HASH_TYPES._md5_hashlib_md5_sha": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/utils.py_hashlib_SUPPORTED_HASH_TYPES._md5_hashlib_md5_sha", "embedding": null, "metadata": {"file_path": "monai/apps/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 41, "span_ids": ["docstring"], "tokens": 242}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import hashlib\nimport logging\nimport os\nimport shutil\nimport sys\nimport tarfile\nimport tempfile\nimport warnings\nimport zipfile\nfrom pathlib import Path\nfrom typing import TYPE_CHECKING, Optional\nfrom urllib.error import ContentTooShortError, HTTPError, URLError\nfrom urllib.request import urlretrieve\n\nfrom monai.config.type_definitions import PathLike\nfrom monai.utils import look_up_option, min_version, optional_import\n\ngdown, has_gdown = optional_import(\"gdown\", \"3.6\")\n\nif TYPE_CHECKING:\n    from tqdm import tqdm\n\n    has_tqdm = True\nelse:\n    tqdm, has_tqdm = optional_import(\"tqdm\", \"4.47.0\", min_version, \"tqdm\")\n\n__all__ = [\"check_hash\", \"download_url\", \"extractall\", \"download_and_extract\", \"get_logger\", \"SUPPORTED_HASH_TYPES\"]\n\nDEFAULT_FMT = \"%(asctime)s - %(levelname)s - %(message)s\"\nSUPPORTED_HASH_TYPES = {\"md5\": hashlib.md5, \"sha1\": hashlib.sha1, \"sha256\": hashlib.sha256, \"sha512\": hashlib.sha512}", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/utils.py_get_logger__basename.return.Path_f_p_rstrip_sep_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/utils.py_get_logger__basename.return.Path_f_p_rstrip_sep_", "embedding": null, "metadata": {"file_path": "monai/apps/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 44, "end_line": 78, "span_ids": ["_basename", "impl:13", "get_logger"], "tokens": 304}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_logger(\n    module_name: str = \"monai.apps\",\n    fmt: str = DEFAULT_FMT,\n    datefmt: Optional[str] = None,\n    logger_handler: Optional[logging.Handler] = None,\n):\n    \"\"\"\n    Get a `module_name` logger with the specified format and date format.\n    By default, the logger will print to `stdout` at the INFO level.\n    If `module_name` is `None`, return the root logger.\n    `fmt` and `datafmt` are passed to a `logging.Formatter` object\n    (https://docs.python.org/3/library/logging.html#formatter-objects).\n    `logger_handler` can be used to add an additional handler.\n    \"\"\"\n    logger = logging.getLogger(module_name)\n    logger.propagate = False\n    logger.setLevel(logging.INFO)\n    handler = logging.StreamHandler(sys.stdout)\n    formatter = logging.Formatter(fmt=fmt, datefmt=datefmt)\n    handler.setFormatter(formatter)\n    logger.addHandler(handler)\n    if logger_handler is not None:\n        logger.addHandler(logger_handler)\n    return logger\n\n\n# apps module-level default logger\nlogger = get_logger(\"monai.apps\")\n__all__.append(\"logger\")\n\n\ndef _basename(p: PathLike) -> str:\n    \"\"\"get the last part of the path (removing the trailing slash if it exists)\"\"\"\n    sep = os.path.sep + (os.path.altsep or \"\") + \"/ \"\n    return Path(f\"{p}\".rstrip(sep)).name", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/config/type_definitions.py_os_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/config/type_definitions.py_os_", "embedding": null, "metadata": {"file_path": "monai/config/type_definitions.py", "file_name": "type_definitions.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 81, "span_ids": ["docstring:39", "docstring"], "tokens": 634}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nfrom typing import Collection, Hashable, Iterable, Sequence, TypeVar, Union\n\nimport numpy as np\nimport torch\n\n# Commonly used concepts\n# This module provides naming and type specifications for commonly used concepts\n# within the MONAI package. The intent is to explicitly identify information\n# that should be used consistently throughout the entire MONAI package.\n#\n# A type would be named as type_definitions.KeysCollection\n# which includes a meaningful name for the consent in the name itself. The\n# definitions in this file map context meaningful names to the underlying\n# object properties that define the expected API.\n#\n# A conceptual type is represented by a new type name but is also one which\n# can be different depending on an environment (i.e. differences for python 3.6 vs 3.9\n# may be implemented). Consistent use of the concept and recorded documentation of\n# the rationale and convention behind it lowers the learning curve for new\n# developers. For readability, short names are preferred.\n__all__ = [\n    \"KeysCollection\",\n    \"IndexSelection\",\n    \"DtypeLike\",\n    \"NdarrayTensor\",\n    \"NdarrayOrTensor\",\n    \"TensorOrList\",\n    \"PathLike\",\n]\n\n\n#: KeysCollection\n#\n# The KeyCollection type is used to for defining variables\n# that store a subset of keys to select items from a dictionary.\n# The container of keys must contain hashable elements.\n# NOTE:  `Hashable` is not a collection, but is provided as a\n#        convenience to end-users.  All supplied values will be\n#        internally converted to a tuple of `Hashable`'s before\n#        use\nKeysCollection = Union[Collection[Hashable], Hashable]\n\n#: IndexSelection\n#\n# The IndexSelection type is used to for defining variables\n# that store a subset of indices to select items from a List or Array like objects.\n# The indices must be integers, and if a container of indices is specified, the\n# container must be iterable.\nIndexSelection = Union[Iterable[int], int]\n\n#: Type of datatypes: Adapted from https://github.com/numpy/numpy/blob/v1.21.4/numpy/typing/_dtype_like.py#L121\nDtypeLike = Union[np.dtype, type, str, None]\n\n#: NdarrayTensor\n#\n# Generic type which can represent either a numpy.ndarray or a torch.Tensor\n# Unlike Union can create a dependence between parameter(s) / return(s)\nNdarrayTensor = TypeVar(\"NdarrayTensor\", np.ndarray, torch.Tensor)\n\n\n#: NdarrayOrTensor: Union of numpy.ndarray and torch.Tensor to be used for typing\nNdarrayOrTensor = Union[np.ndarray, torch.Tensor]\n\n#: TensorOrList: The TensorOrList type is used for defining `batch-first Tensor` or `list of channel-first Tensor`.\nTensorOrList = Union[torch.Tensor, Sequence[torch.Tensor]]\n\n#: PathLike: The PathLike type is used for defining a file path.\nPathLike = Union[str, os.PathLike]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_warnings___all__._ImageReader_ITKReade": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_warnings___all__._ImageReader_ITKReade", "embedding": null, "metadata": {"file_path": "monai/data/image_reader.py", "file_name": "image_reader.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 44, "span_ids": ["docstring"], "tokens": 347}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import warnings\nfrom abc import ABC, abstractmethod\nfrom pathlib import Path\nfrom typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Sequence, Tuple, Union\n\nimport numpy as np\nfrom torch.utils.data._utils.collate import np_str_obj_array_pattern\n\nfrom monai.config import DtypeLike, KeysCollection, PathLike\nfrom monai.data.utils import correct_nifti_header_if_necessary\nfrom monai.transforms.utility.array import EnsureChannelFirst\nfrom monai.utils import ensure_tuple, ensure_tuple_rep, optional_import, require_pkg\n\nfrom .utils import is_supported_format\n\nif TYPE_CHECKING:\n    import itk  # type: ignore\n    import nibabel as nib\n    from nibabel.nifti1 import Nifti1Image\n    from PIL import Image as PILImage\n\n    has_itk = has_nib = has_pil = True\nelse:\n    itk, has_itk = optional_import(\"itk\", allow_namespace_pkg=True)\n    nib, has_nib = optional_import(\"nibabel\")\n    Nifti1Image, _ = optional_import(\"nibabel.nifti1\", name=\"Nifti1Image\")\n    PILImage, has_pil = optional_import(\"PIL.Image\")\n\nOpenSlide, _ = optional_import(\"openslide\", name=\"OpenSlide\")\nCuImage, _ = optional_import(\"cucim\", name=\"CuImage\")\nTiffFile, _ = optional_import(\"tifffile\", name=\"TiffFile\")\n\n__all__ = [\"ImageReader\", \"ITKReader\", \"NibabelReader\", \"NumpyReader\", \"PILReader\", \"WSIReader\"]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/nifti_saver.py_from_typing_import_Dict__NiftiSaver._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/nifti_saver.py_from_typing_import_Dict__NiftiSaver._", "embedding": null, "metadata": {"file_path": "monai/data/nifti_saver.py", "file_name": "nifti_saver.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 35, "span_ids": ["NiftiSaver", "docstring"], "tokens": 213}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Dict, Optional, Union\n\nimport numpy as np\nimport torch\n\nfrom monai.config import DtypeLike, PathLike\nfrom monai.data.nifti_writer import write_nifti\nfrom monai.data.utils import create_file_basename\nfrom monai.utils import GridSampleMode, GridSamplePadMode\nfrom monai.utils import ImageMetaKey as Key\n\n\nclass NiftiSaver:\n    \"\"\"\n    Save the data as NIfTI file, it can support single data content or a batch of data.\n    Typically, the data can be segmentation predictions, call `save` for single data\n    or call `save_batch` to save a batch of data together.\n    The name of saved file will be `{input_image_name}_{output_postfix}{output_ext}`,\n    where the input image name is extracted from the provided meta data dictionary.\n    If no meta data provided, use index from 0 as the filename prefix.\n\n    Note: image should include channel dimension: [B],C,H,W,[D].\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/png_saver.py_from_typing_import_Dict__PNGSaver.__init__.self._data_index.0": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/png_saver.py_from_typing_import_Dict__PNGSaver.__init__.self._data_index.0", "embedding": null, "metadata": {"file_path": "monai/data/png_saver.py", "file_name": "png_saver.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 84, "span_ids": ["PNGSaver.__init__", "PNGSaver", "docstring"], "tokens": 784}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Dict, Optional, Union\n\nimport numpy as np\nimport torch\n\nfrom monai.config.type_definitions import PathLike\nfrom monai.data.png_writer import write_png\nfrom monai.data.utils import create_file_basename\nfrom monai.utils import ImageMetaKey as Key\nfrom monai.utils import InterpolateMode, look_up_option\n\n\nclass PNGSaver:\n    \"\"\"\n    Save the data as png file, it can support single data content or a batch of data.\n    Typically, the data can be segmentation predictions, call `save` for single data\n    or call `save_batch` to save a batch of data together.\n    The name of saved file will be `{input_image_name}_{output_postfix}{output_ext}`,\n    where the input image name is extracted from the provided meta data dictionary.\n    If no meta data provided, use index from 0 as the filename prefix.\n\n    \"\"\"\n\n    def __init__(\n        self,\n        output_dir: PathLike = \"./\",\n        output_postfix: str = \"seg\",\n        output_ext: str = \".png\",\n        resample: bool = True,\n        mode: Union[InterpolateMode, str] = InterpolateMode.NEAREST,\n        scale: Optional[int] = None,\n        data_root_dir: PathLike = \"\",\n        separate_folder: bool = True,\n        print_log: bool = True,\n    ) -> None:\n        \"\"\"\n        Args:\n            output_dir: output image directory.\n            output_postfix: a string appended to all output file names.\n            output_ext: output file extension name.\n            resample: whether to resample and resize if providing spatial_shape in the metadata.\n            mode: {``\"nearest\"``, ``\"linear\"``, ``\"bilinear\"``, ``\"bicubic\"``, ``\"trilinear\"``, ``\"area\"``}\n                The interpolation mode. Defaults to ``\"nearest\"``.\n                See also: https://pytorch.org/docs/stable/nn.functional.html#interpolate\n            scale: {``255``, ``65535``} postprocess data by clipping to [0, 1] and scaling\n                [0, 255] (uint8) or [0, 65535] (uint16). Default is None to disable scaling.\n            data_root_dir: if not empty, it specifies the beginning parts of the input file's\n                absolute path. it's used to compute `input_file_rel_path`, the relative path to the file from\n                `data_root_dir` to preserve folder structure when saving in case there are files in different\n                folders with the same file names. for example:\n                input_file_name: /foo/bar/test1/image.png,\n                postfix: seg\n                output_ext: png\n                output_dir: /output,\n                data_root_dir: /foo/bar,\n                output will be: /output/test1/image/image_seg.png\n            separate_folder: whether to save every file in a separate folder, for example: if input filename is\n                `image.png`, postfix is `seg` and folder_path is `output`, if `True`, save as:\n                `output/image/image_seg.png`, if `False`, save as `output/image_seg.nii`. default to `True`.\n            print_log: whether to print log about the saved PNG file path, etc. default to `True`.\n\n        \"\"\"\n        self.output_dir = output_dir\n        self.output_postfix = output_postfix\n        self.output_ext = output_ext\n        self.resample = resample\n        self.mode: InterpolateMode = look_up_option(mode, InterpolateMode)\n        self.scale = scale\n        self.data_root_dir = data_root_dir\n        self.separate_folder = separate_folder\n        self.print_log = print_log\n\n        self._data_index = 0", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py__non_zipping_check__non_zipping_check.return.batch_size_non_iterable_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py__non_zipping_check__non_zipping_check.return.batch_size_non_iterable_", "embedding": null, "metadata": {"file_path": "monai/data/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 299, "end_line": 321, "span_ids": ["_non_zipping_check"], "tokens": 374}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def _non_zipping_check(batch_data, detach, pad, fill_value):\n    \"\"\"\n    Utility function based on `decollate_batch`, to identify the largest batch size from the collated data.\n    returns batch_size, the list of non-iterable items, and the dictionary or list with their items decollated.\n\n    See `decollate_batch` for more details.\n    \"\"\"\n    if isinstance(batch_data, Mapping):\n        _deco = {key: decollate_batch(batch_data[key], detach, pad=pad, fill_value=fill_value) for key in batch_data}\n    elif isinstance(batch_data, Iterable):\n        _deco = [decollate_batch(b, detach, pad=pad, fill_value=fill_value) for b in batch_data]\n    else:\n        raise NotImplementedError(f\"Unable to de-collate: {batch_data}, type: {type(batch_data)}.\")\n    batch_size, non_iterable = 0, []\n    for k, v in _deco.items() if isinstance(_deco, Mapping) else enumerate(_deco):\n        if not isinstance(v, Iterable) or isinstance(v, (str, bytes)) or (isinstance(v, torch.Tensor) and v.ndim == 0):\n            # Not running the usual list decollate here:\n            # don't decollate ['test', 'test'] into [['t', 't'], ['e', 'e'], ['s', 's'], ['t', 't']]\n            # torch.tensor(0) is iterable but iter(torch.tensor(0)) raises TypeError: iteration over a 0-d tensor\n            non_iterable.append(k)\n        elif hasattr(v, \"__len__\"):\n            batch_size = max(batch_size, len(v))\n    return batch_size, non_iterable, _deco", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_decollate_batch_decollate_batch.if_isinstance_batch_flo.return.batch": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_decollate_batch_decollate_batch.if_isinstance_batch_flo.return.batch", "embedding": null, "metadata": {"file_path": "monai/data/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 324, "end_line": 383, "span_ids": ["decollate_batch"], "tokens": 705}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def decollate_batch(batch, detach: bool = True, pad=True, fill_value=None):\n    \"\"\"De-collate a batch of data (for example, as produced by a `DataLoader`).\n\n    Returns a list of structures with the original tensor's 0-th dimension sliced into elements using `torch.unbind`.\n\n    Images originally stored as (B,C,H,W,[D]) will be returned as (C,H,W,[D]). Other information,\n    such as metadata, may have been stored in a list (or a list inside nested dictionaries). In\n    this case we return the element of the list corresponding to the batch idx.\n\n    Return types aren't guaranteed to be the same as the original, since numpy arrays will have been\n    converted to torch.Tensor, sequences may be converted to lists of tensors,\n    mappings may be converted into dictionaries.\n\n    For example:\n\n    .. code-block:: python\n\n        batch_data = {\n            \"image\": torch.rand((2,1,10,10)),\n            \"image_meta_dict\": {\"scl_slope\": torch.Tensor([0.0, 0.0])}\n        }\n        out = decollate_batch(batch_data)\n        print(len(out))\n        >>> 2\n\n        print(out[0])\n        >>> {'image': tensor([[[4.3549e-01...43e-01]]]), 'image_meta_dict': {'scl_slope': 0.0}}\n\n        batch_data = [torch.rand((2,1,10,10)), torch.rand((2,3,5,5))]\n        out = decollate_batch(batch_data)\n        print(out[0])\n        >>> [tensor([[[4.3549e-01...43e-01]]], tensor([[[5.3435e-01...45e-01]]])]\n\n        batch_data = torch.rand((2,1,10,10))\n        out = decollate_batch(batch_data)\n        print(out[0])\n        >>> tensor([[[4.3549e-01...43e-01]]])\n\n        batch_data = {\n            \"image\": [1, 2, 3], \"meta\": [4, 5],  # undetermined batch size\n        }\n        out = decollate_batch(batch_data, pad=True, fill_value=0)\n        print(out)\n        >>> [{'image': 1, 'meta': 4}, {'image': 2, 'meta': 5}, {'image': 3, 'meta': 0}]\n        out = decollate_batch(batch_data, pad=False)\n        print(out)\n        >>> [{'image': 1, 'meta': 4}, {'image': 2, 'meta': 5}]\n\n    Args:\n        batch: data to be de-collated.\n        detach: whether to detach the tensors. Scalars tensors will be detached into number types\n            instead of torch tensors.\n        pad: when the items in a batch indicate different batch size, whether to pad all the sequences to the longest.\n            If False, the batch size will be the length of the shortest sequence.\n        fill_value: when `pad` is True, the `fillvalue` to use when padding, defaults to `None`.\n    \"\"\"\n    if batch is None:\n        return batch\n    if isinstance(batch, (float, int, str, bytes)):\n        return batch\n    # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_decollate_batch.if_isinstance_batch_torc_decollate_batch.raise_NotImplementedError": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_decollate_batch.if_isinstance_batch_torc_decollate_batch.raise_NotImplementedError", "embedding": null, "metadata": {"file_path": "monai/data/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 384, "end_line": 406, "span_ids": ["decollate_batch"], "tokens": 327}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def decollate_batch(batch, detach: bool = True, pad=True, fill_value=None):\n    # ... other code\n    if isinstance(batch, torch.Tensor):\n        if detach:\n            batch = batch.detach()\n        if batch.ndim == 0:\n            return batch.item() if detach else batch\n        out_list = torch.unbind(batch, dim=0)\n        if out_list[0].ndim == 0 and detach:\n            return [t.item() for t in out_list]\n        return list(out_list)\n\n    b, non_iterable, deco = _non_zipping_check(batch, detach, pad, fill_value)\n    if b <= 0:  # all non-iterable, single item \"batch\"? {\"image\": 1, \"label\": 1}\n        return deco\n    if pad:  # duplicate non-iterable items to the longest batch\n        for k in non_iterable:\n            deco[k] = [deepcopy(deco[k]) for _ in range(b)]\n    if isinstance(deco, Mapping):\n        _gen = zip_longest(*deco.values(), fillvalue=fill_value) if pad else zip(*deco.values())\n        return [dict(zip(deco, item)) for item in _gen]\n    if isinstance(deco, Iterable):\n        _gen = zip_longest(*deco, fillvalue=fill_value) if pad else zip(*deco)\n        return [list(item) for item in _gen]\n    raise NotImplementedError(f\"Unable to de-collate: {batch}, type: {type(batch)}.\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_compute_shape_offset_compute_shape_offset.return.out_shape_astype_int_cop": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_compute_shape_offset_compute_shape_offset.return.out_shape_astype_int_cop", "embedding": null, "metadata": {"file_path": "monai/data/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 590, "end_line": 623, "span_ids": ["compute_shape_offset"], "tokens": 409}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def compute_shape_offset(\n    spatial_shape: Union[np.ndarray, Sequence[int]], in_affine: np.ndarray, out_affine: np.ndarray\n) -> Tuple[np.ndarray, np.ndarray]:\n    \"\"\"\n    Given input and output affine, compute appropriate shapes\n    in the output space based on the input array's shape.\n    This function also returns the offset to put the shape\n    in a good position with respect to the world coordinate system.\n\n    Args:\n        spatial_shape: input array's shape\n        in_affine (matrix): 2D affine matrix\n        out_affine (matrix): 2D affine matrix\n    \"\"\"\n    shape = np.array(spatial_shape, copy=True, dtype=float)\n    sr = len(shape)\n    in_affine = to_affine_nd(sr, in_affine)\n    out_affine = to_affine_nd(sr, out_affine)\n    in_coords = [(0.0, dim - 1.0) for dim in shape]\n    corners = np.asarray(np.meshgrid(*in_coords, indexing=\"ij\")).reshape((len(shape), -1))\n    corners = np.concatenate((corners, np.ones_like(corners[:1])))\n    corners = in_affine @ corners\n    corners_out = np.linalg.inv(out_affine) @ corners\n    corners_out = corners_out[:-1] / corners_out[-1]\n    out_shape = np.round(corners_out.ptp(axis=1) + 1.0)\n    if np.allclose(nib.io_orientation(in_affine), nib.io_orientation(out_affine)):\n        # same orientation, get translate from the origin\n        offset = in_affine @ ([0] * sr + [1])\n        offset = offset[:-1] / offset[-1]\n    else:\n        # different orientation, the min is the origin\n        corners = corners[:-1] / corners[-1]\n        offset = np.min(corners, 1)\n    return out_shape.astype(int, copy=False), offset", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/tensorboard_handlers.py_TensorBoardImageHandler.__init___TensorBoardImageHandler.__init__.self.max_channels.max_channels": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/tensorboard_handlers.py_TensorBoardImageHandler.__init___TensorBoardImageHandler.__init__.self.max_channels.max_channels", "embedding": null, "metadata": {"file_path": "monai/handlers/tensorboard_handlers.py", "file_name": "tensorboard_handlers.py", "file_type": "text/x-python", "category": "implementation", "start_line": 267, "end_line": 318, "span_ids": ["TensorBoardImageHandler.__init__"], "tokens": 727}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TensorBoardImageHandler(TensorBoardHandler):\n\n    def __init__(\n        self,\n        summary_writer: Optional[SummaryWriter] = None,\n        log_dir: str = \"./runs\",\n        interval: int = 1,\n        epoch_level: bool = True,\n        batch_transform: Callable = lambda x: x,\n        output_transform: Callable = lambda x: x,\n        global_iter_transform: Callable = lambda x: x,\n        index: int = 0,\n        max_channels: int = 1,\n        frame_dim: int = -3,\n        max_frames: int = 64,\n    ) -> None:\n        \"\"\"\n        Args:\n            summary_writer: user can specify TensorBoard or TensorBoardX SummaryWriter,\n                default to create a new TensorBoard writer.\n            log_dir: if using default SummaryWriter, write logs to this directory, default is `./runs`.\n            interval: plot content from engine.state every N epochs or every N iterations, default is 1.\n            epoch_level: plot content from engine.state every N epochs or N iterations. `True` is epoch level,\n                `False` is iteration level.\n            batch_transform: a callable that is used to extract `image` and `label` from `ignite.engine.state.batch`,\n                then construct `(image, label)` pair. for example: if `ignite.engine.state.batch` is `{\"image\": xxx,\n                \"label\": xxx, \"other\": xxx}`, `batch_transform` can be `lambda x: (x[\"image\"], x[\"label\"])`.\n                will use the result to plot image from `result[0][index]` and plot label from `result[1][index]`.\n                `engine.state` and `batch_transform` inherit from the ignite concept:\n                https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial:\n                https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb.\n            output_transform: a callable that is used to extract the `predictions` data from\n                `ignite.engine.state.output`, will use the result to plot output from `result[index]`.\n                `engine.state` and `output_transform` inherit from the ignite concept:\n                https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial:\n                https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb.\n            global_iter_transform: a callable that is used to customize global step number for TensorBoard.\n                For example, in evaluation, the evaluator engine needs to know current epoch from trainer.\n            index: plot which element in a data batch, default is the first element.\n            max_channels: number of channels to plot.\n            frame_dim: if plotting 3D image as GIF, specify the dimension used as frames,\n                expect input data shape as `NCHWD`, default to `-3` (the first spatial dim)\n            max_frames: if plot 3D RGB image as video in TensorBoardX, set the FPS to `max_frames`.\n        \"\"\"\n        super().__init__(summary_writer=summary_writer, log_dir=log_dir)\n        self.interval = interval\n        self.epoch_level = epoch_level\n        self.batch_transform = batch_transform\n        self.output_transform = output_transform\n        self.global_iter_transform = global_iter_transform\n        self.index = index\n        self.frame_dim = frame_dim\n        self.max_frames = max_frames\n        self.max_channels = max_channels", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/tensorboard_handlers.py_TensorBoardImageHandler.attach_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/tensorboard_handlers.py_TensorBoardImageHandler.attach_", "embedding": null, "metadata": {"file_path": "monai/handlers/tensorboard_handlers.py", "file_name": "tensorboard_handlers.py", "file_type": "text/x-python", "category": "implementation", "start_line": 320, "end_line": 407, "span_ids": ["TensorBoardImageHandler.__call__", "TensorBoardImageHandler.attach"], "tokens": 748}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TensorBoardImageHandler(TensorBoardHandler):\n\n    def attach(self, engine: Engine) -> None:\n        \"\"\"\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n        \"\"\"\n        if self.epoch_level:\n            engine.add_event_handler(Events.EPOCH_COMPLETED(every=self.interval), self)\n        else:\n            engine.add_event_handler(Events.ITERATION_COMPLETED(every=self.interval), self)\n\n    def __call__(self, engine: Engine) -> None:\n        \"\"\"\n        Args:\n            engine: Ignite Engine, it can be a trainer, validator or evaluator.\n\n        Raises:\n            TypeError: When ``output_transform(engine.state.output)[0]`` type is not in\n                ``Optional[Union[numpy.ndarray, torch.Tensor]]``.\n            TypeError: When ``batch_transform(engine.state.batch)[1]`` type is not in\n                ``Optional[Union[numpy.ndarray, torch.Tensor]]``.\n            TypeError: When ``output_transform(engine.state.output)`` type is not in\n                ``Optional[Union[numpy.ndarray, torch.Tensor]]``.\n\n        \"\"\"\n        step = self.global_iter_transform(engine.state.epoch if self.epoch_level else engine.state.iteration)\n        show_images = self.batch_transform(engine.state.batch)[0][self.index]\n        if isinstance(show_images, torch.Tensor):\n            show_images = show_images.detach().cpu().numpy()\n        if show_images is not None:\n            if not isinstance(show_images, np.ndarray):\n                raise TypeError(\n                    \"output_transform(engine.state.output)[0] must be None or one of \"\n                    f\"(numpy.ndarray, torch.Tensor) but is {type(show_images).__name__}.\"\n                )\n            plot_2d_or_3d_image(\n                # add batch dim and plot the first item\n                data=show_images[None],\n                step=step,\n                writer=self._writer,\n                index=0,\n                max_channels=self.max_channels,\n                frame_dim=self.frame_dim,\n                max_frames=self.max_frames,\n                tag=\"input_0\",\n            )\n\n        show_labels = self.batch_transform(engine.state.batch)[1][self.index]\n        if isinstance(show_labels, torch.Tensor):\n            show_labels = show_labels.detach().cpu().numpy()\n        if show_labels is not None:\n            if not isinstance(show_labels, np.ndarray):\n                raise TypeError(\n                    \"batch_transform(engine.state.batch)[1] must be None or one of \"\n                    f\"(numpy.ndarray, torch.Tensor) but is {type(show_labels).__name__}.\"\n                )\n            plot_2d_or_3d_image(\n                data=show_labels[None],\n                step=step,\n                writer=self._writer,\n                index=0,\n                max_channels=self.max_channels,\n                frame_dim=self.frame_dim,\n                max_frames=self.max_frames,\n                tag=\"input_1\",\n            )\n\n        show_outputs = self.output_transform(engine.state.output)[self.index]\n        if isinstance(show_outputs, torch.Tensor):\n            show_outputs = show_outputs.detach().cpu().numpy()\n        if show_outputs is not None:\n            if not isinstance(show_outputs, np.ndarray):\n                raise TypeError(\n                    \"output_transform(engine.state.output) must be None or one of \"\n                    f\"(numpy.ndarray, torch.Tensor) but is {type(show_outputs).__name__}.\"\n                )\n            plot_2d_or_3d_image(\n                data=show_outputs[None],\n                step=step,\n                writer=self._writer,\n                index=0,\n                max_channels=self.max_channels,\n                frame_dim=self.frame_dim,\n                max_frames=self.max_frames,\n                tag=\"output\",\n            )\n\n        self._writer.flush()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/utils.py_get_mask_edges_get_mask_edges.return.edges_pred_edges_gt": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/metrics/utils.py_get_mask_edges_get_mask_edges.return.edges_pred_edges_gt", "embedding": null, "metadata": {"file_path": "monai/metrics/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 102, "end_line": 164, "span_ids": ["get_mask_edges"], "tokens": 618}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def get_mask_edges(\n    seg_pred: Union[np.ndarray, torch.Tensor],\n    seg_gt: Union[np.ndarray, torch.Tensor],\n    label_idx: int = 1,\n    crop: bool = True,\n) -> Tuple[np.ndarray, np.ndarray]:\n    \"\"\"\n    Do binary erosion and use XOR for input to get the edges. This\n    function is helpful to further calculate metrics such as Average Surface\n    Distance and Hausdorff Distance.\n    The input images can be binary or labelfield images. If labelfield images\n    are supplied, they are converted to binary images using `label_idx`.\n\n    `scipy`'s binary erosion is used to to calculate the edges of the binary\n    labelfield.\n\n    In order to improve the computing efficiency, before getting the edges,\n    the images can be cropped and only keep the foreground if not specifies\n    ``crop = False``.\n\n    We require that images are the same size, and assume that they occupy the\n    same space (spacing, orientation, etc.).\n\n    Args:\n        seg_pred: the predicted binary or labelfield image.\n        seg_gt: the actual binary or labelfield image.\n        label_idx: for labelfield images, convert to binary with\n            `seg_pred = seg_pred == label_idx`.\n        crop: crop input images and only keep the foregrounds. In order to\n            maintain two inputs' shapes, here the bounding box is achieved\n            by ``(seg_pred | seg_gt)`` which represents the union set of two\n            images. Defaults to ``True``.\n    \"\"\"\n\n    # Get both labelfields as np arrays\n    if isinstance(seg_pred, torch.Tensor):\n        seg_pred = seg_pred.detach().cpu().numpy()\n    if isinstance(seg_gt, torch.Tensor):\n        seg_gt = seg_gt.detach().cpu().numpy()\n\n    if seg_pred.shape != seg_gt.shape:\n        raise ValueError(\"seg_pred and seg_gt should have same shapes.\")\n\n    # If not binary images, convert them\n    if seg_pred.dtype != bool:\n        seg_pred = seg_pred == label_idx\n    if seg_gt.dtype != bool:\n        seg_gt = seg_gt == label_idx\n\n    if crop:\n        if not np.any(seg_pred | seg_gt):\n            return np.zeros_like(seg_pred), np.zeros_like(seg_gt)\n\n        seg_pred, seg_gt = np.expand_dims(seg_pred, 0), np.expand_dims(seg_gt, 0)\n        box_start, box_end = generate_spatial_bounding_box(np.asarray(seg_pred | seg_gt))\n        cropper = SpatialCrop(roi_start=box_start, roi_end=box_end)\n        seg_pred, seg_gt = np.squeeze(cropper(seg_pred)), np.squeeze(cropper(seg_gt))\n\n    # Do binary erosion and use XOR to get edges\n    edges_pred = binary_erosion(seg_pred) ^ seg_pred\n    edges_gt = binary_erosion(seg_gt) ^ seg_gt\n\n    return edges_pred, edges_gt", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/dints_block.py_from_typing_import_Tuple__FactorizedIncreaseBlock.__init__.None_4": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/dints_block.py_from_typing_import_Tuple__FactorizedIncreaseBlock.__init__.None_4", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/dints_block.py", "file_name": "dints_block.py", "file_type": "text/x-python", "category": "implementation", "start_line": 13, "end_line": 70, "span_ids": ["FactorizedIncreaseBlock", "FactorizedIncreaseBlock.__init__", "docstring"], "tokens": 450}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Tuple, Union\n\nimport torch\n\nfrom monai.networks.layers.factories import Conv\nfrom monai.networks.layers.utils import get_act_layer, get_norm_layer\n\n__all__ = [\"FactorizedIncreaseBlock\", \"FactorizedReduceBlock\", \"P3DActiConvNormBlock\", \"ActiConvNormBlock\"]\n\n\nclass FactorizedIncreaseBlock(torch.nn.Sequential):\n    \"\"\"\n    Up-sampling the features by two using linear interpolation and convolutions.\n    \"\"\"\n\n    def __init__(\n        self,\n        in_channel: int,\n        out_channel: int,\n        spatial_dims: int = 3,\n        act_name: Union[Tuple, str] = \"RELU\",\n        norm_name: Union[Tuple, str] = \"INSTANCE\",\n    ):\n        \"\"\"\n        Args:\n            in_channel: number of input channels\n            out_channel: number of output channels\n            spatial_dims: number of spatial dimensions\n            act_name: activation layer type and arguments.\n            norm_name: feature normalization type and arguments.\n        \"\"\"\n        super().__init__()\n        self._in_channel = in_channel\n        self._out_channel = out_channel\n        self._spatial_dims = spatial_dims\n        if self._spatial_dims not in (2, 3):\n            raise ValueError(\"spatial_dims must be 2 or 3.\")\n\n        conv_type = Conv[Conv.CONV, self._spatial_dims]\n        mode = \"trilinear\" if self._spatial_dims == 3 else \"bilinear\"\n        self.add_module(\"up\", torch.nn.Upsample(scale_factor=2, mode=mode, align_corners=True))\n        self.add_module(\"acti\", get_act_layer(name=act_name))\n        self.add_module(\n            \"conv\",\n            conv_type(\n                in_channels=self._in_channel,\n                out_channels=self._out_channel,\n                kernel_size=1,\n                stride=1,\n                padding=0,\n                groups=1,\n                bias=False,\n                dilation=1,\n            ),\n        )\n        self.add_module(\n            \"norm\", get_norm_layer(name=norm_name, spatial_dims=self._spatial_dims, channels=self._out_channel)\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/dints_block.py_FactorizedReduceBlock_FactorizedReduceBlock.__init__.self.norm.get_norm_layer_name_norm_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/dints_block.py_FactorizedReduceBlock_FactorizedReduceBlock.__init__.self.norm.get_norm_layer_name_norm_", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/dints_block.py", "file_name": "dints_block.py", "file_type": "text/x-python", "category": "implementation", "start_line": 73, "end_line": 125, "span_ids": ["FactorizedReduceBlock", "FactorizedReduceBlock.__init__"], "tokens": 396}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class FactorizedReduceBlock(torch.nn.Module):\n    \"\"\"\n    Down-sampling the feature by 2 using stride.\n    The length along each spatial dimension must be a multiple of 2.\n    \"\"\"\n\n    def __init__(\n        self,\n        in_channel: int,\n        out_channel: int,\n        spatial_dims: int = 3,\n        act_name: Union[Tuple, str] = \"RELU\",\n        norm_name: Union[Tuple, str] = \"INSTANCE\",\n    ):\n        \"\"\"\n        Args:\n            in_channel: number of input channels\n            out_channel: number of output channels.\n            spatial_dims: number of spatial dimensions.\n            act_name: activation layer type and arguments.\n            norm_name: feature normalization type and arguments.\n        \"\"\"\n        super().__init__()\n        self._in_channel = in_channel\n        self._out_channel = out_channel\n        self._spatial_dims = spatial_dims\n        if self._spatial_dims not in (2, 3):\n            raise ValueError(\"spatial_dims must be 2 or 3.\")\n\n        conv_type = Conv[Conv.CONV, self._spatial_dims]\n\n        self.act = get_act_layer(name=act_name)\n        self.conv_1 = conv_type(\n            in_channels=self._in_channel,\n            out_channels=self._out_channel // 2,\n            kernel_size=1,\n            stride=2,\n            padding=0,\n            groups=1,\n            bias=False,\n            dilation=1,\n        )\n        self.conv_2 = conv_type(\n            in_channels=self._in_channel,\n            out_channels=self._out_channel - self._out_channel // 2,\n            kernel_size=1,\n            stride=2,\n            padding=0,\n            groups=1,\n            bias=False,\n            dilation=1,\n        )\n        self.norm = get_norm_layer(name=norm_name, spatial_dims=self._spatial_dims, channels=self._out_channel)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/dints_block.py_FactorizedReduceBlock.forward_FactorizedReduceBlock.forward.return.out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/dints_block.py_FactorizedReduceBlock.forward_FactorizedReduceBlock.forward.return.out", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/dints_block.py", "file_name": "dints_block.py", "file_type": "text/x-python", "category": "implementation", "start_line": 127, "end_line": 137, "span_ids": ["FactorizedReduceBlock.forward"], "tokens": 136}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class FactorizedReduceBlock(torch.nn.Module):\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        \"\"\"\n        The length along each spatial dimension must be a multiple of 2.\n        \"\"\"\n        x = self.act(x)\n        if self._spatial_dims == 3:\n            out = torch.cat([self.conv_1(x), self.conv_2(x[:, :, 1:, 1:, 1:])], dim=1)\n        else:\n            out = torch.cat([self.conv_1(x), self.conv_2(x[:, :, 1:, 1:])], dim=1)\n        out = self.norm(out)\n        return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/dints_block.py_P3DActiConvNormBlock_P3DActiConvNormBlock.__init__.self_add_module_norm_g": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/dints_block.py_P3DActiConvNormBlock_P3DActiConvNormBlock.__init__.self_add_module_norm_g", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/dints_block.py", "file_name": "dints_block.py", "file_type": "text/x-python", "category": "implementation", "start_line": 140, "end_line": 222, "span_ids": ["P3DActiConvNormBlock", "P3DActiConvNormBlock.__init__"], "tokens": 748}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class P3DActiConvNormBlock(torch.nn.Sequential):\n    \"\"\"\n    -- (act) -- (conv) -- (norm) --\n    \"\"\"\n\n    def __init__(\n        self,\n        in_channel: int,\n        out_channel: int,\n        kernel_size: int,\n        padding: int,\n        mode: int = 0,\n        act_name: Union[Tuple, str] = \"RELU\",\n        norm_name: Union[Tuple, str] = \"INSTANCE\",\n    ):\n        \"\"\"\n        Args:\n            in_channel: number of input channels.\n            out_channel: number of output channels.\n            kernel_size: kernel size to be expanded to 3D.\n            padding: padding size to be expanded to 3D.\n            mode: mode for the anisotropic kernels:\n\n                - 0: ``(k, k, 1)``, ``(1, 1, k)``,\n                - 1: ``(k, 1, k)``, ``(1, k, 1)``,\n                - 2: ``(1, k, k)``. ``(k, 1, 1)``.\n\n            act_name: activation layer type and arguments.\n            norm_name: feature normalization type and arguments.\n        \"\"\"\n        super().__init__()\n        self._in_channel = in_channel\n        self._out_channel = out_channel\n        self._p3dmode = int(mode)\n\n        conv_type = Conv[Conv.CONV, 3]\n\n        if self._p3dmode == 0:  # (k, k, 1), (1, 1, k)\n            kernel_size0 = (kernel_size, kernel_size, 1)\n            kernel_size1 = (1, 1, kernel_size)\n            padding0 = (padding, padding, 0)\n            padding1 = (0, 0, padding)\n        elif self._p3dmode == 1:  # (k, 1, k), (1, k, 1)\n            kernel_size0 = (kernel_size, 1, kernel_size)\n            kernel_size1 = (1, kernel_size, 1)\n            padding0 = (padding, 0, padding)\n            padding1 = (0, padding, 0)\n        elif self._p3dmode == 2:  # (1, k, k), (k, 1, 1)\n            kernel_size0 = (1, kernel_size, kernel_size)\n            kernel_size1 = (kernel_size, 1, 1)\n            padding0 = (0, padding, padding)\n            padding1 = (padding, 0, 0)\n        else:\n            raise ValueError(\"`mode` must be 0, 1, or 2.\")\n\n        self.add_module(\"acti\", get_act_layer(name=act_name))\n        self.add_module(\n            \"conv\",\n            conv_type(\n                in_channels=self._in_channel,\n                out_channels=self._in_channel,\n                kernel_size=kernel_size0,\n                stride=1,\n                padding=padding0,\n                groups=1,\n                bias=False,\n                dilation=1,\n            ),\n        )\n        self.add_module(\n            \"conv_1\",\n            conv_type(\n                in_channels=self._in_channel,\n                out_channels=self._out_channel,\n                kernel_size=kernel_size1,\n                stride=1,\n                padding=padding1,\n                groups=1,\n                bias=False,\n                dilation=1,\n            ),\n        )\n        self.add_module(\"norm\", get_norm_layer(name=norm_name, spatial_dims=3, channels=self._out_channel))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/dints_block.py_ActiConvNormBlock_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/blocks/dints_block.py_ActiConvNormBlock_", "embedding": null, "metadata": {"file_path": "monai/networks/blocks/dints_block.py", "file_name": "dints_block.py", "file_type": "text/x-python", "category": "implementation", "start_line": 225, "end_line": 273, "span_ids": ["ActiConvNormBlock", "ActiConvNormBlock.__init__"], "tokens": 341}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ActiConvNormBlock(torch.nn.Sequential):\n    \"\"\"\n    -- (Acti) -- (Conv) -- (Norm) --\n    \"\"\"\n\n    def __init__(\n        self,\n        in_channel: int,\n        out_channel: int,\n        kernel_size: int = 3,\n        padding: int = 1,\n        spatial_dims: int = 3,\n        act_name: Union[Tuple, str] = \"RELU\",\n        norm_name: Union[Tuple, str] = \"INSTANCE\",\n    ):\n        \"\"\"\n        Args:\n            in_channel: number of input channels.\n            out_channel: number of output channels.\n            kernel_size: kernel size of the convolution.\n            padding: padding size of the convolution.\n            spatial_dims: number of spatial dimensions.\n            act_name: activation layer type and arguments.\n            norm_name: feature normalization type and arguments.\n        \"\"\"\n        super().__init__()\n        self._in_channel = in_channel\n        self._out_channel = out_channel\n        self._spatial_dims = spatial_dims\n\n        conv_type = Conv[Conv.CONV, self._spatial_dims]\n        self.add_module(\"acti\", get_act_layer(name=act_name))\n        self.add_module(\n            \"conv\",\n            conv_type(\n                in_channels=self._in_channel,\n                out_channels=self._out_channel,\n                kernel_size=kernel_size,\n                stride=1,\n                padding=padding,\n                groups=1,\n                bias=False,\n                dilation=1,\n            ),\n        )\n        self.add_module(\n            \"norm\", get_norm_layer(name=norm_name, spatial_dims=self._spatial_dims, channels=self._out_channel)\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dints.py_warnings__CloseWithRAMCost.forward.return.torch_tensor_0_0_require": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dints.py_warnings__CloseWithRAMCost.forward.return.torch_tensor_0_0_require", "embedding": null, "metadata": {"file_path": "monai/networks/nets/dints.py", "file_name": "dints.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 84, "span_ids": ["StemInterface", "CellInterface.forward", "StemTS.forward", "StemTS.__init__", "_IdentityWithRAMCost", "_CloseWithRAMCost.__init__", "_CloseWithRAMCost", "_CloseWithRAMCost.forward", "StemInterface.forward", "docstring", "CellInterface", "_IdentityWithRAMCost.__init__", "_dfs", "StemTS"], "tokens": 489}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import warnings\nfrom typing import List, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom monai.networks.blocks.dints_block import (\n    ActiConvNormBlock,\n    FactorizedIncreaseBlock,\n    FactorizedReduceBlock,\n    P3DActiConvNormBlock,\n)\nfrom monai.networks.layers.factories import Conv\nfrom monai.networks.layers.utils import get_act_layer, get_norm_layer\nfrom monai.utils import optional_import\n\n# solving shortest path problem\ncsr_matrix, _ = optional_import(\"scipy.sparse\", name=\"csr_matrix\")\ndijkstra, _ = optional_import(\"scipy.sparse.csgraph\", name=\"dijkstra\")\n\n__all__ = [\"DiNTS\", \"TopologyConstruction\", \"TopologyInstance\", \"TopologySearch\"]\n\n\n@torch.jit.interface\nclass CellInterface(torch.nn.Module):\n    \"\"\"interface for torchscriptable Cell\"\"\"\n\n    def forward(self, x: torch.Tensor, weight: torch.Tensor) -> torch.Tensor:\n        pass\n\n\n@torch.jit.interface\nclass StemInterface(torch.nn.Module):\n    \"\"\"interface for torchscriptable Stem\"\"\"\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        pass\n\n\nclass StemTS(StemInterface):\n    \"\"\"wrapper for torchscriptable Stem\"\"\"\n\n    def __init__(self, *mod):\n        super().__init__()\n        self.mod = torch.nn.Sequential(*mod)\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        return self.mod(x)  # type: ignore\n\n\ndef _dfs(node, paths):\n    \"\"\"use depth first search to find all path activation combination\"\"\"\n    if node == paths:\n        return [[0], [1]]\n    child = _dfs(node + 1, paths)\n    return [[0] + _ for _ in child] + [[1] + _ for _ in child]\n\n\nclass _IdentityWithRAMCost(nn.Identity):\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        self.ram_cost = 0\n\n\nclass _CloseWithRAMCost(nn.Module):\n    def __init__(self):\n        super().__init__()\n        self.ram_cost = 0\n\n    def forward(self, x):\n        return torch.tensor(0.0, requires_grad=False).to(x)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dints.py__ActiConvNormBlockWithRAMCost__ActiConvNormBlockWithRAMCost.__init__.self.ram_cost.1_in_channel_out_chan": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dints.py__ActiConvNormBlockWithRAMCost__ActiConvNormBlockWithRAMCost.__init__.self.ram_cost.1_in_channel_out_chan", "embedding": null, "metadata": {"file_path": "monai/networks/nets/dints.py", "file_name": "dints.py", "file_type": "text/x-python", "category": "implementation", "start_line": 87, "end_line": 101, "span_ids": ["_ActiConvNormBlockWithRAMCost", "_ActiConvNormBlockWithRAMCost.__init__"], "tokens": 210}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class _ActiConvNormBlockWithRAMCost(ActiConvNormBlock):\n    \"\"\"The class wraps monai layers with ram estimation. The ram_cost = total_ram/output_size is estimated.\n    Here is the estimation:\n     feature_size = output_size/out_channel\n     total_ram = ram_cost * output_size\n     total_ram = in_channel * feature_size (activation map) +\n                 in_channel * feature_size (convolution map) +\n                 out_channel * feature_size (normalization)\n               = (2*in_channel + out_channel) * output_size/out_channel\n     ram_cost = total_ram/output_size = 2 * in_channel/out_channel + 1\n    \"\"\"\n\n    def __init__(self, in_channel: int, out_channel: int, kernel_size: int, padding: int, spatial_dims: int = 3):\n        super().__init__(in_channel, out_channel, kernel_size, padding, spatial_dims)\n        self.ram_cost = 1 + in_channel / out_channel * 2", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dints.py__P3DActiConvNormBlockWithRAMCost__P3DActiConvNormBlockWithRAMCost.__init__.self.ram_cost.2_2_in_channel_out_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dints.py__P3DActiConvNormBlockWithRAMCost__P3DActiConvNormBlockWithRAMCost.__init__.self.ram_cost.2_2_in_channel_out_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/dints.py", "file_name": "dints.py", "file_type": "text/x-python", "category": "implementation", "start_line": 104, "end_line": 109, "span_ids": ["_P3DActiConvNormBlockWithRAMCost", "_P3DActiConvNormBlockWithRAMCost.__init__"], "tokens": 134}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class _P3DActiConvNormBlockWithRAMCost(P3DActiConvNormBlock):\n    def __init__(self, in_channel: int, out_channel: int, kernel_size: int, padding: int, p3dmode: int = 0):\n        super().__init__(in_channel, out_channel, kernel_size, padding, p3dmode)\n        # 1 in_channel (activation) + 1 in_channel (convolution) +\n        # 1 out_channel (convolution) + 1 out_channel (normalization)\n        self.ram_cost = 2 + 2 * in_channel / out_channel", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dints.py__FactorizedIncreaseBlockWithRAMCost__FactorizedIncreaseBlockWithRAMCost.__init__.self.ram_cost.2_in_channel_out_chan": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dints.py__FactorizedIncreaseBlockWithRAMCost__FactorizedIncreaseBlockWithRAMCost.__init__.self.ram_cost.2_in_channel_out_chan", "embedding": null, "metadata": {"file_path": "monai/networks/nets/dints.py", "file_name": "dints.py", "file_type": "text/x-python", "category": "implementation", "start_line": 112, "end_line": 118, "span_ids": ["_FactorizedIncreaseBlockWithRAMCost.__init__", "_FactorizedIncreaseBlockWithRAMCost"], "tokens": 133}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class _FactorizedIncreaseBlockWithRAMCost(FactorizedIncreaseBlock):\n    def __init__(self, in_channel: int, out_channel: int, spatial_dims: int = 3):\n        super().__init__(in_channel, out_channel, spatial_dims)\n        # s0 is upsampled 2x from s1, representing feature sizes at two resolutions.\n        # 2 * in_channel * s0 (upsample + activation) + 2 * out_channel * s0 (conv + normalization)\n        # s0 = output_size/out_channel\n        self.ram_cost = 2 * in_channel / out_channel + 2", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dints.py__FactorizedReduceBlockWithRAMCost__FactorizedReduceBlockWithRAMCost.__init__.self.ram_cost.in_channel_out_channel_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dints.py__FactorizedReduceBlockWithRAMCost__FactorizedReduceBlockWithRAMCost.__init__.self.ram_cost.in_channel_out_channel_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/dints.py", "file_name": "dints.py", "file_type": "text/x-python", "category": "implementation", "start_line": 121, "end_line": 127, "span_ids": ["_FactorizedReduceBlockWithRAMCost.__init__", "_FactorizedReduceBlockWithRAMCost"], "tokens": 156}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class _FactorizedReduceBlockWithRAMCost(FactorizedReduceBlock):\n    def __init__(self, in_channel: int, out_channel: int, spatial_dims: int = 3):\n        super().__init__(in_channel, out_channel, spatial_dims)\n        # s0 is upsampled 2x from s1, representing feature sizes at two resolutions.\n        # in_channel * s0 (activation) + 3 * out_channel * s1 (convolution, concatenation, normalization)\n        # s0 = s1 * 2^(spatial_dims) = output_size / out_channel * 2^(spatial_dims)\n        self.ram_cost = in_channel / out_channel * 2 ** self._spatial_dims + 3", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dints.py_MixedOp_MixedOp.forward.return.out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dints.py_MixedOp_MixedOp.forward.return.out", "embedding": null, "metadata": {"file_path": "monai/networks/nets/dints.py", "file_name": "dints.py", "file_type": "text/x-python", "category": "implementation", "start_line": 130, "end_line": 159, "span_ids": ["MixedOp.forward", "MixedOp.__init__", "MixedOp"], "tokens": 269}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class MixedOp(nn.Module):\n    \"\"\"\n    The weighted averaging of cell operations.\n    Args:\n        c: number of output channels.\n        ops: a dictionary of operations. See also: ``Cell.OPS2D`` or ``Cell.OPS3D``.\n        arch_code_c: binary cell operation code. It represents the operation results added to the output.\n    \"\"\"\n\n    def __init__(self, c: int, ops: dict, arch_code_c=None):\n        super().__init__()\n        if arch_code_c is None:\n            arch_code_c = np.ones(len(ops))\n        self.ops = nn.ModuleList()\n        for arch_c, op_name in zip(arch_code_c, ops):\n            self.ops.append(_CloseWithRAMCost() if arch_c == 0 else ops[op_name](c))\n\n    def forward(self, x: torch.Tensor, weight: torch.Tensor):\n        \"\"\"\n        Args:\n            x: input tensor.\n            weight: learnable architecture weights for cell operations. arch_code_c are derived from it.\n        Return:\n            out: weighted average of the operation results.\n        \"\"\"\n        out = 0.0\n        weight = weight.to(x)\n        for idx, _op in enumerate(self.ops):\n            out = out + _op(x) * weight[idx]\n        return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dints.py_Cell_Cell.OPS3D._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dints.py_Cell_Cell.OPS3D._", "embedding": null, "metadata": {"file_path": "monai/networks/nets/dints.py", "file_name": "dints.py", "file_type": "text/x-python", "category": "implementation", "start_line": 162, "end_line": 206, "span_ids": ["Cell"], "tokens": 541}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Cell(CellInterface):\n    \"\"\"\n    The basic class for cell operation search, which contains a preprocessing operation and a mixed cell operation.\n    Each cell is defined on a `path` in the topology search space.\n    Args:\n        c_prev: number of input channels\n        c: number of output channels\n        rate: resolution change rate. It represents the preprocessing operation before the mixed cell operation.\n            ``-1`` for 2x downsample, ``1`` for 2x upsample, ``0`` for no change of resolution.\n        arch_code_c: cell operation code\n    \"\"\"\n\n    DIRECTIONS = 3\n    # Possible output paths for `Cell`.\n    #\n    #       - UpSample\n    #      /\n    # +--+/\n    # |  |--- Identity or AlignChannels\n    # +--+\\\n    #      \\\n    #       - Downsample\n\n    # Define connection operation set, parameterized by the number of channels\n    ConnOPS = {\n        \"up\": _FactorizedIncreaseBlockWithRAMCost,\n        \"down\": _FactorizedReduceBlockWithRAMCost,\n        \"identity\": _IdentityWithRAMCost,\n        \"align_channels\": _ActiConvNormBlockWithRAMCost,\n    }\n\n    # Define 2D operation set, parameterized by the number of channels\n    OPS2D = {\n        \"skip_connect\": lambda _c: _IdentityWithRAMCost(),\n        \"conv_3x3\": lambda c: _ActiConvNormBlockWithRAMCost(c, c, 3, padding=1, spatial_dims=2),\n    }\n\n    # Define 3D operation set, parameterized by the number of channels\n    OPS3D = {\n        \"skip_connect\": lambda _c: _IdentityWithRAMCost(),\n        \"conv_3x3x3\": lambda c: _ActiConvNormBlockWithRAMCost(c, c, 3, padding=1, spatial_dims=3),\n        \"conv_3x3x1\": lambda c: _P3DActiConvNormBlockWithRAMCost(c, c, 3, padding=1, p3dmode=0),\n        \"conv_3x1x3\": lambda c: _P3DActiConvNormBlockWithRAMCost(c, c, 3, padding=1, p3dmode=1),\n        \"conv_1x3x3\": lambda c: _P3DActiConvNormBlockWithRAMCost(c, c, 3, padding=1, p3dmode=2),\n    }", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dints.py_Cell.__init___Cell.forward.return.x": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dints.py_Cell.__init___Cell.forward.return.x", "embedding": null, "metadata": {"file_path": "monai/networks/nets/dints.py", "file_name": "dints.py", "file_type": "text/x-python", "category": "implementation", "start_line": 208, "end_line": 239, "span_ids": ["Cell.forward", "Cell.__init__"], "tokens": 328}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Cell(CellInterface):\n\n    def __init__(self, c_prev: int, c: int, rate: int, arch_code_c=None, spatial_dims: int = 3):\n        super().__init__()\n        self._spatial_dims = spatial_dims\n        if rate == -1:  # downsample\n            self.preprocess = self.ConnOPS[\"down\"](c_prev, c, spatial_dims=self._spatial_dims)\n        elif rate == 1:  # upsample\n            self.preprocess = self.ConnOPS[\"up\"](c_prev, c, spatial_dims=self._spatial_dims)\n        else:\n            if c_prev == c:\n                self.preprocess = self.ConnOPS[\"identity\"]()\n            else:\n                self.preprocess = self.ConnOPS[\"align_channels\"](c_prev, c, 1, 0, spatial_dims=self._spatial_dims)\n\n        self.OPS = {}\n        if self._spatial_dims == 2:\n            self.OPS = self.OPS2D\n        elif self._spatial_dims == 3:\n            self.OPS = self.OPS3D\n        else:\n            raise NotImplementedError(f\"Spatial dimensions {self._spatial_dims} is not supported.\")\n\n        self.op = MixedOp(c, self.OPS, arch_code_c)\n\n    def forward(self, x: torch.Tensor, weight: torch.Tensor) -> torch.Tensor:\n        \"\"\"\n        Args:\n            x: input tensor\n            weight: weights for different operations.\n        \"\"\"\n        x = self.preprocess(x)\n        x = self.op(x, weight)\n        return x", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dints.py_DiNTS_DiNTS._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dints.py_DiNTS_DiNTS._", "embedding": null, "metadata": {"file_path": "monai/networks/nets/dints.py", "file_name": "dints.py", "file_type": "text/x-python", "category": "implementation", "start_line": 242, "end_line": 278, "span_ids": ["DiNTS"], "tokens": 550}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DiNTS(nn.Module):\n    \"\"\"\n    Reimplementation of DiNTS based on\n    \"DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentation\n    <https://arxiv.org/abs/2103.15954>\".\n\n    The model contains a pre-defined multi-resolution stem block (defined in this class) and a\n    DiNTS space (defined in :py:class:`monai.networks.nets.TopologyInstance` and\n    :py:class:`monai.networks.nets.TopologySearch`).\n\n    The stem block is for: 1) input downsample and 2) output upsample to original size.\n    The model downsamples the input image by 2 (if ``use_downsample=True``).\n    The downsampled image is downsampled by [1, 2, 4, 8] times (``num_depths=4``) and used as input to the\n    DiNTS search space (``TopologySearch``) or the DiNTS instance (``TopologyInstance``).\n\n        - ``TopologyInstance`` is the final searched model. The initialization requires the searched architecture codes.\n        - ``TopologySearch`` is a multi-path topology and cell operation search space.\n          The architecture codes will be initialized as one.\n        - ``TopologyConstruction`` is the parent class which constructs the instance and search space.\n\n    To meet the requirements of the structure, the input size for each spatial dimension should be:\n    divisible by 2 ** (num_depths + 1).\n\n    Args:\n        dints_space: DiNTS search space. The value should be instance of `TopologyInstance` or `TopologySearch`.\n        in_channels: number of input image channels.\n        num_classes: number of output segmentation classes.\n        act_name: activation name, default to 'RELU'.\n        norm_name: normalization used in convolution blocks. Default to `InstanceNorm`.\n        spatial_dims: spatial 2D or 3D inputs.\n        use_downsample: use downsample in the stem.\n            If ``False``, the search space will be in resolution [1, 1/2, 1/4, 1/8],\n            if ``True``, the search space will be in resolution [1/2, 1/4, 1/8, 1/16].\n        node_a: node activation numpy matrix. Its shape is `(num_depths, num_blocks + 1)`.\n            +1 for multi-resolution inputs.\n            In model searching stage, ``node_a`` can be None. In deployment stage, ``node_a`` cannot be None.\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dints.py_DiNTS.__init___DiNTS.__init__.for_res_idx_in_range_self.if_use_downsample_.else_.self_stem_up_str_res_idx_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dints.py_DiNTS.__init___DiNTS.__init__.for_res_idx_in_range_self.if_use_downsample_.else_.self_stem_up_str_res_idx_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/dints.py", "file_name": "dints.py", "file_type": "text/x-python", "category": "implementation", "start_line": 280, "end_line": 403, "span_ids": ["DiNTS.__init__"], "tokens": 984}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DiNTS(nn.Module):\n\n    def __init__(\n        self,\n        dints_space,\n        in_channels: int,\n        num_classes: int,\n        act_name: Union[Tuple, str] = \"RELU\",\n        norm_name: Union[Tuple, str] = \"INSTANCE\",\n        spatial_dims: int = 3,\n        use_downsample: bool = True,\n        node_a=None,\n    ):\n        super().__init__()\n\n        self.dints_space = dints_space\n        self.filter_nums = dints_space.filter_nums\n        self.num_blocks = dints_space.num_blocks\n        self.num_depths = dints_space.num_depths\n        if spatial_dims not in (2, 3):\n            raise NotImplementedError(f\"Spatial dimensions {spatial_dims} is not supported.\")\n        self._spatial_dims = spatial_dims\n        if node_a is None:\n            self.node_a = torch.ones((self.num_blocks + 1, self.num_depths))\n        else:\n            self.node_a = node_a\n\n        # define stem operations for every block\n        conv_type = Conv[Conv.CONV, spatial_dims]\n        self.stem_down = nn.ModuleDict()\n        self.stem_up = nn.ModuleDict()\n        self.stem_finals = nn.Sequential(\n            ActiConvNormBlock(\n                self.filter_nums[0],\n                self.filter_nums[0],\n                act_name=act_name,\n                norm_name=norm_name,\n                spatial_dims=spatial_dims,\n            ),\n            conv_type(\n                in_channels=self.filter_nums[0],\n                out_channels=num_classes,\n                kernel_size=1,\n                stride=1,\n                padding=0,\n                groups=1,\n                bias=True,\n                dilation=1,\n            ),\n        )\n        mode = \"trilinear\" if self._spatial_dims == 3 else \"bilinear\"\n        for res_idx in range(self.num_depths):\n            # define downsample stems before DiNTS search\n            if use_downsample:\n                self.stem_down[str(res_idx)] = StemTS(\n                    nn.Upsample(scale_factor=1 / (2 ** res_idx), mode=mode, align_corners=True),\n                    conv_type(\n                        in_channels=in_channels,\n                        out_channels=self.filter_nums[res_idx],\n                        kernel_size=3,\n                        stride=1,\n                        padding=1,\n                        groups=1,\n                        bias=False,\n                        dilation=1,\n                    ),\n                    get_norm_layer(name=norm_name, spatial_dims=spatial_dims, channels=self.filter_nums[res_idx]),\n                    get_act_layer(name=act_name),\n                    conv_type(\n                        in_channels=self.filter_nums[res_idx],\n                        out_channels=self.filter_nums[res_idx + 1],\n                        kernel_size=3,\n                        stride=2,\n                        padding=1,\n                        groups=1,\n                        bias=False,\n                        dilation=1,\n                    ),\n                    get_norm_layer(name=norm_name, spatial_dims=spatial_dims, channels=self.filter_nums[res_idx + 1]),\n                )\n                self.stem_up[str(res_idx)] = StemTS(\n                    get_act_layer(name=act_name),\n                    conv_type(\n                        in_channels=self.filter_nums[res_idx + 1],\n                        out_channels=self.filter_nums[res_idx],\n                        kernel_size=3,\n                        stride=1,\n                        padding=1,\n                        groups=1,\n                        bias=False,\n                        dilation=1,\n                    ),\n                    get_norm_layer(name=norm_name, spatial_dims=spatial_dims, channels=self.filter_nums[res_idx]),\n                    nn.Upsample(scale_factor=2, mode=mode, align_corners=True),\n                )\n\n            else:\n                self.stem_down[str(res_idx)] = StemTS(\n                    nn.Upsample(scale_factor=1 / (2 ** res_idx), mode=mode, align_corners=True),\n                    conv_type(\n                        in_channels=in_channels,\n                        out_channels=self.filter_nums[res_idx],\n                        kernel_size=3,\n                        stride=1,\n                        padding=1,\n                        groups=1,\n                        bias=False,\n                        dilation=1,\n                    ),\n                    get_norm_layer(name=norm_name, spatial_dims=spatial_dims, channels=self.filter_nums[res_idx]),\n                )\n                self.stem_up[str(res_idx)] = StemTS(\n                    get_act_layer(name=act_name),\n                    conv_type(\n                        in_channels=self.filter_nums[res_idx],\n                        out_channels=self.filter_nums[max(res_idx - 1, 0)],\n                        kernel_size=3,\n                        stride=1,\n                        padding=1,\n                        groups=1,\n                        bias=False,\n                        dilation=1,\n                    ),\n                    get_norm_layer(name=norm_name, spatial_dims=spatial_dims, channels=self.filter_nums[res_idx - 1]),\n                    nn.Upsample(scale_factor=2 ** (res_idx != 0), mode=mode, align_corners=True),\n                )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dints.py_DiNTS.weight_parameters_DiNTS.forward.return.prediction": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dints.py_DiNTS.weight_parameters_DiNTS.forward.return.prediction", "embedding": null, "metadata": {"file_path": "monai/networks/nets/dints.py", "file_name": "dints.py", "file_type": "text/x-python", "category": "implementation", "start_line": 405, "end_line": 438, "span_ids": ["DiNTS.forward", "DiNTS.weight_parameters"], "tokens": 267}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DiNTS(nn.Module):\n\n    def weight_parameters(self):\n        return [param for name, param in self.named_parameters()]\n\n    def forward(self, x: torch.Tensor):\n        \"\"\"\n        Prediction based on dynamic arch_code.\n\n        Args:\n            x: input tensor.\n        \"\"\"\n        inputs = []\n        for d in range(self.num_depths):\n            # allow multi-resolution input\n            _mod_w: StemInterface = self.stem_down[str(d)]\n            x_out = _mod_w.forward(x)\n            if self.node_a[0][d]:\n                inputs.append(x_out)\n            else:\n                inputs.append(torch.zeros_like(x_out))\n\n        outputs = self.dints_space(inputs)\n\n        blk_idx = self.num_blocks - 1\n        start = False\n        _temp: torch.Tensor = torch.empty(0)\n        for res_idx in range(self.num_depths - 1, -1, -1):\n            _mod_up: StemInterface = self.stem_up[str(res_idx)]\n            if start:\n                _temp = _mod_up.forward(outputs[res_idx] + _temp)\n            elif self.node_a[blk_idx + 1][res_idx]:\n                start = True\n                _temp = _mod_up.forward(outputs[res_idx])\n        prediction = self.stem_finals(_temp)\n        return prediction", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dints.py_TopologyConstruction_TopologyConstruction._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dints.py_TopologyConstruction_TopologyConstruction._", "embedding": null, "metadata": {"file_path": "monai/networks/nets/dints.py", "file_name": "dints.py", "file_type": "text/x-python", "category": "implementation", "start_line": 441, "end_line": 477, "span_ids": ["TopologyConstruction"], "tokens": 654}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TopologyConstruction(nn.Module):\n    \"\"\"\n    The base class for `TopologyInstance` and `TopologySearch`.\n\n    Args:\n        arch_code: `[arch_code_a, arch_code_c]`, numpy arrays. The architecture codes defining the model.\n            For example, for a ``num_depths=4, num_blocks=12`` search space:\n\n            - `arch_code_a` is a 12x10 (10 paths) binary matrix representing if a path is activated.\n            - `arch_code_c` is a 12x10x5 (5 operations) binary matrix representing if a cell operation is used.\n            - `arch_code` in ``__init__()`` is used for creating the network and remove unused network blocks. If None,\n\n            all paths and cells operations will be used, and must be in the searching stage (is_search=True).\n        channel_mul: adjust intermediate channel number, default is 1.\n        cell: operation of each node.\n        num_blocks: number of blocks (depth in the horizontal direction) of the DiNTS search space.\n        num_depths: number of image resolutions of the DiNTS search space: 1, 1/2, 1/4 ... in each dimension.\n        use_downsample: use downsample in the stem. If False, the search space will be in resolution [1, 1/2, 1/4, 1/8],\n            if True, the search space will be in resolution [1/2, 1/4, 1/8, 1/16].\n        device: `'cpu'`, `'cuda'`, or device ID.\n\n\n    Predefined variables:\n        `filter_nums`: default to 32. Double the number of channels after downsample.\n        topology related variables:\n\n            - `arch_code2in`: path activation to its incoming node index (resolution). For depth = 4,\n              arch_code2in = [0, 1, 0, 1, 2, 1, 2, 3, 2, 3]. The first path outputs from node 0 (top resolution),\n              the second path outputs from node 1 (second resolution in the search space),\n              the third path outputs from node 0, etc.\n            - `arch_code2ops`: path activation to operations of upsample 1, keep 0, downsample -1. For depth = 4,\n              arch_code2ops = [0, 1, -1, 0, 1, -1, 0, 1, -1, 0]. The first path does not change\n              resolution, the second path perform upsample, the third perform downsample, etc.\n            - `arch_code2out`: path activation to its output node index.\n              For depth = 4, arch_code2out = [0, 0, 1, 1, 1, 2, 2, 2, 3, 3],\n              the first and second paths connects to node 0 (top resolution), the 3,4,5 paths connects to node 1, etc.\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dints.py_TopologyConstruction.__init___TopologyConstruction.forward.pass": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dints.py_TopologyConstruction.__init___TopologyConstruction.forward.pass", "embedding": null, "metadata": {"file_path": "monai/networks/nets/dints.py", "file_name": "dints.py", "file_type": "text/x-python", "category": "implementation", "start_line": 479, "end_line": 542, "span_ids": ["TopologyConstruction.__init__", "TopologyConstruction.forward"], "tokens": 701}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TopologyConstruction(nn.Module):\n\n    def __init__(\n        self,\n        arch_code: Optional[list] = None,\n        channel_mul: float = 1.0,\n        cell=Cell,\n        num_blocks: int = 6,\n        num_depths: int = 3,\n        spatial_dims: int = 3,\n        use_downsample: bool = True,\n        device: str = \"cpu\",\n    ):\n\n        super().__init__()\n\n        self.filter_nums = [int(n_feat * channel_mul) for n_feat in (32, 64, 128, 256, 512)]\n        self.num_blocks = num_blocks\n        self.num_depths = num_depths\n        self._spatial_dims = spatial_dims\n        self.use_downsample = use_downsample\n        self.device = device\n        self.num_cell_ops = 0\n        if self._spatial_dims == 2:\n            self.num_cell_ops = len(cell.OPS2D)\n        elif self._spatial_dims == 3:\n            self.num_cell_ops = len(cell.OPS3D)\n\n        # Calculate predefined parameters for topology search and decoding\n        arch_code2in, arch_code2out = [], []\n        for i in range(Cell.DIRECTIONS * self.num_depths - 2):\n            arch_code2in.append((i + 1) // Cell.DIRECTIONS - 1 + (i + 1) % Cell.DIRECTIONS)\n        arch_code2ops = ([-1, 0, 1] * self.num_depths)[1:-1]\n        for m in range(self.num_depths):\n            arch_code2out.extend([m, m, m])\n        arch_code2out = arch_code2out[1:-1]\n        self.arch_code2in = arch_code2in\n        self.arch_code2ops = arch_code2ops\n        self.arch_code2out = arch_code2out\n\n        # define NAS search space\n        if arch_code is None:\n            arch_code_a = torch.ones((self.num_blocks, len(self.arch_code2out))).to(self.device)\n            arch_code_c = torch.ones((self.num_blocks, len(self.arch_code2out), self.num_cell_ops)).to(self.device)\n        else:\n            arch_code_a = torch.from_numpy(arch_code[0]).to(self.device)\n            arch_code_c = F.one_hot(torch.from_numpy(arch_code[1]).to(torch.int64), self.num_cell_ops).to(self.device)\n\n        self.arch_code_a = arch_code_a\n        self.arch_code_c = arch_code_c\n        # define cell operation on each path\n        self.cell_tree = nn.ModuleDict()\n        for blk_idx in range(self.num_blocks):\n            for res_idx in range(len(self.arch_code2out)):\n                if self.arch_code_a[blk_idx, res_idx] == 1:\n                    self.cell_tree[str((blk_idx, res_idx))] = cell(\n                        self.filter_nums[self.arch_code2in[res_idx] + int(use_downsample)],\n                        self.filter_nums[self.arch_code2out[res_idx] + int(use_downsample)],\n                        self.arch_code2ops[res_idx],\n                        self.arch_code_c[blk_idx, res_idx],\n                        self._spatial_dims,\n                    )\n\n    def forward(self, x):\n        \"\"\"This function to be implemented by the architecture instances or search spaces.\"\"\"\n        pass", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dints.py_TopologyInstance_TopologyInstance.__init__.super___init___": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dints.py_TopologyInstance_TopologyInstance.__init__.super___init___", "embedding": null, "metadata": {"file_path": "monai/networks/nets/dints.py", "file_name": "dints.py", "file_type": "text/x-python", "category": "implementation", "start_line": 545, "end_line": 576, "span_ids": ["TopologyInstance.__init__", "TopologyInstance"], "tokens": 202}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TopologyInstance(TopologyConstruction):\n    \"\"\"\n    Instance of the final searched architecture. Only used in re-training/inference stage.\n    \"\"\"\n\n    def __init__(\n        self,\n        arch_code=None,\n        channel_mul: float = 1.0,\n        cell=Cell,\n        num_blocks: int = 6,\n        num_depths: int = 3,\n        spatial_dims: int = 3,\n        use_downsample: bool = True,\n        device: str = \"cpu\",\n    ):\n        \"\"\"\n        Initialize DiNTS topology search space of neural architectures.\n        \"\"\"\n        if arch_code is None:\n            warnings.warn(\"arch_code not provided when not searching.\")\n\n        super().__init__(\n            arch_code=arch_code,\n            channel_mul=channel_mul,\n            cell=cell,\n            num_blocks=num_blocks,\n            num_depths=num_depths,\n            spatial_dims=spatial_dims,\n            use_downsample=use_downsample,\n            device=device,\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dints.py_TopologyInstance.forward_TopologyInstance.forward.return.inputs": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dints.py_TopologyInstance.forward_TopologyInstance.forward.return.inputs", "embedding": null, "metadata": {"file_path": "monai/networks/nets/dints.py", "file_name": "dints.py", "file_type": "text/x-python", "category": "implementation", "start_line": 578, "end_line": 596, "span_ids": ["TopologyInstance.forward"], "tokens": 212}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TopologyInstance(TopologyConstruction):\n\n    def forward(self, x: List[torch.Tensor]) -> List[torch.Tensor]:\n        \"\"\"\n        Args:\n            x: input tensor.\n        \"\"\"\n        # generate path activation probability\n        inputs, outputs = x, [torch.tensor(0.0).to(x[0])] * self.num_depths\n        for blk_idx in range(self.num_blocks):\n            outputs = [torch.tensor(0.0).to(x[0])] * self.num_depths\n            for res_idx, activation in enumerate(self.arch_code_a[blk_idx].data):\n                if activation:\n                    mod: CellInterface = self.cell_tree[str((blk_idx, res_idx))]\n                    _out = mod.forward(\n                        x=inputs[self.arch_code2in[res_idx]], weight=torch.ones_like(self.arch_code_c[blk_idx, res_idx])\n                    )\n                    outputs[self.arch_code2out[res_idx]] = outputs[self.arch_code2out[res_idx]] + _out\n        inputs = outputs\n\n        return inputs", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dints.py_TopologySearch_TopologySearch._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dints.py_TopologySearch_TopologySearch._", "embedding": null, "metadata": {"file_path": "monai/networks/nets/dints.py", "file_name": "dints.py", "file_type": "text/x-python", "category": "implementation", "start_line": 599, "end_line": 643, "span_ids": ["TopologySearch"], "tokens": 629}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TopologySearch(TopologyConstruction):\n    \"\"\"\n    DiNTS topology search space of neural architectures.\n\n    Examples:\n\n    .. code-block:: python\n\n        from monai.networks.nets.dints import TopologySearch\n\n        topology_search_space = TopologySearch(\n            channel_mul=0.5, num_blocks=8, num_depths=4, use_downsample=True, spatial_dims=3)\n        topology_search_space.get_ram_cost_usage(in_size=(2, 16, 80, 80, 80), full=True)\n        multi_res_images = [\n            torch.randn(2, 16, 80, 80, 80),\n            torch.randn(2, 32, 40, 40, 40),\n            torch.randn(2, 64, 20, 20, 20),\n            torch.randn(2, 128, 10, 10, 10)]\n        prediction = topology_search_space(image)\n        for x in prediction: print(x.shape)\n        # torch.Size([2, 16, 80, 80, 80])\n        # torch.Size([2, 32, 40, 40, 40])\n        # torch.Size([2, 64, 20, 20, 20])\n        # torch.Size([2, 128, 10, 10, 10])\n\n    Class method overview:\n\n        - ``get_prob_a()``: convert learnable architecture weights to path activation probabilities.\n        - ``get_ram_cost_usage()``: get estimated ram cost.\n        - ``get_topology_entropy()``: get topology entropy loss in searching stage.\n        - ``decode()``: get final binarized architecture code.\n        - ``gen_mtx()``: generate variables needed for topology search.\n\n    Predefined variables:\n        - `tidx`: index used to convert path activation matrix T = (depth,depth) in transfer_mtx to\n          path activation arch_code (1,3*depth-2), for depth = 4, tidx = [0, 1, 4, 5, 6, 9, 10, 11, 14, 15],\n          A tidx (10 binary values) represents the path activation.\n        - `transfer_mtx`: feasible path activation matrix (denoted as T) given a node activation pattern.\n          It is used to convert path activation pattern (1, paths) to node activation (1, nodes)\n        - `node_act_list`: all node activation [2^num_depths-1, depth]. For depth = 4, there are 15 node activation\n          patterns, each of length 4. For example, [1,1,0,0] means nodes 0, 1 are activated (with input paths).\n        - `all_connect`: All possible path activations. For depth = 4,\n          all_connection has 1024 vectors of length 10 (10 paths).\n          The return value will exclude path activation of all 0.\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dints.py_TopologySearch.__init___TopologySearch.__init__.self._arch_param_names._log_alpha_a_log_alph": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dints.py_TopologySearch.__init___TopologySearch.__init__.self._arch_param_names._log_alpha_a_log_alph", "embedding": null, "metadata": {"file_path": "monai/networks/nets/dints.py", "file_name": "dints.py", "file_type": "text/x-python", "category": "implementation", "start_line": 645, "end_line": 703, "span_ids": ["TopologySearch.__init__"], "tokens": 574}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TopologySearch(TopologyConstruction):\n\n    def __init__(\n        self,\n        channel_mul: float = 1.0,\n        cell=Cell,\n        arch_code: Optional[list] = None,\n        num_blocks: int = 6,\n        num_depths: int = 3,\n        spatial_dims: int = 3,\n        use_downsample: bool = True,\n        device: str = \"cpu\",\n    ):\n        \"\"\"\n        Initialize DiNTS topology search space of neural architectures.\n        \"\"\"\n        super().__init__(\n            arch_code=arch_code,\n            channel_mul=channel_mul,\n            cell=cell,\n            num_blocks=num_blocks,\n            num_depths=num_depths,\n            spatial_dims=spatial_dims,\n            use_downsample=use_downsample,\n            device=device,\n        )\n\n        tidx = []\n        _d = Cell.DIRECTIONS\n        for i in range(_d * self.num_depths - 2):\n            tidx.append((i + 1) // _d * self.num_depths + (i + 1) // _d - 1 + (i + 1) % _d)\n        self.tidx = tidx\n        transfer_mtx, node_act_list, child_list = self.gen_mtx(num_depths)\n\n        self.node_act_list = np.asarray(node_act_list)\n        self.node_act_dict = {str(self.node_act_list[i]): i for i in range(len(self.node_act_list))}\n        self.transfer_mtx = transfer_mtx\n        self.child_list = np.asarray(child_list)\n\n        self.ram_cost = np.zeros((self.num_blocks, len(self.arch_code2out), self.num_cell_ops))\n        for blk_idx in range(self.num_blocks):\n            for res_idx in range(len(self.arch_code2out)):\n                if self.arch_code_a[blk_idx, res_idx] == 1:\n                    self.ram_cost[blk_idx, res_idx] = np.array(\n                        [\n                            op.ram_cost + self.cell_tree[str((blk_idx, res_idx))].preprocess.ram_cost\n                            for op in self.cell_tree[str((blk_idx, res_idx))].op.ops[: self.num_cell_ops]\n                        ]\n                    )\n\n        # define cell and macro architecture probabilities\n        self.log_alpha_c = nn.Parameter(\n            torch.zeros(self.num_blocks, len(self.arch_code2out), self.num_cell_ops)\n            .normal_(1, 0.01)\n            .to(self.device)\n            .requires_grad_()\n        )\n        self.log_alpha_a = nn.Parameter(\n            torch.zeros(self.num_blocks, len(self.arch_code2out)).normal_(0, 0.01).to(self.device).requires_grad_()\n        )\n        self._arch_param_names = [\"log_alpha_a\", \"log_alpha_c\"]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dints.py_TopologySearch.gen_mtx_TopologySearch.gen_mtx.return.transfer_mtx_node_act_li": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dints.py_TopologySearch.gen_mtx_TopologySearch.gen_mtx.return.transfer_mtx_node_act_li", "embedding": null, "metadata": {"file_path": "monai/networks/nets/dints.py", "file_name": "dints.py", "file_type": "text/x-python", "category": "implementation", "start_line": 705, "end_line": 742, "span_ids": ["TopologySearch.gen_mtx"], "tokens": 531}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TopologySearch(TopologyConstruction):\n\n    def gen_mtx(self, depth: int):\n        \"\"\"\n        Generate elements needed in decoding and topology.\n\n            - `transfer_mtx`: feasible path activation matrix (denoted as T) given a node activation pattern.\n               It is used to convert path activation pattern (1, paths) to node activation (1, nodes)\n            - `node_act_list`: all node activation [2^num_depths-1, depth]. For depth = 4, there are 15 node activation\n               patterns, each of length 4. For example, [1,1,0,0] means nodes 0, 1 are activated (with input paths).\n            - `all_connect`: All possible path activations. For depth = 4,\n              all_connection has 1024 vectors of length 10 (10 paths).\n              The return value will exclude path activation of all 0.\n        \"\"\"\n        # total paths in a block, each node has three output paths,\n        # except the two nodes at the top and the bottom scales\n        paths = Cell.DIRECTIONS * depth - 2\n\n        # for 10 paths, all_connect has 1024 possible path activations. [1 0 0 0 0 0 0 0 0 0] means the top\n        # path is activated.\n        all_connect = _dfs(0, paths - 1)\n\n        # Save all possible connections in mtx (might be redundant and infeasible)\n        mtx = []\n        for m in all_connect:\n            # convert path activation [1,paths] to path activation matrix [depth, depth]\n            ma = np.zeros((depth, depth))\n            for i in range(paths):\n                ma[(i + 1) // Cell.DIRECTIONS, (i + 1) // Cell.DIRECTIONS - 1 + (i + 1) % Cell.DIRECTIONS] = m[i]\n            mtx.append(ma)\n\n        # define all possible node activation\n        node_act_list = _dfs(0, depth - 1)[1:]\n        transfer_mtx = {}\n        for arch_code in node_act_list:\n            # make sure each activated node has an active connection, inactivated node has no connection\n            arch_code_mtx = [_ for _ in mtx if ((np.sum(_, 0) > 0).astype(int) == np.array(arch_code)).all()]\n            transfer_mtx[str(np.array(arch_code))] = arch_code_mtx\n\n        return transfer_mtx, node_act_list, all_connect[1:]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dints.py_TopologySearch.weight_parameters_TopologySearch.get_prob_a.return.None_arch_code_prob_a": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dints.py_TopologySearch.weight_parameters_TopologySearch.get_prob_a.return.None_arch_code_prob_a", "embedding": null, "metadata": {"file_path": "monai/networks/nets/dints.py", "file_name": "dints.py", "file_type": "text/x-python", "category": "implementation", "start_line": 744, "end_line": 777, "span_ids": ["TopologySearch.weight_parameters", "TopologySearch.get_prob_a"], "tokens": 391}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TopologySearch(TopologyConstruction):\n\n    def weight_parameters(self):\n        return [param for name, param in self.named_parameters() if name not in self._arch_param_names]\n\n    def get_prob_a(self, child: bool = False):\n        \"\"\"\n        Get final path and child model probabilities from architecture weights `log_alpha_a`.\n        This is used in forward pass, getting training loss, and final decoding.\n\n        Args:\n            child: return child probability (used in decoding)\n        Return:\n            arch_code_prob_a: the path activation probability of size:\n                `[number of blocks, number of paths in each block]`.\n                For 12 blocks, 4 depths search space, the size is [12,10]\n            probs_a: The probability of all child models (size 1023x10). Each child model is a path activation pattern\n                 (1D vector of length 10 for 10 paths). In total 1023 child models (2^10 -1)\n        \"\"\"\n        _arch_code_prob_a = torch.sigmoid(self.log_alpha_a)\n        # remove the case where all path are zero, and re-normalize.\n        norm = 1 - (1 - _arch_code_prob_a).prod(-1)\n        arch_code_prob_a = _arch_code_prob_a / norm.unsqueeze(1)\n        if child:\n            path_activation = torch.from_numpy(self.child_list).to(self.device)\n            probs_a = [\n                (\n                    path_activation * _arch_code_prob_a[blk_idx]\n                    + (1 - path_activation) * (1 - _arch_code_prob_a[blk_idx])\n                ).prod(-1)\n                / norm[blk_idx]\n                for blk_idx in range(self.num_blocks)\n            ]\n            probs_a = torch.stack(probs_a)  # type: ignore\n            return probs_a, arch_code_prob_a\n        return None, arch_code_prob_a", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dints.py_TopologySearch.get_ram_cost_usage_TopologySearch.get_ram_cost_usage.return.usage_32_8_1024_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dints.py_TopologySearch.get_ram_cost_usage_TopologySearch.get_ram_cost_usage.return.usage_32_8_1024_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/dints.py", "file_name": "dints.py", "file_type": "text/x-python", "category": "implementation", "start_line": 779, "end_line": 810, "span_ids": ["TopologySearch.get_ram_cost_usage"], "tokens": 386}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TopologySearch(TopologyConstruction):\n\n    def get_ram_cost_usage(self, in_size, full: bool = False):\n        \"\"\"\n        Get estimated output tensor size to approximate RAM consumption.\n\n        Args:\n            in_size: input image shape (4D/5D, ``[BCHW[D]]``) at the highest resolution level.\n            full: full ram cost usage with all probability of 1.\n        \"\"\"\n        # convert input image size to feature map size at each level\n        batch_size = in_size[0]\n        image_size = np.array(in_size[-self._spatial_dims :])\n        sizes = []\n        for res_idx in range(self.num_depths):\n            sizes.append(batch_size * self.filter_nums[res_idx] * (image_size // (2 ** res_idx)).prod())\n        sizes = torch.tensor(sizes).to(torch.float32).to(self.device) / (2 ** (int(self.use_downsample)))\n        probs_a, arch_code_prob_a = self.get_prob_a(child=False)\n        cell_prob = F.softmax(self.log_alpha_c, dim=-1)\n        if full:\n            arch_code_prob_a = arch_code_prob_a.detach()\n            arch_code_prob_a.fill_(1)\n        ram_cost = torch.from_numpy(self.ram_cost).to(torch.float32).to(self.device)\n        usage = 0.0\n        for blk_idx in range(self.num_blocks):\n            # node activation for input\n            # cell operation\n            for path_idx in range(len(self.arch_code2out)):\n                usage += (\n                    arch_code_prob_a[blk_idx, path_idx]\n                    * (1 + (ram_cost[blk_idx, path_idx] * cell_prob[blk_idx, path_idx]).sum())\n                    * sizes[self.arch_code2out[path_idx]]\n                )\n        return usage * 32 / 8 / 1024 ** 2", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dints.py_TopologySearch.get_topology_entropy_TopologySearch.get_topology_entropy.return.ent": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dints.py_TopologySearch.get_topology_entropy_TopologySearch.get_topology_entropy.return.ent", "embedding": null, "metadata": {"file_path": "monai/networks/nets/dints.py", "file_name": "dints.py", "file_type": "text/x-python", "category": "implementation", "start_line": 812, "end_line": 848, "span_ids": ["TopologySearch.get_topology_entropy"], "tokens": 447}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TopologySearch(TopologyConstruction):\n\n    def get_topology_entropy(self, probs):\n        \"\"\"\n        Get topology entropy loss at searching stage.\n\n        Args:\n            probs: path activation probabilities\n        \"\"\"\n        if hasattr(self, \"node2in\"):\n            node2in = self.node2in\n            node2out = self.node2out\n        else:\n            # node activation index to feasible input child_idx\n            node2in = [[] for _ in range(len(self.node_act_list))]\n            # node activation index to feasible output child_idx\n            node2out = [[] for _ in range(len(self.node_act_list))]\n            for child_idx in range(len(self.child_list)):\n                _node_in, _node_out = np.zeros(self.num_depths), np.zeros(self.num_depths)\n                for res_idx in range(len(self.arch_code2out)):\n                    _node_out[self.arch_code2out[res_idx]] += self.child_list[child_idx][res_idx]\n                    _node_in[self.arch_code2in[res_idx]] += self.child_list[child_idx][res_idx]\n                _node_in = (_node_in >= 1).astype(int)\n                _node_out = (_node_out >= 1).astype(int)\n                node2in[self.node_act_dict[str(_node_out)]].append(child_idx)\n                node2out[self.node_act_dict[str(_node_in)]].append(child_idx)\n            self.node2in = node2in\n            self.node2out = node2out\n        # calculate entropy\n        ent = 0\n        for blk_idx in range(self.num_blocks - 1):\n            blk_ent = 0\n            # node activation probability\n            for node_idx in range(len(self.node_act_list)):\n                _node_p = probs[blk_idx, node2in[node_idx]].sum()\n                _out_probs = probs[blk_idx + 1, node2out[node_idx]].sum()\n                blk_ent += -(_node_p * torch.log(_out_probs + 1e-5) + (1 - _node_p) * torch.log(1 - _out_probs + 1e-5))\n            ent += blk_ent\n        return ent", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dints.py_TopologySearch.decode_TopologySearch.decode.dist_matrix_predecessors": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dints.py_TopologySearch.decode_TopologySearch.decode.dist_matrix_predecessors", "embedding": null, "metadata": {"file_path": "monai/networks/nets/dints.py", "file_name": "dints.py", "file_type": "text/x-python", "category": "implementation", "start_line": 850, "end_line": 906, "span_ids": ["TopologySearch.decode"], "tokens": 783}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TopologySearch(TopologyConstruction):\n\n    def decode(self):\n        \"\"\"\n        Decode network log_alpha_a/log_alpha_c using dijkstra shortest path algorithm.\n\n        `[node_a, arch_code_a, arch_code_c, arch_code_a_max]` is decoded when using ``self.decode()``.\n\n        For example, for a ``num_depths=4``, ``num_blocks=12`` search space:\n\n            - ``node_a`` is a 4x13 binary matrix representing if a feature node is activated\n              (13 because of multi-resolution inputs).\n            - ``arch_code_a`` is a 12x10 (10 paths) binary matrix representing if a path is activated.\n            - ``arch_code_c`` is a 12x10x5 (5 operations) binary matrix representing if a cell operation is used.\n\n        Return:\n            arch_code with maximum probability\n        \"\"\"\n        probs, arch_code_prob_a = self.get_prob_a(child=True)\n        arch_code_a_max = self.child_list[torch.argmax(probs, -1).data.cpu().numpy()]\n        arch_code_c = torch.argmax(F.softmax(self.log_alpha_c, -1), -1).data.cpu().numpy()\n        probs = probs.data.cpu().numpy()\n\n        # define adjacency matrix\n        amtx = np.zeros(\n            (1 + len(self.child_list) * self.num_blocks + 1, 1 + len(self.child_list) * self.num_blocks + 1)\n        )\n\n        # build a path activation to child index searching dictionary\n        path2child = {str(self.child_list[i]): i for i in range(len(self.child_list))}\n\n        # build a submodel to submodel index\n        sub_amtx = np.zeros((len(self.child_list), len(self.child_list)))\n        for child_idx in range(len(self.child_list)):\n            _node_act = np.zeros(self.num_depths).astype(int)\n            for path_idx in range(len(self.child_list[child_idx])):\n                _node_act[self.arch_code2out[path_idx]] += self.child_list[child_idx][path_idx]\n            _node_act = (_node_act >= 1).astype(int)\n            for mtx in self.transfer_mtx[str(_node_act)]:\n                connect_child_idx = path2child[str(mtx.flatten()[self.tidx].astype(int))]\n                sub_amtx[child_idx, connect_child_idx] = 1\n\n        # fill in source to first block, add 1e-5/1e-3 to avoid log0 and negative edge weights\n        amtx[0, 1 : 1 + len(self.child_list)] = -np.log(probs[0] + 1e-5) + 0.001\n\n        # fill in the rest blocks\n        for blk_idx in range(1, self.num_blocks):\n            amtx[\n                1 + (blk_idx - 1) * len(self.child_list) : 1 + blk_idx * len(self.child_list),\n                1 + blk_idx * len(self.child_list) : 1 + (blk_idx + 1) * len(self.child_list),\n            ] = sub_amtx * np.tile(-np.log(probs[blk_idx] + 1e-5) + 0.001, (len(self.child_list), 1))\n\n        # fill in the last to the sink\n        amtx[1 + (self.num_blocks - 1) * len(self.child_list) : 1 + self.num_blocks * len(self.child_list), -1] = 0.001\n\n        graph = csr_matrix(amtx)\n        dist_matrix, predecessors, sources = dijkstra(\n            csgraph=graph, directed=True, indices=0, min_only=True, return_predecessors=True\n        )\n        # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dints.py_TopologySearch.decode.index_a_idx_1_1_TopologySearch.decode.return.node_a_arch_code_a_arch": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dints.py_TopologySearch.decode.index_a_idx_1_1_TopologySearch.decode.return.node_a_arch_code_a_arch", "embedding": null, "metadata": {"file_path": "monai/networks/nets/dints.py", "file_name": "dints.py", "file_type": "text/x-python", "category": "implementation", "start_line": 907, "end_line": 924, "span_ids": ["TopologySearch.decode"], "tokens": 239}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TopologySearch(TopologyConstruction):\n\n    def decode(self):\n        # ... other code\n        index, a_idx = -1, -1\n        arch_code_a = np.zeros((self.num_blocks, len(self.arch_code2out)))\n        node_a = np.zeros((self.num_blocks + 1, self.num_depths))\n\n        # decoding to paths\n        while True:\n            index = predecessors[index]\n            if index == 0:\n                break\n            child_idx = (index - 1) % len(self.child_list)\n            arch_code_a[a_idx, :] = self.child_list[child_idx]\n            for res_idx in range(len(self.arch_code2out)):\n                node_a[a_idx, self.arch_code2out[res_idx]] += arch_code_a[a_idx, res_idx]\n            a_idx -= 1\n        for res_idx in range(len(self.arch_code2out)):\n            node_a[a_idx, self.arch_code2in[res_idx]] += arch_code_a[0, res_idx]\n        node_a = (node_a >= 1).astype(int)\n        return node_a, arch_code_a, arch_code_c, arch_code_a_max", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dints.py_TopologySearch.forward_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dints.py_TopologySearch.forward_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/dints.py", "file_name": "dints.py", "file_type": "text/x-python", "category": "implementation", "start_line": 926, "end_line": 949, "span_ids": ["TopologySearch.forward"], "tokens": 232}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TopologySearch(TopologyConstruction):\n\n    def forward(self, x):\n        \"\"\"\n        Prediction based on dynamic arch_code.\n\n        Args:\n            x: a list of `num_depths` input tensors as a multi-resolution input.\n                tensor is of shape `BCHW[D]` where `C` must match `self.filter_nums`.\n        \"\"\"\n        # generate path activation probability\n        probs_a, arch_code_prob_a = self.get_prob_a(child=False)\n        inputs = x\n        for blk_idx in range(self.num_blocks):\n            outputs = [0.0] * self.num_depths\n            for res_idx, activation in enumerate(self.arch_code_a[blk_idx].data.cpu().numpy()):\n                if activation:\n                    _w = F.softmax(self.log_alpha_c[blk_idx, res_idx], dim=-1)\n                    outputs[self.arch_code2out[res_idx]] += (\n                        self.cell_tree[str((blk_idx, res_idx))](inputs[self.arch_code2in[res_idx]], weight=_w)\n                        * arch_code_prob_a[blk_idx, res_idx]\n                    )\n            inputs = outputs\n\n        return inputs", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/milmodel.py_from_typing_import_Dict__MILModel._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/milmodel.py_from_typing_import_Dict__MILModel._", "embedding": null, "metadata": {"file_path": "monai/networks/nets/milmodel.py", "file_name": "milmodel.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 50, "span_ids": ["MILModel", "docstring"], "tokens": 444}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Dict, Optional, Union, cast\n\nimport torch\nimport torch.nn as nn\n\nfrom monai.utils.module import optional_import\n\nmodels, _ = optional_import(\"torchvision.models\")\n\n\nclass MILModel(nn.Module):\n    \"\"\"\n    Multiple Instance Learning (MIL) model, with a backbone classification model.\n    Currently, it only works for 2D images, a typical use case is for classification of the\n    digital pathology whole slide images. The expected shape of input data is `[B, N, C, H, W]`,\n    where `B` is the batch_size of PyTorch Dataloader and `N` is the number of instances\n    extracted from every original image in the batch. A tutorial example is available at:\n    https://github.com/Project-MONAI/tutorials/tree/master/pathology/multiple_instance_learning.\n\n    Args:\n        num_classes: number of output classes.\n        mil_mode: MIL algorithm, available values (Defaults to ``\"att\"``):\n\n            - ``\"mean\"`` - average features from all instances, equivalent to pure CNN (non MIL).\n            - ``\"max\"`` - retain only the instance with the max probability for loss calculation.\n            - ``\"att\"`` - attention based MIL https://arxiv.org/abs/1802.04712.\n            - ``\"att_trans\"`` - transformer MIL https://arxiv.org/abs/2111.01556.\n            - ``\"att_trans_pyramid\"`` - transformer pyramid MIL https://arxiv.org/abs/2111.01556.\n\n        pretrained: init backbone with pretrained weights, defaults to ``True``.\n        backbone: Backbone classifier CNN (either ``None``, a ``nn.Module`` that returns features,\n            or a string name of a torchvision model).\n            Defaults to ``None``, in which case ResNet50 is used.\n        backbone_num_features: Number of output features of the backbone CNN\n            Defaults to ``None`` (necessary only when using a custom backbone)\n        trans_blocks: number of the blocks in `TransformEncoder` layer.\n        trans_dropout: dropout rate in `TransformEncoder` layer.\n\n    \"\"\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/milmodel.py_MILModel.__init___MILModel.__init__.self.net.net": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/milmodel.py_MILModel.__init___MILModel.__init__.self.net.net", "embedding": null, "metadata": {"file_path": "monai/networks/nets/milmodel.py", "file_name": "milmodel.py", "file_type": "text/x-python", "category": "implementation", "start_line": 35, "end_line": 155, "span_ids": ["MILModel.__init__"], "tokens": 1036}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class MILModel(nn.Module):\n\n    def __init__(\n        self,\n        num_classes: int,\n        mil_mode: str = \"att\",\n        pretrained: bool = True,\n        backbone: Optional[Union[str, nn.Module]] = None,\n        backbone_num_features: Optional[int] = None,\n        trans_blocks: int = 4,\n        trans_dropout: float = 0.0,\n    ) -> None:\n\n        super().__init__()\n\n        if num_classes <= 0:\n            raise ValueError(\"Number of classes must be positive: \" + str(num_classes))\n\n        if mil_mode.lower() not in [\"mean\", \"max\", \"att\", \"att_trans\", \"att_trans_pyramid\"]:\n            raise ValueError(\"Unsupported mil_mode: \" + str(mil_mode))\n\n        self.mil_mode = mil_mode.lower()\n        self.attention = nn.Sequential()\n        self.transformer = None  # type: Optional[nn.Module]\n\n        if backbone is None:\n\n            net = models.resnet50(pretrained=pretrained)\n            nfc = net.fc.in_features  # save the number of final features\n            net.fc = torch.nn.Identity()  # remove final linear layer\n\n            self.extra_outputs = {}  # type: Dict[str, torch.Tensor]\n\n            if mil_mode == \"att_trans_pyramid\":\n                # register hooks to capture outputs of intermediate layers\n                def forward_hook(layer_name):\n                    def hook(module, input, output):\n                        self.extra_outputs[layer_name] = output\n\n                    return hook\n\n                net.layer1.register_forward_hook(forward_hook(\"layer1\"))\n                net.layer2.register_forward_hook(forward_hook(\"layer2\"))\n                net.layer3.register_forward_hook(forward_hook(\"layer3\"))\n                net.layer4.register_forward_hook(forward_hook(\"layer4\"))\n\n        elif isinstance(backbone, str):\n\n            # assume torchvision model string is provided\n            torch_model = getattr(models, backbone, None)\n            if torch_model is None:\n                raise ValueError(\"Unknown torch vision model\" + str(backbone))\n            net = torch_model(pretrained=pretrained)\n\n            if getattr(net, \"fc\", None) is not None:\n                nfc = net.fc.in_features  # save the number of final features\n                net.fc = torch.nn.Identity()  # remove final linear layer\n            else:\n                raise ValueError(\n                    \"Unable to detect FC layer for the torchvision model \" + str(backbone),\n                    \". Please initialize the backbone model manually.\",\n                )\n\n        elif isinstance(backbone, nn.Module):\n            # use a custom backbone\n            net = backbone\n            nfc = backbone_num_features\n\n            if backbone_num_features is None:\n                raise ValueError(\"Number of endencoder features must be provided for a custom backbone model\")\n\n        else:\n            raise ValueError(\"Unsupported backbone\")\n\n        if backbone is not None and mil_mode not in [\"mean\", \"max\", \"att\", \"att_trans\"]:\n            raise ValueError(\"Custom backbone is not supported for the mode:\" + str(mil_mode))\n\n        if self.mil_mode in [\"mean\", \"max\"]:\n            pass\n        elif self.mil_mode == \"att\":\n            self.attention = nn.Sequential(nn.Linear(nfc, 2048), nn.Tanh(), nn.Linear(2048, 1))\n\n        elif self.mil_mode == \"att_trans\":\n            transformer = nn.TransformerEncoderLayer(d_model=nfc, nhead=8, dropout=trans_dropout)\n            self.transformer = nn.TransformerEncoder(transformer, num_layers=trans_blocks)\n            self.attention = nn.Sequential(nn.Linear(nfc, 2048), nn.Tanh(), nn.Linear(2048, 1))\n\n        elif self.mil_mode == \"att_trans_pyramid\":\n\n            transformer_list = nn.ModuleList(\n                [\n                    nn.TransformerEncoder(\n                        nn.TransformerEncoderLayer(d_model=256, nhead=8, dropout=trans_dropout), num_layers=trans_blocks\n                    ),\n                    nn.Sequential(\n                        nn.Linear(768, 256),\n                        nn.TransformerEncoder(\n                            nn.TransformerEncoderLayer(d_model=256, nhead=8, dropout=trans_dropout),\n                            num_layers=trans_blocks,\n                        ),\n                    ),\n                    nn.Sequential(\n                        nn.Linear(1280, 256),\n                        nn.TransformerEncoder(\n                            nn.TransformerEncoderLayer(d_model=256, nhead=8, dropout=trans_dropout),\n                            num_layers=trans_blocks,\n                        ),\n                    ),\n                    nn.TransformerEncoder(\n                        nn.TransformerEncoderLayer(d_model=2304, nhead=8, dropout=trans_dropout),\n                        num_layers=trans_blocks,\n                    ),\n                ]\n            )\n            self.transformer = transformer_list\n            nfc = nfc + 256\n            self.attention = nn.Sequential(nn.Linear(nfc, 2048), nn.Tanh(), nn.Linear(2048, 1))\n\n        else:\n            raise ValueError(\"Unsupported mil_mode: \" + str(mil_mode))\n\n        self.myfc = nn.Linear(nfc, num_classes)\n        self.net = net", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/milmodel.py_MILModel.calc_head_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/milmodel.py_MILModel.calc_head_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/milmodel.py", "file_name": "milmodel.py", "file_type": "text/x-python", "category": "implementation", "start_line": 157, "end_line": 228, "span_ids": ["MILModel.calc_head", "MILModel.forward"], "tokens": 681}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class MILModel(nn.Module):\n\n    def calc_head(self, x: torch.Tensor) -> torch.Tensor:\n\n        sh = x.shape\n\n        if self.mil_mode == \"mean\":\n            x = self.myfc(x)\n            x = torch.mean(x, dim=1)\n\n        elif self.mil_mode == \"max\":\n            x = self.myfc(x)\n            x, _ = torch.max(x, dim=1)\n\n        elif self.mil_mode == \"att\":\n\n            a = self.attention(x)\n            a = torch.softmax(a, dim=1)\n            x = torch.sum(x * a, dim=1)\n\n            x = self.myfc(x)\n\n        elif self.mil_mode == \"att_trans\" and self.transformer is not None:\n\n            x = x.permute(1, 0, 2)\n            x = self.transformer(x)\n            x = x.permute(1, 0, 2)\n\n            a = self.attention(x)\n            a = torch.softmax(a, dim=1)\n            x = torch.sum(x * a, dim=1)\n\n            x = self.myfc(x)\n\n        elif self.mil_mode == \"att_trans_pyramid\" and self.transformer is not None:\n\n            l1 = torch.mean(self.extra_outputs[\"layer1\"], dim=(2, 3)).reshape(sh[0], sh[1], -1).permute(1, 0, 2)\n            l2 = torch.mean(self.extra_outputs[\"layer2\"], dim=(2, 3)).reshape(sh[0], sh[1], -1).permute(1, 0, 2)\n            l3 = torch.mean(self.extra_outputs[\"layer3\"], dim=(2, 3)).reshape(sh[0], sh[1], -1).permute(1, 0, 2)\n            l4 = torch.mean(self.extra_outputs[\"layer4\"], dim=(2, 3)).reshape(sh[0], sh[1], -1).permute(1, 0, 2)\n\n            transformer_list = cast(nn.ModuleList, self.transformer)\n\n            x = transformer_list[0](l1)\n            x = transformer_list[1](torch.cat((x, l2), dim=2))\n            x = transformer_list[2](torch.cat((x, l3), dim=2))\n            x = transformer_list[3](torch.cat((x, l4), dim=2))\n\n            x = x.permute(1, 0, 2)\n\n            a = self.attention(x)\n            a = torch.softmax(a, dim=1)\n            x = torch.sum(x * a, dim=1)\n\n            x = self.myfc(x)\n\n        else:\n            raise ValueError(\"Wrong model mode\" + str(self.mil_mode))\n\n        return x\n\n    def forward(self, x: torch.Tensor, no_head: bool = False) -> torch.Tensor:\n\n        sh = x.shape\n        x = x.reshape(sh[0] * sh[1], sh[2], sh[3], sh[4])\n\n        x = self.net(x)\n        x = x.reshape(sh[0], sh[1], -1)\n\n        if not no_head:\n            x = self.calc_head(x)\n\n        return x", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/resnet.py_from_functools_import_par_get_avgpool.return._0_1_1_1_1_1_1_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/resnet.py_from_functools_import_par_get_avgpool.return._0_1_1_1_1_1_1_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/resnet.py", "file_name": "resnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 32, "span_ids": ["get_avgpool", "get_inplanes", "docstring"], "tokens": 164}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from functools import partial\nfrom typing import Any, Callable, List, Optional, Type, Union\n\nimport torch\nimport torch.nn as nn\n\nfrom monai.networks.layers.factories import Conv, Norm, Pool\nfrom monai.networks.layers.utils import get_pool_layer\nfrom monai.utils.module import look_up_option\n\n__all__ = [\"ResNet\", \"resnet10\", \"resnet18\", \"resnet34\", \"resnet50\", \"resnet101\", \"resnet152\", \"resnet200\"]\n\nfrom monai.utils import deprecated_arg\n\n\ndef get_inplanes():\n    return [64, 128, 256, 512]\n\n\ndef get_avgpool():\n    return [0, 1, (1, 1), (1, 1, 1)]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/segresnet.py_SegResNetVAE._get_vae_loss_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/segresnet.py_SegResNetVAE._get_vae_loss_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/segresnet.py", "file_name": "segresnet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 286, "end_line": 338, "span_ids": ["SegResNetVAE._get_vae_loss", "SegResNetVAE.forward"], "tokens": 507}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SegResNetVAE(SegResNet):\n\n    def _get_vae_loss(self, net_input: torch.Tensor, vae_input: torch.Tensor):\n        \"\"\"\n        Args:\n            net_input: the original input of the network.\n            vae_input: the input of VAE module, which is also the output of the network's encoder.\n        \"\"\"\n        x_vae = self.vae_down(vae_input)\n        x_vae = x_vae.view(-1, self.vae_fc1.in_features)\n        z_mean = self.vae_fc1(x_vae)\n\n        z_mean_rand = torch.randn_like(z_mean)\n        z_mean_rand.requires_grad_(False)\n\n        if self.vae_estimate_std:\n            z_sigma = self.vae_fc2(x_vae)\n            z_sigma = F.softplus(z_sigma)\n            vae_reg_loss = 0.5 * torch.mean(z_mean ** 2 + z_sigma ** 2 - torch.log(1e-8 + z_sigma ** 2) - 1)\n\n            x_vae = z_mean + z_sigma * z_mean_rand\n        else:\n            z_sigma = self.vae_default_std\n            vae_reg_loss = torch.mean(z_mean ** 2)\n\n            x_vae = z_mean + z_sigma * z_mean_rand\n\n        x_vae = self.vae_fc3(x_vae)\n        x_vae = self.act_mod(x_vae)\n        x_vae = x_vae.view([-1, self.smallest_filters] + self.fc_insize)\n        x_vae = self.vae_fc_up_sample(x_vae)\n\n        for up, upl in zip(self.up_samples, self.up_layers):\n            x_vae = up(x_vae)\n            x_vae = upl(x_vae)\n\n        x_vae = self.vae_conv_final(x_vae)\n        vae_mse_loss = F.mse_loss(net_input, x_vae)\n        vae_loss = vae_reg_loss + vae_mse_loss\n        return vae_loss\n\n    def forward(self, x):\n        net_input = x\n        x, down_x = self.encode(x)\n        down_x.reverse()\n\n        vae_input = x\n        x = self.decode(x, down_x)\n\n        if self.training:\n            vae_loss = self._get_vae_loss(net_input, vae_input)\n            return x, vae_loss\n\n        return x, None", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandGaussianNoise_RandGaussianNoise.__init__.self.noise.None": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandGaussianNoise_RandGaussianNoise.__init__.self.noise.None", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 81, "end_line": 100, "span_ids": ["RandGaussianNoise.__init__", "RandGaussianNoise"], "tokens": 179}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandGaussianNoise(RandomizableTransform):\n    \"\"\"\n    Add Gaussian noise to image.\n\n    Args:\n        prob: Probability to add Gaussian noise.\n        mean: Mean or \u201ccentre\u201d of the distribution.\n        std: Standard deviation (spread) of distribution.\n        dtype: output data type, if None, same as input image. defaults to float32.\n\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __init__(self, prob: float = 0.1, mean: float = 0.0, std: float = 0.1, dtype: DtypeLike = np.float32) -> None:\n        RandomizableTransform.__init__(self, prob)\n        self.mean = mean\n        self.std = std\n        self.dtype = dtype\n        self.noise: Optional[np.ndarray] = None", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandGaussianNoise.randomize_RandGaussianNoise.randomize._type_ignore": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_RandGaussianNoise.randomize_RandGaussianNoise.randomize._type_ignore", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 102, "end_line": 109, "span_ids": ["RandGaussianNoise.randomize"], "tokens": 126}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandGaussianNoise(RandomizableTransform):\n\n    def randomize(self, img: NdarrayOrTensor, mean: Optional[float] = None) -> None:\n        super().randomize(None)\n        if not self._do_transform:\n            return None\n        rand_std = self.R.uniform(0, self.std)\n        noise = self.R.normal(self.mean if mean is None else mean, rand_std, size=img.shape)\n        # noise is float64 array, convert to the output dtype to save memory\n        self.noise, *_ = convert_data_type(noise, dtype=self.dtype)  # type: ignore", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_RandGaussianNoised_RandGaussianNoised.set_random_state.return.self": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_RandGaussianNoised_RandGaussianNoised.set_random_state.return.self", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 147, "end_line": 183, "span_ids": ["RandGaussianNoised.set_random_state", "RandGaussianNoised", "RandGaussianNoised.__init__"], "tokens": 361}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandGaussianNoised(RandomizableTransform, MapTransform):\n    \"\"\"\n    Dictionary-based version :py:class:`monai.transforms.RandGaussianNoise`.\n    Add Gaussian noise to image. This transform assumes all the expected fields have same shape, if want to add\n    different noise for every field, please use this transform separately.\n\n    Args:\n        keys: keys of the corresponding items to be transformed.\n            See also: :py:class:`monai.transforms.compose.MapTransform`\n        prob: Probability to add Gaussian noise.\n        mean: Mean or \u201ccentre\u201d of the distribution.\n        std: Standard deviation (spread) of distribution.\n        dtype: output data type, if None, same as input image. defaults to float32.\n        allow_missing_keys: don't raise exception if key is missing.\n    \"\"\"\n\n    backend = RandGaussianNoise.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        prob: float = 0.1,\n        mean: float = 0.0,\n        std: float = 0.1,\n        dtype: DtypeLike = np.float32,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        MapTransform.__init__(self, keys, allow_missing_keys)\n        RandomizableTransform.__init__(self, prob)\n        self.rand_gaussian_noise = RandGaussianNoise(mean=mean, std=std, prob=1.0, dtype=dtype)\n\n    def set_random_state(\n        self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None\n    ) -> \"RandGaussianNoised\":\n        super().set_random_state(seed, state)\n        self.rand_gaussian_noise.set_random_state(seed, state)\n        return self", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_RandGaussianNoised.__call___RandGaussianNoised.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_RandGaussianNoised.__call___RandGaussianNoised.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 185, "end_line": 199, "span_ids": ["RandGaussianNoised.__call__"], "tokens": 153}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandGaussianNoised(RandomizableTransform, MapTransform):\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        self.randomize(None)\n        if not self._do_transform:\n            return d\n\n        # all the keys share the same random noise\n        first_key: Union[Hashable, List] = self.first_key(d)\n        if first_key == []:\n            return d\n\n        self.rand_gaussian_noise.randomize(d[first_key])  # type: ignore\n        for key in self.key_iterator(d):\n            d[key] = self.rand_gaussian_noise(img=d[key], randomize=False)\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_RandCoarseShuffled_RandCoarseShuffled.set_random_state.return.self": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_RandCoarseShuffled_RandCoarseShuffled.set_random_state.return.self", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1496, "end_line": 1545, "span_ids": ["RandCoarseShuffled.set_random_state", "RandCoarseShuffled", "RandCoarseShuffled.__init__"], "tokens": 615}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandCoarseShuffled(RandomizableTransform, MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.RandCoarseShuffle`.\n    Expect all the data specified by `keys` have same spatial shape and will randomly dropout the same regions\n    for every key, if want to shuffle different regions for every key, please use this transform separately.\n\n    Args:\n        keys: keys of the corresponding items to be transformed.\n            See also: :py:class:`monai.transforms.compose.MapTransform`\n        holes: number of regions to dropout, if `max_holes` is not None, use this arg as the minimum number to\n            randomly select the expected number of regions.\n        spatial_size: spatial size of the regions to dropout, if `max_spatial_size` is not None, use this arg\n            as the minimum spatial size to randomly select size for every region.\n            if some components of the `spatial_size` are non-positive values, the transform will use the\n            corresponding components of input img size. For example, `spatial_size=(32, -1)` will be adapted\n            to `(32, 64)` if the second spatial dimension size of img is `64`.\n        max_holes: if not None, define the maximum number to randomly select the expected number of regions.\n        max_spatial_size: if not None, define the maximum spatial size to randomly select size for every region.\n            if some components of the `max_spatial_size` are non-positive values, the transform will use the\n            corresponding components of input img size. For example, `max_spatial_size=(32, -1)` will be adapted\n            to `(32, 64)` if the second spatial dimension size of img is `64`.\n        prob: probability of applying the transform.\n        allow_missing_keys: don't raise exception if key is missing.\n\n    \"\"\"\n\n    backend = RandCoarseShuffle.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        holes: int,\n        spatial_size: Union[Sequence[int], int],\n        max_holes: Optional[int] = None,\n        max_spatial_size: Optional[Union[Sequence[int], int]] = None,\n        prob: float = 0.1,\n        allow_missing_keys: bool = False,\n    ):\n        MapTransform.__init__(self, keys, allow_missing_keys)\n        RandomizableTransform.__init__(self, prob=prob)\n        self.shuffle = RandCoarseShuffle(\n            holes=holes, spatial_size=spatial_size, max_holes=max_holes, max_spatial_size=max_spatial_size, prob=1.0\n        )\n\n    def set_random_state(\n        self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None\n    ) -> \"RandCoarseShuffled\":\n        super().set_random_state(seed, state)\n        self.shuffle.set_random_state(seed, state)\n        return self", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_RandCoarseShuffled.__call___RandCoarseShuffled.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_RandCoarseShuffled.__call___RandCoarseShuffled.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1547, "end_line": 1562, "span_ids": ["RandCoarseShuffled.__call__"], "tokens": 136}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandCoarseShuffled(RandomizableTransform, MapTransform):\n\n    def __call__(self, data):\n        d = dict(data)\n        self.randomize(None)\n        if not self._do_transform:\n            return d\n\n        # expect all the specified keys have same spatial shape and share same random holes\n        first_key: Union[Hashable, List] = self.first_key(d)\n        if first_key == []:\n            return d\n\n        self.shuffle.randomize(d[first_key].shape[1:])  # type: ignore\n        for key in self.key_iterator(d):\n            d[key] = self.shuffle(img=d[key], randomize=False)\n\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/inverse_batch_transform.py_Decollated_Decollated.__init__.self.fill_value.fill_value": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/inverse_batch_transform.py_Decollated_Decollated.__init__.self.fill_value.fill_value", "embedding": null, "metadata": {"file_path": "monai/transforms/inverse_batch_transform.py", "file_name": "inverse_batch_transform.py", "file_type": "text/x-python", "category": "implementation", "start_line": 108, "end_line": 139, "span_ids": ["Decollated.__init__", "Decollated"], "tokens": 326}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Decollated(MapTransform):\n    \"\"\"\n    Decollate a batch of data. If input is a dictionary, it also supports to only decollate specified keys.\n    Note that unlike most MapTransforms, it will delete the other keys that are not specified.\n    if `keys=None`, it will decollate all the data in the input.\n    It replicates the scalar values to every item of the decollated list.\n\n    Args:\n        keys: keys of the corresponding items to decollate, note that it will delete other keys not specified.\n            if None, will decollate all the keys. see also: :py:class:`monai.transforms.compose.MapTransform`.\n        detach: whether to detach the tensors. Scalars tensors will be detached into number types\n            instead of torch tensors.\n        pad_batch: when the items in a batch indicate different batch size,\n            whether to pad all the sequences to the longest.\n            If False, the batch size will be the length of the shortest sequence.\n        fill_value: the value to fill the padded sequences when `pad_batch=True`.\n        allow_missing_keys: don't raise exception if key is missing.\n\n    \"\"\"\n\n    def __init__(\n        self,\n        keys: Optional[KeysCollection] = None,\n        detach: bool = True,\n        pad_batch: bool = True,\n        fill_value=None,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        super().__init__(keys, allow_missing_keys)\n        self.detach = detach\n        self.pad_batch = pad_batch\n        self.fill_value = fill_value", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/array.py_AsDiscrete_AsDiscrete.backend._TransformBackends_TORCH_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/array.py_AsDiscrete_AsDiscrete.backend._TransformBackends_TORCH_", "embedding": null, "metadata": {"file_path": "monai/transforms/post/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 112, "end_line": 156, "span_ids": ["AsDiscrete"], "tokens": 485}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AsDiscrete(Transform):\n    \"\"\"\n    Execute after model forward to transform model output to discrete values.\n    It can complete below operations:\n\n        -  execute `argmax` for input logits values.\n        -  threshold input value to 0.0 or 1.0.\n        -  convert input value to One-Hot format.\n        -  round the value to the closest integer.\n\n    Args:\n        argmax: whether to execute argmax function on input data before transform.\n            Defaults to ``False``.\n        to_onehot: if not None, convert input data into the one-hot format with specified number of classes.\n            Defaults to ``None``.\n        threshold: if not None, threshold the float values to int number 0 or 1 with specified theashold.\n            Defaults to ``None``.\n        rounding: if not None, round the data according to the specified option,\n            available options: [\"torchrounding\"].\n\n    Example:\n\n        >>> transform = AsDiscrete(argmax=True)\n        >>> print(transform(np.array([[[0.0, 1.0]], [[2.0, 3.0]]])))\n        # [[[1.0, 1.0]]]\n\n        >>> transform = AsDiscrete(threshold=0.6)\n        >>> print(transform(np.array([[[0.0, 0.5], [0.8, 3.0]]])))\n        # [[[0.0, 0.0], [1.0, 1.0]]]\n\n        >>> transform = AsDiscrete(argmax=True, to_onehot=2, threshold=0.5)\n        >>> print(transform(np.array([[[0.0, 1.0]], [[2.0, 3.0]]])))\n        # [[[0.0, 0.0]], [[1.0, 1.0]]]\n\n    .. deprecated:: 0.6.0\n        ``n_classes`` is deprecated, use ``to_onehot`` instead.\n\n    .. deprecated:: 0.7.0\n        ``num_classes`` is deprecated, use ``to_onehot`` instead.\n        ``logit_thresh`` is deprecated, use ``threshold`` instead.\n        ``threshold_values`` is deprecated, use ``threshold`` instead.\n\n    \"\"\"\n\n    backend = [TransformBackends.TORCH]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/array.py_AsDiscrete.__init___AsDiscrete.__init__.self.rounding.rounding": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/array.py_AsDiscrete.__init___AsDiscrete.__init__.self.rounding.rounding", "embedding": null, "metadata": {"file_path": "monai/transforms/post/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 158, "end_line": 186, "span_ids": ["AsDiscrete.__init__"], "tokens": 362}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AsDiscrete(Transform):\n\n    @deprecated_arg(name=\"n_classes\", new_name=\"num_classes\", since=\"0.6\", msg_suffix=\"please use `to_onehot` instead.\")\n    @deprecated_arg(\"num_classes\", since=\"0.7\", msg_suffix=\"please use `to_onehot` instead.\")\n    @deprecated_arg(\"logit_thresh\", since=\"0.7\", msg_suffix=\"please use `threshold` instead.\")\n    @deprecated_arg(\n        name=\"threshold_values\", new_name=\"threshold\", since=\"0.7\", msg_suffix=\"please use `threshold` instead.\"\n    )\n    def __init__(\n        self,\n        argmax: bool = False,\n        to_onehot: Optional[int] = None,\n        threshold: Optional[float] = None,\n        rounding: Optional[str] = None,\n        n_classes: Optional[int] = None,  # deprecated\n        num_classes: Optional[int] = None,  # deprecated\n        logit_thresh: float = 0.5,  # deprecated\n        threshold_values: Optional[bool] = False,  # deprecated\n    ) -> None:\n        self.argmax = argmax\n        if isinstance(to_onehot, bool):  # for backward compatibility\n            warnings.warn(\"`to_onehot=True/False` is deprecated, please use `to_onehot=num_classes` instead.\")\n            to_onehot = num_classes if to_onehot else None\n        self.to_onehot = to_onehot\n\n        if isinstance(threshold, bool):  # for backward compatibility\n            warnings.warn(\"`threshold_values=True/False` is deprecated, please use `threshold=value` instead.\")\n            threshold = logit_thresh if threshold else None\n        self.threshold = threshold\n\n        self.rounding = rounding", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/dictionary.py_AsDiscreted_AsDiscreted.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/dictionary.py_AsDiscreted_AsDiscreted.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/post/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 124, "end_line": 205, "span_ids": ["AsDiscreted", "AsDiscreted.__call__", "AsDiscreted.__init__"], "tokens": 995}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AsDiscreted(MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.AsDiscrete`.\n    \"\"\"\n\n    backend = AsDiscrete.backend\n\n    @deprecated_arg(name=\"n_classes\", new_name=\"num_classes\", since=\"0.6\", msg_suffix=\"please use `to_onehot` instead.\")\n    @deprecated_arg(\"num_classes\", since=\"0.7\", msg_suffix=\"please use `to_onehot` instead.\")\n    @deprecated_arg(\"logit_thresh\", since=\"0.7\", msg_suffix=\"please use `threshold` instead.\")\n    @deprecated_arg(\n        name=\"threshold_values\", new_name=\"threshold\", since=\"0.7\", msg_suffix=\"please use `threshold` instead.\"\n    )\n    def __init__(\n        self,\n        keys: KeysCollection,\n        argmax: Union[Sequence[bool], bool] = False,\n        to_onehot: Union[Sequence[Optional[int]], Optional[int]] = None,\n        threshold: Union[Sequence[Optional[float]], Optional[float]] = None,\n        rounding: Union[Sequence[Optional[str]], Optional[str]] = None,\n        allow_missing_keys: bool = False,\n        n_classes: Optional[Union[Sequence[int], int]] = None,  # deprecated\n        num_classes: Optional[Union[Sequence[int], int]] = None,  # deprecated\n        logit_thresh: Union[Sequence[float], float] = 0.5,  # deprecated\n        threshold_values: Union[Sequence[bool], bool] = False,  # deprecated\n    ) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to model output and label.\n                See also: :py:class:`monai.transforms.compose.MapTransform`\n            argmax: whether to execute argmax function on input data before transform.\n                it also can be a sequence of bool, each element corresponds to a key in ``keys``.\n            to_onehot: if not None, convert input data into the one-hot format with specified number of classes.\n                defaults to ``None``. it also can be a sequence, each element corresponds to a key in ``keys``.\n            threshold: if not None, threshold the float values to int number 0 or 1 with specified theashold value.\n                defaults to ``None``. it also can be a sequence, each element corresponds to a key in ``keys``.\n            rounding: if not None, round the data according to the specified option,\n                available options: [\"torchrounding\"]. it also can be a sequence of str or None,\n                each element corresponds to a key in ``keys``.\n            allow_missing_keys: don't raise exception if key is missing.\n\n        .. deprecated:: 0.6.0\n            ``n_classes`` is deprecated, use ``to_onehot`` instead.\n\n        .. deprecated:: 0.7.0\n            ``num_classes`` is deprecated, use ``to_onehot`` instead.\n            ``logit_thresh`` is deprecated, use ``threshold`` instead.\n            ``threshold_values`` is deprecated, use ``threshold`` instead.\n\n        \"\"\"\n        super().__init__(keys, allow_missing_keys)\n        self.argmax = ensure_tuple_rep(argmax, len(self.keys))\n        to_onehot_ = ensure_tuple_rep(to_onehot, len(self.keys))\n        num_classes = ensure_tuple_rep(num_classes, len(self.keys))\n        self.to_onehot = []\n        for flag, val in zip(to_onehot_, num_classes):\n            if isinstance(flag, bool):\n                warnings.warn(\"`to_onehot=True/False` is deprecated, please use `to_onehot=num_classes` instead.\")\n                self.to_onehot.append(val if flag else None)\n            else:\n                self.to_onehot.append(flag)\n\n        threshold_ = ensure_tuple_rep(threshold, len(self.keys))\n        logit_thresh = ensure_tuple_rep(logit_thresh, len(self.keys))\n        self.threshold = []\n        for flag, val in zip(threshold_, logit_thresh):\n            if isinstance(flag, bool):\n                warnings.warn(\"`threshold_values=True/False` is deprecated, please use `threshold=value` instead.\")\n                self.threshold.append(val if flag else None)\n            else:\n                self.threshold.append(flag)\n\n        self.rounding = ensure_tuple_rep(rounding, len(self.keys))\n        self.converter = AsDiscrete()\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key, argmax, to_onehot, threshold, rounding in self.key_iterator(\n            d, self.argmax, self.to_onehot, self.threshold, self.rounding\n        ):\n            d[key] = self.converter(d[key], argmax, to_onehot, threshold, rounding)\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/dictionary.py_Ensembled_Ensembled.__init__.self.output_key.output_key_if_output_key_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/dictionary.py_Ensembled_Ensembled.__init__.self.output_key.output_key_if_output_key_", "embedding": null, "metadata": {"file_path": "monai/transforms/post/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 342, "end_line": 377, "span_ids": ["Ensembled", "Ensembled.__init__"], "tokens": 350}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Ensembled(MapTransform):\n    \"\"\"\n    Base class of dictionary-based ensemble transforms.\n\n    \"\"\"\n\n    backend = list(set(VoteEnsemble.backend) & set(MeanEnsemble.backend))\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        ensemble: Callable[[Union[Sequence[NdarrayOrTensor], NdarrayOrTensor]], NdarrayOrTensor],\n        output_key: Optional[str] = None,\n        allow_missing_keys: bool = False,\n    ) -> None:\n        \"\"\"\n        Args:\n            keys: keys of the corresponding items to be stack and execute ensemble.\n                if only 1 key provided, suppose it's a PyTorch Tensor with data stacked on dimension `E`.\n            output_key: the key to store ensemble result in the dictionary.\n            ensemble: callable method to execute ensemble on specified data.\n                if only 1 key provided in `keys`, `output_key` can be None and use `keys` as default.\n            allow_missing_keys: don't raise exception if key is missing.\n\n        Raises:\n            TypeError: When ``ensemble`` is not ``callable``.\n            ValueError: When ``len(keys) > 1`` and ``output_key=None``. Incompatible values.\n\n        \"\"\"\n        super().__init__(keys, allow_missing_keys)\n        if not callable(ensemble):\n            raise TypeError(f\"ensemble must be callable but is {type(ensemble).__name__}.\")\n        self.ensemble = ensemble\n        if len(self.keys) > 1 and output_key is None:\n            raise ValueError(\"Incompatible values: len(self.keys) > 1 and output_key=None.\")\n        self.output_key = output_key if output_key is not None else self.keys[0]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/dictionary.py_Ensembled.__call___Ensembled.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/post/dictionary.py_Ensembled.__call___Ensembled.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/post/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 379, "end_line": 390, "span_ids": ["Ensembled.__call__"], "tokens": 127}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Ensembled(MapTransform):\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        items: Union[List[NdarrayOrTensor], NdarrayOrTensor]\n        if len(self.keys) == 1 and self.keys[0] in d:\n            items = d[self.keys[0]]\n        else:\n            items = [d[key] for key in self.key_iterator(d)]\n\n        if len(items) > 0:\n            d[self.output_key] = self.ensemble(items)\n\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/smooth_field/__init__.py__": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/smooth_field/__init__.py__", "embedding": null, "metadata": {"file_path": "monai/transforms/smooth_field/__init__.py", "file_name": "__init__.py", "file_type": "text/x-python", "category": "implementation", "start_line": 11, "end_line": 11, "span_ids": [], "tokens": 0}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/smooth_field/array.py_from_typing_import_Any_O___all__._SmoothField_RandSmoo": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/smooth_field/array.py_from_typing_import_Any_O___all__._SmoothField_RandSmoo", "embedding": null, "metadata": {"file_path": "monai/transforms/smooth_field/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 14, "end_line": 29, "span_ids": ["docstring"], "tokens": 159}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Any, Optional, Sequence, Union\n\nimport numpy as np\nimport torch\nfrom torch.nn.functional import grid_sample, interpolate\n\nimport monai\nfrom monai.config.type_definitions import NdarrayOrTensor\nfrom monai.transforms.transform import Randomizable, RandomizableTransform\nfrom monai.transforms.utils_pytorch_numpy_unification import moveaxis\nfrom monai.utils import GridSampleMode, GridSamplePadMode, InterpolateMode\nfrom monai.utils.enums import TransformBackends\nfrom monai.utils.module import look_up_option, pytorch_after\nfrom monai.utils.type_conversion import convert_to_dst_type, convert_to_tensor\n\n__all__ = [\"SmoothField\", \"RandSmoothFieldAdjustContrast\", \"RandSmoothFieldAdjustIntensity\", \"RandSmoothDeform\"]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/smooth_field/array.py_RandSmoothFieldAdjustContrast.__call___RandSmoothFieldAdjustContrast.__call__.return.out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/smooth_field/array.py_RandSmoothFieldAdjustContrast.__call___RandSmoothFieldAdjustContrast.__call__.return.out", "embedding": null, "metadata": {"file_path": "monai/transforms/smooth_field/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 214, "end_line": 238, "span_ids": ["RandSmoothFieldAdjustContrast.__call__"], "tokens": 234}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandSmoothFieldAdjustContrast(RandomizableTransform):\n\n    def __call__(self, img: NdarrayOrTensor, randomize: bool = True) -> NdarrayOrTensor:\n        \"\"\"\n        Apply the transform to `img`, if `randomize` randomizing the smooth field otherwise reusing the previous.\n        \"\"\"\n        if randomize:\n            self.randomize()\n\n        if not self._do_transform:\n            return img\n\n        img_min = img.min()\n        img_max = img.max()\n        img_rng = img_max - img_min\n\n        field = self.sfield()\n        rfield, *_ = convert_to_dst_type(field, img)\n\n        # everything below here is to be computed using the destination type (numpy, tensor, etc.)\n\n        img = (img - img_min) / (img_rng + 1e-10)  # rescale to unit values\n        img = img ** rfield  # contrast is changed by raising image data to a power, in this case the field\n\n        out = (img * img_rng) + img_min  # rescale back to the original image value range\n\n        return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_from_copy_import_deepcopy_InterpolateModeSequence.Union_Sequence_Union_Inte": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_from_copy_import_deepcopy_InterpolateModeSequence.Union_Sequence_Union_Inte", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 18, "end_line": 129, "span_ids": ["impl:4", "docstring"], "tokens": 739}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from copy import deepcopy\nfrom enum import Enum\nfrom typing import Any, Dict, Hashable, List, Mapping, Optional, Sequence, Tuple, Union\n\nimport numpy as np\nimport torch\n\nfrom monai.config import DtypeLike, KeysCollection\nfrom monai.config.type_definitions import NdarrayOrTensor\nfrom monai.networks.layers import AffineTransform\nfrom monai.networks.layers.simplelayers import GaussianFilter\nfrom monai.transforms.croppad.array import CenterSpatialCrop, SpatialPad\nfrom monai.transforms.inverse import InvertibleTransform\nfrom monai.transforms.spatial.array import (\n    Affine,\n    AffineGrid,\n    Flip,\n    GridDistortion,\n    Orientation,\n    Rand2DElastic,\n    Rand3DElastic,\n    RandAffine,\n    RandAxisFlip,\n    RandFlip,\n    RandGridDistortion,\n    RandRotate,\n    RandZoom,\n    Resize,\n    Rotate,\n    Rotate90,\n    Spacing,\n    Zoom,\n)\nfrom monai.transforms.transform import MapTransform, RandomizableTransform\nfrom monai.transforms.utils import create_grid\nfrom monai.utils import (\n    GridSampleMode,\n    GridSamplePadMode,\n    InterpolateMode,\n    NumpyPadMode,\n    PytorchPadMode,\n    ensure_tuple,\n    ensure_tuple_rep,\n    fall_back_tuple,\n)\nfrom monai.utils.deprecate_utils import deprecated_arg\nfrom monai.utils.enums import TraceKeys\nfrom monai.utils.module import optional_import\nfrom monai.utils.type_conversion import convert_data_type, convert_to_dst_type\n\nnib, _ = optional_import(\"nibabel\")\n\n__all__ = [\n    \"Spacingd\",\n    \"Orientationd\",\n    \"Rotate90d\",\n    \"RandRotate90d\",\n    \"Resized\",\n    \"Affined\",\n    \"RandAffined\",\n    \"Rand2DElasticd\",\n    \"Rand3DElasticd\",\n    \"Flipd\",\n    \"RandFlipd\",\n    \"GridDistortiond\",\n    \"RandGridDistortiond\",\n    \"RandAxisFlipd\",\n    \"Rotated\",\n    \"RandRotated\",\n    \"Zoomd\",\n    \"RandZoomd\",\n    \"SpacingD\",\n    \"SpacingDict\",\n    \"OrientationD\",\n    \"OrientationDict\",\n    \"Rotate90D\",\n    \"Rotate90Dict\",\n    \"RandRotate90D\",\n    \"RandRotate90Dict\",\n    \"ResizeD\",\n    \"ResizeDict\",\n    \"AffineD\",\n    \"AffineDict\",\n    \"RandAffineD\",\n    \"RandAffineDict\",\n    \"Rand2DElasticD\",\n    \"Rand2DElasticDict\",\n    \"Rand3DElasticD\",\n    \"Rand3DElasticDict\",\n    \"FlipD\",\n    \"FlipDict\",\n    \"RandFlipD\",\n    \"RandFlipDict\",\n    \"GridDistortionD\",\n    \"GridDistortionDict\",\n    \"RandGridDistortionD\",\n    \"RandGridDistortionDict\",\n    \"RandAxisFlipD\",\n    \"RandAxisFlipDict\",\n    \"RotateD\",\n    \"RotateDict\",\n    \"RandRotateD\",\n    \"RandRotateDict\",\n    \"ZoomD\",\n    \"ZoomDict\",\n    \"RandZoomD\",\n    \"RandZoomDict\",\n]\n\nGridSampleModeSequence = Union[Sequence[Union[GridSampleMode, str]], GridSampleMode, str]\nGridSamplePadModeSequence = Union[Sequence[Union[GridSamplePadMode, str]], GridSamplePadMode, str]\nInterpolateModeSequence = Union[Sequence[Union[InterpolateMode, str]], InterpolateMode, str]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_PadModeSequence_Spacingd.backend.Spacing_backend": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/spatial/dictionary.py_PadModeSequence_Spacingd.backend.Spacing_backend", "embedding": null, "metadata": {"file_path": "monai/transforms/spatial/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 134, "end_line": 151, "span_ids": ["impl:4", "Spacingd"], "tokens": 167}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "PadModeSequence = Union[Sequence[Union[NumpyPadMode, PytorchPadMode, str]], NumpyPadMode, PytorchPadMode, str]\n\n\nclass Spacingd(MapTransform, InvertibleTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.Spacing`.\n\n    This transform assumes the ``data`` dictionary has a key for the input\n    data's metadata and contains `affine` field.  The key is formed by ``key_{meta_key_postfix}``.\n\n    After resampling the input array, this transform will write the new affine\n    to the `affine` field of metadata which is formed by ``key_{meta_key_postfix}``.\n\n    see also:\n        :py:class:`monai.transforms.Spacing`\n    \"\"\"\n\n    backend = Spacing.backend", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_ToTensor_ToTensor.__call__.return.convert_to_tensor_img_dt": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_ToTensor_ToTensor.__call__.return.convert_to_tensor_img_dt", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 340, "end_line": 369, "span_ids": ["ToTensor", "ToTensor.__init__", "ToTensor.__call__"], "tokens": 327}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ToTensor(Transform):\n    \"\"\"\n    Converts the input image to a tensor without applying any other transformations.\n    Input data can be PyTorch Tensor, numpy array, list, dictionary, int, float, bool, str, etc.\n    Will convert Tensor, Numpy array, float, int, bool to Tensor, strings and objects keep the original.\n    For dictionary, list or tuple, convert every item to a Tensor if applicable and `wrap_sequence=False`.\n\n    Args:\n        dtype: target data type to when converting to Tensor.\n        device: target device to put the converted Tensor data.\n        wrap_sequence: if `False`, then lists will recursively call this function, default to `True`.\n            E.g., if `False`, `[1, 2]` -> `[tensor(1), tensor(2)]`, if `True`, then `[1, 2]` -> `tensor([1, 2])`.\n\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __init__(\n        self, dtype: Optional[torch.dtype] = None, device: Optional[torch.device] = None, wrap_sequence: bool = True\n    ) -> None:\n        super().__init__()\n        self.dtype = dtype\n        self.device = device\n        self.wrap_sequence = wrap_sequence\n\n    def __call__(self, img: NdarrayOrTensor):\n        \"\"\"\n        Apply the transform to `img` and make it contiguous.\n        \"\"\"\n        return convert_to_tensor(img, dtype=self.dtype, device=self.device, wrap_sequence=self.wrap_sequence)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_EnsureType_EnsureType.__init__.self.wrap_sequence.wrap_sequence": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_EnsureType_EnsureType.__init__.self.wrap_sequence.wrap_sequence", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 372, "end_line": 400, "span_ids": ["EnsureType.__init__", "EnsureType"], "tokens": 331}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class EnsureType(Transform):\n    \"\"\"\n    Ensure the input data to be a PyTorch Tensor or numpy array, support: `numpy array`, `PyTorch Tensor`,\n    `float`, `int`, `bool`, `string` and `object` keep the original.\n    If passing a dictionary, list or tuple, still return dictionary, list or tuple will recursively convert\n    every item to the expected data type if `wrap_sequence=False`.\n\n    Args:\n        data_type: target data type to convert, should be \"tensor\" or \"numpy\".\n        dtype: target data content type to convert, for example: np.float32, torch.float, etc.\n        device: for Tensor data type, specify the target device.\n        wrap_sequence: if `False`, then lists will recursively call this function, default to `True`.\n            E.g., if `False`, `[1, 2]` -> `[tensor(1), tensor(2)]`, if `True`, then `[1, 2]` -> `tensor([1, 2])`.\n\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __init__(\n        self,\n        data_type: str = \"tensor\",\n        dtype: Optional[Union[DtypeLike, torch.dtype]] = None,\n        device: Optional[torch.device] = None,\n        wrap_sequence: bool = True,\n    ) -> None:\n        self.data_type = look_up_option(data_type.lower(), {\"tensor\", \"numpy\"})\n        self.dtype = dtype\n        self.device = device\n        self.wrap_sequence = wrap_sequence", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_ToNumpy_ToNumpy.__call__.return.convert_to_numpy_img_dty": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_ToNumpy_ToNumpy.__call__.return.convert_to_numpy_img_dty", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 418, "end_line": 440, "span_ids": ["ToNumpy.__init__", "ToNumpy", "ToNumpy.__call__"], "tokens": 225}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ToNumpy(Transform):\n    \"\"\"\n    Converts the input data to numpy array, can support list or tuple of numbers and PyTorch Tensor.\n\n    Args:\n        dtype: target data type when converting to numpy array.\n        wrap_sequence: if `False`, then lists will recursively call this function, default to `True`.\n            E.g., if `False`, `[1, 2]` -> `[array(1), array(2)]`, if `True`, then `[1, 2]` -> `array([1, 2])`.\n\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __init__(self, dtype: DtypeLike = None, wrap_sequence: bool = True) -> None:\n        super().__init__()\n        self.dtype = dtype\n        self.wrap_sequence = wrap_sequence\n\n    def __call__(self, img: NdarrayOrTensor):\n        \"\"\"\n        Apply the transform to `img` and make it contiguous.\n        \"\"\"\n        return convert_to_numpy(img, dtype=self.dtype, wrap_sequence=self.wrap_sequence)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_ToCupy_ToCupy.__call__.return.convert_to_cupy_data_dty": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_ToCupy_ToCupy.__call__.return.convert_to_cupy_data_dty", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 445, "end_line": 469, "span_ids": ["ToCupy.__init__", "ToCupy", "ToCupy.__call__"], "tokens": 273}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ToCupy(Transform):\n    \"\"\"\n    Converts the input data to CuPy array, can support list or tuple of numbers, NumPy and PyTorch Tensor.\n\n    Args:\n        dtype: data type specifier. It is inferred from the input by default.\n            if not None, must be an argument of `numpy.dtype`, for more details:\n            https://docs.cupy.dev/en/stable/reference/generated/cupy.array.html.\n        wrap_sequence: if `False`, then lists will recursively call this function, default to `True`.\n            E.g., if `False`, `[1, 2]` -> `[array(1), array(2)]`, if `True`, then `[1, 2]` -> `array([1, 2])`.\n\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __init__(self, dtype: Optional[np.dtype] = None, wrap_sequence: bool = True) -> None:\n        super().__init__()\n        self.dtype = dtype\n        self.wrap_sequence = wrap_sequence\n\n    def __call__(self, data: NdarrayOrTensor):\n        \"\"\"\n        Create a CuPy array from `data` and make it contiguous\n        \"\"\"\n        return convert_to_cupy(data, dtype=self.dtype, wrap_sequence=self.wrap_sequence)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils_pytorch_numpy_unification.py_concatenate_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils_pytorch_numpy_unification.py_concatenate_", "embedding": null, "metadata": {"file_path": "monai/transforms/utils_pytorch_numpy_unification.py", "file_name": "utils_pytorch_numpy_unification.py", "file_type": "text/x-python", "category": "implementation", "start_line": 272, "end_line": 319, "span_ids": ["searchsorted", "repeat", "isnan", "isfinite", "concatenate", "cumsum"], "tokens": 409}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def concatenate(to_cat: Sequence[NdarrayOrTensor], axis: int = 0, out=None) -> NdarrayOrTensor:\n    \"\"\"`np.concatenate` with equivalent implementation for torch (`torch.cat`).\"\"\"\n    if isinstance(to_cat[0], np.ndarray):\n        return np.concatenate(to_cat, axis, out)  # type: ignore\n    return torch.cat(to_cat, dim=axis, out=out)  # type: ignore\n\n\ndef cumsum(a: NdarrayOrTensor, axis=None):\n    \"\"\"`np.cumsum` with equivalent implementation for torch.\"\"\"\n    if isinstance(a, np.ndarray):\n        return np.cumsum(a, axis)\n    if axis is None:\n        return torch.cumsum(a[:], 0)\n    return torch.cumsum(a, dim=axis)\n\n\ndef isfinite(x):\n    \"\"\"`np.isfinite` with equivalent implementation for torch.\"\"\"\n    if not isinstance(x, torch.Tensor):\n        return np.isfinite(x)\n    return torch.isfinite(x)\n\n\ndef searchsorted(a: NdarrayOrTensor, v: NdarrayOrTensor, right=False, sorter=None):\n    side = \"right\" if right else \"left\"\n    if isinstance(a, np.ndarray):\n        return np.searchsorted(a, v, side, sorter)  # type: ignore\n    return torch.searchsorted(a, v, right=right)  # type: ignore\n\n\ndef repeat(a: NdarrayOrTensor, repeats: int, axis: Optional[int] = None):\n    \"\"\"`np.repeat` with equivalent implementation for torch (`repeat_interleave`).\"\"\"\n    if isinstance(a, np.ndarray):\n        return np.repeat(a, repeats, axis)\n    return torch.repeat_interleave(a, repeats, dim=axis)\n\n\ndef isnan(x: NdarrayOrTensor):\n    \"\"\"`np.isnan` with equivalent implementation for torch.\n\n    Args:\n        x: array/tensor\n\n    \"\"\"\n    if isinstance(x, np.ndarray):\n        return np.isnan(x)\n    return torch.isnan(x)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/module.py_version_leq_version_leq.return.True": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/module.py_version_leq_version_leq.return.True", "embedding": null, "metadata": {"file_path": "monai/utils/module.py", "file_name": "module.py", "file_type": "text/x-python", "category": "implementation", "start_line": 413, "end_line": 453, "span_ids": ["version_leq"], "tokens": 310}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def version_leq(lhs: str, rhs: str):\n    \"\"\"\n    Returns True if version `lhs` is earlier or equal to `rhs`.\n\n    Args:\n        lhs: version name to compare with `rhs`, return True if earlier or equal to `rhs`.\n        rhs: version name to compare with `lhs`, return True if later or equal to `lhs`.\n\n    \"\"\"\n\n    lhs, rhs = str(lhs), str(rhs)\n    ver, has_ver = optional_import(\"pkg_resources\", name=\"parse_version\")\n    if has_ver:\n        return ver(lhs) <= ver(rhs)\n\n    def _try_cast(val: str):\n        val = val.strip()\n        try:\n            m = match(\"(\\\\d+)(.*)\", val)\n            if m is not None:\n                val = m.groups()[0]\n                return int(val)\n            return val\n        except ValueError:\n            return val\n\n    # remove git version suffixes if present\n    lhs = lhs.split(\"+\", 1)[0]\n    rhs = rhs.split(\"+\", 1)[0]\n\n    # parse the version strings in this basic way without `packaging` package\n    lhs_ = map(_try_cast, lhs.split(\".\"))\n    rhs_ = map(_try_cast, rhs.split(\".\"))\n\n    for l, r in zip(lhs_, rhs_):\n        if l != r:\n            if isinstance(l, int) and isinstance(r, int):\n                return l < r\n            return f\"{l}\" < f\"{r}\"\n\n    return True", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/module.py_pytorch_after_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/module.py_pytorch_after_", "embedding": null, "metadata": {"file_path": "monai/utils/module.py", "file_name": "module.py", "file_type": "text/x-python", "category": "implementation", "start_line": 456, "end_line": 504, "span_ids": ["pytorch_after"], "tokens": 491}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def pytorch_after(major, minor, patch=0, current_ver_string=None) -> bool:\n    \"\"\"\n    Compute whether the current pytorch version is after or equal to the specified version.\n    The current system pytorch version is determined by `torch.__version__` or\n    via system environment variable `PYTORCH_VER`.\n\n    Args:\n        major: major version number to be compared with\n        minor: minor version number to be compared with\n        patch: patch version number to be compared with\n        current_ver_string: if None, `torch.__version__` will be used.\n\n    Returns:\n        True if the current pytorch version is greater than or equal to the specified version.\n    \"\"\"\n\n    try:\n        if current_ver_string is None:\n            _env_var = os.environ.get(\"PYTORCH_VER\", \"\")\n            current_ver_string = _env_var if _env_var else torch.__version__\n        ver, has_ver = optional_import(\"pkg_resources\", name=\"parse_version\")\n        if has_ver:\n            return ver(\".\".join((f\"{major}\", f\"{minor}\", f\"{patch}\"))) <= ver(f\"{current_ver_string}\")  # type: ignore\n        parts = f\"{current_ver_string}\".split(\"+\", 1)[0].split(\".\", 3)\n        while len(parts) < 3:\n            parts += [\"0\"]\n        c_major, c_minor, c_patch = parts[:3]\n    except (AttributeError, ValueError, TypeError):\n        c_major, c_minor = get_torch_version_tuple()\n        c_patch = \"0\"\n    c_mn = int(c_major), int(c_minor)\n    mn = int(major), int(minor)\n    if c_mn != mn:\n        return c_mn > mn\n    is_prerelease = (\"a\" in f\"{c_patch}\".lower()) or (\"rc\" in f\"{c_patch}\".lower())\n    c_p = 0\n    try:\n        p_reg = re.search(r\"\\d+\", f\"{c_patch}\")\n        if p_reg:\n            c_p = int(p_reg.group())\n    except (AttributeError, TypeError, ValueError):\n        is_prerelease = True\n    patch = int(patch)\n    if c_p != patch:\n        return c_p > patch  # type: ignore\n    if is_prerelease:\n        return False\n    return True", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/img2tensorboard.py_plot_2d_or_3d_image_plot_2d_or_3d_image.if_d_ndim_3_.return": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/img2tensorboard.py_plot_2d_or_3d_image_plot_2d_or_3d_image.if_d_ndim_3_.return", "embedding": null, "metadata": {"file_path": "monai/visualize/img2tensorboard.py", "file_name": "img2tensorboard.py", "file_type": "text/x-python", "category": "implementation", "start_line": 137, "end_line": 187, "span_ids": ["plot_2d_or_3d_image"], "tokens": 615}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def plot_2d_or_3d_image(\n    data: Union[NdarrayTensor, List[NdarrayTensor]],\n    step: int,\n    writer: SummaryWriter,\n    index: int = 0,\n    max_channels: int = 1,\n    frame_dim: int = -3,\n    max_frames: int = 24,\n    tag: str = \"output\",\n) -> None:\n    \"\"\"Plot 2D or 3D image on the TensorBoard, 3D image will be converted to GIF image.\n\n    Note:\n        Plot 3D or 2D image(with more than 3 channels) as separate images.\n        And if writer is from TensorBoardX, data has 3 channels and `max_channels=3`, will plot as RGB video.\n\n    Args:\n        data: target data to be plotted as image on the TensorBoard.\n            The data is expected to have 'NCHW[D]' dimensions or a list of data with `CHW[D]` dimensions,\n            and only plot the first in the batch.\n        step: current step to plot in a chart.\n        writer: specify TensorBoard or TensorBoardX SummaryWriter to plot the image.\n        index: plot which element in the input data batch, default is the first element.\n        max_channels: number of channels to plot.\n        frame_dim: if plotting 3D image as GIF, specify the dimension used as frames,\n            expect input data shape as `NCHWD`, default to `-3` (the first spatial dim)\n        max_frames: if plot 3D RGB image as video in TensorBoardX, set the FPS to `max_frames`.\n        tag: tag of the plotted image on TensorBoard.\n    \"\"\"\n    data_index = data[index]\n    # as the `d` data has no batch dim, reduce the spatial dim index if positive\n    frame_dim = frame_dim - 1 if frame_dim > 0 else frame_dim\n\n    d: np.ndarray = data_index.detach().cpu().numpy() if isinstance(data_index, torch.Tensor) else data_index\n\n    if d.ndim == 2:\n        d = rescale_array(d, 0, 1)  # type: ignore\n        dataformats = \"HW\"\n        writer.add_image(f\"{tag}_{dataformats}\", d, step, dataformats=dataformats)\n        return\n\n    if d.ndim == 3:\n        if d.shape[0] == 3 and max_channels == 3:  # RGB\n            dataformats = \"CHW\"\n            writer.add_image(f\"{tag}_{dataformats}\", d, step, dataformats=dataformats)\n            return\n        dataformats = \"HW\"\n        for j, d2 in enumerate(d[:max_channels]):\n            d2 = rescale_array(d2, 0, 1)\n            writer.add_image(f\"{tag}_{dataformats}_{j}\", d2, step, dataformats=dataformats)\n        return\n    # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/img2tensorboard.py_plot_2d_or_3d_image.if_d_ndim_4__": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/img2tensorboard.py_plot_2d_or_3d_image.if_d_ndim_4__", "embedding": null, "metadata": {"file_path": "monai/visualize/img2tensorboard.py", "file_name": "img2tensorboard.py", "file_type": "text/x-python", "category": "implementation", "start_line": 189, "end_line": 203, "span_ids": ["plot_2d_or_3d_image"], "tokens": 312}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def plot_2d_or_3d_image(\n    data: Union[NdarrayTensor, List[NdarrayTensor]],\n    step: int,\n    writer: SummaryWriter,\n    index: int = 0,\n    max_channels: int = 1,\n    frame_dim: int = -3,\n    max_frames: int = 24,\n    tag: str = \"output\",\n) -> None:\n    # ... other code\n\n    if d.ndim >= 4:\n        spatial = d.shape[-3:]\n        d = d.reshape([-1] + list(spatial))\n        if d.shape[0] == 3 and max_channels == 3 and has_tensorboardx and isinstance(writer, SummaryWriterX):  # RGB\n            # move the expected frame dim to the end as `T` dim for video\n            d = np.moveaxis(d, frame_dim, -1)\n            writer.add_video(tag, d[None], step, fps=max_frames, dataformats=\"NCHWT\")\n            return\n        # scale data to 0 - 255 for visualization\n        max_channels = min(max_channels, d.shape[0])\n        d = np.stack([rescale_array(i, 0, 255) for i in d[:max_channels]], axis=0)\n        # will plot every channel as a separate GIF image\n        add_animated_gif(writer, f\"{tag}_HWD\", d, max_out=max_channels, frame_dim=frame_dim, global_step=step)\n        return", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_acn_block.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_acn_block.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_acn_block.py", "file_name": "test_acn_block.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 39, "span_ids": ["impl:3", "TestACNBlock", "TestACNBlock.test_acn_block", "docstring"], "tokens": 235}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks.blocks.dints_block import ActiConvNormBlock\n\nTEST_CASES = [\n    [{\"in_channel\": 32, \"out_channel\": 16, \"kernel_size\": 3, \"padding\": 1}, (7, 32, 16, 31, 7), (7, 16, 16, 31, 7)],\n    [\n        {\"in_channel\": 32, \"out_channel\": 16, \"kernel_size\": 3, \"padding\": 1, \"spatial_dims\": 2},\n        (7, 32, 13, 32),\n        (7, 16, 13, 32),\n    ],\n]\n\n\nclass TestACNBlock(unittest.TestCase):\n    @parameterized.expand(TEST_CASES)\n    def test_acn_block(self, input_param, input_shape, expected_shape):\n        net = ActiConvNormBlock(**input_param)\n        result = net(torch.randn(input_shape))\n        self.assertEqual(result.shape, expected_shape)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affine.py_unittest_for_p_in_TEST_NDARRAYS_.for_device_in_None_cpu.None_7": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affine.py_unittest_for_p_in_TEST_NDARRAYS_.for_device_in_None_cpu.None_7", "embedding": null, "metadata": {"file_path": "tests/test_affine.py", "file_name": "test_affine.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 114, "span_ids": ["docstring"], "tokens": 1272}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import Affine\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    for device in [None, \"cpu\", \"cuda\"] if torch.cuda.is_available() else [None, \"cpu\"]:\n        TESTS.append(\n            [\n                dict(padding_mode=\"zeros\", device=device),\n                {\"img\": p(np.arange(9).reshape((1, 3, 3))), \"spatial_size\": (-1, 0)},\n                p(np.arange(9).reshape(1, 3, 3)),\n            ]\n        )\n        TESTS.append(\n            [\n                dict(padding_mode=\"zeros\", device=device, image_only=True),\n                {\"img\": p(np.arange(9).reshape((1, 3, 3))), \"spatial_size\": (-1, 0)},\n                p(np.arange(9).reshape(1, 3, 3)),\n            ]\n        )\n        TESTS.append(\n            [\n                dict(padding_mode=\"zeros\", device=device),\n                {\"img\": p(np.arange(4).reshape((1, 2, 2)))},\n                p(np.arange(4).reshape(1, 2, 2)),\n            ]\n        )\n        TESTS.append(\n            [\n                dict(padding_mode=\"zeros\", device=device),\n                {\"img\": p(np.arange(4).reshape((1, 2, 2))), \"spatial_size\": (4, 4)},\n                p(np.array([[[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], [0.0, 2.0, 3.0, 0.0], [0.0, 0.0, 0.0, 0.0]]])),\n            ]\n        )\n        TESTS.append(\n            [\n                dict(rotate_params=[np.pi / 2], padding_mode=\"zeros\", device=device),\n                {\"img\": p(np.arange(4).reshape((1, 2, 2))), \"spatial_size\": (4, 4)},\n                p(np.array([[[0.0, 0.0, 0.0, 0.0], [0.0, 2.0, 0.0, 0.0], [0.0, 3.0, 1.0, 0.0], [0.0, 0.0, 0.0, 0.0]]])),\n            ]\n        )\n        TESTS.append(\n            [\n                dict(\n                    affine=p(torch.tensor([[0.0, -1.0, 0.0], [1.0, 0.0, 0.0], [0.0, 0.0, 1.0]])),\n                    padding_mode=\"zeros\",\n                    device=device,\n                ),\n                {\"img\": p(np.arange(4).reshape((1, 2, 2))), \"spatial_size\": (4, 4)},\n                p(np.array([[[0.0, 0.0, 0.0, 0.0], [0.0, 2.0, 0.0, 0.0], [0.0, 3.0, 1.0, 0.0], [0.0, 0.0, 0.0, 0.0]]])),\n            ]\n        )\n        TESTS.append(\n            [\n                dict(padding_mode=\"zeros\", device=device),\n                {\"img\": p(np.arange(27).reshape((1, 3, 3, 3))), \"spatial_size\": (-1, 0, 0)},\n                p(np.arange(27).reshape(1, 3, 3, 3)),\n            ]\n        )\n        TESTS.append(\n            [\n                dict(padding_mode=\"zeros\", device=device),\n                {\"img\": p(np.arange(8).reshape((1, 2, 2, 2))), \"spatial_size\": (4, 4, 4)},\n                p(\n                    np.array(\n                        [\n                            [\n                                [\n                                    [0.0, 0.0, 0.0, 0.0],\n                                    [0.0, 0.0, 0.0, 0.0],\n                                    [0.0, 0.0, 0.0, 0.0],\n                                    [0.0, 0.0, 0.0, 0.0],\n                                ],\n                                [\n                                    [0.0, 0.0, 0.0, 0.0],\n                                    [0.0, 0.0, 1.0, 0.0],\n                                    [0.0, 2.0, 3.0, 0.0],\n                                    [0.0, 0.0, 0.0, 0.0],\n                                ],\n                                [\n                                    [0.0, 0.0, 0.0, 0.0],\n                                    [0.0, 4.0, 5.0, 0.0],\n                                    [0.0, 6.0, 7.0, 0.0],\n                                    [0.0, 0.0, 0.0, 0.0],\n                                ],\n                                [\n                                    [0.0, 0.0, 0.0, 0.0],\n                                    [0.0, 0.0, 0.0, 0.0],\n                                    [0.0, 0.0, 0.0, 0.0],\n                                    [0.0, 0.0, 0.0, 0.0],\n                                ],\n                            ]\n                        ]\n                    )\n                ),\n            ]\n        )\n        # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affine.py_for_p_in_TEST_NDARRAYS_.for_device_in_None_cpu.None_8_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affine.py_for_p_in_TEST_NDARRAYS_.for_device_in_None_cpu.None_8_", "embedding": null, "metadata": {"file_path": "tests/test_affine.py", "file_name": "test_affine.py", "file_type": "text/x-python", "category": "test", "start_line": 115, "end_line": 167, "span_ids": ["TestAffine", "TestAffine.test_affine", "impl:14", "docstring"], "tokens": 578}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "for p in TEST_NDARRAYS:\n    for device in [None, \"cpu\", \"cuda\"] if torch.cuda.is_available() else [None, \"cpu\"]:\n        # ... other code\n        TESTS.append(\n            [\n                dict(rotate_params=[np.pi / 2], padding_mode=\"zeros\", device=device),\n                {\"img\": p(np.arange(8).reshape((1, 2, 2, 2))), \"spatial_size\": (4, 4, 4)},\n                p(\n                    np.array(\n                        [\n                            [\n                                [\n                                    [0.0, 0.0, 0.0, 0.0],\n                                    [0.0, 0.0, 0.0, 0.0],\n                                    [0.0, 0.0, 0.0, 0.0],\n                                    [0.0, 0.0, 0.0, 0.0],\n                                ],\n                                [\n                                    [0.0, 0.0, 0.0, 0.0],\n                                    [0.0, 2.0, 0.0, 0.0],\n                                    [0.0, 3.0, 1.0, 0.0],\n                                    [0.0, 0.0, 0.0, 0.0],\n                                ],\n                                [\n                                    [0.0, 0.0, 0.0, 0.0],\n                                    [0.0, 6.0, 4.0, 0.0],\n                                    [0.0, 7.0, 5.0, 0.0],\n                                    [0.0, 0.0, 0.0, 0.0],\n                                ],\n                                [\n                                    [0.0, 0.0, 0.0, 0.0],\n                                    [0.0, 0.0, 0.0, 0.0],\n                                    [0.0, 0.0, 0.0, 0.0],\n                                    [0.0, 0.0, 0.0, 0.0],\n                                ],\n                            ]\n                        ]\n                    )\n                ),\n            ]\n        )\n\n\nclass TestAffine(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_affine(self, input_param, input_data, expected_val):\n        g = Affine(**input_param)\n        result = g(**input_data)\n        if isinstance(result, tuple):\n            result = result[0]\n        assert_allclose(result, expected_val, rtol=1e-4, atol=1e-4)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affine_grid.py_for_p_in_TEST_NDARRAYS_.for_device_in_None_cpu.None_5_for_p_in_TEST_NDARRAYS_.for_device_in_None_cpu.None_5": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affine_grid.py_for_p_in_TEST_NDARRAYS_.for_device_in_None_cpu.None_5_for_p_in_TEST_NDARRAYS_.for_device_in_None_cpu.None_5", "embedding": null, "metadata": {"file_path": "tests/test_affine_grid.py", "file_name": "test_affine_grid.py", "file_type": "text/x-python", "category": "test", "start_line": 86, "end_line": 129, "span_ids": ["docstring"], "tokens": 822}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "for p in TEST_NDARRAYS:\n    for device in [None, \"cpu\", \"cuda\"] if torch.cuda.is_available() else [None, \"cpu\"]:\n        # ... other code\n        TESTS.append(\n            [\n                {\"rotate_params\": (1.0, 1.0, 1.0), \"scale_params\": (-20, 10), \"device\": device},\n                {\"grid\": p(torch.ones((4, 3, 3, 3)))},\n                p(\n                    torch.tensor(\n                        [\n                            [\n                                [[-9.5435, -9.5435, -9.5435], [-9.5435, -9.5435, -9.5435], [-9.5435, -9.5435, -9.5435]],\n                                [[-9.5435, -9.5435, -9.5435], [-9.5435, -9.5435, -9.5435], [-9.5435, -9.5435, -9.5435]],\n                                [[-9.5435, -9.5435, -9.5435], [-9.5435, -9.5435, -9.5435], [-9.5435, -9.5435, -9.5435]],\n                            ],\n                            [\n                                [\n                                    [-20.2381, -20.2381, -20.2381],\n                                    [-20.2381, -20.2381, -20.2381],\n                                    [-20.2381, -20.2381, -20.2381],\n                                ],\n                                [\n                                    [-20.2381, -20.2381, -20.2381],\n                                    [-20.2381, -20.2381, -20.2381],\n                                    [-20.2381, -20.2381, -20.2381],\n                                ],\n                                [\n                                    [-20.2381, -20.2381, -20.2381],\n                                    [-20.2381, -20.2381, -20.2381],\n                                    [-20.2381, -20.2381, -20.2381],\n                                ],\n                            ],\n                            [\n                                [[-0.5844, -0.5844, -0.5844], [-0.5844, -0.5844, -0.5844], [-0.5844, -0.5844, -0.5844]],\n                                [[-0.5844, -0.5844, -0.5844], [-0.5844, -0.5844, -0.5844], [-0.5844, -0.5844, -0.5844]],\n                                [[-0.5844, -0.5844, -0.5844], [-0.5844, -0.5844, -0.5844], [-0.5844, -0.5844, -0.5844]],\n                            ],\n                            [\n                                [[1.0000, 1.0000, 1.0000], [1.0000, 1.0000, 1.0000], [1.0000, 1.0000, 1.0000]],\n                                [[1.0000, 1.0000, 1.0000], [1.0000, 1.0000, 1.0000], [1.0000, 1.0000, 1.0000]],\n                                [[1.0000, 1.0000, 1.0000], [1.0000, 1.0000, 1.0000], [1.0000, 1.0000, 1.0000]],\n                            ],\n                        ]\n                    )\n                ),\n            ]\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affined.py_unittest_for_p_in_TEST_NDARRAYS_.for_device_in_None_cpu.None_6": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affined.py_unittest_for_p_in_TEST_NDARRAYS_.for_device_in_None_cpu.None_6", "embedding": null, "metadata": {"file_path": "tests/test_affined.py", "file_name": "test_affined.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 109, "span_ids": ["docstring"], "tokens": 1216}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import Affined\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    for device in [None, \"cpu\", \"cuda\"] if torch.cuda.is_available() else [None, \"cpu\"]:\n        TESTS.append(\n            [\n                dict(keys=\"img\", padding_mode=\"zeros\", spatial_size=(-1, 0), device=device),\n                {\"img\": p(np.arange(9).reshape((1, 3, 3)))},\n                p(np.arange(9).reshape(1, 3, 3)),\n            ]\n        )\n        TESTS.append(\n            [\n                dict(keys=\"img\", padding_mode=\"zeros\", device=device),\n                {\"img\": p(np.arange(4).reshape((1, 2, 2)))},\n                p(np.arange(4).reshape(1, 2, 2)),\n            ]\n        )\n        TESTS.append(\n            [\n                dict(keys=\"img\", padding_mode=\"zeros\", spatial_size=(4, 4), device=device),\n                {\"img\": p(np.arange(4).reshape((1, 2, 2)))},\n                p(np.array([[[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], [0.0, 2.0, 3.0, 0.0], [0.0, 0.0, 0.0, 0.0]]])),\n            ]\n        )\n        TESTS.append(\n            [\n                dict(keys=\"img\", rotate_params=[np.pi / 2], padding_mode=\"zeros\", spatial_size=(4, 4), device=device),\n                {\"img\": p(np.arange(4).reshape((1, 2, 2)))},\n                p(np.array([[[0.0, 0.0, 0.0, 0.0], [0.0, 2.0, 0.0, 0.0], [0.0, 3.0, 1.0, 0.0], [0.0, 0.0, 0.0, 0.0]]])),\n            ]\n        )\n        TESTS.append(\n            [\n                dict(\n                    keys=\"img\",\n                    affine=p(torch.tensor([[0.0, -1.0, 0.0], [1.0, 0.0, 0.0], [0.0, 0.0, 1.0]])),\n                    padding_mode=\"zeros\",\n                    spatial_size=(4, 4),\n                    device=device,\n                ),\n                {\"img\": p(np.arange(4).reshape((1, 2, 2)))},\n                p(np.array([[[0.0, 0.0, 0.0, 0.0], [0.0, 2.0, 0.0, 0.0], [0.0, 3.0, 1.0, 0.0], [0.0, 0.0, 0.0, 0.0]]])),\n            ]\n        )\n        TESTS.append(\n            [\n                dict(keys=\"img\", padding_mode=\"zeros\", spatial_size=(-1, 0, 0), device=device),\n                {\"img\": p(np.arange(27).reshape((1, 3, 3, 3)))},\n                p(np.arange(27).reshape(1, 3, 3, 3)),\n            ]\n        )\n        TESTS.append(\n            [\n                dict(keys=\"img\", padding_mode=\"zeros\", spatial_size=(4, 4, 4), device=device),\n                {\"img\": p(np.arange(8).reshape((1, 2, 2, 2)))},\n                p(\n                    np.array(\n                        [\n                            [\n                                [\n                                    [0.0, 0.0, 0.0, 0.0],\n                                    [0.0, 0.0, 0.0, 0.0],\n                                    [0.0, 0.0, 0.0, 0.0],\n                                    [0.0, 0.0, 0.0, 0.0],\n                                ],\n                                [\n                                    [0.0, 0.0, 0.0, 0.0],\n                                    [0.0, 0.0, 1.0, 0.0],\n                                    [0.0, 2.0, 3.0, 0.0],\n                                    [0.0, 0.0, 0.0, 0.0],\n                                ],\n                                [\n                                    [0.0, 0.0, 0.0, 0.0],\n                                    [0.0, 4.0, 5.0, 0.0],\n                                    [0.0, 6.0, 7.0, 0.0],\n                                    [0.0, 0.0, 0.0, 0.0],\n                                ],\n                                [\n                                    [0.0, 0.0, 0.0, 0.0],\n                                    [0.0, 0.0, 0.0, 0.0],\n                                    [0.0, 0.0, 0.0, 0.0],\n                                    [0.0, 0.0, 0.0, 0.0],\n                                ],\n                            ]\n                        ]\n                    )\n                ),\n            ]\n        )\n        # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affined.py_for_p_in_TEST_NDARRAYS_.for_device_in_None_cpu.None_7_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_affined.py_for_p_in_TEST_NDARRAYS_.for_device_in_None_cpu.None_7_", "embedding": null, "metadata": {"file_path": "tests/test_affined.py", "file_name": "test_affined.py", "file_type": "text/x-python", "category": "test", "start_line": 110, "end_line": 162, "span_ids": ["impl:13", "TestAffined.test_affine", "TestAffined", "docstring"], "tokens": 570}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "for p in TEST_NDARRAYS:\n    for device in [None, \"cpu\", \"cuda\"] if torch.cuda.is_available() else [None, \"cpu\"]:\n        # ... other code\n        TESTS.append(\n            [\n                dict(\n                    keys=\"img\", rotate_params=[np.pi / 2], padding_mode=\"zeros\", spatial_size=(4, 4, 4), device=device\n                ),\n                {\"img\": p(np.arange(8).reshape((1, 2, 2, 2)))},\n                p(\n                    np.array(\n                        [\n                            [\n                                [\n                                    [0.0, 0.0, 0.0, 0.0],\n                                    [0.0, 0.0, 0.0, 0.0],\n                                    [0.0, 0.0, 0.0, 0.0],\n                                    [0.0, 0.0, 0.0, 0.0],\n                                ],\n                                [\n                                    [0.0, 0.0, 0.0, 0.0],\n                                    [0.0, 2.0, 0.0, 0.0],\n                                    [0.0, 3.0, 1.0, 0.0],\n                                    [0.0, 0.0, 0.0, 0.0],\n                                ],\n                                [\n                                    [0.0, 0.0, 0.0, 0.0],\n                                    [0.0, 6.0, 4.0, 0.0],\n                                    [0.0, 7.0, 5.0, 0.0],\n                                    [0.0, 0.0, 0.0, 0.0],\n                                ],\n                                [\n                                    [0.0, 0.0, 0.0, 0.0],\n                                    [0.0, 0.0, 0.0, 0.0],\n                                    [0.0, 0.0, 0.0, 0.0],\n                                    [0.0, 0.0, 0.0, 0.0],\n                                ],\n                            ]\n                        ]\n                    )\n                ),\n            ]\n        )\n\n\nclass TestAffined(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_affine(self, input_param, input_data, expected_val):\n        g = Affined(**input_param)\n        result = g(input_data)[\"img\"]\n        assert_allclose(result, expected_val, rtol=1e-4, atol=1e-4)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_as_discreted.py_unittest_for_p_in_TEST_NDARRAYS_.None_5": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_as_discreted.py_unittest_for_p_in_TEST_NDARRAYS_.None_5", "embedding": null, "metadata": {"file_path": "tests/test_as_discreted.py", "file_name": "test_as_discreted.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 81, "span_ids": ["docstring"], "tokens": 899}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nfrom parameterized import parameterized\n\nfrom monai.transforms import AsDiscreted\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTEST_CASES = []\nfor p in TEST_NDARRAYS:\n    TEST_CASES.append(\n        [\n            {\"keys\": [\"pred\", \"label\"], \"argmax\": [True, False], \"to_onehot\": 2, \"threshold\": 0.5},\n            {\"pred\": p([[[0.0, 1.0]], [[2.0, 3.0]]]), \"label\": p([[[0, 1]]])},\n            {\"pred\": p([[[0.0, 0.0]], [[1.0, 1.0]]]), \"label\": p([[[1.0, 0.0]], [[0.0, 1.0]]])},\n            (2, 1, 2),\n        ]\n    )\n\n    TEST_CASES.append(\n        [\n            {\"keys\": [\"pred\", \"label\"], \"argmax\": False, \"to_onehot\": None, \"threshold\": [0.6, None]},\n            {\"pred\": p([[[0.0, 1.0], [2.0, 3.0]]]), \"label\": p([[[0, 1], [1, 1]]])},\n            {\"pred\": p([[[0.0, 1.0], [1.0, 1.0]]]), \"label\": p([[[0.0, 1.0], [1.0, 1.0]]])},\n            (1, 2, 2),\n        ]\n    )\n\n    TEST_CASES.append(\n        [\n            {\"keys\": [\"pred\"], \"argmax\": True, \"to_onehot\": 2, \"threshold\": 0.5},\n            {\"pred\": p([[[0.0, 1.0]], [[2.0, 3.0]]])},\n            {\"pred\": p([[[0.0, 0.0]], [[1.0, 1.0]]])},\n            (2, 1, 2),\n        ]\n    )\n\n    TEST_CASES.append(\n        [\n            {\"keys\": \"pred\", \"rounding\": \"torchrounding\"},\n            {\"pred\": p([[[0.123, 1.345], [2.567, 3.789]]])},\n            {\"pred\": p([[[0.0, 1.0], [3.0, 4.0]]])},\n            (1, 2, 2),\n        ]\n    )\n\n    # test compatible with previous versions\n    TEST_CASES.append(\n        [\n            {\n                \"keys\": [\"pred\", \"label\"],\n                \"argmax\": False,\n                \"to_onehot\": None,\n                \"threshold\": [True, None],\n                \"logit_thresh\": 0.6,\n            },\n            {\"pred\": p([[[0.0, 1.0], [2.0, 3.0]]]), \"label\": p([[[0, 1], [1, 1]]])},\n            {\"pred\": p([[[0.0, 1.0], [1.0, 1.0]]]), \"label\": p([[[0.0, 1.0], [1.0, 1.0]]])},\n            (1, 2, 2),\n        ]\n    )\n\n    # test threshold = 0.0\n    TEST_CASES.append(\n        [\n            {\"keys\": [\"pred\", \"label\"], \"argmax\": False, \"to_onehot\": None, \"threshold\": [0.0, None]},\n            {\"pred\": p([[[0.0, -1.0], [-2.0, 3.0]]]), \"label\": p([[[0, 1], [1, 1]]])},\n            {\"pred\": p([[[1.0, 0.0], [0.0, 1.0]]]), \"label\": p([[[0.0, 1.0], [1.0, 1.0]]])},\n            (1, 2, 2),\n        ]\n    )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_center_scale_cropd.py_unittest_TEST_CASE_4._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_center_scale_cropd.py_unittest_TEST_CASE_4._", "embedding": null, "metadata": {"file_path": "tests/test_center_scale_cropd.py", "file_name": "test_center_scale_cropd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 40, "span_ids": ["docstring"], "tokens": 407}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import CenterScaleCropd\n\nTEST_CASE_0 = [{\"keys\": \"img\", \"roi_scale\": [0.6, 0.3, -1]}, np.random.randint(0, 2, size=[3, 3, 3, 3]), (3, 2, 1, 3)]\n\nTEST_CASE_1 = [{\"keys\": \"img\", \"roi_scale\": 0.6}, np.random.randint(0, 2, size=[3, 3, 3, 3]), (3, 2, 2, 2)]\n\nTEST_CASE_2 = [\n    {\"keys\": \"img\", \"roi_scale\": [0.4, 0.4]},\n    np.array([[[0, 0, 0, 0, 0], [0, 1, 2, 1, 0], [0, 2, 3, 2, 0], [0, 1, 2, 1, 0], [0, 0, 0, 0, 0]]]),\n    np.array([[[1, 2], [2, 3]]]),\n]\n\nTEST_CASE_3 = [\n    {\"keys\": \"img\", \"roi_scale\": 0.5},\n    torch.randint(0, 2, size=[3, 3, 3, 3], device=\"cuda\" if torch.cuda.is_available() else \"cpu\"),\n    (3, 2, 2, 2),\n]\n\nTEST_CASE_4 = [\n    {\"keys\": \"test\", \"roi_scale\": 0.6, \"allow_missing_keys\": True},\n    np.random.randint(0, 2, size=[3, 3, 3, 3]),\n    (3, 3, 3, 3),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_decollate.py_TestBasicDeCollate_TestBasicDeCollate.test_dict_examples._verify_padding_fill_val": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_decollate.py_TestBasicDeCollate_TestBasicDeCollate.test_dict_examples._verify_padding_fill_val", "embedding": null, "metadata": {"file_path": "tests/test_decollate.py", "file_name": "test_decollate.py", "file_type": "text/x-python", "category": "test", "start_line": 167, "end_line": 219, "span_ids": ["TestBasicDeCollate", "TestBasicDeCollate.test_decollation_examples", "TestBasicDeCollate.test_dict_examples"], "tokens": 660}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestBasicDeCollate(unittest.TestCase):\n    @parameterized.expand(TEST_BASIC)\n    def test_decollation_examples(self, input_val, expected_out):\n        out = decollate_batch(input_val)\n        self.assertListEqual(expected_out, out)\n\n    def test_dict_examples(self):\n        test_case = {\"meta\": {\"out\": [\"test\", \"test\"]}, \"image_meta_dict\": {\"scl_slope\": torch.Tensor((0.0, 0.0))}}\n        out = decollate_batch(test_case)\n        self.assertEqual(out[0][\"meta\"][\"out\"], \"test\")\n        self.assertEqual(out[0][\"image_meta_dict\"][\"scl_slope\"], 0.0)\n\n        test_case = [torch.ones((2, 1, 10, 10)), torch.ones((2, 3, 5, 5))]\n        out = decollate_batch(test_case)\n        self.assertTupleEqual(out[0][0].shape, (1, 10, 10))\n        self.assertTupleEqual(out[0][1].shape, (3, 5, 5))\n\n        test_case = torch.rand((2, 1, 10, 10))\n        out = decollate_batch(test_case)\n        self.assertTupleEqual(out[0].shape, (1, 10, 10))\n\n        test_case = [torch.tensor(0), torch.tensor(0)]\n        out = decollate_batch(test_case, detach=True)\n        self.assertListEqual([0, 0], out)\n        self.assertFalse(isinstance(out[0], torch.Tensor))\n\n        test_case = {\"a\": [torch.tensor(0), torch.tensor(0)]}\n        out = decollate_batch(test_case, detach=False)\n        self.assertListEqual([{\"a\": torch.tensor(0)}, {\"a\": torch.tensor(0)}], out)\n        self.assertTrue(isinstance(out[0][\"a\"], torch.Tensor))\n\n        test_case = [torch.tensor(0), torch.tensor(0)]\n        out = decollate_batch(test_case, detach=False)\n        self.assertListEqual(test_case, out)\n\n        test_case = {\n            \"image\": torch.tensor([[[1, 2]], [[3, 4]]]),\n            \"label\": torch.tensor([[[5, 6]], [[7, 8]]]),\n            \"pred\": torch.tensor([[[9, 10]], [[11, 12]]]),\n            \"out\": [\"test\"],\n        }\n        out = decollate_batch(test_case, detach=False)\n        self.assertEqual(out[0][\"out\"], \"test\")\n\n        test_case = {\n            \"image\": torch.tensor([[[1, 2, 3]], [[3, 4, 5]]]),\n            \"label\": torch.tensor([[[5]], [[7]]]),\n            \"out\": [\"test\"],\n        }\n        out = decollate_batch(test_case, detach=False, pad=False)\n        self.assertEqual(len(out), 1)  # no padding\n        out = decollate_batch(test_case, detach=False, pad=True, fill_value=0)\n        self.assertEqual(out[1][\"out\"], 0)  # verify padding fill_value", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dints_cell.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dints_cell.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_dints_cell.py", "file_name": "test_dints_cell.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 78, "span_ids": ["impl:3", "TestCell", "impl:5", "TestCell.test_cell_3d", "docstring"], "tokens": 767}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks.nets.dints import Cell\n\nTEST_CASES_3D = [\n    [\n        {\"c_prev\": 8, \"c\": 8, \"rate\": 1, \"arch_code_c\": None},\n        torch.tensor([1, 1, 1, 1, 1]),\n        torch.tensor([1, 1, 1, 1, 1]),\n        (2, 8, 32, 16, 8),\n        (2, 8, 64, 32, 16),\n    ],\n    [\n        {\"c_prev\": 8, \"c\": 4, \"rate\": 1, \"arch_code_c\": [1, 1, 0, 0, 1]},\n        torch.tensor([1, 1, 0, 0, 1]),\n        torch.tensor([1, 0.2, 1.3, 0, 1]),\n        (2, 8, 32, 16, 8),\n        (2, 4, 64, 32, 16),\n    ],\n    [\n        {\"c_prev\": 8, \"c\": 8, \"rate\": 0, \"arch_code_c\": None},\n        torch.tensor([1, 1, 1, 1, 1]),\n        torch.tensor([0, 0, 0, 1, 0]),\n        (2, 8, 32, 16, 8),\n        (2, 8, 32, 16, 8),\n    ],\n    [\n        {\"c_prev\": 8, \"c\": 8, \"rate\": -1, \"arch_code_c\": None},\n        torch.tensor([1, 1, 1, 1, 1]),\n        torch.tensor([1, 1, 1, 1, 1]),\n        (2, 8, 32, 16, 8),\n        (2, 8, 16, 8, 4),\n    ],\n    [\n        {\"c_prev\": 8, \"c\": 8, \"rate\": -1, \"arch_code_c\": [1, 0, 0, 0, 1]},\n        torch.tensor([1, 0, 0, 0, 1]),\n        torch.tensor([0.2, 0.2, 0.2, 0.2, 0.2]),\n        (2, 8, 32, 16, 8),\n        (2, 8, 16, 8, 4),\n    ],\n]\n\nTEST_CASES_2D = [\n    [\n        {\"c_prev\": 8, \"c\": 7, \"rate\": -1, \"arch_code_c\": [1, 0, 0, 0, 1], \"spatial_dims\": 2},\n        torch.tensor([1, 0]),\n        torch.tensor([0.2, 0.2]),\n        (2, 8, 16, 8),\n        (2, 7, 8, 4),\n    ]\n]\n\n\nclass TestCell(unittest.TestCase):\n    @parameterized.expand(TEST_CASES_2D + TEST_CASES_3D)\n    def test_cell_3d(self, input_param, ops, weight, input_shape, expected_shape):\n        net = Cell(**input_param)\n        result = net(torch.randn(input_shape), weight=weight)\n        self.assertEqual(result.shape, expected_shape)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dints_mixop.py_unittest_TEST_CASES_2D._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dints_mixop.py_unittest_TEST_CASES_2D._", "embedding": null, "metadata": {"file_path": "tests/test_dints_mixop.py", "file_name": "test_dints_mixop.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 58, "span_ids": ["impl:3", "docstring"], "tokens": 503}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks.nets.dints import Cell, MixedOp\nfrom tests.utils import test_script_save\n\nTEST_CASES_3D = [\n    [\n        {\"c\": 8, \"arch_code_c\": None},\n        torch.tensor([1, 1, 1, 1, 1]),\n        torch.tensor([1, 1, 1, 1, 1]),\n        (2, 8, 32, 16, 8),\n        (2, 8, 32, 16, 8),\n    ],\n    [\n        {\"c\": 8, \"arch_code_c\": [1, 1, 0, 0, 1]},\n        torch.tensor([1, 1, 0, 0, 1]),\n        torch.tensor([1, 0.2, 1.3, 0, 1]),\n        (2, 8, 64, 32, 16),\n        (2, 8, 64, 32, 16),\n    ],\n    [\n        {\"c\": 8, \"arch_code_c\": None},\n        torch.tensor([1, 1, 1, 1, 1]),\n        torch.tensor([0, 0, 0, 1, 0]),\n        (2, 8, 32, 16, 8),\n        (2, 8, 32, 16, 8),\n    ],\n    [\n        {\"c\": 8, \"arch_code_c\": [1, 1, 1, 0, 1]},\n        torch.tensor([1, 1, 1, 1, 1]),\n        torch.tensor([0, 0, 0, 1, 0]),\n        (2, 8, 32, 16, 8),\n        (2, 8, 32, 16, 8),\n    ],\n]\nTEST_CASES_2D = [\n    [\n        {\"c\": 32, \"arch_code_c\": [1, 1, 1, 0, 1]},\n        torch.tensor([1, 1]),\n        torch.tensor([0, 0]),\n        (2, 32, 16, 8),\n        (2, 32, 16, 8),\n    ]\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dints_mixop.py_TestMixOP_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dints_mixop.py_TestMixOP_", "embedding": null, "metadata": {"file_path": "tests/test_dints_mixop.py", "file_name": "test_dints_mixop.py", "file_type": "text/x-python", "category": "test", "start_line": 61, "end_line": 83, "span_ids": ["TestMixOP.test_mixop_3d", "TestMixOP.test_script", "TestMixOP", "TestMixOP.test_mixop_2d", "impl:5"], "tokens": 240}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestMixOP(unittest.TestCase):\n    @parameterized.expand(TEST_CASES_3D)\n    def test_mixop_3d(self, input_param, ops, weight, input_shape, expected_shape):\n        net = MixedOp(ops=Cell.OPS3D, **input_param)\n        result = net(torch.randn(input_shape), weight=weight)\n        self.assertEqual(result.shape, expected_shape)\n        self.assertEqual(result.shape, expected_shape)\n\n    @parameterized.expand(TEST_CASES_2D)\n    def test_mixop_2d(self, input_param, ops, weight, input_shape, expected_shape):\n        net = MixedOp(ops=Cell.OPS2D, **input_param)\n        result = net(torch.randn(input_shape), weight=weight)\n        self.assertEqual(result.shape, expected_shape)\n\n    @parameterized.expand(TEST_CASES_3D)\n    def test_script(self, input_param, ops, weight, input_shape, expected_shape):\n        net = MixedOp(ops=Cell.OPS3D, **input_param)\n        test_script_save(net, torch.randn(input_shape), weight)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dints_network.py_unittest_None_1.TEST_CASES_2D_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dints_network.py_unittest_None_1.TEST_CASES_2D_", "embedding": null, "metadata": {"file_path": "tests/test_dints_network.py", "file_name": "test_dints_network.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 111, "span_ids": ["impl:5", "docstring"], "tokens": 686}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks.nets import DiNTS, TopologyInstance, TopologySearch\nfrom monai.networks.nets.dints import Cell\nfrom tests.utils import SkipIfBeforePyTorchVersion, test_script_save\n\nTEST_CASES_3D = [\n    [\n        {\n            \"channel_mul\": 0.2,\n            \"num_blocks\": 6,\n            \"num_depths\": 3,\n            \"device\": \"cpu\",\n            \"use_downsample\": False,\n            \"spatial_dims\": 3,\n        },\n        {\n            \"in_channels\": 1,\n            \"num_classes\": 3,\n            \"act_name\": \"RELU\",\n            \"norm_name\": \"INSTANCE\",\n            \"use_downsample\": False,\n            \"spatial_dims\": 3,\n        },\n        (3, 1, 32, 32, 16),\n        (3, 3, 32, 32, 16),\n    ]\n]\nif torch.cuda.is_available():\n    TEST_CASES_3D += [\n        [\n            {\n                \"channel_mul\": 0.5,\n                \"num_blocks\": 7,\n                \"num_depths\": 4,\n                \"device\": \"cuda\",\n                \"use_downsample\": True,\n                \"spatial_dims\": 3,\n            },\n            {\n                \"in_channels\": 2,\n                \"num_classes\": 2,\n                \"act_name\": \"PRELU\",\n                \"norm_name\": \"BATCH\",\n                \"use_downsample\": True,\n                \"spatial_dims\": 3,\n            },\n            (3, 2, 32, 32, 16),\n            (3, 2, 32, 32, 16),\n        ]\n    ]\nTEST_CASES_2D = [\n    [\n        {\n            \"channel_mul\": 1,\n            \"num_blocks\": 7,\n            \"num_depths\": 4,\n            \"device\": \"cpu\",\n            \"use_downsample\": True,\n            \"spatial_dims\": 2,\n        },\n        {\n            \"in_channels\": 2,\n            \"num_classes\": 2,\n            \"act_name\": \"PRELU\",\n            \"norm_name\": \"BATCH\",\n            \"use_downsample\": True,\n            \"spatial_dims\": 2,\n        },\n        (2, 2, 32, 16),\n        (2, 2, 32, 16),\n    ]\n]\nif torch.cuda.is_available():\n    TEST_CASES_2D += [\n        [\n            {\n                \"channel_mul\": 0.5,\n                \"num_blocks\": 8,\n                \"num_depths\": 4,\n                \"device\": \"cuda\",\n                \"use_downsample\": False,\n                \"spatial_dims\": 2,\n            },\n            {\n                \"in_channels\": 1,\n                \"num_classes\": 4,\n                \"act_name\": \"RELU\",\n                \"norm_name\": \"INSTANCE\",\n                \"use_downsample\": False,\n                \"spatial_dims\": 2,\n            },\n            (2, 1, 32, 16),\n            (2, 4, 32, 16),\n        ]\n    ]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dints_network.py_TestDints_TestDints.test_dints_inference.grid_gen_mtx_depth_4_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dints_network.py_TestDints_TestDints.test_dints_inference.grid_gen_mtx_depth_4_", "embedding": null, "metadata": {"file_path": "tests/test_dints_network.py", "file_name": "test_dints_network.py", "file_type": "text/x-python", "category": "test", "start_line": 114, "end_line": 128, "span_ids": ["TestDints.test_dints_inference", "TestDints"], "tokens": 186}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestDints(unittest.TestCase):\n    @parameterized.expand(TEST_CASES_3D + TEST_CASES_2D)\n    def test_dints_inference(self, dints_grid_params, dints_params, input_shape, expected_shape):\n        grid = TopologySearch(**dints_grid_params)\n        dints_params[\"dints_space\"] = grid\n        net = DiNTS(**dints_params).to(dints_grid_params[\"device\"])\n        result = net(torch.randn(input_shape).to(dints_grid_params[\"device\"]))\n        self.assertEqual(result.shape, expected_shape)\n        # test functions\n        grid.get_ram_cost_usage(in_size=input_shape, full=True)\n        grid.get_ram_cost_usage(in_size=input_shape, full=False)\n        probs_a, _ = grid.get_prob_a(child=True)\n        grid.get_topology_entropy(probs_a)\n        grid.decode()\n        grid.gen_mtx(depth=4)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dints_network.py_TestDints.test_dints_search_TestDints.test_dints_search.self_assertTrue_isinstanc": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dints_network.py_TestDints.test_dints_search_TestDints.test_dints_search.self_assertTrue_isinstanc", "embedding": null, "metadata": {"file_path": "tests/test_dints_network.py", "file_name": "test_dints_network.py", "file_type": "text/x-python", "category": "test", "start_line": 130, "end_line": 150, "span_ids": ["TestDints.test_dints_search"], "tokens": 318}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestDints(unittest.TestCase):\n\n    @parameterized.expand(TEST_CASES_3D + TEST_CASES_2D)\n    def test_dints_search(self, dints_grid_params, dints_params, input_shape, expected_shape):\n        num_blocks = dints_grid_params[\"num_blocks\"]\n        num_depths = dints_grid_params[\"num_depths\"]\n        # init a Cell to obtain cell operation number\n        _cell = Cell(1, 1, 0, spatial_dims=dints_grid_params[\"spatial_dims\"])\n        num_cell_ops = len(_cell.OPS)\n        # define archtecture codes\n        node_a = torch.ones((num_blocks + 1, num_depths))\n        arch_code_a = np.ones((num_blocks, 3 * num_depths - 2))\n        arch_code_c = np.random.randint(num_cell_ops, size=(num_blocks, 3 * num_depths - 2))\n        # initialize with codes\n        dints_grid_params[\"arch_code\"] = [arch_code_a, arch_code_c]\n        grid = TopologyInstance(**dints_grid_params)\n        # set as deploy stage\n        dints_params[\"dints_space\"] = grid\n        dints_params[\"node_a\"] = node_a\n        net = DiNTS(**dints_params).to(dints_grid_params[\"device\"])\n        result = net(torch.randn(input_shape).to(dints_grid_params[\"device\"]))\n        self.assertEqual(result.shape, expected_shape)\n        self.assertTrue(isinstance(net.weight_parameters(), list))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dints_network.py_TestDintsTS_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dints_network.py_TestDintsTS_", "embedding": null, "metadata": {"file_path": "tests/test_dints_network.py", "file_name": "test_dints_network.py", "file_type": "text/x-python", "category": "test", "start_line": 153, "end_line": 166, "span_ids": ["impl:9", "TestDintsTS.test_script", "TestDintsTS"], "tokens": 148}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@SkipIfBeforePyTorchVersion((1, 9))\nclass TestDintsTS(unittest.TestCase):\n    @parameterized.expand(TEST_CASES_3D + TEST_CASES_2D)\n    def test_script(self, dints_grid_params, dints_params, input_shape, _):\n        grid = TopologyInstance(**dints_grid_params)\n        dints_grid_params[\"device\"] = \"cpu\"\n        dints_params[\"dints_space\"] = grid\n        net = DiNTS(**dints_params).to(dints_grid_params[\"device\"])\n        test_script_save(net, torch.randn(input_shape).to(dints_grid_params[\"device\"]))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_factorized_increase.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_factorized_increase.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_factorized_increase.py", "file_name": "test_factorized_increase.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 35, "span_ids": ["TestFactInc.test_factorized_increase_3d", "impl:3", "TestFactInc", "docstring"], "tokens": 207}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks.blocks.dints_block import FactorizedIncreaseBlock\n\nTEST_CASES_3D = [\n    [{\"in_channel\": 32, \"out_channel\": 16}, (7, 32, 24, 16, 8), (7, 16, 48, 32, 16)],\n    [{\"in_channel\": 1, \"out_channel\": 2}, (1, 1, 1, 1, 1), (1, 2, 2, 2, 2)],\n]\n\n\nclass TestFactInc(unittest.TestCase):\n    @parameterized.expand(TEST_CASES_3D)\n    def test_factorized_increase_3d(self, input_param, input_shape, expected_shape):\n        net = FactorizedIncreaseBlock(**input_param)\n        result = net(torch.randn(input_shape))\n        self.assertEqual(result.shape, expected_shape)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_factorized_reduce.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_factorized_reduce.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_factorized_reduce.py", "file_name": "test_factorized_reduce.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 35, "span_ids": ["TestFactRed.test_factorized_reduce_3d", "impl:3", "TestFactRed", "docstring"], "tokens": 207}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks.blocks.dints_block import FactorizedReduceBlock\n\nTEST_CASES_3D = [\n    [{\"in_channel\": 32, \"out_channel\": 16}, (7, 32, 24, 16, 8), (7, 16, 12, 8, 4)],\n    [{\"in_channel\": 16, \"out_channel\": 32}, (7, 16, 22, 14, 6), (7, 32, 11, 7, 3)],\n]\n\n\nclass TestFactRed(unittest.TestCase):\n    @parameterized.expand(TEST_CASES_3D)\n    def test_factorized_reduce_3d(self, input_param, input_shape, expected_shape):\n        net = FactorizedReduceBlock(**input_param)\n        result = net(torch.randn(input_shape))\n        self.assertEqual(result.shape, expected_shape)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_workflows.py_IntegrationWorkflows.train_and_infer._test_saved_files_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_integration_workflows.py_IntegrationWorkflows.train_and_infer._test_saved_files_", "embedding": null, "metadata": {"file_path": "tests/test_integration_workflows.py", "file_name": "test_integration_workflows.py", "file_type": "text/x-python", "category": "test", "start_line": 322, "end_line": 362, "span_ids": ["impl:3", "IntegrationWorkflows.test_training", "IntegrationWorkflows.train_and_infer", "IntegrationWorkflows.test_timing"], "tokens": 351}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_quick\nclass IntegrationWorkflows(DistTestCase):\n\n    def train_and_infer(self, idx=0):\n        # ... other code\n\n        def _test_saved_files(postfix):\n            output_files = sorted(glob(os.path.join(self.data_dir, \"img*\", f\"*{postfix}.nii.gz\")))\n            values = []\n            for output in output_files:\n                ave = np.mean(nib.load(output).get_fdata())\n                values.append(ave)\n            if idx == 2:\n                self.assertTrue(test_integration_value(TASK, key=\"output_sums_2\", data=values, rtol=1e-2))\n            else:\n                self.assertTrue(test_integration_value(TASK, key=\"output_sums\", data=values, rtol=1e-2))\n\n        _test_saved_files(postfix=\"seg_handler\")\n        _test_saved_files(postfix=\"seg_transform\")\n        try:\n            os.remove(model_file)\n        except Exception as e:\n            warnings.warn(f\"Fail to remove {model_file}: {e}.\")\n        if torch.cuda.is_available():\n            try:\n                torch.cuda.empty_cache()\n            except Exception:\n                pass\n\n        return results\n\n    def test_training(self):\n        repeated = []\n        test_rounds = 3\n        for i in range(test_rounds):\n            results = self.train_and_infer(idx=i)\n            repeated.append(results)\n        np.testing.assert_allclose(repeated[0], repeated[1])\n\n    @TimedCall(seconds=300, skip_timing=not torch.cuda.is_available(), daemon=False)\n    def test_timing(self):\n        self.train_and_infer(idx=2)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_milmodel.py_unittest_None_2": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_milmodel.py_unittest_None_2", "embedding": null, "metadata": {"file_path": "tests/test_milmodel.py", "file_name": "test_milmodel.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 62, "span_ids": ["impl:21", "docstring"], "tokens": 465}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks import eval_mode\nfrom monai.networks.nets import MILModel\nfrom monai.utils.module import optional_import\nfrom tests.utils import test_script_save\n\nmodels, _ = optional_import(\"torchvision.models\")\n\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\n\nTEST_CASE_MILMODEL = []\nfor num_classes in [1, 5]:\n    for mil_mode in [\"mean\", \"max\", \"att\", \"att_trans\", \"att_trans_pyramid\"]:\n        test_case = [\n            {\"num_classes\": num_classes, \"mil_mode\": mil_mode, \"pretrained\": False},\n            (1, 2, 3, 512, 512),\n            (1, num_classes),\n        ]\n        TEST_CASE_MILMODEL.append(test_case)\n\n\nfor trans_blocks in [1, 3]:\n    test_case = [\n        {\"num_classes\": 5, \"pretrained\": False, \"trans_blocks\": trans_blocks, \"trans_dropout\": 0.5},\n        (1, 2, 3, 512, 512),\n        (1, 5),\n    ]\n    TEST_CASE_MILMODEL.append(test_case)\n\n# torchvision backbone\nTEST_CASE_MILMODEL.append(\n    [{\"num_classes\": 5, \"backbone\": \"resnet18\", \"pretrained\": False}, (2, 2, 3, 512, 512), (2, 5)]\n)\nTEST_CASE_MILMODEL.append([{\"num_classes\": 5, \"backbone\": \"resnet18\", \"pretrained\": True}, (2, 2, 3, 512, 512), (2, 5)])\n\n# custom backbone\nbackbone = models.densenet121(pretrained=False)\nbackbone_nfeatures = backbone.classifier.in_features\nbackbone.classifier = torch.nn.Identity()\nTEST_CASE_MILMODEL.append(\n    [\n        {\"num_classes\": 5, \"backbone\": backbone, \"backbone_num_features\": backbone_nfeatures, \"pretrained\": False},\n        (2, 2, 3, 512, 512),\n        (2, 5),\n    ]\n)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_milmodel.py_TestMilModel_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_milmodel.py_TestMilModel_", "embedding": null, "metadata": {"file_path": "tests/test_milmodel.py", "file_name": "test_milmodel.py", "file_type": "text/x-python", "category": "test", "start_line": 65, "end_line": 92, "span_ids": ["TestMilModel", "TestMilModel.test_shape", "TestMilModel.test_ill_args", "TestMilModel.test_script", "impl:24"], "tokens": 197}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestMilModel(unittest.TestCase):\n    @parameterized.expand(TEST_CASE_MILMODEL)\n    def test_shape(self, input_param, input_shape, expected_shape):\n        net = MILModel(**input_param).to(device)\n        with eval_mode(net):\n            result = net(torch.randn(input_shape, dtype=torch.float).to(device))\n            self.assertEqual(result.shape, expected_shape)\n\n    def test_ill_args(self):\n        with self.assertRaises(ValueError):\n            MILModel(\n                num_classes=5,\n                pretrained=False,\n                backbone=\"resnet50\",\n                backbone_num_features=2048,\n                mil_mode=\"att_trans_pyramid\",\n            )\n\n    def test_script(self):\n        input_param, input_shape, expected_shape = TEST_CASE_MILMODEL[0]\n        net = MILModel(**input_param)\n        test_data = torch.randn(input_shape, dtype=torch.float)\n        test_script_save(net, test_data)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_p3d_block.py_unittest_TEST_CASES_3D._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_p3d_block.py_unittest_TEST_CASES_3D._", "embedding": null, "metadata": {"file_path": "tests/test_p3d_block.py", "file_name": "test_p3d_block.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 59, "span_ids": ["docstring"], "tokens": 451}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks.blocks.dints_block import P3DActiConvNormBlock\n\nTEST_CASES_3D = [\n    [\n        {\"in_channel\": 32, \"out_channel\": 16, \"kernel_size\": 3, \"padding\": 0, \"mode\": 0},\n        (7, 32, 16, 32, 8),\n        (7, 16, 14, 30, 6),\n    ],\n    [\n        {\"in_channel\": 32, \"out_channel\": 16, \"kernel_size\": 3, \"padding\": 1, \"mode\": 0},  # check padding\n        (7, 32, 16, 32, 8),\n        (7, 16, 16, 32, 8),\n    ],\n    [\n        {\"in_channel\": 32, \"out_channel\": 16, \"kernel_size\": 3, \"padding\": 0, \"mode\": 1},\n        (7, 32, 16, 32, 8),\n        (7, 16, 14, 30, 6),\n    ],\n    [\n        {\n            \"in_channel\": 32,\n            \"out_channel\": 16,\n            \"kernel_size\": 3,\n            \"padding\": 0,\n            \"mode\": 2,\n            \"act_name\": (\"leakyrelu\", {\"inplace\": True, \"negative_slope\": 0.2}),\n        },\n        (7, 32, 16, 32, 8),\n        (7, 16, 14, 30, 6),\n    ],\n    [\n        {\n            \"in_channel\": 32,\n            \"out_channel\": 16,\n            \"kernel_size\": 4,\n            \"padding\": 0,\n            \"mode\": 0,\n            \"norm_name\": (\"INSTANCE\", {\"affine\": True}),\n        },\n        (7, 32, 16, 32, 8),\n        (7, 16, 13, 29, 5),\n    ],\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_p3d_block.py_TestP3D_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_p3d_block.py_TestP3D_", "embedding": null, "metadata": {"file_path": "tests/test_p3d_block.py", "file_name": "test_p3d_block.py", "file_type": "text/x-python", "category": "test", "start_line": 62, "end_line": 76, "span_ids": ["TestP3D.test_ill", "impl:3", "TestP3D.test_3d", "TestP3D"], "tokens": 127}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestP3D(unittest.TestCase):\n    @parameterized.expand(TEST_CASES_3D)\n    def test_3d(self, input_param, input_shape, expected_shape):\n        net = P3DActiConvNormBlock(**input_param)\n        result = net(torch.randn(input_shape))\n        self.assertEqual(result.shape, expected_shape)\n\n    def test_ill(self):\n        with self.assertRaises(ValueError):\n            P3DActiConvNormBlock(in_channel=32, out_channel=16, kernel_size=3, padding=0, mode=3)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_pytorch_version_after.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_pytorch_version_after.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_pytorch_version_after.py", "file_name": "test_pytorch_version_after.py", "file_type": "text/x-python", "category": "test", "start_line": 13, "end_line": 48, "span_ids": ["TestPytorchVersionCompare.test_compare", "impl:3", "TestPytorchVersionCompare", "docstring"], "tokens": 451}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nfrom parameterized import parameterized\n\nfrom monai.utils import pytorch_after\n\nTEST_CASES = (\n    (1, 5, 9, \"1.6.0\"),\n    (1, 6, 0, \"1.6.0\"),\n    (1, 6, 1, \"1.6.0\", False),\n    (1, 7, 0, \"1.6.0\", False),\n    (2, 6, 0, \"1.6.0\", False),\n    (0, 6, 0, \"1.6.0a0+3fd9dcf\"),\n    (1, 5, 9, \"1.6.0a0+3fd9dcf\"),\n    (1, 6, 0, \"1.6.0a0+3fd9dcf\", False),\n    (1, 6, 1, \"1.6.0a0+3fd9dcf\", False),\n    (2, 6, 0, \"1.6.0a0+3fd9dcf\", False),\n    (1, 6, 0, \"1.6.0-rc0+3fd9dcf\", False),  # defaults to prerelease\n    (1, 6, 0, \"1.6.0rc0\", False),\n    (1, 6, 0, \"1.6\", True),\n    (1, 6, 0, \"1\", False),\n    (1, 6, 0, \"1.6.0+cpu\", True),\n    (1, 6, 1, \"1.6.0+cpu\", False),\n)\n\n\nclass TestPytorchVersionCompare(unittest.TestCase):\n    @parameterized.expand(TEST_CASES)\n    def test_compare(self, a, b, p, current, expected=True):\n        \"\"\"Test pytorch_after with a and b\"\"\"\n        self.assertEqual(pytorch_after(a, b, p, current), expected)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_smooth_field.py_TestSmoothField_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_smooth_field.py_TestSmoothField_", "embedding": null, "metadata": {"file_path": "tests/test_smooth_field.py", "file_name": "test_smooth_field.py", "file_type": "text/x-python", "category": "test", "start_line": 81, "end_line": 144, "span_ids": ["TestSmoothField.test_rand_smooth_field_adjust_contrastd_pad", "TestSmoothField", "TestSmoothField.test_rand_smooth_field_adjust_intensityd_pad", "TestSmoothField.test_rand_smooth_deformd_pad", "TestSmoothField.test_rand_smooth_field_adjust_intensityd", "TestSmoothField.test_rand_smooth_deformd", "TestSmoothField.test_rand_smooth_field_adjust_contrastd"], "tokens": 588}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestSmoothField(unittest.TestCase):\n    @parameterized.expand(TESTS_CONTRAST)\n    def test_rand_smooth_field_adjust_contrastd(self, input_param, input_data, expected_val):\n        g = RandSmoothFieldAdjustContrastd(**input_param)\n        g.set_random_state(123)\n\n        res = g(input_data)\n        for key, result in res.items():\n            expected = expected_val[key]\n            assert_allclose(result, expected, rtol=_rtol, atol=1e-1)\n\n    def test_rand_smooth_field_adjust_contrastd_pad(self):\n        input_param, input_data, expected_val = TESTS_CONTRAST[0]\n\n        g = RandSmoothFieldAdjustContrastd(pad=1, **input_param)\n        g.set_random_state(123)\n\n        res = g(input_data)\n        for key, result in res.items():\n            expected = expected_val[key]\n            assert_allclose(result, expected, rtol=_rtol, atol=1e-1)\n\n    @parameterized.expand(TESTS_INTENSITY)\n    def test_rand_smooth_field_adjust_intensityd(self, input_param, input_data, expected_val):\n        g = RandSmoothFieldAdjustIntensityd(**input_param)\n        g.set_random_state(123)\n\n        res = g(input_data)\n        for key, result in res.items():\n            expected = expected_val[key]\n            assert_allclose(result, expected, rtol=_rtol, atol=1e-1)\n\n    def test_rand_smooth_field_adjust_intensityd_pad(self):\n        input_param, input_data, expected_val = TESTS_INTENSITY[0]\n\n        g = RandSmoothFieldAdjustIntensityd(pad=1, **input_param)\n        g.set_random_state(123)\n\n        res = g(input_data)\n        for key, result in res.items():\n            expected = expected_val[key]\n            assert_allclose(result, expected, rtol=_rtol, atol=1e-1)\n\n    @parameterized.expand(TESTS_DEFORM)\n    def test_rand_smooth_deformd(self, input_param, input_data, expected_val):\n        g = RandSmoothDeformd(**input_param)\n        g.set_random_state(123)\n\n        res = g(input_data)\n        for key, result in res.items():\n            expected = expected_val[key]\n            assert_allclose(result, expected, rtol=_rtol, atol=1e-1)\n\n    def test_rand_smooth_deformd_pad(self):\n        input_param, input_data, expected_val = TESTS_DEFORM[0]\n\n        g = RandSmoothDeformd(pad=1, **input_param)\n        g.set_random_state(123)\n\n        res = g(input_data)\n        for key, result in res.items():\n            expected = expected_val[key]\n            assert_allclose(result, expected, rtol=_rtol, atol=1e-1)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_split_on_grid.py_unittest_TEST_MULTIPLE._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_split_on_grid.py_unittest_TEST_MULTIPLE._", "embedding": null, "metadata": {"file_path": "tests/test_split_on_grid.py", "file_name": "test_split_on_grid.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 59, "span_ids": ["impl:33", "docstring"], "tokens": 742}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.apps.pathology.transforms import SplitOnGrid\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nA11 = torch.randn(3, 2, 2)\nA12 = torch.randn(3, 2, 2)\nA21 = torch.randn(3, 2, 2)\nA22 = torch.randn(3, 2, 2)\n\nA1 = torch.cat([A11, A12], 2)\nA2 = torch.cat([A21, A22], 2)\nA = torch.cat([A1, A2], 1)\n\nTEST_CASE_0 = [{\"grid_size\": (2, 2)}, A, torch.stack([A11, A12, A21, A22])]\nTEST_CASE_1 = [{\"grid_size\": (2, 1)}, A, torch.stack([A1, A2])]\nTEST_CASE_2 = [{\"grid_size\": (1, 2)}, A1, torch.stack([A11, A12])]\nTEST_CASE_3 = [{\"grid_size\": (1, 2)}, A2, torch.stack([A21, A22])]\nTEST_CASE_4 = [{\"grid_size\": (1, 1), \"patch_size\": (2, 2)}, A, torch.stack([A11])]\nTEST_CASE_5 = [{\"grid_size\": 1, \"patch_size\": 4}, A, torch.stack([A])]\nTEST_CASE_6 = [{\"grid_size\": 2, \"patch_size\": 2}, A, torch.stack([A11, A12, A21, A22])]\nTEST_CASE_7 = [{\"grid_size\": 1}, A, torch.stack([A])]\nTEST_CASE_8 = [\n    {\"grid_size\": (2, 2), \"patch_size\": 2},\n    torch.arange(12).reshape(1, 3, 4).to(torch.float32),\n    torch.Tensor([[[[0, 1], [4, 5]]], [[[2, 3], [6, 7]]], [[[4, 5], [8, 9]]], [[[6, 7], [10, 11]]]]).to(torch.float32),\n]\n\nTEST_SINGLE = []\nfor p in TEST_NDARRAYS:\n    TEST_SINGLE.append([p, *TEST_CASE_0])\n    TEST_SINGLE.append([p, *TEST_CASE_1])\n    TEST_SINGLE.append([p, *TEST_CASE_2])\n    TEST_SINGLE.append([p, *TEST_CASE_3])\n    TEST_SINGLE.append([p, *TEST_CASE_4])\n    TEST_SINGLE.append([p, *TEST_CASE_5])\n    TEST_SINGLE.append([p, *TEST_CASE_6])\n    TEST_SINGLE.append([p, *TEST_CASE_7])\n    TEST_SINGLE.append([p, *TEST_CASE_8])\n\nTEST_CASE_MC_0 = [{\"grid_size\": (2, 2)}, [A, A], [torch.stack([A11, A12, A21, A22]), torch.stack([A11, A12, A21, A22])]]\nTEST_CASE_MC_1 = [{\"grid_size\": (2, 1)}, [A] * 5, [torch.stack([A1, A2])] * 5]\nTEST_CASE_MC_2 = [{\"grid_size\": (1, 2)}, [A1, A2], [torch.stack([A11, A12]), torch.stack([A21, A22])]]\n\nTEST_MULTIPLE = []", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_split_on_grid.py_None_1_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_split_on_grid.py_None_1_", "embedding": null, "metadata": {"file_path": "tests/test_split_on_grid.py", "file_name": "test_split_on_grid.py", "file_type": "text/x-python", "category": "test", "start_line": 60, "end_line": 85, "span_ids": ["TestSplitOnGrid.test_split_patch_single_call", "TestSplitOnGrid.test_split_patch_multiple_call", "impl:33", "impl:57", "TestSplitOnGrid"], "tokens": 218}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "for p in TEST_NDARRAYS:\n    TEST_MULTIPLE.append([p, *TEST_CASE_MC_0])\n    TEST_MULTIPLE.append([p, *TEST_CASE_MC_1])\n    TEST_MULTIPLE.append([p, *TEST_CASE_MC_2])\n\n\nclass TestSplitOnGrid(unittest.TestCase):\n    @parameterized.expand(TEST_SINGLE)\n    def test_split_patch_single_call(self, in_type, input_parameters, image, expected):\n        input_image = in_type(image)\n        splitter = SplitOnGrid(**input_parameters)\n        output = splitter(input_image)\n        assert_allclose(output, expected, type_test=False)\n\n    @parameterized.expand(TEST_MULTIPLE)\n    def test_split_patch_multiple_call(self, in_type, input_parameters, img_list, expected_list):\n        splitter = SplitOnGrid(**input_parameters)\n        for image, expected in zip(img_list, expected_list):\n            input_image = in_type(image)\n            output = splitter(input_image)\n            assert_allclose(output, expected, type_test=False)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_split_on_grid_dict.py_unittest_TEST_CASE_MC_0._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_split_on_grid_dict.py_unittest_TEST_CASE_MC_0._", "embedding": null, "metadata": {"file_path": "tests/test_split_on_grid_dict.py", "file_name": "test_split_on_grid_dict.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 59, "span_ids": ["impl:29", "docstring"], "tokens": 755}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.apps.pathology.transforms import SplitOnGridDict\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nA11 = torch.randn(3, 2, 2)\nA12 = torch.randn(3, 2, 2)\nA21 = torch.randn(3, 2, 2)\nA22 = torch.randn(3, 2, 2)\n\nA1 = torch.cat([A11, A12], 2)\nA2 = torch.cat([A21, A22], 2)\nA = torch.cat([A1, A2], 1)\n\nTEST_CASE_0 = [{\"keys\": \"image\", \"grid_size\": (2, 2)}, {\"image\": A}, torch.stack([A11, A12, A21, A22])]\nTEST_CASE_1 = [{\"keys\": \"image\", \"grid_size\": (2, 1)}, {\"image\": A}, torch.stack([A1, A2])]\nTEST_CASE_2 = [{\"keys\": \"image\", \"grid_size\": (1, 2)}, {\"image\": A1}, torch.stack([A11, A12])]\nTEST_CASE_3 = [{\"keys\": \"image\", \"grid_size\": (1, 2)}, {\"image\": A2}, torch.stack([A21, A22])]\nTEST_CASE_4 = [{\"keys\": \"image\", \"grid_size\": (1, 1), \"patch_size\": (2, 2)}, {\"image\": A}, torch.stack([A11])]\nTEST_CASE_5 = [{\"keys\": \"image\", \"grid_size\": 1, \"patch_size\": 4}, {\"image\": A}, torch.stack([A])]\nTEST_CASE_6 = [{\"keys\": \"image\", \"grid_size\": 2, \"patch_size\": 2}, {\"image\": A}, torch.stack([A11, A12, A21, A22])]\nTEST_CASE_7 = [{\"keys\": \"image\", \"grid_size\": 1}, {\"image\": A}, torch.stack([A])]\nTEST_CASE_8 = [\n    {\"keys\": \"image\", \"grid_size\": (2, 2), \"patch_size\": 2},\n    {\"image\": torch.arange(12).reshape(1, 3, 4).to(torch.float32)},\n    torch.Tensor([[[[0, 1], [4, 5]]], [[[2, 3], [6, 7]]], [[[4, 5], [8, 9]]], [[[6, 7], [10, 11]]]]).to(torch.float32),\n]\n\nTEST_SINGLE = []\nfor p in TEST_NDARRAYS:\n    TEST_SINGLE.append([p, *TEST_CASE_0])\n    TEST_SINGLE.append([p, *TEST_CASE_1])\n    TEST_SINGLE.append([p, *TEST_CASE_2])\n    TEST_SINGLE.append([p, *TEST_CASE_3])\n    TEST_SINGLE.append([p, *TEST_CASE_4])\n    TEST_SINGLE.append([p, *TEST_CASE_5])\n    TEST_SINGLE.append([p, *TEST_CASE_6])\n    TEST_SINGLE.append([p, *TEST_CASE_7])\n    TEST_SINGLE.append([p, *TEST_CASE_8])\n\nTEST_CASE_MC_0 = [\n    {\"keys\": \"image\", \"grid_size\": (2, 2)},\n    [{\"image\": A}, {\"image\": A}],\n    [torch.stack([A11, A12, A21, A22]), torch.stack([A11, A12, A21, A22])],\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_split_on_grid_dict.py_TEST_CASE_MC_1_None_1.None_2": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_split_on_grid_dict.py_TEST_CASE_MC_1_None_1.None_2", "embedding": null, "metadata": {"file_path": "tests/test_split_on_grid_dict.py", "file_name": "test_split_on_grid_dict.py", "file_type": "text/x-python", "category": "test", "start_line": 60, "end_line": 75, "span_ids": ["impl:29"], "tokens": 174}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "TEST_CASE_MC_1 = [\n    {\"keys\": \"image\", \"grid_size\": (2, 1)},\n    [{\"image\": A}, {\"image\": A}, {\"image\": A}],\n    [torch.stack([A1, A2])] * 3,\n]\nTEST_CASE_MC_2 = [\n    {\"keys\": \"image\", \"grid_size\": (1, 2)},\n    [{\"image\": A1}, {\"image\": A2}],\n    [torch.stack([A11, A12]), torch.stack([A21, A22])],\n]\n\nTEST_MULTIPLE = []\nfor p in TEST_NDARRAYS:\n    TEST_MULTIPLE.append([p, *TEST_CASE_MC_0])\n    TEST_MULTIPLE.append([p, *TEST_CASE_MC_1])\n    TEST_MULTIPLE.append([p, *TEST_CASE_MC_2])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_thread_buffer.py_TestDataLoader.test_time_TestDataLoader.test_time.if_sys_platform_darwi.else_.self_assertTrue_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_thread_buffer.py_TestDataLoader.test_time_TestDataLoader.test_time.if_sys_platform_darwi.else_.self_assertTrue_", "embedding": null, "metadata": {"file_path": "tests/test_thread_buffer.py", "file_name": "test_thread_buffer.py", "file_type": "text/x-python", "category": "test", "start_line": 56, "end_line": 79, "span_ids": ["TestDataLoader.test_time"], "tokens": 254}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestDataLoader(unittest.TestCase):\n\n    def test_time(self):\n        dataset = Dataset(data=self.datalist * 2, transform=self.transform)  # contains data for 2 batches\n        dataloader = DataLoader(dataset=dataset, batch_size=2, num_workers=0)\n\n        tbuffer = ThreadBuffer(dataloader)\n\n        with PerfContext() as pc:\n            for _ in dataloader:\n                time.sleep(0.5)  # each batch takes 0.8 s to generate on top of this time\n\n        unbuffered_time = pc.total_time\n\n        with PerfContext() as pc:\n            for _ in tbuffer:\n                time.sleep(0.5)  # while \"computation\" is happening the next batch is being generated, saving 0.4 s\n\n        buffered_time = pc.total_time\n        if sys.platform == \"darwin\":  # skip macOS measure\n            print(f\"darwin: Buffered time {buffered_time} vs unbuffered time {unbuffered_time}\")\n        else:\n            self.assertTrue(\n                buffered_time < unbuffered_time,\n                f\"Buffered time {buffered_time} should be less than unbuffered time {unbuffered_time}\",\n            )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_thread_buffer.py_TestDataLoader.test_dataloader_repeats_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_thread_buffer.py_TestDataLoader.test_dataloader_repeats_", "embedding": null, "metadata": {"file_path": "tests/test_thread_buffer.py", "file_name": "test_thread_buffer.py", "file_type": "text/x-python", "category": "test", "start_line": 81, "end_line": 100, "span_ids": ["TestDataLoader.test_dataloader_repeats", "impl"], "tokens": 154}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestDataLoader(unittest.TestCase):\n\n    def test_dataloader_repeats(self):\n        dataset = Dataset(data=self.datalist, transform=self.transform)\n        dataloader = ThreadDataLoader(dataset=dataset, batch_size=2, num_workers=0, repeats=2)\n\n        previous_batch = None\n\n        for d in dataloader:\n            self.assertEqual(d[\"image\"][0], \"spleen_19.nii.gz\")\n            self.assertEqual(d[\"image\"][1], \"spleen_31.nii.gz\")\n\n            if previous_batch is None:\n                previous_batch = d\n            else:\n                self.assertTrue(previous_batch is d, \"Batch object was not repeated\")\n                previous_batch = None\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_tile_on_grid.py_unittest_None_4.for_tc_in_TEST_CASES2_.TESTS2_append_p_tc_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_tile_on_grid.py_unittest_None_4.for_tc_in_TEST_CASES2_.TESTS2_append_p_tc_", "embedding": null, "metadata": {"file_path": "tests/test_tile_on_grid.py", "file_name": "test_tile_on_grid.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 67, "span_ids": ["docstring"], "tokens": 371}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom typing import Optional\n\nimport numpy as np\nfrom parameterized import parameterized\n\nfrom monai.apps.pathology.transforms import TileOnGrid\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTEST_CASES = []\nfor tile_count in [16, 64]:\n    for tile_size in [8, 32]:\n        for filter_mode in [\"min\", \"max\", \"random\"]:\n            for background_val in [255, 0]:\n                TEST_CASES.append(\n                    [\n                        {\n                            \"tile_count\": tile_count,\n                            \"tile_size\": tile_size,\n                            \"filter_mode\": filter_mode,\n                            \"random_offset\": False,\n                            \"background_val\": background_val,\n                        }\n                    ]\n                )\n\nfor tile_size in [8, 16]:\n    for step in [4, 8]:\n        TEST_CASES.append([{\"tile_count\": 16, \"step\": step, \"tile_size\": tile_size}])\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    for tc in TEST_CASES:\n        TESTS.append([p, *tc])\n\nTEST_CASES2 = []\nfor tile_count in [16, 64]:\n    for tile_size in [8, 32]:\n        for filter_mode in [\"min\", \"max\", \"random\"]:\n            for background_val in [255, 0]:\n                TEST_CASES2.append(\n                    [\n                        {\n                            \"tile_count\": tile_count,\n                            \"tile_size\": tile_size,\n                            \"filter_mode\": filter_mode,\n                            \"random_offset\": True,\n                            \"background_val\": background_val,\n                        }\n                    ]\n                )\n\nTESTS2 = []\nfor p in TEST_NDARRAYS:\n    for tc in TEST_CASES2:\n        TESTS2.append([p, *tc])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_tile_on_grid.py_make_image_make_image.return.imlarge_tiles": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_tile_on_grid.py_make_image_make_image.return.imlarge_tiles", "embedding": null, "metadata": {"file_path": "tests/test_tile_on_grid.py", "file_name": "test_tile_on_grid.py", "file_type": "text/x-python", "category": "test", "start_line": 70, "end_line": 113, "span_ids": ["make_image"], "tokens": 367}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def make_image(\n    tile_count: int,\n    tile_size: int,\n    step: int = 0,\n    random_offset: bool = False,\n    filter_mode: Optional[str] = None,\n    seed=123,\n    **kwargs,\n):\n\n    tile_count = int(np.sqrt(tile_count))\n    pad = 0\n    if random_offset:\n        pad = 3\n\n    if step == 0:\n        step = tile_size\n\n    image = np.random.randint(\n        200,\n        size=[3, (tile_count - 1) * step + tile_size + pad, (tile_count - 1) * step + tile_size + pad],\n        dtype=np.uint8,\n    )\n    imlarge = image\n\n    random_state = np.random.RandomState(seed)\n\n    if random_offset:\n        pad_h = image.shape[1] % tile_size\n        pad_w = image.shape[2] % tile_size\n        offset = (random_state.randint(pad_h) if pad_h > 0 else 0, random_state.randint(pad_w) if pad_w > 0 else 0)\n        image = image[:, offset[0] :, offset[1] :]\n\n    tiles_list = []\n    for x in range(tile_count):\n        for y in range(tile_count):\n            tiles_list.append(image[:, x * step : x * step + tile_size, y * step : y * step + tile_size])\n\n    tiles = np.stack(tiles_list, axis=0)  # type: ignore\n\n    if (filter_mode == \"min\" or filter_mode == \"max\") and len(tiles) > tile_count ** 2:\n        tiles = tiles[np.argsort(tiles.sum(axis=(1, 2, 3)))]\n\n    return imlarge, tiles", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_tile_on_grid.py_TestTileOnGrid_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_tile_on_grid.py_TestTileOnGrid_", "embedding": null, "metadata": {"file_path": "tests/test_tile_on_grid.py", "file_name": "test_tile_on_grid.py", "file_type": "text/x-python", "category": "test", "start_line": 116, "end_line": 142, "span_ids": ["TestTileOnGrid.test_tile_patch_single_call", "TestTileOnGrid", "TestTileOnGrid.test_tile_patch_random_call", "impl:28"], "tokens": 181}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestTileOnGrid(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_tile_patch_single_call(self, in_type, input_parameters):\n\n        img, tiles = make_image(**input_parameters)\n        input_img = in_type(img)\n\n        tiler = TileOnGrid(**input_parameters)\n        output = tiler(input_img)\n        assert_allclose(output, tiles, type_test=False)\n\n    @parameterized.expand(TESTS2)\n    def test_tile_patch_random_call(self, in_type, input_parameters):\n\n        img, tiles = make_image(**input_parameters, seed=123)\n        input_img = in_type(img)\n\n        tiler = TileOnGrid(**input_parameters)\n        tiler.set_random_state(seed=123)\n\n        output = tiler(input_img)\n        assert_allclose(output, tiles, type_test=False)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_tile_on_grid_dict.py_unittest_None_5.for_step_in_4_8_.TEST_CASES_append_tile": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_tile_on_grid_dict.py_unittest_None_5.for_step_in_4_8_.TEST_CASES_append_tile", "embedding": null, "metadata": {"file_path": "tests/test_tile_on_grid_dict.py", "file_name": "test_tile_on_grid_dict.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 76, "span_ids": ["impl:27", "docstring"], "tokens": 469}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom typing import Optional\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.apps.pathology.transforms import TileOnGridDict\nfrom tests.utils import TEST_NDARRAYS, assert_allclose\n\nTEST_CASES = []\nfor tile_count in [16, 64]:\n    for tile_size in [8, 32]:\n        for filter_mode in [\"min\", \"max\", \"random\"]:\n            for background_val in [255, 0]:\n                for return_list_of_dicts in [False, True]:\n                    TEST_CASES.append(\n                        [\n                            {\n                                \"tile_count\": tile_count,\n                                \"tile_size\": tile_size,\n                                \"filter_mode\": filter_mode,\n                                \"random_offset\": False,\n                                \"background_val\": background_val,\n                                \"return_list_of_dicts\": return_list_of_dicts,\n                            }\n                        ]\n                    )\n\nfor tile_size in [8, 16]:\n    for step in [4, 8]:\n        TEST_CASES.append([{\"tile_count\": 16, \"step\": step, \"tile_size\": tile_size}])\n\nTESTS = []\nfor p in TEST_NDARRAYS:\n    for tc in TEST_CASES:\n        TESTS.append([p, *tc])\n\nTEST_CASES2 = []\nfor tile_count in [16, 64]:\n    for tile_size in [8, 32]:\n        for filter_mode in [\"min\", \"max\", \"random\"]:\n            for background_val in [255, 0]:\n                for return_list_of_dicts in [False, True]:\n                    TEST_CASES2.append(\n                        [\n                            {\n                                \"tile_count\": tile_count,\n                                \"tile_size\": tile_size,\n                                \"filter_mode\": filter_mode,\n                                \"random_offset\": True,\n                                \"background_val\": background_val,\n                                \"return_list_of_dicts\": return_list_of_dicts,\n                            }\n                        ]\n                    )\n\nTESTS2 = []\nfor p in TEST_NDARRAYS:\n    for tc in TEST_CASES2:\n        TESTS2.append([p, *tc])\n\nfor tile_size in [8, 16]:\n    for step in [4, 8]:\n        TEST_CASES.append([{\"tile_count\": 16, \"step\": step, \"tile_size\": tile_size}])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_tile_on_grid_dict.py_make_image_make_image.return.imlarge_tiles": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_tile_on_grid_dict.py_make_image_make_image.return.imlarge_tiles", "embedding": null, "metadata": {"file_path": "tests/test_tile_on_grid_dict.py", "file_name": "test_tile_on_grid_dict.py", "file_type": "text/x-python", "category": "test", "start_line": 79, "end_line": 122, "span_ids": ["make_image"], "tokens": 367}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def make_image(\n    tile_count: int,\n    tile_size: int,\n    step: int = 0,\n    random_offset: bool = False,\n    filter_mode: Optional[str] = None,\n    seed=123,\n    **kwargs,\n):\n\n    tile_count = int(np.sqrt(tile_count))\n    pad = 0\n    if random_offset:\n        pad = 3\n\n    if step == 0:\n        step = tile_size\n\n    image = np.random.randint(\n        200,\n        size=[3, (tile_count - 1) * step + tile_size + pad, (tile_count - 1) * step + tile_size + pad],\n        dtype=np.uint8,\n    )\n    imlarge = image\n\n    random_state = np.random.RandomState(seed)\n\n    if random_offset:\n        pad_h = image.shape[1] % tile_size\n        pad_w = image.shape[2] % tile_size\n        offset = (random_state.randint(pad_h) if pad_h > 0 else 0, random_state.randint(pad_w) if pad_w > 0 else 0)\n        image = image[:, offset[0] :, offset[1] :]\n\n    tiles_list = []\n    for x in range(tile_count):\n        for y in range(tile_count):\n            tiles_list.append(image[:, x * step : x * step + tile_size, y * step : y * step + tile_size])\n\n    tiles = np.stack(tiles_list, axis=0)  # type: ignore\n\n    if (filter_mode == \"min\" or filter_mode == \"max\") and len(tiles) > tile_count ** 2:\n        tiles = tiles[np.argsort(tiles.sum(axis=(1, 2, 3)))]\n\n    return imlarge, tiles", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_tile_on_grid_dict.py_TestTileOnGridDict_TestTileOnGridDict.test_tile_patch_single_call.assert_allclose_output_t": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_tile_on_grid_dict.py_TestTileOnGridDict_TestTileOnGridDict.test_tile_patch_single_call.assert_allclose_output_t", "embedding": null, "metadata": {"file_path": "tests/test_tile_on_grid_dict.py", "file_name": "test_tile_on_grid_dict.py", "file_type": "text/x-python", "category": "test", "start_line": 125, "end_line": 147, "span_ids": ["TestTileOnGridDict.test_tile_patch_single_call", "TestTileOnGridDict"], "tokens": 169}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestTileOnGridDict(unittest.TestCase):\n    @parameterized.expand(TESTS)\n    def test_tile_patch_single_call(self, in_type, input_parameters):\n\n        key = \"image\"\n        input_parameters[\"keys\"] = key\n\n        img, tiles = make_image(**input_parameters)\n        input_img = in_type(img)\n\n        splitter = TileOnGridDict(**input_parameters)\n\n        output = splitter({key: input_img})\n\n        if input_parameters.get(\"return_list_of_dicts\", False):\n            if isinstance(input_img, torch.Tensor):\n                output = torch.stack([ix[key] for ix in output], axis=0)\n            else:\n                output = np.stack([ix[key] for ix in output], axis=0)\n        else:\n            output = output[key]\n\n        assert_allclose(output, tiles, type_test=False)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_tile_on_grid_dict.py_TestTileOnGridDict.test_tile_patch_random_call_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_tile_on_grid_dict.py_TestTileOnGridDict.test_tile_patch_random_call_", "embedding": null, "metadata": {"file_path": "tests/test_tile_on_grid_dict.py", "file_name": "test_tile_on_grid_dict.py", "file_type": "text/x-python", "category": "test", "start_line": 149, "end_line": 177, "span_ids": ["TestTileOnGridDict.test_tile_patch_random_call", "impl:33"], "tokens": 216}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestTileOnGridDict(unittest.TestCase):\n\n    @parameterized.expand(TESTS2)\n    def test_tile_patch_random_call(self, in_type, input_parameters):\n\n        key = \"image\"\n        input_parameters[\"keys\"] = key\n\n        random_state = np.random.RandomState(123)\n        seed = random_state.randint(10000)\n        img, tiles = make_image(**input_parameters, seed=seed)\n        input_img = in_type(img)\n\n        splitter = TileOnGridDict(**input_parameters)\n        splitter.set_random_state(seed=123)\n\n        output = splitter({key: input_img})\n\n        if input_parameters.get(\"return_list_of_dicts\", False):\n            if isinstance(input_img, torch.Tensor):\n                output = torch.stack([ix[key] for ix in output], axis=0)\n            else:\n                output = np.stack([ix[key] for ix in output], axis=0)\n        else:\n            output = output[key]\n        assert_allclose(output, tiles, type_test=False)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_to_numpy.py_TestToNumpy.test_list_tuple_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_to_numpy.py_TestToNumpy.test_list_tuple_", "embedding": null, "metadata": {"file_path": "tests/test_to_numpy.py", "file_name": "test_to_numpy.py", "file_type": "text/x-python", "category": "test", "start_line": 65, "end_line": 84, "span_ids": ["TestToNumpy.test_single_value", "impl:2", "TestToNumpy.test_list_tuple"], "tokens": 207}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestToNumpy(unittest.TestCase):\n\n    def test_list_tuple(self):\n        test_data = [[1, 2], [3, 4]]\n        result = ToNumpy()(test_data)\n        assert_allclose(result, np.asarray(test_data), type_test=False)\n        test_data = ((1, 2), (3, 4))\n        result = ToNumpy(wrap_sequence=False)(test_data)\n        self.assertTrue(type(result), tuple)\n        assert_allclose(result, ((np.asarray(1), np.asarray(2)), (np.asarray(3), np.asarray(4))))\n\n    def test_single_value(self):\n        for test_data in [5, np.array(5), torch.tensor(5)]:\n            result = ToNumpy(dtype=np.uint8)(test_data)\n            self.assertTrue(isinstance(result, np.ndarray))\n            assert_allclose(result, np.asarray(test_data), type_test=False)\n            self.assertEqual(result.ndim, 0)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/datasets.py_MedNISTDataset_MedNISTDataset.dataset_folder_name._MedNIST_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/datasets.py_MedNISTDataset_MedNISTDataset.dataset_folder_name._MedNIST_", "embedding": null, "metadata": {"file_path": "monai/apps/datasets.py", "file_name": "datasets.py", "file_type": "text/x-python", "category": "implementation", "start_line": 33, "end_line": 70, "span_ids": ["MedNISTDataset"], "tokens": 548}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class MedNISTDataset(Randomizable, CacheDataset):\n    \"\"\"\n    The Dataset to automatically download MedNIST data and generate items for training, validation or test.\n    It's based on `CacheDataset` to accelerate the training process.\n\n    Args:\n        root_dir: target directory to download and load MedNIST dataset.\n        section: expected data section, can be: `training`, `validation` or `test`.\n        transform: transforms to execute operations on input data.\n        download: whether to download and extract the MedNIST from resource link, default is False.\n            if expected file already exists, skip downloading even set it to True.\n            user can manually copy `MedNIST.tar.gz` file or `MedNIST` folder to root directory.\n        seed: random seed to randomly split training, validation and test datasets, default is 0.\n        val_frac: percentage of of validation fraction in the whole dataset, default is 0.1.\n        test_frac: percentage of of test fraction in the whole dataset, default is 0.1.\n        cache_num: number of items to be cached. Default is `sys.maxsize`.\n            will take the minimum of (cache_num, data_length x cache_rate, data_length).\n        cache_rate: percentage of cached data in total, default is 1.0 (cache all).\n            will take the minimum of (cache_num, data_length x cache_rate, data_length).\n        num_workers: the number of worker threads to use.\n            if 0 a single thread will be used. Default is 0.\n        progress: whether to display a progress bar when downloading dataset and computing the transform cache content.\n        copy_cache: whether to `deepcopy` the cache content before applying the random transforms,\n            default to `True`. if the random transforms don't modify the cached content\n            (for example, randomly crop from the cached image and deepcopy the crop region)\n            or if every cache item is only used once in a `multi-processing` environment,\n            may set `copy=False` for better performance.\n\n    Raises:\n        ValueError: When ``root_dir`` is not a directory.\n        RuntimeError: When ``dataset_dir`` doesn't exist and downloading is not selected (``download=False``).\n\n    \"\"\"\n\n    resource = \"https://drive.google.com/uc?id=1QsnnkvZyJPcbRoV_ArW8SnE1OTuoVbKE\"\n    md5 = \"0bc7306e7427e00ad1c5526a6677552d\"\n    compressed_file_name = \"MedNIST.tar.gz\"\n    dataset_folder_name = \"MedNIST\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/datasets.py_MedNISTDataset.__init___MedNISTDataset.get_num_classes.return.self_num_class": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/datasets.py_MedNISTDataset.__init___MedNISTDataset.get_num_classes.return.self_num_class", "embedding": null, "metadata": {"file_path": "monai/apps/datasets.py", "file_name": "datasets.py", "file_type": "text/x-python", "category": "implementation", "start_line": 72, "end_line": 130, "span_ids": ["MedNISTDataset.__init__", "MedNISTDataset.randomize", "MedNISTDataset.get_num_classes"], "tokens": 427}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class MedNISTDataset(Randomizable, CacheDataset):\n\n    def __init__(\n        self,\n        root_dir: PathLike,\n        section: str,\n        transform: Union[Sequence[Callable], Callable] = (),\n        download: bool = False,\n        seed: int = 0,\n        val_frac: float = 0.1,\n        test_frac: float = 0.1,\n        cache_num: int = sys.maxsize,\n        cache_rate: float = 1.0,\n        num_workers: int = 0,\n        progress: bool = True,\n        copy_cache: bool = True,\n    ) -> None:\n        root_dir = Path(root_dir)\n        if not root_dir.is_dir():\n            raise ValueError(\"Root directory root_dir must be a directory.\")\n        self.section = section\n        self.val_frac = val_frac\n        self.test_frac = test_frac\n        self.set_random_state(seed=seed)\n        tarfile_name = root_dir / self.compressed_file_name\n        dataset_dir = root_dir / self.dataset_folder_name\n        self.num_class = 0\n        if download:\n            download_and_extract(\n                url=self.resource,\n                filepath=tarfile_name,\n                output_dir=root_dir,\n                hash_val=self.md5,\n                hash_type=\"md5\",\n                progress=progress,\n            )\n\n        if not dataset_dir.is_dir():\n            raise RuntimeError(\n                f\"Cannot find dataset directory: {dataset_dir}, please use download=True to download it.\"\n            )\n        data = self._generate_data_list(dataset_dir)\n        if transform == ():\n            transform = LoadImaged(\"image\")\n        CacheDataset.__init__(\n            self,\n            data=data,\n            transform=transform,\n            cache_num=cache_num,\n            cache_rate=cache_rate,\n            num_workers=num_workers,\n            progress=progress,\n            copy_cache=copy_cache,\n        )\n\n    def randomize(self, data: np.ndarray) -> None:\n        self.R.shuffle(data)\n\n    def get_num_classes(self) -> int:\n        \"\"\"Get number of classes.\"\"\"\n        return self.num_class", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/datasets.py_MedNISTDataset._generate_data_list_MedNISTDataset._generate_data_list.return._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/apps/datasets.py_MedNISTDataset._generate_data_list_MedNISTDataset._generate_data_list.return._", "embedding": null, "metadata": {"file_path": "monai/apps/datasets.py", "file_name": "datasets.py", "file_type": "text/x-python", "category": "implementation", "start_line": 132, "end_line": 171, "span_ids": ["MedNISTDataset._generate_data_list"], "tokens": 405}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class MedNISTDataset(Randomizable, CacheDataset):\n\n    def _generate_data_list(self, dataset_dir: PathLike) -> List[Dict]:\n        \"\"\"\n        Raises:\n            ValueError: When ``section`` is not one of [\"training\", \"validation\", \"test\"].\n\n        \"\"\"\n        dataset_dir = Path(dataset_dir)\n        class_names = sorted(f\"{x.name}\" for x in dataset_dir.iterdir() if x.is_dir())  # folder name as the class name\n        self.num_class = len(class_names)\n        image_files = [[f\"{x}\" for x in (dataset_dir / class_names[i]).iterdir()] for i in range(self.num_class)]\n        num_each = [len(image_files[i]) for i in range(self.num_class)]\n        image_files_list = []\n        image_class = []\n        class_name = []\n        for i in range(self.num_class):\n            image_files_list.extend(image_files[i])\n            image_class.extend([i] * num_each[i])\n            class_name.extend([class_names[i]] * num_each[i])\n\n        length = len(image_files_list)\n        indices = np.arange(length)\n        self.randomize(indices)\n\n        test_length = int(length * self.test_frac)\n        val_length = int(length * self.val_frac)\n        if self.section == \"test\":\n            section_indices = indices[:test_length]\n        elif self.section == \"validation\":\n            section_indices = indices[test_length : test_length + val_length]\n        elif self.section == \"training\":\n            section_indices = indices[test_length + val_length :]\n        else:\n            raise ValueError(\n                f'Unsupported section: {self.section}, available options are [\"training\", \"validation\", \"test\"].'\n            )\n        # the types of label and class name should be compatible with the pytorch dataloader\n        return [\n            {\"image\": image_files_list[i], \"label\": image_class[i], \"class_name\": class_name[i]}\n            for i in section_indices\n        ]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_DatasetFunc_DatasetFunc.__init__.self_reset_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_DatasetFunc_DatasetFunc.__init__.self_reset_", "embedding": null, "metadata": {"file_path": "monai/data/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 100, "end_line": 134, "span_ids": ["DatasetFunc.__init__", "DatasetFunc"], "tokens": 323}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DatasetFunc(Dataset):\n    \"\"\"\n    Execute function on the input dataset and leverage the output to act as a new Dataset.\n    It can be used to load / fetch the basic dataset items, like the list of `image, label` paths.\n    Or chain together to execute more complicated logic, like `partition_dataset`, `resample_datalist`, etc.\n    The `data` arg of `Dataset` will be applied to the first arg of callable `func`.\n    Usage example::\n\n        data_list = DatasetFunc(\n            data=\"path to file\",\n            func=monai.data.load_decathlon_datalist,\n            data_list_key=\"validation\",\n            base_dir=\"path to base dir\",\n        )\n        # partition dataset for every rank\n        data_partition = DatasetFunc(\n            data=data_list,\n            func=lambda **kwargs: monai.data.partition_dataset(**kwargs)[torch.distributed.get_rank()],\n            num_partitions=torch.distributed.get_world_size(),\n        )\n        dataset = Dataset(data=data_partition, transform=transforms)\n\n    Args:\n        data: input data for the func to process, will apply to `func` as the first arg.\n        func: callable function to generate dataset items.\n        kwargs: other arguments for the `func` except for the first arg.\n\n    \"\"\"\n\n    def __init__(self, data: Any, func: Callable, **kwargs) -> None:\n        super().__init__(data=None, transform=None)  # type:ignore\n        self.src = data\n        self.func = func\n        self.kwargs = kwargs\n        self.reset()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_DatasetFunc.reset_DatasetFunc.reset.self.data.self_func_src_self_kwa": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/dataset.py_DatasetFunc.reset_DatasetFunc.reset.self.data.self_func_src_self_kwa", "embedding": null, "metadata": {"file_path": "monai/data/dataset.py", "file_name": "dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 136, "end_line": 147, "span_ids": ["DatasetFunc.reset"], "tokens": 142}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DatasetFunc(Dataset):\n\n    def reset(self, data: Optional[Any] = None, func: Optional[Callable] = None, **kwargs):\n        \"\"\"\n        Reset the dataset items with specified `func`.\n\n        Args:\n            data: if not None, execute `func` on it, default to `self.src`.\n            func: if not None, execute the `func` with specified `kwargs`, default to `self.func`.\n            kwargs: other arguments for the `func` except for the first arg.\n\n        \"\"\"\n        src = self.src if data is None else data\n        self.data = self.func(src, **self.kwargs) if func is None else func(src, **kwargs)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_ITKReader._get_array_data_ITKReader._get_array_data.return.np_img_if_self_reverse_in": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_ITKReader._get_array_data_ITKReader._get_array_data.return.np_img_if_self_reverse_in", "embedding": null, "metadata": {"file_path": "monai/data/image_reader.py", "file_name": "image_reader.py", "file_type": "text/x-python", "category": "implementation", "start_line": 312, "end_line": 332, "span_ids": ["ITKReader._get_array_data"], "tokens": 230}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@require_pkg(pkg_name=\"itk\")\nclass ITKReader(ImageReader):\n\n    def _get_array_data(self, img):\n        \"\"\"\n        Get the raw array data of the image, converted to Numpy array.\n\n        Following PyTorch conventions, the returned array data has contiguous channels,\n        e.g. for an RGB image, all red channel image pixels are contiguous in memory.\n        The last axis of the returned array is the channel axis.\n\n        See also:\n\n            - https://github.com/InsightSoftwareConsortium/ITK/blob/v5.2.1/Modules/Bridge/NumPy/wrapping/PyBuffer.i.in\n\n        Args:\n            img: an ITK image object loaded from an image file.\n\n        \"\"\"\n        np_img = itk.array_view_from_image(img, keep_axes=False)\n        if img.GetNumberOfComponentsPerPixel() == 1:  # handling spatial images\n            return np_img if self.reverse_indexing else np_img.T\n        # handling multi-channel images\n        return np_img if self.reverse_indexing else np.moveaxis(np_img.T, 0, -1)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_WSIReader._check_level_WSIReader._check_level.return.level": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_WSIReader._check_level_WSIReader._check_level.return.level", "embedding": null, "metadata": {"file_path": "monai/data/image_reader.py", "file_name": "image_reader.py", "file_type": "text/x-python", "category": "implementation", "start_line": 803, "end_line": 817, "span_ids": ["WSIReader._check_level"], "tokens": 127}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class WSIReader(ImageReader):\n\n    def _check_level(self, img, level):\n        level = self.level\n\n        level_count = 0\n        if self.backend == \"openslide\":\n            level_count = img.level_count\n        elif self.backend == \"cucim\":\n            level_count = img.resolutions[\"level_count\"]\n        elif self.backend == \"tifffile\":\n            level_count = len(img.pages)\n\n        if level > level_count - 1:\n            raise ValueError(f\"The maximum level of this image is {level_count - 1} while level={level} is requested)!\")\n\n        return level", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_WSIReader._get_image_size_WSIReader._get_image_size.return.size": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/image_reader.py_WSIReader._get_image_size_WSIReader._get_image_size.return.size", "embedding": null, "metadata": {"file_path": "monai/data/image_reader.py", "file_name": "image_reader.py", "file_type": "text/x-python", "category": "implementation", "start_line": 819, "end_line": 840, "span_ids": ["WSIReader._get_image_size"], "tokens": 236}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class WSIReader(ImageReader):\n\n    def _get_image_size(self, img, size, level, location):\n        \"\"\"\n        Calculate the maximum region size for the given level and starting location (if size is None).\n        Note that region size in OpenSlide and cuCIM are WxH (but the final image output would be HxW)\n        \"\"\"\n        if size is not None:\n            return size[::-1]\n\n        max_size = []\n        downsampling_factor = []\n        if self.backend == \"openslide\":\n            downsampling_factor = img.level_downsamples[level]\n            max_size = img.level_dimensions[level]\n        elif self.backend == \"cucim\":\n            downsampling_factor = img.resolutions[\"level_downsamples\"][level]\n            max_size = img.resolutions[\"level_dimensions\"][level]\n\n        # subtract the top left corner of the patch (at given level) from maximum size\n        location_at_level = (round(location[1] / downsampling_factor), round(location[0] / downsampling_factor))\n        size = [max_size[i] - location_at_level[i] for i in range(len(max_size))]\n\n        return size", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/iterable_dataset.py_CSVIterableDataset.__init___CSVIterableDataset.__init__._type_ignore": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/iterable_dataset.py_CSVIterableDataset.__init___CSVIterableDataset.__init__._type_ignore", "embedding": null, "metadata": {"file_path": "monai/data/iterable_dataset.py", "file_name": "iterable_dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 186, "end_line": 213, "span_ids": ["CSVIterableDataset.__init__"], "tokens": 296}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CSVIterableDataset(IterableDataset):\n\n    @deprecated_arg(name=\"filename\", new_name=\"src\", since=\"0.8\", msg_suffix=\"please use `src` instead.\")\n    def __init__(\n        self,\n        src: Union[Union[str, Sequence[str]], Union[Iterable, Sequence[Iterable]]],\n        chunksize: int = 1000,\n        buffer_size: Optional[int] = None,\n        col_names: Optional[Sequence[str]] = None,\n        col_types: Optional[Dict[str, Optional[Dict[str, Any]]]] = None,\n        col_groups: Optional[Dict[str, Sequence[str]]] = None,\n        transform: Optional[Callable] = None,\n        shuffle: bool = False,\n        seed: int = 0,\n        **kwargs,\n    ):\n        self.src = src\n        self.chunksize = chunksize\n        self.buffer_size = 2 * chunksize if buffer_size is None else buffer_size\n        self.col_names = col_names\n        self.col_types = col_types\n        self.col_groups = col_groups\n        self.shuffle = shuffle\n        self.seed = seed\n        # in case treating deprecated arg `filename` as kwargs, remove it from `kwargs`\n        kwargs.pop(\"filename\", None)\n        self.kwargs = kwargs\n\n        self.iters: List[Iterable] = self.reset()\n        super().__init__(data=None, transform=transform)  # type: ignore", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/iterable_dataset.py_CSVIterableDataset.reset_CSVIterableDataset.reset.return.self_iters": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/iterable_dataset.py_CSVIterableDataset.reset_CSVIterableDataset.reset.return.self_iters", "embedding": null, "metadata": {"file_path": "monai/data/iterable_dataset.py", "file_name": "iterable_dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 215, "end_line": 238, "span_ids": ["CSVIterableDataset.reset"], "tokens": 294}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CSVIterableDataset(IterableDataset):\n\n    @deprecated_arg(name=\"filename\", new_name=\"src\", since=\"0.8\", msg_suffix=\"please use `src` instead.\")\n    def reset(self, src: Optional[Union[Union[str, Sequence[str]], Union[Iterable, Sequence[Iterable]]]] = None):\n        \"\"\"\n        Reset the pandas `TextFileReader` iterable object to read data. For more details, please check:\n        https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html?#iteration.\n\n        Args:\n            src: if not None and provided the filename of CSV file, it can be a str, URL, path object\n                or file-like object to load. also support to provide iter for stream input directly,\n                will skip loading from filename. if provided a list of filenames or iters, it will join the tables.\n                default to `self.src`.\n\n        \"\"\"\n        src = self.src if src is None else src\n        srcs = (src,) if not isinstance(src, (tuple, list)) else src\n        self.iters = []\n        for i in srcs:\n            if isinstance(i, str):\n                self.iters.append(pd.read_csv(i, chunksize=self.chunksize))\n            elif isinstance(i, Iterable):\n                self.iters.append(i)\n            else:\n                raise ValueError(\"`src` must be file path or iterable object.\")\n        return self.iters", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/iterable_dataset.py_CSVIterableDataset.close_CSVIterableDataset.close.for_i_in_self_iters_.i_close_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/iterable_dataset.py_CSVIterableDataset.close_CSVIterableDataset.close.for_i_in_self_iters_.i_close_", "embedding": null, "metadata": {"file_path": "monai/data/iterable_dataset.py", "file_name": "iterable_dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 240, "end_line": 250, "span_ids": ["CSVIterableDataset.close"], "tokens": 113}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CSVIterableDataset(IterableDataset):\n\n    def close(self):\n        \"\"\"\n        Close the pandas `TextFileReader` iterable objects.\n        If the input src is file path, TextFileReader was created internally, need to close it.\n        If the input src is iterable object, depends on users requirements whether to close it in this function.\n        For more details, please check:\n        https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html?#iteration.\n\n        \"\"\"\n        for i in self.iters:\n            i.close()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/iterable_dataset.py_CSVIterableDataset._flattened_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/iterable_dataset.py_CSVIterableDataset._flattened_", "embedding": null, "metadata": {"file_path": "monai/data/iterable_dataset.py", "file_name": "iterable_dataset.py", "file_type": "text/x-python", "category": "implementation", "start_line": 252, "end_line": 270, "span_ids": ["CSVIterableDataset._flattened", "CSVIterableDataset.__iter__"], "tokens": 137}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class CSVIterableDataset(IterableDataset):\n\n    def _flattened(self):\n        for chunks in zip(*self.iters):\n            yield from convert_tables_to_dicts(\n                dfs=chunks,\n                col_names=self.col_names,\n                col_types=self.col_types,\n                col_groups=self.col_groups,\n                **self.kwargs,\n            )\n\n    def __iter__(self):\n        if self.shuffle:\n            self.seed += 1\n            buffer = ShuffleBuffer(\n                data=self._flattened(), transform=self.transform, buffer_size=self.buffer_size, seed=self.seed\n            )\n            yield from buffer\n        yield from IterableDataset(data=self._flattened(), transform=self.transform)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/torchscript_utils.py_datetime_save_net_with_metadata.torch_jit_save_jit_obj_f": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/torchscript_utils.py_datetime_save_net_with_metadata.torch_jit_save_jit_obj_f", "embedding": null, "metadata": {"file_path": "monai/data/torchscript_utils.py", "file_name": "torchscript_utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 107, "span_ids": ["save_net_with_metadata", "docstring"], "tokens": 910}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import datetime\nimport json\nimport os\nfrom typing import IO, Any, Mapping, Optional, Sequence, Tuple, Union\n\nimport torch\n\nfrom monai.config import get_config_values\nfrom monai.utils import JITMetadataKeys\nfrom monai.utils.module import pytorch_after\n\nMETADATA_FILENAME = \"metadata.json\"\n\n\ndef save_net_with_metadata(\n    jit_obj: torch.nn.Module,\n    filename_prefix_or_stream: Union[str, IO[Any]],\n    include_config_vals: bool = True,\n    append_timestamp: bool = False,\n    meta_values: Optional[Mapping[str, Any]] = None,\n    more_extra_files: Optional[Mapping[str, bytes]] = None,\n) -> None:\n    \"\"\"\n    Save the JIT object (script or trace produced object) `jit_obj` to the given file or stream with metadata\n    included as a JSON file. The Torchscript format is a zip file which can contain extra file data which is used\n    here as a mechanism for storing metadata about the network being saved. The data in `meta_values` should be\n    compatible with conversion to JSON using the standard library function `dumps`. The intent is this metadata will\n    include information about the network applicable to some use case, such as describing the input and output format,\n    a network name and version, a plain language description of what the network does, and other relevant scientific\n    information. Clients can use this information to determine automatically how to use the network, and users can\n    read what the network does and keep track of versions.\n\n    Examples::\n\n        net = torch.jit.script(monai.networks.nets.UNet(2, 1, 1, [8, 16], [2]))\n\n        meta = {\n            \"name\": \"Test UNet\",\n            \"used_for\": \"demonstration purposes\",\n            \"input_dims\": 2,\n            \"output_dims\": 2\n        }\n\n        # save the Torchscript bundle with the above dictionary stored as an extra file\n        save_net_with_metadata(m, \"test\", meta_values=meta)\n\n        # load the network back, `loaded_meta` has same data as `meta` plus version information\n        loaded_net, loaded_meta, _ = load_net_with_metadata(\"test.pt\")\n\n\n    Args:\n        jit_obj: object to save, should be generated by `script` or `trace`.\n        filename_prefix_or_stream: filename or file-like stream object, if filename has no extension it becomes `.pt`.\n        include_config_vals: if True, MONAI, Pytorch, and Numpy versions are included in metadata.\n        append_timestamp: if True, a timestamp for \"now\" is appended to the file's name before the extension.\n        meta_values: metadata values to store with the object, not limited just to keys in `JITMetadataKeys`.\n        more_extra_files: other extra file data items to include in bundle, see `_extra_files` of `torch.jit.save`.\n    \"\"\"\n\n    now = datetime.datetime.now()\n    metadict = {}\n\n    if include_config_vals:\n        metadict.update(get_config_values())\n        metadict[JITMetadataKeys.TIMESTAMP.value] = now.astimezone().isoformat()\n\n    if meta_values is not None:\n        metadict.update(meta_values)\n\n    json_data = json.dumps(metadict)\n\n    # Pytorch>1.6 can use dictionaries directly, otherwise need to use special map object\n    if pytorch_after(1, 7):\n        extra_files = {METADATA_FILENAME: json_data.encode()}\n\n        if more_extra_files is not None:\n            extra_files.update(more_extra_files)\n    else:\n        extra_files = torch._C.ExtraFilesMap()  # type:ignore[attr-defined]\n        extra_files[METADATA_FILENAME] = json_data.encode()\n\n        if more_extra_files is not None:\n            for k, v in more_extra_files.items():\n                extra_files[k] = v\n\n    if isinstance(filename_prefix_or_stream, str):\n        filename_no_ext, ext = os.path.splitext(filename_prefix_or_stream)\n        if ext == \"\":\n            ext = \".pt\"\n\n        if append_timestamp:\n            filename_prefix_or_stream = now.strftime(f\"{filename_no_ext}_%Y%m%d%H%M%S{ext}\")\n        else:\n            filename_prefix_or_stream = filename_no_ext + ext\n\n    torch.jit.save(jit_obj, filename_prefix_or_stream, extra_files)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/torchscript_utils.py_load_net_with_metadata_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/torchscript_utils.py_load_net_with_metadata_", "embedding": null, "metadata": {"file_path": "monai/data/torchscript_utils.py", "file_name": "torchscript_utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 110, "end_line": 150, "span_ids": ["load_net_with_metadata"], "tokens": 379}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def load_net_with_metadata(\n    filename_prefix_or_stream: Union[str, IO[Any]],\n    map_location: Optional[torch.device] = None,\n    more_extra_files: Sequence[str] = (),\n) -> Tuple[torch.nn.Module, dict, dict]:\n    \"\"\"\n    Load the module object from the given Torchscript filename or stream, and convert the stored JSON metadata\n    back to a dict object. This will produce an empty dict if the metadata file is not present.\n\n    Args:\n        filename_prefix_or_stream: filename or file-like stream object.\n        map_location: network map location as in `torch.jit.load`.\n        more_extra_files: other extra file data names to load from bundle, see `_extra_files` of `torch.jit.load`.\n    Returns:\n        Triple containing loaded object, metadata dict, and extra files dict containing other file data if present\n    \"\"\"\n    # Pytorch>1.6 can use dictionaries directly, otherwise need to use special map object\n    if pytorch_after(1, 7):\n        extra_files = {f: \"\" for f in more_extra_files}\n        extra_files[METADATA_FILENAME] = \"\"\n    else:\n        extra_files = torch._C.ExtraFilesMap()  # type:ignore[attr-defined]\n        extra_files[METADATA_FILENAME] = \"\"\n\n        for f in more_extra_files:\n            extra_files[f] = \"\"\n\n    jit_obj = torch.jit.load(filename_prefix_or_stream, map_location, extra_files)\n\n    extra_files = dict(extra_files.items())  # compatibility with ExtraFilesMap\n\n    if METADATA_FILENAME in extra_files:\n        json_data = extra_files[METADATA_FILENAME]\n        del extra_files[METADATA_FILENAME]\n    else:\n        json_data = \"{}\"\n\n    json_data_dict = json.loads(json_data)\n\n    return jit_obj, json_data_dict, extra_files", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_hashlib_SUPPORTED_PICKLE_MOD._pickle_pickle_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_hashlib_SUPPORTED_PICKLE_MOD._pickle_pickle_", "embedding": null, "metadata": {"file_path": "monai/data/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 83, "span_ids": ["docstring:11", "docstring"], "tokens": 436}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import hashlib\nimport json\nimport math\nimport os\nimport pickle\nimport warnings\nfrom collections import defaultdict\nfrom copy import deepcopy\nfrom functools import reduce\nfrom itertools import product, starmap, zip_longest\nfrom pathlib import PurePath\nfrom typing import Any, Dict, Generator, Iterable, List, Mapping, Optional, Sequence, Tuple, Union\n\nimport numpy as np\nimport torch\nfrom torch.utils.data._utils.collate import default_collate\n\nfrom monai.config.type_definitions import PathLike\nfrom monai.networks.layers.simplelayers import GaussianFilter\nfrom monai.utils import (\n    MAX_SEED,\n    BlendMode,\n    NumpyPadMode,\n    ensure_tuple,\n    ensure_tuple_rep,\n    ensure_tuple_size,\n    fall_back_tuple,\n    first,\n    issequenceiterable,\n    look_up_option,\n    optional_import,\n)\nfrom monai.utils.enums import Method\n\npd, _ = optional_import(\"pandas\")\nDataFrame, _ = optional_import(\"pandas\", name=\"DataFrame\")\nnib, _ = optional_import(\"nibabel\")\n\n\n__all__ = [\n    \"get_random_patch\",\n    \"iter_patch_slices\",\n    \"dense_patch_slices\",\n    \"iter_patch\",\n    \"get_valid_patch_size\",\n    \"list_data_collate\",\n    \"worker_init_fn\",\n    \"set_rnd\",\n    \"correct_nifti_header_if_necessary\",\n    \"rectify_header_sform_qform\",\n    \"zoom_affine\",\n    \"compute_shape_offset\",\n    \"to_affine_nd\",\n    \"create_file_basename\",\n    \"compute_importance_map\",\n    \"is_supported_format\",\n    \"partition_dataset\",\n    \"partition_dataset_classes\",\n    \"resample_datalist\",\n    \"select_cross_validation_folds\",\n    \"json_hashing\",\n    \"pickle_hashing\",\n    \"sorted_dict\",\n    \"decollate_batch\",\n    \"pad_list_data_collate\",\n    \"no_collation\",\n    \"convert_tables_to_dicts\",\n    \"SUPPORTED_PICKLE_MOD\",\n]\n\n# module to be used by `torch.save`\nSUPPORTED_PICKLE_MOD = {\"pickle\": pickle}", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_create_file_basename_create_file_basename.return.os_path_normpath_output_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/data/utils.py_create_file_basename_create_file_basename.return.os_path_normpath_output_", "embedding": null, "metadata": {"file_path": "monai/data/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 673, "end_line": 738, "span_ids": ["create_file_basename"], "tokens": 644}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def create_file_basename(\n    postfix: str,\n    input_file_name: PathLike,\n    folder_path: PathLike,\n    data_root_dir: PathLike = \"\",\n    separate_folder: bool = True,\n    patch_index=None,\n    makedirs: bool = True,\n) -> str:\n    \"\"\"\n    Utility function to create the path to the output file based on the input\n    filename (file name extension is not added by this function).\n    When `data_root_dir` is not specified, the output file name is:\n\n        `folder_path/input_file_name (no ext.) /input_file_name (no ext.)[_postfix][_patch_index]`\n\n    otherwise the relative path with respect to `data_root_dir` will be inserted, for example:\n    input_file_name: /foo/bar/test1/image.png,\n    postfix: seg\n    folder_path: /output,\n    data_root_dir: /foo/bar,\n    output will be: /output/test1/image/image_seg\n\n    Args:\n        postfix: output name's postfix\n        input_file_name: path to the input image file.\n        folder_path: path for the output file\n        data_root_dir: if not empty, it specifies the beginning parts of the input file's\n            absolute path. This is used to compute `input_file_rel_path`, the relative path to the file from\n            `data_root_dir` to preserve folder structure when saving in case there are files in different\n            folders with the same file names.\n        separate_folder: whether to save every file in a separate folder, for example: if input filename is\n            `image.nii`, postfix is `seg` and folder_path is `output`, if `True`, save as:\n            `output/image/image_seg.nii`, if `False`, save as `output/image_seg.nii`. default to `True`.\n        patch_index: if not None, append the patch index to filename.\n        makedirs: whether to create the folder if it does not exist.\n    \"\"\"\n\n    # get the filename and directory\n    filedir, filename = os.path.split(input_file_name)\n    # remove extension\n    filename, ext = os.path.splitext(filename)\n    if ext == \".gz\":\n        filename, ext = os.path.splitext(filename)\n    # use data_root_dir to find relative path to file\n    filedir_rel_path = \"\"\n    if data_root_dir and filedir:\n        filedir_rel_path = os.path.relpath(filedir, data_root_dir)\n\n    # output folder path will be original name without the extension\n    output = os.path.join(folder_path, filedir_rel_path)\n\n    if separate_folder:\n        output = os.path.join(output, filename)\n\n    if makedirs:\n        # create target folder if no existing\n        os.makedirs(output, exist_ok=True)\n\n    # add the sub-folder plus the postfix name to become the file basename in the output path\n    output = os.path.join(output, (filename + \"_\" + postfix) if len(postfix) > 0 else filename)\n\n    if patch_index is not None:\n        output += f\"_{patch_index}\"\n\n    return os.path.normpath(output)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/workflow.py_warnings_BaseWorkflow.run.raise_NotImplementedError": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/engines/workflow.py_warnings_BaseWorkflow.run.raise_NotImplementedError", "embedding": null, "metadata": {"file_path": "monai/engines/workflow.py", "file_name": "workflow.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 50, "span_ids": ["BaseWorkflow.run", "BaseWorkflow", "docstring"], "tokens": 370}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import warnings\nfrom abc import ABC, abstractmethod\nfrom typing import TYPE_CHECKING, Any, Callable, Dict, Iterable, List, Optional, Sequence, Union\n\nimport torch\nimport torch.distributed as dist\nfrom torch.utils.data import DataLoader\nfrom torch.utils.data.distributed import DistributedSampler\n\nfrom monai.config import IgniteInfo\nfrom monai.engines.utils import IterationEvents, default_metric_cmp_fn, default_prepare_batch\nfrom monai.transforms import Decollated\nfrom monai.utils import ensure_tuple, is_scalar, min_version, optional_import\n\nfrom .utils import engine_apply_transform\n\nIgniteEngine, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Engine\")\nState, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"State\")\nEvents, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Events\")\n\nif TYPE_CHECKING:\n    from ignite.engine import Engine, EventEnum\n    from ignite.metrics import Metric\nelse:\n    Engine, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Engine\")\n    Metric, _ = optional_import(\"ignite.metrics\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Metric\")\n    EventEnum, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"EventEnum\")\n\n\nclass BaseWorkflow(ABC):\n    \"\"\"\n    Base class for any MONAI style workflow.\n    `run()` is designed to execute the train, evaluation or inference logic.\n\n    \"\"\"\n\n    @abstractmethod\n    def run(self, *args, **kwargs):\n        raise NotImplementedError(f\"Subclass {self.__class__.__name__} must implement this method.\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/tensorboard_handlers.py_TensorBoardStatsHandler._write_scalar_TensorBoardStatsHandler._write_scalar.writer_add_scalar_tag_va": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/handlers/tensorboard_handlers.py_TensorBoardStatsHandler._write_scalar_TensorBoardStatsHandler._write_scalar.writer_add_scalar_tag_va", "embedding": null, "metadata": {"file_path": "monai/handlers/tensorboard_handlers.py", "file_name": "tensorboard_handlers.py", "file_type": "text/x-python", "category": "implementation", "start_line": 176, "end_line": 189, "span_ids": ["TensorBoardStatsHandler._write_scalar"], "tokens": 138}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TensorBoardStatsHandler(TensorBoardHandler):\n\n    def _write_scalar(self, _engine: Engine, writer: SummaryWriter, tag: str, value: Any, step: int) -> None:\n        \"\"\"\n        Write scale value into TensorBoard.\n        Default to call `SummaryWriter.add_scalar()`.\n\n        Args:\n            _engine: Ignite Engine, unused argument.\n            writer: TensorBoard or TensorBoardX writer, passed or created in TensorBoardHandler.\n            tag: tag name in the TensorBoard.\n            value: value of the scalar data for current step.\n            step: index of current step.\n\n        \"\"\"\n        writer.add_scalar(tag, value, step)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/contrastive.py_torch_ContrastiveLoss.__init__.self.temperature.temperature": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/contrastive.py_torch_ContrastiveLoss.__init__.self.temperature.temperature", "embedding": null, "metadata": {"file_path": "monai/losses/contrastive.py", "file_name": "contrastive.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 51, "span_ids": ["ContrastiveLoss.__init__", "ContrastiveLoss", "docstring"], "tokens": 321}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import torch\nfrom torch.nn import functional as F\nfrom torch.nn.modules.loss import _Loss\n\nfrom monai.utils import deprecated_arg\n\n\nclass ContrastiveLoss(_Loss):\n\n    \"\"\"\n    Compute the Contrastive loss defined in:\n\n        Chen, Ting, et al. \"A simple framework for contrastive learning of visual representations.\" International\n        conference on machine learning. PMLR, 2020. (http://proceedings.mlr.press/v119/chen20j.html)\n\n    Adapted from:\n        https://github.com/Sara-Ahmed/SiT/blob/1aacd6adcd39b71efc903d16b4e9095b97dda76f/losses.py#L5\n\n    \"\"\"\n\n    @deprecated_arg(name=\"reduction\", since=\"0.8\", msg_suffix=\"`reduction` is no longer supported.\")\n    def __init__(self, temperature: float = 0.5, batch_size: int = 1, reduction=\"sum\") -> None:\n        \"\"\"\n        Args:\n            temperature: Can be scaled between 0 and 1 for learning from negative samples, ideally set to 0.5.\n            batch_size: The number of samples.\n\n        Raises:\n            ValueError: When an input of dimension length > 2 is passed\n            ValueError: When input and target are of different shapes\n\n        .. deprecated:: 0.8.0\n\n            `reduction` is no longer supported.\n\n        \"\"\"\n        super().__init__()\n\n        self.batch_size = batch_size\n        self.temperature = temperature", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/deform.py_BendingEnergyLoss_BendingEnergyLoss.__init__.self.normalize.normalize": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/losses/deform.py_BendingEnergyLoss_BendingEnergyLoss.__init__.self.normalize.normalize", "embedding": null, "metadata": {"file_path": "monai/losses/deform.py", "file_name": "deform.py", "file_type": "text/x-python", "category": "implementation", "start_line": 47, "end_line": 69, "span_ids": ["BendingEnergyLoss.__init__", "BendingEnergyLoss"], "tokens": 239}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class BendingEnergyLoss(_Loss):\n    \"\"\"\n    Calculate the bending energy based on second-order differentiation of pred using central finite difference.\n\n    Adapted from:\n        DeepReg (https://github.com/DeepRegNet/DeepReg)\n    \"\"\"\n\n    def __init__(self, normalize: bool = False, reduction: Union[LossReduction, str] = LossReduction.MEAN) -> None:\n        \"\"\"\n        Args:\n            normalize:\n                Whether to divide out spatial sizes in order to make the computation roughly\n                invariant to image scale (i.e. vector field sampling resolution). Defaults to False.\n            reduction: {``\"none\"``, ``\"mean\"``, ``\"sum\"``}\n                Specifies the reduction to apply to the output. Defaults to ``\"mean\"``.\n\n                - ``\"none\"``: no reduction will be applied.\n                - ``\"mean\"``: the sum of the output will be divided by the number of elements in the output.\n                - ``\"sum\"``: the output will be summed.\n        \"\"\"\n        super().__init__(reduction=LossReduction(reduction).value)\n        self.normalize = normalize", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dynunet.py_DynUNet.__init___DynUNet.__init__.if_not_self_deep_supervis.else_.self.skip_layers.create_skips_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/dynunet.py_DynUNet.__init___DynUNet.__init__.if_not_self_deep_supervis.else_.self.skip_layers.create_skips_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/dynunet.py", "file_name": "dynunet.py", "file_type": "text/x-python", "category": "implementation", "start_line": 123, "end_line": 227, "span_ids": ["DynUNet.__init__"], "tokens": 1069}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DynUNet(nn.Module):\n\n    def __init__(\n        self,\n        spatial_dims: int,\n        in_channels: int,\n        out_channels: int,\n        kernel_size: Sequence[Union[Sequence[int], int]],\n        strides: Sequence[Union[Sequence[int], int]],\n        upsample_kernel_size: Sequence[Union[Sequence[int], int]],\n        filters: Optional[Sequence[int]] = None,\n        dropout: Optional[Union[Tuple, str, float]] = None,\n        norm_name: Union[Tuple, str] = (\"INSTANCE\", {\"affine\": True}),\n        act_name: Union[Tuple, str] = (\"leakyrelu\", {\"inplace\": True, \"negative_slope\": 0.01}),\n        deep_supervision: bool = False,\n        deep_supr_num: int = 1,\n        res_block: bool = False,\n        trans_bias: bool = False,\n    ):\n        super().__init__()\n        self.spatial_dims = spatial_dims\n        self.in_channels = in_channels\n        self.out_channels = out_channels\n        self.kernel_size = kernel_size\n        self.strides = strides\n        self.upsample_kernel_size = upsample_kernel_size\n        self.norm_name = norm_name\n        self.act_name = act_name\n        self.dropout = dropout\n        self.conv_block = UnetResBlock if res_block else UnetBasicBlock\n        self.trans_bias = trans_bias\n        if filters is not None:\n            self.filters = filters\n            self.check_filters()\n        else:\n            self.filters = [min(2 ** (5 + i), 320 if spatial_dims == 3 else 512) for i in range(len(strides))]\n        self.input_block = self.get_input_block()\n        self.downsamples = self.get_downsamples()\n        self.bottleneck = self.get_bottleneck()\n        self.upsamples = self.get_upsamples()\n        self.output_block = self.get_output_block(0)\n        self.deep_supervision = deep_supervision\n        self.deep_supr_num = deep_supr_num\n        # initialize the typed list of supervision head outputs so that Torchscript can recognize what's going on\n        self.heads: List[torch.Tensor] = [torch.rand(1)] * self.deep_supr_num\n        if self.deep_supervision:\n            self.deep_supervision_heads = self.get_deep_supervision_heads()\n            self.check_deep_supr_num()\n\n        self.apply(self.initialize_weights)\n        self.check_kernel_stride()\n\n        def create_skips(index, downsamples, upsamples, bottleneck, superheads=None):\n            \"\"\"\n            Construct the UNet topology as a sequence of skip layers terminating with the bottleneck layer. This is\n            done recursively from the top down since a recursive nn.Module subclass is being used to be compatible\n            with Torchscript. Initially the length of `downsamples` will be one more than that of `superheads`\n            since the `input_block` is passed to this function as the first item in `downsamples`, however this\n            shouldn't be associated with a supervision head.\n            \"\"\"\n\n            if len(downsamples) != len(upsamples):\n                raise ValueError(f\"{len(downsamples)} != {len(upsamples)}\")\n\n            if len(downsamples) == 0:  # bottom of the network, pass the bottleneck block\n                return bottleneck\n\n            if superheads is None:\n                next_layer = create_skips(1 + index, downsamples[1:], upsamples[1:], bottleneck)\n                return DynUNetSkipLayer(index, downsample=downsamples[0], upsample=upsamples[0], next_layer=next_layer)\n\n            super_head_flag = False\n            if index == 0:  # don't associate a supervision head with self.input_block\n                rest_heads = superheads\n            else:\n                if len(superheads) > 0:\n                    super_head_flag = True\n                    rest_heads = superheads[1:]\n                else:\n                    rest_heads = nn.ModuleList()\n\n            # create the next layer down, this will stop at the bottleneck layer\n            next_layer = create_skips(1 + index, downsamples[1:], upsamples[1:], bottleneck, superheads=rest_heads)\n            if super_head_flag:\n                return DynUNetSkipLayer(\n                    index,\n                    downsample=downsamples[0],\n                    upsample=upsamples[0],\n                    next_layer=next_layer,\n                    heads=self.heads,\n                    super_head=superheads[0],\n                )\n\n            return DynUNetSkipLayer(index, downsample=downsamples[0], upsample=upsamples[0], next_layer=next_layer)\n\n        if not self.deep_supervision:\n            self.skip_layers = create_skips(\n                0, [self.input_block] + list(self.downsamples), self.upsamples[::-1], self.bottleneck\n            )\n        else:\n            self.skip_layers = create_skips(\n                0,\n                [self.input_block] + list(self.downsamples),\n                self.upsamples[::-1],\n                self.bottleneck,\n                superheads=self.deep_supervision_heads,\n            )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/vitautoenc.py_math_ViTAutoEnc.__init__.self.conv3d_transpose_1.conv_trans_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/networks/nets/vitautoenc.py_math_ViTAutoEnc.__init__.self.conv3d_transpose_1.conv_trans_", "embedding": null, "metadata": {"file_path": "monai/networks/nets/vitautoenc.py", "file_name": "vitautoenc.py", "file_type": "text/x-python", "category": "implementation", "start_line": 13, "end_line": 100, "span_ids": ["ViTAutoEnc", "ViTAutoEnc.__init__", "docstring"], "tokens": 792}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import math\nfrom typing import Sequence, Union\n\nimport torch\nimport torch.nn as nn\n\nfrom monai.networks.blocks.patchembedding import PatchEmbeddingBlock\nfrom monai.networks.blocks.transformerblock import TransformerBlock\nfrom monai.networks.layers import Conv\n\n__all__ = [\"ViTAutoEnc\"]\n\n\nclass ViTAutoEnc(nn.Module):\n    \"\"\"\n    Vision Transformer (ViT), based on: \"Dosovitskiy et al.,\n    An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale <https://arxiv.org/abs/2010.11929>\"\n\n    Modified to also give same dimension outputs as the input size of the image\n    \"\"\"\n\n    def __init__(\n        self,\n        in_channels: int,\n        img_size: Union[Sequence[int], int],\n        patch_size: Union[Sequence[int], int],\n        out_channels: int = 1,\n        deconv_chns: int = 16,\n        hidden_size: int = 768,\n        mlp_dim: int = 3072,\n        num_layers: int = 12,\n        num_heads: int = 12,\n        pos_embed: str = \"conv\",\n        dropout_rate: float = 0.0,\n        spatial_dims: int = 3,\n    ) -> None:\n        \"\"\"\n        Args:\n            in_channels: dimension of input channels or the number of channels for input\n            img_size: dimension of input image.\n            patch_size: dimension of patch size.\n            hidden_size: dimension of hidden layer.\n            out_channels: number of output channels.\n            deconv_chns: number of channels for the deconvolution layers.\n            mlp_dim: dimension of feedforward layer.\n            num_layers: number of transformer blocks.\n            num_heads: number of attention heads.\n            pos_embed: position embedding layer type.\n            dropout_rate: faction of the input units to drop.\n            spatial_dims: number of spatial dimensions.\n\n        Examples::\n\n            # for single channel input with image size of (96,96,96), conv position embedding and segmentation backbone\n            # It will provide an output of same size as that of the input\n            >>> net = ViTAutoEnc(in_channels=1, patch_size=(16,16,16), img_size=(96,96,96), pos_embed='conv')\n\n            # for 3-channel with image size of (128,128,128), output will be same size as of input\n            >>> net = ViTAutoEnc(in_channels=3, patch_size=(16,16,16), img_size=(128,128,128), pos_embed='conv')\n\n        \"\"\"\n\n        super().__init__()\n\n        self.spatial_dims = spatial_dims\n\n        self.patch_embedding = PatchEmbeddingBlock(\n            in_channels=in_channels,\n            img_size=img_size,\n            patch_size=patch_size,\n            hidden_size=hidden_size,\n            num_heads=num_heads,\n            pos_embed=pos_embed,\n            dropout_rate=dropout_rate,\n            spatial_dims=self.spatial_dims,\n        )\n        self.blocks = nn.ModuleList(\n            [TransformerBlock(hidden_size, mlp_dim, num_heads, dropout_rate) for i in range(num_layers)]\n        )\n        self.norm = nn.LayerNorm(hidden_size)\n\n        new_patch_size = [4] * self.spatial_dims\n        conv_trans = Conv[Conv.CONVTRANS, self.spatial_dims]\n        # self.conv3d_transpose* is to be compatible with existing 3d model weights.\n        self.conv3d_transpose = conv_trans(hidden_size, deconv_chns, kernel_size=new_patch_size, stride=new_patch_size)\n        self.conv3d_transpose_1 = conv_trans(\n            in_channels=deconv_chns, out_channels=out_channels, kernel_size=new_patch_size, stride=new_patch_size\n        )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/optimizers/lr_finder.py_pickle___all__._LearningRateFinder_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/optimizers/lr_finder.py_pickle___all__._LearningRateFinder_", "embedding": null, "metadata": {"file_path": "monai/optimizers/lr_finder.py", "file_name": "lr_finder.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 39, "span_ids": ["docstring"], "tokens": 178}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import pickle\nimport warnings\nfrom functools import partial\nfrom typing import TYPE_CHECKING, Any, Callable, Dict, Optional, Tuple, Type, Union\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom torch.optim import Optimizer\nfrom torch.serialization import DEFAULT_PROTOCOL\nfrom torch.utils.data import DataLoader\n\nfrom monai.networks.utils import eval_mode\nfrom monai.optimizers.lr_scheduler import ExponentialLR, LinearLR\nfrom monai.utils import StateCacher, copy_to_device, optional_import\n\nif TYPE_CHECKING:\n    import matplotlib.pyplot as plt\n\n    has_matplotlib = True\n    import tqdm\n\n    has_tqdm = True\nelse:\n    plt, has_matplotlib = optional_import(\"matplotlib.pyplot\")\n    tqdm, has_tqdm = optional_import(\"tqdm\")\n\n__all__ = [\"LearningRateFinder\"]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/__init__.py_AdjustContrastd_LoadImaged": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/__init__.py_AdjustContrastd_LoadImaged", "embedding": null, "metadata": {"file_path": "monai/transforms/__init__.py", "file_name": "__init__.py", "file_type": "text/x-python", "category": "implementation", "start_line": 114, "end_line": 206, "span_ids": ["imports:8", "imports:7"], "tokens": 647}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from .intensity.dictionary import (\n    AdjustContrastd,\n    AdjustContrastD,\n    AdjustContrastDict,\n    GaussianSharpend,\n    GaussianSharpenD,\n    GaussianSharpenDict,\n    GaussianSmoothd,\n    GaussianSmoothD,\n    GaussianSmoothDict,\n    GibbsNoised,\n    GibbsNoiseD,\n    GibbsNoiseDict,\n    HistogramNormalized,\n    HistogramNormalizeD,\n    HistogramNormalizeDict,\n    KSpaceSpikeNoised,\n    KSpaceSpikeNoiseD,\n    KSpaceSpikeNoiseDict,\n    MaskIntensityd,\n    MaskIntensityD,\n    MaskIntensityDict,\n    NormalizeIntensityd,\n    NormalizeIntensityD,\n    NormalizeIntensityDict,\n    RandAdjustContrastd,\n    RandAdjustContrastD,\n    RandAdjustContrastDict,\n    RandBiasFieldd,\n    RandBiasFieldD,\n    RandBiasFieldDict,\n    RandCoarseDropoutd,\n    RandCoarseDropoutD,\n    RandCoarseDropoutDict,\n    RandCoarseShuffled,\n    RandCoarseShuffleD,\n    RandCoarseShuffleDict,\n    RandGaussianNoised,\n    RandGaussianNoiseD,\n    RandGaussianNoiseDict,\n    RandGaussianSharpend,\n    RandGaussianSharpenD,\n    RandGaussianSharpenDict,\n    RandGaussianSmoothd,\n    RandGaussianSmoothD,\n    RandGaussianSmoothDict,\n    RandGibbsNoised,\n    RandGibbsNoiseD,\n    RandGibbsNoiseDict,\n    RandHistogramShiftd,\n    RandHistogramShiftD,\n    RandHistogramShiftDict,\n    RandKSpaceSpikeNoised,\n    RandKSpaceSpikeNoiseD,\n    RandKSpaceSpikeNoiseDict,\n    RandRicianNoised,\n    RandRicianNoiseD,\n    RandRicianNoiseDict,\n    RandScaleIntensityd,\n    RandScaleIntensityD,\n    RandScaleIntensityDict,\n    RandShiftIntensityd,\n    RandShiftIntensityD,\n    RandShiftIntensityDict,\n    RandStdShiftIntensityd,\n    RandStdShiftIntensityD,\n    RandStdShiftIntensityDict,\n    SavitzkyGolaySmoothd,\n    SavitzkyGolaySmoothD,\n    SavitzkyGolaySmoothDict,\n    ScaleIntensityd,\n    ScaleIntensityD,\n    ScaleIntensityDict,\n    ScaleIntensityRanged,\n    ScaleIntensityRangeD,\n    ScaleIntensityRangeDict,\n    ScaleIntensityRangePercentilesd,\n    ScaleIntensityRangePercentilesD,\n    ScaleIntensityRangePercentilesDict,\n    ShiftIntensityd,\n    ShiftIntensityD,\n    ShiftIntensityDict,\n    StdShiftIntensityd,\n    StdShiftIntensityD,\n    StdShiftIntensityDict,\n    ThresholdIntensityd,\n    ThresholdIntensityD,\n    ThresholdIntensityDict,\n)\nfrom .inverse import InvertibleTransform, TraceableTransform\nfrom .inverse_batch_transform import BatchInverseTransform, Decollated\nfrom .io.array import SUPPORTED_READERS, LoadImage, SaveImage\nfrom .io.dictionary import LoadImaged, LoadImageD, LoadImageDict, SaveImaged, SaveImageD, SaveImageDict", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/__init__.py_Mark_Affine": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/__init__.py_Mark_Affine", "embedding": null, "metadata": {"file_path": "monai/transforms/__init__.py", "file_name": "__init__.py", "file_type": "text/x-python", "category": "implementation", "start_line": 207, "end_line": 310, "span_ids": ["imports:16", "imports:8"], "tokens": 547}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from .nvtx import (\n    Mark,\n    Markd,\n    MarkD,\n    MarkDict,\n    RandMark,\n    RandMarkd,\n    RandMarkD,\n    RandMarkDict,\n    RandRangePop,\n    RandRangePopd,\n    RandRangePopD,\n    RandRangePopDict,\n    RandRangePush,\n    RandRangePushd,\n    RandRangePushD,\n    RandRangePushDict,\n    RangePop,\n    RangePopd,\n    RangePopD,\n    RangePopDict,\n    RangePush,\n    RangePushd,\n    RangePushD,\n    RangePushDict,\n)\nfrom .post.array import (\n    Activations,\n    AsDiscrete,\n    FillHoles,\n    KeepLargestConnectedComponent,\n    LabelFilter,\n    LabelToContour,\n    MeanEnsemble,\n    ProbNMS,\n    VoteEnsemble,\n)\nfrom .post.dictionary import (\n    ActivationsD,\n    Activationsd,\n    ActivationsDict,\n    AsDiscreteD,\n    AsDiscreted,\n    AsDiscreteDict,\n    Ensembled,\n    FillHolesD,\n    FillHolesd,\n    FillHolesDict,\n    InvertD,\n    Invertd,\n    InvertDict,\n    KeepLargestConnectedComponentD,\n    KeepLargestConnectedComponentd,\n    KeepLargestConnectedComponentDict,\n    LabelFilterD,\n    LabelFilterd,\n    LabelFilterDict,\n    LabelToContourD,\n    LabelToContourd,\n    LabelToContourDict,\n    MeanEnsembleD,\n    MeanEnsembled,\n    MeanEnsembleDict,\n    ProbNMSD,\n    ProbNMSd,\n    ProbNMSDict,\n    SaveClassificationD,\n    SaveClassificationd,\n    SaveClassificationDict,\n    VoteEnsembleD,\n    VoteEnsembled,\n    VoteEnsembleDict,\n)\nfrom .smooth_field.array import (\n    RandSmoothDeform,\n    RandSmoothFieldAdjustContrast,\n    RandSmoothFieldAdjustIntensity,\n    SmoothField,\n)\nfrom .smooth_field.dictionary import RandSmoothDeformd, RandSmoothFieldAdjustContrastd, RandSmoothFieldAdjustIntensityd\nfrom .spatial.array import (\n    Affine,\n    AffineGrid,\n    Flip,\n    GridDistortion,\n    Orientation,\n    Rand2DElastic,\n    Rand3DElastic,\n    RandAffine,\n    RandAffineGrid,\n    RandAxisFlip,\n    RandDeformGrid,\n    RandFlip,\n    RandGridDistortion,\n    RandRotate,\n    RandRotate90,\n    RandZoom,\n    Resample,\n    Resize,\n    Rotate,\n    Rotate90,\n    Spacing,\n    Zoom,\n)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/__init__.py_Affined_AddChannel": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/__init__.py_Affined_AddChannel", "embedding": null, "metadata": {"file_path": "monai/transforms/__init__.py", "file_name": "__init__.py", "file_type": "text/x-python", "category": "implementation", "start_line": 311, "end_line": 401, "span_ids": ["imports:20", "imports:16"], "tokens": 476}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from .spatial.dictionary import (\n    Affined,\n    AffineD,\n    AffineDict,\n    Flipd,\n    FlipD,\n    FlipDict,\n    GridDistortiond,\n    GridDistortionD,\n    GridDistortionDict,\n    Orientationd,\n    OrientationD,\n    OrientationDict,\n    Rand2DElasticd,\n    Rand2DElasticD,\n    Rand2DElasticDict,\n    Rand3DElasticd,\n    Rand3DElasticD,\n    Rand3DElasticDict,\n    RandAffined,\n    RandAffineD,\n    RandAffineDict,\n    RandAxisFlipd,\n    RandAxisFlipD,\n    RandAxisFlipDict,\n    RandFlipd,\n    RandFlipD,\n    RandFlipDict,\n    RandGridDistortiond,\n    RandGridDistortionD,\n    RandGridDistortionDict,\n    RandRotate90d,\n    RandRotate90D,\n    RandRotate90Dict,\n    RandRotated,\n    RandRotateD,\n    RandRotateDict,\n    RandZoomd,\n    RandZoomD,\n    RandZoomDict,\n    Resized,\n    ResizeD,\n    ResizeDict,\n    Rotate90d,\n    Rotate90D,\n    Rotate90Dict,\n    Rotated,\n    RotateD,\n    RotateDict,\n    Spacingd,\n    SpacingD,\n    SpacingDict,\n    Zoomd,\n    ZoomD,\n    ZoomDict,\n)\nfrom .transform import MapTransform, Randomizable, RandomizableTransform, ThreadUnsafe, Transform, apply_transform\nfrom .utility.array import (\n    AddChannel,\n    AddCoordinateChannels,\n    AddExtremePointsChannel,\n    AsChannelFirst,\n    AsChannelLast,\n    CastToType,\n    ClassesToIndices,\n    ConvertToMultiChannelBasedOnBratsClasses,\n    CuCIM,\n    DataStats,\n    EnsureChannelFirst,\n    EnsureType,\n    FgBgToIndices,\n    Identity,\n    IntensityStats,\n    LabelToMask,\n    Lambda,\n    MapLabelValue,\n    RandCuCIM,\n    RandLambda,\n    RemoveRepeatedChannel,\n    RepeatChannel,\n    SimulateDelay,\n    SplitChannel,\n    SqueezeDim,\n    ToCupy,\n    ToDevice,\n    ToNumpy,\n    ToPIL,\n    TorchVision,\n    ToTensor,\n    Transpose,\n)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_ScaleIntensityRangePercentiles._normalize_ScaleIntensityRangePercentiles.__call__.return.img": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/array.py_ScaleIntensityRangePercentiles._normalize_ScaleIntensityRangePercentiles.__call__.return.img", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 953, "end_line": 981, "span_ids": ["ScaleIntensityRangePercentiles._normalize", "ScaleIntensityRangePercentiles.__call__"], "tokens": 304}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class ScaleIntensityRangePercentiles(Transform):\n\n    def _normalize(self, img: NdarrayOrTensor) -> NdarrayOrTensor:\n        a_min: float = percentile(img, self.lower)  # type: ignore\n        a_max: float = percentile(img, self.upper)  # type: ignore\n        b_min = self.b_min\n        b_max = self.b_max\n\n        if self.relative:\n            if (self.b_min is None) or (self.b_max is None):\n                raise ValueError(\"If it is relative, b_min and b_max should not be None.\")\n            b_min = ((self.b_max - self.b_min) * (self.lower / 100.0)) + self.b_min\n            b_max = ((self.b_max - self.b_min) * (self.upper / 100.0)) + self.b_min\n\n        scalar = ScaleIntensityRange(\n            a_min=a_min, a_max=a_max, b_min=b_min, b_max=b_max, clip=self.clip, dtype=self.dtype\n        )\n        img = scalar(img)\n        return img\n\n    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:\n        \"\"\"\n        Apply the transform to `img`.\n        \"\"\"\n        if self.channel_wise:\n            for i, d in enumerate(img):\n                img[i] = self._normalize(img=d)  # type: ignore\n        else:\n            img = self._normalize(img=img)\n\n        return img", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_SavitzkyGolaySmoothd_SavitzkyGolaySmoothd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/intensity/dictionary.py_SavitzkyGolaySmoothd_SavitzkyGolaySmoothd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/intensity/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 924, "end_line": 958, "span_ids": ["SavitzkyGolaySmoothd.__call__", "SavitzkyGolaySmoothd.__init__", "SavitzkyGolaySmoothd"], "tokens": 364}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SavitzkyGolaySmoothd(MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.SavitzkyGolaySmooth`.\n\n    Args:\n        keys: keys of the corresponding items to be transformed.\n            See also: :py:class:`monai.transforms.compose.MapTransform`\n        window_length: length of the filter window, must be a positive odd integer.\n        order: order of the polynomial to fit to each window, must be less than ``window_length``.\n        axis: optional axis along which to apply the filter kernel. Default 1 (first spatial dimension).\n        mode: optional padding mode, passed to convolution class. ``'zeros'``, ``'reflect'``, ``'replicate'``\n            or ``'circular'``. default: ``'zeros'``. See ``torch.nn.Conv1d()`` for more information.\n        allow_missing_keys: don't raise exception if key is missing.\n\n    \"\"\"\n\n    backend = SavitzkyGolaySmooth.backend\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        window_length: int,\n        order: int,\n        axis: int = 1,\n        mode: str = \"zeros\",\n        allow_missing_keys: bool = False,\n    ) -> None:\n        super().__init__(keys, allow_missing_keys)\n        self.converter = SavitzkyGolaySmooth(window_length=window_length, order=order, axis=axis, mode=mode)\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key in self.key_iterator(d):\n            d[key] = self.converter(d[key])\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/transform.py_Transform_Transform.backend._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/transform.py_Transform_Transform.backend._", "embedding": null, "metadata": {"file_path": "monai/transforms/transform.py", "file_name": "transform.py", "file_type": "text/x-python", "category": "implementation", "start_line": 185, "end_line": 209, "span_ids": ["Transform"], "tokens": 248}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class Transform(ABC):\n    \"\"\"\n    An abstract class of a ``Transform``.\n    A transform is callable that processes ``data``.\n\n    It could be stateful and may modify ``data`` in place,\n    the implementation should be aware of:\n\n        #. thread safety when mutating its own states.\n           When used from a multi-process context, transform's instance variables are read-only.\n           thread-unsafe transforms should inherit :py:class:`monai.transforms.ThreadUnsafe`.\n        #. ``data`` content unused by this transform may still be used in the\n           subsequent transforms in a composed transform.\n        #. storing too much information in ``data`` may cause some memory issue or IPC sync issue,\n           especially in the multi-processing environment of PyTorch DataLoader.\n\n    See Also\n\n        :py:class:`monai.transforms.Compose`\n    \"\"\"\n\n    # Transforms should add data types to this list if they are capable of performing a transform without\n    # modifying the input type. For example, [\"torch.Tensor\", \"np.ndarray\"] means that no copies of the data\n    # are required if the input is either `torch.Tensor` or `np.ndarray`.\n    backend: List[TransformBackends] = []", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_RandCuCIM_RandCuCIM.__call__.return.super___call___data_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_RandCuCIM_RandCuCIM.__call__.return.super___call___data_", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1219, "end_line": 1258, "span_ids": ["RandCuCIM.__init__", "RandCuCIM.__call__", "RandCuCIM"], "tokens": 437}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandCuCIM(CuCIM, RandomizableTransform):\n    \"\"\"\n    Wrap a randomized cuCIM transform, defined based on the transform name and args,\n    or randomly apply a non-randomized transform.\n    For deterministic non-randomized transforms use :py:class:`monai.transforms.CuCIM`.\n\n    Args:\n        name: the transform name in CuCIM package.\n        apply_prob: the probability to apply the transform (default=1.0)\n        args: parameters for the CuCIM transform.\n        kwargs: parameters for the CuCIM transform.\n\n    Note:\n        - CuCIM transform only work with CuPy arrays, so this transform expects input data to be `cupy.ndarray`.\n          Users can call `ToCuPy` transform to convert a numpy array or torch tensor to cupy array.\n        - If the cuCIM transform is already randomized the `apply_prob` argument has nothing to do with\n          the randomness of the underlying cuCIM transform. `apply_prob` defines if the transform (either randomized\n          or non-randomized) being applied randomly, so it can apply non-randomized transforms randomly but be careful\n          with setting `apply_prob` to anything than 1.0 when using along with cuCIM's randomized transforms.\n        - If the random factor of the underlying cuCIM transform is not derived from `self.R`,\n          the results may not be deterministic. See Also: :py:class:`monai.transforms.Randomizable`.\n    \"\"\"\n\n    def __init__(self, name: str, apply_prob: float = 1.0, *args, **kwargs) -> None:\n        CuCIM.__init__(self, name, *args, **kwargs)\n        RandomizableTransform.__init__(self, prob=apply_prob)\n\n    def __call__(self, data):\n        \"\"\"\n        Args:\n            data: a CuPy array (`cupy.ndarray`) for the cuCIM transform\n\n        Returns:\n            `cupy.ndarray`\n\n        \"\"\"\n        self.randomize(data)\n        if not self._do_transform:\n            return data\n        return super().__call__(data)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_AddCoordinateChannels_AddCoordinateChannels.__init__.self.spatial_dims.spatial_dims": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_AddCoordinateChannels_AddCoordinateChannels.__init__.self.spatial_dims.spatial_dims", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1261, "end_line": 1286, "span_ids": ["AddCoordinateChannels", "AddCoordinateChannels.__init__"], "tokens": 272}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AddCoordinateChannels(Transform):\n    \"\"\"\n    Appends additional channels encoding coordinates of the input. Useful when e.g. training using patch-based sampling,\n    to allow feeding of the patch's location into the network.\n\n    This can be seen as a input-only version of CoordConv:\n\n    Liu, R. et al. An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution, NeurIPS 2018.\n\n    Args:\n        spatial_dims: the spatial dimensions that are to have their coordinates encoded in a channel and\n            appended to the input image. E.g., `(0, 1, 2)` represents `H, W, D` dims and append three channels\n            to the input image, encoding the coordinates of the input's three spatial dimensions.\n\n    .. deprecated:: 0.8.0\n        ``spatial_channels`` is deprecated, use ``spatial_dims`` instead.\n\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    @deprecated_arg(\n        name=\"spatial_channels\", new_name=\"spatial_dims\", since=\"0.8\", msg_suffix=\"please use `spatial_dims` instead.\"\n    )\n    def __init__(self, spatial_dims: Sequence[int]) -> None:\n        self.spatial_dims = spatial_dims", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_AddCoordinateChannels.__call___": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/array.py_AddCoordinateChannels.__call___", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1288, "end_line": 1301, "span_ids": ["AddCoordinateChannels.__call__"], "tokens": 185}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AddCoordinateChannels(Transform):\n\n    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:\n        \"\"\"\n        Args:\n            img: data to be transformed, assuming `img` is channel first.\n        \"\"\"\n        if max(self.spatial_dims) > img.ndim - 2 or min(self.spatial_dims) < 0:\n            raise ValueError(f\"`spatial_dims` values must be within [0, {img.ndim - 2}]\")\n\n        spatial_size = img.shape[1:]\n        coord_channels = np.array(np.meshgrid(*tuple(np.linspace(-0.5, 0.5, s) for s in spatial_size), indexing=\"ij\"))\n        coord_channels, *_ = convert_to_dst_type(coord_channels, img)  # type: ignore\n        coord_channels = coord_channels[list(self.spatial_dims)]\n        return concatenate((img, coord_channels), axis=0)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_AddCoordinateChannelsd_AddCoordinateChannelsd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utility/dictionary.py_AddCoordinateChannelsd_AddCoordinateChannelsd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/utility/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 1596, "end_line": 1626, "span_ids": ["AddCoordinateChannelsd", "AddCoordinateChannelsd.__init__", "AddCoordinateChannelsd.__call__"], "tokens": 333}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class AddCoordinateChannelsd(MapTransform):\n    \"\"\"\n    Dictionary-based wrapper of :py:class:`monai.transforms.AddCoordinateChannels`.\n\n    Args:\n        keys: keys of the corresponding items to be transformed.\n            See also: :py:class:`monai.transforms.compose.MapTransform`\n        spatial_dims: the spatial dimensions that are to have their coordinates encoded in a channel and\n            appended to the input image. E.g., `(0, 1, 2)` represents `H, W, D` dims and append three channels\n            to the input image, encoding the coordinates of the input's three spatial dimensions.\n        allow_missing_keys: don't raise exception if key is missing.\n\n    .. deprecated:: 0.8.0\n        ``spatial_channels`` is deprecated, use ``spatial_dims`` instead.\n\n    \"\"\"\n\n    backend = AddCoordinateChannels.backend\n\n    @deprecated_arg(\n        name=\"spatial_channels\", new_name=\"spatial_dims\", since=\"0.8\", msg_suffix=\"please use `spatial_dims` instead.\"\n    )\n    def __init__(self, keys: KeysCollection, spatial_dims: Sequence[int], allow_missing_keys: bool = False) -> None:\n        super().__init__(keys, allow_missing_keys)\n        self.add_coordinate_channels = AddCoordinateChannels(spatial_dims=spatial_dims)\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:\n        d = dict(data)\n        for key in self.key_iterator(d):\n            d[key] = self.add_coordinate_channels(d[key])\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_correct_crop_centers_correct_crop_centers.return.valid_centers": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils.py_correct_crop_centers_correct_crop_centers.return.valid_centers", "embedding": null, "metadata": {"file_path": "monai/transforms/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 411, "end_line": 450, "span_ids": ["correct_crop_centers"], "tokens": 482}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def correct_crop_centers(\n    centers: List[Union[int, torch.Tensor]],\n    spatial_size: Union[Sequence[int], int],\n    label_spatial_shape: Sequence[int],\n    allow_smaller: bool = False,\n):\n    \"\"\"\n    Utility to correct the crop center if the crop size and centers are not compatible with the image size.\n\n    Args:\n        centers: pre-computed crop centers of every dim, will correct based on the valid region.\n        spatial_size: spatial size of the ROIs to be sampled.\n        label_spatial_shape: spatial shape of the original label data to compare with ROI.\n        allow_smaller: if `False`, an exception will be raised if the image is smaller than\n            the requested ROI in any dimension. If `True`, any smaller dimensions will be set to\n            match the cropped size (i.e., no cropping in that dimension).\n\n    \"\"\"\n    spatial_size = fall_back_tuple(spatial_size, default=label_spatial_shape)\n    if any(np.subtract(label_spatial_shape, spatial_size) < 0):\n        if not allow_smaller:\n            raise ValueError(\"The size of the proposed random crop ROI is larger than the image size.\")\n        spatial_size = tuple(min(l, s) for l, s in zip(label_spatial_shape, spatial_size))\n\n    # Select subregion to assure valid roi\n    valid_start = np.floor_divide(spatial_size, 2)\n    # add 1 for random\n    valid_end = np.subtract(label_spatial_shape + np.array(1), spatial_size / np.array(2)).astype(np.uint16)\n    # int generation to have full range on upper side, but subtract unfloored size/2 to prevent rounded range\n    # from being too high\n    for i, valid_s in enumerate(valid_start):\n        # need this because np.random.randint does not work with same start and end\n        if valid_s == valid_end[i]:\n            valid_end[i] += 1\n    valid_centers = []\n    for c, v_s, v_e in zip(centers, valid_start, valid_end):\n        _c = int(convert_data_type(c, np.ndarray)[0])  # type: ignore\n        center_i = min(max(_c, v_s), v_e - 1)\n        valid_centers.append(int(center_i))\n    return valid_centers", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils_create_transform_ims.py_os_from_monai_transforms_pos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils_create_transform_ims.py_os_from_monai_transforms_pos", "embedding": null, "metadata": {"file_path": "monai/transforms/utils_create_transform_ims.py", "file_name": "utils_create_transform_ims.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 146, "span_ids": ["imports:14", "docstring"], "tokens": 744}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport pathlib\nimport tempfile\nimport textwrap\nfrom copy import deepcopy\nfrom glob import glob\nfrom typing import TYPE_CHECKING, Callable\n\nimport numpy as np\nimport torch\n\nfrom monai.apps import download_and_extract\nfrom monai.transforms import (\n    AddChanneld,\n    Affine,\n    Affined,\n    AsDiscrete,\n    Compose,\n    Flip,\n    Flipd,\n    LoadImaged,\n    MapTransform,\n    Orientation,\n    Orientationd,\n    Rand3DElastic,\n    Rand3DElasticd,\n    RandFlip,\n    RandFlipd,\n    Randomizable,\n    RandRotate,\n    RandRotated,\n    RandZoom,\n    RandZoomd,\n    Rotate,\n    Rotate90,\n    Rotate90d,\n    Rotated,\n    ScaleIntensity,\n    ScaleIntensityd,\n    SpatialPadd,\n    Zoom,\n    Zoomd,\n)\nfrom monai.transforms.croppad.array import (\n    BorderPad,\n    CenterScaleCrop,\n    CenterSpatialCrop,\n    CropForeground,\n    DivisiblePad,\n    RandCropByLabelClasses,\n    RandCropByPosNegLabel,\n    RandScaleCrop,\n    RandSpatialCrop,\n    RandSpatialCropSamples,\n    RandWeightedCrop,\n    ResizeWithPadOrCrop,\n    SpatialCrop,\n    SpatialPad,\n)\nfrom monai.transforms.croppad.dictionary import (\n    BorderPadd,\n    CenterScaleCropd,\n    CenterSpatialCropd,\n    CropForegroundd,\n    DivisiblePadd,\n    RandCropByLabelClassesd,\n    RandCropByPosNegLabeld,\n    RandScaleCropd,\n    RandSpatialCropd,\n    RandSpatialCropSamplesd,\n    RandWeightedCropd,\n    ResizeWithPadOrCropd,\n    SpatialCropd,\n)\nfrom monai.transforms.intensity.array import (\n    AdjustContrast,\n    GaussianSharpen,\n    GaussianSmooth,\n    GibbsNoise,\n    HistogramNormalize,\n    KSpaceSpikeNoise,\n    MaskIntensity,\n    NormalizeIntensity,\n    RandAdjustContrast,\n    RandBiasField,\n    RandCoarseDropout,\n    RandCoarseShuffle,\n    RandGaussianNoise,\n    RandGaussianSharpen,\n    RandGaussianSmooth,\n    RandGibbsNoise,\n    RandHistogramShift,\n    RandKSpaceSpikeNoise,\n    RandRicianNoise,\n    RandScaleIntensity,\n    RandShiftIntensity,\n    RandStdShiftIntensity,\n    SavitzkyGolaySmooth,\n    ScaleIntensityRange,\n    ScaleIntensityRangePercentiles,\n    ShiftIntensity,\n    StdShiftIntensity,\n    ThresholdIntensity,\n)\nfrom monai.transforms.intensity.dictionary import (\n    AdjustContrastd,\n    GaussianSharpend,\n    GaussianSmoothd,\n    GibbsNoised,\n    HistogramNormalized,\n    KSpaceSpikeNoised,\n    MaskIntensityd,\n    NormalizeIntensityd,\n    RandAdjustContrastd,\n    RandBiasFieldd,\n    RandCoarseDropoutd,\n    RandCoarseShuffled,\n    RandGaussianNoised,\n    RandGaussianSharpend,\n    RandGaussianSmoothd,\n    RandGibbsNoised,\n    RandHistogramShiftd,\n    RandKSpaceSpikeNoised,\n    RandRicianNoised,\n    RandScaleIntensityd,\n    RandShiftIntensityd,\n    RandStdShiftIntensityd,\n    SavitzkyGolaySmoothd,\n    ScaleIntensityRanged,\n    ScaleIntensityRangePercentilesd,\n    ShiftIntensityd,\n    StdShiftIntensityd,\n    ThresholdIntensityd,\n)\nfrom monai.transforms.post.array import KeepLargestConnectedComponent, LabelFilter, LabelToContour", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils_create_transform_ims.py_None_16_if_TYPE_CHECKING_.else_.plt_has_matplotlib_opt": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils_create_transform_ims.py_None_16_if_TYPE_CHECKING_.else_.plt_has_matplotlib_opt", "embedding": null, "metadata": {"file_path": "monai/transforms/utils_create_transform_ims.py", "file_name": "utils_create_transform_ims.py", "file_type": "text/x-python", "category": "implementation", "start_line": 147, "end_line": 187, "span_ids": ["imports:20", "imports:14"], "tokens": 250}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from monai.transforms.post.dictionary import AsDiscreted, KeepLargestConnectedComponentd, LabelFilterd, LabelToContourd\nfrom monai.transforms.smooth_field.array import (\n    RandSmoothDeform,\n    RandSmoothFieldAdjustContrast,\n    RandSmoothFieldAdjustIntensity,\n)\nfrom monai.transforms.smooth_field.dictionary import (\n    RandSmoothDeformd,\n    RandSmoothFieldAdjustContrastd,\n    RandSmoothFieldAdjustIntensityd,\n)\nfrom monai.transforms.spatial.array import (\n    GridDistortion,\n    Rand2DElastic,\n    RandAffine,\n    RandAxisFlip,\n    RandGridDistortion,\n    RandRotate90,\n    Resize,\n    Spacing,\n)\nfrom monai.transforms.spatial.dictionary import (\n    GridDistortiond,\n    Rand2DElasticd,\n    RandAffined,\n    RandAxisFlipd,\n    RandGridDistortiond,\n    RandRotate90d,\n    Resized,\n    Spacingd,\n)\nfrom monai.utils.enums import CommonKeys\nfrom monai.utils.module import optional_import\n\nif TYPE_CHECKING:\n    import matplotlib.pyplot as plt\n\n    has_matplotlib = True\n\nelse:\n    plt, has_matplotlib = optional_import(\"matplotlib.pyplot\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils_create_transform_ims.py_if___name_____main____if___name_____main___.None_49": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils_create_transform_ims.py_if___name_____main____if___name_____main___.None_49", "embedding": null, "metadata": {"file_path": "monai/transforms/utils_create_transform_ims.py", "file_name": "utils_create_transform_ims.py", "file_type": "text/x-python", "category": "implementation", "start_line": 444, "end_line": 538, "span_ids": ["impl:6"], "tokens": 1545}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "if __name__ == \"__main__\":\n\n    keys = [CommonKeys.IMAGE, CommonKeys.LABEL]\n    data = get_data(keys)\n    create_transform_im(RandFlip, dict(prob=1, spatial_axis=1), data)\n    create_transform_im(RandFlipd, dict(keys=keys, prob=1, spatial_axis=2), data)\n    create_transform_im(Flip, dict(spatial_axis=1), data)\n    create_transform_im(Flipd, dict(keys=keys, spatial_axis=2), data)\n    create_transform_im(Flipd, dict(keys=keys, spatial_axis=2), data)\n    create_transform_im(Orientation, dict(axcodes=\"RPI\", image_only=True), data)\n    create_transform_im(Orientationd, dict(keys=keys, axcodes=\"RPI\"), data)\n    create_transform_im(\n        Rand3DElastic, dict(prob=1.0, sigma_range=(1, 2), magnitude_range=(0.5, 0.5), shear_range=(1, 1, 1)), data\n    )\n    create_transform_im(Affine, dict(shear_params=(0, 0.5, 0), image_only=True, padding_mode=\"zeros\"), data)\n    create_transform_im(\n        Affined, dict(keys=keys, shear_params=(0, 0.5, 0), mode=[\"bilinear\", \"nearest\"], padding_mode=\"zeros\"), data\n    )\n    create_transform_im(RandAffine, dict(prob=1, shear_range=(0.5, 0.5), padding_mode=\"zeros\"), data)\n    create_transform_im(\n        RandAffined,\n        dict(keys=keys, prob=1, shear_range=(0.5, 0.5), mode=[\"bilinear\", \"nearest\"], padding_mode=\"zeros\"),\n        data,\n    )\n    create_transform_im(\n        Rand3DElastic, dict(sigma_range=(5, 7), magnitude_range=(50, 150), prob=1, padding_mode=\"zeros\"), data\n    )\n    create_transform_im(\n        Rand2DElastic, dict(prob=1, spacing=(20, 20), magnitude_range=(1, 2), padding_mode=\"zeros\"), data, 2\n    )\n    create_transform_im(\n        Rand2DElasticd,\n        dict(\n            keys=keys,\n            prob=1,\n            spacing=(20, 20),\n            magnitude_range=(1, 2),\n            padding_mode=\"zeros\",\n            mode=[\"bilinear\", \"nearest\"],\n        ),\n        data,\n        2,\n    )\n    create_transform_im(\n        Rand3DElasticd,\n        dict(\n            keys=keys,\n            sigma_range=(5, 7),\n            magnitude_range=(50, 150),\n            prob=1,\n            padding_mode=\"zeros\",\n            mode=[\"bilinear\", \"nearest\"],\n        ),\n        data,\n    )\n    create_transform_im(Rotate90, dict(spatial_axes=(1, 2)), data)\n    create_transform_im(Rotate90d, dict(keys=keys, spatial_axes=(1, 2)), data)\n    create_transform_im(RandRotate90, dict(prob=1), data)\n    create_transform_im(RandRotate90d, dict(keys=keys, prob=1), data)\n    create_transform_im(Rotate, dict(angle=0.1), data)\n    create_transform_im(Rotated, dict(keys=keys, angle=0.1, mode=[\"bilinear\", \"nearest\"]), data)\n    create_transform_im(RandRotate, dict(prob=1, range_x=[0.4, 0.4]), data)\n    create_transform_im(RandRotated, dict(keys=keys, prob=1, range_x=[0.4, 0.4], mode=[\"bilinear\", \"nearest\"]), data)\n    create_transform_im(Zoom, dict(zoom=0.6), data)\n    create_transform_im(Zoomd, dict(keys=keys, zoom=1.3, mode=[\"area\", \"nearest\"]), data)\n    create_transform_im(RandZoom, dict(prob=1, min_zoom=0.6, max_zoom=0.8), data)\n    create_transform_im(RandZoomd, dict(keys=keys, prob=1, min_zoom=1.3, max_zoom=1.5, mode=[\"area\", \"nearest\"]), data)\n    create_transform_im(ScaleIntensity, dict(minv=0, maxv=10), data, colorbar=True)\n    create_transform_im(ScaleIntensityd, dict(keys=CommonKeys.IMAGE, minv=0, maxv=10), data, colorbar=True)\n    create_transform_im(RandScaleIntensity, dict(prob=1.0, factors=(5, 10)), data, colorbar=True)\n    create_transform_im(\n        RandScaleIntensityd, dict(keys=CommonKeys.IMAGE, prob=1.0, factors=(5, 10)), data, colorbar=True\n    )\n    create_transform_im(DivisiblePad, dict(k=64), data)\n    create_transform_im(DivisiblePadd, dict(keys=keys, k=64), data)\n    create_transform_im(CropForeground, dict(), data)\n    create_transform_im(CropForegroundd, dict(keys=keys, source_key=CommonKeys.IMAGE), data)\n    create_transform_im(RandGaussianNoise, dict(prob=1, mean=0, std=0.1), data)\n    create_transform_im(RandGaussianNoised, dict(keys=CommonKeys.IMAGE, prob=1, mean=0, std=0.1), data)\n    create_transform_im(KSpaceSpikeNoise, dict(loc=(100, 100, 100), k_intensity=13), data)\n    create_transform_im(KSpaceSpikeNoised, dict(keys=CommonKeys.IMAGE, loc=(100, 100, 100), k_intensity=13), data)\n    create_transform_im(RandKSpaceSpikeNoise, dict(prob=1, intensity_range=(10, 13)), data)\n    create_transform_im(\n        RandKSpaceSpikeNoised,\n        dict(keys=CommonKeys.IMAGE, global_prob=1, prob=1, common_sampling=True, intensity_range=(13, 15)),\n        data,\n    )\n    create_transform_im(RandRicianNoise, dict(prob=1.0, mean=1, std=0.5), data)\n    create_transform_im(RandRicianNoised, dict(keys=CommonKeys.IMAGE, prob=1.0, mean=1, std=0.5), data)\n    create_transform_im(SavitzkyGolaySmooth, dict(window_length=5, order=1), data)\n    create_transform_im(SavitzkyGolaySmoothd, dict(keys=CommonKeys.IMAGE, window_length=5, order=1), data)\n    create_transform_im(GibbsNoise, dict(alpha=0.8), data)\n    create_transform_im(GibbsNoised, dict(keys=CommonKeys.IMAGE, alpha=0.8), data)\n    create_transform_im(RandGibbsNoise, dict(prob=1.0, alpha=(0.6, 0.8)), data)\n    create_transform_im(RandGibbsNoised, dict(keys=CommonKeys.IMAGE, prob=1.0, alpha=(0.6, 0.8)), data)\n    # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils_create_transform_ims.py_if___name_____main___.create_transform_im_Shift_if___name_____main___.None_100": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils_create_transform_ims.py_if___name_____main___.create_transform_im_Shift_if___name_____main___.None_100", "embedding": null, "metadata": {"file_path": "monai/transforms/utils_create_transform_ims.py", "file_name": "utils_create_transform_ims.py", "file_type": "text/x-python", "category": "implementation", "start_line": 539, "end_line": 610, "span_ids": ["impl:6"], "tokens": 1552}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "if __name__ == \"__main__\":\n    # ... other code\n    create_transform_im(ShiftIntensity, dict(offset=1), data, colorbar=True)\n    create_transform_im(ShiftIntensityd, dict(keys=CommonKeys.IMAGE, offset=1), data, colorbar=True)\n    create_transform_im(RandShiftIntensity, dict(prob=1.0, offsets=(10, 20)), data, colorbar=True)\n    create_transform_im(\n        RandShiftIntensityd, dict(keys=CommonKeys.IMAGE, prob=1.0, offsets=(10, 20)), data, colorbar=True\n    )\n    create_transform_im(StdShiftIntensity, dict(factor=10), data, colorbar=True)\n    create_transform_im(StdShiftIntensityd, dict(keys=CommonKeys.IMAGE, factor=10), data, colorbar=True)\n    create_transform_im(RandStdShiftIntensity, dict(prob=1.0, factors=(5, 10)), data, colorbar=True)\n    create_transform_im(\n        RandStdShiftIntensityd, dict(keys=CommonKeys.IMAGE, prob=1.0, factors=(5, 10)), data, colorbar=True\n    )\n    create_transform_im(RandBiasField, dict(prob=1, coeff_range=(0.2, 0.3)), data)\n    create_transform_im(RandBiasFieldd, dict(keys=CommonKeys.IMAGE, prob=1, coeff_range=(0.2, 0.3)), data)\n    create_transform_im(NormalizeIntensity, dict(subtrahend=0, divisor=10), data, colorbar=True)\n    create_transform_im(NormalizeIntensityd, dict(keys=CommonKeys.IMAGE, subtrahend=0, divisor=10), data, colorbar=True)\n    create_transform_im(ThresholdIntensity, dict(threshold=0.4, above=False, cval=0.9), data, colorbar=True)\n    create_transform_im(\n        ThresholdIntensityd, dict(keys=CommonKeys.IMAGE, threshold=0.4, above=False, cval=0.9), data, colorbar=True\n    )\n    create_transform_im(ScaleIntensityRange, dict(a_min=0, a_max=1, b_min=1, b_max=10), data, colorbar=True)\n    create_transform_im(\n        ScaleIntensityRanged, dict(keys=CommonKeys.IMAGE, a_min=0, a_max=1, b_min=1, b_max=10), data, colorbar=True\n    )\n    create_transform_im(ScaleIntensityRangePercentiles, dict(lower=5, upper=95, b_min=1, b_max=10), data, colorbar=True)\n    create_transform_im(\n        ScaleIntensityRangePercentilesd,\n        dict(keys=CommonKeys.IMAGE, lower=5, upper=95, b_min=1, b_max=10),\n        data,\n        colorbar=True,\n    )\n    create_transform_im(AdjustContrast, dict(gamma=2), data, colorbar=True)\n    create_transform_im(AdjustContrastd, dict(keys=CommonKeys.IMAGE, gamma=2), data, colorbar=True)\n    create_transform_im(RandAdjustContrast, dict(prob=1, gamma=(1.5, 2)), data, colorbar=True)\n    create_transform_im(RandAdjustContrastd, dict(keys=CommonKeys.IMAGE, prob=1, gamma=(1.5, 2)), data, colorbar=True)\n    create_transform_im(MaskIntensity, dict(mask_data=data[CommonKeys.IMAGE], select_fn=lambda x: x > 0.3), data)\n    create_transform_im(\n        MaskIntensityd, dict(keys=CommonKeys.IMAGE, mask_key=CommonKeys.IMAGE, select_fn=lambda x: x > 0.3), data\n    )\n    create_transform_im(GaussianSmooth, dict(sigma=2), data)\n    create_transform_im(GaussianSmoothd, dict(keys=CommonKeys.IMAGE, sigma=2), data)\n    create_transform_im(RandGaussianSmooth, dict(prob=1.0, sigma_x=(1, 2)), data)\n    create_transform_im(RandGaussianSmoothd, dict(keys=CommonKeys.IMAGE, prob=1.0, sigma_x=(1, 2)), data)\n    create_transform_im(GaussianSharpen, dict(), GaussianSmoothd(CommonKeys.IMAGE, 2)(data))\n    create_transform_im(GaussianSharpend, dict(keys=CommonKeys.IMAGE), GaussianSmoothd(CommonKeys.IMAGE, 2)(data))\n    create_transform_im(RandGaussianSharpen, dict(prob=1), GaussianSmoothd(CommonKeys.IMAGE, 2)(data))\n    create_transform_im(\n        RandGaussianSharpend, dict(keys=CommonKeys.IMAGE, prob=1), GaussianSmoothd(CommonKeys.IMAGE, 2)(data)\n    )\n    create_transform_im(RandHistogramShift, dict(prob=1, num_control_points=3), data, colorbar=True)\n    create_transform_im(\n        RandHistogramShiftd, dict(keys=CommonKeys.IMAGE, prob=1, num_control_points=3), data, colorbar=True\n    )\n    create_transform_im(RandCoarseDropout, dict(prob=1, holes=200, spatial_size=20, fill_value=0), data)\n    create_transform_im(\n        RandCoarseDropoutd, dict(keys=CommonKeys.IMAGE, prob=1, holes=200, spatial_size=20, fill_value=0), data\n    )\n    create_transform_im(RandCoarseShuffle, dict(prob=1, holes=200, spatial_size=20), data)\n    create_transform_im(RandCoarseShuffled, dict(keys=CommonKeys.IMAGE, prob=1, holes=200, spatial_size=20), data)\n    create_transform_im(HistogramNormalize, dict(num_bins=10), data)\n    create_transform_im(HistogramNormalized, dict(keys=CommonKeys.IMAGE, num_bins=10), data)\n    create_transform_im(SpatialPad, dict(spatial_size=(300, 300, 300)), data)\n    create_transform_im(SpatialPadd, dict(keys=keys, spatial_size=(300, 300, 300)), data)\n    create_transform_im(BorderPad, dict(spatial_border=10), data)\n    create_transform_im(BorderPadd, dict(keys=keys, spatial_border=10), data)\n    create_transform_im(SpatialCrop, dict(roi_center=(75, 75, 75), roi_size=(100, 100, 100)), data)\n    create_transform_im(SpatialCropd, dict(keys=keys, roi_center=(75, 75, 75), roi_size=(100, 100, 100)), data)\n    create_transform_im(CenterSpatialCrop, dict(roi_size=(100, 100, 100)), data)\n    create_transform_im(CenterSpatialCropd, dict(keys=keys, roi_size=(100, 100, 100)), data)\n    create_transform_im(RandSpatialCrop, dict(roi_size=(100, 100, 100), random_size=False), data)\n    create_transform_im(RandSpatialCropd, dict(keys=keys, roi_size=(100, 100, 100), random_size=False), data)\n    create_transform_im(RandSpatialCropSamples, dict(num_samples=4, roi_size=(100, 100, 100), random_size=False), data)\n    # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils_create_transform_ims.py_if___name_____main___.None_101_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/utils_create_transform_ims.py_if___name_____main___.None_101_", "embedding": null, "metadata": {"file_path": "monai/transforms/utils_create_transform_ims.py", "file_name": "utils_create_transform_ims.py", "file_type": "text/x-python", "category": "implementation", "start_line": 619, "end_line": 735, "span_ids": ["impl:6"], "tokens": 1514}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "if __name__ == \"__main__\":\n    # ... other code\n    create_transform_im(\n        RandSpatialCropSamplesd, dict(keys=keys, num_samples=4, roi_size=(100, 100, 100), random_size=False), data\n    )\n    create_transform_im(\n        RandWeightedCrop, dict(spatial_size=(100, 100, 100), num_samples=4, weight_map=data[CommonKeys.IMAGE] > 0), data\n    )\n    create_transform_im(\n        RandWeightedCropd, dict(keys=keys, spatial_size=(100, 100, 100), num_samples=4, w_key=CommonKeys.IMAGE), data\n    )\n    create_transform_im(\n        RandCropByPosNegLabel,\n        dict(spatial_size=(100, 100, 100), label=data[CommonKeys.LABEL], neg=0, num_samples=4),\n        data,\n    )\n    create_transform_im(\n        RandCropByPosNegLabeld,\n        dict(keys=keys, spatial_size=(100, 100, 100), label_key=CommonKeys.LABEL, neg=0, num_samples=4),\n        data,\n    )\n    create_transform_im(\n        RandCropByLabelClasses,\n        dict(\n            spatial_size=(100, 100, 100), label=data[CommonKeys.LABEL] > 0, num_classes=2, ratios=[0, 1], num_samples=4\n        ),\n        data,\n    )\n    create_transform_im(\n        RandCropByLabelClassesd,\n        dict(\n            keys=keys,\n            spatial_size=(100, 100, 100),\n            label_key=CommonKeys.LABEL,\n            num_classes=2,\n            ratios=[0, 1],\n            num_samples=4,\n        ),\n        data,\n    )\n    create_transform_im(ResizeWithPadOrCrop, dict(spatial_size=(100, 100, 100)), data)\n    create_transform_im(ResizeWithPadOrCropd, dict(keys=keys, spatial_size=(100, 100, 100)), data)\n    create_transform_im(RandScaleCrop, dict(roi_scale=0.4), data)\n    create_transform_im(RandScaleCropd, dict(keys=keys, roi_scale=0.4), data)\n    create_transform_im(CenterScaleCrop, dict(roi_scale=0.4), data)\n    create_transform_im(CenterScaleCropd, dict(keys=keys, roi_scale=0.4), data)\n    create_transform_im(AsDiscrete, dict(to_onehot=None, threshold=10), data, is_post=True, colorbar=True)\n    create_transform_im(AsDiscreted, dict(keys=CommonKeys.LABEL, to_onehot=None, threshold=10), data, is_post=True)\n    create_transform_im(LabelFilter, dict(applied_labels=(1, 2, 3, 4, 5, 6)), data, is_post=True)\n    create_transform_im(\n        LabelFilterd, dict(keys=CommonKeys.LABEL, applied_labels=(1, 2, 3, 4, 5, 6)), data, is_post=True\n    )\n    create_transform_im(LabelToContour, dict(), data, is_post=True)\n    create_transform_im(LabelToContourd, dict(keys=CommonKeys.LABEL), data, is_post=True)\n    create_transform_im(Spacing, dict(pixdim=(5, 5, 5), image_only=True), data)\n    create_transform_im(Spacingd, dict(keys=keys, pixdim=(5, 5, 5), mode=[\"bilinear\", \"nearest\"]), data)\n    create_transform_im(RandAxisFlip, dict(prob=1), data)\n    create_transform_im(RandAxisFlipd, dict(keys=keys, prob=1), data)\n    create_transform_im(Resize, dict(spatial_size=(100, 100, 100)), data)\n    create_transform_im(Resized, dict(keys=keys, spatial_size=(100, 100, 100), mode=[\"area\", \"nearest\"]), data)\n    data_binary = deepcopy(data)\n    data_binary[CommonKeys.LABEL] = (data_binary[CommonKeys.LABEL] > 0).astype(np.float32)\n    create_transform_im(KeepLargestConnectedComponent, dict(applied_labels=1), data_binary, is_post=True, ndim=2)\n    create_transform_im(\n        KeepLargestConnectedComponentd, dict(keys=CommonKeys.LABEL, applied_labels=1), data_binary, is_post=True, ndim=2\n    )\n    create_transform_im(\n        GridDistortion, dict(num_cells=3, distort_steps=[(1.5,) * 4] * 3, mode=\"nearest\", padding_mode=\"zeros\"), data\n    )\n    create_transform_im(\n        GridDistortiond,\n        dict(\n            keys=keys, num_cells=3, distort_steps=[(1.5,) * 4] * 3, mode=[\"bilinear\", \"nearest\"], padding_mode=\"zeros\"\n        ),\n        data,\n    )\n    create_transform_im(RandGridDistortion, dict(num_cells=3, prob=1.0, distort_limit=(-0.1, 0.1)), data)\n    create_transform_im(\n        RandGridDistortiond,\n        dict(keys=keys, num_cells=4, prob=1.0, distort_limit=(-0.2, 0.2), mode=[\"bilinear\", \"nearest\"]),\n        data,\n    )\n    create_transform_im(\n        RandSmoothFieldAdjustContrast, dict(spatial_size=(217, 217, 217), rand_size=(10, 10, 10), prob=1.0), data\n    )\n    create_transform_im(\n        RandSmoothFieldAdjustContrastd,\n        dict(keys=keys, spatial_size=(217, 217, 217), rand_size=(10, 10, 10), prob=1.0),\n        data,\n    )\n    create_transform_im(\n        RandSmoothFieldAdjustIntensity,\n        dict(spatial_size=(217, 217, 217), rand_size=(10, 10, 10), prob=1.0, gamma=(0.5, 4.5)),\n        data,\n    )\n    create_transform_im(\n        RandSmoothFieldAdjustIntensityd,\n        dict(keys=keys, spatial_size=(217, 217, 217), rand_size=(10, 10, 10), prob=1.0, gamma=(0.5, 4.5)),\n        data,\n    )\n\n    create_transform_im(\n        RandSmoothDeform,\n        dict(spatial_size=(217, 217, 217), rand_size=(10, 10, 10), prob=1.0, def_range=0.05, grid_mode=\"blinear\"),\n        data,\n    )\n    create_transform_im(\n        RandSmoothDeformd,\n        dict(\n            keys=keys,\n            spatial_size=(217, 217, 217),\n            rand_size=(10, 10, 10),\n            prob=1.0,\n            def_range=0.05,\n            grid_mode=\"blinear\",\n        ),\n        data,\n    )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/misc.py_collections.abc_ensure_tuple_size.return.new_tup_dim_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/utils/misc.py_collections.abc_ensure_tuple_size.return.new_tup_dim_", "embedding": null, "metadata": {"file_path": "monai/utils/misc.py", "file_name": "misc.py", "file_type": "text/x-python", "category": "implementation", "start_line": 12, "end_line": 102, "span_ids": ["zip_with", "ensure_tuple_size", "first", "docstring", "ensure_tuple", "star_zip_with", "issequenceiterable"], "tokens": 615}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import collections.abc\nimport inspect\nimport itertools\nimport random\nimport types\nimport warnings\nfrom ast import literal_eval\nfrom distutils.util import strtobool\nfrom typing import Any, Callable, Optional, Sequence, Tuple, Union, cast\n\nimport numpy as np\nimport torch\n\nfrom monai.utils.module import version_leq\n\n__all__ = [\n    \"zip_with\",\n    \"star_zip_with\",\n    \"first\",\n    \"issequenceiterable\",\n    \"ensure_tuple\",\n    \"ensure_tuple_size\",\n    \"ensure_tuple_rep\",\n    \"fall_back_tuple\",\n    \"is_scalar_tensor\",\n    \"is_scalar\",\n    \"progress_bar\",\n    \"get_seed\",\n    \"set_determinism\",\n    \"list_to_dict\",\n    \"MAX_SEED\",\n    \"copy_to_device\",\n    \"ImageMetaKey\",\n    \"is_module_ver_at_least\",\n    \"has_option\",\n]\n\n_seed = None\n_flag_deterministic = torch.backends.cudnn.deterministic\n_flag_cudnn_benchmark = torch.backends.cudnn.benchmark\nMAX_SEED = np.iinfo(np.uint32).max + 1  # 2**32, the actual seed should be in [0, MAX_SEED - 1] for uint32\n\n\ndef zip_with(op, *vals, mapfunc=map):\n    \"\"\"\n    Map `op`, using `mapfunc`, to each tuple derived from zipping the iterables in `vals`.\n    \"\"\"\n    return mapfunc(op, zip(*vals))\n\n\ndef star_zip_with(op, *vals):\n    \"\"\"\n    Use starmap as the mapping function in zipWith.\n    \"\"\"\n    return zip_with(op, *vals, mapfunc=itertools.starmap)\n\n\ndef first(iterable, default=None):\n    \"\"\"\n    Returns the first item in the given iterable or `default` if empty, meaningful mostly with 'for' expressions.\n    \"\"\"\n    for i in iterable:\n        return i\n    return default\n\n\ndef issequenceiterable(obj: Any) -> bool:\n    \"\"\"\n    Determine if the object is an iterable sequence and is not a string.\n    \"\"\"\n    if isinstance(obj, torch.Tensor):\n        return int(obj.dim()) > 0  # a 0-d tensor is not iterable\n    return isinstance(obj, collections.abc.Iterable) and not isinstance(obj, (str, bytes))\n\n\ndef ensure_tuple(vals: Any) -> Tuple[Any, ...]:\n    \"\"\"\n    Returns a tuple of `vals`.\n    \"\"\"\n    if not issequenceiterable(vals):\n        return (vals,)\n\n    return tuple(vals)\n\n\ndef ensure_tuple_size(tup: Any, dim: int, pad_val: Any = 0) -> Tuple[Any, ...]:\n    \"\"\"\n    Returns a copy of `tup` with `dim` values by either shortened or padded with `pad_val` as necessary.\n    \"\"\"\n    new_tup = ensure_tuple(tup) + (pad_val,) * dim\n    return new_tup[:dim]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/utils.py_matshow3d_matshow3d._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/utils.py_matshow3d_matshow3d._", "embedding": null, "metadata": {"file_path": "monai/visualize/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 29, "end_line": 95, "span_ids": ["matshow3d"], "tokens": 772}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def matshow3d(\n    volume,\n    fig=None,\n    title: Optional[str] = None,\n    figsize=(10, 10),\n    frames_per_row: Optional[int] = None,\n    frame_dim: int = -3,\n    channel_dim: Optional[int] = None,\n    vmin=None,\n    vmax=None,\n    every_n: int = 1,\n    interpolation: str = \"none\",\n    show=False,\n    fill_value=np.nan,\n    margin: int = 1,\n    dtype=np.float32,\n    **kwargs,\n):\n    \"\"\"\n    Create a 3D volume figure as a grid of images.\n\n    Args:\n        volume: 3D volume to display. data shape can be `BCHWD`, `CHWD` or `HWD`.\n            Higher dimensional arrays will be reshaped into (-1, H, W, [C]), `C` depends on `channel_dim` arg.\n            A list of channel-first (C, H[, W, D]) arrays can also be passed in,\n            in which case they will be displayed as a padded and stacked volume.\n        fig: matplotlib figure to use. If None, a new figure will be created.\n        title: title of the figure.\n        figsize: size of the figure.\n        frames_per_row: number of frames to display in each row. If None, sqrt(firstdim) will be used.\n        frame_dim: for higher dimensional arrays, which dimension from (`-1`, `-2`, `-3`) is moved to\n            the `-3` dimension. dim and reshape to (-1, H, W) shape to construct frames, default to `-3`.\n        channel_dim: if not None, explicitly specify the channel dimension to be transposed to the\n            last dimensionas shape (-1, H, W, C). this can be used to plot RGB color image.\n            if None, the channel dimension will be flattened with `frame_dim` and `batch_dim` as shape (-1, H, W).\n            note that it can only support 3D input image. default is None.\n        vmin: `vmin` for the matplotlib `imshow`.\n        vmax: `vmax` for the matplotlib `imshow`.\n        every_n: factor to subsample the frames so that only every n-th frame is displayed.\n        interpolation: interpolation to use for the matplotlib `matshow`.\n        show: if True, show the figure.\n        fill_value: value to use for the empty part of the grid.\n        margin: margin to use for the grid.\n        dtype: data type of the output stacked frames.\n        kwargs: additional keyword arguments to matplotlib `matshow` and `imshow`.\n\n    See Also:\n        - https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.imshow.html\n        - https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.matshow.html\n\n    Example:\n\n        >>> import numpy as np\n        >>> import matplotlib.pyplot as plt\n        >>> from monai.visualize import matshow3d\n        # create a figure of a 3D volume\n        >>> volume = np.random.rand(10, 10, 10)\n        >>> fig = plt.figure()\n        >>> matshow3d(volume, fig=fig, title=\"3D Volume\")\n        >>> plt.show()\n        # create a figure of a list of channel-first 3D volumes\n        >>> volumes = [np.random.rand(1, 10, 10, 10), np.random.rand(1, 10, 10, 10)]\n        >>> fig = plt.figure()\n        >>> matshow3d(volumes, fig=fig, title=\"List of Volumes\")\n        >>> plt.show()\n\n    \"\"\"\n    # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/utils.py_matshow3d.vol_matshow3d.return.fig_im": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/visualize/utils.py_matshow3d.vol_matshow3d.return.fig_im", "embedding": null, "metadata": {"file_path": "monai/visualize/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 96, "end_line": 153, "span_ids": ["matshow3d"], "tokens": 802}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def matshow3d(\n    volume,\n    fig=None,\n    title: Optional[str] = None,\n    figsize=(10, 10),\n    frames_per_row: Optional[int] = None,\n    frame_dim: int = -3,\n    channel_dim: Optional[int] = None,\n    vmin=None,\n    vmax=None,\n    every_n: int = 1,\n    interpolation: str = \"none\",\n    show=False,\n    fill_value=np.nan,\n    margin: int = 1,\n    dtype=np.float32,\n    **kwargs,\n):\n    vol: np.ndarray = convert_data_type(data=volume, output_type=np.ndarray)[0]  # type: ignore\n    if channel_dim is not None:\n        if channel_dim not in [0, 1] or vol.shape[channel_dim] not in [1, 3, 4]:\n            raise ValueError(\"channel_dim must be: None, 0 or 1, and channels of image must be 1, 3 or 4.\")\n\n    if isinstance(vol, (list, tuple)):\n        # a sequence of channel-first volumes\n        if not isinstance(vol[0], np.ndarray):\n            raise ValueError(\"volume must be a list of arrays.\")\n        pad_size = np.max(np.asarray([v.shape for v in vol]), axis=0)\n        pad = SpatialPad(pad_size[1:])  # assuming channel-first for item in vol\n        vol = np.concatenate([pad(v) for v in vol], axis=0)\n    else:  # ndarray\n        while len(vol.shape) < 3:\n            vol = np.expand_dims(vol, 0)  # so that we display 2d as well\n\n    if channel_dim is not None:\n        vol = np.moveaxis(vol, frame_dim, -4)  # move the expected dim to construct frames with `B` dim\n        vol = vol.reshape((-1, vol.shape[-3], vol.shape[-2], vol.shape[-1]))\n    else:\n        vol = np.moveaxis(vol, frame_dim, -3)\n        vol = vol.reshape((-1, vol.shape[-2], vol.shape[-1]))\n    vmin = np.nanmin(vol) if vmin is None else vmin\n    vmax = np.nanmax(vol) if vmax is None else vmax\n\n    # subsample every_n-th frame of the 3D volume\n    vol = vol[:: max(every_n, 1)]\n    if not frames_per_row:\n        frames_per_row = int(np.ceil(np.sqrt(len(vol))))\n    # create the grid of frames\n    cols = max(min(len(vol), frames_per_row), 1)\n    rows = int(np.ceil(len(vol) / cols))\n    width = [[0, cols * rows - len(vol)]]\n    if channel_dim is not None:\n        width += [[0, 0]]  # add pad width for the channel dim\n    width += [[margin, margin]] * 2\n    vol = np.pad(vol.astype(dtype, copy=False), width, mode=\"constant\", constant_values=fill_value)\n    im = np.block([[vol[i * cols + j] for j in range(cols)] for i in range(rows)])\n    if channel_dim is not None:\n        # move channel dim to the end\n        im = np.moveaxis(im, 0, -1)\n\n    # figure related configurations\n    if fig is None:\n        fig = plt.figure(tight_layout=True)\n    if not fig.axes:\n        fig.add_subplot(111)\n    ax = fig.axes[0]\n    ax.matshow(im, vmin=vmin, vmax=vmax, interpolation=interpolation, **kwargs)\n    ax.axis(\"off\")\n\n    if title is not None:\n        ax.set_title(title)\n    if figsize is not None:\n        fig.set_size_inches(figsize)\n    if show:\n        plt.show()\n    return fig, im", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_csv_dataset.py_TestCSVDataset.test_values.with_tempfile_TemporaryDi.dataset_10_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_csv_dataset.py_TestCSVDataset.test_values.with_tempfile_TemporaryDi.dataset_10_", "embedding": null, "metadata": {"file_path": "tests/test_csv_dataset.py", "file_name": "test_csv_dataset.py", "file_type": "text/x-python", "category": "test", "start_line": 124, "end_line": 190, "span_ids": ["TestCSVDataset.test_values", "impl"], "tokens": 917}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestCSVDataset(unittest.TestCase):\n    def test_values(self):\n        with tempfile.TemporaryDirectory() as tempdir:\n            # ... other code\n            dataset = CSVDataset(\n                src=filepaths,\n                row_indices=[1, 3],  # load row: 1, 3\n                col_names=[\"subject_id\", \"image\", *[f\"ehr_{i}\" for i in range(11)], \"meta_0\", \"meta_1\", \"meta_2\"],\n                col_groups={\"ehr\": [f\"ehr_{i}\" for i in range(11)], \"meta12\": [\"meta_1\", \"meta_2\"]},\n            )\n            np.testing.assert_allclose(\n                [round(i, 4) for i in dataset[-1][\"ehr\"]],\n                [3.3333, 3.2353, 3.4000, 3.1647, 3.0863, 3.7255, 3.6980, 3.6980, 3.7020, 3.3098, 3.7294],\n            )\n            np.testing.assert_allclose(dataset[-1][\"meta12\"], [False, True])\n\n            # test transform\n            dataset = CSVDataset(\n                src=filepaths, col_groups={\"ehr\": [f\"ehr_{i}\" for i in range(5)]}, transform=ToNumpyd(keys=\"ehr\")\n            )\n            self.assertEqual(len(dataset), 5)\n            expected = [\n                [2.0078, 2.2902, 2.0549, 3.0196, 3.8078],\n                [6.8392, 6.4745, 5.8627, 5.1922, 5.2745],\n                [3.7725, 4.2118, 4.6353, 5.2980, 9.5451],\n                [3.3333, 3.2353, 3.4000, 3.1647, 3.0863],\n                [6.4275, 6.2549, 5.9765, 6.2627, 7.7176],\n            ]\n            for item, exp in zip(dataset, expected):\n                self.assertTrue(isinstance(item[\"ehr\"], np.ndarray))\n                np.testing.assert_allclose(np.around(item[\"ehr\"], 4), exp)\n\n            # test default values and dtype\n            dataset = CSVDataset(\n                src=filepaths,\n                col_names=[\"subject_id\", \"image\", \"ehr_1\", \"ehr_9\", \"meta_1\"],\n                col_types={\"image\": {\"type\": str, \"default\": \"No image\"}, \"ehr_1\": {\"type\": int, \"default\": 0}},\n                how=\"outer\",  # generate NaN values in this merge mode\n            )\n            self.assertEqual(len(dataset), 6)\n            self.assertEqual(dataset[-1][\"image\"], \"No image\")\n            self.assertEqual(type(dataset[-1][\"ehr_1\"]), int)\n            np.testing.assert_allclose(dataset[-1][\"ehr_9\"], 3.3537, rtol=1e-2)\n\n            # test pre-loaded DataFrame\n            df = pd.read_csv(filepath1)\n            dataset = CSVDataset(src=df)\n            self.assertDictEqual(\n                {k: round(v, 4) if not isinstance(v, str) else v for k, v in dataset[2].items()},\n                {\n                    \"subject_id\": \"s000002\",\n                    \"label\": 4,\n                    \"image\": \"./imgs/s000002.png\",\n                    \"ehr_0\": 3.7725,\n                    \"ehr_1\": 4.2118,\n                    \"ehr_2\": 4.6353,\n                },\n            )\n\n            # test pre-loaded multiple DataFrames, join tables with kwargs\n            dfs = [pd.read_csv(i) for i in filepaths]\n            dataset = CSVDataset(src=dfs, on=\"subject_id\")\n            self.assertEqual(dataset[3][\"subject_id\"], \"s000003\")\n            self.assertEqual(dataset[3][\"label\"], 1)\n            self.assertEqual(round(dataset[3][\"ehr_0\"], 4), 3.3333)\n            self.assertEqual(dataset[3][\"meta_0\"], False)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_csv_iterable_dataset.py_os_TestCSVIterableDataset": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_csv_iterable_dataset.py_os_TestCSVIterableDataset", "embedding": null, "metadata": {"file_path": "tests/test_csv_iterable_dataset.py", "file_name": "test_csv_iterable_dataset.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 246, "span_ids": ["TestCSVIterableDataset", "docstring"], "tokens": 64}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport sys\nimport tempfile\nimport unittest\n\nimport numpy as np\nimport pandas as pd\n\nfrom monai.data import CSVIterableDataset, DataLoader\nfrom monai.transforms import ToNumpyd\nfrom tests.utils import skip_if_windows\n\n\n@skip_if_windows\nclass TestCSVIterableDataset(unittest.TestCase):", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_csv_iterable_dataset.py_TestCSVIterableDataset.test_values_TestCSVIterableDataset.test_values.with_tempfile_TemporaryDi._test_group_columns": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_csv_iterable_dataset.py_TestCSVIterableDataset.test_values_TestCSVIterableDataset.test_values.with_tempfile_TemporaryDi._test_group_columns", "embedding": null, "metadata": {"file_path": "tests/test_csv_iterable_dataset.py", "file_name": "test_csv_iterable_dataset.py", "file_type": "text/x-python", "category": "test", "start_line": 27, "end_line": 150, "span_ids": ["TestCSVIterableDataset.test_values"], "tokens": 1614}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_windows\nclass TestCSVIterableDataset(unittest.TestCase):\n    def test_values(self):\n        with tempfile.TemporaryDirectory() as tempdir:\n            test_data1 = [\n                [\"subject_id\", \"label\", \"image\", \"ehr_0\", \"ehr_1\", \"ehr_2\"],\n                [\"s000000\", 5, \"./imgs/s000000.png\", 2.007843256, 2.29019618, 2.054902077],\n                [\"s000001\", 0, \"./imgs/s000001.png\", 6.839215755, 6.474509716, 5.862744808],\n                [\"s000002\", 4, \"./imgs/s000002.png\", 3.772548914, 4.211764812, 4.635294437],\n                [\"s000003\", 1, \"./imgs/s000003.png\", 3.333333254, 3.235294342, 3.400000095],\n                [\"s000004\", 9, \"./imgs/s000004.png\", 6.427451134, 6.254901886, 5.976470947],\n            ]\n            test_data2 = [\n                [\"subject_id\", \"ehr_3\", \"ehr_4\", \"ehr_5\", \"ehr_6\", \"ehr_7\", \"ehr_8\"],\n                [\"s000000\", 3.019608021, 3.807843208, 3.584313869, 3.141176462, 3.1960783, 4.211764812],\n                [\"s000001\", 5.192157269, 5.274509907, 5.250980377, 4.647058964, 4.886274338, 4.392156601],\n                [\"s000002\", 5.298039436, 9.545097351, 12.57254887, 6.799999714, 2.1960783, 1.882352948],\n                [\"s000003\", 3.164705753, 3.086274624, 3.725490093, 3.698039293, 3.698039055, 3.701960802],\n                [\"s000004\", 6.26274538, 7.717647076, 9.584313393, 6.082352638, 2.662744999, 2.34117651],\n            ]\n            test_data3 = [\n                [\"subject_id\", \"ehr_9\", \"ehr_10\", \"meta_0\", \"meta_1\", \"meta_2\"],\n                [\"s000000\", 6.301961422, 6.470588684, \"TRUE\", \"TRUE\", \"TRUE\"],\n                [\"s000001\", 5.219608307, 7.827450752, \"FALSE\", \"TRUE\", \"FALSE\"],\n                [\"s000002\", 1.882352948, 2.031372547, \"TRUE\", \"FALSE\", \"TRUE\"],\n                [\"s000003\", 3.309803963, 3.729412079, \"FALSE\", \"FALSE\", \"TRUE\"],\n                [\"s000004\", 2.062745094, 2.34117651, \"FALSE\", \"TRUE\", \"TRUE\"],\n            ]\n\n            def prepare_csv_file(data, filepath):\n                with open(filepath, \"a\") as f:\n                    for d in data:\n                        f.write((\",\".join([str(i) for i in d])) + \"\\n\")\n\n            filepath1 = os.path.join(tempdir, \"test_data1.csv\")\n            filepath2 = os.path.join(tempdir, \"test_data2.csv\")\n            filepath3 = os.path.join(tempdir, \"test_data3.csv\")\n            filepaths = [filepath1, filepath2, filepath3]\n            prepare_csv_file(test_data1, filepath1)\n            prepare_csv_file(test_data2, filepath2)\n            prepare_csv_file(test_data3, filepath3)\n\n            # test single CSV file\n            dataset = CSVIterableDataset(filepath1, shuffle=False)\n            count = 0\n            for item in dataset:\n                count += 1\n                if count == 3:\n                    self.assertDictEqual(\n                        {k: round(v, 4) if not isinstance(v, str) else v for k, v in item.items()},\n                        {\n                            \"subject_id\": \"s000002\",\n                            \"label\": 4,\n                            \"image\": \"./imgs/s000002.png\",\n                            \"ehr_0\": 3.7725,\n                            \"ehr_1\": 4.2118,\n                            \"ehr_2\": 4.6353,\n                        },\n                    )\n                    break\n            self.assertEqual(count, 3)\n            dataset.close()\n\n            # test reset iterables\n            dataset.reset(src=filepath3)\n            count = 0\n            for i, item in enumerate(dataset):\n                count += 1\n                if i == 4:\n                    self.assertEqual(item[\"meta_0\"], False)\n            self.assertEqual(count, 5)\n            dataset.close()\n\n            # test multiple CSV files, join tables with kwargs\n            dataset = CSVIterableDataset(filepaths, on=\"subject_id\", shuffle=False)\n            count = 0\n            for item in dataset:\n                count += 1\n                if count == 4:\n                    self.assertDictEqual(\n                        {k: round(v, 4) if not isinstance(v, (str, np.bool_)) else v for k, v in item.items()},\n                        {\n                            \"subject_id\": \"s000003\",\n                            \"label\": 1,\n                            \"image\": \"./imgs/s000003.png\",\n                            \"ehr_0\": 3.3333,\n                            \"ehr_1\": 3.2353,\n                            \"ehr_2\": 3.4000,\n                            \"ehr_3\": 3.1647,\n                            \"ehr_4\": 3.0863,\n                            \"ehr_5\": 3.7255,\n                            \"ehr_6\": 3.6980,\n                            \"ehr_7\": 3.6980,\n                            \"ehr_8\": 3.7020,\n                            \"ehr_9\": 3.3098,\n                            \"ehr_10\": 3.7294,\n                            \"meta_0\": False,\n                            \"meta_1\": False,\n                            \"meta_2\": True,\n                        },\n                    )\n            self.assertEqual(count, 5)\n            dataset.close()\n\n            # test selected columns and chunk size\n            dataset = CSVIterableDataset(\n                src=filepaths, chunksize=2, col_names=[\"subject_id\", \"image\", \"ehr_1\", \"ehr_7\", \"meta_1\"], shuffle=False\n            )\n            count = 0\n            for item in dataset:\n                count += 1\n                if count == 4:\n                    self.assertDictEqual(\n                        {k: round(v, 4) if not isinstance(v, (str, np.bool_)) else v for k, v in item.items()},\n                        {\n                            \"subject_id\": \"s000003\",\n                            \"image\": \"./imgs/s000003.png\",\n                            \"ehr_1\": 3.2353,\n                            \"ehr_7\": 3.6980,\n                            \"meta_1\": False,\n                        },\n                    )\n            self.assertEqual(count, 5)\n            dataset.close()\n\n            # test group columns\n            # ... other code\n            for item in dataset:\n                # ... other code\n            # ... other code\n            for item in dataset:\n                # ... other code\n            # ... other code", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_csv_iterable_dataset.py_TestCSVIterableDataset.test_values.with_tempfile_TemporaryDi.dataset_14_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_csv_iterable_dataset.py_TestCSVIterableDataset.test_values.with_tempfile_TemporaryDi.dataset_14_", "embedding": null, "metadata": {"file_path": "tests/test_csv_iterable_dataset.py", "file_name": "test_csv_iterable_dataset.py", "file_type": "text/x-python", "category": "test", "start_line": 151, "end_line": 251, "span_ids": ["TestCSVIterableDataset.test_values", "impl"], "tokens": 1127}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@skip_if_windows\nclass TestCSVIterableDataset(unittest.TestCase):\n    def test_values(self):\n        with tempfile.TemporaryDirectory() as tempdir:\n            # ... other code\n            dataset.close()\n            # ... other code\n            self.assertEqual(count, 5)\n            # ... other code\n            dataset = CSVIterableDataset(\n                src=filepaths,\n                col_names=[\"subject_id\", \"image\", *[f\"ehr_{i}\" for i in range(11)], \"meta_0\", \"meta_1\", \"meta_2\"],\n                col_groups={\"ehr\": [f\"ehr_{i}\" for i in range(11)], \"meta12\": [\"meta_1\", \"meta_2\"]},\n                shuffle=False,\n            )\n            count = 0\n            for item in dataset:\n                count += 1\n                if count == 4:\n                    np.testing.assert_allclose(\n                        [round(i, 4) for i in item[\"ehr\"]],\n                        [3.3333, 3.2353, 3.4000, 3.1647, 3.0863, 3.7255, 3.6980, 3.6980, 3.7020, 3.3098, 3.7294],\n                    )\n                    np.testing.assert_allclose(item[\"meta12\"], [False, True])\n            self.assertEqual(count, 5)\n            dataset.close()\n\n            # test transform\n            dataset = CSVIterableDataset(\n                chunksize=2,\n                buffer_size=4,\n                src=filepaths,\n                col_groups={\"ehr\": [f\"ehr_{i}\" for i in range(5)]},\n                transform=ToNumpyd(keys=\"ehr\"),\n                shuffle=True,\n                seed=123,\n            )\n            expected = [\n                [6.8392, 6.4745, 5.8627, 5.1922, 5.2745],\n                [3.3333, 3.2353, 3.4000, 3.1647, 3.0863],\n                [3.7725, 4.2118, 4.6353, 5.298, 9.5451],\n                [6.4275, 6.2549, 5.9765, 6.2627, 7.7176],\n                [2.0078, 2.2902, 2.0549, 3.0196, 3.8078],\n            ]\n            count = 0\n            for item, exp in zip(dataset, expected):\n                count += 1\n                self.assertTrue(isinstance(item[\"ehr\"], np.ndarray))\n                np.testing.assert_allclose(np.around(item[\"ehr\"], 4), exp)\n            self.assertEqual(count, 5)\n            dataset.close()\n\n            # test multiple processes loading\n            dataset = CSVIterableDataset(filepath1, transform=ToNumpyd(keys=\"label\"), shuffle=False)\n            # set num workers = 0 for mac / win\n            num_workers = 2 if sys.platform == \"linux\" else 0\n            dataloader = DataLoader(dataset=dataset, num_workers=num_workers, batch_size=2)\n            count = 0\n            for item in dataloader:\n                count += 1\n                # test the last item which only has 1 data\n                if len(item) == 1:\n                    self.assertListEqual(item[\"subject_id\"], [\"s000002\"])\n                    np.testing.assert_allclose(item[\"label\"], [4])\n                    self.assertListEqual(item[\"image\"], [\"./imgs/s000002.png\"])\n            self.assertEqual(count, 3)\n            dataset.close()\n\n            # test iterable stream\n            iters = pd.read_csv(filepath1, chunksize=1000)\n            dataset = CSVIterableDataset(src=iters, shuffle=False)\n            count = 0\n            for item in dataset:\n                count += 1\n                if count == 3:\n                    self.assertDictEqual(\n                        {k: round(v, 4) if not isinstance(v, str) else v for k, v in item.items()},\n                        {\n                            \"subject_id\": \"s000002\",\n                            \"label\": 4,\n                            \"image\": \"./imgs/s000002.png\",\n                            \"ehr_0\": 3.7725,\n                            \"ehr_1\": 4.2118,\n                            \"ehr_2\": 4.6353,\n                        },\n                    )\n                    break\n            self.assertEqual(count, 3)\n            dataset.close()\n\n            # test multiple iterable streams, join tables with kwargs\n            iters = [pd.read_csv(i, chunksize=1000) for i in filepaths]\n            dataset = CSVIterableDataset(src=iters, on=\"subject_id\", shuffle=False)\n            count = 0\n            for item in dataset:\n                count += 1\n                if count == 4:\n                    self.assertEqual(item[\"subject_id\"], \"s000003\")\n                    self.assertEqual(item[\"label\"], 1)\n                    self.assertEqual(round(item[\"ehr_0\"], 4), 3.3333)\n                    self.assertEqual(item[\"meta_0\"], False)\n            self.assertEqual(count, 5)\n            # manually close the pre-loaded iterables instead of `dataset.close()`\n            for i in iters:\n                i.close()\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dataset_func.py_json_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dataset_func.py_json_", "embedding": null, "metadata": {"file_path": "tests/test_dataset_func.py", "file_name": "test_dataset_func.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 53, "span_ids": ["TestDatasetFunc.test_seg_values", "TestDatasetFunc", "impl", "docstring"], "tokens": 383}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import json\nimport os\nimport tempfile\nimport unittest\n\nfrom monai.data import Dataset, DatasetFunc, load_decathlon_datalist, partition_dataset\n\n\nclass TestDatasetFunc(unittest.TestCase):\n    def test_seg_values(self):\n        with tempfile.TemporaryDirectory() as tempdir:\n            # prepare test datalist file\n            test_data = {\n                \"name\": \"Spleen\",\n                \"description\": \"Spleen Segmentation\",\n                \"labels\": {\"0\": \"background\", \"1\": \"spleen\"},\n                \"training\": [\n                    {\"image\": \"spleen_19.nii.gz\", \"label\": \"spleen_19.nii.gz\"},\n                    {\"image\": \"spleen_31.nii.gz\", \"label\": \"spleen_31.nii.gz\"},\n                ],\n                \"test\": [\"spleen_15.nii.gz\", \"spleen_23.nii.gz\"],\n            }\n            json_str = json.dumps(test_data)\n            file_path = os.path.join(tempdir, \"test_data.json\")\n            with open(file_path, \"w\") as json_file:\n                json_file.write(json_str)\n\n            data_list = DatasetFunc(\n                data=file_path, func=load_decathlon_datalist, data_list_key=\"training\", base_dir=tempdir\n            )\n            # partition dataset for train / validation\n            data_partition = DatasetFunc(\n                data=data_list, func=lambda x, **kwargs: partition_dataset(x, **kwargs)[0], num_partitions=2\n            )\n            dataset = Dataset(data=data_partition, transform=None)\n            self.assertEqual(dataset[0][\"image\"], os.path.join(tempdir, \"spleen_19.nii.gz\"))\n            self.assertEqual(dataset[0][\"label\"], os.path.join(tempdir, \"spleen_19.nii.gz\"))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deprecated.py_TestDeprecated.test_arg_except3_unknown_TestDeprecated.test_arg_except3_unknown.None_1": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deprecated.py_TestDeprecated.test_arg_except3_unknown_TestDeprecated.test_arg_except3_unknown.None_1", "embedding": null, "metadata": {"file_path": "tests/test_deprecated.py", "file_name": "test_deprecated.py", "file_type": "text/x-python", "category": "test", "start_line": 238, "end_line": 249, "span_ids": ["TestDeprecated.test_arg_except3_unknown"], "tokens": 128}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestDeprecated(unittest.TestCase):\n\n    def test_arg_except3_unknown(self):\n        \"\"\"\n        Test deprecated_arg decorator raises exception with `removed` set in the past.\n        with unknown version and kwargs\n        \"\"\"\n\n        @deprecated_arg(\"b\", removed=self.prev_version, version_val=\"0+untagged.1.g3131155\")\n        def afoo4(a, b=None, **kwargs):\n            pass\n\n        self.assertRaises(DeprecatedError, lambda: afoo4(1, b=2))\n        self.assertRaises(DeprecatedError, lambda: afoo4(1, b=2, c=3))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deprecated.py_TestDeprecated.test_replacement_arg_TestDeprecated.test_replacement_arg._prefers_the_new_arg": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deprecated.py_TestDeprecated.test_replacement_arg_TestDeprecated.test_replacement_arg._prefers_the_new_arg", "embedding": null, "metadata": {"file_path": "tests/test_deprecated.py", "file_name": "test_deprecated.py", "file_type": "text/x-python", "category": "test", "start_line": 251, "end_line": 262, "span_ids": ["TestDeprecated.test_replacement_arg"], "tokens": 122}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestDeprecated(unittest.TestCase):\n\n    def test_replacement_arg(self):\n        \"\"\"\n        Test deprecated arg being replaced.\n        \"\"\"\n\n        @deprecated_arg(\"b\", new_name=\"a\", since=self.prev_version, version_val=self.test_version)\n        def afoo4(a, b=None):\n            return a\n\n        self.assertEqual(afoo4(b=2), 2)\n        self.assertEqual(afoo4(1, b=2), 1)  # new name is in use\n        self.assertEqual(afoo4(a=1, b=2), 1)  # prefers the new arg", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deprecated.py_TestDeprecated.test_replacement_arg1_TestDeprecated.test_replacement_arg1._prefers_the_new_arg": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deprecated.py_TestDeprecated.test_replacement_arg1_TestDeprecated.test_replacement_arg1._prefers_the_new_arg", "embedding": null, "metadata": {"file_path": "tests/test_deprecated.py", "file_name": "test_deprecated.py", "file_type": "text/x-python", "category": "test", "start_line": 264, "end_line": 275, "span_ids": ["TestDeprecated.test_replacement_arg1"], "tokens": 136}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestDeprecated(unittest.TestCase):\n\n    def test_replacement_arg1(self):\n        \"\"\"\n        Test deprecated arg being replaced with kwargs.\n        \"\"\"\n\n        @deprecated_arg(\"b\", new_name=\"a\", since=self.prev_version, version_val=self.test_version)\n        def afoo4(a, *args, **kwargs):\n            return a\n\n        self.assertEqual(afoo4(b=2), 2)\n        self.assertEqual(afoo4(1, b=2, c=3), 1)  # new name is in use\n        self.assertEqual(afoo4(a=1, b=2, c=3), 1)  # prefers the new arg", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deprecated.py_TestDeprecated.test_replacement_arg2_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_deprecated.py_TestDeprecated.test_replacement_arg2_", "embedding": null, "metadata": {"file_path": "tests/test_deprecated.py", "file_name": "test_deprecated.py", "file_type": "text/x-python", "category": "test", "start_line": 277, "end_line": 294, "span_ids": ["TestDeprecated.test_replacement_arg2", "impl"], "tokens": 210}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestDeprecated(unittest.TestCase):\n\n    def test_replacement_arg2(self):\n        \"\"\"\n        Test deprecated arg (with a default value) being replaced.\n        \"\"\"\n\n        @deprecated_arg(\"b\", new_name=\"a\", since=self.prev_version, version_val=self.test_version)\n        def afoo4(a, b=None, **kwargs):\n            return a, kwargs\n\n        self.assertEqual(afoo4(b=2, c=3), (2, {\"c\": 3}))\n        self.assertEqual(afoo4(1, b=2, c=3), (1, {\"c\": 3}))  # new name is in use\n        self.assertEqual(afoo4(a=1, b=2, c=3), (1, {\"c\": 3}))  # prefers the new arg\n        self.assertEqual(afoo4(1, 2, c=3), (1, {\"c\": 3}))  # prefers the new positional arg\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_image.py_TEST_CASE_12_TestLoadImage.test_nibabel_reader.with_tempfile_TemporaryDi.self_assertTupleEqual_res": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_image.py_TEST_CASE_12_TestLoadImage.test_nibabel_reader.with_tempfile_TemporaryDi.self_assertTupleEqual_res", "embedding": null, "metadata": {"file_path": "tests/test_load_image.py", "file_name": "test_load_image.py", "file_type": "text/x-python", "category": "test", "start_line": 103, "end_line": 129, "span_ids": ["TestLoadImage", "TestLoadImage.test_nibabel_reader", "impl:25"], "tokens": 313}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "TEST_CASE_12 = [\n    {\"image_only\": False, \"reader\": \"ITKReader\", \"pixel_type\": itk.UC, \"reverse_indexing\": True},\n    \"tests/testing_data/CT_DICOM\",\n    (16, 16, 4),\n    (4, 16, 16),\n]\n\n\nclass TestLoadImage(unittest.TestCase):\n    @parameterized.expand(\n        [TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_3_1, TEST_CASE_4, TEST_CASE_4_1, TEST_CASE_5]\n    )\n    def test_nibabel_reader(self, input_param, filenames, expected_shape):\n        test_image = np.random.rand(128, 128, 128)\n        with tempfile.TemporaryDirectory() as tempdir:\n            for i, name in enumerate(filenames):\n                filenames[i] = os.path.join(tempdir, name)\n                nib.save(nib.Nifti1Image(test_image, np.eye(4)), filenames[i])\n            result = LoadImage(**input_param)(filenames)\n\n            if isinstance(result, tuple):\n                result, header = result\n                self.assertTrue(\"affine\" in header)\n                self.assertEqual(header[\"filename_or_obj\"], os.path.join(tempdir, \"test_image.nii.gz\"))\n                np.testing.assert_allclose(header[\"affine\"], np.eye(4))\n                np.testing.assert_allclose(header[\"original_affine\"], np.eye(4))\n            self.assertTupleEqual(result.shape, expected_shape)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_image.py_TestLoadImage.test_itk_dicom_series_reader_TestLoadImage.test_itk_dicom_series_reader.self_assertTupleEqual_res": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_image.py_TestLoadImage.test_itk_dicom_series_reader_TestLoadImage.test_itk_dicom_series_reader.self_assertTupleEqual_res", "embedding": null, "metadata": {"file_path": "tests/test_load_image.py", "file_name": "test_load_image.py", "file_type": "text/x-python", "category": "test", "start_line": 150, "end_line": 167, "span_ids": ["TestLoadImage.test_itk_dicom_series_reader"], "tokens": 233}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestLoadImage(unittest.TestCase):\n\n    @parameterized.expand([TEST_CASE_10, TEST_CASE_11, TEST_CASE_12])\n    def test_itk_dicom_series_reader(self, input_param, filenames, expected_shape, expected_np_shape):\n        result, header = LoadImage(**input_param)(filenames)\n        self.assertTrue(\"affine\" in header)\n        self.assertEqual(header[\"filename_or_obj\"], f\"{Path(filenames)}\")\n        np.testing.assert_allclose(\n            header[\"affine\"],\n            np.array(\n                [\n                    [-0.488281, 0.0, 0.0, 125.0],\n                    [0.0, -0.488281, 0.0, 128.100006],\n                    [0.0, 0.0, 68.33333333, -99.480003],\n                    [0.0, 0.0, 0.0, 1.0],\n                ]\n            ),\n        )\n        self.assertTupleEqual(tuple(header[\"spatial_shape\"]), expected_shape)\n        self.assertTupleEqual(result.shape, expected_np_shape)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_image.py_TestLoadImage.test_itk_reader_multichannel_TestLoadImage.test_itk_reader_multichannel.with_tempfile_TemporaryDi.for_flag_in_False_True_.None_3": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_image.py_TestLoadImage.test_itk_reader_multichannel_TestLoadImage.test_itk_reader_multichannel.with_tempfile_TemporaryDi.for_flag_in_False_True_.None_3", "embedding": null, "metadata": {"file_path": "tests/test_load_image.py", "file_name": "test_load_image.py", "file_type": "text/x-python", "category": "test", "start_line": 169, "end_line": 182, "span_ids": ["TestLoadImage.test_itk_reader_multichannel"], "tokens": 219}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestLoadImage(unittest.TestCase):\n\n    def test_itk_reader_multichannel(self):\n        test_image = np.random.randint(0, 256, size=(256, 224, 3)).astype(\"uint8\")\n        with tempfile.TemporaryDirectory() as tempdir:\n            filename = os.path.join(tempdir, \"test_image.png\")\n            itk_np_view = itk.image_view_from_array(test_image, is_vector=True)\n            itk.imwrite(itk_np_view, filename)\n            for flag in (False, True):\n                result, header = LoadImage(reader=ITKReader(reverse_indexing=flag))(Path(filename))\n\n                self.assertTupleEqual(tuple(header[\"spatial_shape\"]), (224, 256))\n                test_image = test_image.transpose(1, 0, 2)\n                np.testing.assert_allclose(result[:, :, 0], test_image[:, :, 0])\n                np.testing.assert_allclose(result[:, :, 1], test_image[:, :, 1])\n                np.testing.assert_allclose(result[:, :, 2], test_image[:, :, 2])", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_image.py_TestLoadImage.test_load_nifti_multichannel_TestLoadImage.test_load_nifti_multichannel.with_tempfile_TemporaryDi.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_load_image.py_TestLoadImage.test_load_nifti_multichannel_TestLoadImage.test_load_nifti_multichannel.with_tempfile_TemporaryDi.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_load_image.py", "file_name": "test_load_image.py", "file_type": "text/x-python", "category": "test", "start_line": 184, "end_line": 199, "span_ids": ["TestLoadImage.test_load_nifti_multichannel"], "tokens": 267}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestLoadImage(unittest.TestCase):\n\n    def test_load_nifti_multichannel(self):\n        test_image = np.random.randint(0, 256, size=(31, 64, 16, 2)).astype(np.float32)\n        with tempfile.TemporaryDirectory() as tempdir:\n            filename = os.path.join(tempdir, \"test_image.nii.gz\")\n            itk_np_view = itk.image_view_from_array(test_image, is_vector=True)\n            itk.imwrite(itk_np_view, filename)\n\n            itk_img, itk_header = LoadImage(reader=ITKReader())(Path(filename))\n            self.assertTupleEqual(tuple(itk_header[\"spatial_shape\"]), (16, 64, 31))\n            self.assertTupleEqual(tuple(itk_img.shape), (16, 64, 31, 2))\n\n            nib_image, nib_header = LoadImage(reader=NibabelReader(squeeze_non_spatial_dims=True))(Path(filename))\n            self.assertTupleEqual(tuple(nib_header[\"spatial_shape\"]), (16, 64, 31))\n            self.assertTupleEqual(tuple(nib_image.shape), (16, 64, 31, 2))\n\n            np.testing.assert_allclose(itk_img, nib_image, atol=1e-3, rtol=1e-3)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_matshow3d.py_os_pyplot_has_pyplot_opti": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_matshow3d.py_os_pyplot_has_pyplot_opti", "embedding": null, "metadata": {"file_path": "tests/test_matshow3d.py", "file_name": "test_matshow3d.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 24, "span_ids": ["docstring"], "tokens": 109}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport tempfile\nimport unittest\n\nimport numpy as np\n\nfrom monai.transforms import AddChanneld, Compose, LoadImaged, RandSpatialCropSamplesd, RepeatChanneld, ScaleIntensityd\nfrom monai.utils import optional_import\nfrom monai.visualize.utils import matshow3d\nfrom tests.utils import SkipIfNoModule\n\ncompare_images, _ = optional_import(\"matplotlib.testing.compare\", name=\"compare_images\")\npyplot, has_pyplot = optional_import(\"matplotlib\", name=\"pyplot\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_matshow3d.py_TestMatshow3d_TestMatshow3d.test_3d.with_tempfile_TemporaryDi._None_indicates_test_pas": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_matshow3d.py_TestMatshow3d_TestMatshow3d.test_3d.with_tempfile_TemporaryDi._None_indicates_test_pas", "embedding": null, "metadata": {"file_path": "tests/test_matshow3d.py", "file_name": "test_matshow3d.py", "file_type": "text/x-python", "category": "test", "start_line": 27, "end_line": 43, "span_ids": ["TestMatshow3d", "TestMatshow3d.test_3d"], "tokens": 241}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@SkipIfNoModule(\"matplotlib\")\nclass TestMatshow3d(unittest.TestCase):\n    def test_3d(self):\n        testing_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"testing_data\")\n        keys = \"image\"\n        xforms = Compose([LoadImaged(keys=keys), AddChanneld(keys=keys), ScaleIntensityd(keys=keys)])\n        image_path = os.path.join(testing_dir, \"anatomical.nii\")\n        ims = xforms({keys: image_path})\n\n        fig = pyplot.figure()  # external figure\n        fig, _ = matshow3d(ims[keys], fig=fig, figsize=(2, 2), frames_per_row=5, every_n=2, frame_dim=-1, show=False)\n\n        with tempfile.TemporaryDirectory() as tempdir:\n            tempimg = f\"{tempdir}/matshow3d_test.png\"\n            fig.savefig(tempimg)\n            comp = compare_images(f\"{testing_dir}/matshow3d_test.png\", tempimg, 5e-2)\n            self.assertIsNone(comp, f\"value of comp={comp}\")  # None indicates test passed", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_matshow3d.py_TestMatshow3d.test_samples_TestMatshow3d.test_samples.with_tempfile_TemporaryDi.if_comp_.else_._None_indicates_test_pas": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_matshow3d.py_TestMatshow3d.test_samples_TestMatshow3d.test_samples.with_tempfile_TemporaryDi.if_comp_.else_._None_indicates_test_pas", "embedding": null, "metadata": {"file_path": "tests/test_matshow3d.py", "file_name": "test_matshow3d.py", "file_type": "text/x-python", "category": "test", "start_line": 45, "end_line": 72, "span_ids": ["TestMatshow3d.test_samples"], "tokens": 356}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@SkipIfNoModule(\"matplotlib\")\nclass TestMatshow3d(unittest.TestCase):\n\n    def test_samples(self):\n        testing_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"testing_data\")\n        keys = \"image\"\n        xforms = Compose(\n            [\n                LoadImaged(keys=keys),\n                AddChanneld(keys=keys),\n                ScaleIntensityd(keys=keys),\n                RandSpatialCropSamplesd(keys=keys, roi_size=(8, 8, 5), random_size=True, num_samples=10),\n            ]\n        )\n        image_path = os.path.join(testing_dir, \"anatomical.nii\")\n        xforms.set_random_state(0)\n        ims = xforms({keys: image_path})\n        fig, mat = matshow3d(\n            [im[keys] for im in ims], title=f\"testing {keys}\", figsize=(2, 2), frames_per_row=5, every_n=2, show=False\n        )\n        self.assertTrue(mat.dtype == np.float32)\n\n        with tempfile.TemporaryDirectory() as tempdir:\n            tempimg = f\"{tempdir}/matshow3d_patch_test.png\"\n            fig.savefig(tempimg)\n            comp = compare_images(f\"{testing_dir}/matshow3d_patch_test.png\", tempimg, 5e-2, in_decorator=True)\n            if comp:\n                print(\"not none comp: \", comp)  # matplotlib 3.2.2\n                np.testing.assert_allclose(comp[\"rms\"], 30.786983, atol=1e-3, rtol=1e-3)\n            else:\n                self.assertIsNone(comp, f\"value of comp={comp}\")  # None indicates test passed", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_matshow3d.py_TestMatshow3d.test_3d_rgb_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_matshow3d.py_TestMatshow3d.test_3d_rgb_", "embedding": null, "metadata": {"file_path": "tests/test_matshow3d.py", "file_name": "test_matshow3d.py", "file_type": "text/x-python", "category": "test", "start_line": 74, "end_line": 110, "span_ids": ["TestMatshow3d.test_3d_rgb", "impl:3"], "tokens": 304}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@SkipIfNoModule(\"matplotlib\")\nclass TestMatshow3d(unittest.TestCase):\n\n    def test_3d_rgb(self):\n        testing_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"testing_data\")\n        keys = \"image\"\n        xforms = Compose(\n            [\n                LoadImaged(keys=keys),\n                AddChanneld(keys=keys),\n                ScaleIntensityd(keys=keys),\n                # change to RGB color image\n                RepeatChanneld(keys=keys, repeats=3),\n            ]\n        )\n        image_path = os.path.join(testing_dir, \"anatomical.nii\")\n        ims = xforms({keys: image_path})\n\n        fig = pyplot.figure()  # external figure\n        fig, _ = matshow3d(\n            volume=ims[keys],\n            fig=fig,\n            figsize=(2, 2),\n            frames_per_row=5,\n            every_n=2,\n            frame_dim=-1,\n            channel_dim=0,\n            show=False,\n        )\n\n        with tempfile.TemporaryDirectory() as tempdir:\n            tempimg = f\"{tempdir}/matshow3d_rgb_test.png\"\n            fig.savefig(tempimg)\n            comp = compare_images(f\"{testing_dir}/matshow3d_rgb_test.png\", tempimg, 5e-2)\n            self.assertIsNone(comp, f\"value of comp={comp}\")  # None indicates test passed\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nvtx_decorator.py_unittest_TEST_CASE_WRAPPER._np_random_randn_3_10_1": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nvtx_decorator.py_unittest_TEST_CASE_WRAPPER._np_random_randn_3_10_1", "embedding": null, "metadata": {"file_path": "tests/test_nvtx_decorator.py", "file_name": "test_nvtx_decorator.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 50, "span_ids": ["docstring"], "tokens": 293}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import (\n    Compose,\n    CuCIM,\n    Flip,\n    FlipD,\n    RandAdjustContrast,\n    RandCuCIM,\n    RandFlip,\n    Randomizable,\n    Rotate90,\n    ToCupy,\n    TorchVision,\n    ToTensor,\n    ToTensorD,\n)\nfrom monai.utils import Range, optional_import\nfrom tests.utils import HAS_CUPY\n\n_, has_nvtx = optional_import(\"torch._C._nvtx\", descriptor=\"NVTX is not installed. Are you sure you have a CUDA build?\")\n_, has_tvt = optional_import(\"torchvision.transforms\")\n_, has_cut = optional_import(\"cucim.core.operations.expose.transform\")\n\n\nTEST_CASE_ARRAY_0 = [np.random.randn(3, 3)]\nTEST_CASE_ARRAY_1 = [np.random.randn(3, 10, 10)]\n\nTEST_CASE_DICT_0 = [{\"image\": np.random.randn(3, 3)}]\nTEST_CASE_DICT_1 = [{\"image\": np.random.randn(3, 10, 10)}]\n\nTEST_CASE_TORCH_0 = [torch.randn(3, 3)]\nTEST_CASE_TORCH_1 = [torch.randn(3, 10, 10)]\n\nTEST_CASE_WRAPPER = [np.random.randn(3, 10, 10)]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nvtx_decorator.py_TestNVTXRangeDecorator.test_wrapper_tranforms_TestNVTXRangeDecorator.test_wrapper_tranforms.np_testing_assert_equal_o": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_nvtx_decorator.py_TestNVTXRangeDecorator.test_wrapper_tranforms_TestNVTXRangeDecorator.test_wrapper_tranforms.np_testing_assert_equal_o", "embedding": null, "metadata": {"file_path": "tests/test_nvtx_decorator.py", "file_name": "test_nvtx_decorator.py", "file_type": "text/x-python", "category": "test", "start_line": 105, "end_line": 128, "span_ids": ["TestNVTXRangeDecorator.test_wrapper_tranforms"], "tokens": 239}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "@unittest.skipUnless(has_nvtx, \"CUDA is required for NVTX Range!\")\nclass TestNVTXRangeDecorator(unittest.TestCase):\n\n    @parameterized.expand([TEST_CASE_WRAPPER])\n    @unittest.skipUnless(HAS_CUPY, \"Requires CuPy.\")\n    @unittest.skipUnless(has_cut, \"Requires cuCIM transforms.\")\n    @unittest.skipUnless(has_tvt, \"Requires torchvision transforms.\")\n    def test_wrapper_tranforms(self, input):\n        transform_list = [\n            ToTensor(),\n            TorchVision(name=\"RandomHorizontalFlip\", p=1.0),\n            ToCupy(),\n            CuCIM(name=\"image_flip\", spatial_axis=-1),\n            RandCuCIM(name=\"rand_image_rotate_90\", prob=1.0, max_k=1, spatial_axis=(-2, -1)),\n        ]\n\n        transforms = Compose(transform_list)\n        transforms_range = Compose([Range()(t) for t in transform_list])\n\n        # Apply transforms\n        output = transforms(input)\n\n        # Apply transforms with Range\n        output_r = transforms_range(input)\n\n        # Check the outputs\n        np.testing.assert_equal(output.get(), output_r.get())", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_one_of.py_TestOneOf.test_inverse_TestOneOf.test_inverse.None_1.else_.self_assertDictEqual_fwd_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_one_of.py_TestOneOf.test_inverse_TestOneOf.test_inverse.None_1.else_.self_assertDictEqual_fwd_", "embedding": null, "metadata": {"file_path": "tests/test_one_of.py", "file_name": "test_one_of.py", "file_type": "text/x-python", "category": "test", "start_line": 150, "end_line": 177, "span_ids": ["TestOneOf.test_inverse"], "tokens": 283}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestOneOf(unittest.TestCase):\n\n    @parameterized.expand(TEST_INVERSES)\n    def test_inverse(self, transform, invertible):\n        data = {k: (i + 1) * 10.0 for i, k in enumerate(KEYS)}\n        fwd_data = transform(data)\n\n        if invertible:\n            for k in KEYS:\n                t = fwd_data[TraceableTransform.trace_key(k)][-1]\n                # make sure the OneOf index was stored\n                self.assertEqual(t[TraceKeys.CLASS_NAME], OneOf.__name__)\n                # make sure index exists and is in bounds\n                self.assertTrue(0 <= t[TraceKeys.EXTRA_INFO][\"index\"] < len(transform))\n\n        # call the inverse\n        fwd_inv_data = transform.inverse(fwd_data)\n\n        if invertible:\n            for k in KEYS:\n                # check transform was removed\n                self.assertTrue(\n                    len(fwd_inv_data[TraceableTransform.trace_key(k)]) < len(fwd_data[TraceableTransform.trace_key(k)])\n                )\n                # check data is same as original (and different from forward)\n                self.assertEqual(fwd_inv_data[k], data[k])\n                self.assertNotEqual(fwd_inv_data[k], fwd_data[k])\n        else:\n            # if not invertible, should not change the data\n            self.assertDictEqual(fwd_data, fwd_inv_data)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_one_of.py_TestOneOf.test_inverse_compose_TestOneOf.test_inverse_compose.self_assertTupleEqual_res": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_one_of.py_TestOneOf.test_inverse_compose_TestOneOf.test_inverse_compose.self_assertTupleEqual_res", "embedding": null, "metadata": {"file_path": "tests/test_one_of.py", "file_name": "test_one_of.py", "file_type": "text/x-python", "category": "test", "start_line": 179, "end_line": 196, "span_ids": ["TestOneOf.test_inverse_compose"], "tokens": 167}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestOneOf(unittest.TestCase):\n\n    def test_inverse_compose(self):\n        transform = Compose(\n            [\n                Resized(keys=\"img\", spatial_size=[100, 100, 100]),\n                OneOf(\n                    [\n                        RandScaleIntensityd(keys=\"img\", factors=0.5, prob=1.0),\n                        RandShiftIntensityd(keys=\"img\", offsets=0.5, prob=1.0),\n                    ]\n                ),\n            ]\n        )\n        transform.set_random_state(seed=0)\n        result = transform({\"img\": np.ones((1, 101, 102, 103))})\n\n        result = transform.inverse(result)\n        # invert to the original spatial shape\n        self.assertTupleEqual(result[\"img\"].shape, (1, 101, 102, 103))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_savitzky_golay_smooth.py_TestSavitzkyGolaySmooth_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_savitzky_golay_smooth.py_TestSavitzkyGolaySmooth_", "embedding": null, "metadata": {"file_path": "tests/test_savitzky_golay_smooth.py", "file_name": "test_savitzky_golay_smooth.py", "file_type": "text/x-python", "category": "test", "start_line": 61, "end_line": 73, "span_ids": ["impl:9", "TestSavitzkyGolaySmooth", "TestSavitzkyGolaySmooth.test_value"], "tokens": 137}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestSavitzkyGolaySmooth(unittest.TestCase):\n    @parameterized.expand(\n        [TEST_CASE_SINGLE_VALUE, TEST_CASE_2D_AXIS_2, TEST_CASE_SINE_SMOOTH, TEST_CASE_SINGLE_VALUE_REP]\n    )\n    def test_value(self, arguments, image, expected_data, atol):\n        for p in TEST_NDARRAYS:\n            result = SavitzkyGolaySmooth(**arguments)(p(image.astype(np.float32)))\n            torch.testing.assert_allclose(result, p(expected_data.astype(np.float32)), rtol=1e-4, atol=atol)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_savitzky_golay_smoothd.py_unittest_TEST_CASE_SINE_SMOOTH._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_savitzky_golay_smoothd.py_unittest_TEST_CASE_SINE_SMOOTH._", "embedding": null, "metadata": {"file_path": "tests/test_savitzky_golay_smoothd.py", "file_name": "test_savitzky_golay_smoothd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 58, "span_ids": ["docstring:13", "docstring"], "tokens": 586}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import SavitzkyGolaySmoothd\nfrom tests.utils import TEST_NDARRAYS\n\n# Zero-padding trivial tests\n\nTEST_CASE_SINGLE_VALUE = [\n    {\"keys\": \"img\", \"window_length\": 3, \"order\": 1},\n    np.expand_dims(np.array([1.0]), 0),  # Input data: Single value\n    np.expand_dims(np.array([1 / 3]), 0),  # Expected output: With a window length of 3 and polyorder 1\n    # output should be equal to mean of 0, 1 and 0 = 1/3 (because input will be zero-padded and a linear fit performed)\n    1e-5,  # absolute tolerance\n]\n\nTEST_CASE_2D_AXIS_2 = [\n    {\"keys\": \"img\", \"window_length\": 3, \"order\": 1, \"axis\": 2},  # along axis 2 (second spatial dim)\n    np.expand_dims(np.ones((2, 3)), 0),\n    np.expand_dims(np.array([[2 / 3, 1.0, 2 / 3], [2 / 3, 1.0, 2 / 3]]), 0),\n    1e-5,  # absolute tolerance\n]\n\n# Replicated-padding trivial tests\n\nTEST_CASE_SINGLE_VALUE_REP = [\n    {\"keys\": \"img\", \"window_length\": 3, \"order\": 1, \"mode\": \"replicate\"},\n    np.expand_dims(np.array([1.0]), 0),  # Input data: Single value\n    np.expand_dims(np.array([1.0]), 0),  # Expected output: With a window length of 3 and polyorder 1\n    # output will be equal to mean of [1, 1, 1] = 1 (input will be nearest-neighbour-padded and a linear fit performed)\n    1e-5,  # absolute tolerance\n]\n\n# Sine smoothing\n\nTEST_CASE_SINE_SMOOTH = [\n    {\"keys\": \"img\", \"window_length\": 3, \"order\": 1},\n    # Sine wave with period equal to savgol window length (windowed to reduce edge effects).\n    np.expand_dims(np.sin(2 * np.pi * 1 / 3 * np.arange(100)) * np.hanning(100), 0),\n    # Should be smoothed out to zeros\n    np.expand_dims(np.zeros(100), 0),\n    # tolerance chosen by examining output of SciPy.signal.savgol_filter() when provided the above input\n    2e-2,  # absolute tolerance\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_savitzky_golay_smoothd.py_TestSavitzkyGolaySmoothd_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_savitzky_golay_smoothd.py_TestSavitzkyGolaySmoothd_", "embedding": null, "metadata": {"file_path": "tests/test_savitzky_golay_smoothd.py", "file_name": "test_savitzky_golay_smoothd.py", "file_type": "text/x-python", "category": "test", "start_line": 61, "end_line": 73, "span_ids": ["impl:9", "TestSavitzkyGolaySmoothd", "TestSavitzkyGolaySmoothd.test_value"], "tokens": 146}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestSavitzkyGolaySmoothd(unittest.TestCase):\n    @parameterized.expand(\n        [TEST_CASE_SINGLE_VALUE, TEST_CASE_2D_AXIS_2, TEST_CASE_SINE_SMOOTH, TEST_CASE_SINGLE_VALUE_REP]\n    )\n    def test_value(self, arguments, image, expected_data, atol):\n        for p in TEST_NDARRAYS:\n            result = SavitzkyGolaySmoothd(**arguments)({\"img\": p(image.astype(np.float32))})[\"img\"]\n            torch.testing.assert_allclose(result, p(expected_data.astype(np.float32)), rtol=1e-4, atol=atol)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_scale_intensity_range_percentiles.py_TestScaleIntensityRangePercentiles.test_invalid_instantiation_TestScaleIntensityRangePercentiles.test_invalid_instantiation.None_3": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_scale_intensity_range_percentiles.py_TestScaleIntensityRangePercentiles.test_invalid_instantiation_TestScaleIntensityRangePercentiles.test_invalid_instantiation.None_3", "embedding": null, "metadata": {"file_path": "tests/test_scale_intensity_range_percentiles.py", "file_name": "test_scale_intensity_range_percentiles.py", "file_type": "text/x-python", "category": "test", "start_line": 62, "end_line": 66, "span_ids": ["TestScaleIntensityRangePercentiles.test_invalid_instantiation"], "tokens": 139}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestScaleIntensityRangePercentiles(NumpyImageTestCase2D):\n\n    def test_invalid_instantiation(self):\n        self.assertRaises(ValueError, ScaleIntensityRangePercentiles, lower=-10, upper=99, b_min=0, b_max=255)\n        self.assertRaises(ValueError, ScaleIntensityRangePercentiles, lower=101, upper=99, b_min=0, b_max=255)\n        self.assertRaises(ValueError, ScaleIntensityRangePercentiles, lower=30, upper=-20, b_min=0, b_max=255)\n        self.assertRaises(ValueError, ScaleIntensityRangePercentiles, lower=30, upper=900, b_min=0, b_max=255)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_scale_intensity_range_percentiles.py_TestScaleIntensityRangePercentiles.test_channel_wise_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_scale_intensity_range_percentiles.py_TestScaleIntensityRangePercentiles.test_channel_wise_", "embedding": null, "metadata": {"file_path": "tests/test_scale_intensity_range_percentiles.py", "file_name": "test_scale_intensity_range_percentiles.py", "file_type": "text/x-python", "category": "test", "start_line": 68, "end_line": 91, "span_ids": ["TestScaleIntensityRangePercentiles.test_channel_wise", "impl"], "tokens": 216}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestScaleIntensityRangePercentiles(NumpyImageTestCase2D):\n\n    def test_channel_wise(self):\n        img = self.imt[0]\n        lower = 10\n        upper = 99\n        b_min = 0\n        b_max = 255\n        scaler = ScaleIntensityRangePercentiles(\n            lower=lower, upper=upper, b_min=b_min, b_max=b_max, channel_wise=True, dtype=np.uint8\n        )\n        expected = []\n        for c in img:\n            a_min = np.percentile(c, lower)\n            a_max = np.percentile(c, upper)\n            expected.append(((c - a_min) / (a_max - a_min)) * (b_max - b_min) + b_min)\n        expected = np.stack(expected).astype(np.uint8)\n\n        for p in TEST_NDARRAYS:\n            result = scaler(p(img))\n            assert_allclose(result, p(expected), rtol=1e-4)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_scale_intensity_range_percentilesd.py_TestScaleIntensityRangePercentilesd.test_invalid_instantiation_TestScaleIntensityRangePercentilesd.test_invalid_instantiation.with_self_assertRaises_Va.s_self_imt_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_scale_intensity_range_percentilesd.py_TestScaleIntensityRangePercentilesd.test_invalid_instantiation_TestScaleIntensityRangePercentilesd.test_invalid_instantiation.with_self_assertRaises_Va.s_self_imt_", "embedding": null, "metadata": {"file_path": "tests/test_scale_intensity_range_percentilesd.py", "file_name": "test_scale_intensity_range_percentilesd.py", "file_type": "text/x-python", "category": "test", "start_line": 60, "end_line": 75, "span_ids": ["TestScaleIntensityRangePercentilesd.test_invalid_instantiation"], "tokens": 222}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestScaleIntensityRangePercentilesd(NumpyImageTestCase2D):\n\n    def test_invalid_instantiation(self):\n        self.assertRaises(\n            ValueError, ScaleIntensityRangePercentilesd, keys=[\"img\"], lower=-1, upper=99, b_min=0, b_max=255\n        )\n        self.assertRaises(\n            ValueError, ScaleIntensityRangePercentilesd, keys=[\"img\"], lower=101, upper=99, b_min=0, b_max=255\n        )\n        self.assertRaises(\n            ValueError, ScaleIntensityRangePercentilesd, keys=[\"img\"], lower=30, upper=-2, b_min=0, b_max=255\n        )\n        self.assertRaises(\n            ValueError, ScaleIntensityRangePercentilesd, keys=[\"img\"], lower=30, upper=1000, b_min=0, b_max=255\n        )\n        with self.assertRaises(ValueError):\n            s = ScaleIntensityRangePercentilesd(keys=[\"img\"], lower=30, upper=90, b_min=None, b_max=20, relative=True)\n            s(self.imt)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_scale_intensity_range_percentilesd.py_TestScaleIntensityRangePercentilesd.test_channel_wise_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_scale_intensity_range_percentilesd.py_TestScaleIntensityRangePercentilesd.test_channel_wise_", "embedding": null, "metadata": {"file_path": "tests/test_scale_intensity_range_percentilesd.py", "file_name": "test_scale_intensity_range_percentilesd.py", "file_type": "text/x-python", "category": "test", "start_line": 77, "end_line": 100, "span_ids": ["TestScaleIntensityRangePercentilesd.test_channel_wise", "impl"], "tokens": 226}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestScaleIntensityRangePercentilesd(NumpyImageTestCase2D):\n\n    def test_channel_wise(self):\n        img = self.imt\n        lower = 10\n        upper = 99\n        b_min = 0\n        b_max = 255\n        scaler = ScaleIntensityRangePercentilesd(\n            keys=\"img\", lower=lower, upper=upper, b_min=b_min, b_max=b_max, channel_wise=True, dtype=np.uint8\n        )\n        expected = []\n        for c in img:\n            a_min = np.percentile(c, lower)\n            a_max = np.percentile(c, upper)\n            expected.append((((c - a_min) / (a_max - a_min)) * (b_max - b_min) + b_min).astype(np.uint8))\n        expected = np.stack(expected)\n\n        for p in TEST_NDARRAYS:\n            data = {\"img\": p(img)}\n            assert_allclose(scaler(data)[\"img\"], p(expected), rtol=1e-4)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_state_cacher.py_pickle_TEST_CASES._TEST_CASE_0_TEST_CASE_1": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_state_cacher.py_pickle_TEST_CASES._TEST_CASE_0_TEST_CASE_1", "embedding": null, "metadata": {"file_path": "tests/test_state_cacher.py", "file_name": "test_state_cacher.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 33, "span_ids": ["docstring"], "tokens": 216}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import pickle\nimport unittest\nfrom os.path import exists, join\nfrom pathlib import Path\nfrom tempfile import gettempdir\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.utils import StateCacher\n\nDEVICE = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n\nTEST_CASE_0 = [torch.Tensor([1]).to(DEVICE), {\"in_memory\": True}]\nTEST_CASE_1 = [\n    torch.Tensor([1]).to(DEVICE),\n    {\"in_memory\": False, \"cache_dir\": gettempdir(), \"pickle_module\": None, \"pickle_protocol\": pickle.HIGHEST_PROTOCOL},\n]\nTEST_CASE_2 = [torch.Tensor([1]).to(DEVICE), {\"in_memory\": False, \"allow_overwrite\": False}]\nTEST_CASE_3 = [torch.Tensor([1]).to(DEVICE), {\"in_memory\": False, \"cache_dir\": Path(gettempdir())}]\n\nTEST_CASES = [TEST_CASE_0, TEST_CASE_1, TEST_CASE_2, TEST_CASE_3]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_to_cupyd.py_unittest_TestToCupyd.test_tensor_input.cp_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_to_cupyd.py_unittest_TestToCupyd.test_tensor_input.cp_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_to_cupyd.py", "file_name": "test_to_cupyd.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 52, "span_ids": ["TestToCupyd.test_tensor_input", "TestToCupyd", "docstring", "TestToCupyd.test_numpy_input", "TestToCupyd.test_cupy_input"], "tokens": 380}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom unittest import skipUnless\n\nimport numpy as np\nimport torch\n\nfrom monai.transforms import ToCupyd\nfrom monai.utils import optional_import\nfrom tests.utils import HAS_CUPY, skip_if_no_cuda\n\ncp, _ = optional_import(\"cupy\")\n\n\n@skipUnless(HAS_CUPY, \"CuPy is required.\")\nclass TestToCupyd(unittest.TestCase):\n    def test_cupy_input(self):\n        test_data = cp.array([[1, 2], [3, 4]])\n        test_data = cp.rot90(test_data)\n        self.assertFalse(test_data.flags[\"C_CONTIGUOUS\"])\n        result = ToCupyd(keys=\"img\")({\"img\": test_data})[\"img\"]\n        self.assertTrue(isinstance(result, cp.ndarray))\n        self.assertTrue(result.flags[\"C_CONTIGUOUS\"])\n        cp.testing.assert_allclose(result, test_data)\n\n    def test_numpy_input(self):\n        test_data = np.array([[1, 2], [3, 4]])\n        test_data = np.rot90(test_data)\n        self.assertFalse(test_data.flags[\"C_CONTIGUOUS\"])\n        result = ToCupyd(keys=\"img\")({\"img\": test_data})[\"img\"]\n        self.assertTrue(isinstance(result, cp.ndarray))\n        self.assertTrue(result.flags[\"C_CONTIGUOUS\"])\n        cp.testing.assert_allclose(result, test_data)\n\n    def test_tensor_input(self):\n        test_data = torch.tensor([[1, 2], [3, 4]])\n        test_data = test_data.rot90()\n        self.assertFalse(test_data.is_contiguous())\n        result = ToCupyd(keys=\"img\")({\"img\": test_data})[\"img\"]\n        self.assertTrue(isinstance(result, cp.ndarray))\n        self.assertTrue(result.flags[\"C_CONTIGUOUS\"])\n        cp.testing.assert_allclose(result, test_data.numpy())", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_to_numpy.py_unittest_TestToNumpy.test_cupy_input.assert_allclose_result_t": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_to_numpy.py_unittest_TestToNumpy.test_cupy_input.assert_allclose_result_t", "embedding": null, "metadata": {"file_path": "tests/test_to_numpy.py", "file_name": "test_to_numpy.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 34, "span_ids": ["TestToNumpy", "TestToNumpy.test_cupy_input", "docstring"], "tokens": 180}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom unittest import skipUnless\n\nimport numpy as np\nimport torch\n\nfrom monai.transforms import ToNumpy\nfrom monai.utils import optional_import\nfrom tests.utils import HAS_CUPY, assert_allclose, skip_if_no_cuda\n\ncp, _ = optional_import(\"cupy\")\n\n\nclass TestToNumpy(unittest.TestCase):\n    @skipUnless(HAS_CUPY, \"CuPy is required.\")\n    def test_cupy_input(self):\n        test_data = cp.array([[1, 2], [3, 4]])\n        test_data = cp.rot90(test_data)\n        self.assertFalse(test_data.flags[\"C_CONTIGUOUS\"])\n        result = ToNumpy()(test_data)\n        self.assertTrue(isinstance(result, np.ndarray))\n        self.assertTrue(result.flags[\"C_CONTIGUOUS\"])\n        assert_allclose(result, test_data.get(), type_test=False)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_to_numpy.py_TestToNumpy.test_numpy_input_TestToNumpy.test_tensor_cuda_input.assert_allclose_result_t": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_to_numpy.py_TestToNumpy.test_numpy_input_TestToNumpy.test_tensor_cuda_input.assert_allclose_result_t", "embedding": null, "metadata": {"file_path": "tests/test_to_numpy.py", "file_name": "test_to_numpy.py", "file_type": "text/x-python", "category": "test", "start_line": 36, "end_line": 63, "span_ids": ["TestToNumpy.test_tensor_input", "TestToNumpy.test_tensor_cuda_input", "TestToNumpy.test_numpy_input"], "tokens": 303}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestToNumpy(unittest.TestCase):\n\n    def test_numpy_input(self):\n        test_data = np.array([[1, 2], [3, 4]])\n        test_data = np.rot90(test_data)\n        self.assertFalse(test_data.flags[\"C_CONTIGUOUS\"])\n        result = ToNumpy(dtype=\"float32\")(test_data)\n        self.assertTrue(isinstance(result, np.ndarray))\n        self.assertTrue(result.dtype == np.float32)\n        self.assertTrue(result.flags[\"C_CONTIGUOUS\"])\n        assert_allclose(result, test_data, type_test=False)\n\n    def test_tensor_input(self):\n        test_data = torch.tensor([[1, 2], [3, 4]])\n        test_data = test_data.rot90()\n        self.assertFalse(test_data.is_contiguous())\n        result = ToNumpy(dtype=torch.uint8)(test_data)\n        self.assertTrue(isinstance(result, np.ndarray))\n        self.assertTrue(result.flags[\"C_CONTIGUOUS\"])\n        assert_allclose(result, test_data, type_test=False)\n\n    @skip_if_no_cuda\n    def test_tensor_cuda_input(self):\n        test_data = torch.tensor([[1, 2], [3, 4]]).cuda()\n        test_data = test_data.rot90()\n        self.assertFalse(test_data.is_contiguous())\n        result = ToNumpy()(test_data)\n        self.assertTrue(isinstance(result, np.ndarray))\n        self.assertTrue(result.flags[\"C_CONTIGUOUS\"])\n        assert_allclose(result, test_data, type_test=False)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_torchscript_utils.py_os_TestTorchscript.test_save_net_with_metadata_with_extra.with_tempfile_TemporaryDi.self_assertTrue_os_path_i": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_torchscript_utils.py_os_TestTorchscript.test_save_net_with_metadata_with_extra.with_tempfile_TemporaryDi.self_assertTrue_os_path_i", "embedding": null, "metadata": {"file_path": "tests/test_torchscript_utils.py", "file_name": "test_torchscript_utils.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 57, "span_ids": ["TestTorchscript.test_save_net_with_metadata_ext", "TestModule.forward", "TestModule", "TestTorchscript.test_save_net_with_metadata_with_extra", "TestTorchscript", "TestTorchscript.test_save_net_with_metadata", "docstring"], "tokens": 320}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport tempfile\nimport unittest\n\nimport torch\n\nfrom monai.config import get_config_values\nfrom monai.data import load_net_with_metadata, save_net_with_metadata\nfrom monai.utils import JITMetadataKeys\nfrom monai.utils.module import pytorch_after\n\n\nclass TestModule(torch.nn.Module):\n    def forward(self, x):\n        return x + 10\n\n\nclass TestTorchscript(unittest.TestCase):\n    def test_save_net_with_metadata(self):\n        \"\"\"Save a network without metadata to a file.\"\"\"\n        m = torch.jit.script(TestModule())\n\n        with tempfile.TemporaryDirectory() as tempdir:\n            save_net_with_metadata(m, f\"{tempdir}/test\")\n\n            self.assertTrue(os.path.isfile(f\"{tempdir}/test.pt\"))\n\n    def test_save_net_with_metadata_ext(self):\n        \"\"\"Save a network without metadata to a file.\"\"\"\n        m = torch.jit.script(TestModule())\n\n        with tempfile.TemporaryDirectory() as tempdir:\n            save_net_with_metadata(m, f\"{tempdir}/test.zip\")\n\n            self.assertTrue(os.path.isfile(f\"{tempdir}/test.zip\"))\n\n    def test_save_net_with_metadata_with_extra(self):\n        \"\"\"Save a network with simple metadata to a file.\"\"\"\n        m = torch.jit.script(TestModule())\n\n        test_metadata = {\"foo\": [1, 2], \"bar\": \"string\"}\n\n        with tempfile.TemporaryDirectory() as tempdir:\n            save_net_with_metadata(m, f\"{tempdir}/test\", meta_values=test_metadata)\n\n            self.assertTrue(os.path.isfile(f\"{tempdir}/test.pt\"))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_torchscript_utils.py_TestTorchscript.test_load_net_with_metadata_TestTorchscript.test_load_net_with_metadata.self_assertEqual_extra_fi": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_torchscript_utils.py_TestTorchscript.test_load_net_with_metadata_TestTorchscript.test_load_net_with_metadata.self_assertEqual_extra_fi", "embedding": null, "metadata": {"file_path": "tests/test_torchscript_utils.py", "file_name": "test_torchscript_utils.py", "file_type": "text/x-python", "category": "test", "start_line": 59, "end_line": 70, "span_ids": ["TestTorchscript.test_load_net_with_metadata"], "tokens": 126}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestTorchscript(unittest.TestCase):\n\n    def test_load_net_with_metadata(self):\n        \"\"\"Save then load a network with no metadata or other extra files.\"\"\"\n        m = torch.jit.script(TestModule())\n\n        with tempfile.TemporaryDirectory() as tempdir:\n            save_net_with_metadata(m, f\"{tempdir}/test\")\n            _, meta, extra_files = load_net_with_metadata(f\"{tempdir}/test.pt\")\n\n        del meta[JITMetadataKeys.TIMESTAMP.value]  # no way of knowing precisely what this value would be\n\n        self.assertEqual(meta, get_config_values())\n        self.assertEqual(extra_files, {})", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_torchscript_utils.py_TestTorchscript.test_load_net_with_metadata_with_extra_TestTorchscript.test_load_net_with_metadata_with_extra.self_assertEqual_extra_fi": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_torchscript_utils.py_TestTorchscript.test_load_net_with_metadata_with_extra_TestTorchscript.test_load_net_with_metadata_with_extra.self_assertEqual_extra_fi", "embedding": null, "metadata": {"file_path": "tests/test_torchscript_utils.py", "file_name": "test_torchscript_utils.py", "file_type": "text/x-python", "category": "test", "start_line": 72, "end_line": 88, "span_ids": ["TestTorchscript.test_load_net_with_metadata_with_extra"], "tokens": 162}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestTorchscript(unittest.TestCase):\n\n    def test_load_net_with_metadata_with_extra(self):\n        \"\"\"Save then load a network with basic metadata.\"\"\"\n        m = torch.jit.script(TestModule())\n\n        test_metadata = {\"foo\": [1, 2], \"bar\": \"string\"}\n\n        with tempfile.TemporaryDirectory() as tempdir:\n            save_net_with_metadata(m, f\"{tempdir}/test\", meta_values=test_metadata)\n            _, meta, extra_files = load_net_with_metadata(f\"{tempdir}/test.pt\")\n\n        del meta[JITMetadataKeys.TIMESTAMP.value]  # no way of knowing precisely what this value would be\n\n        test_compare = get_config_values()\n        test_compare.update(test_metadata)\n\n        self.assertEqual(meta, test_compare)\n        self.assertEqual(extra_files, {})", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_torchscript_utils.py_TestTorchscript.test_save_load_more_extra_files_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_torchscript_utils.py_TestTorchscript.test_save_load_more_extra_files_", "embedding": null, "metadata": {"file_path": "tests/test_torchscript_utils.py", "file_name": "test_torchscript_utils.py", "file_type": "text/x-python", "category": "test", "start_line": 90, "end_line": 113, "span_ids": ["TestTorchscript.test_save_load_more_extra_files", "impl"], "tokens": 220}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestTorchscript(unittest.TestCase):\n\n    def test_save_load_more_extra_files(self):\n        \"\"\"Save then load extra file data from a torchscript file.\"\"\"\n        m = torch.jit.script(TestModule())\n\n        test_metadata = {\"foo\": [1, 2], \"bar\": \"string\"}\n\n        more_extra_files = {\"test.txt\": b\"This is test data\"}\n\n        with tempfile.TemporaryDirectory() as tempdir:\n            save_net_with_metadata(m, f\"{tempdir}/test\", meta_values=test_metadata, more_extra_files=more_extra_files)\n\n            self.assertTrue(os.path.isfile(f\"{tempdir}/test.pt\"))\n\n            _, _, loaded_extra_files = load_net_with_metadata(f\"{tempdir}/test.pt\", more_extra_files=(\"test.txt\",))\n\n            if pytorch_after(1, 7):\n                self.assertEqual(more_extra_files[\"test.txt\"], loaded_extra_files[\"test.txt\"])\n            else:\n                self.assertEqual(more_extra_files[\"test.txt\"].decode(), loaded_extra_files[\"test.txt\"])\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_utils_pytorch_numpy_unification.py_unittest_TestPytorchNumpyUnification.test_percentile.for_size_in_1_100_.for_p_in_TEST_NDARRAYS_.assert_allclose_results_0": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_utils_pytorch_numpy_unification.py_unittest_TestPytorchNumpyUnification.test_percentile.for_size_in_1_100_.for_p_in_TEST_NDARRAYS_.assert_allclose_results_0", "embedding": null, "metadata": {"file_path": "tests/test_utils_pytorch_numpy_unification.py", "file_name": "test_utils_pytorch_numpy_unification.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 36, "span_ids": ["TestPytorchNumpyUnification.test_percentile", "TestPytorchNumpyUnification", "TestPytorchNumpyUnification.setUp", "docstring"], "tokens": 244}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\n\nfrom monai.transforms.utils_pytorch_numpy_unification import percentile\nfrom monai.utils import set_determinism\nfrom tests.utils import TEST_NDARRAYS, SkipIfBeforePyTorchVersion, assert_allclose\n\n\nclass TestPytorchNumpyUnification(unittest.TestCase):\n    def setUp(self) -> None:\n        set_determinism(0)\n\n    def test_percentile(self):\n        for size in (1, 100):\n            q = np.random.randint(0, 100, size=size)\n            results = []\n            for p in TEST_NDARRAYS:\n                arr = p(np.arange(100 * 101).reshape(1, 100, 101).astype(np.float32))\n                results.append(percentile(arr, q))\n                # pre torch 1.7, no `quantile`. Our own method doesn't interpolate,\n                # so we can only be accurate to 0.5\n                atol = 0.5 if not hasattr(torch, \"quantile\") else 1e-4\n                assert_allclose(results[0], results[-1], type_test=False, atol=atol)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_utils_pytorch_numpy_unification.py_TestPytorchNumpyUnification.test_fails_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_utils_pytorch_numpy_unification.py_TestPytorchNumpyUnification.test_fails_", "embedding": null, "metadata": {"file_path": "tests/test_utils_pytorch_numpy_unification.py", "file_name": "test_utils_pytorch_numpy_unification.py", "file_type": "text/x-python", "category": "test", "start_line": 38, "end_line": 60, "span_ids": ["TestPytorchNumpyUnification.test_fails", "TestPytorchNumpyUnification.test_dim", "impl"], "tokens": 256}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestPytorchNumpyUnification(unittest.TestCase):\n\n    def test_fails(self):\n        for p in TEST_NDARRAYS:\n            for q in (-1, 101):\n                arr = p(np.arange(100 * 101).reshape(1, 100, 101).astype(np.float32))\n                with self.assertRaises(ValueError):\n                    percentile(arr, q)\n\n    @SkipIfBeforePyTorchVersion((1, 7))\n    def test_dim(self):\n        q = np.random.randint(0, 100, size=50)\n        results = []\n        for p in TEST_NDARRAYS:\n            arr = p(np.arange(6).reshape(1, 2, 3).astype(np.float32))\n            results.append(percentile(arr, q, dim=1))\n            # pre torch 1.7, no `quantile`. Our own method doesn't interpolate,\n            # so we can only be accurate to 0.5\n            atol = 0.5 if not hasattr(torch, \"quantile\") else 1e-4\n            assert_allclose(results[0], results[-1], type_test=False, atol=atol)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_vitautoenc.py_unittest_for_in_channels_in_1_4_.for_img_size_in_64_96_.for_patch_size_in_16_.for_pos_embed_in_conv_.for_nd_in_2_3_.TEST_CASE_Vitautoenc_appe": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_vitautoenc.py_unittest_for_in_channels_in_1_4_.for_img_size_in_64_96_.for_patch_size_in_16_.for_pos_embed_in_conv_.for_nd_in_2_3_.TEST_CASE_Vitautoenc_appe", "embedding": null, "metadata": {"file_path": "tests/test_vitautoenc.py", "file_name": "test_vitautoenc.py", "file_type": "text/x-python", "category": "test", "start_line": 11, "end_line": 42, "span_ids": ["docstring"], "tokens": 246}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.networks import eval_mode\nfrom monai.networks.nets.vitautoenc import ViTAutoEnc\n\nTEST_CASE_Vitautoenc = []\nfor in_channels in [1, 4]:\n    for img_size in [64, 96, 128]:\n        for patch_size in [16]:\n            for pos_embed in [\"conv\", \"perceptron\"]:\n                for nd in [2, 3]:\n                    test_case = [\n                        {\n                            \"in_channels\": in_channels,\n                            \"img_size\": (img_size,) * nd,\n                            \"patch_size\": (patch_size,) * nd,\n                            \"hidden_size\": 768,\n                            \"mlp_dim\": 3072,\n                            \"num_layers\": 4,\n                            \"num_heads\": 12,\n                            \"pos_embed\": pos_embed,\n                            \"dropout_rate\": 0.6,\n                            \"spatial_dims\": nd,\n                        },\n                        (2, in_channels, *([img_size] * nd)),\n                        (2, 1, *([img_size] * nd)),\n                    ]\n\n                    TEST_CASE_Vitautoenc.append(test_case)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_wsireader.py_os_TEST_CASE_5._": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_wsireader.py_os_TEST_CASE_5._", "embedding": null, "metadata": {"file_path": "tests/test_wsireader.py", "file_name": "test_wsireader.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 78, "span_ids": ["impl:24", "docstring"], "tokens": 759}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import os\nimport unittest\nfrom unittest import skipUnless\n\nimport numpy as np\nimport torch\nfrom numpy.testing import assert_array_equal\nfrom parameterized import parameterized\n\nfrom monai.apps.utils import download_url\nfrom monai.data import DataLoader, Dataset\nfrom monai.data.image_reader import WSIReader\nfrom monai.transforms import Compose, LoadImaged, ToTensord\nfrom monai.utils import first, optional_import\n\ncucim, has_cucim = optional_import(\"cucim\")\nhas_cucim = has_cucim and hasattr(cucim, \"CuImage\")\n_, has_osl = optional_import(\"openslide\")\nimsave, has_tiff = optional_import(\"tifffile\", name=\"imsave\")\n_, has_codec = optional_import(\"imagecodecs\")\nhas_tiff = has_tiff and has_codec\n\nFILE_URL = \"https://drive.google.com/uc?id=1sGTKZlJBIz53pfqTxoTqiIQzIoEzHLAe\"\nbase_name, extension = FILE_URL.split(\"id=\")[1], \".tiff\"\nFILE_PATH = os.path.join(os.path.dirname(__file__), \"testing_data\", \"temp_\" + base_name + extension)\n\nHEIGHT = 32914\nWIDTH = 46000\n\nTEST_CASE_0 = [FILE_PATH, 2, (3, HEIGHT // 4, WIDTH // 4)]\n\nTEST_CASE_TRANSFORM_0 = [FILE_PATH, 4, (HEIGHT // 16, WIDTH // 16), (1, 3, HEIGHT // 16, WIDTH // 16)]\n\nTEST_CASE_1 = [\n    FILE_PATH,\n    {\"location\": (HEIGHT // 2, WIDTH // 2), \"size\": (2, 1), \"level\": 0},\n    np.array([[[246], [246]], [[246], [246]], [[246], [246]]]),\n]\n\nTEST_CASE_2 = [\n    FILE_PATH,\n    {\"location\": (0, 0), \"size\": (2, 1), \"level\": 2},\n    np.array([[[239], [239]], [[239], [239]], [[239], [239]]]),\n]\n\nTEST_CASE_3 = [\n    FILE_PATH,\n    {\"location\": (0, 0), \"size\": (8, 8), \"level\": 2, \"grid_shape\": (2, 1), \"patch_size\": 2},\n    np.array(\n        [\n            [[[239, 239], [239, 239]], [[239, 239], [239, 239]], [[239, 239], [239, 239]]],\n            [[[242, 242], [242, 243]], [[242, 242], [242, 243]], [[242, 242], [242, 243]]],\n        ]\n    ),\n]\n\nTEST_CASE_4 = [\n    FILE_PATH,\n    {\"location\": (0, 0), \"size\": (8, 8), \"level\": 2, \"grid_shape\": (2, 1), \"patch_size\": 1},\n    np.array([[[[239]], [[239]], [[239]]], [[[243]], [[243]], [[243]]]]),\n]\n\nTEST_CASE_5 = [\n    FILE_PATH,\n    {\"location\": (HEIGHT - 2, WIDTH - 2), \"level\": 0, \"grid_shape\": (1, 1)},\n    np.array([[[239, 239], [239, 239]], [[239, 239], [239, 239]], [[237, 237], [237, 237]]]),\n]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_wsireader.py_TEST_CASE_RGB_0_setUpModule.download_url_FILE_URL_FI": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_wsireader.py_TEST_CASE_RGB_0_setUpModule.download_url_FILE_URL_FI", "embedding": null, "metadata": {"file_path": "tests/test_wsireader.py", "file_name": "test_wsireader.py", "file_type": "text/x-python", "category": "test", "start_line": 81, "end_line": 106, "span_ids": ["impl:24", "setUpModule", "save_rgba_tiff"], "tokens": 299}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "TEST_CASE_RGB_0 = [np.ones((3, 2, 2), dtype=np.uint8)]  # CHW\n\nTEST_CASE_RGB_1 = [np.ones((3, 100, 100), dtype=np.uint8)]  # CHW\n\n\ndef save_rgba_tiff(array: np.ndarray, filename: str, mode: str):\n    \"\"\"\n    Save numpy array into a TIFF RGB/RGBA file\n\n    Args:\n        array: numpy ndarray with the shape of CxHxW and C==3 representing a RGB image\n        file_prefix: the filename to be used for the tiff file. '_RGB.tiff' or '_RGBA.tiff' will be appended to this filename.\n        mode: RGB or RGBA\n    \"\"\"\n    if mode == \"RGBA\":\n        array = np.concatenate([array, 255 * np.ones_like(array[0])[np.newaxis]]).astype(np.uint8)\n\n    img_rgb = array.transpose(1, 2, 0)\n    imsave(filename, img_rgb, shape=img_rgb.shape, tile=(16, 16))\n\n    return filename\n\n\n@skipUnless(has_cucim or has_osl or has_tiff, \"Requires cucim, openslide, or tifffile!\")\ndef setUpModule():  # noqa: N802\n    download_url(FILE_URL, FILE_PATH, \"5a3cfd4fd725c50578ddb80b517b759f\")", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_wsireader.py_WSIReaderTests_WSIReaderTests.Tests.test_read_region.with_reader_read_file_pat.if_self_backend_tifff.else_.None_3": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_wsireader.py_WSIReaderTests_WSIReaderTests.Tests.test_read_region.with_reader_read_file_pat.if_self_backend_tifff.else_.None_3", "embedding": null, "metadata": {"file_path": "tests/test_wsireader.py", "file_name": "test_wsireader.py", "file_type": "text/x-python", "category": "test", "start_line": 109, "end_line": 134, "span_ids": ["WSIReaderTests.Tests.test_read_whole_image", "WSIReaderTests.Tests", "WSIReaderTests", "WSIReaderTests.Tests.test_read_region"], "tokens": 268}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class WSIReaderTests:\n    class Tests(unittest.TestCase):\n        backend = None\n\n        @parameterized.expand([TEST_CASE_0])\n        def test_read_whole_image(self, file_path, level, expected_shape):\n            reader = WSIReader(self.backend, level=level)\n            with reader.read(file_path) as img_obj:\n                img = reader.get_data(img_obj)[0]\n            self.assertTupleEqual(img.shape, expected_shape)\n\n        @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_5])\n        def test_read_region(self, file_path, patch_info, expected_img):\n            reader = WSIReader(self.backend)\n            with reader.read(file_path) as img_obj:\n                if self.backend == \"tifffile\":\n                    with self.assertRaises(ValueError):\n                        reader.get_data(img_obj, **patch_info)[0]\n                else:\n                    # Read twice to check multiple calls\n                    img = reader.get_data(img_obj, **patch_info)[0]\n                    img2 = reader.get_data(img_obj, **patch_info)[0]\n                    self.assertTupleEqual(img.shape, img2.shape)\n                    self.assertIsNone(assert_array_equal(img, img2))\n                    self.assertTupleEqual(img.shape, expected_img.shape)\n                    self.assertIsNone(assert_array_equal(img, expected_img))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_wsireader.py_WSIReaderTests.Tests.test_read_patches_WSIReaderTests.Tests.test_read_patches.with_reader_read_file_pat.if_self_backend_tifff.else_.self_assertIsNone_assert_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_wsireader.py_WSIReaderTests.Tests.test_read_patches_WSIReaderTests.Tests.test_read_patches.with_reader_read_file_pat.if_self_backend_tifff.else_.self_assertIsNone_assert_", "embedding": null, "metadata": {"file_path": "tests/test_wsireader.py", "file_name": "test_wsireader.py", "file_type": "text/x-python", "category": "test", "start_line": 136, "end_line": 146, "span_ids": ["WSIReaderTests.Tests.test_read_patches"], "tokens": 135}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class WSIReaderTests:\n    class Tests(unittest.TestCase):\n\n        @parameterized.expand([TEST_CASE_3, TEST_CASE_4])\n        def test_read_patches(self, file_path, patch_info, expected_img):\n            reader = WSIReader(self.backend)\n            with reader.read(file_path) as img_obj:\n                if self.backend == \"tifffile\":\n                    with self.assertRaises(ValueError):\n                        reader.get_data(img_obj, **patch_info)[0]\n                else:\n                    img = reader.get_data(img_obj, **patch_info)[0]\n                    self.assertTupleEqual(img.shape, expected_img.shape)\n                    self.assertIsNone(assert_array_equal(img, expected_img))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_wsireader.py_WSIReaderTests.Tests.test_read_rgba_WSIReaderTests.Tests.test_read_rgba.None_1": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_wsireader.py_WSIReaderTests.Tests.test_read_rgba_WSIReaderTests.Tests.test_read_rgba.None_1", "embedding": null, "metadata": {"file_path": "tests/test_wsireader.py", "file_name": "test_wsireader.py", "file_type": "text/x-python", "category": "test", "start_line": 148, "end_line": 166, "span_ids": ["WSIReaderTests.Tests.test_read_rgba"], "tokens": 202}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class WSIReaderTests:\n    class Tests(unittest.TestCase):\n\n        @parameterized.expand([TEST_CASE_RGB_0, TEST_CASE_RGB_1])\n        @skipUnless(has_tiff, \"Requires tifffile.\")\n        def test_read_rgba(self, img_expected):\n            # skip for OpenSlide since not working with images without tiles\n            if self.backend == \"openslide\":\n                return\n            image = {}\n            reader = WSIReader(self.backend)\n            for mode in [\"RGB\", \"RGBA\"]:\n                file_path = save_rgba_tiff(\n                    img_expected,\n                    os.path.join(os.path.dirname(__file__), \"testing_data\", f\"temp_tiff_image_{mode}.tiff\"),\n                    mode=mode,\n                )\n                with reader.read(file_path) as img_obj:\n                    image[mode], _ = reader.get_data(img_obj)\n\n            self.assertIsNone(assert_array_equal(image[\"RGB\"], img_expected))\n            self.assertIsNone(assert_array_equal(image[\"RGBA\"], img_expected))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/utils.py_has_cupy_has_cupy.try_.except_Exception_.return.False": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/utils.py_has_cupy_has_cupy.try_.except_Exception_.return.False", "embedding": null, "metadata": {"file_path": "tests/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 217, "end_line": 232, "span_ids": ["has_cupy"], "tokens": 171}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def has_cupy():\n    \"\"\"\n    Returns True if the user has installed a version of cupy.\n    \"\"\"\n    cp, has_cp = optional_import(\"cupy\")\n    if not has_cp:\n        return False\n    try:  # test cupy installation with a basic example\n        x = cp.arange(6, dtype=\"f\").reshape(2, 3)\n        y = cp.arange(3, dtype=\"f\")\n        kernel = cp.ElementwiseKernel(\n            \"float32 x, float32 y\", \"float32 z\", \"\"\" if (x - 2 > y) { z = x * y; } else { z = x + y; } \"\"\", \"my_kernel\"\n        )\n        return kernel(x, y)[0, 0] == 0\n    except Exception:\n        return False", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/utils.py_HAS_CUPY_make_nifti_image.return.image_name": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/utils.py_HAS_CUPY_make_nifti_image.return.image_name", "embedding": null, "metadata": {"file_path": "tests/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 235, "end_line": 254, "span_ids": ["make_nifti_image", "impl:6"], "tokens": 170}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "HAS_CUPY = has_cupy()\n\n\ndef make_nifti_image(array: NdarrayOrTensor, affine=None):\n    \"\"\"\n    Create a temporary nifti image on the disk and return the image name.\n    User is responsible for deleting the temporary file when done with it.\n    \"\"\"\n    if isinstance(array, torch.Tensor):\n        array, *_ = convert_data_type(array, np.ndarray)\n    if isinstance(affine, torch.Tensor):\n        affine, *_ = convert_data_type(affine, np.ndarray)\n    if affine is None:\n        affine = np.eye(4)\n    test_image = nib.Nifti1Image(array, affine)\n\n    temp_f, image_name = tempfile.mkstemp(suffix=\".nii.gz\")\n    nib.save(test_image, image_name)\n    os.close(temp_f)\n    return image_name", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/smooth_field/array.py_SmoothField_SmoothField.randomize.self_field_self_rand_slic": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/smooth_field/array.py_SmoothField_SmoothField.randomize.self_field_self_rand_slic", "embedding": null, "metadata": {"file_path": "monai/transforms/smooth_field/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 32, "end_line": 94, "span_ids": ["SmoothField.randomize", "SmoothField.__init__", "SmoothField"], "tokens": 642}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SmoothField(Randomizable):\n    \"\"\"\n    Generate a smooth field array by defining a smaller randomized field and then reinterpolating to the desired size.\n\n    This exploits interpolation to create a smoothly varying field used for other applications. An initial randomized\n    field is defined with `rand_size` dimensions with `pad` number of values padding it along each dimension using\n    `pad_val` as the value. If `spatial_size` is given this is interpolated to that size, otherwise if None the random\n    array is produced uninterpolated. The output is always a Pytorch tensor allocated on the specified device.\n\n    Args:\n        rand_size: size of the randomized field to start from\n        pad: number of pixels/voxels along the edges of the field to pad with `pad_val`\n        pad_val: value with which to pad field edges\n        low: low value for randomized field\n        high: high value for randomized field\n        channels: number of channels of final output\n        spatial_size: final output size of the array, None to produce original uninterpolated field\n        mode: interpolation mode for resizing the field\n        align_corners: if True align the corners when upsampling field\n        device: Pytorch device to define field on\n    \"\"\"\n\n    def __init__(\n        self,\n        rand_size: Sequence[int],\n        pad: int = 0,\n        pad_val: float = 0,\n        low: float = -1.0,\n        high: float = 1.0,\n        channels: int = 1,\n        spatial_size: Optional[Sequence[int]] = None,\n        mode: Union[InterpolateMode, str] = InterpolateMode.AREA,\n        align_corners: Optional[bool] = None,\n        device: Optional[torch.device] = None,\n    ):\n        self.rand_size = tuple(rand_size)\n        self.pad = pad\n        self.low = low\n        self.high = high\n        self.channels = channels\n        self.mode = mode\n        self.align_corners = align_corners\n        self.device = device\n\n        self.spatial_size: Optional[Sequence[int]] = None\n        self.spatial_zoom: Optional[Sequence[float]] = None\n\n        if low >= high:\n            raise ValueError(\"Value for `low` must be less than `high` otherwise field will be zeros\")\n\n        self.total_rand_size = tuple(rs + self.pad * 2 for rs in self.rand_size)\n\n        self.field = torch.ones((1, self.channels) + self.total_rand_size, device=self.device) * pad_val\n\n        self.crand_size = (self.channels,) + self.rand_size\n\n        pad_slice = slice(None) if self.pad == 0 else slice(self.pad, -self.pad)\n        self.rand_slices = (0, slice(None)) + (pad_slice,) * len(self.rand_size)\n\n        self.set_spatial_size(spatial_size)\n\n    def randomize(self, data: Optional[Any] = None) -> None:\n        self.field[self.rand_slices] = torch.from_numpy(self.R.uniform(self.low, self.high, self.crand_size))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/smooth_field/array.py_SmoothField.set_spatial_size_SmoothField.set_mode.self.mode.mode": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/smooth_field/array.py_SmoothField.set_spatial_size_SmoothField.set_mode.self.mode.mode", "embedding": null, "metadata": {"file_path": "monai/transforms/smooth_field/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 96, "end_line": 112, "span_ids": ["SmoothField.set_mode", "SmoothField.set_spatial_size"], "tokens": 169}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SmoothField(Randomizable):\n\n    def set_spatial_size(self, spatial_size: Optional[Sequence[int]]) -> None:\n        \"\"\"\n        Set the `spatial_size` and `spatial_zoom` attributes used for interpolating the field to the given\n        dimension, or not interpolate at all if None.\n\n        Args:\n            spatial_size: new size to interpolate to, or None to not interpolate\n        \"\"\"\n        if spatial_size is None:\n            self.spatial_size = None\n            self.spatial_zoom = None\n        else:\n            self.spatial_size = tuple(spatial_size)\n            self.spatial_zoom = tuple(s / f for s, f in zip(self.spatial_size, self.total_rand_size))\n\n    def set_mode(self, mode: Union[monai.utils.InterpolateMode, str]) -> None:\n        self.mode = mode", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/smooth_field/array.py_SmoothField.__call___SmoothField.__call__.return.field": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/smooth_field/array.py_SmoothField.__call___SmoothField.__call__.return.field", "embedding": null, "metadata": {"file_path": "monai/transforms/smooth_field/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 114, "end_line": 138, "span_ids": ["SmoothField.__call__"], "tokens": 207}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class SmoothField(Randomizable):\n\n    def __call__(self, randomize=False) -> torch.Tensor:\n        if randomize:\n            self.randomize()\n\n        field = self.field.clone()\n\n        if self.spatial_zoom is not None:\n            resized_field = interpolate(  # type: ignore\n                input=field,  # type: ignore\n                scale_factor=self.spatial_zoom,\n                mode=look_up_option(self.mode, InterpolateMode).value,\n                align_corners=self.align_corners,\n                recompute_scale_factor=False,\n            )\n\n            mina = resized_field.min()\n            maxa = resized_field.max()\n            minv = self.field.min()\n            maxv = self.field.max()\n\n            # faster than rescale_array, this uses in-place operations and doesn't perform unneeded range checks\n            norm_field = (resized_field.squeeze(0) - mina).div_(maxa - mina)\n            field = norm_field.mul_(maxv - minv).add_(minv)\n\n        return field", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/smooth_field/array.py_RandSmoothFieldAdjustContrast_RandSmoothFieldAdjustContrast.set_mode.self_sfield_set_mode_mode": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/smooth_field/array.py_RandSmoothFieldAdjustContrast_RandSmoothFieldAdjustContrast.set_mode.self_sfield_set_mode_mode", "embedding": null, "metadata": {"file_path": "monai/transforms/smooth_field/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 141, "end_line": 212, "span_ids": ["RandSmoothFieldAdjustContrast.__init__", "RandSmoothFieldAdjustContrast.randomize", "RandSmoothFieldAdjustContrast.set_mode", "RandSmoothFieldAdjustContrast.set_random_state", "RandSmoothFieldAdjustContrast"], "tokens": 656}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandSmoothFieldAdjustContrast(RandomizableTransform):\n    \"\"\"\n    Randomly adjust the contrast of input images by calculating a randomized smooth field for each invocation.\n\n    This uses SmoothField internally to define the adjustment over the image. If `pad` is greater than 0 the\n    edges of the input volume of that width will be mostly unchanged. Contrast is changed by raising input\n    values by the power of the smooth field so the range of values given by `gamma` should be chosen with this\n    in mind. For example, a minimum value of 0 in `gamma` will produce white areas so this should be avoided.\n    Afte the contrast is adjusted the values of the result are rescaled to the range of the original input.\n\n    Args:\n        spatial_size: size of input array's spatial dimensions\n        rand_size: size of the randomized field to start from\n        pad: number of pixels/voxels along the edges of the field to pad with 1\n        mode: interpolation mode to use when upsampling\n        align_corners: if True align the corners when upsampling field\n        prob: probability transform is applied\n        gamma: (min, max) range for exponential field\n        device: Pytorch device to define field on\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __init__(\n        self,\n        spatial_size: Sequence[int],\n        rand_size: Sequence[int],\n        pad: int = 0,\n        mode: Union[InterpolateMode, str] = InterpolateMode.AREA,\n        align_corners: Optional[bool] = None,\n        prob: float = 0.1,\n        gamma: Union[Sequence[float], float] = (0.5, 4.5),\n        device: Optional[torch.device] = None,\n    ):\n        super().__init__(prob)\n\n        if isinstance(gamma, (int, float)):\n            self.gamma = (0.5, gamma)\n        else:\n            if len(gamma) != 2:\n                raise ValueError(\"Argument `gamma` should be a number or pair of numbers.\")\n\n            self.gamma = (min(gamma), max(gamma))\n\n        self.sfield = SmoothField(\n            rand_size=rand_size,\n            pad=pad,\n            pad_val=1,\n            low=self.gamma[0],\n            high=self.gamma[1],\n            channels=1,\n            spatial_size=spatial_size,\n            mode=mode,\n            align_corners=align_corners,\n            device=device,\n        )\n\n    def set_random_state(\n        self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None\n    ) -> \"RandSmoothFieldAdjustContrast\":\n        super().set_random_state(seed, state)\n        self.sfield.set_random_state(seed, state)\n        return self\n\n    def randomize(self, data: Optional[Any] = None) -> None:\n        super().randomize(None)\n\n        if self._do_transform:\n            self.sfield.randomize()\n\n    def set_mode(self, mode: Union[monai.utils.InterpolateMode, str]) -> None:\n        self.sfield.set_mode(mode)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/smooth_field/array.py_RandSmoothFieldAdjustIntensity_RandSmoothFieldAdjustIntensity.set_mode.self_sfield_set_mode_mode": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/smooth_field/array.py_RandSmoothFieldAdjustIntensity_RandSmoothFieldAdjustIntensity.set_mode.self_sfield_set_mode_mode", "embedding": null, "metadata": {"file_path": "monai/transforms/smooth_field/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 241, "end_line": 312, "span_ids": ["RandSmoothFieldAdjustIntensity.set_mode", "RandSmoothFieldAdjustIntensity.__init__", "RandSmoothFieldAdjustIntensity.set_random_state", "RandSmoothFieldAdjustIntensity", "RandSmoothFieldAdjustIntensity.randomize"], "tokens": 638}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandSmoothFieldAdjustIntensity(RandomizableTransform):\n    \"\"\"\n    Randomly adjust the intensity of input images by calculating a randomized smooth field for each invocation.\n\n    This uses SmoothField internally to define the adjustment over the image. If `pad` is greater than 0 the\n    edges of the input volume of that width will be mostly unchanged. Intensity is changed by multiplying the\n    inputs by the smooth field, so the values of `gamma` should be chosen with this in mind. The default values\n    of `(0.1, 1.0)` are sensible in that values will not be zeroed out by the field nor multiplied greater than\n    the original value range.\n\n    Args:\n        spatial_size: size of input array\n        rand_size: size of the randomized field to start from\n        pad: number of pixels/voxels along the edges of the field to pad with 1\n        mode: interpolation mode to use when upsampling\n        align_corners: if True align the corners when upsampling field\n        prob: probability transform is applied\n        gamma: (min, max) range of intensity multipliers\n        device: Pytorch device to define field on\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __init__(\n        self,\n        spatial_size: Sequence[int],\n        rand_size: Sequence[int],\n        pad: int = 0,\n        mode: Union[InterpolateMode, str] = InterpolateMode.AREA,\n        align_corners: Optional[bool] = None,\n        prob: float = 0.1,\n        gamma: Union[Sequence[float], float] = (0.1, 1.0),\n        device: Optional[torch.device] = None,\n    ):\n        super().__init__(prob)\n\n        if isinstance(gamma, (int, float)):\n            self.gamma = (0.5, gamma)\n        else:\n            if len(gamma) != 2:\n                raise ValueError(\"Argument `gamma` should be a number or pair of numbers.\")\n\n            self.gamma = (min(gamma), max(gamma))\n\n        self.sfield = SmoothField(\n            rand_size=rand_size,\n            pad=pad,\n            pad_val=1,\n            low=self.gamma[0],\n            high=self.gamma[1],\n            channels=1,\n            spatial_size=spatial_size,\n            mode=mode,\n            align_corners=align_corners,\n            device=device,\n        )\n\n    def set_random_state(\n        self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None\n    ) -> \"RandSmoothFieldAdjustIntensity\":\n        super().set_random_state(seed, state)\n        self.sfield.set_random_state(seed, state)\n        return self\n\n    def randomize(self, data: Optional[Any] = None) -> None:\n        super().randomize(None)\n\n        if self._do_transform:\n            self.sfield.randomize()\n\n    def set_mode(self, mode: Union[InterpolateMode, str]) -> None:\n        self.sfield.set_mode(mode)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/smooth_field/array.py_RandSmoothFieldAdjustIntensity.__call___RandSmoothFieldAdjustIntensity.__call__.return.out": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/smooth_field/array.py_RandSmoothFieldAdjustIntensity.__call___RandSmoothFieldAdjustIntensity.__call__.return.out", "embedding": null, "metadata": {"file_path": "monai/transforms/smooth_field/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 314, "end_line": 332, "span_ids": ["RandSmoothFieldAdjustIntensity.__call__"], "tokens": 139}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandSmoothFieldAdjustIntensity(RandomizableTransform):\n\n    def __call__(self, img: NdarrayOrTensor, randomize: bool = True) -> NdarrayOrTensor:\n        \"\"\"\n        Apply the transform to `img`, if `randomize` randomizing the smooth field otherwise reusing the previous.\n        \"\"\"\n\n        if randomize:\n            self.randomize()\n\n        if not self._do_transform:\n            return img\n\n        field = self.sfield()\n        rfield, *_ = convert_to_dst_type(field, img)\n\n        # everything below here is to be computed using the destination type (numpy, tensor, etc.)\n\n        out = img * rfield\n\n        return out", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/smooth_field/array.py_RandSmoothDeform_RandSmoothDeform.__init__.self.grid.torch_stack_grid_unsquee": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/smooth_field/array.py_RandSmoothDeform_RandSmoothDeform.__init__.self.grid.torch_stack_grid_unsquee", "embedding": null, "metadata": {"file_path": "monai/transforms/smooth_field/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 335, "end_line": 412, "span_ids": ["RandSmoothDeform.__init__", "RandSmoothDeform"], "tokens": 766}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandSmoothDeform(RandomizableTransform):\n    \"\"\"\n    Deform an image using a random smooth field and Pytorch's grid_sample.\n\n    The amount of deformation is given by `def_range` in fractions of the size of the image. The size of each dimension\n    of the input image is always defined as 2 regardless of actual image voxel dimensions, that is the coordinates in\n    every dimension range from -1 to 1. A value of 0.1 means pixels/voxels can be moved by up to 5% of the image's size.\n\n    Args:\n        spatial_size: input array size to which deformation grid is interpolated\n        rand_size: size of the randomized field to start from\n        pad: number of pixels/voxels along the edges of the field to pad with 0\n        field_mode: interpolation mode to use when upsampling the deformation field\n        align_corners: if True align the corners when upsampling field\n        prob: probability transform is applied\n        def_range: value of the deformation range in image size fractions, single min/max value  or min/max pair\n        grid_dtype: type for the deformation grid calculated from the field\n        grid_mode: interpolation mode used for sampling input using deformation grid\n        grid_padding_mode: padding mode used for sampling input using deformation grid\n        grid_align_corners: if True align the corners when sampling the deformation grid\n        device: Pytorch device to define field on\n    \"\"\"\n\n    backend = [TransformBackends.TORCH]\n\n    def __init__(\n        self,\n        spatial_size: Sequence[int],\n        rand_size: Sequence[int],\n        pad: int = 0,\n        field_mode: Union[InterpolateMode, str] = InterpolateMode.AREA,\n        align_corners: Optional[bool] = None,\n        prob: float = 0.1,\n        def_range: Union[Sequence[float], float] = 1.0,\n        grid_dtype=torch.float32,\n        grid_mode: Union[GridSampleMode, str] = GridSampleMode.NEAREST,\n        grid_padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER,\n        grid_align_corners: Optional[bool] = False,\n        device: Optional[torch.device] = None,\n    ):\n        super().__init__(prob)\n\n        self.grid_dtype = grid_dtype\n        self.grid_mode = grid_mode\n        self.def_range = def_range\n        self.device = device\n        self.grid_align_corners = grid_align_corners\n        self.grid_padding_mode = grid_padding_mode\n\n        if isinstance(def_range, (int, float)):\n            self.def_range = (-def_range, def_range)\n        else:\n            if len(def_range) != 2:\n                raise ValueError(\"Argument `def_range` should be a number or pair of numbers.\")\n\n            self.def_range = (min(def_range), max(def_range))\n\n        self.sfield = SmoothField(\n            spatial_size=spatial_size,\n            rand_size=rand_size,\n            pad=pad,\n            low=self.def_range[0],\n            high=self.def_range[1],\n            channels=len(rand_size),\n            mode=field_mode,\n            align_corners=align_corners,\n            device=device,\n        )\n\n        grid_space = spatial_size if spatial_size is not None else self.sfield.field.shape[2:]\n        grid_ranges = [torch.linspace(-1, 1, d) for d in grid_space]\n\n        if pytorch_after(1, 10):\n            grid = torch.meshgrid(*grid_ranges, indexing=\"ij\")\n        else:\n            grid = torch.meshgrid(*grid_ranges)\n\n        self.grid = torch.stack(grid).unsqueeze(0).to(self.device, self.grid_dtype)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/smooth_field/array.py_RandSmoothDeform.set_random_state_RandSmoothDeform.set_grid_mode.self.grid_mode.mode": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/smooth_field/array.py_RandSmoothDeform.set_random_state_RandSmoothDeform.set_grid_mode.self.grid_mode.mode", "embedding": null, "metadata": {"file_path": "monai/transforms/smooth_field/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 414, "end_line": 431, "span_ids": ["RandSmoothDeform.set_field_mode", "RandSmoothDeform.set_random_state", "RandSmoothDeform.randomize", "RandSmoothDeform.set_grid_mode"], "tokens": 167}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandSmoothDeform(RandomizableTransform):\n\n    def set_random_state(\n        self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None\n    ) -> \"Randomizable\":\n        super().set_random_state(seed, state)\n        self.sfield.set_random_state(seed, state)\n        return self\n\n    def randomize(self, data: Optional[Any] = None) -> None:\n        super().randomize(None)\n\n        if self._do_transform:\n            self.sfield.randomize()\n\n    def set_field_mode(self, mode: Union[monai.utils.InterpolateMode, str]) -> None:\n        self.sfield.set_mode(mode)\n\n    def set_grid_mode(self, mode: Union[monai.utils.GridSampleMode, str]) -> None:\n        self.grid_mode = mode", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/smooth_field/array.py_RandSmoothDeform.__call___": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/smooth_field/array.py_RandSmoothDeform.__call___", "embedding": null, "metadata": {"file_path": "monai/transforms/smooth_field/array.py", "file_name": "array.py", "file_type": "text/x-python", "category": "implementation", "start_line": 433, "end_line": 462, "span_ids": ["RandSmoothDeform.__call__"], "tokens": 224}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandSmoothDeform(RandomizableTransform):\n\n    def __call__(\n        self, img: NdarrayOrTensor, randomize: bool = True, device: Optional[torch.device] = None\n    ) -> NdarrayOrTensor:\n        if randomize:\n            self.randomize()\n\n        if not self._do_transform:\n            return img\n\n        device = device if device is not None else self.device\n\n        field = self.sfield()\n\n        dgrid = self.grid + field.to(self.grid_dtype)\n        dgrid = moveaxis(dgrid, 1, -1)  # type: ignore\n\n        img_t = convert_to_tensor(img[None], torch.float32, device)\n\n        out = grid_sample(\n            input=img_t,\n            grid=dgrid,\n            mode=look_up_option(self.grid_mode, GridSampleMode).value,\n            align_corners=self.grid_align_corners,\n            padding_mode=look_up_option(self.grid_padding_mode, GridSamplePadMode).value,\n        )\n\n        out_t, *_ = convert_to_dst_type(out.squeeze(0), img)\n\n        return out_t", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/smooth_field/dictionary.py_from_typing_import_Any_H_GridSampleModeType.Union_GridSampleMode_str": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/smooth_field/dictionary.py_from_typing_import_Any_H_GridSampleModeType.Union_GridSampleMode_str", "embedding": null, "metadata": {"file_path": "monai/transforms/smooth_field/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 13, "end_line": 33, "span_ids": ["docstring"], "tokens": 176}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "from typing import Any, Hashable, Mapping, Optional, Sequence, Union\n\nimport numpy as np\nimport torch\n\nfrom monai.config import KeysCollection\nfrom monai.config.type_definitions import NdarrayOrTensor\nfrom monai.transforms.smooth_field.array import (\n    RandSmoothDeform,\n    RandSmoothFieldAdjustContrast,\n    RandSmoothFieldAdjustIntensity,\n)\nfrom monai.transforms.transform import MapTransform, RandomizableTransform\nfrom monai.utils import GridSampleMode, GridSamplePadMode, InterpolateMode, ensure_tuple_rep\nfrom monai.utils.enums import TransformBackends\n\n__all__ = [\"RandSmoothFieldAdjustContrastd\", \"RandSmoothFieldAdjustIntensityd\", \"RandSmoothDeformd\"]\n\n\nInterpolateModeType = Union[InterpolateMode, str]\nGridSampleModeType = Union[GridSampleMode, str]", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/smooth_field/dictionary.py_RandSmoothFieldAdjustContrastd_RandSmoothFieldAdjustContrastd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/smooth_field/dictionary.py_RandSmoothFieldAdjustContrastd_RandSmoothFieldAdjustContrastd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/smooth_field/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 36, "end_line": 111, "span_ids": ["RandSmoothFieldAdjustContrastd.set_random_state", "RandSmoothFieldAdjustContrastd", "RandSmoothFieldAdjustContrastd.__call__", "RandSmoothFieldAdjustContrastd.randomize", "RandSmoothFieldAdjustContrastd.__init__"], "tokens": 634}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandSmoothFieldAdjustContrastd(RandomizableTransform, MapTransform):\n    \"\"\"\n    Dictionary version of RandSmoothFieldAdjustContrast.\n\n    The field is randomized once per invocation by default so the same field is applied to every selected key. The\n    `mode` parameter specifying interpolation mode for the field can be a single value or a sequence of values with\n    one for each key in `keys`.\n\n    Args:\n        keys: key names to apply the augment to\n        spatial_size: size of input arrays, all arrays stated in `keys` must have same dimensions\n        rand_size: size of the randomized field to start from\n        pad: number of pixels/voxels along the edges of the field to pad with 0\n        mode: interpolation mode to use when upsampling\n        align_corners: if True align the corners when upsampling field\n        prob: probability transform is applied\n        gamma: (min, max) range for exponential field\n        device: Pytorch device to define field on\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        spatial_size: Sequence[int],\n        rand_size: Sequence[int],\n        pad: int = 0,\n        mode: Union[InterpolateModeType, Sequence[InterpolateModeType]] = InterpolateMode.AREA,\n        align_corners: Optional[bool] = None,\n        prob: float = 0.1,\n        gamma: Union[Sequence[float], float] = (0.5, 4.5),\n        device: Optional[torch.device] = None,\n    ):\n        RandomizableTransform.__init__(self, prob)\n        MapTransform.__init__(self, keys)\n\n        self.mode = ensure_tuple_rep(mode, len(self.keys))\n\n        self.trans = RandSmoothFieldAdjustContrast(\n            spatial_size=spatial_size,\n            rand_size=rand_size,\n            pad=pad,\n            mode=self.mode[0],\n            align_corners=align_corners,\n            prob=1.0,\n            gamma=gamma,\n            device=device,\n        )\n\n    def set_random_state(\n        self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None\n    ) -> \"RandSmoothFieldAdjustContrastd\":\n        super().set_random_state(seed, state)\n        self.trans.set_random_state(seed, state)\n        return self\n\n    def randomize(self, data: Optional[Any] = None) -> None:\n        super().randomize(None)\n\n        if self._do_transform:\n            self.trans.randomize()\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Mapping[Hashable, NdarrayOrTensor]:\n        self.randomize()\n\n        if not self._do_transform:\n            return data\n\n        d = dict(data)\n\n        for idx, key in enumerate(self.key_iterator(d)):\n            self.trans.set_mode(self.mode[idx % len(self.mode)])\n            d[key] = self.trans(d[key], False)\n\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/smooth_field/dictionary.py_RandSmoothFieldAdjustIntensityd_RandSmoothFieldAdjustIntensityd.__call__.return.d": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/smooth_field/dictionary.py_RandSmoothFieldAdjustIntensityd_RandSmoothFieldAdjustIntensityd.__call__.return.d", "embedding": null, "metadata": {"file_path": "monai/transforms/smooth_field/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 114, "end_line": 187, "span_ids": ["RandSmoothFieldAdjustIntensityd.__init__", "RandSmoothFieldAdjustIntensityd.randomize", "RandSmoothFieldAdjustIntensityd.set_random_state", "RandSmoothFieldAdjustIntensityd", "RandSmoothFieldAdjustIntensityd.__call__"], "tokens": 624}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandSmoothFieldAdjustIntensityd(RandomizableTransform, MapTransform):\n    \"\"\"\n    Dictionary version of RandSmoothFieldAdjustIntensity.\n\n    The field is randomized once per invocation by default so the same field is applied to every selected key. The\n    `mode` parameter specifying interpolation mode for the field can be a single value or a sequence of values with\n    one for each key in `keys`.\n\n    Args:\n        keys: key names to apply the augment to\n        spatial_size: size of input arrays, all arrays stated in `keys` must have same dimensions\n        rand_size: size of the randomized field to start from\n        pad: number of pixels/voxels along the edges of the field to pad with 0\n        mode: interpolation mode to use when upsampling\n        align_corners: if True align the corners when upsampling field\n        prob: probability transform is applied\n        gamma: (min, max) range of intensity multipliers\n        device: Pytorch device to define field on\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        spatial_size: Sequence[int],\n        rand_size: Sequence[int],\n        pad: int = 0,\n        mode: Union[InterpolateModeType, Sequence[InterpolateModeType]] = InterpolateMode.AREA,\n        align_corners: Optional[bool] = None,\n        prob: float = 0.1,\n        gamma: Union[Sequence[float], float] = (0.1, 1.0),\n        device: Optional[torch.device] = None,\n    ):\n        RandomizableTransform.__init__(self, prob)\n        MapTransform.__init__(self, keys)\n\n        self.mode = ensure_tuple_rep(mode, len(self.keys))\n\n        self.trans = RandSmoothFieldAdjustIntensity(\n            spatial_size=spatial_size,\n            rand_size=rand_size,\n            pad=pad,\n            mode=self.mode[0],\n            align_corners=align_corners,\n            prob=1.0,\n            gamma=gamma,\n            device=device,\n        )\n\n    def set_random_state(\n        self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None\n    ) -> \"RandSmoothFieldAdjustIntensityd\":\n        super().set_random_state(seed, state)\n        self.trans.set_random_state(seed, state)\n        return self\n\n    def randomize(self, data: Optional[Any] = None) -> None:\n        super().randomize(None)\n        self.trans.randomize()\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Mapping[Hashable, NdarrayOrTensor]:\n        self.randomize()\n\n        if not self._do_transform:\n            return data\n\n        d = dict(data)\n\n        for idx, key in enumerate(self.key_iterator(d)):\n            self.trans.set_mode(self.mode[idx % len(self.mode)])\n            d[key] = self.trans(d[key], False)\n\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/smooth_field/dictionary.py_RandSmoothDeformd_RandSmoothDeformd.randomize.self_trans_randomize_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/smooth_field/dictionary.py_RandSmoothDeformd_RandSmoothDeformd.randomize.self_trans_randomize_", "embedding": null, "metadata": {"file_path": "monai/transforms/smooth_field/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 190, "end_line": 262, "span_ids": ["RandSmoothDeformd.__init__", "RandSmoothDeformd.randomize", "RandSmoothDeformd", "RandSmoothDeformd.set_random_state"], "tokens": 719}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandSmoothDeformd(RandomizableTransform, MapTransform):\n    \"\"\"\n    Dictionary version of RandSmoothDeform.\n\n    The field is randomized once per invocation by default so the same field is applied to every selected key. The\n    `field_mode` parameter specifying interpolation mode for the field can be a single value or a sequence of values\n    with one for each key in `keys`. Similarly the `grid_mode` parameter can be one value or one per key.\n\n    Args:\n        keys: key names to apply the augment to\n        spatial_size: input array size to which deformation grid is interpolated\n        rand_size: size of the randomized field to start from\n        pad: number of pixels/voxels along the edges of the field to pad with 0\n        field_mode: interpolation mode to use when upsampling the deformation field\n        align_corners: if True align the corners when upsampling field\n        prob: probability transform is applied\n        def_range: value of the deformation range in image size fractions\n        grid_dtype: type for the deformation grid calculated from the field\n        grid_mode: interpolation mode used for sampling input using deformation grid\n        grid_padding_mode: padding mode used for sampling input using deformation grid\n        grid_align_corners: if True align the corners when sampling the deformation grid\n        device: Pytorch device to define field on\n    \"\"\"\n\n    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]\n\n    def __init__(\n        self,\n        keys: KeysCollection,\n        spatial_size: Sequence[int],\n        rand_size: Sequence[int],\n        pad: int = 0,\n        field_mode: Union[InterpolateModeType, Sequence[InterpolateModeType]] = InterpolateMode.AREA,\n        align_corners: Optional[bool] = None,\n        prob: float = 0.1,\n        def_range: Union[Sequence[float], float] = 1.0,\n        grid_dtype=torch.float32,\n        grid_mode: Union[GridSampleModeType, Sequence[GridSampleModeType]] = GridSampleMode.NEAREST,\n        grid_padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER,\n        grid_align_corners: Optional[bool] = False,\n        device: Optional[torch.device] = None,\n    ):\n        RandomizableTransform.__init__(self, prob)\n        MapTransform.__init__(self, keys)\n\n        self.field_mode = ensure_tuple_rep(field_mode, len(self.keys))\n        self.grid_mode = ensure_tuple_rep(grid_mode, len(self.keys))\n\n        self.trans = RandSmoothDeform(\n            rand_size=rand_size,\n            spatial_size=spatial_size,\n            pad=pad,\n            field_mode=self.field_mode[0],\n            align_corners=align_corners,\n            prob=1.0,\n            def_range=def_range,\n            grid_dtype=grid_dtype,\n            grid_mode=self.grid_mode[0],\n            grid_padding_mode=grid_padding_mode,\n            grid_align_corners=grid_align_corners,\n            device=device,\n        )\n\n    def set_random_state(\n        self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None\n    ) -> \"RandSmoothDeformd\":\n        super().set_random_state(seed, state)\n        self.trans.set_random_state(seed, state)\n        return self\n\n    def randomize(self, data: Optional[Any] = None) -> None:\n        super().randomize(None)\n        self.trans.randomize()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/smooth_field/dictionary.py_RandSmoothDeformd.__call___": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/monai/transforms/smooth_field/dictionary.py_RandSmoothDeformd.__call___", "embedding": null, "metadata": {"file_path": "monai/transforms/smooth_field/dictionary.py", "file_name": "dictionary.py", "file_type": "text/x-python", "category": "implementation", "start_line": 264, "end_line": 279, "span_ids": ["RandSmoothDeformd.__call__"], "tokens": 129}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class RandSmoothDeformd(RandomizableTransform, MapTransform):\n\n    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Mapping[Hashable, NdarrayOrTensor]:\n        self.randomize()\n\n        if not self._do_transform:\n            return data\n\n        d = dict(data)\n\n        for idx, key in enumerate(self.key_iterator(d)):\n            self.trans.set_field_mode(self.field_mode[idx % len(self.field_mode)])\n            self.trans.set_grid_mode(self.grid_mode[idx % len(self.grid_mode)])\n\n            d[key] = self.trans(d[key], False, self.trans.device)\n\n        return d", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_call_dist.py_unittest_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_call_dist.py_unittest_", "embedding": null, "metadata": {"file_path": "tests/test_call_dist.py", "file_name": "test_call_dist.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 30, "span_ids": ["DistributedCallTest", "DistributedCallTest.test_constructor", "impl", "docstring"], "tokens": 119}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nfrom tests.utils import DistCall, DistTestCase\n\n\nclass DistributedCallTest(DistTestCase):\n    def test_constructor(self):\n        with self.assertRaises(ValueError):\n            DistCall(nnodes=1, nproc_per_node=0)\n        with self.assertRaises(ValueError):\n            DistCall(nnodes=0, nproc_per_node=0)\n        with self.assertRaises(ValueError):\n            DistCall(nnodes=0, nproc_per_node=1)\n        _ = DistCall(nnodes=1, nproc_per_node=1)\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dataset_summary.py_glob_test_collate.if_isinstance_elem_np_nd.elif_isinstance_elem_dic.return.elem_type_key_test_coll": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dataset_summary.py_glob_test_collate.if_isinstance_elem_np_nd.elif_isinstance_elem_dic.return.elem_type_key_test_coll", "embedding": null, "metadata": {"file_path": "tests/test_dataset_summary.py", "file_name": "test_dataset_summary.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 31, "span_ids": ["test_collate", "docstring"], "tokens": 127}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import glob\nimport os\nimport tempfile\nimport unittest\n\nimport nibabel as nib\nimport numpy as np\n\nfrom monai.data import Dataset, DatasetSummary, create_test_image_3d\nfrom monai.transforms import LoadImaged\nfrom monai.utils import set_determinism\n\n\ndef test_collate(batch):\n    elem = batch[0]\n    elem_type = type(elem)\n    if isinstance(elem, np.ndarray):\n        return np.stack(batch, 0)\n    elif isinstance(elem, dict):\n        return elem_type({key: test_collate([d[key] for d in batch]) for key in elem})", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dataset_summary.py_TestDatasetSummary_TestDatasetSummary.test_spacing_intensity.with_tempfile_TemporaryDi.None_6": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_dataset_summary.py_TestDatasetSummary_TestDatasetSummary.test_spacing_intensity.with_tempfile_TemporaryDi.None_6", "embedding": null, "metadata": {"file_path": "tests/test_dataset_summary.py", "file_name": "test_dataset_summary.py", "file_type": "text/x-python", "category": "test", "start_line": 34, "end_line": 66, "span_ids": ["TestDatasetSummary.test_spacing_intensity", "TestDatasetSummary"], "tokens": 469}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestDatasetSummary(unittest.TestCase):\n    def test_spacing_intensity(self):\n        set_determinism(seed=0)\n        with tempfile.TemporaryDirectory() as tempdir:\n\n            for i in range(5):\n                im, seg = create_test_image_3d(32, 32, 32, num_seg_classes=1, num_objs=3, rad_max=6, channel_dim=0)\n                n = nib.Nifti1Image(im, np.eye(4))\n                nib.save(n, os.path.join(tempdir, f\"img{i:d}.nii.gz\"))\n                n = nib.Nifti1Image(seg, np.eye(4))\n                nib.save(n, os.path.join(tempdir, f\"seg{i:d}.nii.gz\"))\n\n            train_images = sorted(glob.glob(os.path.join(tempdir, \"img*.nii.gz\")))\n            train_labels = sorted(glob.glob(os.path.join(tempdir, \"seg*.nii.gz\")))\n            data_dicts = [\n                {\"image\": image_name, \"label\": label_name} for image_name, label_name in zip(train_images, train_labels)\n            ]\n\n            dataset = Dataset(\n                data=data_dicts, transform=LoadImaged(keys=[\"image\", \"label\"], meta_keys=[\"test1\", \"test2\"])\n            )\n\n            # test **kwargs of `DatasetSummary` for `DataLoader`\n            calculator = DatasetSummary(dataset, num_workers=4, meta_key=\"test1\", collate_fn=test_collate)\n\n            target_spacing = calculator.get_target_spacing()\n            self.assertEqual(target_spacing, (1.0, 1.0, 1.0))\n            calculator.calculate_statistics()\n            np.testing.assert_allclose(calculator.data_mean, 0.892599, rtol=1e-5, atol=1e-5)\n            np.testing.assert_allclose(calculator.data_std, 0.131731, rtol=1e-5, atol=1e-5)\n            calculator.calculate_percentiles(sampling_flag=True, interval=2)\n            self.assertEqual(calculator.data_max_percentile, 1.0)\n            np.testing.assert_allclose(calculator.data_min_percentile, 0.556411, rtol=1e-5, atol=1e-5)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_lmdbdataset_dist.py_shutil_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_lmdbdataset_dist.py_shutil_", "embedding": null, "metadata": {"file_path": "tests/test_lmdbdataset_dist.py", "file_name": "test_lmdbdataset_dist.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 73, "span_ids": ["_InplaceXform.__call__", "TestMPLMDBDataset.test_mp_cache", "TestMPLMDBDataset", "impl", "TestMPLMDBDataset.tearDown", "docstring", "_InplaceXform", "TestMPLMDBDataset.setUp"], "tokens": 544}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import shutil\nimport tempfile\nimport unittest\n\nimport numpy as np\n\nfrom monai.data import LMDBDataset, json_hashing\nfrom monai.transforms import Transform\nfrom tests.utils import DistCall, DistTestCase, skip_if_windows\n\n\nclass _InplaceXform(Transform):\n    def __call__(self, data):\n        if data:\n            data[0] = data[0] + np.pi\n        else:\n            data.append(1)\n        return data\n\n\n@skip_if_windows\nclass TestMPLMDBDataset(DistTestCase):\n    def setUp(self):\n        self.tempdir = tempfile.mkdtemp()\n\n    def tearDown(self):\n        shutil.rmtree(self.tempdir)\n\n    @DistCall(nnodes=1, nproc_per_node=1)\n    def test_mp_cache(self):\n        items = [[list(range(i))] for i in range(5)]\n\n        ds = LMDBDataset(items, transform=_InplaceXform(), cache_dir=self.tempdir, lmdb_kwargs={\"map_size\": 10 * 1024})\n        self.assertEqual(items, [[[]], [[0]], [[0, 1]], [[0, 1, 2]], [[0, 1, 2, 3]]])\n        ds1 = LMDBDataset(items, transform=_InplaceXform(), cache_dir=self.tempdir, lmdb_kwargs={\"map_size\": 10 * 1024})\n        self.assertEqual(list(ds1), list(ds))\n        self.assertEqual(items, [[[]], [[0]], [[0, 1]], [[0, 1, 2]], [[0, 1, 2, 3]]])\n\n        ds = LMDBDataset(\n            items,\n            transform=_InplaceXform(),\n            cache_dir=self.tempdir,\n            lmdb_kwargs={\"map_size\": 10 * 1024},\n            hash_func=json_hashing,\n        )\n        self.assertEqual(items, [[[]], [[0]], [[0, 1]], [[0, 1, 2]], [[0, 1, 2, 3]]])\n        ds1 = LMDBDataset(\n            items,\n            transform=_InplaceXform(),\n            cache_dir=self.tempdir,\n            lmdb_kwargs={\"map_size\": 10 * 1024},\n            hash_func=json_hashing,\n        )\n        self.assertEqual(list(ds1), list(ds))\n        self.assertEqual(items, [[[]], [[0]], [[0, 1]], [[0, 1, 2]], [[0, 1, 2, 3]]])\n\n        self.assertTrue(isinstance(ds1.info(), dict))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_sampler_dist.py_unittest_DistributedSamplerTest.test_even.if_dist_get_rank_1_.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_sampler_dist.py_unittest_DistributedSamplerTest.test_even.if_dist_get_rank_1_.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_sampler_dist.py", "file_name": "test_sampler_dist.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 32, "span_ids": ["DistributedSamplerTest.test_even", "DistributedSamplerTest", "docstring"], "tokens": 176}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch.distributed as dist\n\nfrom monai.data import DistributedSampler\nfrom tests.utils import DistCall, DistTestCase\n\n\nclass DistributedSamplerTest(DistTestCase):\n    @DistCall(nnodes=1, nproc_per_node=2)\n    def test_even(self):\n        data = [1, 2, 3, 4, 5]\n        sampler = DistributedSampler(dataset=data, shuffle=False)\n        samples = np.array([data[i] for i in list(sampler)])\n        self.assertEqual(dist.get_rank(), sampler.rank)\n        if dist.get_rank() == 0:\n            np.testing.assert_allclose(samples, np.array([1, 3, 5]))\n\n        if dist.get_rank() == 1:\n            np.testing.assert_allclose(samples, np.array([2, 4, 1]))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_sampler_dist.py_DistributedSamplerTest.test_uneven_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_sampler_dist.py_DistributedSamplerTest.test_uneven_", "embedding": null, "metadata": {"file_path": "tests/test_sampler_dist.py", "file_name": "test_sampler_dist.py", "file_type": "text/x-python", "category": "test", "start_line": 34, "end_line": 49, "span_ids": ["DistributedSamplerTest.test_uneven", "impl"], "tokens": 160}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DistributedSamplerTest(DistTestCase):\n\n    @DistCall(nnodes=1, nproc_per_node=2)\n    def test_uneven(self):\n        data = [1, 2, 3, 4, 5]\n        sampler = DistributedSampler(dataset=data, shuffle=False, even_divisible=False)\n        samples = np.array([data[i] for i in list(sampler)])\n        self.assertEqual(dist.get_rank(), sampler.rank)\n        if dist.get_rank() == 0:\n            np.testing.assert_allclose(samples, np.array([1, 3, 5]))\n\n        if dist.get_rank() == 1:\n            np.testing.assert_allclose(samples, np.array([2, 4]))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_smooth_field.py_unittest_for_arr_type_shape_in_pr.TESTS_DEFORM_append_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_smooth_field.py_unittest_for_arr_type_shape_in_pr.TESTS_DEFORM_append_", "embedding": null, "metadata": {"file_path": "tests/test_smooth_field.py", "file_name": "test_smooth_field.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 78, "span_ids": ["docstring"], "tokens": 477}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\nfrom itertools import product\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom monai.transforms import RandSmoothDeformd, RandSmoothFieldAdjustContrastd, RandSmoothFieldAdjustIntensityd\nfrom tests.utils import TEST_NDARRAYS, assert_allclose, is_tf32_env\n\n_rtol = 5e-3 if is_tf32_env() else 1e-4\n\nINPUT_SHAPES = ((1, 8, 8), (2, 8, 8), (1, 8, 8, 8))\n\nTESTS_CONTRAST = []\nTESTS_INTENSITY = []\nTESTS_DEFORM = []\n\nKEY = \"test\"\n\nfor arr_type, shape in product(TEST_NDARRAYS, INPUT_SHAPES):\n    in_arr = arr_type(np.ones(shape, np.float32))\n    exp_arr = arr_type(np.ones(shape, np.float32))\n    rand_size = (4,) * (len(shape) - 1)\n\n    device = torch.device(\"cpu\")\n\n    if isinstance(in_arr, torch.Tensor) and in_arr.get_device() >= 0:\n        device = torch.device(in_arr.get_device())\n\n    TESTS_CONTRAST.append(\n        (\n            {\"keys\": (KEY,), \"spatial_size\": shape[1:], \"rand_size\": rand_size, \"prob\": 1.0, \"device\": device},\n            {KEY: in_arr},\n            {KEY: exp_arr},\n        )\n    )\n\n    TESTS_INTENSITY.append(\n        (\n            {\n                \"keys\": (KEY,),\n                \"spatial_size\": shape[1:],\n                \"rand_size\": rand_size,\n                \"prob\": 1.0,\n                \"device\": device,\n                \"gamma\": (0.9, 1),\n            },\n            {KEY: in_arr},\n            {KEY: exp_arr},\n        )\n    )\n\n    TESTS_DEFORM.append(\n        (\n            {\n                \"keys\": (KEY,),\n                \"spatial_size\": shape[1:],\n                \"rand_size\": rand_size,\n                \"prob\": 1.0,\n                \"device\": device,\n                \"def_range\": 0.1,\n            },\n            {KEY: in_arr},\n            {KEY: exp_arr},\n        )\n    )", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_timedcall_dist.py_multiprocessing_case_1_seconds_bad.assert_0_1_wrong_cas": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_timedcall_dist.py_multiprocessing_case_1_seconds_bad.assert_0_1_wrong_cas", "embedding": null, "metadata": {"file_path": "tests/test_timedcall_dist.py", "file_name": "test_timedcall_dist.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 47, "span_ids": ["docstring", "case_1_seconds_bad", "case_1_seconds_skip", "case_1_seconds_timeout", "case_1_seconds_timeout_warning", "case_1_seconds"], "tokens": 233}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import multiprocessing\nimport sys\nimport time\nimport unittest\n\nfrom tests.utils import TimedCall\n\n\n@TimedCall(seconds=10 if sys.platform == \"linux\" else 60, force_quit=False)\ndef case_1_seconds(arg=None):\n    time.sleep(1)\n    return \"good\" if not arg else arg\n\n\n@TimedCall(seconds=0.1, skip_timing=True, force_quit=True)\ndef case_1_seconds_skip(arg=None):\n    time.sleep(1)\n    return \"good\" if not arg else arg\n\n\n@TimedCall(seconds=0.1, force_quit=True)\ndef case_1_seconds_timeout(arg=None):\n    time.sleep(1)\n    return \"good\" if not arg else arg\n\n\n@TimedCall(seconds=0.1, force_quit=False)\ndef case_1_seconds_timeout_warning(arg=None):\n    time.sleep(1)\n    return \"good\" if not arg else arg\n\n\n@TimedCall(seconds=0.1, force_quit=True)\ndef case_1_seconds_bad(arg=None):\n    time.sleep(1)\n    assert 0 == 1, \"wrong case\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_timedcall_dist.py_TestTimedCall_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_timedcall_dist.py_TestTimedCall_", "embedding": null, "metadata": {"file_path": "tests/test_timedcall_dist.py", "file_name": "test_timedcall_dist.py", "file_type": "text/x-python", "category": "test", "start_line": 50, "end_line": 76, "span_ids": ["impl", "TestTimedCall.test_timeout_bad", "TestTimedCall.test_good_call", "TestTimedCall.test_timeout", "TestTimedCall.test_skip_timing", "TestTimedCall.test_timeout_not_force_quit", "TestTimedCall"], "tokens": 161}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class TestTimedCall(unittest.TestCase):\n    def test_good_call(self):\n        output = case_1_seconds()\n        self.assertEqual(output, \"good\")\n\n    def test_skip_timing(self):\n        output = case_1_seconds_skip(\"testing\")\n        self.assertEqual(output, \"testing\")\n\n    def test_timeout(self):\n        with self.assertRaises(multiprocessing.TimeoutError):\n            case_1_seconds_timeout()\n\n    def test_timeout_not_force_quit(self):\n        with self.assertWarns(Warning):\n            with self.assertRaises(multiprocessing.TimeoutError):\n                case_1_seconds_timeout_warning()\n\n    def test_timeout_bad(self):\n        # timeout before the method's error\n        with self.assertRaises(multiprocessing.TimeoutError):\n            case_1_seconds_bad()\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_weighted_random_sampler_dist.py_unittest_DistributedWeightedRandomSamplerTest.test_sampling.if_dist_get_rank_1_.np_testing_assert_allclos": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_weighted_random_sampler_dist.py_unittest_DistributedWeightedRandomSamplerTest.test_sampling.if_dist_get_rank_1_.np_testing_assert_allclos", "embedding": null, "metadata": {"file_path": "tests/test_weighted_random_sampler_dist.py", "file_name": "test_weighted_random_sampler_dist.py", "file_type": "text/x-python", "category": "test", "start_line": 12, "end_line": 36, "span_ids": ["DistributedWeightedRandomSamplerTest", "DistributedWeightedRandomSamplerTest.test_sampling", "docstring"], "tokens": 214}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "import unittest\n\nimport numpy as np\nimport torch\nimport torch.distributed as dist\n\nfrom monai.data import DistributedWeightedRandomSampler\nfrom tests.utils import DistCall, DistTestCase\n\n\nclass DistributedWeightedRandomSamplerTest(DistTestCase):\n    @DistCall(nnodes=1, nproc_per_node=2)\n    def test_sampling(self):\n        data = [1, 2, 3, 4, 5]\n        weights = [1, 2, 3, 4, 5]\n        sampler = DistributedWeightedRandomSampler(\n            weights=weights, dataset=data, shuffle=False, generator=torch.Generator().manual_seed(0)\n        )\n        samples = np.array([data[i] for i in list(sampler)])\n\n        if dist.get_rank() == 0:\n            np.testing.assert_allclose(samples, np.array([5, 5, 5]))\n\n        if dist.get_rank() == 1:\n            np.testing.assert_allclose(samples, np.array([1, 4, 4]))", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_weighted_random_sampler_dist.py_DistributedWeightedRandomSamplerTest.test_num_samples_": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/test_weighted_random_sampler_dist.py_DistributedWeightedRandomSamplerTest.test_num_samples_", "embedding": null, "metadata": {"file_path": "tests/test_weighted_random_sampler_dist.py", "file_name": "test_weighted_random_sampler_dist.py", "file_type": "text/x-python", "category": "test", "start_line": 38, "end_line": 60, "span_ids": ["DistributedWeightedRandomSamplerTest.test_num_samples", "impl"], "tokens": 212}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "class DistributedWeightedRandomSamplerTest(DistTestCase):\n\n    @DistCall(nnodes=1, nproc_per_node=2)\n    def test_num_samples(self):\n        data = [1, 2, 3, 4, 5]\n        weights = [1, 2, 3, 4, 5]\n        sampler = DistributedWeightedRandomSampler(\n            weights=weights,\n            num_samples_per_rank=5,\n            dataset=data,\n            shuffle=False,\n            generator=torch.Generator().manual_seed(123),\n        )\n        samples = np.array([data[i] for i in list(sampler)])\n\n        if dist.get_rank() == 0:\n            np.testing.assert_allclose(samples, np.array([3, 1, 5, 1, 5]))\n\n        if dist.get_rank() == 1:\n            np.testing.assert_allclose(samples, np.array([4, 2, 4, 2, 4]))\n\n\nif __name__ == \"__main__\":\n    unittest.main()", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/utils.py_test_pretrained_networks_test_is_quick.return.os_environ_get_quick_test": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/utils.py_test_pretrained_networks_test_is_quick.return.os_environ_get_quick_test", "embedding": null, "metadata": {"file_path": "tests/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 95, "end_line": 107, "span_ids": ["test_is_quick", "test_pretrained_networks"], "tokens": 115}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def test_pretrained_networks(network, input_param, device):\n    try:\n        return network(**input_param).to(device)\n    except (URLError, HTTPError) as e:\n        raise unittest.SkipTest(e) from e\n    except RuntimeError as r_error:\n        if \"unexpected EOF\" in f\"{r_error}\":  # The file might be corrupted.\n            raise unittest.SkipTest(f\"{r_error}\") from r_error\n        raise\n\n\ndef test_is_quick():\n    return os.environ.get(quick_test_var, \"\").lower() == \"true\"", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/utils.py_is_tf32_env_is_tf32_env.return._tf32_enabled": {"__data__": {"id_": "/home/jiayipan/code/24FA/temp/ml-01/moatless-tools/t/repos/swe-train_Project-MONAI__MONAI/tests/utils.py_is_tf32_env_is_tf32_env.return._tf32_enabled", "embedding": null, "metadata": {"file_path": "tests/utils.py", "file_name": "utils.py", "file_type": "text/x-python", "category": "implementation", "start_line": 110, "end_line": 135, "span_ids": ["is_tf32_env"], "tokens": 318}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date", "start_line", "end_line", "tokens"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {}, "text": "def is_tf32_env():\n    \"\"\"\n    The environment variable NVIDIA_TF32_OVERRIDE=0 will override any defaults\n    or programmatic configuration of NVIDIA libraries, and consequently,\n    cuBLAS will not accelerate FP32 computations with TF32 tensor cores.\n    \"\"\"\n    global _tf32_enabled\n    if _tf32_enabled is None:\n        _tf32_enabled = False\n        if (\n            torch.cuda.is_available()\n            and not version_leq(f\"{torch.version.cuda}\", \"10.100\")\n            and os.environ.get(\"NVIDIA_TF32_OVERRIDE\", \"1\") != \"0\"\n            and torch.cuda.device_count() > 0  # at least 11.0\n        ):\n            try:\n                # with TF32 enabled, the speed is ~8x faster, but the precision has ~2 digits less in the result\n                g_gpu = torch.Generator(device=\"cuda\")\n                g_gpu.manual_seed(2147483647)\n                a_full = torch.randn(1024, 1024, dtype=torch.double, device=\"cuda\", generator=g_gpu)\n                b_full = torch.randn(1024, 1024, dtype=torch.double, device=\"cuda\", generator=g_gpu)\n                _tf32_enabled = (a_full.float() @ b_full.float() - a_full @ b_full).abs().max().item() > 0.001  # 0.1713\n            except BaseException:\n                pass\n        print(f\"tf32 enabled: {_tf32_enabled}\")\n    return _tf32_enabled", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}}}
\ No newline at end of file